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Mini review article, farmers’ perception of climate change: a review of the literature for latin america.

• 1 Tecnologico de Monterrey, School of Social Science and Government, Mexico and Economics Department, Centro de Investigación y Docencia Económicas, Mexico City, Mexico
• 2 Economics Department, Centro de Investigación y Docencia Económicas, Mexico City, Mexico

Global climate is changing rapidly, and it is not clear if agricultural producers in developing countries will be able to adapt fast enough in order to mitigate its negative effects. In order to be willing to take adaptation measures, farmers need to perceive that the climate is changing or could change, and they need to attribute enough weight to this perception to take action. During the last two decades, the literature that examines farmers’ perception of climate change has gained ground, but it is still scant. This is particularly true for Latin America, which is highly vulnerable to climate change. Based on a review of original research articles published between 2000 and 2020, this article presents the status of knowledge about the topic in the region to identify research gaps and inform future research. The review found that the available research has been based mostly on qualitative analyses of case studies for a few countries. More research that identifies causal relationships is necessary. Data from surveys that are representative at the national or subnational levels, as well as longitudinal data, will be very helpful to better understand farmer’s perceptions. Finally, the use of field experiments and choice experiments can complement the use of observational data.

Introduction

Throughout human history, farmers have adapted to changing environmental, social and economic conditions ( Kurukulasuriya and Rosenthal, 2013 ). Nonetheless, it is not clear if agricultural producers will be able to keep up with the unprecedented speed at which climate is expected to change in the coming years ( Jones et al., 2012 ). The negative effects of these changes will be higher for agricultural producers that practice rainfed agriculture, as well as for those with limited access to credit and insurance, and those that are disconnected from regional or national markets ( Skoufias et al., 2011 ; Quiroga et al., 2015 ; IFAD, 2016 ; Castells-Quintana et al., 2018 ). In order to ameliorate these negative effects, public policies and interventions to promote and facilitate adaptation will be needed ( Howden et al., 2007 ; Kumar et al., 2020 ). Nonetheless, in order to be willing to implement adaptation measures, farmers need to be aware of climate change ( Silvestri et al., 2012 ; Simelton et al., 2013 ; Meldrum et al., 2018 ). In that sense, the perception that farmers have about climate change not only informs their planting decisions, but also determines the adoption of adaptation measures ( Meldrum et al., 2018 ; De Matos Carlos et al., 2020 ). Therefore, understanding farmers’ perceptions about climate change can be seen as a condition for the design and successful implementation of adaptation policies in agriculture ( Hansen et al., 2004 ; Silvestri et al., 2012 ; De Matos Carlos et al., 2020 ).

The number of studies that focus on understanding farmers’ climate change perception has been increasing, but it is still scant. This is particularly true for Latin America ( Dang et al., 2019 ; Karki et al., 2020 ), a region highly vulnerable to climate change ( López-Feldman and Hernández Cort ). This phenomenon is expected to have serious negative impacts on the income, consumption and health of agricultural producers in the region ( Reyer et al., 2017 ; IPCC et al., 2018 ), leading to increases in poverty and inequality ( Skoufias et al., 2011 ; Harvey et al., 2018 ; López-Feldman and Mora Rivera, 2018 ). Given this scenario, the lack of research on the determinants of climate change perception is worrisome. The objective of this work is to present an overview of the studies on this topic available for Latin America while identifying research gaps and potential paths for future research.

Climate Change Perception

Climate change perception is a complex process that encompasses a range of psychological constructs such as knowledge, beliefs, attitudes and concerns about if and how the climate is changing ( Whitmarsh and Capstick, 2018 ). Perception is influenced and shaped, among other things, by the individuals’ characteristics, their experience, the information that they receive, and the cultural and geographic context in which they live ( van der Linden, 2015 ; Whitmarsh and Capstick, 2018 ). Therefore, measuring climate change perception and trying to find its determinants is not an easy task.

The variability that local weather can have from one day to the other, from one season to the next, and between years, is one of the many challenges that a person faces when trying to distinguish between normal short-run variations and climate change manifestations ( Hansen et al., 2012 ). In fact, local short-term variations tend to be more salient than long-term trends and hence can have a key impact on the formation of climate change perceptions ( Lehner and Stocker, 2015 ). Although the perception of those that directly depend on the weather for at least part of their income, such as farmers, tend to be more accurate than that of their counterparts, they might still have problems using their own experience with weather variables to correctly interpret changes as being big enough as to feel worried and compelled to do something about it ( Weber, 2010 ; Whitmarsh and Capstick, 2018 ).

Life experiences influence perception, individuals who have been directly affected by extreme climatic events tend to report that the probability of such event happening again is relatively high ( Patt and Schröter, 2008 ; De Matos Carlos et al., 2020 ). Furthermore, the perception that a person has about climate change can be influenced or modified by the information that she receives ( Weber, 2010 ). Finally, it should be noted that perception is in part a subjective phenomenon, therefore, different people in the same locality might construct different perceptions of climate change even though they experience the same weather patterns ( Simelton et al., 2013 ).

The Link Between Perception and Adaptation to Climate Change

In order to protect the livelihoods of the population that directly depends on agriculture, adaptation of the agricultural sector to the adverse effects of climate change is crucial ( Asfaw et al., 2016 ). In a world with perfect information, complete markets, and adequate incentives, the decision to adopt or implement a particular adaptation measure would simply be a matter of evaluating the net benefits of said measure. That is certainly not the setting in which small and subsistence farmers in developing countries operate ( Castells-Quintana et al., 2018 ). Therefore, the adoption of adaptation measures is not an automatic or smooth process, quite the contrary. The evidence has shown that factors like inadequate access to insurance or credit, limited information about adaptation alternatives, and incomplete property rights, constitute barriers that small and subsistence farmers face in relation to technology adoption ( Asfaw et al., 2016 ). Furthermore, the decision to adopt a new technology or production method frequently entails cognitive processes, like mental accounting ( Thaler, 1999 ), loss aversion ( Kahneman and Tversky, 1979 ), and hyperbolic discounting ( Laibson, 1997 ), which can lead to suboptimal levels of adoption ( Zilberman et al., 2012 ). This is particularly relevant for adaptation to climate change, as even farmers with access to weather information and climate forecasts face considerable levels of uncertainty ( Silvestri et al., 2012 ). Under these conditions, the perception that farmers have about climate change is a key component to understanding their adaptation decisions ( Clarke, et al., 2012 ).

Adaptation requires not only that individuals perceive that something is changing or could change, but also that they attribute enough weight to this perception to be willing to take action and try to do something about it ( Eakin et al., 2014 ). In this sense, perceiving that the climate is changing can be seen as a pre-condition for the adoption of agricultural adaptation measures ( Simelton et al., 2013 ; Makuvaro et al., 2018 ). Furthermore, the successful implementation of public policies aimed towards the promotion of adaptation requires, among many other things, the cooperation and participation of the intended beneficiaries. If their perception about the consequences or immediacy of climate change is different from that of the policy makers, then it is likely that the implementation of the policy will fail ( Patt and Schrö ).

Climate Change Perception of Farmers in Latin America

Hansen et al. (2004) were the first to analyze the climate perceptions of farmers in a Latin American country (Argentina). The literature on this topic has slowly grown since then, although it is still scarce compared to that from Africa and South-East Asia ( Altea, 2020 ; Karki et al., 2020 ). Here we briefly summarize some of the main findings of the studies about Latin America published, in either English or Spanish, during the period 2000–2020. The articles’ selection process was based on some of the steps used in systematic reviews, in particular we followed Karki et al. (2020) and Dang et al. (2019) . For our search, we used the following combinations of keywords or closely related words: climate change (climate, climate variability, global warming, temperature, rainfall), extreme weather events (droughts, hurricanes, tropical storms), perception (understanding), Latin America (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay, Venezuela, North America, Central America, South America), family farms (farms, small producers, farmers, subsistence farms, household, communities, villages), indigenous (indigeneity) . In our search, in addition to Science Direct and Web of Science , we also used Google Scholar. It has been shown that Google Scholar has a very good coverage in areas where Web of Science does not ( Martín-Martín, et al., 2018 ), therefore, by using these three databases we have a comprehensive coverage of the literature. The title and abstract of 112 published papers that resulted from the search were analyzed to check if at least one of the objectives of the paper was to empirically analyze the climate change perceptions of farmers in a Latin American country; if that was the case, the paper was included in the revision. We focused on research published in peer-reviewed journals, the only exception was ( Hansen et al., 2004 ) which was published as a technical report and was the first study to analyze the topic in a Latin American country. At the end of this procedure, 21 scientific articles met the pre-established criteria.

As Table 1 shows, the existing studies come from a limited number of countries in the region; Mexico being the country with the highest number of studies available with five. Case based analysis was conducted for most, allowing for a more in depth understanding of local actors and weather ( Funatsu et al., 2019 ), while excluding generalizations at greater scales. Only two studies ( Eakin et al., 2014 ; Leroy 2019 ), covered more than one Latin American country. The studies are based on small samples; the average sample size of the papers included in Table 1 is 240, with a range of 23–1,267 observations. Most of the studies are qualitative, only three use an econometric approach as part of the analysis. Latin America’s diversity in terms of ecosystems, climate, and agricultural production systems is reflected in the studies. The papers in Table 1 analyze farmers in settings that go from semiarid environments to high mountain ecosystems, intertropical alpine ecosystems ( páramos ), and tropical forests, and, although the majority of them are of subsistence farmers, there are also studies that look at small commercial farmers, such as winegrowers. Coffee is the crop that farmers were planting in most of the studies, followed by maize, banana, cacao, potatoes, sugar cane, beans, tomatoes, and cocoa.

TABLE 1 . Basic information for studies regarding climate change perception in Latin America.

The papers reviewed look at the perception that farmers have about changes in, among other climate and weather-related variables, temperature, precipitation, and droughts. Results show that most of the farmers have in fact perceived changes in these variables. A common approach used in many of the studies is to compare farmers perceptions with the actual measured variations in the respective variables. In this way, in addition to testing if farmers perceive changes in climate-related variables, it is also possible to test if farmers perceptions coincide with actual changes. The reported results are mixed, in some cases there is a clear correspondence between changes reported by farmers and actual changes ( Pinilla et al., 2012 ; Roco et al., 2015 ; Fourment et al., 2020 ), while in other contexts, farmers’ perceptions are less aligned with observed changes ( Valdivia et al., 2010 ; Gurgiser et at., 2016; Funatsu et al., 2019 ). However, even in those cases where farmers disagree in the direction in which weather variables are changing (e.g., more or less precipitation), they tend to agree in reporting that there is more variability and in mentioning that a less reliable and more unpredictable weather complicates their farming related decisions ( Eakin et al., 2014 ; Meli et al., 2015 ; López-García and Manzano, 2016 ). Nonetheless, in some cases even when farmers perceive climate variability, they do not attribute it to climate change as they see it as a future and long-term issue ( Fourment et al., 2020 ).

Even though the focus of this review was not farmers’ adoption of adaptation practices, the articles that do look at adoption show that, in general, farmers try to adapt to the changing environmental circumstances that they are facing ( Eakin et al., 2014 ; Jacobi et al., 2015 ; Gurgiser et al., 2016 ; Meldrum et al., 2018 ; De Matos Carlos et al., 2020 ). Particularly relevant for the focus of this review is the result reported by De Matos Carlos et al. (2020) showing that there is a positive correlation between the adoption of adaptation practices and perceiving a change in climate.

The literature for Africa and Asia has shown that factors such as age, gender, education, and culture, play an important role in the processes that determine farmers’ perception of climate change ( Karki et al., 2020 ). This seems to be the case in Latin America as well. Results for Chile show that younger and more educated household heads tend to have a perception of climate change that is more aligned with the observed changes in weather variables than the perception of their older and less educated counterparts ( Roco et al., 2015 ). Nonetheless, there is also evidence showing that, in other contexts, farmers might have similar perceptions of climate change irrespective of their age; that is the case for Southern Mexico ( Meli et al., 2015 ). Meanwhile, results for Brazil ( Funatsu et al., 2019 ), Peru ( Altea, 2020 ), and Mexico (Sánchez-Cortés and Lazos, 2011; Orduño et al., 2019 ) show that women are less involved than men in agricultural activities and in general in decision making. Furthermore, they tend to be less perceptive of climate change, and, at least according to the evidence for Brazil and Peru, when they perceive it, they do not think of it as an anthropogenic phenomenon. Similarly, some indigenous farmers in Bolivia see climate change as a punishment of God to inappropriate human behavior ( Boillat and Berkes, 2013 ). Results from an analysis of indigenous farmers in Mexico, show another relevant cultural aspect behind climate change perception; the Zoques in Chiapas use biological indicators (e.g., ants, birds and some plants), in addition to their observation of weather variables, to explain perceived changes in climate variability (Sánchez-Cortés and Lazos, 2011).

In addition to the aforementioned characteristics, agroclimatic conditions can also play a relevant role as a determinant of climate change perception ( Karki et al., 2020 ). In Chile, for example, farmers living in dryland areas, where rainfall is always marginal, seem to be more aware of climate change than those located in places where irrigation infrastructure is widely available ( Roco et al., 2015 ). Something similar, although less conclusive, is reported for Ecuador ( VanderMolen, 2011 ). Altea (2020) presents evidence suggesting that in Peru perception of climate change varies with the altitude in which the agricultural land is located. Meanwhile, in the case of Brazil, although droughts affect farmers located in the tropical rainforest as well as those living in shrubland areas (characterized by low and irregular levels of precipitation), rainforest farmers seem to be less aware of the effects of climate change ( De Matos Carlos et al., 2020 ). Farmers’ location can be related to perception for another reason: access to meteorological information. This seems to be the case of Chilean farmers, those located close to the regional capital are more aware of the actual changes in weather ( Roco et al., 2015 ). Finally, perception could be affected by recent experience with climate events. Barrucand et al. (2017) report that the perception of changes in precipitation could be biased upwards when farmers have been recently affected by a weather phenomenon; La Niña occurred a few months before farmers participating in their case study were interviewed.

Discussion, Research Gaps and Opportunities for Future Research

The “finite pool of worry” hypothesis proposes that climate change concern is a finite resource, that is, it diminishes as other worries rise in prominence ( Weber, 2006 ; Weber, 2015 ). Other than the work from Hansen et al. (2004) , this is something that has not been carefully studied for Latin American farmers. Understanding how the presence of more immediate threats (e.g., violence) might hinder concern, and therefore action, about the implications of climate change is crucial in a region with high levels of poverty, inequality and social unrest. In particular, it has been shown that exposure to violence can induce higher levels of risk aversion, which in turn hampers productive investments ( Moya, 2018 ). Given the relatively high levels of violence experienced by rural populations in many Latin American countries, understanding the effects that exposure to violence can have on climate change perceptions, as well as on adaptation decisions, is crucial for the successful adaptation of farmers in the region.

The studies available for Latin America are mostly qualitative in nature and based on case studies and small samples. While these studies provide abundant information in terms of the local context, it is desirable to complement them with quantitative studies, in particular with econometric studies. Econometric studies have the potential to identify the main factors behind climate change perceptions as well as the relationship between perception and adaptation. Furthermore, given the adequate data and the correct identification strategy, econometric tools can help establish causal relationships. Moreover, data from surveys that are representative at the national or sub-national levels are necessary to obtain results that can be generalized and used to scale-up adaptation policies and programs. Ideally, these data should be longitudinal in order to better understand how information and the occurrence of extreme climatic events affect perception and adaptation over time. The national statistical offices of all Latin American countries should regularly collect information on perception of climate change and adoption of adaptation mechanisms.

The use of field experiments and choice experiments is an alternative approach which can complement the use of observational data. These tools are used widely in behavioral, environmental and experimental economics, among other disciplines. The use of hypothetical scenarios, a characteristic of these two methods, allows for the construction of mental simulations of the negative effects of climate change. By being based on hypothetical scenarios, these methods have an important advantage over observational studies: they can be used to analyze policies before they are actually implemented. These methods could also be useful to test how successful different policies might be in terms of promoting adoption of adaptation measures. Furthermore, they can help us analyze the effect that different approaches to communicate climate change information has on perception. The issue of the perception of climate change in a context where concern is in fact a finite resource could also be analyzed using these methods. Applying field and choice experiments to study perception and adaptation to climate change in Latin America is a very promising agenda from a purely academic perspective, but, more importantly, it could be very relevant in terms of providing valuable information that could aid in the design and successful implementation of public policies.

The complexity behind the analysis of farmers’ climate change perception implies that the collaboration between researchers from different disciplines, such as economics, geography, meteorology, psychology, and sociology, among others, is almost a necessity. If such collaboration is successfully achieved, the results could generate recommendations for the design of adaptation policies that are better tailored to local conditions, less costly, more efficient, and conducive to rural development.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

The Postdoctoral Fellowships Program of CONACYT and the Centro de Investigación y Docencia Económicas provided support for IF during the development of the present investigation.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: perception, climate change, adaptation, Latin America, farmer, agriculture

Citation: Fierros-González I and López-Feldman A (2021) Farmers’ Perception of Climate Change: A Review of the Literature for Latin America. Front. Environ. Sci. 9:672399. doi: 10.3389/fenvs.2021.672399

Received: 26 February 2021; Accepted: 26 May 2021; Published: 07 June 2021.

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Copyright © 2021 Fierros-González and López-Feldman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Alejandro López-Feldman, [email protected]

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Environmental impacts of organic agriculture and the controversial scientific debates

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The environmental impacts of organic agriculture have been controversially discussed in the scientific community for many years. There are still conflicting views on how far organic agriculture can help address environmental and resource challenges, and whether its promotion is an appropriate policy approach to solving existing socioecological problems. So far, no clear perspective on these questions has been established. How can this be explained? And is there a “lock-in” of the scientific discourse? The aim of this paper is to retrace the scientific discourse on this topic and to derive possible explanations as to why environmental impacts of organic agriculture continue to be assessed differently. To this end, a qualitative content analysis was conducted with a sample of n = 93 scientific publications. In addition, expert interviews were conducted to verify the results of the literature analysis. Two main lines of discussion were identified: first, the extent to which aspects of food security should be included in the assessment of environmental aspects (thematic frame); second, the extent to which net environmental impacts or possible leakage effects because of lower yield levels should be considered (spatial frame). It is concluded that the polarizing debate mainly results from the often-binary initial question (is organic agriculture superior to conventional agriculture?). Further, aspects that have been insufficiently illuminated so far, such as the choice of reference units or normative basic assumptions in scientific sustainability assessments, should be given greater consideration in the discourse.

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Introduction

Organic agriculture (OA) is considered a particularly environmentally friendly way of farming based on the interconnected principles of health, ecology, fairness, and care (IFOAM 2021 ). Especially in the European Union (EU), policymakers have therefore advocated an expansion of the area under organic management. In Germany, for example, a growth target was set in 2001: the aim is to achieve a 20% share of organically managed land (BMEL 2019 ). More recently, the EU Commission’s Farm to Fork Strategy has called for at least 25% of agricultural land in the EU to be farmed organically by 2030, in view of the expected positive environmental and resource-related effects (European Commission 2020 ).

The political support of OA and its advantages in environmental protection have been the subject of intense political and scientific discussions for more than twenty years (Sanders 2016 ). Repeatedly, several scholars have provided empirical evidence for the relative advantages of OA (cf. Reganold and Wachter 2016 ; Stolze 2000 ), whereas others have produced contrary findings and concluded the opposite (cf. Bergström and Kirchmann 2016 ; Trewavas 2001 ).

Thus, there is reason to assume that scientific debates on the relative merits of OA have taken place in an overall fruitless way since the beginning of the political support debates. This is especially true for the question of what role OA is to play nationally and internationally in addressing the critical socioecological problems facing agriculture. Footnote 1 In this context, there is an urgent need to solve such environmental issues related to critically exceeded planetary boundaries, as proposed by Steffen et al. ( 2015 ), that are primarily impacted by agriculture, e.g., biosphere integrity and biogeochemical flows. This highlights the importance of science being able to provide clear and well-justified conclusions about environmental impacts of alternative agricultural systems. The question thus arises as to whether there is a “lock-in” of scientific debate.

Against this background, this paper does not provide additional evidence whether or in which areas OA provides greater environmental performance than conventional agriculture (CA). Rather, it attempts to analyze comprehensively the controversial assessments of OA in scientific debates in terms of the underlying argumentation. Further, it explores the question of why such disparate views still exist in the scientific community. Specifically, the aim of this paper is to retrace the scientific discourse on this topic in order to derive possible explanations why environmental impacts of OA continue to be assessed controversially in the scientific community.

Material and methods

Systematic literature search.

The analyzed material is scientific publications that were obtained through a systematic literature search. The literature search was based on the four-phase flow diagram of the PRISMA Statement Footnote 2 (cf. Moher et al. 2009 ) and is illustrated in Fig.  1 . It consisted of a search string-based query Footnote 3 of the online database Scopus (n = 22 cases) and a complementary web-based search via Google and Google Scholar, mainly using the snowball system (n = 71 cases).

Flow diagram of the systematic literature search combined of a string-based Scopus search and a web-based search (modified according to Moher et al. ( 2009 ))

The search string query was conducted in English as it could be assumed that the publications relevant to the discourse to be analyzed are mainly written in English. The search string was not limited to specific environmental dimensions as the interest in knowledge was focused on argumentations that move across different performance areas. Further, it was assumed that the term “yield” is a strong indicator of a publication’s relevance to the subject matter, as studies that do not consider yield in any form are unlikely to comprehensively address the question of how to evaluate environmental performance and impacts of any agricultural system.

The complementary search was intended to include relevant literature not captured by the Scopus inquiry, as well as literature that is not subject to peer-reviewed publication but contributes to the debates under investigation. In total, the dataset consisted of n = 93 scientific publications. The full record of the analyzed cases is provided in Online Resource 1 .

Qualitative content analysis

To obtain the desired information from the retrieved cases, a qualitative content analysis was carried out. The content structuring qualitative content analysis applied here is based on Mayring ( 2015 ) and Kuckartz ( 2018 ). The analysis was conducted using the data analysis software MAXQDA 2020 (VERBI Software 2019 ). To obtain the content-related information from the analysis units, i.e., scientific publications, these were coded, which is equivalent to categorizing text segments (Kuckartz 2018 ). The codebook (including the coding frame with all main and sub-codes that were used and the code descriptions with application examples) is provided in Online Resource 2 .

Due to the explorative and descriptive orientation of the research aim, a mixed form of a-priori code creation and code creation directly on the material, i.e., deductive-inductive coding, was applied (Kuckartz 2018 ). The starting point for code creation was a coding frame consisting of relatively few codes, which were derived from the first examination of texts during the process of literature search as described above. Central publications in this examination were Gomiero et al. ( 2011 ), Meemken and Qaim ( 2018 ), and Sanders and Heß ( 2019 ).

Expert interviews

In addition to the scientific publications, qualitative data were obtained in four expert interviews. The interviews specifically aimed at exploring i) possible explanations for the course of the scientific debates and ii) lessons to be learned for the ongoing discourse.

The interviewees were considered suitable experts based on their academic careers and scientific research that has contributed and is closely related to the debates under investigation. All interviewees hold professorships at various international universities, including the research areas of organic agriculture, sustainable land use and food systems, ecology, agricultural economics and development, and sustainability science.

The interviews were conducted via video calls and followed a semi-structured guideline to meet the explorative research objective. All the interviews took place after the literature analysis had been completed. The transcripts of the interviews (provided in German language in Online Resource 3 , including the transcription system in Table S1) were then qualitatively analyzed.

The environmental impacts of OA were first comprehensively described by Stolze ( 2000 ). Based on the literature available at the time, the authors concluded that OA—like any type of agriculture—entails environmental impacts, but that these impacts are less harmful than in CA. This finding was subsequently affirmed by further literature (cf. Gomiero et al. 2011 ; Reganold and Wachter 2016 ; Sanders and Heß 2019). However, the conclusion that the environmental performance of OA is superior to that of CA, or simply put, “OA is more environmentally friendly than CA,” is not shared by all scientific studies.

Over the past twenty years or so, two key counterarguments have been raised claiming that OA is not superior to CA regarding environmental impacts. As discussed further in detail below and illustrated schematically in Fig.  2 , numerous studies argue that impacts on food security should be considered in the face of productivity issues when assessing environmental impacts. Second, it is argued that the assessment should also consider potential leakage effects given different land use (LU) efficiencies, i.e., it should not only consider the spatially immediate impacts of organic systems. Based on these intertwined counterarguments, two lines of scientific discussion have emerged in which the two counterarguments are reinforced or relativized, respectively. Thus, the main ambiguity is how broadly to draw the thematic (chapter Importance of food security in assessments of environmental impacts ) and spatial (chapter Importance of leakage effects in assessments of environmental impacts ) frames in the assessments.

Two identified lines of discussion that trace back to two key counterarguments against environmental benefits of organic agriculture (OA). The two lines illustrate the ambiguity regarding the thematic and spatial boundary in the debates. Each box depicts a set of subsumed arguments. A change of color between two boxes indicates the relativization of the preceding one. Mixed-colored boxes indicate that both relativizing and affirming arguments are subsumed in the box. The terms in bold type are highlighted in italics in the text (own illustration)

Importance of food security in assessments of environmental impacts

It becomes clear that the first line of argumentation (Fig.  2 ) can be traced back to the fundamental critique of OA regarding lower productivity. This is commonly considered problematic with reference to increasing population growth and the overarching goal of food security (cf. Goklany 2002 ; Kirchmann et al. 2007 ). Consequently, a relevant component of these debates is the yield gap between organic and conventional systems, which are mainly discussed in light of a few key meta-studies (cf. Ponisio et al. 2015 ; Ponti et al. 2012 ; Seufert et al. 2012 ). In addition, the concept of yield stability, i.e., the temporal variability and reliability of production, has been argued to be important when comparing organic and conventional agriculture regarding food security (cf. Knapp and van der Heijden 2018 ).

In the context of yield gaps, the empirical evidence to date clearly points to lower average yields in OA (cf. Meemken and Qaim 2018 ). However, beyond averages, it has also been noted that the available data are highly context-dependent , i.e., there is considerable variability depending on system and site characteristics; it is also argued that biases in study selection (e.g., by geographic location) should be taken into account in meta-analyses, as well as the multitude of yield-limiting factors that have been insufficiently understood to date (cf. Lorenz and Lal 2016 ; Seufert 2019 ). In general, it is increasingly recognized that yield is only one factor among a multitude of complex economic and ecological interrelationships that need to be included in the sustainability assessment of different farming systems (cf. Ponisio and Ehrlich 2016 ; Seufert and Ramankutty 2017 ). This argument has been put forward by researchers calling for a more holistic agri-food systems perspective beyond productivity aspects (cf. IPES-Food 2016 ) by greater inclusion of ecosystem services (cf. van der Werf et al. 2020 ) when it comes to assessing the relative merits of alternative farming systems. In particular, it is argued that a primary focus on yields and “eco-efficiency” assessments does not sufficiently address ecological or nutritional issues, as, for example, rebound effects may occur in complex LU systems (cf. Ponisio and Kremen 2016 ) or efforts to reduce crop and food waste need to be taken into account regarding the goal of food security (cf. Müller et al. 2016 ).

Accordingly, some researchers emphasize the benefits of OA for sustainable food systems and argue that yield gaps could be closed in the long term if, for example, agroecological conditions and changes in dietary behavior were promoted or possible synergistic effects of large contiguous areas of OA were taken more into consideration (cf. Fess and Benedito 2018 ; Müller et al. 2017 ; Ponisio et al. 2015 ). Others disagree, sometimes vehemently, invoking nutrient limitations in organic systems or the erroneous equation of yield ratios between individual crops with system productivity in some comparative studies, as additional land for nitrogen fixation would be needed in OA (cf. Connor 2018 ; Kirchmann et al. 2016 ; Leifeld 2016 ).

Consequently, the existing limitations of empirical evidence on yield gaps not only influence discussions on food security, but also significantly influence discussions on the assessment of environmental impacts of OA. Although a “conventional wisdom” in scientific discourse has already been described by Holt-Giménez et al. ( 2012 ), which advocates a combination of organic and conventional methods with the aim of increasing productivity in a sustainable manner (cf. Meemken and Qaim 2018 ), this has not led to a reduction in controversial debates. For example, Tal ( 2018 : 9) notes that the binary organic vs. conventional debates foster “a tendency on both sides of the […] divide to caricaturize the other and cherry pick extreme examples of environmentally problematic practices.” Seufert and Ramankutty ( 2017 : 1) also roughly divide the discourse into those researchers promoting OA as a solution to sustainable food security challenges and others who “condemn it as a backward and romanticized version of agriculture that would lead to hunger and environmental devastation.”

Accordingly, along the debates on the role of OA in global food security, arguments have been identified that address the policy relevance of certain scientific issues. In this context, it is striking that the question of whether OA can “ feed the world ” is a type of framing (cf. IPES-Food 2016 ) that has persisted throughout the period in which the analyzed literature was published (cf. Goklany 2002 ; Meemken and Qaim 2018 ; Müller et al. 2017 ; Ponti et al. 2012 ). Again, however, there is disagreement regarding the appropriate focus of research questions. Tittonell ( 2013 ), for example, considers the “feed the world” framing as oversimplified and thus not very policy-relevant, whereas others argue that this very question is crucial (Niggli 2015 ) or an interesting thought experiment (Meemken and Qaim 2018 ).

In addition, there are previously marginalized narratives that argue from the political economy perspective of unequal global power relations, thus criticizing Western industrialized development narratives, and highlighting the importance of food sovereignty (cf. Scoones et al. 2019 ). Overall, it becomes clear that the discussions about the role of OA in the context of food security are strongly influenced by normative assumptions on socioeconomic and agricultural development and are correspondingly divergent.

Importance of leakage effects in assessments of environmental impacts

Regarding environmental impacts, which are condensed in a second line of discussion (Fig.  2 ), the lower yield performance of OA and the resulting lower land use (LU) efficiency emerge as the main points of criticism, analogous to the first line of discussion. Here, the aspects regarding yield gaps, as described above, are reflected in the use of the concept of leakage effects as a prominent reasoning.

Overall, the discussions on environmental merits of OA predominantly appear as tradeoff analyses. Regarding biodiversity effects, for example, the general argumentation dominates that local biodiversity benefits of OA are offset or even turn into disadvantages due to higher land requirements when expanded (cf. Tuck et al. 2014 ). In this context, the logic of leakage effects assumes that an expansion of generally more extensive OA may lead to LU intensification elsewhere, resulting in net negative environmental impacts, e.g., higher greenhouse gas (GHG) emissions through LU change or biodiversity loss through habitat conversion (cf. Bergström and Kirchmann 2016 ; Gabriel et al. 2013 ; Kirchmann et al. 2007 ; Kirchmann 2019 ; Leifeld 2016 ; Searchinger et al. 2018 ).

By the same token, OA is criticized in terms of increased nutrient leaching, assuming that large-scale conversion would lead to arable land expansion to meet the unchanged (or increasing) demand for agricultural products due to yield gaps (cf. Bergström and Kirchmann 2016 ; Tuomisto et al. 2012 ). Although there are studies that find lower eutrophication potential in OA (cf. Schader et al. 2012 ) and more efficient nutrient use on a given area (cf. Mäder et al. 2002 ; Niggli 2015 ) due to system boundaries, some researchers also point out that a lack of data , especially on water conservation, does not allow robust general conclusions (cf. Kusche et al. 2019 ; Seufert and Ramankutty 2017 ). Further, regarding biodiversity and GHG emissions, estimating the effects of large-scale adoption of OA is argued to be ambiguous because there exists uncertainty about the relationship between yield-levels and land in production or conversion of natural habitat (cf. Ponisio and Kremen 2016 ; Reganold and Wachter 2016 ; van der Werf et al. 2020 ).

In addition, there are arguments indicating that so far unmeasured and potentially positive effects of OA are not covered by comparative studies conducted to date (cf. Clark and Tilman 2017 ; Tuck et al. 2014 ); e.g., positive biodiversity effects from large contiguous areas of OA (cf. Meng et al. 2017 ; Stein-Bachinger et al. 2019 ). Hence, some authors argue that expanding OA might be the most cost-effective strategy from the perspective of integrated policy measures that address improvements in multiple environmental dimensions simultaneously (cf. Jespersen et al. 2017 ).

The role of reference units in environmental impact assessments

What further becomes clear from the above is that study results and their conclusions regarding the benefits of OA significantly depend on the choice of reference unit . That is, whether environmental impacts are expressed per unit of farmed area or per unit of produced output. The central role of the reference units in environmental impact assessments becomes particularly clear regarding nutrient leaching and GHG emissions (cf. Halberg et al. 2005 ; Schader et al. 2012 ).

As Meemken and Qaim ( 2018 ) summarize, most evidence suggests that OA has lower environmental impacts in terms of GHG emissions when expressed per unit area, and higher impacts per unit output, respectively. However, as Sanders and Heß (2019) point out, in many studies the choice of the appropriate reference unit—despite its centrality to the results and conclusions—is inadequately justified. The latter authors argue that the question of the appropriate reference unit from a societal perspective Footnote 4 needs further scrutiny by considering i) the spatial scope of a solution to reduce environmental impacts (is the public environmental good to be provided on a local or global scale?), ii) the regional characteristics of environmental impacts (how scarce are specific public environmental goods in a region?), and iii) the risk and extent of leakage effects (does the provision of a public environmental good in one region result in negative environmental impacts in another region?).

Regarding the (rarely explicit) backing argumentation for the use of different reference units, c ontradictory justifications could be identified in the present study. Some scholars argue that the primary use of the output-reference is misleading because absolute, rather than relative (to the yield), environmental impacts are decisive; thus, the primary focus on the output-reference would not do justice to the complexity of goods and services provided as well as to the systems approach of OA Footnote 5 (cf. Müller et al. 2016 ; Niggli 2015 ; Ponisio and Kremen 2016 ).

On the other hand, it is argued that expressing environmental impacts per unit area is misleading if it does not take into account system productivity (which is usually lower in OA) and LU efficiency; thus, in the context of a growing world population and global environmental impacts, yield units would be the primarily relevant reference (cf. Kirchmann 2019 ; Meemken and Qaim 2018 ; Tuomisto et al. 2012 ). It is noteworthy in this context that already about twenty years ago, Geier ( 2000 ) stated that there is no consensus on the use of the functional unit within the life cycle assessment (LCA) methodology Footnote 6 and thus the main problem is the question of when it is reasonable to relate environmental impacts to the output and when to the area. The disparate views on the appropriate choice of reference units that since have been brought forward illustrate the difficulty to debate environmental impacts within consistent thematic and spatial boundaries.

The partly contrary argumentation is aggravated by a weak empirical evidence base on leakage effects that could result from an expansion of OA, especially on a global scale. For example, Seufert and Ramankutty ( 2017 ) note that potential impacts of a large-scale shift to OA are highly uncertain due to, among other issues, existing knowledge gaps on system-level feedback effects that ultimately influence future food production and demand. Other studies emphasize that regarding leakage effects, analogous to the implications for food security, it is crucial in an assessment of OA to also include dietary habits and the origin of demanded foods (cf. Haller et al. 2020 ; Müller et al. 2017 ). Accordingly, the German Advisory Council on Global Change has recently pointed out that the argument of leakage effects cannot be the sole focus when aiming to safeguard globally important ecosystems, but the various dimensions of leakage must be embedded in cross-sectoral measures that go far beyond issues of domestic LU efficiency (WBGU 2020 ).

Synopsis of the expert interviews

In addition to the qualitative content analysis of the literature, four expert interviews were conducted and qualitatively analyzed. Across all interviews, it became clear that the nexus of science, policy, and values Footnote 7 that has so far led to research agendas and political initiatives to promote OA (or the general transformation toward sustainable agricultural systems) needs to be adapted to the increasingly complex problem situation described in the Introduction. At the same time, the barriers that might impede such adaptation were addressed. In this context, the interviews also repeatedly referred to the formation of entrenched positions (sometimes referred to as “paradigms” or “camps”) through established academic networks and associated normative foundations that may be dominant in the investigated scientific discourse. The resulting implications are discussed in the chapter Reasons for the lock-in and what to learn from it .

Table 1 shows the synopsis of statements across all interviews that are related to possible explanations for the course of the scientific debates or to possible ways to alleviate the persisting controversies.

Lock-in of scientific discourse

The analysis at hand shows that two lines of discussion have emerged along two main arguments that relativize the environmental performance of OA in terms of lower productivity. Strikingly, from an argumentative point of view, these lines do not show a substantial development over the course of the last twenty years or so.

Against this background, the present analysis provides evidence for the validity of the assumption formulated at the beginning that the scientific discourse on the relative environmental merits of OA have taken place in an altogether little fruitful manner. In summary, since the beginning of the political support debates, no scientific consensus could be formulated on the extent to which an expansion of organically managed land, which is politically embedded in many places, will help address the environmental and resource challenges.

Certainly, it is not the goal of research to produce as homogeneous scientific knowledge as possible. However, in view of the long period of debates and the partly opposing positions that continue to exist in academic circles, it is remarkable that the productive nature of scientific research in the sense of formulating syntheses has not sufficiently taken place. Given the urgency of environmental and resource problems to be solved and that OA has gained much attention as a possible strategy, the course of scientific debates appears even more problematic.

Thus, we argue that a “lock-in” of scientific debate prevails. Various reasons for and implications of this development are conceivable and will be discussed in the following chapter.

Reasons for the lock-in and what to learn from it

First and foremost, it appears that the binary initial question regarding relative merits of OA compared to CA favors a polarizing discussion space. Accordingly, conclusions are likely to move in dichotomies. This has already been addressed by Mehrabi et al. ( 2017 ) in the context of alternative approaches to conventional intensification. The authors argue that binary “organic versus conventional” system classifications have exceedingly poor explanatory power; this holds, especially for making clear evidence-based decisions regarding socioecological outcomes of different farming systems on a global scale. Thus, they advocate “more contextual and outcome-based experiments of farming practices” to turn away from “divisive discourse” (Mehrabi et al. 2017 : 721) and to promote socioecological benefits of different farming systems.

Further, the expert interviews suggest that the research and development of “hybrid” farming systems might be a way to foster the debates on sustainable agriculture. Other researchers already have called for the deliberate reframing of binary research questions regarding a more differentiated consideration of the multilayered ecological problems and approaches to solutions (cf. Kremen 2015 ; Seufert and Ramankutty 2017 ; Shennan et al. 2017 ). In this context, a final settlement of the “ideologically charged ‘organic versus conventional’ debate” (Seufert et al. 2012 : 231) seems important to avoid fruitless discourse.

Indeed, alternative concepts beyond the organic-conventional dichotomy increasingly diversify both scientific and societal discussions about sustainable agriculture. For example, the agroecology concept is gaining recognition in policy-making (cf. Bisoffi 2019 ; FAO 2018 ), but other (partly interrelated) concepts such as conservation agriculture (cf. Kassam et al. 2019 ; Page et al. 2020 ), sustainable intensification (cf. Cassman and Grassini 2020 ; Pretty et al. 2018 ), ecological intensification (cf. Kernecker et al. 2021 ; Kremen 2020 ), or regenerative agriculture (cf. LaCanne and Lundgren 2018 ; Lal 2020 ) are also being debated internationally. Footnote 8 However, in the European context, OA continues to be the key benchmark for “greening” conventional systems (WBAE 2020 ). Regarding the “growing enthusiasm” for regenerative agriculture, Giller et al. ( 2021 : 22), in line with the reasoning of this paper, see “the need for agronomists to be more explicit about the fact that many of the […] dichotomies that frame public, and to some degree the scientific debates about agriculture, have little if any analytical purchase.”

Moreover, although the emphasis on inter- and transdisciplinary research (cf. Veerman et al. 2020 ) to meet the complex problem space seems like a logical conclusion, it can be assumed that it is no panacea. As Bruhn et al. ( 2019 ) point out, such endeavors would ideally be structured in a reflexive and co-creative way to advise transformative policy. However, it is not only the issue of lacking standardized frameworks and different traditions and vocabularies of the various disciplines involved (cf. Garibaldi et al. 2017 ) that needs to be overcome. When operationalizing sustainability in agri-food systems, also different value systems and related normative assumptions of the involved researchers must be considered (cf. Fischer et al. 2014 ; Halberg et al. 2005 ; Kuyper and Struik 2014 ; Thompson 2010 ).

Consequently, overcoming ideological barriers between supporters and critics of OA is also recognized as a prerequisite for developing and implementing more sustainable farming systems and their research (cf. Eyhorn et al. 2019 ; Meemken and Qaim 2018 ). In general, however, the expert interviews suggest that “path dependencies” regarding certain narratives of agricultural development and a lack of awareness in natural sciences as to how the framing of research questions are embedded in scientific discourse might be major obstacles for such deliberation.

In this context, the argument made by Sanders and Heß (2019) on inadequate justifications for appropriate reference units can be taken further in light of the present results. The backing argumentation, as described in the chapter The role of reference units in environmental impact assessments , reveals that basic normative assumptions in the choice of a reference unit are an implicit part of the discussions and likely are conducive to a polarizing overall debate. For example, there is the question of whether (arable) land is understood as a substitutable input to the agricultural production process or as an integral part of the agroecosystem (cf. Berlin and Uhlin 2004 ; Tuomisto et al. 2012 ). Or the question of which “purpose” agricultural systems primarily are to fulfill in the societal context (cf. Leifeld 2016 ; Ponisio and Kremen 2016 ) and which framework is to be prioritized in the assessment of environmental impacts accordingly. Here, it becomes clear that different understandings of sustainability are implicitly involved, which can be subsumed under the terms “resource sufficiency” and “functional integrity” Footnote 9 (cf. Halberg 2012 ; Thompson 2010 ).

Table 2 characterizes these two concepts according to Thompson ( 2010 ) and the “three schools of defining agricultural sustainability” (Halberg 2012 : 983–984) according to Douglass ( 1984 ), on which the former are based.

Importantly, Halberg ( 2012 ) recognizes that the different schools of agricultural sustainability according to Douglass ( 1984 ) are still present in contemporary debates, “but many users of the sustainability term seem not to be fully aware of the normative content” (Halberg 2012 : 983). Hence, the confounding influence that the sustainability term potentially has on binary scientific debates at hand is pointed out by Seufert ( 2019 : 196): “critics argue that organic agriculture may actually not be more sustainable than conventional agriculture […]” while advocates of OA “argue that the jury on comparative yields […] is still out […] or that yields are not the right metric to assess farming systems by.”

However, no substantial discussion of these frameworks, which are influenced by value systems when at work in the assessment of environmental impacts of OA, could be identified in the analyzed literature. It can therefore be assumed that the choice of a reference unit can be an entry point for critical reflection on the inevitable associated normative basic assumptions in environmental impact assessments and that the overall discourse could thus gain in transparency.

The paper aimed at retracing the scientific discourse on environmental impacts of OA and exploring why these continue to be assessed controversially. It could be shown that the debates are characterized by a “lock-in” which is complicated by persisting disagreement in the scientific community on appropriate thematic and spatial boundaries for the assessment of environmental impacts.

We conclude that it appears central to overcome binary questions to alleviate the consequent polarizing logic of the debates under investigation. Thus, the question arises, for example, to what extent comparative case studies that aim to quantify environmental impacts between OA and CA under controlled conditions can make a substantial contribution to the political debate on the future role of OA.

The paper further suggests that the insufficient empirical evidence, particularly on leakage effects and on studies directly linking yield data and environmental impacts on the same fields or farms, complicates the debates. It cannot be assumed, however, that gathering more data will be the sole key to reducing controversy. Consequently, it is increasingly appropriate to discuss the usefulness of research questions by considering a broader view of societies’ underpinnings facing increasing global crises. Researchers engaged with environmental impact assessments of agriculture should therefore be aware of their role in the process of co-creating narratives and thus exerting power (cf. Scoones et al. 2019 ). This is especially true for the implicit operationalization of different sustainability concepts, which is often mediated by the choice of reference units.

Against this background, basic normative assumptions should be more strongly reflected and disclosed when assessing environmental impacts of alternative farming systems. As Nielsen et al. ( 2019 ) point out, when considering agricultural LU from the perspective of complex human–environment land systems, there is a need for increased discussion about the normative implications of the scientific research process. Here, it appears crucial to create discussion spaces for agricultural research to appropriately consider the normative aspects that are intrinsic to the sustainability assessments of alternative farming systems. This could make the scientific debates at hand more productive and lead to greater transparency in advising political transformation processes of agri-food systems.

Availability of data and material

The analyzed literature is all published literature. A full record list with bibliographic data is available in Online Resource 1 . The analyzed interview transcripts are available in German language in Online Resource 3 .

Today, the scope of agricultural production is extended far beyond the provision of food and includes numerous environmental and resource-related challenges. In a global context, agriculture’s most critical environmental impacts include soil and water degradation, habitat fragmentation and biodiversity loss, freshwater withdrawal, disrupted nitrogen and phosphorus cycles, and greenhouse gas emissions (Foley et al. 2011 ). At the same time, hunger is on the rise again with over 800 million people undernourished or lacking sufficient nutrients, while overweight and obesity are also increasing rapidly across the globe, leading to a “triple burden” of malnutrition (Gómez et al. 2013 ; HLPE 2017 ; Ingram 2020 ). Such challenges increasingly gain traction in science and policy arenas, not least due to the overarching debate on climate change, making evident the interconnectedness between global warming and food systems and thus its socioecological consequences (IPCC 2019 ).

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement comprises guidelines that address conceptual and practical advances in the science of systematic reviews (cf. Moher et al. 2009 ).

Search string (applied on 28/01/2020): TITLE-ABS-KEY ((“organic farm*” OR “organic agricul*”) AND (“environment* impact” OR “environment* effect”) AND yield) .

This refers to the environmental dimensions of biodiversity, water protection, climate protection, and climate adaptation. The reference units for assessing impacts on soil fertility (area) and resource (N and energy) efficiency (output) were considered immanent (Sanders and Heß 2019).

For example, Müller et al. ( 2016 : 16) argue that single-criteria assessments such as emissions per unit output disregard negative externalities, e.g., through the production of synthetic inputs or concentrate feed. Similarly, the IPES-Food ( 2016 : 68) finds that classical measures of agricultural productivity systematically undervalue benefits of diversified systems; thus, new “measures of success” should be established which account for, e.g., total resource flows and interactions between the agricultural sector and the wider economy.

Within the LCA methodology, the term "functional unit" is used (according to the “function” attributed to a studied system) and “serves as the reference basis for all calculations regarding impact assessment” (Arzoumanidis et al. 2020 : 1). Thus, in the context of this study, "functional unit" can be considered synonymous with the term "reference unit" (cf. van der Werf et al. 2020 ).

As Douglas ( 2016 : 475) states, “Policy influences which science we pursue and how we pursue it in practice, as well as how science ultimately informs policy. Values inform our choices in these areas, as values shape the research agendas scientists pursue, the issues debated as we decide on policy, and what counts as sufficient warrant in any given case”.

For a characterization of some of the mentioned concepts, see Garibaldi et al. ( 2017 ). For discussions about different perspectives on agricultural intensification to foster sustainability and the associated scientific controversy, see Kuyper and Struik ( 2014 ) and Struik et al. ( 2014 ).

Note that Müller et al. ( 2016 ), for example, use the term “resource sufficiency” for describing approaches that reduce wastage or the consumption of animal products regarding climate change mitigation in food systems. They further argue that for an encompassing sustainability assessment of OA it is crucial to consider not only “efficiency” and “sufficiency” measures but also the "consistency" of resource use, i.e., approaches to optimal resource use that address “the question of the roles different resources play in the context of a sustainable food system” (Müller et al. 2016 : 42).

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The authors would like to thank Dr. Christian Schleyer for valuable suggestions on and supervision of the master's thesis that resulted in this paper. Further, the authors would like to thank the reviewers for valuable comments and suggestions on the first draft of the manuscript.

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Debuschewitz, E., Sanders, J. Environmental impacts of organic agriculture and the controversial scientific debates. Org. Agr. 12 , 1–15 (2022). https://doi.org/10.1007/s13165-021-00381-z

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• Margitta Minah   ORCID: orcid.org/0000-0002-4484-2707 3 ,
• Markus Hanisch 3 ,
• Rama Mohana Rao Turaga   ORCID: orcid.org/0000-0002-4207-8821 4 ,
• Chinwe Ifejika Speranza   ORCID: orcid.org/0000-0003-1927-7635 5 ,
• Muthumariappan Karthikeyan   ORCID: orcid.org/0000-0001-8171-6868 6 ,
• Lixia Tang 7 ,
• Kate Ghezzi-Kopel   ORCID: orcid.org/0000-0002-8777-402X 8 ,
• Julie Kelly   ORCID: orcid.org/0000-0003-0796-0461 9 ,
• Ashley Casandra Celestin 8 &
• Beth Timmers   ORCID: orcid.org/0000-0001-8526-4280 10  

Nature Food volume  1 ,  pages 620–630 ( 2020 ) Cite this article

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• Agriculture
• Development studies
• Environmental sciences
• Social sciences

An Author Correction to this article was published on 02 November 2020

A Publisher Correction to this article was published on 20 October 2020

This article has been updated

Farmers’ organizations (FOs), such as associations, cooperatives, self-help and women’s groups, are common in developing countries and provide services that are widely viewed as contributing to income and productivity for small-scale producers. Here, we conducted a scoping review of the literature on FO services and their impacts on small-scale producers in sub-Saharan Africa and India. Most reviewed studies (57%) reported positive FO impacts on farmer income, but much fewer reported positive impacts on crop yield (19%) and production quality (20%). Environmental benefits, such as resilience-building and improved water quality and quantity were documented in 24% of the studies. Our analysis indicates that having access to markets through information, infrastructure, and logistical support at the centre of FO design could help integrate FOs into policy. Natural resource management should also be more widely incorporated in the services provided by FOs to mitigate risks associated with environmental degradation and climate change. Finally, farmers who are already marginalized because of poor education, land access, social status and market accessibility may require additional support systems to improve their capacities, skills and resources before they are able to benefit from FO membership.

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The adoption of the United Nations Sustainable Development Goals (SDGs) in 2015 signalled a global commitment to combat hunger and improve the well-being of small-scale producers and the environment. Small-scale producers contribute substantially to the food supply 1 , 2 , 3 , yet many experience food insecurity 4 . They are also highly vulnerable to climate change and environmental degradation 5 with particular severity in sub-Saharan Africa (SSA) and South and East Asia 6 .

Farmer organizations (FOs) such as associations, cooperatives, producer organizations, self-help and women’s groups, are collective institutions intended to support members’ interests 7 , 8 . FOs may help small-scale producers access markets, credit and rural extension services 9 , 10 as well as manage shared natural resources 11 . FOs can build farmer skills in production, marketing and leadership and strengthen psychological well-being 12 . Building on these contributions to farmers, FOs have become core elements of rural development, agricultural productivity and anti-poverty policies—especially in Africa and South Asia 13 , 14 .

Questions have arisen about the equity of FOs, including whether they serve mainly middle-class farmers, rather than the poorest and most vulnerable farmers 15 , 16 . In some contexts, FO benefits have been shown to vary depending on the crops grown, farmers’ access to resources and membership heterogeneity 14 , 17 . Experience from Kenya, Ethiopia and South Africa also indicates that FOs often depend on support from governments and other agencies 18 , 19 and that the benefits of FOs to individual members can be limited by production volumes, infrastructure challenges and inadequate banking services, as well as limited managerial and leadership skills 16 , 20 .

More evidence on the impact of FOs is urgently needed for governments and donor organizations to identify effective interventions to achieve the SDGs, including target 2.1 to fight hunger, 2.3 to improve the income of smallholders and 2.4 to promote environmentally friendly agricultural practices and responses to climate change. Although several studies have reviewed the contributions of FOs towards those objectives, most have focused on a subset of FO types and/ or individual countries 12 , 21 , 22 . Many have not applied a systematic approach 23 .

Here we explore the contributions of FO membership by reviewing the scientific literature on the impacts of FOs on small-scale producers in SSA and India—both of which have a long tradition of cooperatives and other FOs 24 , 25 . More specifically, we analyse the findings of 239 studies to elicit the contributions of FOs to income, empowerment, agricultural production, food security and the environment. Details of the literature screening and eligibility criteria can be found in the Methods and in Box 1 .

Overview of the included studies

The 239 studies included in this scoping review document FOs in 24 countries (Fig. 1 ). All studies were published between 2000 and 2019, most (192, or 80% of the total) since 2010. The majority used quantitative methodologies (53%) and involved at least 100 respondents (64%).

The map shows the number of studies analysing FOs included in the review by country ( n  = 239) in each of the 24 countries considered.

The reviewed studies included seven types of FOs (Fig. 2b ): agricultural cooperatives, farmer associations and groups, rural self-help groups and women’s groups, dairy cooperatives, producer groups, natural resource management groups and rural financial cooperatives.

a , Representation of services in the included studies ( n  = 239). Most of the studies reported the delivery of multiple services, thus the sum across all the services is above 100%. b , Services with the highest representation in the studies by FO type. c , Services with the lowest representation in the studies by FO type. Most of the studies delivered multiple services, thus the sum across all the services is greater than 100%. Financial cooperatives did not provide any of these services.

We characterized the studies by FO membership and crop type, where relevant. Out of 228 studies that provided data on membership, 171 studies (75%) involved FOs with open membership, unrestricted by gender, age or any other qualification. The other studies (25%) had exclusively or mostly women members (Supplementary Fig. 1.3 ).

Of the 238 studies that provided data on type of production, more than half (132, or 55% of the total) focused on crop production alone and included FOs working with cereals, vegetables, coffee and fruits; 24% (56 studies) focused on livestock only, and 21% (51 studies) focused on both crop and livestock production (Supplementary Figure 1.3 ). Agricultural cooperatives and farmers’ associations had the strongest focus on crop production (73% and 68% respectively). We found only limited information on other FO characteristics, such as membership costs (found for 37 studies, 15%) (Supplementary Fig. 1.4 ).

FO services for members

The services FOs provided to their members can be grouped into 11 categories (Fig. 2 ), of which the most common (129 studies, 54%) was ‘marketing services to increase product sales’ (such as connecting to specific markets to sell products, shared transport or storage of the products and the establishment of contacts between FO members and buyers). The second most common category was ‘providing access to market information’ on product prices and trends, seasonality and regional changes (111 studies, 46%). The third most common was ‘extension and educational services’, which both promote improved production and marketing practices, as well as build financial literacy (89 studies, 37%). The first and third categories were widely represented regardless of the type of FO or membership. Other services, such as linking farmers to external programmes, infrastructure development/management and policy advocacy with local/sub-national governments, were also found in some FOs, but their frequency in the reviewed studies is low.

Most studies described FOs that provided multiple services, but 25 of the studies (4%) focused on FOs that solely provided financial services, including financial cooperatives and rural self-help and women’s groups. FOs offering multiple services typically addressed output marketing, market information and extension services and were analysed by 32% of the studies. They were mostly agricultural or dairy cooperatives, farmer associations and groups. Studies focused on the FOs from India show that rural self-help groups and women’s groups tended to deliver financial, extension and education services such as certification and improved production practices, financial literacy, marketing skills and skills for income generation, strengthening members’ access to income, savings, credit and empowerment.

FO membership impacts

The observed FO impacts could be grouped into six categories: income, yield, production quality, environment, empowerment and food security (Fig. 3a ). Of the 239 studies, 98 (41%) focused on a single measurable impact (that is ‘improved’ or ‘not improved’) in response to FO membership.

a , Proportions of studies reporting different types of impact (as percentage of studies in that category, n  = 239). b , Positive impacts by type of production and membership in the studies. c , Positive impacts by FO type. d , Positive impacts by FO services. The sum of improvements and no improvements does not add up to 100% for each impact because not all the studies analysed the respective impacts. Most (59%) of the studies delivered multiple impacts, thus the sum across all of the services is greater than 100%.

Sixty-seven per cent of the studies (161) reported only cases of improvement (in one or more impact categories) associated with FO membership; 21% (50) reported both cases of improvements and cases of non-improvements (in one or more impact categories). Finally, 12% of studies (28) reported only cases of no measurable improvement (in one or more of the impact categories studied).

Changes in income are the most investigated impact, included in 174 studies (73%). Of the 239 studies, 58% identified positive impacts on income and only 15% saw no improvements at all. These income improvements were mostly delivered by FOs engaged in crop production (55%) and with no restriction on membership (67%) (Fig. 3b ). The proportion of studies that reported improvement in incomes is similar across FO types (Fig. 3c ), except for natural resource management groups (mostly water and forest user associations), for which only a third of the studies reported positive effects. More than two-thirds of the studies analysing self-help and women’s groups reported improvement in incomes.

Among the services offered by FOs, marketing assistance for farm products and services that provide access to market information have the highest association with improvement in incomes (Fig. 3d ). Extension and financial services also seem to play a positive role, but natural resource management services do not seem to translate into short-term improvement in incomes. Our data, however, do not indicate whether income gains are achieved through a combination of these services or whether a few services on their own have a large influence on improving incomes.

In the studies that quantified changes in income (33, or 14%), increases ranged widely from 3% to 70% over the studied period (often between 2 and 5 years). Out of our 239 studies, 7 (3%) reported inconsistent income gains characterized by fluctuations over years and seasons. Such fluctuations were attributed to external and socio-demographic factors such as commodity prices, weather and climate impacts, crop and livestock losses caused by pests and diseases, varying product quality and insufficient family labour, or illness of household members 26 , 27 . However, 25 studies (10%) mentioned that FOs assisted famers to stabilize their income through access to reliable markets, higher bargaining power with wholesalers and retailers, and more stable prices through access to consistent and reliable markets. This indicates that FOs have the ability to mitigate risks that cause fluctuations in the incomes of their members.

Production quality

After income gains, improved production quality was the next most commonly reported impact. Changes in production quality were typically measured in terms of improved quality of crops, especially fruits and coffee, as well as dairy products. Positive contributions to production quality were reported in 48 studies (20%) whereas no improvements in production quality could be identified in 13 studies (5%). Positive impacts on production quality were mostly delivered by FOs engaged in crop production (65%) and in FOs with no restriction on membership (79%) (Fig. 3b ).

With few exceptions, the share of studies that find positive impacts of FOs on production quality is similar across FO types. Studies analysing rural self-help and women’s groups provide few accounts of production quality improvements and there seems to be no association between financial cooperatives and quality improvements (Fig. 3c ).

The reported improvements in production quality are mostly driven by marketing information and output marketing services, as reported in around two-thirds of the studies (Fig. 3d ). This mostly related to a switch to organic production, stronger connections with buyers and improved value chains, as found by Bezecon 28 . The provision of extension and input marketing services also seems to matter, as indicated in one-third of the studies, mostly focused on improved practices in the field, collection and storage. Other types of FO services seem to have a limited association with production quality improvements.

Yield changes

Typically, indicators to measure changes in yield include amount produced per hectare or per animal for livestock, volume of dairy products and reductions in crop losses. Positive contributions to yield were delivered in 46 studies (19%), while no improvements in yield were listed in 27 studies (11%). Positive impacts on yield were mostly delivered by FOs engaged in crop production (70%) with no restriction on membership (87%) (Fig. 3b ).

Improvements in yield were mostly driven by producer groups, farmers’ associations and agricultural cooperatives, for which approximately one-quarter of the studies reported yield improvements. Studies analysing other FO types reported yield improvements much less often or, in the case of financial cooperatives, did not report any improvements (Fig. 3c ).

As in the case of impacts on incomes, output marketing services seem to matter the most for yield improvements. Extension services and access to market information are the other two services that are associated with higher yields (Fig. 3d ). A greater capacity of producers to deploy sophisticated inputs and management practices, as a result mainly of FO extension services in combination with access to inputs, may have a strong effect on members’ yield levels, as found in Chindi et al. 29 and Wassie at al. 30 . Extension services provided by FOs have been shown to have positive impacts specifically on the use of fertilizers or high-quality and climate-resilient seeds 31 .

Environment

In 57 studies (24%), there were documented improvements in environmental parameters mostly in terms of resilience-building such as flood protection, wetland management to promote nature-based solutions to climate change, water and land conservation practices to respond to climate change impacts, improved water quality and quantity and soil conditions, and reduced erosion. All these factors contribute to longer-term yield improvement, sustainable production and risk reduction, so they can be expected to have measurable long-term effects on farmer income (beyond the period of study).

However, 15 studies (6%) mentioned no improvements or negative impacts on the environment, mostly relating to water pollution and land clearing. Positive impacts on the environment were mostly delivered by FOs engaged in crop production (53%) with no restriction on membership (78%) (Fig. 3b ).

Unsurprisingly, positive environmental impacts are predominantly reported by studies focused on natural resource management FOs. Only a few studies concerned with FOs for economic support, such as agricultural and dairy cooperatives, report positive environmental impacts (Fig. 3c ). However, it is possible that studies that focus on FOs oriented towards economic support do not measure environmental impacts. In these cases, any positive impacts in terms of income and yield may have actually resulted from sustainable practices such as improved soil and water management as well as adaptation responses to climate change impacts. The only substantial impacts were adaptation to climate change and resilience-building (11 studies, or 4.6%) and implementation of organic farming methods (10 studies, or 4.2%). There were also examples of engagement in forest and biodiversity management, addressing water quality and availability and the use of renewable energy. These activities were motivated by production needs such as irrigation or energy for processing and storage (for example Bekele and Ando 32 ) or as the outcome of particular government support campaigns to improve irrigation, for example 33 .

Environmental improvements were delivered by specific services targeting natural resource management (mostly water, forest and pasture) as well as outcomes of market information and output marketing (Fig. 3d ). In addition, studies focusing on other types of FOs that deliver extension and marketing services also reported environmental improvements, as some of the promoted management practices aimed at better yields (such as small-scale irrigation and targeted fertilizer application) were provided by FO extension services that in turn contribute to improved water quality and quantity 34 . Management practices promoted by such FOs, aimed at improving yields and/or resource use efficiency (such as small-scale irrigation and targeted fertilizer application), were also found to contribute to improved water quality and quantity 34 .

For natural resource management groups, livelihoods were strengthened and made more resilient through improvements in the quality or quantity of forest resources, irrigation water or pasture. More predictable and secure access to forest resources also provided a risk management strategy to deal with income fluctuation, as illustrated by Maretzki 26 , Ingabire et al. 27 and others.

Other impacts

Of the studies on self-help and women’s groups—predominantly located in India—about 20% reported improvements in food security and 31% in social empowerment. Natural resource management groups are the other type of FOs reporting such benefits, although present in very few studies. Empowerment was measured through self-reported increases in confidence and psychological well-being and participation in domestic decision-making, as well as improved business knowledge, leadership and management skills, and engagement in civic affairs. Approximately 20% of the studies mentioned the importance of higher income and access to credit to pay school fees, health care costs or to increase savings. The information on food security benefits is limited, with only 19 studies (8%) addressing this parameter. These studies focused on assistance related to food access through income fluctuations as well as through increasing food availability due to extension support and access to inputs resulting in yield improvements.

Factors affecting FO service delivery

Studies were also assessed for their reporting of factors that could have mitigated or strengthened the impacts of the FOs’ membership and service delivery. These were placed in two groups, concerned with external and socio-economic factors, as detailed below.

External factors

To assess the reported role of external factors on FO services, we first focused on support provided by national governments to FOs (Fig. 4 ). Of the studies reviewed, 40% reported that FOs received government support in the form of input and investment subsidies, conditional and unconditional cash transfers, infrastructure support programmes to develop roads, irrigation, storage facilities and others, non-targeted support to assist with start-up costs, government-financed extension services and tax exemptions on FO products. Besides government support, 25% of the reviewed studies mentioned support from local non-governmental organization (NGOs), international projects or donor initiatives. Across the various types of FO, the highest rate of government support was reported in studies of natural resource management groups (60%), although a higher share of farmers’ associations and groups received external support when NGOs were included.

Bars indicate the proportion of each type of FO receiving support from national governments. In total, 40% of the reviewed studies reported some sort of support from national governments to FOs. Government support may include input and investment subsidies, conditional and unconditional cash transfers, infrastructure support programmes to develop roads, irrigation, storage facilities and others, non-targeted support to assist with start-up costs, government-financed extension services and tax exemptions on FO products.

Other external factors beyond government or NGO support have been reported relating to climatic, weather and extreme events that affected production, changes in local administration and migration. From these three factors, climate variability and related effects were mentioned in 30 studies (12.6%) because of their negative implications for production and yield. Local administration was listed in 17 studies (7.1%) which typically stressed the importance of relationships with local governments to improve the ability of FOs to successfully deliver services.

Some of the reviewed studies identified specific recommendations for government policies to assist in service delivery and strengthen the impacts of FOs. The most common suggestion was to direct government support to FOs through extension services, access to credit and support for market access, as well as infrastructure investment (28 studies, 12%); and strengthening natural resource management policies, mostly on water management and climate change adaptation (27 studies, 11%).

Finally, our scoping review identified a small number of studies (14, or 6%) that referred to interactions with the private sector in terms of FOs’ contracts with input companies, interactions with private-sector buyers, engagement in contract farming and private sector-driven extension provision.

Socio-economic factors

FO impacts can vary between members as households are highly heterogeneous in terms of their socio-economic characteristics and ability to take advantage of FO services. Sixty-eight of our studies identified factors influencing membership and service delivery (Table 1 ). These factors (which are inter-related) include gender and gender relations, access to land, education and poverty levels and remoteness/access to infrastructure. We also found four studies (1.7%) that identified support to purchase inputs for production or access education for poor households 21 , 35 .

As our scoping review shows, the literature on the impacts of FO membership on small-scale agricultural producers covers different types of FO in multiple countries of SSA and India. Positive impacts on farmers’ income, yield and production were found, as well as some benefits for food security and the environment.

FO services and members’ incomes

Our review revealed that FO services that enhance access to markets—for example, product marketing and market information—have positive impacts on member income as well as yield and product quality. This is consistent with the broader literature, which argues that the diverse services that FOs provide to connect small-scale producers to markets lead to positive impacts by assisting the individual members to overcome challenges such as low quantity or quality of products and frequent supply constraints, as well as by assisting with skill development and access to inputs 22 . In addition, access to financial services was shown in our findings to be critical to achieve improved income 23 . Member access to credit will be even more crucial for FOs to respond to future challenges such as climate change impacts and risk management, which require additional investments in climate-resilient crops, irrigation or insurance 36 .

Extension and educational services delivered by FOs have a substantial presence across all types of FOs in our review and delivered positive impacts. These services addressed skill, knowledge and information deficiencies that the members faced in relation to production decisions and practices. Types of services included information about input application, farming practices and production systems; market information; health and safety; and managerial and business skills—as well as knowledge about environmental stewardship and sustainability. These services would ideally be bundled flexibly and responsively to meet specific and dynamic local production constraints and market opportunities. In practice, however, providing these services to individual ssmall farmers is costly; collectives such as FOs make extension services more cost effective and feasible 23 . FOs can provide the institutional infrastructure for effective knowledge management, applied research and practical innovation to respond to diverse local production constraints or changing market conditions. Our results reinforce the value of extension services in the context of FOs and are consistent with literature findings that FO extension services benefit smallholders by improving financial literacy and the uptake of sustainable practices to achieve productivity and income gains 36 , 37 .

We infer from these results that policy development and programming should support FOs in the effective delivery of services that provide access to markets—both input and output—through targeted market information, infrastructure investment to improve market access mostly focused on road development, logistical support and extension to improve outcomes across different forms of FOs. Smallholders would probably benefit from FO provision of financial services such as consolidating and administering small-scale loans, seasonal input financing or crop insurance schemes based on measurable climate parameters (such as rainfall) rather than complex, case-by-case yield calculations. This set of multiple services for extension, infrastructure, market and financial services should be central to the design of FOs.

In terms of avenues for future research, our scoping review indicates that the benefits provided by a given FO may differ between individual members 14 , 38 . Although we found information comparing benefits for marginalized groups (as discussed below), this aspect of the analysis warrants further research. Similarly, further investigation of the positive spill-over effects 39 of FOs on non-members and local communities would strengthen the case for FOs in supporting smallholder livelihoods.

Limited FO benefits for marginal producers

Reviewed studies mostly focused on those smallholder households with sufficient resources to benefit from engagement in FOs. Although the broader literature identifies several characteristics, such as farm size, gender of the household head, education and age, that influence FO membership and the heterogeneity of impacts 40 , our findings reveal that distance of households from markets is also an important variable hindering FO benefits. Gassner et al. 41 argue for differentiating among smallholders on the basis of the availability of resources. Households engaged in small-scale farming as a livelihood may have varying income and assets, resources to reinvest in agriculture or access to better-paid non-farm jobs to transition out of farming 38 . Those households that are on the margin and lack resources are likely to incur higher transaction costs to access FO services 39 and thus need to be supported, while possible barriers and incentives need to be carefully revisited to make FOs more accessible 42 .

The gender of the household head was a prominent factor; studies suggest that benefits such as income, yield and production quality are lower for female-headed farm households 40 . FOs seem to be less effective for younger, less literate and female farmers, even if they become members. In addition, women (both married and unmarried) are often constrained in their ability to take advantage of FO services to improve crop yield, production systems and marketing. Some studies suggest that the homogeneity of women’s self-help groups positively affected women’s likelihood of joining, as a higher proportion of female members is more appealing to other women 43 . In India, rural self-help groups and women’s cooperatives show positive impacts on women’s empowerment and access to credit, but often limited impact on domestic gender relations 44 .

Our results on gender, combined with our results on the other characteristics of marginalization (for example, distance to market) indicate that marginalized groups of farmers are less likely to participate in or to benefit from participation in FOs. This implies that policy development and programming in Africa and India should focus on the levers that induce them to more actively engage in FOs. Marginalized small-scale farmers may require different support systems to first improve their capacities, skills and resources as well as connections to infrastructure before they are able to benefit from FO membership. With regards to gender, policy development and programming should focus on improving the participation of women in FOs. One way is to mobilize women to form female-focused FOs and provide support through agricultural extension aimed at building the abilities of women farmers in areas such as production technology uptake and marketing 45 .

Limited food security benefits

This scoping review found a very low number of studies evaluating the contributions of FOs to food security compared with studies on improving income. This may also be due to our sample selection criteria, which may have resulted in studies that focused on non-marginalized small farmers for whom food security may not be a research outcome of interest.

Gains to food security attributable to FOs require additional research, as few previous studies examined this relationship. Although marginal, remote and socially disadvantaged households are the ones who typically suffer from food insecurity and who would gain most from FO participation, the studies show that marginalized producers are particularly difficult to engage in FOs for the reasons discussed above. It is also worth further studying food security impacts among more prosperous farmers, as improvements in indicators such as income or yield do not always translate into better food security or nutrition if, for example, households spend additional income on non-food items 46 .

We suggest that a distinction be made by policymakers between food security versus income or poverty reduction when prioritizing interventions in smallholder agriculture. For marginalized farmers who have limited capacity to benefit from FO membership, food security challenges require different interventions. Instead of improving production systems or market access, these might instead focus on, for example, basic social protections, income support, nutritional supplements or seasonal food security packs 41 .

FO services and natural resource management

Natural resource-based FOs were able to address soil erosion, improve water availability and contribute to reforestation and forest rehabilitation, thereby improving member resilience through access to higher-quality resources. These impacts were mostly achieved using targeted services to strengthen collective management of water, forest and pasture. The extensive work on common pool resources has demonstrated the ability of self-organized collectives to sustain key resources 47 and our results align well with this body of work. Research more specific to FOs has shown, for example, that FOs designed for collective forest, water and pasture resource management in Africa and other parts of the world 48 , 49 have resulted in positive impacts for members.

Some studies reported that climate change and weather events affected FO members’ ability to produce and sell crops due to negative impacts on harvest and impacts on markets and related infrastructure. To promote sustainable agricultural practices and address climate risk, FOs should reassess whether input use, extension services, production technologies and resource management practices are consistent with sustainability and climate resilience criteria. This could lead to greater attention to sustainable production practices and more judicious natural resource management to preserve ecosystem function under increased climate stress. These additional complexities will challenge FOs to devote more resources to innovation but they will become increasingly important to ensure the sustainability of agricultural production systems and risk-adjusted returns to farmers 50 .

Our findings show that fluctuations in farmers’ incomes in FOs is at least partly because of climate change-induced uncertainties, but at the same time we find that very few types of FO offer natural resource management services. The type of FOs that predominantly focus on natural resource management seem to be successful in delivering positive environmental impacts. The literature also suggests that other types of FO targeting the environment may improve yields, but not report on these services 51 . The implication of these findings for policy development and programming is that broader ecosystem and natural resource management should be more widely incorporated in the extension services of FOs to mitigate the risk induced by environmental degradation and climate change. This may require better documentation of current practices that contribute to the environment, as well as training and investment in innovation for FOs to demonstrate the benefits of new, more sustainable practices—so that they feel confident promoting such practices in agricultural systems.

Government role in supporting FOs

The literature shows that, on the one hand, governments play a substantial role in creating and supporting FOs. They can provide initial financial assistance 15 , 16 as well as long-term support to increase asset levels that contribute to FOs’ competitiveness and investment opportunities 9 . Moreover, government-subsidized FOs can become a buyer of last resort for farmers to sell their products, but often at lower prices than they would receive in a market 52 . Product price fluctuations were a substantial feature in many of the reviewed studies, so improved price stability was an important benefit of FO membership. Contrastingly, external support can also prop up weak and dysfunctional FOs and prolong inefficiencies 53 , with FO membership possibly representing a way of insulating small-scale producers from the hardships of essential structural change 53 .

Given the important role of governments in creating and supporting FOs, as well as the potential for political interference, the data extraction criteria used here identified available information on government and/or donor support for FOs, as well as cases where FOs do not provide the details of such support.

Final remarks

Our findings suggest generally positive evidence for the ability of FOs to provide important benefits to their members, and although only a minority of studies explicitly identify the role of government in the FOs that they study, this role was mostly a constructive one. There is abundant support in the broader literature 23 for widespread participation in FOs; governments can be more proactive in supporting them by promoting legal frameworks for FO operation and providing access to credit and extension services to enable more widespread and effective engagement of small-scale farmers in FOs. Finally, while the contribution of government and support of NGOs can be substantial, the connections between this support and FO benefits has not been well documented in our sample of studies, indicating the need for additional research to explore the supporting role of governments and other entities in FO performance. Specific investigation of FO engagement in politics and policy, as well as the influence of governmental and other programmes on these FOs, would be beneficial to gain a fuller picture of FO contributions to members’ livelihoods and environmental sustainability.

In addition to the government and NGO support to FOs, there is a growing interest in engagement with the private sector 54 . The number of studies assessing the impacts of such engagement was low in our review. Future research should focus on exploring whether the nature of supporting organization (government/NGO/private players) makes much difference in the performance of FOs.

A final caveat is that the papers in our sample may be subject to publication bias, as studies reporting positive results concerning FO impacts are more likely to be published than studies reporting insignificant or negative results. Twenty-eight of the studies included in our review (12%) provide accounts of no measurable improvement in FO members’ livelihoods. However, we cannot rule out the possibility of a larger publication bias because of this preference for positive results 55 , 56 .

Scoping review and protocol pre-registration

Scoping reviews do not seek to ‘synthesize’ evidence nor aggregate findings from different studies 57 , 58 , but rather provide a narrative or descriptive account of available research without focusing on the strength of evidence 58 . Other types of review that do require quality appraisal, such as systematic reviews, often include a lower number of studies than scoping reviews 57 . The outcomes of scoping reviews can include policy and practice recommendations and suggestions for areas of study that are not currently well addressed in the literature.

This scoping review was prepared following guidelines from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) 59 . This approach comprises five steps: (1) identifying the research question (that is, “what are the services that farmer organizations provide to members, and what impacts do those services have on small-scale producers’ livelihoods and the environment?”); (2) identifying relevant studies using pre-determined definitions (see Box 1 ); (3) study selection; (4) extracting and charting the data; and (5) collating, summarizing and reporting the results.

Box 1 Key definitions for the identification of relevant studies

Small-scale producers.

Rural producers that meet at least two of the four following criteria: land size, labour availability (especially family members), market orientation (that is, whether production is for personal consumption or sale/barter in markets) and economic size.

Farmer organization

Formal or informal membership-based, collective action institution with the purpose of assembling and possessing established organizational structure to support members in pursuing their individual and collective interests. One essential function is to organize relations with the external world to mediate between members and others who act in their economic, institutional and political environment. This definition includes farmers’ associations, farmer cooperatives, farmer clubs, farmer groups, producer organizations and women’s groups.

FO services

Actions, strategies or activities undertaken by FOs to help small-scale producers/smallholder farmers generate more income and have better access to food and other raw materials. Typical examples are agricultural extension, education, training and other ways to work with or for farmers.

Environmental impacts

Positive or negative impacts of FO services on the environment. Positive impacts may include improved water quality, greater water availability, reduced erosion, reduced pollution, greater use of renewable energy, greater climate change resilience and lower vulnerability. Negative impacts could include water, soil and air pollution, deforestation and so on.

Livelihood impacts

Changes to the capabilities, assets (stores, resources, claims and access) and activities required for living.

Sustainable livelihood

A livelihood that can cope with and recover from stresses and shocks, maintain or enhance its capabilities and assets, and provide livelihood opportunities for the next generation; it also contributes net benefits to other livelihoods at the local and global levels and in the short and long terms. In this scoping review, income and food security are the two most important components for measuring impacts on livelihoods.

Databases, search methods and citation management

A search strategy was developed and tested by the authors to identify all available publications pertaining to the research question. Search terms included variations of the key concepts in the research question (that is FOs and the geographic regions of interest). Searches included the following electronic databases: CAB Abstracts and Global Health (accessed via Web of Science); Web of Science Core Collection (accessed via Web of Science); and Scopus (accessed via Elsevier). Full search strategies used for each database, including grey literature, can be accessed in their entirety at https://osf.io/4gt3b/ .

In addition to scholarly literature, the authors also conducted a comprehensive search of grey literature using custom web-scraping scripts. The authors tested search strings on each website before initiating web-scraping. An existing Google Chrome extension was needed to scrape dynamically generated websites. The authors combined and removed duplicated results from the databases and the grey literature searches using a Python script.

Eligibility criteria

A total of 239 studies were included in the review on the basis of the following inclusion criteria: (1) explicit reference to small-scale farmers, small-scale producers or smallholders; (2) explicit reference to farmer organizations, as defined in the protocol ( https://osf.io/4gt3b/ ); (3) explicit reference to SSA, individual SSA countries or India; (4) published after the year 2000; (5) explicit reference to the impacts of FOs on livelihoods, including food security, income or the environment; (6) focus on agricultural production (crop or animal) for human and animal consumption; (7) no focus on stallholder activities in forestry, agroforestry, fisheries and aquaculture; (8) use of primary and secondary data to demonstrate contribution to outcomes; (9) published in English or French. The PRISMA flow diagram summarizes the study selection process and indicates the number of articles excluded at each phase of screening (Supplementary Fig. 1.6 ). The data extraction template (available in the Supplementary Information ) documented the study type and various aspects of FOs and their membership.

Study selection

Studies were selected following a three-stage process. The first stage involved title screening, a process where the main elements of each study are reviewed, such as the PICo components (participants, intervention and comparator, but not outcomes) that can help identify the corpus of relevant studies 60 . Title screening helped to considerably reduce the workload of citation screening while maintaining high recall of relevant studies 60 . In this study, manual title screening was enhanced by machine learning to accelerate the process. The machine learning model provided additional metadata about each study, including the identification of a study population and study geography. The additional metadata accelerated the speed with which title screening could be conducted. The second stage consisted of uploading the remaining articles to Covidence, a systematic review software package that performs title and abstract screening to exclude articles that did not meet the inclusion criteria. Two independent authors reviewed each title and abstract, and a third independent author resolved discrepancies. In the third stage, a single reviewer performed full-text screening of papers that met all inclusion criteria and those whose eligibility could not be established during title and abstract screening. Supplementary Fig. 1.6 presents the study selection process and indicates the number of articles excluded at each phase of screening. Some of the papers presented multiple studies such as ref. 61 covering two studies from Ethiopia, ref.  62 covering studies from Kenya and Uganda, ref.  63 covering India and Ethiopia, ref.  64 covering two studies from Kenya and ref.  65 covering two studies from India. Thus, the number of studies that this review refers to ( n  = 239) exceeds the number of papers ( N  = 234) included in the review. In addition, some of the included studies used aggregated household data that did not allow us to clearly separate FOs of the same type and that, in some cases, operate in adjacent locations and/or belonged to the same umbrella organization. Because the studies often discuss services and impacts across the multiple FOs, we were not able to clearly separate these FOs in the studies; this could have led to underreporting of the total number of FOs that have been studied in the individual papers.

Data extraction and analysis

A data extraction template for scoping reviews originally developed in ref.  66 was adapted for this scoping review. The data extraction template is available in Supplementary Data 1 . Extracted data included all basic citation information and each study’s location, design and methodology. We also extracted data about FOs in the studies, including their type and cost of membership, number of years in operation and focal activities of crops and livestock. These indicators were selected because of their reported potential influence on achieving impacts in the literature 9 , 52 , 64 . We also collected information about the services FOs provide to members, including marketing services, output marketing, market information, financial services, technology services such as education, extension, research, skills, technology access, infrastructure development and management, managing common property resources and others.

The impacts of FOs were separated into categories, detailing impacts of FOs services on livelihoods, agricultural production and the environment. As stated in Box 1 , livelihood impacts include changes in income and food security. We also collected impacts that are often reported on the literature on FOs’ impacts such as improvements in yield, production quality and empowerment 67 , 68 , 69 .

Given that SDG target 2.4 concerns the linkages between agricultural production and the environment, information about the impacts of FOs on the environment was also collected. The environmental impacts were identified as the outcomes of services primarily aimed at improving the benefits to members such as income, yield or production quality (for example through access to irrigation, improved grazing land or reduced impacts of climate change on production). Environmental impacts included resilience-building and responses to climate change such as flood protection and changes in water quality and quantity, soil characteristics and erosion, land in production/set aside, biodiversity, the use of renewable energy sources/reduced used of fossil fuel-based energy and others. To specify the impacts, we also collected any quantification noted in the studies such as percentage change in income, change in yield and production quality, percentage of change in land use and others. Similarly, we documented the presence or lack thereof any external and socio-demographic factors that could potentially influence the impacts of FO services.

The data extraction also included an assessment of the quality of the methodology used in each of the included papers. We examined whether sampling methods were clearly specified and whether the sampling strategy for both qualitative and quantitative studies were suitable—in particular, if the sample selection was based on specific criteria to select the FOs’ members and non-members of the FOs and if these criteria were explicitly listed in the study. Next, the studies were reviewed for their methodology justification based on the studies’ research design, focusing on two criteria: if the methodology used control groups and/or conducted pre- and post- assessments when assessing the FOs’ benefits to the members. Finally, we assessed whether a clear description of the method and methods used for data analysis and its appropriateness to make sure reported FO’s benefits to the members are based on data collected from the sample instead of for example based on literature. Based on these criteria, studies lacking clearly-stated methodological approaches and/or deemed inadequate were classified as low quality (Supplementary Table 1.1 ).

We synthesized data on FO services and their impacts on livelihood and the environment in the context of documented external and socio-demographic factors. Contextual details on the basic characteristics of FOs included in the studies, such as their geographical location, years of operation, membership type and fees can be found in Supplementary Figs. 1.3 and 1.4 .

Data availability

All data are available from the corresponding author on reasonable request.

Code availability

The scripts used for literature screening/selection and data analysis are available on request from the corresponding author. The protocol for this study was registered on the Open Science Framework before study selection, and can be accessed at https://osf.io/cxrwb/ .

Change history

20 october 2020.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

02 November 2020

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Acknowledgements

We thank the Federal Ministry of Economic Cooperation (BMZ Germany) and the Bill & Melinda Gates Foundation for funding under the project Ceres2030: Sustainable Solutions to End Hunger.

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Livia Bizikova

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Ephraim Nkonya

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Margitta Minah & Markus Hanisch

Public Systems Group, Indian Institute of Management Ahmedabad, Ahmedabad, India

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Institute of Geography, University of Bern, Bern, Switzerland

Chinwe Ifejika Speranza

Department of Cooperatives, College of Business and Economics, Wollo University, Dessie, Ethiopia

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China Agricultural University, Beijing, China

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Contributions

K.G.-K. and J.K. led the search process, contributed to title screening and writing. L.B. liaised with M.M., R.M.R.T., M.H., A.C.C. and B.T. on the search process, coordinated the paper screening, contributed to screening at all stages, developed the data extraction template and contributed to data extraction, data analysis and writing. E.N. and L.T. identified the overall research question and contributed to article screening at the abstract stage and writing. R.M.R.T., C.I.S., E.N. and M.K. supplied specific aspects of cooperatives in Africa and India and FOs focused on natural resource management expertise, and contributed to writing. L.B., M.M. and R.M.R.T. led the data analysis and the policy recommendations.

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Supplementary information.

Supplementary Figs. 1.1–1.6, tables, methods and references.

Supplementary Data 1

Data collected from the included papers using the data extraction categories.

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Bizikova, L., Nkonya, E., Minah, M. et al. A scoping review of the contributions of farmers’ organizations to smallholder agriculture. Nat Food 1 , 620–630 (2020). https://doi.org/10.1038/s43016-020-00164-x

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Farming for Life Quality and Sustainability: A Literature Review of Green Care Research Trends in Europe

Marina garcía-llorente.

1 Department of Applied Research and Agricultural Extension, Madrid Institute for Rural, Agricultural and Food Research and Development (IMIDRA), Finca Experimental ‘‘El Encín’’Ctra N-II, Km 38, Madrid 28800, Spain

Radha Rubio-Olivar

2 Social-Ecological Systems Laboratory, Department of Ecology, Edificio de Biología, Universidad Autónoma de Madrid, C/Darwin 2, Madrid 28049, Spain; [email protected] (R.R.O.); [email protected] (I.G.B.)

Inés Gutierrez-Briceño

Associated data.

Green care is an innovative approach that combines simultaneously caring for people and caring for land through three elements that have not been previously connected: (1) multifunctional agriculture and recognition of the plurality of agricultural system values; (2) social services and health care; and (3) the possibility of strengthening the farming sector and local communities. The current research provides a comprehensive overview of green care in Europe as a scientific discipline through a literature review ( n = 98 studies). According to our results, the Netherlands, the UK, Norway and Sweden followed by Italy have led the scientific studies published in English. Green care research comprises a wide range of perspectives and frameworks (social farming, care farming, nature-based solutions, etc.) with differences in their specificities. Green care studies have mainly focused on measuring the effectiveness of therapeutic interventions. Studies that evaluate its relevance in socio-economic and environmental terms are still limited. According to our results, the most common users studied were people suffering from psychological and mental ill health, while the most common activities were horticulture, animal husbandry and gardening. Finally, we discuss the potential of green care to reconnect people with nature and to diversify the farming sector providing new public services associated with the relational values society obtains from the contact with agricultural systems.

1. Introduction

Agriculture has been performed by our species for approximately 10,000 years [ 1 ], and practices have been altered according to human needs and preferences. The agricultural industrialization of the 20th century dramatically changed agricultural activities and relations between agriculture and our culture; for example, agriculture now focuses largely on the maximization of both production and profit [ 2 ]. This change has become even more severe over the last 50 years, during the green revolution, with the intensification of large-scale agricultural production and the abandonment of the countryside in traditionally agricultural rural areas [ 3 , 4 ].

The consequences of this transition not only has environmental impacts (i.e., loss of agricultural landscapes, water pollution, loss of genetic heritage related to local varieties and breeds), economic impacts (i.e., loss in profitability) and cultural impacts (i.e., loss of local knowledge and identity linked to agricultural management) but also affects our general nutrition, relationships, health and quality of life. The value and importance of the relation between humans and nature has been overlooked during recent decades. However, currently, it is known that contact with nature has a positive influence on quality of life in terms of both physical and psychological health [ 5 , 6 , 7 ]. Nevertheless, this disconnection (in terms of access and appreciation) with agroecosystems and the ecosystem services that agroecosystems provide is increasing in western and urbanized societies. It is argued by Pretty [ 8 ] that as urbanized societies we have become disconnected from the land that sustains us and we cultivate; thus, we are losing part of our culture and identity.

Human beings, as part of nature, have always coexisted with it; thus, the association between people and nature has always existed. This concept has been formalized in the academic world through the study of social-ecological systems [ 9 ]. Following the biophilia theory [ 10 ], this connection should be more important and integrated into our lives, but the ability to connect with and understand nature often depends on our experiences as children, and such experiences should be reinforced in our society [ 11 ]. In addition to the biophilia theory, Kaplan’s attention restoration theory [ 12 ] and Ulrich’s psycho-evolutionary theory [ 13 ] should be highlighted, as these theories defend and explain why and how our surrounding natural environment influences our lives and is important for us. In socio-cultural terms, the current individualist lifestyle in western societies has resulted in a disregard for social well-being, deriving in a disconnection from other people and lack of community [ 14 ]. According to Spain’s Millennium Ecosystem Assessment, in recent decades in urbanized societies’ good social relations have deteriorated, with a specific tendency toward the loss of social cohesion and an increase in individualistic, sedentary and isolated lifestyles [ 15 ]. These trends are reflected by various indicators such the number of people living alone and the amount of television consumption [ 3 ]. This situation affects the most vulnerable people in the system more dramatically, placing them at risk of social exclusion.

Green care is an approach that aims to combine, simultaneously, caring for people and caring for land. It promotes health and well-being for people at risk of social exclusion through the use of natural environments as the central element [ 14 , 16 ]. In green care, a series of activities are carried out in the context of agricultural and natural environments where activities and interactions with nature take place (i.e., activities performed on farms, orchards and gardens, forests, etc.) to produce physical, psychological, emotional, social, cognitive-educational, social and labor-integration benefits for people at risk of social exclusion [ 7 , 14 , 16 , 17 , 18 ]. At those interventions, diverse social groups could be involved, including elderly people, people with mental disabilities, people with various mental disorders or mental health problems (i.e., dementia, stress, anxiety, depression and schizophrenia), refugees, teenagers with problems, ex-prisoners, people with addiction or abuse problems, women suffering from male violence, people with various physical disorders (cancer, obesity, hearing impairment and other disabilities), migrants with difficulties, long-term unemployed people, persons belonging to ethnic minorities, etc.

Green care is an inclusive and umbrella term that includes a broad variety of interventions such as nature-based rehabilitation, care farming, social farming, therapeutic horticulture, animal-assisted intervention, etc. While these concepts are sometimes used as synonyms, all of them are sustained by different backgrounds and theories and have different representations in each country. In this study we will refer to the term green care in order to cover a broad area of research. Over recent decades, in many European countries, the use of agriculture as a tool of public health and social integration has been developed in different forms. Many projects and initiatives have arisen, with the existence of more than 170 care farms in the UK as of 2011 [ 19 ], nearly 600 care farms in the Netherlands as of 2005 [ 20 ], and nearly 700 social farms in Italy [ 17 ]. In this way, in many European countries, green care is a practice with a long history; however, numerous research projects and studies have been developed to formalize the concept only in the last decade. In fact, in 2007, a cost action called “COST Action 866 Green Care in Agriculture” was created as one of the first attempts to increase scientific knowledge of green care, as one of the main limitations of green care has been the lack of evidence about the effectiveness of its various practices [ 16 ].

Since the end of the 20th century and the beginning of the 21st century, there has been an increase in the number of scientific studies focused on green care throughout Europe. Therefore, the current paper uses Europe as a case study with the intention of better understanding the main research trends and pathways that have been taken in terms of green care development to obtain a comprehensive understanding of the progress and dimensions of this new discipline in Europe. The proposed specific objectives of this systematic review have focused on analyzing: (1) which countries have published more, within which approach and which research areas have been emphasized by studies related to green care; (2) the temporal evolution of these studies and the research objectives investigated; (3) the targeted populations of green care studies as well as the activities carried out with each population; and (4) the methods used for assessing green care interventions. Finally, we discuss how our analysis can contribute to future research and green care practices.

2. Materials and Methods

2.1. search procedure.

The methodology of this study consists of a systematic review of the existing scientific literature on green care in Europe. Specifically, we gathered and selected all studies published in peer-reviewed journals via the search engine Web of Science. To encompass the spectrum of terminology used to refer to green care, we considered this term as well as all related terms that have been used. The complete list of English keywords included “care farm”, “ecotherapy”, “farm animal-assisted”, “gardening-based intervention”, “green care”, “horticultural therapy”, “nature-based rehabilitation”, “nature-assisted therapy”, “social farm”, “therapeutic garden”, “therapeutic horticulture”, “working in nature”.

The search was restricted according to the following criteria: (1) all studies published until 2017 were included to avoid incomplete years (i.e., 2018); (2) original articles were from scientific journals to avoid double counting (and excluded short communications, letters to the editor or editorials, communications in congresses and reviews); (3) scientific articles were restricted to those published in English; and (4) scientific articles were published in European countries.

Initially, 128 scientific articles were gathered in the search. Following the application of the above selection criteria and an inspection of the abstracts, 98 valid articles were selected ( Supplementary material, Table S1 ). The remaining articles were excluded from the study because they did not meet any of the above criteria or because a read through of the publication indicated that they did not correspond to the topic in question ( Figure 1 ).

Flow diagram with the different phases of a systematic review (adapted from PRISMA, [ 21 ]).

2.2. Database Generation and Analysis

We extracted the following information from these publications: (1) publication identification (title, authors, year and journal); (2) discipline (level of disciplinary integration, i.e., uni-disciplinary or interdisciplinary, discipline area and research labels); (3) study characteristics (country studied, study type—theoretical or empirical); (4) study approach (following the terminology used in the study) and purpose; (5) target population; (6) type of activities conducted; and (7) methodological approach used to assess the intervention (when an intervention was implemented) ( Table 1 ).

List of variables extracted from the database.

Regarding the purpose of publications, the published studies were classified into three main categories: (1) therapeutic assessments, including all the studies from the health sector that analyzed the effectiveness of different interventions; (2) concept, development and relevance of green care, including all the studies that practically or theoretically addressed the emergence of this new approach or aimed to define concepts, hypothesize potential benefits, or consider the impacts of its implementation; and (3) publications where the professionals were the cornerstone of the article and defined their preferences, views, needs to provide this health and social service as well as their networks (i.e., how are they organized).

First, we explored the current state of knowledge of green care through a general descriptive analysis of all included studies. To do so, we analyzed the countries that have published more studies, the theoretical framework used (care farming, nature-based rehabilitation, etc.), the field-specific disciplines related to the subject, the temporal evolution of the studies that included green care as their main research goal, the activities conducted, the main stakeholders and the methods used. Then, chi-square tests were performed to detect significant associations between specific variables. Specifically, chi-square tests were used to assess the relationship between countries and theoretical frameworks used, countries and discipline areas, countries and user types, countries and activities conducted, and finally, between activities and user types.

3.1. Overview of the Scientific Studies on Green Care Carried out in Europe

A comparison of the studies published in different European countries showed that four countries led the scientific research on green care: the Netherlands (24%), the UK (22%), Norway (17%) and Sweden (16%). These top four countries were followed by Italy, which accounted for 7% of the publications, and other countries, such as Denmark, Spain, Germany, Switzerland, Belgium, Finland and France, which had low representation (approximately 1–4% each) (see Figure 2 ). The differences in the percentages of studies published in different countries may be due to the language restrictions used during the search process, as we analyzed only papers published in English.

Number of publications per country, including the approach used.

Green care research comprises a wide range of perspectives and frameworks with differences in their specificities. In this regard, we identified seven different terminologies associated with those frameworks: care farming (used at 31% of the publications), nature-based rehabilitation (which includes forest interventions and ecotherapy, used at 16% of the publications), green care (15%), therapeutic horticulture (13%), therapeutic gardening (11%), social farming (8%) and farm animal-assisted interventions (5%). We detected significant differences performing chi-squared contingency-table test showing that some countries follow specific approaches. In this regard, the Netherlands used green care concept in its broadest sense more than other terms in their research studies (χ 2 = 27.46; p < 0.05). In the UK most of the studies came from the therapeutic horticulture approach (χ 2 = 21.64; p < 0.05). In Norway we found a significantly higher number of studies using the farm animal-assisted intervention approach (χ 2 = 30.06; p < 0.05). Publications conducted in Sweden used the term nature-based rehabilitation significantly more than other terms (χ 2 = 52.87; p < 0.05). Finally, studies from Italy used mainly the term social farming (χ 2 = 35.46; p < 0.05) ( Figure 2 ).

Most of the articles (63% of the studies analyzed) were interdisciplinary in nature, which allowed for a holistic approach to assessing the field of green care. Concerning the disciplines that assessed the subject of green care, health sciences and environmental sciences were the dominant areas (45% each of them), followed by social sciences (10%). In Europe, green care has been frequently framed in the field of health sciences (including areas such as rehabilitation, geriatrics and gerontology, occupational health, public health, psychiatry, dietetic and nutrition and oncology) and has included research on the therapeutic effects of green care and its impact on indicators of health and well-being. Such research includes publications on the impacts of therapeutic landscapes for older people [ 22 ], horticulture for clinical depression [ 23 ], and farm animal-assisted interventions for people with clinical depression [ 24 ]. From the environmental perspective (including researchers from the fields of vegetal science, agriculture, ecology and forest science), examples of published studies have focused on the values of landscapes and their management [ 25 ] or on the conceptualization of terms and the capacity of green care farms to promote social-ecological sustainability and ecosystem services [ 26 ]. A lower number of authors came from social sciences backgrounds emphasizing socioeconomic aspects; such as analyzing the economic impacts of green care, including indicators of expenditure and employment [ 27 ]; or investigating the evolution of rural social cooperatives engaged in green care farm practices [ 28 ]. When we performed the chi-squared contingency table test we detected significant differences, showing that the Netherlands and the UK were specialized in specific research areas. Such specialization was specifically seen in the Netherlands, where there was a predominance of studies coming from the environmental sciences (χ 2 = 9.21; p < 0.05). In the UK most of the studies came from the health sector (χ 2 = 11.88; p < 0.05), which is consistent with the therapeutic horticulture approach used with clear health goals defined ( Figure 2 ).

3.2. Temporal Evolution of Green Care Studies and Their Research Objectives

The first study was published in the UK in 1979, and it focused on the requirements of horticultural training programs for people with mental health disabilities [ 29 ]. During the 1990s, two studies were published in relation to the concept, development and relevance of green care. These two theoretical studies were conducted in the health sector and explored the role of horticultural therapy [ 30 ] and gardens [ 31 ] in supporting people with disabilities, and they emphasized the elderly population. These types of studies had the purpose of providing confidence to caregivers regarding the use of green tools in human well-being interventions. Since 2004, a progressive increase in the number of studies has been observed, and this increase has been exponential since 2010 ( Figure 3 ). In 2004, a network was created to promote knowledge sharing in European countries; it was the community of practice (Cop) “farming for health”. Later, in 2007, a project called “COST Action 866 Green Care in Agriculture” was launched, and it aimed to further investigate the concept of green care and its development in different European countries. The COST Action 866 Green Care Initiative was born in 2007 as a network in which researchers, engineers and scientists cooperated and whose main objective was to increase knowledge within the framework of green care. This project involved researchers from 22 countries, and it aimed to promote scientific knowledge in relation to green care, develop and deepen the concept, and highlight the potential of this new discipline in different European countries [ 16 ]). Thus, COST 866 was one of the first initiatives to formalize green care as a scientific discipline. Subsequently, at the scientific level, the European SoFar (Social Farming in Multifunctional Farms) project was financed by the Sixth Framework Programme during the 2006–2009 period. More recently, the SoFab Project (Social Farming across Borders) has been approved and implemented (2014–2017) in Ireland and Northern Ireland through INTERREG IVA Cross-border Programme funding. All these academic initiatives may explain the increase in the number of published studies.

Temporal trends in published research by the study purpose.

Considering the general purposes of these publications, articles assessing health interventions have a long tradition, while studies exploring the concept, development and implementation of this discipline have been present but to a much lesser extent. During recent years, articles from the perspective of green care providers and how they are organized have become more visible ( Figure 3 ). In our sample, we found that 58% of the studies were assessments on therapeutic intervention. Specifically, these studies from the health sector analyze the effectiveness of different treatments with different user types. Währborg et al. conducted a study comparing the effects of therapeutic gardening with the effects of conventional therapy on the rehabilitation of people suffering from depression or stress [ 32 ]. The results obtained after therapy concluded that people who had been treated in nature required less medical help than the other group. The study carried out by [ 33 ] aimed to evaluate whether the results of therapy that used activities in boreal forests could be utilized for the rehabilitation of patients suffering from exhaustion disorder. One of the results obtained suggested that the effect of this therapy is transitory, indicating that activities in nature should not be temporary in our lives; rather, these activities should be incorporated into our daily lives. The influence of contact with nature on children with attention deficit hyperactivity disorder was examined by [ 34 ]. The way in which women with stress-related illnesses experienced rehabilitation in a therapeutic garden was described by [ 35 ].

Then, 27% of the studies emphasized the concept, development and relevance of green care and included practical or theoretical publications that addressed the emergence of this novel approach; these studies aimed to identify the concepts and potential benefits, implementation possibilities and legislative frames that supported its implementation. These aspects differed by country, and many of these studies analyzed the evolution of green care in different countries that had their own particularities and trends, as seen by the evolution in the Netherlands [ 36 , 37 ], Flanders [ 36 ], Italy [ 28 ], and Switzerland [ 38 ]. Finally, in 15% of the publications, professionals were the cornerstone of the research, and they defined their preferences, views, need to provide this social service and health care, as well as their networks and organizational strategies and the benefits that they could obtain by including green care (mainly care and social farms) in their enterprises [ 39 , 40 , 41 ].

3.3. Target Population and Greem Care Activities

Green care research covers a wide range of users who benefit from the interventions in which they participate. Following our sample, 10 categories of users have been identified, and two of these categories stand out ( Figure 4 ): people suffering from psychological health illnesses such as depression, burnout and/or stress (e.g., [ 35 , 42 ]; in 30% of the studies), and people suffering from mental health illnesses, such as cases of dementia, schizophrenia, personality and behavioral disorders and other mental health problems (e.g., [ 43 , 44 ]; in 21% of the studies). Other publications focused on children and young people at risk of exclusion (e.g., those with behavioral problems or with dysfunctional family backgrounds; such as [ 11 ]; in 8% of the studies), on people with learning disabilities (e.g., [ 45 ]; in 7% of the studies), on elderly populations (e.g., [ 22 ]; in 7% of the studies), and on people suffering from physical disabilities or physical health illnesses (e.g., people with chronic muscle pain, coronary and pulmonary diseases or cancer; [ 46 ]; in 6% of the studies). Finally, a more limited number of studies focused on people suffering from addictions (4%), offenders (e.g., [ 47 ]; in 3% of the studies), people experiencing long-term unemployment (e.g., [ 48 ]; in 1% of the studies), and refugees and displaced people (e.g., [ 49 ]; in 1% of the studies).

Type of users involved in green care programs.

Most of the studies were concentrated on a particular type of user (in 90% of the studies). We found a higher number of studies on people suffering from mental health illnesses in the Netherlands than in other countries (χ 2 = 4.71; p < 0.05). We found a significantly higher number of studies focused on people suffering from psychological health illnesses in Sweden than in other countries (χ 2 = 23.67; p < 0.001). Finally, we found a significantly higher number of studies focused on people with learning disabilities in the UK than in other countries (χ 2 = 7.74; p < 0.05).

A wide variety of activities and tasks have been analyzed in the literature review conducted. Horticulture stands out as the most widely performed activity (32%), followed by animal husbandry by feeding and taking care of farm animals and working in stables (27%), gardening (26%), and outdoor activities, such as forest walks and other physical activities in green spaces (24%; Figure 5 ). Other types of activities that were carried out included being in contact with nature (e.g., passive exposure to vegetated environments) and contemplation (12%); food processing, cooking and preparing meals from farm products for sale (9%); agriculture production, including viticulture and olive orchards (9%); relaxation (6%); conversation with the farmers, other staff and the farm community (7%); firewood collection (2%) and equine-assisted therapy (2%). There were also mentions of training and educational activities through combined workshops (e.g., textile, carpentry, ceramics and art) focused on agricultural education and user training for labor market integration.

Green care activities carried out during interventions.

In 60% of the publications analyzed, a unique principal activity was studied, with 27% of the studies having two or three activities and 13% of the publications describing more than four activities. The typology of activities also differed from country to country in some cases, and we found a higher number of studies on engaging in outdoor activities (χ 2 = 6.73; p < 0.05), relaxation activities in nature (χ 2 = 18.38; p < 0.001) and gardening (χ 2 = 6.03; p < 0.001) in Sweden than in other countries. Gardening was significantly more studied in the UK than in other countries (χ 2 = 5.11; p < 0.05). In addition, Norway and the Netherlands produced more studies related to animal-assisted interventions, including activities such as animal husbandry (χ 2 = 7.36 and χ 2 = 5.25, respectively; p < 0.05). Finally, we tested associations between activities and user types. Following chi-square tests, we found that research publications studied the impact of relaxation activities on people suffering from psychological health illness (χ 2 = 7.00; p < 0.05).

3.4. Methodological Tools for Assessing Green Care Interventions

The most common methods used to evaluate green care interventions were interviews (43%) and surveys (41%; Figure 6 ). Interviews involved semi-structured guides and open-ended questions to explore users’ experiences with green care practices. This was the case in the work conducted by [ 50 ], who analyzed forest-based rehabilitation through semi-structured interviews and analyzed the results from the perspective of the grounded theory. Interviews were carried out by [ 51 ] with care farmer professionals to explore the characteristics of diverse types of care farms in the Netherlands. Interviews were conducted by [ 52 ] with therapeutic garden users who had stress-related disorders to explore how they experienced the rehabilitation process.

Methodological tools to assess intervention effectiveness.

Other studies have used quantitative data collected from questionnaires using experimental or quasi-experimental designs at clinical assesments. How a woodland program improved the psychological well-being of members of deprived urban communities was assessed by [ 53 ] using the perceived stress scale. Horticultural therapy as a physical health, mental health and social interaction with patients with chronic musculoskeletal pain was used by [ 54 ]. They used an experimental design and assessed indicators measured by the West Haven-Yale multidimensional pain inventory or the hospital anxiety and depression scale. A lower number of other studies gathered information from participant observations (8%), official statistics (7%), focal groups (7%), participatory methods (2%) and recordings (2%).

4. Discussion

4.1. overview of green care discipline across europe.

This study builds on previous literature reviews of green care interventions. A literature review was completed by [ 55 ] ( n = 38 studies) on nature-assisted therapy that used controlled and observational studies to evaluate the scientific evidence, while five other publications were dedicated to specific user groups. The impacts on military veterans of sport and physical activity, including nature-based physical activities, were analyzed by [ 56 ] , ( n = 11 studies). In the same way, [ 57 ] focused their literature review ( n = 20 studies) on military veterans suffering traumatic experiences after active service and their participation in nature-assisted therapies. The evidence on the effectiveness of farm-based interventions for people with mental health disorders was reviewed by [ 58 ] ( n = 11 studies). The benefits of gardening-based mental health interventions was evaluated by [ 59 ] ( n = 10). Regarding dementia care, Whear et al. used qualitative and qualitative studies to examine the impacts of gardens and outdoor spaces on people with dementia ([ 60 ]; n = 17 studies), while González et al. evaluated the benefits of sensory gardens and horticultural activities ([ 61 ]; n = 16). Those reviews aimed to evaluate evidence that supported the effectiveness of green care interventions to significantly improve public health, mainly the health of specific users. Finally, a descriptive review was conducted by [ 62 ] of research on care farms for adults with mental health problems in Norway.

This research provides the first attempt to complete a comprehensive review of green care as a scientific discipline and includes studies assessing not only the effectiveness of interventions from the perspective of health but also other key aspects that require scientific attention, such as the concept, development and relevance of green care, as well as publications where professionals’ preferences, views, needs and networks were explored. Here, we analyzed trends in green care research using 98 publications that were conducted in different European countries. Although this study covered all Europe, we specifically reviewed scientific articles published in English. This limit provided a systematic method of searching for publications and avoided duplication; simultaneously, there was a limitation imposed by not collecting works published in other languages. For instance, there is evidence concerning the situation of green care under the approach of social farming in Catalonia in studies written in Spanish [ 63 ] or Catalan [ 64 ]. Much research conducted in Italy, mainly within the framework of social farming, has been published in Italian [ 17 ]; thus, such research has been underrepresented in the current study. In addition, Pawelczyk et al. attributed the lack of knowledge and research in Poland to the lack of knowledge about the usefulness of farming activities as a tool for tackling socio-health problems [ 65 ].

In this study we decided to use the broadest framework (green care) in order to cover the larger number of studies developed in Europe. However, as presented in our findings and also pointed out by other authors, there is a diversity of terms associated with different interpretations of the synergy between being in contact with natural and agricultural landscapes and the promotion of health together with other quality of life dimensions (e.g., employment, good social relationships, equity, education) [ 14 , 48 , 66 ]. Here, we identified seven terms used in research publications, the most popular being care farming. Nevertheless there were differences among countries, for example the green care term was used more in the Netherlands research, therapeutic horticulture approach in UK, farm animal-assisted intervention in Norway, the concept of nature-based rehabilitation in Sweden, and studies from Italy mainly used the term social farming. Some of the differences between those approaches are in the level of care and therapy provided [ 16 ]. Those interventions done within structured rehabilitation or health programs with clearly defined patient-orientated goals are commonly defined with the terms therapy or care such as therapeutic horticulture, therapeutic gardening or care farming [ 67 ]. In care farming and social farming the objectives are more related to conducting meaningful occupational activities and achieving employment goals at real production and commercial farms [ 40 ] and especially within social farming the therapeutic intent is not so explicit, with the aim being to promote innovation and collaboration pathways between sectors in local communities following social and employment inclusion and integration principles [ 68 ]. Other studies differ by the key element or tool used during the intervention, such as being in contact with nature at outdoor surroundings (at nature-based rehabilitation, [ 50 ]) or farm animal-assisted therapy being essential to the interactions established with animals (such as empathy, expression emotions or not being judged; [ 69 ]). In this way, green care is an umbrella term that represents a complex interaction between nature–people with different goals and specificities that determines the formalization of the approach. Green care is a dynamic concept, that has developed rapidly during the last 10 years and that will continue in progress as it represents a mirror of the different European countries and societies in terms of its culture, path dependence, needs and future expectations. This study reflects the green care research trends, giving the opportunity to offer an overview of the recent years and present time and to draw conclusions for the future. As presented by Di Iacovo et al., in Europe there are two models derived from two welfare systems: the northern European specialized model and the Mediterranean communitarian one. While in the first (followed by countries such as Sweden and the Netherlands), green care farms provide a health service (delivered by specialized facilities and skills) in private farms they receive direct payments (from the state or from the market being directly paid by users) for those services [ 70 ]. In the Mediterranean model (e.g., Italy or Spain) usually farmers do not receive a direct payment but receive other benefits more related with enhancing their reputation and expanding their networks. In this model, the goals pursued are more linked to social inclusion and justice than therapy. This situation may also explain the larger number of studies found in northern countries compared with those in the Mediterranean area; the number of studies being higher when the green care activities are more explicitly defined and where it is essential to measure the therapeutic effectiveness of the interventions conducted. In fact, there are small cooperatives or enterprises operating at the agrifood sector sustained by social economy and following agroecological principles (e.g., community supported agriculture) which are closely connected with social farming (e.g., justice, inclusion, solidarity, promotion of rural economies) but this is not explicitly stated, and it would be interesting to study the association between both approaches.

4.2. Target Population and Green Care Activities

It has increasingly been seen that green care responds to the needs of diverse groups, such as the training and working skills required by people who have experienced long-term unemployment or low employability, and the social integration of marginalized communities or spaces for community dialogue and interaction [ 71 ]. It improves not only their health but also their physical, psychological and emotional well-being (e.g., [ 55 ]). Green care provides opportunities to allow people to actively participate in society and agricultural landscape conservation. Green care has the potential to stress the relationships established between people and nature, uncovering the relational values obtained from agricultural landscapes. In an increasingly urban society, spending time in more natural, greener and more rural environments can help to meet new food, labor and social needs [ 72 ]. It has been proposed that to go beyond the classical duality to sustain landscape conservation based on intrinsic vs. instrumental values, policies should take into consideration relational values derived from the relationships establish between people and nature (e.g., cultural identity, stewardship principles), including relationships that are between people but involve natural surroundings (e.g., social cohesion) [ 73 ]. Active exposure to nature can promote a healthier lifestyle in the long term, which can help people cope with the effects of rapid lifestyles experienced in cities (e.g., stress, depression, fatigue) and address problems (quality food or lack of physical activity) facing people with increasingly sedentary futures [ 74 ].

Regarding green care activities, according to our findings, the most researched activities are horticulture, feeding and taking care of farm animals, gardening activities and outdoor activities, such as forest walks and green exercise. We found some significant associations between users and activities. In this regard, [ 75 ] analyzed different green care farming activities in terms of their suitability for different type of users taking into account aspects such as previous knowledge needed, need of support, risk due to the use of tools, etc. It would be a step forward to carry out further research to connect practices and specific well-being objectives to reach different users.

5. Conclusions

Some of the difficulties that a new science, movement and practice such as green care can face include gaining scientific, political and social credibility. Despite the advances in research publications, the potential of green care is still poorly understood [ 19 , 38 ]. As shown, in the last decade, researchers have started to study the effectiveness of green care compared to other therapeutic processes. However, since green care (mainly its orientation through social farming) contributes to rural revitalization—and the conservation of the agricultural landscape—it requires more scientific research that evaluates its relevance in socio-economic and environmental terms. It has been stressed that there is a need to recognize the complexity of views required to evaluate green care and to go beyond health indicators, since the mainstream measures of those indicators could mask and underestimate key components necessary to assess the development of green care practices (e.g., management procedures, networks of actors involved, certifications, consumer knowledge and acceptance of green care farms products, private or public policies to support them, etc.) [ 76 ]. Further research that proposes indicators and measures to analyses it as an innovative practice to diversify the farming sector, conserve agricultural landscapes and improve human well-being is required to ensure its establishment. In this regard, green care farming can be a major source of income for farmers [ 19 , 20 ] and a way to increase their visibility and reputation [ 26 ], which can stimulate the economy of the sector. It is necessary to determine which strategies farmers use, whether they are sufficiently innovative and whether they favor economic development [ 77 ]. It is also important to analyze the key factors that contribute to the success of green care projects by focusing on the point of view of producers and their willingness to innovate [ 40 ]. According to our findings, during recent years, the number of publications from the perspective of green care providers has been increasing ( Figure 3 ). A shift in production models on farms can attract new types of workers by offering diversified activities through other approaches, skills, interests, benefits and resources that break with traditional farming and livestock activities. The diversification of agricultural activities can offer farm owners opportunities to provide new services. Green care, together with agro-tourism, has also been seen as motivation for women to diversify farming activities and promote female succession in farm properties in Norway, helping to counterbalance the masculinization of rural areas [ 78 ]. This can provide an incentive to significantly halt population declines in rural areas and could stimulate an increase in the number of women owners at the head of green care activities that occur on farms.

Green care activities can play a key role in enhancing life quality and sustainability in rural areas by providing economic and social benefits, as seen by recent cases of rural social cooperatives that have emerged in Italy [ 28 ]. Such cooperatives create a new relationship between urban and rural areas, as urban people are attracted to local markets in which they can find organic and ethical products with added social value. As was shown by [ 79 ], people were willing to support a green care initiative in the UK and were willing to contribute their money and voluntary time. Similarly, Carbone, A. et al. found that consumers’ buying groups in short food supply chains in Italy hold a strong concern for ethical issues when purchasing products and had an interest in supporting social farming products [ 80 ]. Unlike other economic sectors, agricultural activity can be understood as a transversal field with the capacity to influence a diversity of well-being components, not only in terms of production but also in terms of nutritional, educational, social and relational components as well as a new way of understanding the food system and our relationship with natural environments. This viewpoint aims to intensify social capital over intensive technological capital. From this perspective, farmers are essential actors since they can provide new services to society.

Acknowledgments

We would like to thank the two anonymous referees for providing thoughtful and valuable comments and suggestions.

Supplementary Materials

The following are available online at http://www.mdpi.com/1660-4601/15/6/1282/s1 , Table S1: Publications included in the systematic review.

Author Contributions

M.G.L. conceived and designed the study; R.R.O. and I.G.B. conducted the literature review and data extraction; M.G.L. provided conceptual and analytical advice; R.R.O., I.G.B. and M.G.L. conducted the data analysis, M.G.L. wrote most of the paper.

This research was funded by a grant from the Spanish National Institute for Agriculture and Food Research and Technology, co-funded by the Social European Fund (Doc-INIA CCAA); and the IMIDRA research projects: Social Farming viability at the Madrid Region (FP16 VAS) and Assessment of Ecosystem Services provided by Agroecosystems (FP16 ECO).

Conflicts of Interest

The authors declare no conflict of interest.

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Research Article

Research on corn production efficiency and influencing factors of typical farms: Based on data from 12 corn-producing countries from 2012 to 2019

Roles Conceptualization, Data curation, Investigation, Methodology, Resources, Software, Writing – original draft

Affiliation Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing, China

Roles Conceptualization, Funding acquisition, Validation, Visualization, Writing – review & editing

* E-mail: [email protected]

• Jiamei Wang, 
• Xiangdong Hu

• Published: July 9, 2021
• https://doi.org/10.1371/journal.pone.0254423
• Reader Comments

Globally, corn is characterised by high production and high export concentrations, yet the world is experiencing an unprecedented, huge change in this regard. Ensuring the global supply of corn, and thereby the energy and food security of nations has become particularly important. To understand the importance of corn production as an influencing mechanism of global food supplies, the present study researched the corn production of typical farms in major corn-producing and importing countries around the world. I selected the corn input and output data of 18 typical farms in 12 countries from 2012 to 2019, used the data envelopment analysis (DEA) model to calculate the technical efficiency of corn production, and built a tobit model to explore the impact of farming methods, input elements, supporting services, and other factors on efficiency. The study established that the average comprehensive technical efficiency of corn production on a typical farm was 0.863, and the average loss was 13.7%. In addition, it concluded that intensive tillage and conservation tillage have high technical efficiency. It also demonstrated that the proportion of mechanical labour and technical efficiency is in a ‘U’-shaped relationship, among others.

Citation: Wang J, Hu X (2021) Research on corn production efficiency and influencing factors of typical farms: Based on data from 12 corn-producing countries from 2012 to 2019. PLoS ONE 16(7): e0254423. https://doi.org/10.1371/journal.pone.0254423

Editor: Carlos Alberto Zúniga-González, Universidad Nacional Autonoma de Nicaragua Leon, NICARAGUA

Received: March 26, 2021; Accepted: June 25, 2021; Published: July 9, 2021

Copyright: © 2021 Wang, Hu. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This research is supported by Science and Technology Innovation Engineering Talent Project of Chinese Academy of Agricultural Sciences (ASTIP-IAED-2021-RC-05) and Science and Technology Innovation Project of Chinese Academy of Agricultural Sciences (ASTIP-IAED-2021- 01). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Corn is one of the most widely-planted crops in the world. It is grown in more than 170 regions globally. Corn production is highly concentrated in certain regions like North America, Asia, and South America. According to the United States Department of Agriculture, in 2020, corn production in the United States (US), China, Brazil, and Argentina accounted for 64.63% of global production [ 1 ]. In addition to holding inventory, a portion of the corn produced is consumed domestically, while the rest is exported. Corn exports and production are also highly concentrated. The main corn exporting countries are the US, Brazil, Argentina, and Ukraine. During 2020–2021, the cumulative corn exports of these four countries accounted for 88.12% of global exports [ 1 ]. This indicates that, although China is a major corn producer, it is not a major corn exporter. Global corn production is showing a slight downward trend, and the growth rate of consumption is higher than that of production. Global corn consumption is also highly concentrated. The US and China are the two largest corn consumers. In 2020, China’s corn consumption reached 279 million tons, an increase of 2 million tons from the previous year [ 1 ]. In recent years, the destocking speed of corn in China has accelerated, and nearly 260 million tons of stock have been consumed in 4 years [ 2 ]. With the continuous consumption of temporarily-stored corn, the overall corn supply has tightened, and the gap between supply and demand has gradually increased. According to UN Comtrade, China’s corn imports reached 11.3 million tons in 2020, a yearly increase of 135.73% [ 3 ]. China is a large corn consumer, most of which is from domestic production and a small part of it is from imports, but the import volume is showing an upward trend. Ukraine and the US are the main sources of China’s corn imports. In 2020, China’s corn imports from these two countries accounted for 94.20% of the total imports [ 3 ].

Grain production has achieved bumper harvests for 17 consecutive years, since China attaches great importance to food security, corn production has increased steadily in the past five years. However, there are still major problems in China’s corn production. From the perspective of supply and demand, the rapid development of animal husbandry has caused China’s corn consumption to exceed corn production since 2016, resulting in a large amount of imported corn. According to UN Comtrade, in terms of imports, corn reached 3.52 million tons in 2018, a yearly increase of 24.38%. In 2019, corn imports reached 4.79 million tons, a yearly increase of 36.08%. Simultaneously, since 2008, corn exports have reduced to 270,000 tons, a yearly decrease of 94.5%. Corn exports have been below 300,000 tons in the past 12 years [ 3 ]. Regarding production costs, China’s demographic dividend period has passed, and labour costs have increased significantly; land costs, seeds, fertilisers, and other agricultural materials costs have also increased, increasing the total cost of corn. From a pecuniary point of view, due to China’s policy of supporting the food market for many years, coupled with the increase in corn production costs, the phenomenon of domestic and foreign corn prices is inverted. Effectively ensuring corn production, increasing corn productivity, and ensuring food security have become particularly important. This study examines the production technology efficiency of 12 major corn-producing countries around the world from a macro perspective, analyses the main influencing factors, explores its influence mechanism, and proposes policy recommendations for improving China’s corn production capacity.

Literature review

Agricultural growth mainly depends on the improvement of production efficiency, which is a key indicator of agricultural progress [ 4 ]. Improvements in technical efficiency are necessary to increase food production and release production potential [ 5 ]. To effectively improve technical efficiency, it is required to accurately estimate technical efficiency, analyse important factors affecting corn production efficiency, and propose targeted policy recommendations [ 6 ]. This article reviews the research on the technical efficiency of corn and other important agricultural products. One is to compare and sort out the methods of measuring efficiency in existing research, the other is to provide reference for the methods used in their own research programs, and the third is to compare and verify the existing research results with their own conclusions. Many scholars use certain methods to measure the technical efficiency of corn production, which can be roughly divided into two categories: parametric and non-parametric methods. One is to use the non-parametric data envelopment analysis (DEA) method to measure technical efficiency. Zhang, Meng and Gao [ 7 ] calculated the average technical efficiency of corn production in the Xiliao River Basin to be 0.88, based on the DEA of investment-oriented BCC model. They established that, compared with the traditional technology, the use of the non-film shallow drip irrigation technology improves the technical efficiency. Gao, Liu and Dai [ 8 ] conducted efficiency calculations on nine major corn-producing areas in Xinjiang in 2006. Two output indicators and two input indicators are used. The output indicators are mainly corn yield and output value per unit area. The input indicators use the working price and material cost per hectare in turn. Pei and Zhou [ 9 ] used the DEA method to evaluate corn production efficiency in Heilongjiang Province in 2014. The study established that the overall efficiency of corn production in Heilongjiang was 78.80%. Koc, Gul and Parlakay [ 10 ] measured through the DEA model that the technical efficiency of corn farms in the Eastern Mediterranean region of Turkey is 81%. Mulwa, Emrouznejad and Muhammad [ 11 ] used the DEA model to measure the technical efficiency of corn production in western Kenya and determined that the production efficiency was only 67.70%, which is technically inefficient. However, agricultural training can reduce this inefficiency. The extension agents organize farmer training sessions to inform of farmer field schools to inform farmers about modern farming methods. DEA is used to model efficiencies as an explicit function of seed, fertiliser, family labour, hired labour and hired capital costs. This study constructs a meta-frontier for the two regions.

Another way is to use a parametric method to measure technical efficiency using the stochastic frontier model. Based on the stochastic frontier analysis, at the end of the 20th century and the beginning of the 21st century, the average technical efficiency of corn production in China was above 0.8 [ 12 , 13 ]. Moreover, there are obvious gaps in technical efficiency between regions, and the degree of technology utilisation is different [ 14 ]. The application of scientific research and development in corn production has an advancing effect on corn production, and technological progress is conducive to improving the efficiency of corn production technology [ 15 ]. By 2015, technical efficiency had slightly improved, stabilising above 0.9 [ 16 ]. Abdallah and Awal [ 17 ] reported that the technical efficiency of Ghana’s corn production from 2001 to 2004 was 53%. A Cobb-Douglas production function was used as the functional form of the stochastic frontier production function to define the relationship between outputs and inputs. Chiona, Kalinda and Tembo [ 18 ] conducted a stochastic frontier analysis of the technical efficiency of smallholder corn growers in the central province of Zambia and measured an average technical efficiency of 50%. This study specifies the stochastic frontier production function using the flexible translog specification. A likelihood test was conducted that the translog stochastic frontier production function can be reduced to a Cobb Douglas. Using the stochastic frontier method, Siaw et al. [ 19 ] measured the average technical efficiency of corn production in Ghana to be 74% and established that agricultural credit can increase technical efficiency by 8%. Some scholars use DEA and stochastic frontier method to measure the technical efficiency of corn production at the same time, and find that the efficiency calculated by DEA method is higher than that by stochastic frontier method. Hassan et al. [ 20 ] measured the technical efficiency of corn production in Nigeria from 1971 to 2010. The efficiencies measured by the stochastic frontier method and DEA were 64.1% and 87.7%, respectively. The average level of corn production efficiency in Indonesia calculated by Asmara using the stochastic frontier method was 0.78, and the technical efficiency calculated using the DEA method was 0.91 [ 21 ].

Technical efficiency measurement of other important agricultural products. The above summarizes the measurement of the transnational technical efficiency of corn. Some scholars have also measured the technical efficiency of other agricultural products. Here I mainly review the measurement of the efficiency of soybean, wheat, cotton, and coffee bulk agricultural products. For soybean varieties, some scholars use the super-logarithmic stochastic frontier model to measure the efficiency. Otitoju and Arene [ 22 ] measured the technical efficiency of soybean production in Benue State, Nigeria as 0.73, and Asodina et al. [ 23 ] measured 0.58 in Upper West Region, Ghana. For wheat varieties, Tuna and Oren [ 24 ] used DEA to measure the technical efficiency of wheat production in south-eastern Anatolia, Turkey to be 0.78. Some scholars use the stochastic frontier production function to measure the efficiency of wheat production technology. Jaime and Salazar [ 25 ] measured the efficiency of Chile as 0.6, and Kamruzzaman and Islam [ 26 ] measured the efficiency of Dinajpur District, Bangladesh as 0.7. Ojo [ 27 ] measured the technical efficiency of food crops in Swaziland to be 0.77. Some scholars used DEA to measure the technical efficiency of other agricultural products. Poudel, Yamamoto and Johnson [ 28 ] used DEA to measure the technical efficiency of coffee production in Nepal as 0.83. Gul [ 29 ] measured the cotton technical efficiency in Turkey to be 0.89. The above-mentioned scholars’ technical efficiency measurement and use methods of corn and other agricultural products in different regions are shown in Table 1 . More about the measurement method of efficiency, from the beginning of Farrell to the improvement and innovation of the original method by later scholars, is presented by Zuniga who carried out a detailed and systematic combing [ 30 ].

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https://doi.org/10.1371/journal.pone.0254423.t001

Numerous scholars have researched the factors affecting corn production efficiency from different perspectives. Miho [ 31 ] studied corn planting efficiency in two areas of Tanzania and established that there is a positive relationship between the number of people who can work in the family and technical inefficiency. Production input inhibits the improvement of technical efficiency, and good living conditions can promote efficiency. Olarinde [ 32 ] established that the main determinants of the technical efficiency of maize planting in Nigeria include extension services, farming experience, and farm distance. Boundeth, Nanseki and Takeuchi [ 33 ] established that the technical inefficiency of corn growers in the northern provinces of Laos decreases with an increase in farm size. When studying the factors affecting corn production technology in China, Liu et al. [ 16 ] determined that the power of agricultural machinery has a positive effect on the technical efficiency of corn production and that the basic conditions of agriculture will affect the loss of technical efficiency. They proposed an increase in machinery investment to promote mechanised production. Wang and Wu [ 34 ] established that the use of biochemical inputs such as chemical fertilisers and the use of mechanical agricultural materials improved the technical efficiency of corn production and proposed increasing chemical fertiliser subsidies and promoting large-scale production. Jia and Xia [ 35 ] established that the scale efficiency and the corn planting area indicated an ‘inverted U-shaped relationship’. It can be seen that promoting the scale operation of grain is not the bigger the better. The government should give the guidance and standardization to farmers and make them seek the appropriate degree of scale operation.

Based on the above literature, it can be determined that many scholars use non-parametric analysis methods to measure efficiency in addition to parametric analysis methods. The parametric analysis method has higher requirements for the correct construction of the model and the selection of variables. Scholars have mostly used the envelope analysis method and linear programming to measure efficiency. Concurrently, it can also be established that most scholars focused on the perspective of corn production in a certain country, region, province, or state. They rarely explored the technical efficiency and factors influencing corn production across multiple countries. Therefore, in this study, the corn input and output data of 18 typical farms in 12 countries worldwide from 2012 to 2019 were selected. I used the DEA model to measure technical efficiency and the tobit model, considering the measured technical efficiency as the dependent variable, to explore the impact of the farming system, business scale, and quantity and structure of factor inputs on the efficiency of corn production technology.

Materials and methods

Model settings.

The production unit that needs to be measured in DEA is called a decision-making unit (DMU). There are n units that measure efficiency, denoted as DMU j , each of which has m types of inputs denoted as x i , and q types of outputs denoted as y r . The current DMU to be measured is denoted as DMU K , and the linear combination coefficient of the DMU is represented by λ. With a certain input, technical efficiency is the ratio of the actual output of a production unit to the production frontier. Under the assumption that the return to scale is constant, the production frontier can be represented by the OB ray in Fig 1 , and B is the only effective production unit. In the case of variable returns to scale, the production frontier is a curve formed by MABD that is convex to the left.

https://doi.org/10.1371/journal.pone.0254423.g001

Tobit model.

To further study the influencing factors of technical efficiency, the technical efficiency measured by DEA was used as the dependent variable, and the influencing factors were regressed. As the efficiency value of a typical farm calculated by DEA is truncated data between 0 and 1, estimating it using the ordinary least squares method will cause bias and inconsistency. The tobit model can effectively reduce the deviation [ 38 , 39 ] and is suitable for analysing the factors influencing technical efficiency [ 40 ]. Therefore, this study uses the tobit model, which is expressed as formula ( 2 ), and the maximum likelihood estimation method is used for the regression analysis. The data used in this study are from 2012 to 2019, which are short panel data. Due to the lack of sufficient statistics for individual heterogeneity, the fixed-effects tobit model cannot perform a maximum likelihood estimation, and the regression results are usually biased [ 41 ]. The use of the random effects estimation is effective [ 42 ]; therefore, this study adopts the tobit model of random effects.

Data source

The data used in this study are sourced from agri-benchmark, a global, non-profit network of agricultural economists, advisors, producers and specialists. It is managed by the Thünen Institute and global networks under the German Federal Ministry of Food and Agriculture. According to agri-benchmark, the standard definition of a typical farm is that a region or country has medium-scale and large-scale farms to reflect the average management level of most farms, that is, the average profit level. The areas where the selected typical farms are located are the intensive and main production areas of a particular crop. The cost-benefit data of a typical farm are collected in a comprehensive group with the participation of farmers and consultants, and standard questionnaires are issued to ensure that each figure reflects a typical situation.

This study selects corn input and output data of typical farms in 12 countries, that is, Argentina, Brazil, and Uruguay (South America), Russia, France, Ukraine, Bulgaria, Poland, Czech Republic, and Hungary (Europe), as well as the US (North America) and South Africa (Africa) including 18 typical farms such as AR330ZN, AR700SBA, AR900WBA, BR65PR, BR1300MT, US700IA, US1300ND, etc. from 2012 to 2019. The first two digits of the farm code represent the country, the number represents the size of the farm, and the last few alphabets represent the area where the farm is located. Consider US700IA as an example, which means a 700-hectare farm in Iowa, USA.

Variable selection and descriptive analysis

Based on the scholarly research of Yang and Lu [ 43 ], Zhao, Wang and Zhang [ 44 ], and Xiao and Zhao [ 45 ], this study selected corn yield per unit area as the output indicator, and land, labour, machinery and fuel, construction costs, and other miscellaneous expenses as input indicators. The land input is the corn planting and operating area of the farm, and the labour input is the amount of labour per unit area of corn production. Machinery and fuel inputs are machinery and fuel costs, and construction costs include depreciation, repair, and financial costs. Other miscellaneous expenses include inventory insurance premiums and taxes, consulting fees, and accounting costs. A descriptive analysis of the corn input and output indicators for typical farms is presented in Table 2 .

https://doi.org/10.1371/journal.pone.0254423.t002

Liu et al. [ 46 ] used the comprehensive technical efficiency measured by DEA as the dependent variable and land, machinery, and seed prices as independent variables to study the factors affecting corn production efficiency. They established that land cost, machinery cost, and seed price affect comprehensive technical efficiency. All of these factors have a positive impact. Tian and Zhu [ 39 ] used grain-sown area, labour input (indicated by agriculture, forestry, animal husbandry, and fishery), and fertiliser application as explanatory variables when studying the factors affecting food production efficiency in China. The results showed that chemical fertilisers and machinery promoted the efficiency of food production. The main factors for the improvement, the grain-sown area, and labour input have no significant impact on efficiency.

The technical efficiency measured by the DEA model was selected as the dependent variable, and farming methods, input elements, supporting services, and other factors were used as independent variables to perform the random-effects tobit model regression analysis. The statistical descriptions of the explanatory variables are presented in Table 3 . The farming system is divided into five methods: no-tillage, conservation farming (reducing stubble and covering seeds), conservation farming (covering seeds), intensive farming, traditional farming, and deep farming. I constructed dummy variables for the farming system. The proportion of mechanical labour is the proportion of mechanical labour to total labour, which is used to reflect the degree of mechanisation. To explore the influence of the degree of mechanisation on technical efficiency, the square term of the proportion of mechanical labour was added to the constructed model. The proportion of hired workers indicates the ratio of the number of employees to the total labour force. The total labour force includes family and hired labour. Land cost refers to the cost of renting a unit area of land or the opportunity cost of land. Drying costs refer to the costs incurred when drying corn. Insurance premium is the cost of purchasing agricultural insurance for farms. Consultation fees refer to the expenses incurred by consulting experts in agricultural production. The addition of time-trend variables reduces the impact of time on technical efficiency. Regional dummy variables were set up, and North America was used as the benchmark group to compare regional technical efficiency differences with South America, Europe, and Africa.

https://doi.org/10.1371/journal.pone.0254423.t003

Results and discussion

The use of the DEA model for efficiency measurement needs higher requirements for the selection of input items, and the difference in the selection of input items directly leads to differences in the results of the efficiency calculation. After the input items were determined, multiple collinear tests were performed to ensure that the selected input items were not redundant. Using Stata to test this, the results show that the input variables selected by the DEA model are all less than 10, with an average value of 2.85, indicating that the input variables selected by the DEA model are not redundant. A multicollinearity test was also performed on the explanatory variables selected by the tobit model. The results indicated that the variance expansion factors of the selected explanatory variables were all less than 10, with an average value of 2.02, indicating that there was no multicollinearity in the variable settings of the tobit model [ 47 , 48 ].

Technical efficiency and decomposition of corn production in typical farms

DEAP2.1 software was used to calculate the technical efficiency of 18 typical farms, which is shown in Table 4 . The average level of technical efficiency of corn production was 0.863, and the average loss of efficiency was 13.7%. Table 3 shows that the comprehensive technical efficiency of nine typical farms is 1, indicating that half of the farms in the research group have corn production at the forefront of production, and corn production is DEA-effective. These farms are in Europe and South America, of which three are in Argentina, one is in Brazil, and the others are in the Czech Republic, Poland, Russia, Ukraine, and Uruguay. The comprehensive technical efficiency of US1215INC farms in Bulgaria, Hungary, and the US was approximately 0.5. This is because the pure technical efficiency of corn production is low, which reduces the contribution of scale efficiency. From the perspective of scale efficiency, the average level is 0.939, which is relatively high. However, half of the farms are still in a state of ineffective scale. Farms in Bulgaria, Brazil, Hungary, and South Africa are in a state of increasing returns to scale, and five farms in France and the US are experiencing diminishing returns to scale.

https://doi.org/10.1371/journal.pone.0254423.t004

Based on the two aspects of the main corn-producing countries and the production technology being at the forefront, the corn production conditions in the US, Brazil, and Argentina are analysed.

The pure technical efficiency of corn production in two typical farms in the US is 1, the scale efficiency is above 0.9, and the returns to scale are both in a diminishing stage. As the main agricultural business entity in the US, a large number of family farms have a long history of development. In 2010, the number of family farms in the US reached more than 1.9 million. On average, each family farm has a large area of arable land. The development of family farms requires a higher degree of mechanisation. Therefore, the US attaches great importance to the research and development of agricultural science and technology. The use of agricultural technology reduces labour input and saves transaction costs propagated by hired labour, which is an important reason for maintaining a high level of agricultural production efficiency [ 49 – 51 ]. Furthermore, the US government has always attached great importance to agricultural protection. Due to the surplus of agricultural production, the return to scale of farm corn production is in a diminishing stage. To avoid excessive production by farmers, the government has issued several compensation policies to encourage farmers to participate in fallow programs to adjust supply and protect farmers’ income [ 52 ].

The comprehensive technical efficiency, pure technical efficiency, and scale efficiency of the BR1300MT farms in Brazil and three farms in Argentina are all 1, and the return to scale remains unchanged.

Natural conditions. Brazil, Uruguay, and Argentina are all located in South America. Corn growth has more intensive water requirement. Abundant rainfall and vertical and horizontal river networks provide irrigation conditions, and corn has become the main crop in these areas.

Government support. The Brazilian government has issued several policies to support the soybean planting industry. Brazil convened soybean farmers across the country to form farm consortiums, which purchase materials for soybean planting and production in a unified manner and provide farmers with financing, product transportation, and storage services [ 53 ]. It is beneficial to reducing procurement costs, promoting the consistency of soybean product quality, and achieving large-scale mass production. Soybean varieties grown in Brazil were introduced in the US and transferred to the country for research and breeding. The new varieties cultivated by scientific research institutions based on the country’s soil and climatic conditions are more adaptable to the country’s natural conditions and more suitable for growth in the tropics and subtropics. Consequently, the yield of corn improved [ 54 ]. The Argentine government has always attached great importance to the research, development, and promotion of agricultural science and technology [ 55 ]. It has established several scientific research institutions to promote the development of agricultural science and technology, establish extension stations to provide farmers with agricultural knowledge, train the use of new technologies, promote the implementation and transformation of scientific research results [ 56 ], and establish specialised corn production areas; production specialisation has greatly improved the production efficiency of corn. The export strategy has been formulated to focus on reducing production costs and increasing output per unit area. Consequently, its competitiveness in international trade has improved [ 56 ].

Influencing factors of corn production technology efficiency

The regression results of the tobit model are presented in Table 5 .

https://doi.org/10.1371/journal.pone.0254423.t005

Regional difference.

The North American region represented by the US was considered a benchmark. The regression results indicate that the average technical efficiency level of South America does not show a significant difference from that of North America. At the 1% level, the technical efficiency of Europe and Africa is significantly lower than that of North America to varying degrees. Among them, Africa, represented by South Africa, has the lowest technical efficiency. North America, represented by the US, and South America, represented by Brazil and Argentina, have the highest maize production efficiency. The US, Brazil, and Argentina are the main corn producers and exporters. In addition to having a unique natural resource endowment, government agencies have invested heavily in science and technology and issued several policies to protect the interests of farmers and promote and guarantee the effective production of corn [ 56 ].

Time difference.

At the 1% significance level, the time variable had a positive effect on technical efficiency. This shows that over time, the technical efficiency of agricultural production is constantly increasing, and the level of agricultural technology is constantly improving. The advancement of agricultural production technology relies on economic growth. In the exogenous technological progress growth model, the technological factor is regarded as a function of time.

Tillage system.

The regression coefficients of deep tillage and no-tillage did not pass the significance test, indicating that the technical efficiency of deep tillage and no-tillage did not show obvious differences compared with conservation tillage (reducing stubble and covering seeds). At a significance level of 1%, seed mulching and intensive farming have higher technical efficiency than conservation tillage, and seed-covering farming modes have higher technical efficiency. Covering seeds can replace fallow in summer, protect soil organic carbon, and have beneficial effects on maintaining soil fertility and improving soil quality, thereby effectively disposing biological waste [ 57 ]. Conservation tillage integrated mulch technology can effectively coordinate crop yield, water consumption, and reducing carbon emissions, which can increase the water use efficiency of corn and improve corn production efficiency [ 58 ].

Supporting service.

The input of the drying fee reflects that typical farms dry harvested corn. However, not all production entities dry corn, but the harvested corn has a high moisture content. If stored improperly, the corn will deteriorate and produce mildew, resulting in food waste and economic losses [ 59 ]. In the regression results, the drying fee has a significant promoting effect on the improvement of technical efficiency, indicating that the drying treatment of corn significantly reduced the loss of harvested fruits, promoted agricultural efficiency, and improved corn production efficiency.

The farm insurance premium did not pass the significance test in the regression results, indicating that agricultural insurance did not fully promote the production of planting entities. Due to the risks of agricultural production that are difficult to control, the agricultural insurance market system is unsophisticated [ 60 ], the implementation of insurance is arbitrary, and the protection of agricultural production is not enough; therefore, the interests of farmers have not been effectively protected [ 61 ]. Expert consulting fees have a significant inhibitory effect on corn production efficiency at the 1% level. On the one hand, the methods used to collect farmers’ knowledge are flawed, resulting in inaccurate or incomplete information. The use of inaccurate information or misunderstandings between farmers and scientists result in the development and extension of unprofitable, unsustainable or inappropriate management recommendations [ 62 ]. On the other hand, a small number of experts are far from the reality of agricultural production and fail to propose practical solutions to farmers. Moreover, farmers are restricted by their level, unable to achieve effective docking between farmers and experts, and have not played a role in improving agricultural production efficiency [ 63 ].

Regarding input factors, at a significance level of 1%, land cost has a positive impact on technical efficiency, but the impact is relatively small. This indicates that when the cost of land input factors increases, it will curb the input of land factors, which in turn will increase the utilisation rate of existing land and promote an increase in land productivity. They cope with the increase in the cost of production materials by increasing the output per unit area of land and promoting the improvement of production technology. The regression coefficient of the proportion of hired workers is significantly positive at the 1% level, indicating that it is difficult to improve technical efficiency by relying solely on family labour. The increase in the proportion of hired labour in labour input has a positive effect on the development of agricultural production. Additionally, as agricultural production reaches a certain stage, the demand for labour will inevitably increase. The limited family labour force has led to a large demand for hired labour, which has met the expansion of agricultural production and stimulate technical efficiency.

At a significance level of 5%, the proportion of mechanical labour can promote technical efficiency. Due to the continuous increase in labour costs, the main body of production has increased the use of machinery [ 64 ], shortened the operation time of unit agricultural production, substantially increased labour productivity [ 65 ], and improved corn production efficiency. However, at the 1% significance level, the regression coefficient of the square term of the proportion of mechanical labour is negative, indicating that there is an inverted U-shaped relationship between the proportion of mechanical labour and technical efficiency. When the proportion of mechanical labour exceeds the critical value, continuing to invest in machinery will lead to a decline in technical efficiency, which is due to the use of machinery restricted by topographical characteristics and agricultural production conditions [ 66 ]. Plain areas are easier to mechanise than hilly areas. Small machinery can be used in hilly areas. Excessive machinery investment leads to low agricultural production efficiency and economic losses. The use of machinery also has requirements for the extent of the entire land. It is difficult for these fragmented lands to implement large-scale mechanised production. Agricultural operations still rely mostly on human labour. The proportion of machinery investment is too high, and it is difficult for them to be effective, resulting in technical inefficiency.

Conclusions

The main conclusions of this study are as follows: first, the overall level of technical efficiency of corn production on family farms in Argentina, the US, Ukraine, and other countries is at a relatively high level and is at the forefront of production technology. However, some farms are affected by the low efficiency of pure technology. Technical inefficiency reached 50%, and the average loss of technical efficiency was 13.7%. Second, among the five farming modes, seed-covering, intensive farming, and conservation farming have higher production efficiency than no-tillage and deep tillage. The increase in the cost of production factors of land and labour forces has caused the continuous improvement of technology, thereby increasing the efficiency of corn production. Third, corn drying can promote production efficiency. Farm insurance premiums and expert consultation fees have not played a role in stabilising agricultural production and promoting crop management. The transformation of the economic benefits of agricultural production equipment services is still lacking. Finally, the input and use of machinery are within a certain threshold, which improve labour productivity and promote the development efficiency of corn planting agriculture. However, if the mechanical input exceeds the turning point, it will cause redundancy in the input of elements, resulting in waste and loss of efficiency.

The findings of this study are as follows. First, to achieve the goal of improving corn production efficiency, reducing biological waste, and realizing green and economical production, it is necessary to innovate the production and farming mode of corn, promote the diversification of production methods, and develop a protection farming method that combines with bean crops. Second, improving and refining the construction of agricultural insurance regulations and systems, effectively protecting farmers’ interests, and sharing the risks faced by agricultural production for farmers, are all conducive to improving the efficiency of food production and its sustainability. Third, it is high time to promote the landing and transformation of agricultural scientific research results, strengthen the training and support of scientific research institutions, experts and scholars to production entities, and earnestly take farmers as the main resource for agricultural production ideas. Fourth, it is imperative to consider to promote land circulation and reduce fragmented land to create conditions for mechanised agricultural production and provide basic guarantees for modern agricultural production. Simultaneously, various types of machinery should be developed as well as a technical strategy that combines large-scale and small-scale machinery. For areas where it is difficult to operate large-scale machinery, small-scale agricultural machinery with characteristics of adapting to local conditions should be considered. Finally, strengthen the construction and improvement of the agricultural service system and provide farmers with various forms of information services so that agricultural products can better meet the market demand. At present, there is a large gap between socialised service funds and unreasonable use. Agricultural socialised services must be considered with high importance to effectively serve both farmers and agriculture, ensure agricultural production, and improve economic benefits.

Supporting information

S1 table. corn production input and output indicators in typical farms for 2012–2019..

https://doi.org/10.1371/journal.pone.0254423.s001

S2 Table. Model variable settings and descriptive statistics.

https://doi.org/10.1371/journal.pone.0254423.s002

Acknowledgments

The authors thank agri-benchmark for providing the data. We would like to thank the editor and three reviewers whose comments helped to greatly improve this manuscript.

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News from the Columbia Climate School

The Emerging Field of Sustainable Agriculture

Steve Cohen

I grew up in Brooklyn and have spent most of my life living in Morningside Heights in Manhattan; my only exposure to farming life was during the last of my five years living in Franklin, Indiana, when I delivered the Daily Journal Newspaper to farmers in rural Johnson and Brown counties. Occasionally, when the farmers were a little short of cash, they paid for their newspapers with produce. I know very little about farming, except that farmers seem to be the hardest-working people I’ve ever known. Modern industrial farming has made American agriculture the most productive in the world, but it is capital-intensive, risky, and polluting. An emerging movement in sustainable agriculture is developing, which promises continued productivity with less pollution. According to the Union of Concerned Scientists :

“There’s a transformation taking place on farms across the United States. For decades, we’ve produced the bulk of our food through industrial agriculture—a system dominated by large farms growing the same crops year after year, using enormous amounts of chemical pesticides and fertilizers that damage our soil, water, air, and climate. This system is not built to last, because it squanders and degrades the resources it depends on. But a growing number of innovative farmers and scientists are taking a different path, moving toward a farming system that is more sustainable—environmentally, economically, and socially. This system has room for farms of all sizes, producing a diverse range of foods, fibers, and fuels adapted to local conditions and regional markets. It uses state-of-the-art, science-based practices that maximize productivity and profit while minimizing environmental damage. Sustainability also means the whole system is more resilient to droughts, floods, and other impacts of climate change that farmers are already seeing. Though the move to this type of system often involves some up-front costs, smart public policies can help farmers make the shift.”

Techniques such as rotating crops and integrating livestock and crops can reduce costs and maintain soil productivity with less need for chemicals and other costly interventions. Notably, some of the “sustainable” techniques represent a return to traditional methods of farming. The issue for many farmers is the capital requirements needed for some of the technology required for sustainable farming and the revenue deferred when giving the soil time to regenerate itself. One company that has addressed these issues is Land O’Lakes, which is a cooperative populated by farmers who are part owners of the company. According to a 2021 Press Release on the Land O’Lakes website:

“Land O’Lakes, Inc. today announced new on-farm sustainability commitments to be adopted by its more than 1,600 member-dairy farms by 2025. Within the next four years, all Land O’Lakes’ dairy farmer-owners will complete an intensive, industry-leading on-farm sustainability assessment aligned with the U.S. Dairy Stewardship Commitment while maintaining universal compliance with the National Milk Producers Federation’s National Dairy Farmers Assuring Responsible Management (FARM) program. This announcement is the next step in Land O’Lakes’ enterprise-wide approach to on-farm sustainability.”

This company has learned that sustainability practices can reduce both costs and pollution. By using satellites, automation, GPS, and other technologies, they can precisely target water, fertilizer, and pesticides to plants, thereby reducing resource use, costs, and pollution. Managing manure from their many dairy cows enables Land O’Lakes to utilize this resource for fertilizer and energy. Efforts are underway to promote these methods globally, with limited success. According to Rochelle Toplensky of the Wall Street Journal :

“The Sustainable Markets Initiative, a private-sector group launched in 2020, set up its Agribusiness Task Force to accelerate regenerative agriculture adoption and includes senior leaders from Mars, McDonald’s, PepsiCo , Bayer , McCain, Mondelez and others. The task force’s 2022 report concluded the main hurdle to adopting regenerative practices was that farmers’ short-term economics don’t add up, but it also found there was a knowledge gap and not everyone in the value-chain was aligned. Follow-up work concluded that farmers need financial incentives and derisking mechanisms as well as technical and peer-to-peer support. Also important were agreeing [to] environmental outcome metrics and creating supportive policy and payments for so-called ecosystem services such as rebuilding biodiversity and water quality.”

In the United States—and throughout the world—there is potential for a transformation of agricultural practices to make them more efficient and less polluting. But agriculture is an industry characterized by a wide variety of cultural traditions, business models, and geographic conditions. Sustainable practices make economic and environmental sense, and farmers who practice them will outcompete those who don’t. Nevertheless, the transition requires capital, technical expertise, and the willingness and training to experiment with new production processes. The piece by the Union of Concerned Scientists recognizes this and calls for public policy to provide the incentives needed to bring about this transition. The United States has had an activist federal farm policy since the 19 th century. It dates back to the establishment of Land Grant Colleges in the Morrill Act of 1862, where the federal government gave states federal lands in exchange for the establishment of agricultural colleges. The federal government also developed agricultural extension services to train farmers in the latest methods of farming.

In the United States, agricultural policies and subsidies are legislated in the “Farm Bill,” which has been renewed eighteen times since it was first enacted during the New Deal of the 1930s. According to the Congressional Research Services Primer on the Farm Bill , last updated on February 29, 2024:

“Farm bills traditionally have focused on farm commodity program support for a handful of staple commodities—corn, soybeans, wheat, cotton, rice, peanuts, dairy, and sugar. Farm bills have become increasingly expansive in nature since 1973, when a nutrition title was first included. Other prominent additions since then include horticulture and bioenergy titles and expansion of conservation, research, and rural development titles.”

Traditionally, agriculture policy in the United States was dominated by rural farm states due to their over-representation in the United States Senate. Lightly populated farm states have the same number of senators (two) as heavily populated industrial states. Farm policy was more important in rural states, and in exchange for votes from industrial states on urban initiatives, farm-state senators traditionally dominated U.S. agriculture policy. This changed in the 1970s when food subsidies for poor people were added to the farm bill, and today, over 75% of the funding in the farm bill subsidizes these “nutrition” programs. In the most recent farm bill, nutrition funding totaled $1.1 billion, crop insurance $124 million, and conservation funding was about $58 million. The politics of agriculture policy is no longer dominated by the farm states. According to the Congressional Research Service :

“The omnibus nature of the farm bill can create broad coalitions of support among sometimes conflicting interests for policies that individually might have greater difficulty achieving majority support in the legislative process. In recent years, more stakeholders have become involved in the debate on farm bills, including national farm groups; commodity associations; state organizations; nutrition and public health officials; and advocacy groups representing conservation, recreation, rural development, faith-based interests, local food systems, and organic production. These factors can contribute to increased interest in the allocation of funds provided in a farm bill.”

This broader coalition might be drawn upon to support an expansion of agricultural subsidies to enable farms to receive the financial support needed to transition to renewable agricultural practices in the United States. Farm policy and environmental/climate policy might well be brought together to modernize American agriculture and reduce its release of toxics and greenhouse gasses into the environment. Funding the transition to renewable agriculture does not need to be justified as climate policy, although it would have the impact of reducing greenhouse gas pollution. It could well be sold as modernizing American agriculture to better position it for global competition.

Views and opinions expressed here are those of the authors, and do not necessarily reflect the official position of the Columbia Climate School, Earth Institute or Columbia University.

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Making Small Farms More Sustainable — and Profitable

• Lino Miguel Dias,
• Robert S. Kaplan,
• Harmanpreet Singh

A case study of Better Life Farming, an innovative public-private partnership in India, Indonesia, and Bangladesh.

Smallholder farms provide a large proportion of food supply in developing economies, but 40% of these farmers live on less than U.S.$2/day.  With a rapidly growing global population it is imperative to improve the productivity and security of farmers making up this sector.  This article presents the results of Better Life Farming, an ecosystem that connects smallholder farmers in India, Indonesia, and Bangladesh to the capabilities, products, and services of corporations and NGOs.

More than 2 billion people currently live on about 550 million small farms, with 40% of them on incomes of less than U.S. $2 per day. Despite high rates of poverty and malnutrition, these smallholders produce food for more than 50% of the population in low-and middle-income countries, and they have to be part of any solution for achieving the 50% higher food production required to feed the world’s projected 2050 population of nearly 10 billion people.

• LD Lino Miguel Dias is Vice-President Smallholder Farming in the Crop Science Division at Bayer AG, a global pharmaceuticals and life sciences company based in Germany, and Invited Professor at University of Lisbon, Portugal.
• Robert S. Kaplan is a senior fellow and the Marvin Bower Professor of Leadership Development emeritus at Harvard Business School. He coauthored the McKinsey Award–winning HBR article “ Accounting for Climate Change ” (November–December 2021).
• HS Harmanpreet Singh is Smallholder Partnerships Lead for the Asia Pacific region at Bayer AG, a global pharmaceutical and life Sciences company.

Partner Center

Improving Data Collection and Measurement of Complex Farms (2019)

Chapter: 4 conceptual issues: defining farming, farms, farmers, and agriculture, 4 conceptual issues: defining farming, farms, farmers, and agriculture.

In this chapter, we identify and clarify definitions and concepts used in the measurement of the farm economy and propose alternatives that may be more useful for measuring the activities of complex operations. The material here bridges the discussions of complexity in the previous chapter and proposals for the statistical framework in the next chapter.

4.1. FARMING

Observing the complexity of modern farms raises the basic question: What makes a farm a farm? It is essential to address this question before turning to others, such as what a complex farm structure is and how such operations should be dealt with in statistics, research, and policy making.

In the United States, even children who have never seen a farming operation firsthand develop an intuitive notion of what a farm is. Their toys are likely to include plastic cows, sheep, horses, and tractors. They are taught songs like “Old MacDonald.” And with the advent of electronic gaming devices and smartphones, it is now possible for anyone to “run” a virtual farm. These experiences tend to reinforce the idea that the typical farm is an entity that perfectly overlaps the activities of a farmer who centrally manages a diverse set of production activities.

In the contemporary economy, however, this heuristic of the farm has become outdated as more complex organizational forms have emerged and become commonplace. For example, some traditional farming activities, like harvesting crops, are now often carried out by hiring specialized companies or using workers who are employed not by the farm but

rather by an employment agency. Some farms are parts of vertically integrated businesses, such as feedlots attached to slaughterhouses or vineyards attached to wineries. And some farms derive income from related activities that are not necessarily farming, such as agritourism, food processing, or energy production.

All of these examples create problems for statisticians the moment they want to classify a company (or operation, business, firm, or holding—for now, we use these terms interchangeably) as either a farm or another type of business. Before turning to that classification question, we therefore address an even more basic question: What is farming?

The Merriam-Webster Dictionary defines farming as “the practice of agriculture or aquaculture,” providing a synonym to be discussed later, in section 4.5 . The Oxford English Dictionary (second edition, 1989) defines farming as “the activity or business of growing crops and raising livestock.” This latter day-to-day definition seems fairly close to what social scientists use in some of the disciplines relevant to this study. For instance, the International Accounting Standards Board (IASB) 1 describes agriculture (an imperfect synonym of farming) as follows: “Agricultural activity is the management by an entity of the biological transformation and harvest of biological assets for sale or for conversion into agricultural produce or into additional biological assets” ( International Financial Reporting Standards Foundation, 2017 ). The essence of this definition is of a business activity that manages a biological process that leads to either products (such as milk, potatoes, or oranges) or biological means of production (such as animals or seeds).

Definitions Current at Major Statistical Agencies

International statisticians rely on a definition similar to that used by the IASB. Both the Statistical Classification of Economic Activities in the European Community (NACE) 2 and the international integrated system of economic classifications (ISIC) managed by the UN Statistical Commission (UNSTAT) group agriculture (again, used as a synonym of farming) together with fisheries and forestry in one category for national accounting purposes. This broad category is defined as “the exploitation of vegetal

___________________

1 The IASB is an independent, private-sector body that develops and approves International Financial Reporting Standards (IFRS). The IASB operates under the oversight of the IFRS Foundation. The IFRS Foundation is a not-for-profit public interest organization established to develop a single set of high-quality, understandable, enforceable and globally accepted accounting standards—IFRS Standards—and to promote and facilitate adoption of the standards.

2 Commonly referred to as NACE, based in the French term, “nomenclature statistique des activités économiques dans la Communauté européenne. ”

and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.” 3 Note that the addition of natural habitats includes fisheries, hunting, and forestry in the category.

Likewise, the North American Industrial Classification System (NAICS) groups farming together with fisheries and forestry activities:

The Agriculture, Forestry, Fishing and Hunting sector comprises establishments primarily engaged in growing crops, raising animals, harvesting timber, and harvesting fish and other animals from a farm, ranch, or their natural habitats.

The establishments in this sector are often described as farms, ranches, dairies, greenhouses, nurseries, orchards, or hatcheries. A farm may consist of a single tract of land or a number of separate tracts which may be held under different tenures. For example, one tract may be owned by the farm operator and another rented. It may be operated by the operator alone or with the assistance of members of the household or hired employees, or it may be operated by a partnership, corporation, or other type of organization. When a landowner has one or more tenants, renters, croppers, or managers, the land operated by each is considered a farm. 4

The sector distinguishes two basic activities: agricultural production and agricultural support activities. Agricultural production includes establishments performing the complete farm or ranch operation, such as farm owner-operators and tenant farm operators. Agricultural support activities include establishments that perform one or more activities associated with farm operation, such as soil preparation, planting, harvesting, and management, on a contract or fee basis.

Excluded from the Agriculture, Forestry, Fishing and Hunting sector are establishments primarily engaged in agricultural research and establishments primarily engaged in administering programs for regulating and conserving land, mineral, wildlife, and forest use. These establishments are classified in Industry 54171, Research and Development in the Physical,

3 See http://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=MSG_PRINT_NOHDR&StrLanguageCode=EN&ml=NACE_REV2__18493724__0 .

4 Conceptually, this panel agrees with the NAICS position that households that own land and rent to operators should not be considered farm businesses. If a household (or any other sector of the economy, such as government or a business) is engaged only in renting the land, then it should be classified as part of the rental and leasing industry. The farming activity undertaken on such rented land would be measured by the establishment/enterprise using the land, that is, the lessor, not the lessee. Practically speaking, if the lessor and lessee of the land were both considered to be farms, then there would be a risk of double counting.

Engineering, and Life Sciences; and Industry 92412, Administration of Conservation Programs, respectively ( North American Industrial Classification System, 2017 ).

Agricultural activities as classified by NAICS are listed in Annex 4.1 , together with forestry, fishing, and hunting activities. Within this activities listing, Codes 111 (Crop production) and 112 (Animal production and aquaculture) are farming activities. Codes 1151 and 1152 include support activities for agriculture, covering the important set of nonfarm businesses that serve as potential substitutes for direct management and operation of production activities by farmers. Indeed, these businesses introduce two possible sources of statistical complexity: some farms purchase agricultural production services from other entities and some farms sell their production services to other farms.

Neither the National Agricultural Statistics Service (NASS) nor the Economic Research Service (ERS) offers explicit definitions of farming. 5 However, the Census of Agriculture questionnaire follows an enumeration that is similar to NAICS’s list of “agricultural activities.” The census includes questions on field crops (Section 6); hay and forage crops (Section 7); cut Christmas trees and maple syrup (Section 8); nursery, greenhouse, and floriculture (Section 9); vegetables and melons (Section 10); fruits and nuts (Section 11); berries (Section 12); cattle and calves (Section 13); hogs and pigs (Section 14); equine (Section 15); sheep and goats (Section 16); aquaculture (Section 17); poultry (Section 18); apiculture (Section 19); and other livestock (Section 20) ( National Agricultural Statistics Service, 2014 ).

This emphasis on the management of biological processes in crops and livestock makes it clear that farming is not defined on the basis of the purpose to which products are put: it is not necessary to produce food or animal feed. Biological processes are also managed to produce fiber (flax, cotton, and wool), flowers, fur, pharmaceutical products, and fuel, to give just a few examples. However, there are some activities based on biological processes that are classified not as farming but as industrial—such as the industrial production of yeast or the use of microbes in sewage sludge.

Rather than describing activities on the basis of the purpose of the products or even on the basis of the products themselves, the NAICS objective is to describe a certain production function associated with the way a process creates outputs (products or services) from inputs. It is not a product classification and so differs from the National Income and Product Accounts, which is. When biomass is produced and transformed into ethanol or electricity, this includes a farming activity, very often as a primary

5 See Annex 2.1 in Chapter 2 for a table with references to definitions used in USDA documents.

activity, as well as a manufacturing activity, in this case energy production that turns biomass into ethanol.

In some cases, the production activity is very different from farming, even if plants or animals are used. Examples include petting farms, dude ranches, or farms offering yoga with goats. Although these operations may have been conventional farms in the past, focused strictly on crop or livestock production, they do not necessarily fit farm definitions any longer, even if their animals are registered for animal health reasons.

A side remark is that statisticians employ a variety of approaches for dealing with “gray” or illegal activities, for example, when the goal is to assess the total size of an economy. The United Kingdom has added income from illegal activities, such as sex work and illegal drug sales, to its gross domestic product (GDP) calculations to conform with reporting rules from the European Union. In Canada, now that recreational marijuana use will soon become legal, Statistics Canada is preparing to add estimates of the plant’s production and sale to assess its economic impact. 6 The U.S. Bureau of Economic Analysis, which produces GDP estimates, only includes legal activities, so in the United States marijuana growing would only be counted as a farming activity in states, such as Washington and Colorado, where marijuana production, sale, and use are currently legal.

These examples also show that to operationalize the concept of farming as the management of a biological process, an agreed-upon list of activities is needed that can determine the exact borders where farming stops and nonfarm production and services begin (or, as we argue in section 4.5 , where it may make sense to introduce a new category of agriculture that lies between farming and industry). Such a list of activities could make clear that a tree nursery and an energy plantation (where miscanthus or willows are grown, for example) are still farming, while a forest is not, even if it undergoes some pruning. Fish farming (aquaculture) is considered an agricultural activity, as is the growing of seaweed (algae), 7 but the capture of wild fish from the ocean and harvesting of kelp from the open ocean are not.

Defining Agricultural Support Activities

Essential to this definition of farming is that biological products must be managed. Simply harvesting (as a contractor) is not farming if it is done

6 See https://unstats.un.org/unsd/nationalaccount/aeg/2017/M11_8_2_Satellite_Accounting_at_Statistics_Canada.pdf .

7 NAICS defines algae harvesting as an agricultural activity (112519).

as a service but is rather an agricultural support activity. 8 The less technical Organisation for Economic Co-operation and Development (OECD) definition is “the activity or business of growing crops and raising livestock,” one could add “and their preparation for the primary market” to make clear that harvesting, storing, and packaging are included.

Another example of agricultural-related activities that has posed problems for classification purposes is the making of wine and cheese. Are the biological processes in cheese making and wine making characteristic enough to call them farming activities instead of industrial activities? NACE has tried to solve this dilemma by introducing the idea of a primary market of a farm:

Agricultural activities exclude any subsequent processing of the agricultural products classified under divisions Manufacture of food products and beverages and Manufacture of tobacco products, beyond that needed to prepare them for the primary markets. The preparation of products for the primary markets is included here (that is in farming). The division excludes field construction (e.g. agricultural land terracing, drainage, preparing rice paddies etc.) classified in the section Construction, and buyers and cooperative associations engaged in the marketing of farm products, classified in the section Wholesale and retail. Also excluded is landscape care and maintenance, which is classified in the class Landscape service activities [ text slightly altered for readability by deleting codes ]. 9

In agriculture, one frequent situation where the breakdown of the value added presents difficulties is when the unit produces grapes and manufactures wine from the own-produced grapes, or when it produces olives and manufactures oil from the own-produced olives. In these cases [. . .] these vertically integrated activities would generally lead to classification of the units under agriculture ( Federal Statistical Office, 2008 , p. 18).

In other words, by NACE standards, wine and cheese making are not part of farming but of manufacturing. However, because these activities are sometimes not fully separable from the farming activities, for a given entity they may in practice be counted as farming. As discussed in Chapter 3 , this is one characteristic that makes farms more complex, because they have the

8 However, custom work performed by a farmer is a category of income on the Schedule F federal tax form that farmers complete. There are many farms that engage in such work, and often they do not have a separate business for it; it is just lumped under their farming income, as the equipment that they utilize is first and foremost for their farming purposes.

9 See http://ec.europa.eu/eurostat/documents/1965800/1978839/NACE_rev2_explanatory_notes_EN.pdf/b09f2cb4-5dac-4118-9164-bcc39b791ef5 (p. 2).

management skill and technical capabilities to run a significant value-added component as part of their farming business.

Under code 115, NACE also includes in its definition of agriculture activities incidental to agricultural production and activities similar to agriculture but not undertaken for production purposes (in the sense of harvesting agricultural products) that are done on a fee or contract basis. Also included are post-harvest crop activities aimed at preparing agricultural products for the primary market, such as contract work for sorting or grading products or packaging them. Such contract work may be viewed as an agricultural support activity, and this is one of the differences between farming (this section) and agriculture (see section 4.5 ). Companies specializing in such activities are agricultural support firms that perform farming activities on farms. They are engaged in a farming activity —that is, farming (managing the whole biological process from inputs to outputs)—which is different from being a farm.

To summarize, farming is the characteristic activity that takes place on a farm, and typically it involves the management of a biological process, such as growing crops or raising livestock, for the purpose of harvesting products or reproducing a biological means of production. A list of activities and products such as those included in NAICS industry codes 111 and 112 is useful for precisely delineating between farming, agriculture, and manufacturing activities. Conceptualizing farming activities in this way does not imply a change in data collection by NASS and ERS, but it may help both agencies clarify and separate discussions about complex farms as businesses (farms), locations (farms, fields), and activities.

4.2. THE FARM

Having defined farming, or at least described it, the next step is to define what a farm is. In principle, this is the entity that carries out farming, whether it is a firm, business, holding, or operation. Applying this principle to the modern economy, however, turns out to be less straightforward than one might perhaps expect.

The Merriam-Webster Dictionary defines a farm as “a tract of land devoted to agricultural purposes;” the Oxford English Dictionary widens this to “an area of land and its buildings used for growing crops and rearing animals.” However, as argued above, farming does not necessarily demand either land or buildings: pigs and poultry farms may consist only of buildings with no crop production present, but they are still farms; as are greenhouses and buildings used to grow chicory or flowers from roots and bulbs, sometimes cultivating their crops in a substrate instead of soil.

And the vertical farms 10 which are the latest development in urban farming are certainly farms. The more scientific definitions, such as those used by statistical agencies, use terms, such as “entities,” “businesses,” “holdings,” and “units,” that engage in farming activities.

Examining the Principal Activities of a Farm Business

More importantly, the organizational forms that farming activities can take create definitional problems. Statistical agencies that count and describe farms must classify organizations instead of activities; businesses receive a census form and report multiple activities whose operating costs often cannot be disentangled. This is not a problem when businesses are fully specialized in farming, narrowly speaking, and have no other activities, whether agricultural, industrial, retail, or service.

Even in these simpler cases, however, the results of such a classification may raise questions if farms have moved production activities that were once commonly carried out on the farm to other producers downstream in the production pipeline. Historically, for example, cheese and butter making were farm activities but, beginning during the industrial revolution of the 19th century, such activities were increasingly transferred to the food industry. On the input side, support tasks involving contract work or machines, advice from risk-management firms, and work now done by other specialized companies either on the farm or elsewhere are all examples of activities that have displaced comparable ones that were once commonly handled “on the farm.” This shift toward specialization means that functions previously classified as farming are now more typically classified in industrial sectors. Therefore, as captured by economic statistics, the farm sector has contracted, relatively, while industry and service sectors have grown. This evolution suggests that, for some purposes, it may make sense to view agriculture as a sector that is broader than farming (see section 4.5 ).

More problematic from a classification perspective is the low level or even lack of specialization in mixed enterprises, such as mixed farms and integrated companies. For entities engaged in several activities, some of them farming and some of them not, there is a need for a rule to classify them as belonging to a certain sector or industry (a class of activities). The

10 Vertical farming is the practice of growing food or medicine in vertically stacked layers, vertically inclined surfaces, or integrated in other structures. The modern idea of vertical farming uses Controlled Environment Agriculture technology, where all environmental factors can be controlled. These facilities utilize artificial control of light, environmental control (humidity, temperature, gases) and fertigation. Some vertical farms make use of techniques similar to greenhouses, where natural sunlight can be augmented with artificial lighting. The Association of Vertical Farming has its own typology; see https://vertical-farming.net/vertical-farming/integration-typology .

horticulturalist who occasionally sells Christmas trees at the roadside is still a farm and not a retailer, but conceptually we enter a fuzzy area where, at the opposite end of the spectrum, there might be a retail garden center that also supplies a few percent of its sales in December from self-grown Christmas trees (a farming activity). Such examples indicate the need for a criterion and a threshold whereby entities may be classified by sector.

In macroeconomic statistics, guided by such frameworks as the NAICS or ISIC/NACE, it is recognized that a unit may perform one or more economic activities described in one or more categories. According to the OECD Manual on Business Demography Statistics :

In such cases, the principal activity of a statistical unit is the activity, which contributes most to the total value added of that unit. The principle activity is identified according a top-down method and does not necessarily account for 50% or more of the unit’s total value added. A secondary activity is any other activity of the unit, whose outputs are goods or services, which are suitable for delivery to third parties. The value added of a secondary activity must be less than that of the principal activity. A distinction should be made between principal and secondary activities, on the one hand, and ancillary activities, on the other. Principal and secondary activities are generally carried out with the support of a number of ancillary activities, such as accounting, transportation, storage, purchasing, sales promotion, repair and maintenance, etc. Thus, ancillary activities are those that exist solely to support the principal or secondary economic activities of a unit, by providing goods or services for the use of that unit only. 11

By applying such a rule and threshold, mixed enterprises such as the Christmas tree sellers described above can be classified as farms (if the tree nursery is the principal activity) or as retail (if the tree nursery is a secondary activity). Adoption of such a method would bring agricultural statistics in line with statistics covering other sectors of the economy, which the panel finds to be an important consideration guiding decisions on the agricultural statistical framework. The value of statistical agencies taking this approach would be generated because the modern agricultural sector is an integral element of the economy, with many medium-sized businesses and much linkage with the rest of the economy. A status aparte in statistical methods is therefore to be prevented as much as possible.

RECOMMENDATION 4.1: In line with statistics for other parts of the economy for classifying a business as a farm or as an entity operating in a nonfarming sector with secondary activities in farming, the

11 Eurostat, OECD Manual on Business Demography Statistics. See https://www.oecd.org/sdd/39974599.pdf (p. 67).

National Agricultural Statistics Service and the Economic Research Service should apply clear rules based on the nature of the business’s principal productive activities.

This recommendation does not imply that only entities classified as farms with farming as a primary activity are of interest to NASS and ERS. On the contrary, the agencies should be interested in all businesses engaging in farm activities, even those for which it is a minority activity. Counting farming activities in businesses that are classified in sectors other than farming is necessary to accurately and consistently estimate totals for the sector when there is a change in the role of large companies in farming. There are good reasons to try to survey agricultural production that takes place within these companies, especially if a large share of the production takes place in them. This may be appropriate because, among other reasons, such mixed production businesses are affected by agricultural policies (see Chapter 2 ). For reporting on entities engaged in farming as a secondary or tertiary activity, specific classification categories may be needed, such as part-time farms and multi-functional farms . At the sector or macro level, the size of agricultural business complexes can be calculated with input-output tables (see section 4.5 ).

Applying this method of classification requires careful specification of how entities or units are defined. For a company like Smithfield Foods Inc., 12 which owns both slaughterhouses and farms, should the farms be classed as different units, or should the full multinational company be classified under its principal activity as a slaughterhouse and therefore part of the food industry? Are Walmart stores individual units or is all corporate activity one business? The answers to such questions—presented in detail in Chapter 5 —determine the number of farms counted in an agricultural census.

Distinguishing Between Firms and Establishments

In economic statistics, the answer to the above-posed question is dictated by the difference between firms and establishments. A firm is “ an organization conducting a business . . . . A firm may operate one place of business or more.” An establishment is “a single physical location where a firm’s business is conducted” ( National Research Council, 2007 ). 13 For our

12 Smithfield Foods, Inc., is a meat processing company and wholly owned subsidiary of the WH Group.

13 In line with Bureau of Economic Analysis’s November 2017 update to its handbook Concepts and Methods of the U.S. National Income and Product Accounts : “Companies consist of one or more establishments owned by the same legal entity or group of affiliated entities. Establishments are economic units, generally at a single physical location, where business is conducted or where services or industrial operations are performed (e.g., a factory, mill, store, hotel, movie theater, mine, farm, airline terminal, sales office, warehouse, or central administrative office). Establishments are classified into an industry on the basis of their principal production method, and companies are classified into an industry on the basis of the principal industry of all their establishments.”

example, this means that Walmart is a firm with many establishments; and that the farms of Smithfield Foods can be counted as farms, if we define a farm as an establishment and Smithfield Foods is organized in such a way that data from the farms can be separated from other activities.

Eurostat has followed this accounting approach, stating “A farm is a single unit, both technically and economically, which has single management and which produces agricultural products . . . either as its primary or secondary activity.” 14

As covered in Chapter 2 , the official definition of a farm used by NASS is as “any place 15 from which $1,000 or more of agricultural products were produced and sold, or normally would have been sold, during the year.” 16 In line with the day-to-day use of the word “farm” and the current practice in NASS and Eurostat, a farm should be defined in a way that focuses on the productive entity as a business engaged in clearly specified types of activities.

RECOMMENDATION 4.2: For conceptual purposes, the National Agricultural Statistics Service and the Economic Research Service should define a farm as an establishment (single unit with a legal or informal management structure) that (1) has its principal or secondary activity in farming with the production of agricultural products and biological assets such as seeds and animals; and (2) for which full economic data on key business variables, such as costs and revenues, can be collected and made available.

This recommendation is intended to help NASS and ERS unravel the struc-

14 From Eurostat’s online Glossary , under “Agricultural Holding,” see http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Agricultural_holding . The online text further states: “An agricultural holding, or holding or farm is a single unit, both technically and economically, operating under a single management and which undertakes agricultural activities within the economic territory of the European Union, either as its primary or secondary activity. Other supplementary (non-agricultural) products and services may also be provided by the holding.”

15 USDA uses “place” in a nonstandard way. A “place” in its usage does not imply contiguous land parcels where ownership and management overlap.

16 For the USDA/ERS Glossary, see https://www.ers.usda.gov/topics/farm-economy/farmhousehold-well-being/glossary , which covers the following terms: farm; farm operator and principal farm operator; family farm; farm operator household; farm operator household income; farm operator household wealth; farm typology; commodity specialization; disposable personal income of farm and nonfarm residents.

ture of some common types of complex holdings. When a person or businesses has two clearly distinguishable farms with separate accounts, such as one farm in Wyoming and one in California, or one on either side of the same county, the farms should be treated as two farms that happen to have the same owner. This definition is quite close to the USDA’s approach, which defines a farm as a management unit.

Regarding the practicalities of implementing Recommendation 4.2, the panel recognizes that there may be scant political will to change the definition of a farm, regardless of how antiquated the dollar threshold may be, due to the implications for federal funding and other reasons. Indeed, the transfer in 1997 of the data collection for and publication of the Census of Agriculture from the Census Bureau to NASS resulted, in part, from congressional concerns about a proposal to modify the definition of a farm.

Discussions of the politicized aspect of the farm definition often take place in the context of proposals to increase the farm-size threshold (e.g., O’Donoghue et al., 2011 ), which would invariably reduce the number of farms. The recommendation above, which is expanded on in Chapter 5 , is quite different: it recommends that NASS and ERS use a farm enterprise concept and a farm establishment concept when collecting data in order to (i) make the concepts as consistent as possible with those used by other statistical agencies, (ii) provide clarity to survey respondents, and (iii) identify business units that are likely to correspond better with the way respondents organize their own data.

The establishment concept is not fully equivalent to the current farm concept used by USDA to estimate the number of farms, but it probably comes close. The majority of farms, especially the smaller ones, have a simple structure and will report their current operations as establishments, resulting in the same number of farms. For complex farms, it is currently unclear whether they would report one of their establishments or report a complex farm corresponding to a statistical enterprise. If they were to report the latter, the number of farms (farm establishments) would increase; otherwise it would stay unchanged. Recommendation 4.2 should therefore be interpreted as a recommendation to help obtain a clearer picture of the farm structure and reduce the administrative burden for complex farm structures, and not as a recommendation that intends to reduce the number of farms.

However, if administrative or political reasons required that there be a check with the traditional farm-count number, ERS/NASS could develop a mapping from enterprises and establishments to farms. It could then provide counts at the county, state, and national levels that would be consistent with those that have historically been produced. Such a mapping could be done to preserve the distribution of farms across space or in total in a given base year.

Counting and Measuring Farms and Fields for a Complex Farm Business

The definition of a farm as an establishment means that there are businesses that may be involved in two or more farms. (The logic of this is elaborated on in Chapter 5 , where it is also clarified why in statistical terms businesses are equated with firms.) Even defined in this comparatively granular way, a single farm may engage in several different activities, such as growing different crops or raising different types of animals or doing both. A cropping farm will have different fields that are not necessarily adjacent to each other, and so, as noted above, the “place” that defines a farm need not be contiguous. Likewise, an operation that raises livestock or fish does not necessarily have all of its barns, ponds, and so forth on the same site. A farm may engage in relatively minor activities in other industries, such as providing support services to agriculture or being involved in retail. Farmers sometimes use the terminology “different enterprises,” if they produce more than one type of crop or livestock within the farming operation; for example, corn, soybeans, and a herd of beef cows or calves could each be classified as a separate enterprise. 17

Another issue is that a farm, as defined above, is an economic concept. That is, a farm might be organized as a complex legal structure for reasons of tax, inheritance, or otherwise. For instance, a father and son might run a farm wherein the former owns most of the land while both own the machinery together. Or some of the land might be part of a family trust. In any case, the use of these different legal entities does not necessarily change the status of a farm from a single farm to more than one farm.

Defining the Farm as an Establishment

Defining a farm as an establishment means that, as measured by a statistical agency, the number of farms as well as the size of the average farm both depend on how farmers organize their activities. The farmer who buys out a neighbor, adds that land to his own property, and then rents out the purchased farm building or uses it for his contract work operations or for an agritourism business is enlarging his establishment. He (or she) still has one farm, though he also may now have other income sources or may own some other establishments, such as the agritourism business. But, if a similar farmer also buys out a neighbor but instead uses the farm buildings for another farm activity—such as when pig breeding continues on the original farm and hog finishing occurs on the recently acquired farm—proper categorization then starts to depend on the way the business is organized.

17 As in general statistics, the term “enterprise” is used for the level of units (firms) that include several establishments; this report does not use the term.

In the second scenario, if the two farms are or can be separated in a technical and economic sense, such as by having different management accounts each with its own profit-and-loss account and balance sheet, then there are two establishments. This conceptualization is in line with the day-to-day language: the farmer owns two farms. This can of course easily become a source of complexity in data collection if statistical agencies view such a situation as two establishments even though the farmer manages them as one—or the other way around. The solution is to take the farmer’s reality as a point of departure, even if this reality is mainly shaped by the history of the farm or by the legal, fiscal, or risk-management considerations of the farm.

Giving farmers clear guidance concerning the fact that a farm is an establishment and that the data collection aligns with how they see and organize their own farms is important for reducing confusion among respondents about how to report. This implies that NASS needs to be clear to farmers about what “a place” means in its definition of a farm. Where the farm business’s establishment and legal entity structures align, opportunities for exploiting linkages with administrative (e.g., tax) data will exist.

The Census Bureau faces a similar challenge, although on a much larger scale, when it measures the number of “businesses” in the U.S. economy overall. Although it never formally defines what a business is in its technical documentation, the bureau nevertheless reports statistics on businesses in the U.S. economy. This is possible only because it uses the word “business” (synonymous with “company” and “firm”) to simplify its presentation of the more nuanced concept of an “institutional unit,” such as an establishment or firm, which it does describe in its technical documentation. An institutional unit exercises decision-making control and has rights to residual profit; it can be a single-location corporation or LLC, or a multilocation (perhaps multinational) conglomerate with many subsidiary units, each operating under a distinctive legal form of ownership.

The Census Bureau attempts to measure some of this organizational complexity using a mix of administrative and survey data, but of course it cannot capture it all. Two elements of the Census Bureau’s approach that may offer helpful guidance for NASS are (i) the use of a “company organization” instrument to identify and link related reporting units prior to conducting its census enumeration and mailing; and (ii) the sampling of smaller units to free resources for more intensive focus on larger units. We elaborate on this in Chapter 5 in the context of data collection, where it becomes crucial to distinguish between entities from which data are being collected and entities in the statistical structure that we want to measure. The latter should be well defined and set in stone; in contrast, the collection entity may have to conform more to the availability of data as maintained by the businesses.

Refining terms in this way can have an influence on the number of farms that are reported and therefore also on the average size of farms, although it is not clear in which direction. As argued above, most likely the number of farms would increase and the average size would decrease, since this approach invites complex holdings to report separately for each of their component establishments. In judging this advice, the agencies should consider the fact that statistics will also have to report on the number of farming businesses that own more than one farm (see below, and Chapter 5 ).

Defining the farm as an establishment would be an important step toward improving data collection on complex holdings. It would imply that some farmers own, operate, or are the residual claimants of more than one farm, instances that indicate a type of complex farm holding, that is, management structures that extend over a combination of establishments. Grouping multiple establishments under a single management unit for the purposes of data collection and reporting is a common feature of measuring business activity outside the agricultural sector: as mentioned earlier, Walmart is a single firm (business), but the different stores it owns are separate establishments.

If the farm is defined as an establishment, research into the organization of farming, agriculture, and the food chain would benefit from the use of a consistent definition of the firm in the agricultural context. This could be one business that owns several farms (establishments); a business that owns one farm as well other nonfarm establishments; or a business for which the farmer is involved in several farms that have complex ownership and managerial structures. Such situations can increase the complexity of a farming business, at least relative to the classical situation in which the establishment level and the business level are equivalent.

In Chapter 5 , we discuss the value of implementing in the measurement infrastructure a definition of business entities based on shared management structures. This includes both cases in which one business owns and operates one establishment (a simple farm business) and cases in which one business owns and operates a group of establishments (a complex farm business). Using this type of definition would facilitate collecting and reporting statistics at both the “establishment” level and the “farm business” level. Chapter 5 discusses how this can be implemented.

Sharecropping and Noncommercial Farming

Having defined the terms farm and farm business , we end this section by discussing two phenomena that also complicate measurement and reporting. One is the existence of sharecropping, and the other is farming for purposes other than selling agricultural products.

Sharecropping is an institutional form of renting out land that has

engaged economists since Adam Smith. If a landlord leases land to a farm owner, that activity is an investment activity, which does not make that landlord (or a company like an LLC acting as a landlord) either a farmer or a farm business (simple or complex). However, if a share-contract is in place, the rent is not charged as a fixed-dollar value but rather is charged in the form of the products produced, that is, as a share of the crop. In this case, the landowner becomes a risk-bearing managing partner, and one could argue that the operation is a (separate) farm and part of a complex farming business.

However, in line with the Farm Service Agency’s payment eligibility criteria and the Internal Revenue Service (IRS) definition, 18 if someone is receiving rental income or farm income, sharecropping arrangements should not been seen as separate farms unless the landowner is “actively engaged in farming.” Actively engaged here means that all participants, whether individuals or legal entities (such as partnerships or corporations), must provide significant contributions to the farming operation. These contributions may consist of capital, land, equipment, active personal labor, active personal management, or some combination of these. If it is a management contribution, it must be critical to the profitability of the farming operation, and the contributions must be at risk.

Another phenomenon that requires attention is the question whether a business has to be actively engaged in the sale of agricultural products to be considered a farm. For subsistence farms in developing countries, the answer is “no,” but in developed countries it is unusual to include hobby-activities for leisure, like gardening or unpaid housekeeping work, in official economic statistics. From an agricultural policy perspective, activities that are outside markets and not sensitive to agricultural policies are also not very interesting, even if some types of registration can be useful, such as registering all horses in a country for animal health reasons.

Therefore, for most purposes, it makes sense to define a farm as an establishment that at least has the objective to market its produce. In Europe, Eurostat uses a lower threshold of at least 1 hectare (2.5 acres), and includes farms with less than 1 hectare provided they market a certain proportion of their output or produce more than a specified amount (regionally specified in Euros); this latter threshold would normally exclude household gardens providing incidental sales. The Eurostat method guarantees that farms that do not sell products in a certain calendar year, for example due to extreme weather events or harvest loss or if they store their harvest to sell later, are still considered farms.

18 See IRS Publication 225, Farmer’s Tax Guide .

4.3. THE FARMER

Now that the terms farming and farm have been defined and elements of their complexity revealed, we turn to the question, who is a farmer ? This is a relevant question because researchers and policy makers are not only interested in the farm itself but also in the farmers behind that business. Understanding their decision making, for example regarding trade-offs between food production and environmental or other public aspects, is crucial, and monitoring their well-being and any income problems associated with farming is equally crucial.

Not all persons that work on a farm are farmers. Many farm workers are employees or are hired through an agricultural work agency or contractor, as in the horticulture sector. On some farms, family members provide a helping hand in busy periods or on weekends. In most cases, while all of these persons are active in farming, they are not typically treated as “the farmer” either in commonplace discussion or from a statistical agency perspective. According to the Merriam-Webster Dictionary, a farmer is “a person who cultivates land or crops or raises animals (such as livestock or fish).” The Oxford English Dictionary defines farmers as persons “who own or manage farms.”

Owners or Managers?

The Oxford English Dictionary definition exposes a quandary for statisticians: Are farmers owners or managers? Both categories are relevant to various research and policy questions. While they overlap and in many cases are the same person, on some farms they are different persons, creating additional complexity in data collection and interpretation.

Defining the farmer as the owner of the business entity and the person who makes management decisions (or appoints managers to do so) signals the importance of those who are responsible for the operation and who bear all the financial risks. They are the entrepreneurs, the residual claimants, in a view that sees the farm as a bundle of contracts ( Allan and Lueck, 2002 ). Risk management is an important aspect of American farm policy, implying that this is an important dimension when identifying farmers.

Defining the farmer as the owner of a farm means that there will be some farmers (as owners of a farm) who do not work there and are absentee owners. It also means that there may be family members working occasionally on a farm who will be designated as owners for reasons of income tax, marriage, or with an eye to intergenerational transfer and inheritance taxes. This may be broad but at least it is a jurisdictionally precise criterion, and the distribution of profits would make clear who is and who is not an owner.

In principle, both categories—owners and decision makers—are of interest. They could be folded into one group under the term producer , a term that NASS introduced for 2017 19 to indicate any person involved in farm decision making (governance structure), from day-to-day decision making to the work of absentee owners who may only make investment decisions once a year. The term also includes hired managers.

Compared with ownership, there is less clarity with the term decision maker : Are these the persons who are involved in day-to-day operational decision making concerning, for example, spraying and environmental management? 20 The term farm operator also hints at a level of active working and decision making. Or are these the persons who make the important strategic decisions on the choice of marketing channels or about investments and finances? Or are they the owners, while it is a farm manager who “runs the show”? From a user point of view, the appropriate designation of the farmer will depend very much on the type of (policy) research to be informed.

If a farm is a corporation, it could be argued that, from a legal perspective, the corporation is the farmer, and the officers of the corporation (the CEO and others) are the persons who express decisions on behalf of the business. In several ways, however, this seems not to be a very informative approach. A farmer or producer is always a person, even in the case of a corporation that does farming, where most likely at least the CEO is the farmer/producer.

What if a Farm Has Multiple Farmers?

It follows from the analysis above that a farm may be associated with more than one farmer, and similarly with more than one manager and more than one owner. It makes sense that NASS and ERS are now asking respondents to the Census of Agricultural to identify multiple producers at a farm and farming businesses, where the farm has more than one producer. In the 2017 Census of Agriculture and Agricultural Resource Management Survey (ARMS), respondents may list multiple men and women who have been engaged in making decisions for the operation; they are then asked to

19 See Publication of Agriculture Census Data on Farm Operator Demographics , a report by the National Institute of Statistical Sciences Technical Expert Panel, October 12, 2017. There it was recommended to replace the label “Operator” with “Producer” in all publications. The 2017 Census of Agriculture and future censuses use these terms: “All Producers,” “Principal Producers,” “non-Principal Producers.” These terms span the breadth of agriculture and are seen as consistent with current terminology used by producers and by professional agriculture organizations.

20 The 2017 Census of Agriculture and ARMS were broadened to capture farm decision making beyond the day-to-day.

provide more detailed information on up to four people per farm. Of those four people, the respondent may choose to identify one or more of them as a principal operator or senior partner on the census, which is different from previous census surveys, which only allowed one principal operator to be identified. However, the term principal operator is being used in the 2017 census for bridging purposes only and will cease to exist in the future. The ARMS survey continues to ask for a single principal operator to be identified for the farm household.

This raises the question whether or not a single person can be designated as most important in operating the farm or in share of ownership. In larger, more complex farm businesses it can be difficult to rank the “most important manager” as more or less important than the most important owner. But even within one of these subgroups of producers, persons can have more or less equal decision-making or ownership rights. In more traditional family farms, there have often been cultural gender issues and generational issues that have led to misrepresenting the reality of who is contributing to operating the farm, and in this regard data could very much be influenced by who responds to a survey in any given year. 21 The maximum that can be done is to ask, in ARMS or special surveys, for data on each farmer/producer that identifies how many hours each worked and who was involved in which decisions, but such questions are potentially time-consuming and thus costly.

In the case of multiple operators, during the data collection process currently it is the respondent for the farm who identifies the principal farm operator. This identifier also serves as the link to a household for the collection of data on income and other household variables. Such an approach, however, does not lead to an accurately defined household population. (We return to the issue of the relevant household population in the next section, and then again in Chapter 5 .)

It is also clear that, based on the definitions above, a farmer may be involved in more than one farm and even in more than one farm business. In the case of a farmer who owns or co-owns a complex farm business that includes more than one farm, the farm business is a holding company. However, this firm is often a conceptual construct: the presence of a company that oversees the whole structure might be lacking, in practice, even for cases where a farmer owns or co-owns several farms or is involved in LLCs with family members or others. It is these constructions that are especially

21 In the past, as documented by NASS, see https://www.usda.gov/media/blog/2016/01/19/counting-all-farmers-capturing-many-faces-agriculture-2017-census , there has been bias as to who has been reported as “operator,” and even with the new questions and approach on the Census it is unlikely one will see a total change, due to the “culture” of agriculture. Although a woman may be the decision-maker on a farm, she may not be reported as such by some families because women are not viewed as “the farmer.”

complex for data collection. For agricultural statistics, in cases like these the thinking should be clear, namely that the farm establishment is often the point of entry from which higher-level structures, like farm businesses, as well as households can be profiled.

Finally, it is worth remarking that the producer/farmer (whether owner or manager) identified in the Census of Agriculture or ARMS is not necessarily the respondent to the survey. Especially in complex farm businesses, the respondent could very well be an administrator, an outside accountant, or another staff member involved in farm management. Conceptually, the issue of who the respondent is should not influence the view of who the farmer is, but we will return to this issue in Chapter 5 , where survey designs are discussed, because this issue also has consequences for what can be asked and to whom.

4.4. THE FAMILY FARM AND THE FARM HOUSEHOLD

Policy makers and researchers are not only interested in farms and farmers, but also in farm households, because the household situation can influence the behavior of the farmer and the activity of the farm. A well-known example of this influence is the way investment decisions by farmers over long time horizons, such as 30 years, are influenced by whether or not they have a successor, which affects supply responses to policies ( Gasson and Errington, 1993 ; Calus, 2009 ). Investment decisions can also be influenced by income from sources other than the farm, because such incomes can reduce the cash flow needed for consumption or even be allocated to farm investments. Agricultural policies are also sometimes justified as needed to sustain the family farm in times of low production or income, making the interest in family households a legitimate question.

This interest in the total income and well-being of the farmer and the farm household—which factors into ERS mandates—is especially relevant for the family farm. On very large farms and in complex farm businesses, the family dimensions are relatively less important to the functioning and stability of the operation. Such organizations are more like big family firms in other sectors: it is hard to imagine that family income from other sources plays as big a role in the investment decisions made by Walmart as it does in decisions made by small retail businesses.

The family farm is a dominant concept in the public mind and in political debates on agriculture. Historically, family farms are what brought most of the United States into cultivation. In 1930, one of the most iconic American paintings (now in the Art Institute of Chicago), was popularly called “Iowa Farmer and Wife,” although the painter Grant Wood originally named it “American Gothic,” after the unusual window in the building pictured. In reality, the couple in the painting was not a couple, nor was

either of them a farmer (the man was Wood’s dentist). 22 Perceptions of the family farm in agricultural statistics create a similar distortion: already in 1977, James Bonnen, in his Assessment of the Current Agricultural Data Base: An Information System Approach, questioned the concept:

The idea of the “family farm,” with all its value and organizational assumptions, constitutes the central concept around which most of our food and fiber statistics are designed and collected. Yet, it has become an increasingly obsolete representation of the reality of the food and fiber sector. . . . The world has changed and the concept has not. (p. 387)

Given that this observation is 40 years old, it seems even harder to adapt an ingrained image, such as “American Gothic,” to current reality. Adapting the concept to reality would nevertheless follow a tradition. Reinhardt and Bartlett (1989) point out that the concept of the family farm was originally used for homesteading farms, which had no outside labor or capital nor used contractors, and over time it was broadened to keep up with changes in the organization of farming.

The interest in family farms and farm households raises the question of how to define them. The fact that a farm can have several farmers (producers) means that it can be associated with several different households, although this is not necessarily so: a man and his spouse can both be producers, as can one or more of the children, while they are living in the same household. But just as typically, children or brothers may set up their own households, which results in several households being associated with a single farm. Such households may also contain persons who are not necessarily direct relatives of the farmer: children from an earlier relationship of their spouse, interns, and so on. Meanwhile, some family members, such as children away at college, are only members of the household for part of the year.

All these factors make it useful to have a clear definition of both family and household. The Census Bureau defines a family as “a group of two people or more (one of whom is the householder) related by birth, marriage, or adoption and residing together; all such people (including related subfamily members) are considered as members of one family.” Beginning with the 1980 Current Population Survey, unrelated subfamilies (referred to in the past as secondary families) are no longer included in the count of families, nor are the members of unrelated subfamilies included in the count of family members. The number of families is equal to the number of family households, but the count of family members differs from the count

22 See http://mentalfloss.com/article/64853/15-things-you-might-not-know-about-americangothic .

of family household members because the latter count also includes any nonrelatives living in the household.

According to the Census Bureau, a household consists of all the people who occupy a housing unit. A house, an apartment, a group of rooms, or even a single room is regarded as a housing unit when it is occupied or intended for occupancy as separate living quarters; that is, when the occupants do not live with any other persons in the structure and there is direct access from the outside or through a common hall. A household includes both the related family members and all the unrelated people, if there are any, who share the housing unit, including lodgers, foster children, wards, or employees. A person living alone in a housing unit is counted as a household, and so is a group of unrelated people sharing a housing unit, such as partners or roomers. The count of households excludes group quarters. There are two major categories of households: “family” and “nonfamily.”

NASS and ERS use these same definitions for household and family, which are used broadly across the statistical system and should continue to be so used. For NASS and ERS, a family is a group of two people or more related by birth, marriage, or adoption and residing together. A household consists of all of the people who occupy a housing unit, that is, a house, an apartment or other group of rooms, or a single room, occupied or intended for occupancy as separate living quarters.

Even with precise definitions for farm and family, however, linking the two in the term family farm is problematic. First, there are many cases in which a farmer lives alone, unmarried or as a widow(er), and such farms are nevertheless called family farms. More problematic is that an overwhelming number of farms, including those that are part of complex farm businesses, are owned and operated by one or two related families. Therefore, the terms simple farm and family farm are not synonyms. Indeed, in its publications, ERS highlights the fact that family farms can be very complex, with multiple households sharing in the farm’s income.

In practice, data collection from household units can be restricted to the households of farmers who are owners, assuming that the nonfarm activities of salaried managers do not affect farm management. Data collection on those owner-households, for instance regarding nonfarm income, can also be restricted to those persons who are farmers/owners and their spouses, assuming that the nonfarm income of children who might live at home is used for their own personal expenses and savings and not for financing the farm or for reducing the amount of farm income needed for household expenditure. However, this last assumption may be questionable if the child is the potential successor on the family farm.

Off-farm income, under this approach, consists of all income from sources other than farming. This may include other agricultural (support) activities, self-employed income, wage income, and capital income, among

other things. Neither is it particularly relevant if this income is earned at a location on the farm or elsewhere; whether it is income from a tractor repair shop or bed and breakfast on the farm premises or income earned at a job in the town does not matter.

From the above line of reasoning, it follows that the definition of a farmer is not influenced by his or her income streams from other, nonfarm sources, nor by his or her age. A 75-year-old farmer who receives 75 percent of his income from a retirement pension is still a farmer, and so is the 40-year-old farmer who earns 80 percent of her income from working outside of agriculture or from owning another enterprise. Even if a farmer earns all of his or her income outside farming and makes a loss running a farm—perhaps to enjoy the living environment, or to benefit from certain social security payments or tax facilities—he or she is still a farmer (producer).

4.5. AGRICULTURE AND AGRIBUSINESS

Farming and agriculture are often taken to mean the same thing. In section 4.1 , we provided several examples of agencies that use agriculture as a term to describe farming, but we also argued, based on the NAICS classification, that it is useful to restrict the term farming to the management of biological processes that excludes support activities to farming (NAICS 115000) 23 —such as harvesting, cotton ginning, and farm management—if they are provided as services by other firms. In other words, if farmers harvest their own grain, it is an activity that is part of farming, but if these activities are carried out by another firm or farm as a service it is a support activity to farming (see section 4.1 ).

The essence of the problem, and an important source of complexity in agricultural production, arises from the way farming activities are located within the larger food and agriculture supply chain. Compare the following stylized economies, where gray shading denotes activities undertaken by farmers:

In the most extreme case (diagrammed above), farming is a matter of subsistence for household members, and the activities of such farmers

23 NAICS lists them as support activities to Agriculture and Forestry, but if this difference is made between agriculture and farming, they should logically be called support activities to farming.

span the entire supply chain. Households in such cases are responsible for all steps in the production of farm output, and there is no marketintermediated transfer of intermediate goods between input suppliers, farmers, and processors, prior to final consumption by consumers. If such a household were farming wheat, the members of the household would be responsible for collecting seed and manure, preparing the fields, planting, harvesting, threshing, winnowing, grinding, baking bread, and ultimately consuming their own product. This is a model of agricultural production devoid of any trade or specialization and is perhaps approximated by very poor societies in developing parts of the world.

Less extreme is the case where farmers specialize in one part of the production process. This supply chain (diagrammed below) represents the case in which farm businesses only undertake the agricultural production activities and transfers of intermediate goods that arise from inter-firm transactions. Returning to our wheat example, the farm purchases seed and fertilizer from elsewhere, and the harvested wheat is sold to a flour mill. 24 The rest—all of the planting, cultivation, and harvesting—is undertaken under the direct management of the farm.

Of course, there are definitional challenges in precisely demarcating the distinction between production and processing. Are threshing and winnowing grain the former or the latter? What about the drying of tobacco leaves? Or the packing of citrus fruit? Nevertheless, for the purposes of discussing complexity in agricultural production, these distinctions may be taken at face value, and under the current NAICS system all the aforementioned examples are classified as agricultural production activities (see section 4.1 ).

Agricultural production is constantly evolving, and it has generally increased in complexity over the course of human civilization. In particular, three phenomena stand out. First, trade across economies has existed for millennia, but the breadth and depth of such trade are greater today than ever before and occur at every stage of the supply chain. Second, the number of links within the supply chain has grown tremendously. Third, the boundary of the farm increasingly fails to align with the definition of agricultural production activities.

24 There may be additional steps; for example, the farmer may sell to a cooperative or dealer who does not process the wheat but sells it to flour mills.

In the supply chain diagrammed below, the farm business is no longer responsible for all the agricultural production activities:

Instead, in this example the farm hires a specialized service provider who assumes responsibility for soil preparation and planting (NAICS 115112). When the crop is ready, a potentially different service provider is contracted to harvest (NAICS 115114). And while some management tasks are undertaken by the farm business itself, others are delegated to a specialist provider (NAICS 115116).

The increasing decentralization of production activities through the use of specialist service providers is of particular interest because, while these businesses are not farmers themselves, they engage in on-farm production, covering the whole range of activities from soil preparation, planting, and cultivating, to harvesting, packing, and management. More than 50 years ago, the USDA and the Census Bureau recognized the increasing importance of these businesses to the overall farm economy and conducted the first Census of Agricultural Services as a follow-on survey to the 1969 Census of Agriculture. The following paragraph from the 1974 Census of Agriculture succinctly summarizes the motivation for expanding its scope to include such service providers:

Until the 1940s, agriculture in America was largely self-reliant in regard to many production and harvesting practices now available from off-farm sources in the form of agricultural services. During the last three decades agricultural services have become an increasingly specialized industry. The technological and scientific changes in American agriculture have been directly related to the development of the agricultural service industry. A census of this industry is essential to provide facts necessary for

• a broader view of today’s farm production,
• a better understanding and interpretation of long-term agricultural changes and trends, and
• a more meaningful analysis of the interrelationships of agriculture and agricultural services. 25

25 1974 Census of Agriculture, Volume III: Agricultural Services , Appendix A, pg. A-1.

Despite these well-intentioned exhortations, the Census of Agricultural Services was discontinued in 1978 and no current Census Bureau or NASS survey program specifically targets these businesses. While a dedicated data collection effort has long since disappeared, the importance of specialist service providers to farm production and their contribution to farm complexity has only continued to increase. Indeed, these businesses fall into a coverage void between NASS, which surveys farms, and the Census Bureau, which surveys nonfarm, nonagricultural businesses. A critical component of the modern agricultural supply chain has simply fallen between the statistical cracks.

This situation is problematic for two reasons. First, key measures of the overall agricultural production sector are increasingly being mismeasured. For example, we currently cannot construct a reliable estimate for the number of individuals employed on farms. From the Census of Agriculture, we know how many workers farms hire and how much they spend on contract labor, but the actual number of farm workers, how much they are paid, and their take-home wages at the national, state, or county level are all unavailable in any current economic survey. The increasing concentration of land and output is a commonly recognized source of intra-firm complexity, but the vertical disintegration of farm production activities is another source of complexity that also requires attention.

Second, service providers may themselves be farm operators. The Census of Agriculture currently asks farmers to report related income from the provision of specialist services, but only if these are not stand-alone businesses. Indeed, in the past, explicit dollar thresholds were employed to define farm-related versus stand-alone businesses. Documenting the relationships within farm businesses between the constituent establishments—farm establishments and nonfarm establishments—is key to overcoming the challenge of reporting on complex farms.

To the extent that USDA should be reporting on agricultural production activities in the United States, regardless of the business entity carrying out that activity, these agricultural production activities should, in future, be surveyed.

RECOMMENDATION 4.3: A program akin to the defunct Census of Agricultural Services, perhaps undertaken as a follow-on survey to the Census of Agriculture, should be developed to collect and report economic activity undertaken by establishments and firms engaged in agricultural production activities through the provision of support services to farms. To accomplish this task, such providers must be identified and included in a Farm Register (described in detail in Chapter 5 ).

Defining and Enumerating Secondary Activities

The other boundary issue, discussed earlier in this chapter, is that farms may engage in secondary activities that are not farming but rather food processing or retail. Cheese making and produce selling by the roadside or at a farmers market stall are examples of activities closely linked to farming. Notice that, in the last supply chain diagrammed above, the farm business engages in economic activity beyond agricultural production. As an example, if the farm business is a berry farm, it might process some fruit on-site to produce jams or preserves (manufacturing). Some of the fruit may also be used in baking pies and muffins (manufacturing). These items may be sold at an on-site store (retailing) to patrons who visit the farm to pick their own berries and enjoy recreational activities (services). It follows from the recommendations in section 4.2 (and as illustrated with the wine and cheese examples) that these are very often secondary activities of the farm, because they are not organized in a separate establishment; nor do they account for the majority of the work (or value added) in the farm. However, it also follows that, if activities of this kind are organized in a separate establishment or where farming is not a primary activity (such as a garden center that grows some Christmas trees itself), the establishment is not a farm and these are not agricultural support activities. Often, an establishment of this kind will be classified as a food processor or retailer.

These sectoral spillovers mean that a census of farms (farm establishments) is not a census of all farming or agricultural activities, because some of the included farms also engage in a subset of activities outside farming; meanwhile, some farming and agricultural activities are carried out by establishments that are not classified in a statistical framework as farms but instead as agricultural support firms, food processing companies, or retailers. This is the direct consequence of the fact that the Census of Agriculture and ARMS seek to survey organizations and their managers and are interested in the decision making in those organizations.

It is therefore often necessary to extend survey coverage to include these secondary or smaller activities. Farms could be asked how big (and what) these other activities are, and other types of firms could be asked if they engage in farm activities and farm support activities. Based on such an estimate of activities, statistics could be generated to identify the size of farming or agriculture in the total economy.

Two key features of agricultural production in the United States are missing in the supply-chain diagrams above depicting stylized economies. First, the diagrams ignore the central role of international trade. While long-distance trade in agricultural commodities in either their raw or manufactured state is nearly as old as human civilization itself, the world economy has never been more integrated that it is today. Second, the diagrams

ignore the relative contribution of each link in the supply chain. The contribution of agricultural production activities to total economic value added in the United States is now lower than at any point in history. Although food is no less important to human well-being, the sources of value added have shifted to other links in the supply chain—toward companies like Monsanto, ADM, and John Deere on the input side and ConAgra, Coca-Cola, and McDonalds on the processing side.

In addition to agriculture, an agribusiness complex has been created in the United States and other large economies ( Davis and Goldberg, 1957 ). That also means that agricultural policy, along with environmental policies that target farming, has effects on other sectors than farming and agriculture. To give policy makers and the public insight into these interdependencies, statistics on the agribusiness complex are needed. This can be done with a methodology based on input-output tables of the national accounts (and its satellite accounts) that link farming to activities in other sectors.

RECOMMENDATION 4.4: The National Agricultural Statistics Service, the Census Bureau, and the Bureau of Economic Analysis should all report on the size of the agribusiness complex and its components in terms of income, employment, and environmental impacts and develop a program that harnesses existing data collection efforts to create a new satellite account for reporting on the food and agriculture industries.

4.6. FARM AND NONFARM INCOME

A farm is an establishment and should be economically identifiable. This implies that any farm either has a profit-and-loss account and a balance sheet or, at least, that the farmer should be able to create them for a survey. In the real world, terms like income and net worth are not always very clearly defined, and many farmers restrict their accounting to fiscal accounts that satisfy the tax authorities.

Accounting standards as issued by the International Financial Reporting Standards (IFRS) Foundation give clear guidance on how to create a balance sheet, a profit-and-loss account, and a flow of funds for the farm based on accrual accounting. Their accounting standard IAS41 gives clear definitions on valuations in agriculture that ARMS could use. For farm households, an income statement can be added. Off-farm activities generate substantial income and thus contribute to the well-being of farm households. Income at the household level thus may originate from different sources: farm income and nonfarm income. Nonfarm income can be defined as the net income from all nonfarm businesses, wage and nonwage categories.

An important issue in complex farm businesses is that of transfer pric-

ing between different farms and other establishments that are part of the farm business. IFRS accounting standards provide guidance for transfer pricing, but the complexity of this accounting could be a reason to create consolidated accounts for complex farm businesses.

Providing information to policy makers and the public on the real structure of agriculture and the inequalities in the farm sector makes it necessary to provide data not only at the farm (establishment) level but, even more, on the financial situation of complex farm businesses. This means that it is preferable to select farms for ARMS from a register that includes both simple and complex farm businesses. Alternatively, if census data are only available at the establishment (farm) level, ARMS could use these data as a basis for selection, but whenever a farm is part of a complex farm business it should collect data for the complex business as a whole.

4.7. IMPLICATIONS

To find a solution to the issue of data collection on complex agricultural holdings, as specified in Chapter 3 , this chapter analyzed concepts from general statistics and accounting. The analysis suggests that complex agricultural holdings should not be fitted into a definition of a farm (counting them all as one farm); nor should such holdings be pressed into a definition imposed on them that fails to recognize the juridical or fiscal organization or the informal management arrangements that are present in such complex holdings.

Adopting a generalized statistical framework and integrating agricultural statistics into it could help solve the problems in data collection and interpretation created by the presence of complex farm operations. Central to this framework is the recognition that a farm business may have either a simple organization, in which it is one establishment—the “classical” farm—or a more complex organization, in which it consists of several establishments.

For the simple farm businesses, not much needs to be changed in data collection. Such farms can be questioned through census forms that query all that NASS wants to know about them, their farm activities, their farmers, and their households. The current structure of ARMS measures land uses, farming practices, input use, and financial statements well. The use of administrative means or surveillance to collect data on these farms will enhance the quality of the information collected and reduce the burden for the farmer. For data collection from complex farm businesses, the differences between farms and farm businesses and the decisions about whom to ask about farming activities, farmers, and households all have to be taken into account. Farms, farm businesses, and farm households—and even farm fields—each inhabit their own universes that can be represented in registers

or list frames designed to capture them. This aspect of data collection is discussed in detail in Chapter 5 . The presence of these distinct universes also implies that different data items should be associated with different reporting units.

The main objective of the Census of Agriculture is to report on the structure of farming in terms of the number of holdings, their activities, and their size. That distribution is fairly stable over time, which means that a yearly survey may not be needed for most purposes and that the current five-year interval may be sufficient. As argued in section 4.2 , complex farm businesses could benefit from more guidance in profiling their activities, revenues, land, and assets for the Census of Agriculture. In addition to the structure of farms, this census could also be adapted to report on farm businesses.

Information on yields and prices has to be reported comparatively more often to improve the functioning of markets. However, on this score, experts from upstream and downstream industries who visit multiple farms are often more knowledgeable than individual farmers; and remote sensing technology is another valuable source of information about fields, cropping activities, and yields. As a result, there is a diminishing need to include such variables in the Census of Agriculture.

By contrast, to report on revenues and income for the purpose of carrying out policy evaluations, farm businesses (both simple and complex) are the most important level of analysis and should be a major focus of reporting for ARMS. In addition, as argued above, attention must be paid to agricultural support activities and agricultural business complexes. This increased attention to complex farm businesses will require additional resources, which may be freed up by reducing the number of questions in the Census of Agriculture (especially concerning monetary aspects), by reduced attention to very small farms, and by the deployment of modern information technology solutions. Chapter 5 provides additional guidance on these strategies.

ANNEX 4.1. AGRICULTURAL ACTIVITIES LISTED IN NAICS SECTOR 11: AGRICULTURE, FORESTRY, FISHING AND HUNTING

SOURCE: U.S. Census Bureau, see https://www.census.gov/eos/www/naics/2012NAICS/2012_Definition_File.pdf .

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America’s farms and farmers are integral to the U.S. economy and, more broadly, to the nation’s social and cultural fabric. A healthy agricultural sector helps ensure a safe and reliable food supply, improves energy security, and contributes to employment and economic development, traditionally in small towns and rural areas where farming serves as a nexus for related sectors from farm machinery manufacturing to food processing. The agricultural sector also plays a role in the nation’s overall economic growth by providing crucial raw inputs for the production of a wide range of goods and services, including many that generate substantial export value.

If the agricultural sector is to be accurately understood and the policies that affect its functioning are to remain well informed, the statistical system’s data collection programs must be periodically revisited to ensure they are keeping up with current realities. This report reviews current information and makes recommendations to the U.S. Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) and Economic Research Service (ERS) to help identify effective methods for collecting data and reporting information about American agriculture, given increased complexity and other changes in farm business structure in recent decades.

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How to determine temporal yield variances of various cropping systems for modelling farmers' production risk - Illustrated by results from a long-term field trial

• Pergner, Isabell
• Lippert, Christian
• Piepho, Hans-Peter
• Schwarz, Jürgen
• Kehlenbeck, Hella

When making choices among different cropping systems like conventional farming, organic farming or cropping systems without pesticides, but with mineral fertilizers, risk averse farmers will not only account for expected (i.e. average) farm income but also for income stability. The smaller the variance of a farm's total contribution margin, the more stable is the income from a cropping system. The overall income variance can be calculated and included in quadratic risk programming models that apply the expected value-variance criterion when temporal (co-)variances of the contribution margins of the single crops are known. In this context, there is a lack in empirically well-founded approaches to identify the latter. Thus, taking a single farmer's perspective, we first outline a way to derive temporal (co-)variances of contribution margins for crops grown at one location. Second, neglecting variances of crop prices and variable costs, we show for different cropping systems how to consistently estimate temporal crop yield (co-)variances based on available yield data from a long-term field trial by the Julius Kühn-Institut in Dahnsdorf (Germany). The four cropping systems were (b1) without fertilizer and pesticides; (b2) without fertilizer but with pesticides; (b3) with fertilizer but without pesticides; and (b4) with fertilizer and pesticides. For each system, we estimated a covariance matrix for yield data of winter rye, winter barley and pea grown from 1998 to 2021 using a mixed effects model. Moreover, we calculated means, standard deviations and coefficients of variation for the different crop yields. The size of the (co-)variances found is a valuable indicator for their order of magnitude to be used as input in quadratic risk programming with the intention of optimizing a cultivation program that accounts for individual farmers' preferred levels of risk. Further, our statistical approach should be applied to other long-term field experiments to get deeper insights into average yield level and (co-)variance structures of crops grown in different cropping systems.

• Cropping systems;
• Estimating temporal covariance matrices;
• Mixed effects model;
• Production risk;
• Contribution margin;
• Yield data;
• Pesticide-free agriculture

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Home » Term Paper – Format, Examples and Writing Guide

Term Paper – Format, Examples and Writing Guide

Table of Contents

Definition:

Term paper is a type of academic writing assignment that is typically assigned to students at the end of a semester or term. It is usually a research-based paper that is meant to demonstrate the student’s understanding of a particular topic, as well as their ability to analyze and synthesize information from various sources.

Term papers are usually longer than other types of academic writing assignments and can range anywhere from 5 to 20 pages or more, depending on the level of study and the specific requirements of the assignment. They often require extensive research and the use of a variety of sources, including books, articles, and other academic publications.

Term Paper Format

The format of a term paper may vary depending on the specific requirements of your professor or institution. However, a typical term paper usually consists of the following sections:

• Title page: This should include the title of your paper, your name, the course name and number, your instructor’s name, and the date.
• Abstract : This is a brief summary of your paper, usually no more than 250 words. It should provide an overview of your topic, the research question or hypothesis, your methodology, and your main findings or conclusions.
• Introduction : This section should introduce your topic and provide background information on the subject. You should also state your research question or hypothesis and explain the importance of your research.
• Literature review : This section should review the existing literature on your topic. You should summarize the key findings and arguments made by other scholars and identify any gaps in the literature that your research aims to address.
• Methodology: This section should describe the methods you used to collect and analyze your data. You should explain your research design, sampling strategy, data collection methods, and data analysis techniques.
• Results : This section should present your findings. You can use tables, graphs, and charts to illustrate your data.
• Discussion : This section should interpret your findings and explain what they mean in relation to your research question or hypothesis. You should also discuss any limitations of your study and suggest areas for future research.
• Conclusion : This section should summarize your main findings and conclusions. You should also restate the importance of your research and its implications for the field.
• References : This section should list all the sources you cited in your paper using a specific citation style (e.g., APA, MLA, Chicago).
• Appendices : This section should include any additional materials that are relevant to your study but not essential to your main argument (e.g., survey questions, interview transcripts).

Structure of Term Paper

Here’s an example structure for a term paper:

I. Introduction

A. Background information on the topic

B. Thesis statement

II. Literature Review

A. Overview of current literature on the topic

B. Discussion of key themes and findings from literature

C. Identification of gaps in current literature

III. Methodology

A. Description of research design

B. Discussion of data collection methods

C. Explanation of data analysis techniques

IV. Results

A. Presentation of findings

B. Analysis and interpretation of results

C. Comparison of results with previous studies

V. Discussion

A. Summary of key findings

B. Explanation of how results address the research questions

C. Implications of results for the field

VI. Conclusion

A. Recap of key points

B. Significance of findings

C. Future directions for research

VII. References

A. List of sources cited in the paper

How to Write Term Paper

Here are some steps to help you write a term paper:

• Choose a topic: Choose a topic that interests you and is relevant to your course. If your professor has assigned a topic, make sure you understand it and clarify any doubts before you start.
• Research : Conduct research on your topic by gathering information from various sources such as books, academic journals, and online resources. Take notes and organize your information systematically.
• Create an outline : Create an outline of your term paper by arranging your ideas and information in a logical sequence. Your outline should include an introduction, body paragraphs, and a conclusion.
• Write a thesis statement: Write a clear and concise thesis statement that states the main idea of your paper. Your thesis statement should be included in your introduction.
• Write the introduction: The introduction should grab the reader’s attention, provide background information on your topic, and introduce your thesis statement.
• Write the body : The body of your paper should provide supporting evidence for your thesis statement. Use your research to provide details and examples to support your argument. Make sure to organize your ideas logically and use transition words to connect paragraphs.
• Write the conclusion : The conclusion should summarize your main points and restate your thesis statement. Avoid introducing new information in the conclusion.
• Edit and proofread: Edit and proofread your term paper carefully to ensure that it is free of errors and flows smoothly. Check for grammar, spelling, and punctuation errors.
• Format and cite your sources: Follow the formatting guidelines provided by your professor and cite your sources properly using the appropriate citation style.
• Submit your paper : Submit your paper on time and according to the instructions provided by your professor.

Term Paper Example

Here’s an example of a term paper:

Title : The Role of Artificial Intelligence in Cybersecurity

As the world becomes more digitally interconnected, cybersecurity threats are increasing in frequency and sophistication. Traditional security measures are no longer enough to protect against these threats. This paper explores the role of artificial intelligence (AI) in cybersecurity, including how AI can be used to detect and respond to threats in real-time, the challenges of implementing AI in cybersecurity, and the potential ethical implications of AI-powered security systems. The paper concludes with recommendations for organizations looking to integrate AI into their cybersecurity strategies.

Introduction :

The increasing number of cybersecurity threats in recent years has led to a growing interest in the potential of artificial intelligence (AI) to improve cybersecurity. AI has the ability to analyze vast amounts of data and identify patterns and anomalies that may indicate a security breach. Additionally, AI can automate responses to threats, allowing for faster and more effective mitigation of security incidents. However, there are also challenges associated with implementing AI in cybersecurity, such as the need for large amounts of high-quality data, the potential for AI systems to make mistakes, and the ethical considerations surrounding the use of AI in security.

Literature Review:

This section of the paper reviews existing research on the use of AI in cybersecurity. It begins by discussing the types of AI techniques used in cybersecurity, including machine learning, natural language processing, and neural networks. The literature review then explores the advantages of using AI in cybersecurity, such as its ability to detect previously unknown threats and its potential to reduce the workload of security analysts. However, the review also highlights some of the challenges associated with implementing AI in cybersecurity, such as the need for high-quality training data and the potential for AI systems to be fooled by sophisticated attacks.

Methodology :

To better understand the challenges and opportunities associated with using AI in cybersecurity, this paper conducted a survey of cybersecurity professionals working in a variety of industries. The survey included questions about the types of AI techniques used in their organizations, the challenges they faced when implementing AI in cybersecurity, and their perceptions of the ethical implications of using AI in security.

The results of the survey showed that while many organizations are interested in using AI in cybersecurity, they face several challenges when implementing these systems. These challenges include the need for high-quality training data, the potential for AI systems to be fooled by sophisticated attacks, and the difficulty of integrating AI with existing security systems. Additionally, many respondents expressed concerns about the ethical implications of using AI in security, such as the potential for AI to be biased or to make decisions that are harmful to individuals or society as a whole.

Discussion :

Based on the results of the survey and the existing literature, this paper discusses the potential benefits and risks of using AI in cybersecurity. It also provides recommendations for organizations looking to integrate AI into their security strategies, such as the need to prioritize data quality and to ensure that AI systems are transparent and accountable.

Conclusion :

While there are challenges associated with implementing AI in cybersecurity, the potential benefits of using these systems are significant. AI can help organizations detect and respond to threats more quickly and effectively, reducing the risk of security breaches. However, it is important for organizations to be aware of the potential ethical implications of using AI in security and to take steps to ensure that these systems are transparent and accountable.

References:

• Alkhaldi, S., Al-Daraiseh, A., & Lutfiyya, H. (2019). A Survey on Artificial Intelligence Techniques in Cyber Security. Journal of Information Security, 10(03), 191-207.
• Gartner. (2019). Gartner Top 10 Strategic Technology Trends for 2020. Retrieved from https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2020/
• Kshetri, N. (2018). Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80-89.
• Lipton, Z. C. (2018). The mythos of model interpretability. arXiv preprint arXiv:1606.03490.
• Schneier, B. (2019). Click Here to Kill Everybody: Security and Survival in a Hyper-Connected World. WW Norton & Company.
• Wahab, M. A., Rahman, M. S., & Islam, M. R. (2020). A Survey on AI Techniques in Cybersecurity. International Journal of Scientific & Engineering Research, 11(2), 22-27.

When to Write Term Paper

A term paper is usually a lengthy research paper that is assigned to students at the end of a term or semester. There are several situations when writing a term paper may be required, including:

• As a course requirement: In most cases, a term paper is required as part of the coursework for a particular course. It may be assigned by the instructor as a way of assessing the student’s understanding of the course material.
• To explore a specific topic : A term paper can be an excellent opportunity for students to explore a specific topic of interest in-depth. It allows them to conduct extensive research on the topic and develop their understanding of it.
• To develop critical thinking skills : Writing a term paper requires students to engage in critical thinking and analysis. It helps them to develop their ability to evaluate and interpret information, as well as to present their ideas in a clear and coherent manner.
• To prepare for future academic or professional pursuits: Writing a term paper can be an excellent way for students to prepare for future academic or professional pursuits. It can help them to develop the research and writing skills necessary for success in higher education or in a professional career.

Purpose of Term Paper

The main purposes of a term paper are:

• Demonstrate mastery of a subject: A term paper provides an opportunity for students to showcase their knowledge and understanding of a particular subject. It requires students to research and analyze the topic, and then present their findings in a clear and organized manner.
• Develop critical thinking skills: Writing a term paper requires students to think critically about their subject matter, analyzing various sources and viewpoints, and evaluating evidence to support their arguments.
• Improve writing skills : Writing a term paper helps students improve their writing skills, including organization, clarity, and coherence. It also requires them to follow specific formatting and citation guidelines, which can be valuable skills for future academic and professional endeavors.
• Contribute to academic discourse : A well-written term paper can contribute to academic discourse by presenting new insights, ideas, and arguments that add to the existing body of knowledge on a particular topic.
• Prepare for future research : Writing a term paper can help prepare students for future research, by teaching them how to conduct a literature review, evaluate sources, and formulate research questions and hypotheses. It can also help them develop research skills that they can apply in future academic or professional endeavors.

Advantages of Term Paper

There are several advantages of writing a term paper, including:

• In-depth exploration: Writing a term paper allows you to delve deeper into a specific topic, allowing you to gain a more comprehensive understanding of the subject matter.
• Improved writing skills: Writing a term paper involves extensive research, critical thinking, and the organization of ideas into a cohesive written document. As a result, writing a term paper can improve your writing skills significantly.
• Demonstration of knowledge: A well-written term paper demonstrates your knowledge and understanding of the subject matter, which can be beneficial for academic or professional purposes.
• Development of research skills : Writing a term paper requires conducting thorough research, analyzing data, and synthesizing information from various sources. This process can help you develop essential research skills that can be applied in many other areas.
• Enhancement of critical thinking : Writing a term paper encourages you to think critically, evaluate information, and develop well-supported arguments. These skills can be useful in many areas of life, including personal and professional decision-making.
• Preparation for further academic work : Writing a term paper is excellent preparation for more extensive academic projects, such as a thesis or dissertation.

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