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Agriculture articles from across Nature Portfolio

Agriculture is the cultivation of plants, animals, and some other organisms, such as fungi, for the production of food, fibre, fuel, and medicines used by society.

research for agriculture

Nitrogen-hungry bacteria added to farm soil curb greenhouse-gas emissions

Innovative solutions are needed to decrease greenhouse-gas emissions. Field trials show that supplementing farm soil with a bacterium that consumes the greenhouse gas nitrous oxide can substantially lower harmful emissions.

  • Frank E. Löffler

research for agriculture

A more ecological chocolate

A sweet gel from the endocarp of cocoa pods and the concentrated juice of the cocoa fruit pulp can replace sugar in a chocolate recipe, reducing the environmental impact associated with its production and improving the nutritional value of chocolate.

  • Alejandro G. Marangoni

research for agriculture

Fertilizer application improvements in China

Management practices including augmenting the ratio of organic to chemical fertilizers, implementing deep application techniques and reintegrating straw into fields can bolster food production while optimizing resource use efficiency and abating nitrogen pollution in China.

  • Zhenling Cui

Latest Research and Reviews

research for agriculture

Consistency in climate change impact reports among indigenous peoples and local communities depends on site contexts

  • Christoph Schunko
  • Santiago Álvarez-Fernández
  • Victoria Reyes-García

research for agriculture

Alternative protein sources: science powered startups to fuel food innovation

Harnessing the potential of considerable food security efforts requires the ability to translate them into commercial applications. In this Perspective, the author explores the alternative protein source start-up landscape.

  • Elena Lurie-Luke

research for agriculture

Circular food system approaches can support current European protein intake levels while reducing land use and greenhouse gas emissions

Almost half of land use and nearly three-quarters of greenhouse gas emissions can be reduced by adopting circularity principles and reducing the ratio of animal-sourced protein to plant-sourced protein from 60:40 to 40:60 in European diets.

  • Wolfram J. Simon
  • Renske Hijbeek
  • Hannah H. E. van Zanten

research for agriculture

Food fraud threats in UK post-harvest seafood supply chains; an assessment of current vulnerabilities

  • Sophie Lawrence
  • Christopher Elliott
  • Saskia van Ruth

research for agriculture

Metricizing policy texts: Comprehensive dataset on China’s Agri-policy intensity spanning 1982–2023

  • Jianping Yang

research for agriculture

Multi-decadal climate services help farmers assess and manage future risks

How climate services support on-farm management is not well understood. Here research shows that multi-decadal projections help farmers better identify future climate risks through reducing complexity and psychological distance, although this may be impeded by lack of confidence in data.

  • Yuwan Malakar
  • Stephen Snow
  • Rebecca Darbyshire


News and Comment

Emergency loan.

  • Lingxiao Yan

research for agriculture

Knowledge democratization approaches for food systems transformation

Current narrow views of what constitutes evidence have left blind spots in food system decision-making. Yet, alternative ways of facilitating the production and exchange of transdisciplinary knowledge enable key lessons for more equitable and informed policy processes.

  • Samara Brock
  • Lauren Baker

research for agriculture

Bird flu in US cows: is the milk supply safe?

Pasteurized milk is probably not a threat to people, but fresh milk droplets on milking equipment could be spreading the virus in a herd.

  • Julian Nowogrodzki

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Research and Science

From fostering continued economic growth to adapting to the effects of climate change and addressing food security, the United States can continue to be a leader in global agriculture. Each day, the work of USDA scientists and researchers touches the lives of all Americans - from the farm field to the kitchen table and from the air we breathe to the energy that powers our country.

The challenges facing agriculture, natural resources, and conservation are immense and can be addressed through robust research enterprise and educational programs. USDA intramural and extramural science helps to protect, secure, and improve our food, agricultural and natural resources systems.

USDA Science and Research Strategy, 2023-2026: Cultivating Scientific Innovation

The “ USDA Science and Research Strategy, 2023-2026: Cultivating Scientific Innovation (PDF, 21.4 MB)” presents a near-term vision for transforming U.S. agriculture through science and innovation, and outlines USDA’s highest scientific priorities. The S&RS is a call to action for USDA partners, stakeholders, and customers to join the conversation and help identify innovative research strategies that lead to real-world, practical solutions that help farmers, producers, and communities thrive.

Learn more and engage below:

USDA Science and Research Strategy

AGARDA: A Vision for Disruptive Science to Confront Audacious Challenges

Agriculture Advanced Research and Development Authority (AGARDA) Implementation Strategy (PDF, 1.8 MB) is a framework outlining a new approach for delivering disruptive breakthrough discoveries for agriculture.

Strengthening Our Research System

USDA has refocused its science agencies to ensure the most effective and efficient use of its resources, while leveraging the strengths of our partners across the scientific community.

The Office of the Chief Scientist (OCS) coordinates USDA research, education and Extension with scientists and researchers across the federal government and university and private partners, to make the best use of taxpayer investments. In 2012, OCS continued focus on the Research, Education and Economics Action Plan (PDF, 486 KB) and identified seven priority research topics:

  • Global Food Supply and Security
  • Climate and Energy Needs
  • Sustainable Use of Natural Resources
  • Nutrition and Childhood Obesity
  • Food Safety
  • Education and Science Literacy
  • Rural-urban Interdependence/Rural Prosperity

The Agricultural Research Service (ARS) conducts research to develop and transfer solutions to agricultural problems of high national priority.

The Economic Research Service (ERS) , through science-based economic research and analysis, informs public policy and other decisions about agriculture, food, rural development, and environmental challenges.

The National Agricultural Statistics Service (NASS) conducts hundreds of surveys every year and prepares reports covering virtually every aspect of U.S. agriculture.

The National Institute of Food and Agriculture (NIFA) supports research, education and Extension programs in the Land-Grant University System and other partner organizations.

Enhancing the Productivity of American Agriculture and Ensuring the Safety of our Food Supply

USDA invests in research, development, and outreach of new varieties and technologies to mitigate animal/plant diseases and increase productivity, sustainability, and product quality. USDA research has supported America's farmers and ranchers in their work to produce a safe and abundant food supply for over 100 years. This work has helped feed the nation and sustain an agricultural trade surplus since the 1960s.

An additional focus is to establish more sustainable systems that enhance crop and animal health. Our scientists and university partners have revealed the genetic blueprints of a host of plants and animals including the genomes of apples, pigs, and turkeys, and in 2012, they furthered understanding of the tomato, bean, wheat and barley genomes -- key drivers in developing the resilience of those crops to feed growing populations.

NASS has developed animated U.S. crop progress and topsoil moisture maps , along with other resources, to help experts assess farmland data. USDA researchers also created the Maize Genome Database, an important tool to help farmers improve traits in a crop vital to the world. Meeting growing global demand for food, fiber, and biofuel requires robust investment in agricultural research and development (R&D) from both public and private sectors. USDA is a leader in remote sensing and mapping to visualize data in support of agricultural policy and business decision making as well as program operation. We ranked first worldwide among research institutions publishing on priority diseases in animal health including salmonellosis, avian influenza , mycobacterial disease, coccidiosis, campylobacterosis, mastitis and others.

USDA conducts and supports science that informs decisions and policies contributing to a safe food supply and the reduction of foodborne hazards. Our scientists found the primary site where the virus that causes foot-and-mouth disease begins infection in cattle and developed an improved vaccine against the disease. They are also working on new strategies to control mites and other major honey bee problems such as colony collapse disorder .

Improving Nutrition and Confronting Obesity

USDA builds the evidence base for food-based and physical activity strategies and develops effective education activities to promote health and reduce malnutrition and obesity in children and high-risk populations. For example, ARS evaluated school characteristics associated with healthier or less healthy food preparation practices and offerings and found that the school nutrition environment could be improved by requiring food service managers to hold nutrition-related college degrees, pass a food service training program, and by participating in a school-based nutrition program such as USDA Team Nutrition .

USDA-supported science is investigating the causes of childhood obesity so that our country can address the epidemic. In these efforts, USDA supports nutrition education programs and encourages Americans to consume more nutritious foods like fruits and vegetables. Our scientists are part of an international team that has found a way to boost the nutritional value of broccoli, tomatoes and corn, and have worked to find ways to bolster the nutritional content of other staple crops like oats and rice. USDA research has supported these efforts, showing how healthy foods can often cost less than foods that are high in saturated fat, added sugar and/or sodium.

In 2013, USDA updated the national assessment of urban and rural food deserts - low-income areas with limited access to affordable and nutritious food - and provided information on the socioeconomic and demographic characteristics that distinguish food deserts from other areas, for decision-makers and stakeholders concerned about access to healthy foods.

Conserving Natural Resources and Combating Climate Change

USDA develops and delivers science-based knowledge that empowers farmers, foresters, ranchers, landowners, resource managers, policymakers, and Federal agencies to manage the risks, challenges, and opportunities of climate variability, and that informs decision-making and improves practices in environmental conservation.

Our scientists are developing rice and corn crops that are drought- and flood-resistant and helping to improve the productivity of soil, as well as production systems that require increasing smaller amounts of pesticides or none at all.

Vegetation indices contained in VegScape have proven useful for assessing crop condition and identifying the aerial extent of floods, drought, major weather anomalies, and vulnerabilities of early/late season crops. This tool allows users to monitor and track weather anomalies' effects on crops in near real time and compare this information to historical data on localized levels or across States.

Additionally, our researchers have examined the potential impacts of a suite of climate scenarios on U.S. crop production. Studies like these will help policymakers, farmers, industry leaders and others better understand and adapt to a changing climate on America's crop production.

Our researchers created i-Tree , urban forest management software to help cities understand the value of urban trees through carbon sequestration, erosion protection, energy conservation and water filtration, and since 2009 have continued building on the success of the tool and expanding its use. Our scientists are conducting research on uses of wood, helping companies meet green building design standards and creating jobs using forest products. We have also worked with Major League Baseball to reduce the occurrence of broken baseball bats.

USDA supports families managing through tough economic times by helping residents save energy at home and conserve water, with a program run by Cooperative Extension and our land-grant university partners. Cooperative Extension-affiliated volunteer monitoring programs have engaged citizens in water monitoring to better understand the effects of climate change and/or aquatic invasive species on local waters. Collectively, these programs interacted with hundreds of local, State, and Federal partners. The programs help citizens detect the presence of invasive species and harmful algal blooms.

Science Education and Extension

USDA recognizes the importance of recruiting, cultivating, and developing the next generation of scientists, leaders, and a highly skilled workforce for food, agriculture, natural resources, forestry, environmental systems, and life sciences.

The NIFA interagency agreement with the U.S. Fish and Wildlife Service leverages technology and innovation and involves youth in STEM outreach and exposure. Youth participants developed science process skills related to using GIS and research design, analyzing and interpreting data, and reporting findings to the community which has enabled them to become better consumers of science and citizens capable of making wise STEM policy choices.

USDA strives to provide effective research, education, and extension activities that inform public and private decision-making in support of rural and community development . NASS holds outreach events throughout the Census cycle with underserved and minority and disadvantaged farming groups to promote participation in the Census of Agriculture . With funding and support from NIFA, many Tribal Colleges are offering Reservation citizens training ranging from basic financial literacy to business start-up and marketing information so that families not only survive, but thrive.

In addition, the ERS Atlas of Rural and Small Town America brings together over 80 demographic, economic, and agricultural statistics for every county in all 50 states and assembles statistics in four broad categories -- people, jobs, agriculture, and geography.

Research and Science Centers and Databases

  • Agricultural Network Information Center (AGNIC)
  • Agricultural Online Access (AGRICOLA)
  • Alternative Farming Systems Information Center (AFSIC)
  • Animal Welfare Information Center (AWIC)
  • Current Research Information Center (CRIS)
  • Digital Desktop (DigiTop) for Employees
  • Food and Nutrition Assistance Research Database
  • Food and Nutrition Information Center
  • Production, Supply and Distribution Online (PSD Online) Database
  • Rural Information Center
  • Water and Agricultural Information Center

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PLOS publishes a broad range of Open interdisciplinary precision agriculture research that is essential to the wider understanding, advancement and adoption of precision agriculture practices and technology.

Discover precision agriculture research from PLOS

PLOS’ precision agriculture research explores and assesses the very latest agricultural technologies. Whether in controlled environments or directly in the field, our research highlights new methods and technologies for agricultural surveillance and intervention, such as sensors and chemical testing, or high-tech farm machinery and machine learning that measures, analyses, and improves crop yield, soil quality, and animal husbandry practices.

These efforts are fundamental to guaranteeing food security and sustainable crop production in the context of rising demand and increasingly challenging farming environments.

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As a leading publisher in the field, these articles showcase research that has influenced academia, industry and/or policy.

research for agriculture

Role of biotechnology in creating sustainable agriculture

research for agriculture

Maize yield in smallholder agriculture system—An approach integrating socio-economic and crop management factors

research for agriculture

Forecasting severe grape downy mildew attacks using machine learning

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Study the latest insights, technologies, and strategies that are shaping the global agricultural landscape and helping drive sustainable, efficient farming practices.

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Estimation of forage biomass and vegetation cover in grasslands using UAV imagery

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Harnessing digital technology to improve agricultural productivity?

Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis

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Seedlings planted for future crops

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Sustainable Agriculture Research & Education Program | A program of UC Agriculture & Natural Resources

Sustainable Agriculture Research & Education Program

  • What is Sustainable Agriculture?

The goal of sustainable agriculture is to meet society’s food and textile needs in the present without compromising the ability of future generations to meet their own needs.

Practitioners of sustainable agriculture seek to integrate three main objectives into their work: a healthy environment, economic profitability, and social and economic equity. Every person involved in the food system—growers, food processors, distributors, retailers, consumers, and waste managers—can play a role in ensuring a sustainable agricultural system.

There are many practices commonly used by people working in sustainable agriculture and sustainable food systems. Growers may use methods to promote  soil health , minimize  water use , and lower  pollution levels  on the farm. Consumers and retailers concerned with sustainability can look for “ values-based ” foods that are grown using methods promoting  farmworker wellbeing , that are  environmentally friendly , or that strengthen the local economy. And researchers in sustainable agriculture often cross disciplinary lines with their work: combining biology, economics, engineering, chemistry, community development, and many others. However, sustainable agriculture is more than a collection of practices. It is also process of negotiation: a push and pull between the sometimes competing interests of an individual farmer or of people in a community as they work to solve complex problems about how we grow our food and fiber.

Topics in sustainable agriculture

  • Addressing Food Insecurity
  • Agritourism
  • Agroforestry
  • Conservation Tillage
  • Controlled Environment Agriculture (CEA)
  • Cooperatives
  • Cover Crops
  • Dairy Waste Management
  • Direct Marketing
  • Energy Efficiency & Conservation
  • Food and Agricultural Employment
  • Food Labeling/Certifications
  • Food Waste Management
  • Genetically Modified Crops
  • Global Sustainable Sourcing of Commodities
  • Institutional Sustainable Food Procurement
  • Biologically Integrated Farming Systems
  • Integrated Pest Management (IPM)
  • Nutrition & Food Systems Education
  • Organic Farming
  • Precision Agriculture (SSM)
  • Soil Nutrient Management
  • Postharvest Management Practices
  • Technological Innovation in Agriculture
  • Urban Agriculture
  • Value-Based Supply Chains
  • Water Use Efficiency
  • Water Quality Management
  • Zero-Emissions Freight Transport

Directory of UC Programs in Sustainable Agriculture

This directory is a catalog of UC's programmatic activities in sustainable agriculture and food systems. All programs are sorted by activities and topic areas.

Screenshot of Directory Programs

The Philosophy & Practices of Sustainable Agriculture

Agriculture has changed dramatically, especially since the end of World War II. Food and fiber productivity soared due to new technologies, mechanization, increased chemical use, specialization and government policies that favored maximizing production. These changes allowed fewer farmers with reduced labor demands to produce the majority of the food and fiber in the U.S.

Although these changes have had many positive effects and reduced many risks in farming, there have also been significant costs. Prominent among these are topsoil depletion, groundwater contamination, the decline of family farms, continued neglect of the living and working conditions for farm laborers, increasing costs of production, and the disintegration of economic and social conditions in rural communities.

Potential Costs of Modern Agricultural Techniques

A growing movement has emerged during the past two decades to question the role of the agricultural establishment in promoting practices that contribute to these social problems. Today this movement for sustainable agriculture is garnering increasing support and acceptance within mainstream agriculture. Not only does sustainable agriculture address many environmental and social concerns, but it offers innovative and economically viable opportunities for growers, laborers, consumers, policymakers and many others in the entire food system.

This page is an effort to identify the ideas, practices and policies that constitute our concept of sustainable agriculture. We do so for two reasons: 1) to clarify the research agenda and priorities of our program, and 2) to suggest to others practical steps that may be appropriate for them in moving toward sustainable agriculture. Because the concept of sustainable agriculture is still evolving, we intend this page not as a definitive or final statement, but as an invitation to continue the dialogue

what is sustainable ag venn diagram

Despite the diversity of people and perspectives, the following themes commonly weave through definitions of sustainable agriculture:

Sustainability rests on the principle that we must meet the needs of the present without compromising the ability of future generations to meet their own needs. Therefore,  stewardship of both natural and human resources  is of prime importance.  Stewardship of human resources  includes consideration of social responsibilities such as working and living conditions of laborers, the needs of rural communities, and consumer health and safety both in the present and the future.  Stewardship of land and natural resources  involves maintaining or enhancing this vital resource base for the long term.

A  systems perspective  is essential to understanding sustainability. The system is envisioned in its broadest sense, from the individual farm, to the local ecosystem,  and  to communities affected by this farming system both locally and globally. An emphasis on the system allows a larger and more thorough view of the consequences of farming practices on both human communities and the environment. A systems approach gives us the tools to explore the interconnections between farming and other aspects of our environment.

Everyone plays a role in creating a sustainable food system.

Ag infographic

Making the transition to sustainable agriculture is a process.   For farmers, the transition to sustainable agriculture normally requires  a series of small ,  realistic   steps . Family economics and personal goals influence how fast or how far participants can go in the transition. It is important to realize that each small decision can make a difference and contribute to advancing the entire system further on the "sustainable agriculture continuum." The key to moving forward is the will to take the next step. Finally, it is important to point out that   reaching toward the goal of sustainable agriculture is the responsibility of all participants in the system ,  including farmers, laborers, policymakers, researchers, retailers, and consumers. Each group has its own part to play, its own unique contribution to make to strengthen the sustainable agriculture community. The remainder of this page considers specific strategies for realizing these broad themes or goals. The strategies are grouped according to three separate though related areas of concern:  Farming and Natural Resources ,  Plant and Animal Production Practices , and the  Economic, Social and Political Context . They represent a range of potential ideas for individuals committed to interpreting the vision of sustainable agriculture within their own circumstances.

  • Farming and Natural Resources

When the production of food and fiber degrades the natural resource base, the ability of future generations to produce and flourish decreases. The decline of ancient civilizations in Mesopotamia, the Mediterranean region, Pre-Columbian southwest U.S. and Central America is believed to have been strongly influenced by natural resource degradation from non-sustainable farming and forestry practices. 

Water is the principal resource that has helped agriculture and society to prosper, and it has been a major limiting factor when mismanaged.

Water supply and use.  In California, an extensive  water storage and transfer system  has been established which has allowed crop production to expand to very arid regions. In drought years, limited surface water supplies have prompted overdraft of groundwater and consequent intrusion of salt water, or permanent collapse of aquifers. Periodic droughts, some lasting up to 50 years, have occurred in California.

Several steps should be taken to develop drought-resistant farming systems even in "normal" years, including both policy and management actions:

1) improving  water conservation  and storage measures,

2) providing incentives for selection of drought-tolerant crop species,

3) using  reduced-volume irrigation  systems,

4) managing crops to reduce water loss, or

5) not planting at all.

Water quality.  The most important issues related to water quality involve salinization and contamination of ground and surface waters by pesticides, nitrates and selenium. Salinity has become a problem wherever water of even relatively low salt content is used on shallow soils in arid regions and/or where the water table is near the root zone of crops. Tile drainage can remove the water and salts, but the disposal of the salts and other contaminants may negatively affect the environment depending upon where they are deposited. Temporary solutions include the use of salt-tolerant crops, low-volume irrigation, and various management techniques to minimize the effects of salts on crops. In the long-term, some farmland may need to be removed from production or converted to other uses. Other uses include conversion of row crop land to production of drought-tolerant forages, the restoration of wildlife habitat or the use of agroforestry to minimize the impacts of salinity and high water tables. Pesticide and nitrate contamination of water can be reduced using many of the practices discussed later in the  Plant Production Practices  and  Animal Production Practices  sections.

Wildlife . Another way in which agriculture affects water resources is through the destruction of riparian habitats within watersheds. The conversion of wild habitat to agricultural land reduces fish and wildlife through erosion and sedimentation, the effects of pesticides, removal of riparian plants, and the diversion of water. The plant diversity in and around both riparian and agricultural areas should be maintained in order to support a diversity of wildlife. This diversity will enhance natural ecosystems and could aid in agricultural pest management.

Modern agriculture is heavily dependent on non-renewable energy sources, especially petroleum. The continued use of these energy sources cannot be sustained indefinitely, yet to abruptly abandon our reliance on them would be economically catastrophic. However, a sudden cutoff in energy supply would be equally disruptive. In sustainable agricultural systems, there is reduced reliance on non-renewable energy sources and a substitution of renewable sources or labor to the extent that is economically feasible.

Many agricultural activities affect air quality. These include smoke from agricultural burning; dust from tillage, traffic and harvest; pesticide drift from spraying; and nitrous oxide emissions from the use of nitrogen fertilizer. Options to improve air quality include:

      - incorporating crop residue into the soil       - using appropriate levels of tillage       - and planting wind breaks, cover crops or strips of native perennial grasses to reduce dust.

Soil erosion continues to be a serious threat to our continued ability to produce adequate food. Numerous practices have been developed to keep soil in place, which include:

      - reducing or eliminating tillage       - managing irrigation to reduce runoff       - and keeping the soil covered with plants or mulch. 

Enhancement of soil quality is discussed in the next section.

  • Plant Production Practices

Sustainable production practices involve a variety of approaches. Specific strategies must take into account topography, soil characteristics, climate, pests, local availability of inputs and the individual grower's goals.  Despite the site-specific and individual nature of sustainable agriculture, several general principles can be applied to help growers select appropriate management practices:

      - Selection of species and varieties that are well suited to the site and to conditions on the farm;       - Diversification of crops (including livestock) and cultural practices to enhance the biological and economic stability of the farm;       - Management of the soil to enhance and protect soil quality;       - Efficient and humane use of inputs; and       - Consideration of farmers' goals and lifestyle choices.

Selection of site, species and variety

Preventive strategies, adopted early, can reduce inputs and help establish a sustainable production system. When possible, pest-resistant crops should be selected which are tolerant of existing soil or site conditions. When site selection is an option, factors such as soil type and depth, previous crop history, and location (e.g. climate, topography) should be taken into account before planting.

Diversified farms are usually more economically and ecologically resilient.  While monoculture farming has advantages in terms of efficiency and ease of management, the loss of the crop in any one year could put a farm out of business and/or seriously disrupt the stability of a community dependent on that crop. By growing a variety of crops, farmers spread economic risk and are less susceptible to the radical price fluctuations associated with changes in supply and demand.

Properly managed, diversity can also buffer a farm in a biological sense. For example, in annual cropping systems,  crop rotation can be used to suppress weeds, pathogens and insect pests. Also, cover crops can have stabilizing effects on the agroecosystem by holding soil and nutrients in place, conserving soil moisture with mowed or standing dead mulches, and by increasing the water infiltration rate and soil water holding capacity.  Cover crops  in orchards and vineyards can buffer the system against pest infestations by increasing beneficial arthropod populations and can therefore reduce the need for chemical inputs. Using a variety of cover crops is also important in order to protect against the failure of a particular species to grow and to attract and sustain a wide range of beneficial arthropods.

Optimum diversity may be obtained by integrating both crops and livestock in the same farming operation. This was the common practice for centuries until the mid-1900s when technology, government policy and economics compelled farms to become more specialized. Mixed crop and livestock operations have several advantages. First, growing row crops only on more level land and pasture or forages on steeper slopes will reduce soil erosion. Second, pasture and forage crops in rotation enhance soil quality and reduce erosion; livestock manure, in turn, contributes to soil fertility. Third, livestock can buffer the negative impacts of low rainfall periods by consuming crop residue that in "plant only" systems would have been considered crop failures. Finally, feeding and marketing are flexible in animal production systems. This can help cushion farmers against trade and price fluctuations and, in conjunction with cropping operations, make more efficient use of farm labor.

Soil management

A common philosophy among sustainable agriculture practitioners is that a "healthy" soil is a key component of sustainability; that is, a healthy soil will produce healthy crop plants that have optimum vigor and are less susceptible to pests. While many crops have key pests that attack even the healthiest of plants, proper soil, water and nutrient management can help prevent some pest problems brought on by crop stress or nutrient imbalance. Furthermore, crop management systems that impair soil quality often result in greater inputs of water, nutrients, pesticides, and/or energy for tillage to maintain yields.

In sustainable systems, the soil is viewed as a fragile and living medium that must be protected and nurtured to ensure its long-term productivity and stability.   Methods to protect and enhance the productivity of the soil include:

      - using cover crops, compost and/or manures       - reducing tillage       - avoiding traffic on wet soils       - maintaining soil cover with plants and/or mulches

Conditions in most California soils (warm, irrigated, and tilled) do not favor the buildup of organic matter. Regular additions of organic matter or the use of cover crops can increase soil aggregate stability, soil tilth, and diversity of soil microbial life.

Efficient use of inputs

Many inputs and practices used by conventional farmers are also used in sustainable agriculture. Sustainable farmers, however, maximize reliance on natural, renewable, and on-farm inputs.  Equally important are the environmental, social, and economic impacts of a particular strategy. Converting to sustainable practices does not mean simple input substitution. Frequently, it substitutes enhanced management and scientific knowledge for conventional inputs, especially chemical inputs that harm the environment on farms and in rural communities. The goal is to develop efficient, biological systems which do not need high levels of material inputs.

Growers frequently ask if synthetic chemicals are appropriate in a sustainable farming system. Sustainable approaches are those that are the least toxic and least energy intensive, and yet maintain productivity and profitability. Preventive strategies and other alternatives should be employed before using chemical inputs from any source. However, there may be situations where the use of synthetic chemicals would be more "sustainable" than a strictly non-chemical approach or an approach using toxic "organic" chemicals. For example, one grape grower switched from tillage to a few applications of a broad spectrum contact herbicide in the vine row. This approach may use less energy and may compact the soil less than numerous passes with a cultivator or mower.

Consideration of farmer goals and lifestyle choices

Management decisions should reflect not only environmental and broad social considerations, but also individual goals and lifestyle choices. For example, adoption of some technologies or practices that promise profitability may also require such intensive management that one's lifestyle actually deteriorates. Management decisions that promote sustainability, nourish the environment, the community and the individual.

  • Animal Production Practices

In the early part of this century, most farms integrated both crop and livestock operations. Indeed, the two were highly complementary both biologically and economically. The current picture has changed quite drastically since then. Crop and animal producers now are still dependent on one another to some degree, but the integration now most commonly takes place at a higher level-- between  farmers, through intermediaries, rather than  within  the farm itself. This is the result of a trend toward separation and specialization of crop and animal production systems. Despite this trend, there are still many farmers, particularly in the Midwest and Northeastern U.S. that integrate crop and animal systems--either on dairy farms, or with range cattle, sheep or hog operations.

Even with the growing specialization of livestock and crop producers, many of the principles outlined in the crop production section apply to both groups. The actual management practices will, of course, be quite different. Some of the specific points that livestock producers need to address are listed below.

Management Planning

Including livestock in the farming system increases the complexity of biological and economic relationships. The mobility of the stock, daily feeding, health concerns, breeding operations, seasonal feed and forage sources, and complex marketing are sources of this complexity. Therefore, a successful ranch plan should include enterprise calendars of operations, stock flows, forage flows, labor needs, herd production records and land use plans to give the manager control and a means of monitoring progress toward goals.

Animal Selection

The animal enterprise must be appropriate for the farm or ranch resources. Farm capabilities and constraints such as feed and forage sources, landscape, climate and skill of the manager must be considered in selecting which animals to produce. For example, ruminant animals can be raised on a variety of feed sources including range and pasture, cultivated forage, cover crops, shrubs, weeds, and crop residues. There is a wide range of breeds available in each of the major ruminant species, i.e., cattle, sheep and goats. Hardier breeds that, in general, have lower growth and milk production potential, are better adapted to less favorable environments with sparse or highly seasonal forage growth.

Animal nutrition

Feed costs are the largest single variable cost in any livestock operation. While most of the feed may come from other enterprises on the ranch, some purchased feed is usually imported from off the farm. Feed costs can be kept to a minimum by monitoring animal condition and performance and understanding seasonal variations in feed and forage quality on the farm. Determining the optimal use of farm-generated by-products is an important challenge of diversified farming.


Use of quality germplasm to improve herd performance is another key to sustainability. In combination with good genetic stock, adapting the reproduction season to fit the climate and sources of feed and forage reduce health problems and feed costs.

Herd Health

Animal health greatly influences reproductive success and weight gains, two key aspects of successful livestock production. Unhealthy stock waste feed and require additional labor. A herd health program is critical to sustainable livestock production.

Grazing Management

Most adverse environmental impacts associated with grazing can be prevented or mitigated with proper grazing management. First, the number of stock per unit area (stocking rate) must be correct for the landscape and the forage sources. There will need to be compromises between the convenience of tilling large, unfenced fields and the fencing needs of livestock operations. Use of modern, temporary fencing may provide one practical solution to this dilemma. Second, the long term carrying capacity and the stocking rate must take into account short and long-term droughts. Especially in Mediterranean climates such as in California, properly managed grazing significantly reduces fire hazards by reducing fuel build-up in grasslands and brushlands. Finally, the manager must achieve sufficient control to reduce overuse in some areas while other areas go unused. Prolonged concentration of stock that results in permanent loss of vegetative cover on uplands or in riparian zones should be avoided. However, small scale loss of vegetative cover around water or feed troughs may be tolerated if surrounding vegetative cover is adequate.

Confined Livestock Production

Animal health and waste management are key issues in confined livestock operations. The moral and ethical debate taking place today regarding animal welfare is particularly intense for confined livestock production systems. The issues raised in this debate need to be addressed.

Confinement livestock production is increasingly a source of surface and ground water pollutants, particularly where there are large numbers of animals per unit area. Expensive waste management facilities are now a necessary cost of confined production systems. Waste is a problem of almost all operations and must be managed with respect to both the environment and the quality of life in nearby communities. Livestock production systems that disperse stock in pastures so the wastes are not concentrated and do not overwhelm natural nutrient cycling processes have become a subject of renewed interest.

  • The Economic, Social & Political Context

In addition to strategies for preserving natural resources and changing production practices, sustainable agriculture requires a commitment to changing public policies, economic institutions, and social values.  Strategies for change must take into account the complex, reciprocal and ever-changing relationship between agricultural production and the broader society.

The "food system" extends far beyond the farm and involves the interaction of individuals and institutions with contrasting and often competing goals including farmers, researchers, input suppliers, farmworkers, unions, farm advisors, processors, retailers, consumers, and policymakers. Relationships among these actors shift over time as new technologies spawn economic, social and political changes.

A wide diversity of strategies and approaches are necessary to create a more sustainable food system. These will range from specific and concentrated efforts to alter specific policies or practices, to the longer-term tasks of reforming key institutions, rethinking economic priorities, and challenging widely-held social values. Areas of concern where change is most needed include the following:

Food and agricultural policy

Existing federal, state and local government policies often impede the goals of sustainable agriculture. New policies are needed to simultaneously promote environmental health, economic profitability, and social and economic equity. For example, commodity and price support programs could be restructured to allow farmers to realize the full benefits of the productivity gains made possible through alternative practices. Tax and credit policies could be modified to encourage a diverse and decentralized system of family farms rather than corporate concentration and absentee ownership. Government and land grant university research policies could be modified to emphasize the development of sustainable alternatives. Marketing orders and cosmetic standards could be amended to encourage reduced pesticide use. Coalitions must be created to address these policy concerns at the local, regional, and national level.

Conversion of agricultural land to urban uses is a particular concern in California, as rapid growth and escalating land values threaten farming on prime soils. Existing farmland conversion patterns often discourage farmers from adopting sustainable practices and a long-term perspective on the value of land. At the same time, the close proximity of newly developed residential areas to farms is increasing the public demand for environmentally safe farming practices. Comprehensive new policies to protect prime soils and regulate development are needed, particularly in California's Central Valley. By helping farmers to adopt practices that reduce chemical use and conserve scarce resources, sustainable agriculture research and education can play a key role in building public support for agricultural land preservation. Educating land use planners and decision-makers about sustainable agriculture is an important priority.

In California, the conditions of agricultural labor are generally far below accepted social standards and legal protections in other forms of employment. Policies and programs are needed to address this problem, working toward socially just and safe employment that provides adequate wages, working conditions, health benefits, and chances for economic stability. The needs of migrant labor for year-around employment and adequate housing are a particularly crucial problem needing immediate attention. To be more sustainable over the long-term, labor must be acknowledged and supported by government policies, recognized as important constituents of land grant universities, and carefully considered when assessing the impacts of new technologies and practices.

Rural Community Development

Rural communities in California are currently characterized by economic and environmental deterioration. Many are among the poorest locations in the nation. The reasons for the decline are complex, but changes in farm structure have played a significant role. Sustainable agriculture presents an opportunity to rethink the importance of family farms and rural communities. Economic development policies are needed that encourage more diversified agricultural production on family farms as a foundation for healthy economies in rural communities. In combination with other strategies, sustainable agriculture practices and policies can help foster community institutions that meet employment, educational, health, cultural and spiritual needs.

Consumers and the Food System

Consumers can play a critical role in creating a sustainable food system. Through their purchases, they send strong messages to producers, retailers and others in the system about what they think is important.  Food cost and nutritional quality have always influenced consumer choices. The challenge now is to find strategies that broaden consumer perspectives, so that environmental quality, resource use, and social equity issues are also considered in shopping decisions. At the same time, new policies and institutions must be created to enable producers using sustainable practices to market their goods to a wider public. Coalitions organized around improving the food system are one specific method of creating a dialogue among consumers, retailers, producers and others. These coalitions or other public forums can be important vehicles for clarifying issues, suggesting new policies, increasing mutual trust, and encouraging a long-term view of food production, distribution and consumption.  

Contributors : Written by  Gail Feenstra , Writer; Chuck Ingels, Perennial Cropping Systems Analyst; and David Campbell, Economic and Public Policy Analyst with contributions from David Chaney, Melvin R. George, Eric Bradford, the staff and advisory committees of the UC Sustainable Agriculture Research and Education Program.

How to cite this page UC Sustainable Agriculture Research and Education Program. 2021. "What is Sustainable Agriculture?" UC Agriculture and Natural Resources. <>

This page was last updated August 3, 2021.

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The World Bank

Agriculture and Food

Agriculture can help reduce poverty, raise incomes and improve food security for 80% of the world's poor, who live in rural areas and work mainly in farming. The World Bank Group is a leading financier of agriculture.

Healthy, sustainable and inclusive food systems are critical to achieve the world’s development goals. Agricultural development is one of the most powerful tools to end extreme poverty, boost shared prosperity, and feed a projected  10 billion people by 2050 . Growth in the agriculture sector is  two to four times more effective  in raising incomes among the poorest compared to other sectors.

Agriculture is also crucial to economic growth: accounting for 4% of global gross domestic product (GDP) and in some least developing countries,  it can account for more than 25% of GDP .

But agriculture-driven growth, poverty reduction, and food security are at risk: Multiple shocks – from COVID-19 related disruptions to extreme weather, pests, and conflicts – are impacting food systems. The goal of ending global hunger by 2030 is currently off track. Conflicts, climate change, and high food prices are driving food and nutrition insecurity, pushing millions into extreme poverty, and reversing hard-won development gains. Around a quarter of a billion people now face acute food insecurity .

The growing impact of climate change could further cut crop yields, especially in the world’s most food-insecure regions. At the same time, our food systems are responsible for about 30% of greenhouse gas emissions.

Current food systems also threaten the health of people and the planet and generate unsustainable levels of pollution and waste. One third of food produced globally is either lost or wasted. Addressing food loss and waste is critical to improving food and nutrition security, as well as helping to meet climate goals and reduce stress on the environment.

Risks associated with poor diets are also the leading cause of death worldwide. Millions of people are either not eating enough or eating the wrong types of food, resulting in a  double burden of malnutrition  that can lead to illnesses and health crises. Food insecurity can worsen diet quality and increase the risk of various forms of malnutrition, potentially leading to undernutrition as well as people being overweight and obese. An estimated 3 billion people in the world cannot afford a healthy diet.

Last Updated: Mar 15, 2024

The World Bank Group provides knowledge, advice, and financial resources in low- and middle-income countries to transform food systems to reduce poverty and achieve green, resilient, and inclusive development.

Our work in food and agriculture focuses on: 

  • Food and nutrition security , where we work with efforts to share information, and to rapidly provide resources where they are needed, while helping countries design the long-term reforms needed to build resilient food and nutrition systems.
  • Climate-smart agriculture by working with client governments to provide solutions that address global climate priorities, while recognizing national contexts and development objectives.
  • Data-driven digital agriculture by expand the frontier of financing and expertise for digital agriculture.
  • Mobilizing capital for development in agriculture & food . We identify and leverage growth areas for productive investments, focusing on innovation and impact. And we design projects to ensure that financing boosts sustainable productivity gains, reaches smallholders and SMEs, and creates jobs to end poverty and hunger.
  • Public policy and expenditure by working with governments to facilitate the adoption of more sustainable approaches, technologies, and practices, alongside policies that promote public and private sector investment.
  • Sustainable and health diets to ensure that food can support a healthy population.

For fiscal year 2024, a total of $2.98 billion in new IBRD/IDA commitments to agriculture and related sectors are being delivered. Around half of this investment will directly support climate action.

 As part of a comprehensive, global response to the food and nutrition crises, the World Bank is scaling up its responses , making $45 billion available in 90 countries. Our intervention is expected to benefit 335 million people, equivalent to 44% of the number of undernourished people. More than half of the beneficiaries are women, who are disproportionately affected by the crisis. It includes both short term interventions such as expanding social protection, also longer-term resilience such as boosting productivity and climate-smart agriculture. The World Bank has also included food and nutriton security as part of the global challenges that it will address at scale.

Increasingly, the Bank supports country efforts to transform their food systems by taking a holistic look at public policies and spending for agriculture and food. A Multi-Donor Trust Fund,  Food Systems 2030 , provides a platform for change in this area.

In Angola, a project  co-financed by the World Bank and the French Agency for Development, contributed to the government economic diversification agenda by supporting the transition from subsistence to a more market-oriented, competitive agriculture sector. The project is helping producers or small and medium enterprises prepare and finance agriculture investments. As of December 2023, 268 projects have been approved, equivalent to about $37 million in agriculture investment. The project funded the first partial credit guarantees scheme ever dedicated to the agriculture sector in Angola – an innovation for the country’s agribusiness sector – mobilizing so far $4.1 million in private bank financing. 

In  Argentina , the Bank supported 14,630 families who benefited from better socioeconomic inclusion. Under the project, 2,409 families accessed water for human and animal consumption, also irrigation; 7,499 rural families improved their productive capacity; and over 900 families accessed infrastructure, equipment and training that improved their marketing. Based on the model of productive alliances, 2,801 families from different regions became beneficiaries by linking their production with the markets. Among the funded activities, the production of honey, orchards, forage, livestock, nuts, spices, yerba mate and tea, among others, stand out.

In Benin, between 2011-2021, the Agricultural Productivity and Diversification Project facilitated the adoption of productivity-enhancing technologies for 327,503 crop producers, leading to 135,549 hectares of land cultivated with improved technologies. The project interventions resulted in increased yields from 0.45 ton to 0.81 ton for cashew; from 1.2 tons to 2.97 tons for maize, from 4 tons to 6.2 tons for rice, and from 50 tons to 70 tons for pineapple. The project led to significant increases of milled rice and fish output. Combined with support for crop production and processing, support to exports has led to increases in the export of cashew and pineapple.

For the past 18 years, Bolivia has been developing a strategy to improve agricultural production and marketing through the productive alliances model. This model links small rural producers with markets, and facilitates their participation in value chains, and access to technical assistance and technology for better market access. Currently, over 2,600 productive alliances have been implemented, benefiting 107,308 producer families. In 2023, the third phase of productive alliances model was launched, expecting to have a significant impact on nearly 130, 000 rural producer’s communities, with a focus on food security, adoption of innovative practices for resilient agriculture and the increased participation of women producers.

A Bank-supported project implemented in partnership with the Government of Rio Grande do Norte, one of Brazil's poorest and most violent states, has aimed to improve agricultural productivity, the quality of and access to health, public security, education and public sector management across the state. The project has implemented 131 subprojects in family farming, renovated 274km of roads, renovated and strengthened the safety of an important dam, and built 22 modern, multi-service Citizen Centers.

In Bhutan,  a project  is supporting the government's efforts to reduce rural poverty and malnutrition through climate-smart agriculture. Irrigation technology and greenhouses introduced through the project have helped farmers to increase their access to local and export markets. More than 6,500 people have increased the quality and quantity of produce like rice, maize, potato, vegetables, quinoa, citrus, apples, and potatoes, as well as high-value spices such as cardamom and ginger. 

In Burkina Faso, the Bank supported the Burkina Faso Livestock Sector Development Project which ran from 2017 to 2022. By project completion, beneficiaries among selected value chains increased their yield by 8.4%. Yield increase for cattle, sheep, and egg production reached 6.76%, 11.93%, and 6.50%, respectively. Sales increased by 45% exceeding the target of a 30% increase. The volume of loans granted by partner financial institutions reached $5.02 million, exceeding the original target of $4.38 million. The project reached a total of 329,000 beneficiaries, out of which 138,314 were women and 112,573 were youth.

In the Central African Republic, through the Emergency Food Security Response project, 330,000 smallholder farmers received seeds, farming tools, and training in agricultural and post-harvest techniques. The project helped farmers boost their crop production and become more resilient to climate and conflict risks. Local food production increased by 250%, from 28,000 tons in September 2022 to 73,000 tons in June 2023. Moreover, 21,006 agricultural households received training on post-harvest loss management and provided equipment, such as mobile storage units, to enhance packaging of agricultural products, leading to higher selling prices.

In  Colombia, since 2010, the adoption of environmentally friendly silvopastoral production systems  (SPS) for over 4,100 cattle ranches has converted 100,522 hectares of degraded pastures into more productive landscapes and captured 1,565,026 tons of CO2 equivalent. In addition, almost 40,000 hectares of pastureland were transformed to SPS and 4,640 hectares into intensive Silvopastoral Production Systems (iSPS). Moreover, 4,100 direct farmers beneficiaries, of which 17% were women, were trained in SPS and iSPS, and over 21,000 farmers, technicians and producers were also trained, visited demonstration farms, and participated in workshops and events and technology brigades. A network of 116 plant nurseries were also established, which produced around 3.1 million fodder trees that were delivered to beneficiary farmers. 

In Cote d’Ivoire, between 2013 and 2017, the Agriculture Sector Project  boosted the productivity of 200,000 farmers and rehabilitated 6,500 kilometers of rural roads allowing farmers to better transport their products  and reduce post-harvest losses. To aid the cashew industry, the Bank also supported a research program that helped disseminate 209 genotypes of high-performing trees and establish 18 nurseries. The Bank-financed project also helped leverage $27.5 million in private investment to boost productivity on at least 26,500 hectares.

In Ethiopia, since 2015 a project has helped 2.5 million smallholder farmers increase agricultural productivity and commercialization by establishing market linkages, increasing access to agricultural public services, building smallholder farmer capacity in efficient water and crop management to implement climate change mitigation and adaptation, and improving diet diversification. The project has also been promoting the use of nutrition sensitive agriculture and gender and climate-smart agriculture including dietary diversity through nutrient-dense crops, livestock products, post-harvest processing/handling and social behavioral change communication, along with food safety and child and maternal health. The project has supported farmers increase yield in crops and livestock by 19% and 52% respectively and their revenue by 96.2%. To date the project has also provided 58,823 hectares of land with irrigation and water related services, and over 1.6 million farmers have adopted improved agriculture technologies promoted by the project. Nearly one million jobs for rural people, including for women and youth in fragile and conflict affected areas have been created as a result of the project interventions.

In Grenada , the World Bank supported local farmers and fisherfolk, along with aggregators and agro-processors to enhance their access to markets and sales from 2017 to 2023 through the OECS Regional Agriculture Competitiveness Project. The project provided vouchers to 206 farmers and fisherfolk and offered co-financing opportunities for 10 agro-processors, leading to significant improvements in their production facilities and market access. Additionally, 260 employees and 53 extension workers received training, improving their skills in agricultural production and market reach. Through the project, 150 producers adopted various climate-smart technologies, such as solar panels and rainwater harvesting systems, underscoring the project's dedication to sustainability and efficiency.

In Guinea, from 2018 to 2023, through the  Guinea Integrated Agricultural Development Project , local farmers increased agriculture's productivity, and sustainability. To help local communities, the project disseminated high-yielding seeds, improve irrigation, and trained women and youth to access funds to create jobs. The project also promoted the use of climate-smart, gender-sensitive digital technologies with local producers. The project has reached 149,000 farmers (of whom 38% are women and 30% are youth). The project’s results include a 30% increase in yield of rice and maize; a 42% increase in commodity sales; a 47,470-hectare area covered by improved technologies; over 97,000 users of improved technologies, and more than 2,000 jobs created for women and youth.

In Haiti , a World Bank project strengthened the institutional capacity of Haiti’s Ministry of Agriculture and Rural Development by accessing technologies to increase not only agricultural productivity and production but also improved livelihoods and resilience. The project developed irrigation and drainage on 2,244 hectares; established 115 farmer field schools, and trained facilitators in agricultural extension techniques. A total of 78,242 small producers increased their market access, half of whom were women; more than 3,368 private and public sector staff (including staff from the Ministry of Agriculture, municipal staff, among others) and 600 farmers were trained on surveillance and vaccination, the use of fruit fly traps, mealybugs control, and protection of animals against rabies and anthrax and more than 3.6 million animals were vaccinated.

In Honduras, since 2010 , 12,878 small farmers, of which 27% are women, have used productive alliances to improve productivity and access to markets, which has leveraged $33.5 million in finance from commercial banks and microfinance institutions. Under the project, gross sales of producer organizations rose by 25.3%. Also, support to Honduras’ Dry Corridor Alliance, has helped 12,202 households implement food security and agricultural business plans, and improved agricultural yields, nutrition, and food diversity of project beneficiaries.

In India, the  Assam Agribusiness and Rural Transformation Project  supported over 400,000 farm families and 1,270 businesses and over 100 of industry associations and producer organizations in improving their productivity and incomes and helping develop new marketing channels since 2017.

In  Kenya, since 2016,   1.5 million farmers , where over 60% are women, have increased their productivity , climate resilience and access to markets. The digital registry (including geo tagging) of these 1.5 million farmers enables them to access agro-weather and market advisories. In addition, the Bank is facilitating partnerships between the government and 26 ag-tech support agencies which enables almost 500,000 farmers to access a range of services (inputs, financial services and markets) by leveraging digital technologies.  

In Kosovo , the Bank provided 775 grants to farmers and 103 grants to agri-processors to increase production capacities and enhance market competitiveness in the livestock and horticulture sector. This was done through upgrading facilities, adopting new technologies, and introducing food safety and environmental standards. Further, support was provided for the rehabilitation of irrigation schemes covering an area of 7,750 hectares which had an impact on the production, yield, quality, and variety of products cultivated in the area.

In the Kyrgyz Republic, the Additional Financing to the Integrated Dairy Productivity Improvement Project is improving productivity through better technologies and breeds of dairy animals rather than increasing their numbers. The project provides training, artificial insemination services, and monitoring milk yields per cow and the quality of milk to processing companies. To date, 10,000 small farmers including 5,000 women farmers, have received training to enhance productivity and climate-smart agriculture. Over 13,000 cows received artificial insemination for breed improvement with positive pregnancy rate of 67.3% which is above the global average. With improved breeds of dairy animals, the market value of the crossbred calves is higher than local calves and the average milk yield per cow has increased by nearly 15%. The project has also established a digital tool to monitor milk quality which is being used by eight dairy processing companies. The project established 30 milk collection points through famers’ Jamaats that are equipped with refrigerated tanks and advanced testing equipment, strategically located to ensure consistent milk quality and timely delivery, especially during hot summers.

In Madagascar, since 2016 , the Bank has boosted the productivity of over 130,000 farmers. Sixty-thousand hectares of irrigated rice fields have been rehabilitated. The Bank also supported the cocoa sector through research, the development of certified seeds, and promotion of improved production and processing techniques. This allowed 4,000 cocoa producers to increase their incomes and increase production and export volumes by 50%. The Bank also financed the country’s largest land rights registration, facilitating the delivery of over 200,000 land certificates to farmers. 

In Mauritania, between 2016 and 2021, the intervention of the Sahel regional support project offered agricultural assets and services to more than 400,000 farmers/pastoralists, where nearly 30% are women. More than 1.9 million hectares of land under sustainable management practices, in addition to the construction of 133 vaccination parks and the realization of 118 water points (wells and boreholes) as well as other infrastructure of valorization and trade of animals were provided to agro-pastoralist communities. Additionally, from April 2023- June 2028, the Bank offered to support the  Agriculture Development and Innovation Support Project (PADISAM)  to improve land resources management and foster inclusive and sustainable commercial agriculture in selected areas of Mauritania. It is anticipated that by the end of the project, there will be 72,000 direct beneficiaries and about 5,000 Ha of land under sustainable landscape management practices.

Following Russia’s invasion of Ukraine and the resulting spikes in wheat prices in 2022, the World Bank provided emergency support to several countries in the Middle East and North Africa to mitigate the negative socio-economic consequences on the poor and vulnerable. These emergency projects secured access to affordable bread for over 89 million people across the region. In Lebanon, a project ($150 million) has been financing wheat imports that supports universal access to affordable Arabic bread for over a year to 5.36 million people living in Lebanon, of which 1 million are Syrian, Palestinian, and other refugees. In Egypt, a project helped procure around 1.15 million metric tons of wheat – equivalent to at least a 2-month supply to cover the needs of 72 million vulnerable people. A project in In Tunisia procured 160,099 metric tons of soft wheat, equivalent to seven weeks of bread supply for a population of 12 million.

In Moldova, since 2012 , the Bank has helped more than 7,500 farmers gain access to local and regional high-value markets for fresh fruit and vegetables and boosted land productivity through the promotion of sustainable land management practices on 120,000 hectares of farmland.

In  Montenegro , the bank, through the Second Institutional Development and Agriculture Strengthening (MIDAS2), helped the government launch the very first Instrument for Pre-accession Assistance for Agriculture and Rural Development (IPARD)-like agro-environmental measure in a manner compliant with EU requirements, increasing the amount of meadows and pasture lands recorded in the Land Parcel Identification System (LPIS) from 13,600 hectares (ha) to 92,000 ha. The Bank has also supported almost 4,000 farmers working on orchards, vineyards, livestock and aromatic plants, 224 agro-processors, and 59 farmers working on processing on-farm complying with the European Union requirements for food safety and 278 agricultural households adopting agro-environmental measures, improving their competitiveness and sustainability.

In Morocco , the Strengthening Agri-food Value Chains Program for Results has financed the construction of the first modern regional wholesale market in Rabat, which will improve the distribution of agricultural products throughout the region, benefiting more than 4.6 million inhabitants. The program also financed the establishment of the male sterile Ceratite production center, which will enable citrus producers in the Souss-Massa and Berkane regions, which represent 52% of national citrus production and generate about 6 million working days per year, to protect their production from damage caused by the Mediterranean fruit fly. The program also enabled more than 1,000 agri-food SMEs to obtain sanitary approval after upgrading, leading to an increase in employment by almost 61%. The program co-financed more than 70 units of packaging, cold storage and processing, which leveraged about US$86 million as private investment and led to an overall increase in production value of around 34%.

In Niger , through  the Climate Smart Agriculture Support Project , the World Bank supported over 370,000 farmers, where 145,000 of whom are women. The farmers benefited from the project’s investments in small and large-scale irrigation, improved climate-smart agriculture, and sustainable land management practices. Over 154,000 hectares of land were developed with sustainable land management practices, and 4,400 hectares of cropland were brought under irrigation. In collaboration with the International Crops Research Institute for the Semi-Arid Tropics and FAO, the project promoted good agriculture practices through farmer led e-extension services and technical assistance. The project investments led to significant increases in agriculture productivity: yields of cowpea, millet, and sorghum increased by 169, 164, and 142 percent, respectively. The project also strengthened the national climate information system by building the capacity of the National Meteorology Department (the project installed 30 meteorological stations and 600 rain gauges). Through its support to the Sahel Regional Center for Hydro and Agrometeorology, the project strengthened the early warning systems of national institutes such as National Meteorology and the National Hydrology Directorate.

In Nigeria,  APPEALS Project   was designed to enhance agricultural productivity of small and medium scale farmers and improve value addition along priority value chains. Since 2017, the project has demonstrated 204 improved technologies to 93,000 farmers. Food crop production has surged, with 304,516 metric tons produced, representing 3.1% of the national output. Furthermore, the project has reached 61,171 farmers with processing assets to improve the quality of their produce. The project also trained 10,346 women and youth, including persons with disability, providing them with business, technical and life skills training, support to business planning and facilitation of business name registration, start-up grant to establish a commercially viable business, and mentorship to provide the beneficiaries with continued support from established agribusiness entrepreneurs. The project linked farmers to market through the facilitation of commercial partnerships resulting in a total of 327 business alliances with 147 off-takers already buying farmers’ produce across the 11 value chains, with a transaction worth of US$ 59.7 million. Similarly, the project has linked 200 agribusiness clusters to infrastructures which includes 55km rural farm access road, 75 aggregation and cottage processing centers, 102 solar-powered water intervention and energy supplies.

In Paraguay, since 2008, 20,863 farmers  increased their agricultural income by at least 30% and 18,951 adopted improved agricultural practices, boosting the productivity of their land.

In the  Philippines, since 2015 , the Bank helped raise rural incomes, enhance farm and fishery productivity, improve market access and mainstream institutional and operational reforms, as well as science-based planning for agricultural commodities in 81 provinces. The project has benefitted a total of 323,501 people–46% of them women–with farm roads, irrigation, and agricultural enterprise projects, boosting incomes by up to 36%. 

In  Rwanda, since  2010, the Bank helped support over 410,000 farmers – half are women – in improving their agricultural production by developing over 7,400 hectares for marshland irrigation, providing hillside irrigation on over 2,500 hectares, and several hundreds of farmers benefitted matching grants to support their investments in Farmer-Led Irrigation Development (FLID) technologies on over 1,200 hectares of their land. Interventions also included improving soil conservation and erosion on more than 39,000 hectares of hillside. Maize, rice, beans, and potato yields have all more than doubled and around 2.5 tons of vegetables are exported to Europe and the Middle-East every week from intervention areas, or locally, where more horticulture produce is sold to premium markets including 5-star hotels or the national airline, RwandAir. Less than two years after  one of the Bank supported projects  introduced greenhouse farming in its intervention areas to minimize the impacts of unfavorable weather conditions and better manage crop pests and diseases, by 2023, the demand for these technologies has seen a rapid increase in these areas and 132 units have been acquired and installed through the matching grants program under the project. Evidence shows relatively high revenues for farmers investing in greenhouse technology, with revenues increasing up to 15 times for vegetable growers.

Since 2019, the ongoing Serbia Competitive Agriculture Project has been supporting the government economic diversification and competitiveness agenda for small and medium scale farmers and their participation in a more market-oriented agriculture sector. The productive alliance model supported by the project has contributed to the improvement of the agri-food market linkages of 823 farmers, of which 330 are women farmers. Through the project, 4,356 farmers have received technical assistance to prepare their business ideas and plans (1,307 are women), while 1,319 business plans have received support in various forms, such as matching grants, technical assistance, and business development support. The farmers have signed their loans with commercial banks to invest in farm innovations, including equipment, on-farm irrigation, digital agriculture, climate-smart agriculture technologies. By providing co-financing with EUR 24.17 million in matching grants, the project-supported business plans have leveraged an additional EUR 24.17 million in private capital so far, including commercial loans to farmers at market interest rate from 11 local banks, and cash contributions from the beneficiary farmers. Amongst them, 1,117 beneficiary farmers are first-time users of credit.

In  Tajikistan , the Bank supported the establishment of 545 farmer groups in horticulture value chains, specifically apricot, apple, pear, lemon, cucumber, and tomato, and dairy value chain benefiting a total of 13,516 farmers out of which 48% were women. The Bank also supported the establishment of 342 productive partnerships benefitting 4,340 smallholder farmers. A total of 21,882 beneficiaries achieved an increase in commercial activity. The project supported training for 13, 516 farmers, on value chain development.

In  Tunisia, the Bank helped 113 remote rural villages improve  land management practices on 37,000 hectares of land to increase productivity and improve 930 kilometers of rural roads serving some 160 villages. 

In  Uruguay, since 2014, climate-smart agriculture techniques  have been adopted on 2.7 million hectares and adopted by 5,541 farmers, providing for a carbon sequestration potential of up to 9 million tons of CO2 annually.

In Uganda, since 2015 , the  Agriculture Cluster Development Project’s e-voucher scheme has leveraged over $12 million of farmer investments enabling over 450,000 farm households access and use improved agro-inputs resulting in higher farm yields. Provision of matching grants has enhanced storage capacity by 55,000MT, acquiring value addition equipment and machinery thereby facilitating Producer Organizations to add value and undertake collective marketing. Additional infrastructure support addressing road chokes has also led to improved market access.

The Bank has also made investments into strengthening regulatory and administrative functions of the Ministry of Agriculture through the development of IT Platforms and tools facilitating timely planning and decision making.

In the Uganda Multi-Sectoral Food and Nutrition Security Project, the Bank has supported enhanced knowledge on nutrition resulting in improved household nutrition and incomes for 1.55 million direct project beneficiaries.

In Uzbekistan, the Horticulture Development Project has helped create, 34,520 jobs, including 13,124 for women; increase beneficiary productivity by 24% and profitability by 124%, including through entry into new export markets. The  Livestock Sector Development Project  supported a sub-loans benefitting 560 large scale commercial livestock farmers, and a total of 135 value chain development projects benefiting 1,456 smallholder farmers (Dekhans). As a result, the share of improved and high yielding livestock breeds increased by 98.7%; increasing milk and meat productivity by 33% and 38% respectively. The Ferghana Valley Rural Enterprise Project has supported the establishment and operation of nine business incubation hubs in Andijan, Namangan, and Ferghana regions, to support local entrepreneurs in business plan preparation, and facilitated access to finance, technology infusion, also organized training among 5,000 project initiators in 36 districts of Ferghana Valley. The project, under its credit line activities, financed a total of 501 investment sub-projects with $119.6 million of the project fund, of which 77.8% were for small business entrepreneurs This created substantial number of new jobs, and increased the incomes of rural enterprises,

In Vietnam, since 2010, the Bank has promoted sustainable livelihoods by helping develop 9,000 “common interest groups” comprising over 15,500 households and partnering them with agricultural enterprises. The Bank also helped  over 20,000 farmers  improve their livestock production and benefited an additional 130,000 people through capacity building in food safety. 

Under the  West African Agricultural Productivity Program , the Bank supported a research and development effort that promoted technology generation, dissemination, and support to local farming systems in 13  ECOWAS  countries. The project reached over 2.7 million beneficiaries, 41% of whom were women. It also generated 112 technologies that reached over 1,850,000 hectares.

The Yemen Food Security Response and Resilience Project has directly benefited over 1 million beneficiaries to date. The project is focusing on resilience building amidst protracted crisis – including conflict, insecurity, and climate-related shocks. The project has created around 20,000 short-term jobs and benefited over 50,000 smallholder farm households through various agricultural infrastructure improvements. The project invested in the vaccination of 11 million small ruminants and treated a similar number for parasites. In addition to building resilience, as a short-term response, the project supported 20,000 vulnerable households with kitchen gardens and livestock kits, business development training and start-up grants to vulnerable women. Furthermore, the project facilitated a supplemental feeding program for over 740,000 most vulnerable beneficiaries.

Last Updated: Apr 09, 2024

The World Bank works with a range of partners to achieve ambitious development goals: transforming food systems, boosting food security and empowering smallholder farmers, to realize zero hunger and poverty by 2030. 

The World Bank Group is a joint convener, with the G7 Presidency, of the Global Alliance for Food Security (GAFS) . A key outcome of the Global Alliance is the  Global Food and Nutrition Security Dashboard , a key tool to fast-track a rapid response to the unfolding global food security crisis, designed to consolidate and present up-to-date data on food crisis severity, track global food security financing, and make available global and country-level research and analysis to improve coordination of the policy and financial response to the crisis.

The Bank hosts a  Multi-Donor Trust Fund,  Food Systems 2030 , that helps countries build better food systems, fostering healthy people, a healthy planet and healthy economies. The Trust Fund aims to deliver improved livelihoods and affordable, and nutritious diets for all, and progress towards the Sustainable Development Goals of zero poverty and hunger by 2030 and the climate goals of the Paris Agreement. Food Systems 2030 provides advice and analytical products to underpin policy options, funds to pilot innovative approaches, and information to build support for change in different country contexts. It engages with the private sector by supporting the design, piloting and de-risking of innovative public-private partnerships that advance development and climate goals.   

The  Global Agriculture and Food Security Program , a multilateral financing platform, is dedicated to improving food and nutrition security worldwide. Launched by the  G20 in the wake of the global response to the 2007–08 food price crisis, GAFSP works to build sustainable and resilient agriculture and food systems in the world’s poorest and most vulnerable countries. Since its inception in 2010, the Program has mobilized more than US$2 billion in donor funds to reach more than 16.6 million people. GAFSP provides financial and technical resources – investment grants, technical assistance, concessional finance, and advisory services – to demand-driven projects along the food chain to accelerate the transformation of food systems at scale.

The World Bank leads the  Food Systems, Land use and Restoration Global Platform (FOLUR) , financed by the Global Environment Facility, in partnership with UNDP, the UN Food and Agriculture Organization (FAO), the Global Landscapes Forum and the Food and Land-use Coalition. FOLUR is a $345 million, seven-year program that aims to improve the health and sustainability of landscapes that produce the world’s food. FOLUR targets sustainable production landscapes in 27 country projects for eight major commodities (livestock, cocoa, coffee, maize, palm oil, rice, soy, and wheat).

The World Bank chairs the System Council of  CGIAR , a global partnership that advances cutting-edge science to reduce rural poverty, increase food security, improve human health and nutrition, and ensure sustainable management of natural resources.

For more information, contact Clare Murphy-McGreevey on [email protected].

Last Updated: Sep 19, 2023


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Machine Learning in Agriculture: A Comprehensive Updated Review

Lefteris benos.

1 Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; [email protected] (L.B.); [email protected] (A.C.T.); [email protected] (G.D.); [email protected] (D.K.)

Aristotelis C. Tagarakis

Georgios dolias, remigio berruto.

2 Department of Agriculture, Forestry and Food Science (DISAFA), University of Turin, Largo Braccini 2, 10095 Grugliasco, Italy; [email protected]

Dimitrios Kateris

Dionysis bochtis.

3 FarmB Digital Agriculture P.C., Doiranis 17, GR 54639 Thessaloniki, Greece

The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

1. Introduction

1.1. general context of machine learning in agriculture.

Modern agriculture has to cope with several challenges, including the increasing call for food, as a consequence of the global explosion of earth’s population, climate changes [ 1 ], natural resources depletion [ 2 ], alteration of dietary choices [ 3 ], as well as safety and health concerns [ 4 ]. As a means of addressing the above issues, placing pressure on the agricultural sector, there exists an urgent necessity for optimizing the effectiveness of agricultural practices by, simultaneously, lessening the environmental burden. In particular, these two essentials have driven the transformation of agriculture into precision agriculture. This modernization of farming has a great potential to assure sustainability, maximal productivity, and a safe environment [ 5 ]. In general, smart farming is based on four key pillars in order to deal with the increasing needs; (a) optimal natural resources’ management, (b) conservation of the ecosystem, (c) development of adequate services, and (d) utilization of modern technologies [ 6 ]. An essential prerequisite of modern agriculture is, definitely, the adoption of Information and Communication Technology (ICT), which is promoted by policy-makers around the world. ICT can indicatively include farm management information systems, humidity and soil sensors, accelerometers, wireless sensor networks, cameras, drones, low-cost satellites, online services, and automated guided vehicles [ 7 ].

The large volume of data, which is produced by digital technologies and usually referred to as “big data”, needs large storage capabilities in addition to editing, analyzing, and interpreting. The latter has a considerable potential to add value for society, environment, and decision-makers [ 8 ]. Nevertheless, big data encompass challenges on account of their so-called “5-V” requirements; (a) Volume, (b) Variety, (c) Velocity, (d) Veracity, and (e) Value [ 9 ]. The conventional data processing techniques are incapable of meeting the constantly growing demands in the new era of smart farming, which is an important obstacle for extracting valuable information from field data [ 10 ]. To that end, Machine Learning (ML) has emerged, which is a subset of artificial intelligence [ 11 ], by taking advantage of the exponential computational power capacity growth.

There is a plethora of applications of ML in agriculture. According to the recent literature survey by Liakos et al. [ 12 ], regarding the time period of 2004 to 2018, four generic categories were identified ( Figure 1 ). These categories refer to crop, water, soil, and livestock management. In particular, as far as crop management is concerned, it represented the majority of the articles amongst all categories (61% of the total articles) and was further sub-divided into:

  • Yield prediction;
  • Disease detection;
  • Weed detection;
  • Crop recognition;
  • Crop quality.

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The four generic categories in agriculture exploiting machine learning techniques, as presented in [ 12 ].

The generic categories dealing with the management of water and soil were found to be less investigated, corresponding cumulatively to 20% of the total number of papers (10% for each category).

Finally, two main sub-categories were identified for the livestock-related applications corresponding to a total 19% of journal papers:

  • Livestock production;
  • Animal welfare.

1.2. Open Problems Associated with Machine Learning in Agriculture

Due to the broad range of applications of ML in agriculture, several reviews have been published in this research field. The majority of these review studies have been dedicated to crop disease detection [ 13 , 14 , 15 , 16 ], weed detection [ 17 , 18 ], yield prediction [ 19 , 20 ], crop recognition [ 21 , 22 ], water management [ 23 , 24 ], animal welfare [ 25 , 26 ], and livestock production [ 27 , 28 ]. Furthermore, other studies were concerned with the implementation of ML methods regarding the main grain crops by investigating different aspects including quality and disease detection [ 29 ]. Finally, focus has been paid on big data analysis using ML, aiming at finding out real-life problems that originated from smart farming [ 30 ], or dealing with methods to analyze hyperspectral and multispectral data [ 31 ].

Although ML in agriculture has made considerable progress, several open problems remain, which have some common points of reference, despite the fact that the topic covers a variety of sub-fields. According to [ 23 , 24 , 28 , 32 ], the main problems are associated with the implementation of sensors on farms for numerous reasons, including high costs of ICT, traditional practices, and lack of information. In addition, the majority of the available datasets do not reflect realistic cases, since they are normally generated by a few people getting images or specimens in a short time period and from a limited area [ 15 , 21 , 22 , 23 ]. Consequently, more practical datasets coming from fields are required [ 18 , 20 ]. Moreover, the need for more efficient ML algorithms and scalable computational architectures has been pointed out, which can lead to rapid information processing [ 18 , 22 , 23 , 31 ]. The challenging background, when it comes to obtaining images, video, or audio recordings, has also been mentioned owing to changes in lighting [ 16 , 29 ], blind spots of cameras, environmental noise, and simultaneous vocalizations [ 25 ]. Another important open problem is that the vast majority of farmers are non-experts in ML and, thus, they cannot fully comprehend the underlying patterns obtained by ML algorithms. For this reason, more user-friendly systems should be developed. In particular, simple systems, being easy to understand and operate, would be valuable, as for example a visualization tool with a user-friendly interface for the correct presentation and manipulation of data [ 25 , 30 , 31 ]. Taking into account that farmers are getting more and more familiar with smartphones, specific smartphone applications have been proposed as a possible solution to address the above challenge [ 15 , 16 , 21 ]. Last but not least, the development of efficient ML techniques by incorporating expert knowledge from different stakeholders should be fostered, particularly regarding computing science, agriculture, and the private sector, as a means of designing realistic solutions [ 19 , 22 , 24 , 33 ]. As stated in [ 12 ], currently, all of the efforts pertain to individual solutions, which are not always connected with the process of decision-making, as seen for example in other domains.

1.3. Aim of the Present Study

As pointed out above, because of the multiple applications of ML in agriculture, several review studies have been published recently. However, these studies usually concentrate purely on one sub-field of agricultural production. Motivated by the current tremendous progress in ML, the increasing interest worldwide, and its impact in various do-mains of agriculture, a systematic bibliographic survey is presented on the range of the categories proposed in [ 12 ], which were summarized in Figure 1 . In particular, we focus on reviewing the relevant literature of the last three years (2018–2020) for the intention of providing an updated view of ML applications in agricultural systems. In fact, this work is an updated continuation of the work presented at [ 12 ]; following, consequently, exactly the same framework and inclusion criteria. As a consequence, the scholarly literature was screened in order to cover a broad spectrum of important features for capturing the current progress and trends, including the identification of: (a) the research areas which are interested mostly in ML in agriculture along with the geographical distribution of the contributing organizations, (b) the most efficient ML models, (c) the most investigated crops and animals, and (d) the most implemented features and technologies.

As will be discussed next, overall, a 745% increase in the number of journal papers took place in the last three years as compared to [ 12 ], thus justifying the need for a new updated review on the specific topic. Moreover, crop management remained as the most investigated topic, with a number of ML algorithms having been exploited as a means of tackling the heterogeneous data that originated from agricultural fields. As compared to [ 12 ], more crop and animal species have been investigated by using an extensive range of input parameters coming mainly from remote sensing, such as satellites and drones. In addition, people from different research fields have dealt with ML in agriculture, hence, contributing to the remarkable advancement in this field.

1.4. Outline of the Paper

The remainder of this paper is structured as follows. The second section briefly describes the fundamentals of ML along with the subject of the four generic categories for the sake of better comprehension of the scope of the present study. The implemented methodology, along with the inclusive criteria and the search engines, is analyzed in the third section. The main performance metrics, which were used in the selected articles, are also presented in this section. The main results are shown in the fourth section in the form of bar and pie charts, while in the fifth section, the main conclusions are drawn by also discussing the results from a broader perspective. Finally, all the selected journal papers are summarized in Table A1 , Table A2 , Table A3 , Table A4 , Table A5 , Table A6 , Table A7 , Table A8 and Table A9 , in accordance with their field of application, and presented in the Appendix A , together with Table A10 and Table A11 that contain commonly used abbreviations, with the intention of not disrupting the flow of the main text.

2. Background

2.1. fundamentals of machine learning: a brief overview.

In general, the objective of ML algorithms is to optimize the performance of a task, via exploiting examples or past experience. In particular, ML can generate efficient relationships regarding data inputs and reconstruct a knowledge scheme. In this data-driven methodology, the more data are used, the better ML works. This is similar to how well a human being performs a particular task by gaining more experience [ 34 ]. The central outcome of ML is a measure of generalizability; the degree to which the ML algorithm has the ability to provide correct predictions, when new data are presented, on the basis of learned rules originated from preceding exposure to similar data [ 35 ]. More specifically, data involve a set of examples, which are described by a group of characteristics, usually called features. Broadly speaking, ML systems operate at two processes, namely the learning (used for training) and testing. In order to facilitate the former process, these features commonly form a feature vector that can be binary, numeric, ordinal, or nominal [ 36 ]. This vector is utilized as an input within the learning phase. In brief, by relying on training data, within the learning phase, the machine learns to perform the task from experience. Once the learning performance reaches a satisfactory point (expressed through mathematical and statistical relationships), it ends. Subsequently, the model that was developed through the training process can be used to classify, cluster, or predict.

An overview of a typical ML system is illustrated in Figure 2 . With the intention of forming the derived complex raw data into a suitable state, a pre-processing effort is required. This usually includes: (a) data cleaning for removing inconsistent or missing items and noise, (b) data integration, when many data sources exist and (c) data transformation, such as normalization and discretization [ 37 ]. The extraction/selection feature aims at creating or/and identifying the most informative subset of features in which, subsequently, the learning model is going to be implemented throughout the training phase [ 38 ]. Regarding the feedback loop, which is depicted in Figure 2 , it serves for adjustments pertaining to the feature extraction/selection unit as well as the pre-processing one that further improves the overall learning model’s performance. During the phase of testing, previously unseen samples are imported to the trained model, which are usually represented as feature vectors. Finally, an appropriate decision is made by the model (for example, classification or regression) in reliance of the features existing in each sample. Deep learning, a subfield of ML, utilizes an alternative architecture via shifting the process of converting raw data to features (feature engineering) to the corresponding learning system. Consequently, the feature extraction/selection unit is absent, resulting in a fully trainable system; it starts from a raw input and ends with the desired output [ 39 , 40 ].

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A graphical illustration of a typical machine learning system.

Based on the learning type, ML can be classified according to the relative literature [ 41 , 42 ] as:

  • Supervised learning: The input and output are known and the machine tries to find the optimal way to reach an output given an input;
  • Unsupervised learning: No labels are provided, leaving the learning algorithm itself to generate structure within its input;
  • Semi-supervised learning: Input data constitute a mixture of labeled and non-labeled data;
  • Reinforcement learning: Decisions are made towards finding out actions that can lead to the more positive outcome, while it is solely determined by trial and error method and delayed outcome.

Nowadays, ML is used in facilitating several management aspects in agriculture [ 12 ] and in a plethora of other applications, such as image recognition [ 43 ], speech recognition [ 44 ], autonomous driving [ 45 ], credit card fraud detection [ 46 ], stock market forecasting [ 47 ], fluid mechanics [ 48 ], email, spam and malware filtering [ 49 ], medical diagnosis [ 40 ], contamination detection in urban water networks [ 50 ], and activity recognition [ 51 ], to mention but a few.

2.2. Brief Description of the Four Generic Categories

2.2.1. crop management.

The crop management category involves versatile aspects that originated from the combination of farming techniques in the direction of managing the biological, chemical and physical crop environment with the aim of reaching both quantitative and qualitative targets [ 52 ]. Using advanced approaches to manage crops, such as yield prediction, disease detection, weed detection, crop recognition, and crop quality, contributes to the increase of productivity and, consequently, the financial income. The above aspects constitute key goals of precision agriculture.

Yield Prediction

In general, yield prediction is one of the most important and challenging topics in modern agriculture. An accurate model can help, for instance, the farm owners to take informed management decisions on what to grow towards matching the crop to the existing market’s demands [ 20 ]. However, this is not a trivial task; it consists of various steps. Yield prediction can be determined by several factors such as environment, management practices, crop genotypic and phenotypic characteristics, and their interactions. Hence, it necessitates a fundamental comprehension of the relationship between these interactive factors and yield. In turn, identifying such kinds of relationships mandates comprehensive datasets along with powerful algorithms such as ML techniques [ 53 ].

Disease Detection

Crop diseases constitute a major threat in agricultural production systems that deteriorate yield quality and quantity at production, storage, and transportation level. At farm level, reports on yield losses, due to plant diseases, are very common [ 54 ]. Furthermore, crop diseases pose significant risks to food security at a global scale. Timely identification of plant diseases is a key aspect for efficient management. Plant diseases may be provoked by various kinds of bacteria, fungi, pests, viruses, and other agents. Disease symptoms, namely the physical evidence of the presence of pathogens and the changes in the plants’ phenotype, may consist of leaf and fruit spots, wilting and color change [ 55 ], curling of leaves, etc. Historically, disease detection was conducted by expert agronomists, by performing field scouting. However, this process is time-consuming and solely based on visual inspection. Recent technological advances have made commercially available sensing systems able to identify diseased plants before the symptoms become visible. Furthermore, in the past few years, computer vision, especially by employing deep learning, has made remarkable progress. As highlighted by Zhang et al. [ 56 ], who focused on identifying cucumber leaf diseases by utilizing deep learning, due to the complex environmental background, it is beneficial to eliminate background before model training. Moreover, accurate image classifiers for disease diagnosis need a large dataset of both healthy and diseased plant images. In reference to large-scale cultivations, such kinds of automated processes can be combined with autonomous vehicles, to timely identify phytopathological problems by implementing regular inspections. Furthermore, maps of the spatial distribution of the plant disease can be created, depicting the zones in the farm where the infection has been spread [ 57 ].

Weed Detection

As a result of their prolific seed production and longevity, weeds usually grow and spread invasively over large parts of the field very fast, competing with crops for the resources, including space, sunlight, nutrients, and water availability. Besides, weeds frequently arise sooner than crops without having to face natural enemies, a fact that adversely affects crop growth [ 18 ]. In order to prevent crop yield reduction, weed control is an important management task by either mechanical treatment or application of herbicides. Mechanical treatment is, in most cases, difficult to be performed and ineffective if not properly performed, making herbicide application the most widely used operation. Using large quantities of herbicides, however, turns out to be both costly and detrimental for the environment, especially in the case of uniform application without taking into account the spatial distribution of the weeds. Remarkably, long-term herbicide use is very likely to make weeds more resistant, thus, resulting in more demanding and expensive weed control. In recent years, considerable achievements have been made pertaining to the differentiation of weeds from crops on the basis of smart agriculture. This discrimination can be accomplished by using remote or proximal sensing with sensors attached on satellites, aerial, and ground vehicles, as well as unmanned vehicles (both ground (UGV) and aerial (UAV)). The transformation of data gathered by UAVs into meaningful information is, however, still a challenging task, since both data collection and classification need painstaking effort [ 58 ]. ML algorithms coupled with imaging technologies or non-imaging spectroscopy can allow for real-time differentiation and localization of target weeds, enabling precise application of herbicides to specific zones, instead of spraying the entire fields [ 59 ] and planning of the shortest weeding path [ 60 ].

Crop Recognition

Automatic recognition of crops has gained considerable attention in several scientific fields, such as plant taxonomy, botanical gardens, and new species discovery. Plant species can be recognized and classified via analysis of various organs, including leaves, stems, fruits, flowers, roots, and seeds [ 61 , 62 ]. Using leaf-based plant recognition seems to be the most common approach by examining specific leaf’s characteristics like color, shape, and texture [ 63 ]. With the broader use of satellites and aerial vehicles as means of sensing crop properties, crop classification through remote sensing has become particularly popular. As in the above sub-categories, the advancement on computer software and image processing devices combined with ML has led to the automatic recognition and classification of crops.

Crop Quality

Crop quality is very consequential for the market and, in general, is related to soil and climate conditions, cultivation practices and crop characteristics, to name a few. High quality agricultural products are typically sold at better prices, hence, offering larger earnings to farmers. For instance, as regards fruit quality, flesh firmness, soluble solids content, and skin color are among the most ordinary maturity indices utilized for harvesting [ 64 ]. The timing of harvesting greatly affects the quality characteristics of the harvested products in both high value crops (tree crops, grapes, vegetables, herbs, etc.) and arable crops. Therefore, developing decision support systems can aid farmers in taking appropriate management decisions for increased quality of production. For example, selective harvesting is a management practice that may considerably increase quality. Furthermore, crop quality is closely linked with food waste, an additional challenge that modern agriculture has to cope with, since if the crop deviates from the desired shape, color, or size, it may be thrown away. Similarly to the above sub-section, ML algorithms combined with imaging technologies can provide encouraging results.

2.2.2. Water Management

The agricultural sector constitutes the main consumer of available fresh water on a global scale, as plant growth largely relies on water availability. Taking into account the rapid depletion rate of a lot of aquifers with negligible recharge, more effective water management is needed for the purpose of better conserving water in terms of accomplishing a sustainable crop production [ 65 ]. Effective water management can also lead to the improvement of water quality as well as reduction of pollution and health risks [ 66 ]. Recent research on precision agriculture offers the potential of variable rate irrigation so as to attain water savings. This can be realized by implementing irrigation at rates, which vary according to field variability on the basis of specific water requirements of separate management zones, instead of using a uniform rate in the entire field. The effectiveness and feasibility of the variable rate irrigation approach depend on agronomic factors, including topography, soil properties, and their effect on soil water in order to accomplish both water savings and yield optimization [ 67 ]. Carefully monitoring the status of soil water, crop growth conditions, and temporal and spatial patterns in combination with weather conditions monitoring and forecasting, can help in irrigation programming and efficient management of water. Among the utilized ICTs, remote sensing can provide images with spatial and temporal variability associated with the soil moisture status and crop growth parameters for precision water management. Interestingly, water management is challenging enough in arid areas, where groundwater sources are used for irrigation, with the precipitation providing only part of the total crop evapotranspiration (ET) demands [ 68 ].

2.2.3. Soil Management

Soil, a heterogeneous natural resource, involves mechanisms and processes that are very complex. Precise information regarding soil on a regional scale is vital, as it contributes towards better soil management consistent with land potential and, in general, sustainable agriculture [ 5 ]. Better management of soil is also of great interest owing to issues like land degradation (loss of the biological productivity), soil-nutrient imbalance (due to fertilizers overuse), and soil erosion (as a result of vegetation overcutting, improper crop rotations rather than balanced ones, livestock overgrazing, and unsustainable fallow periods) [ 69 ]. Useful soil properties can entail texture, organic matter, and nutrients content, to mention but a few. Traditional soil assessment methods include soil sampling and laboratory analysis, which are normally expensive and take considerable time and effort. However, remote sensing and soil mapping sensors can provide low-cost and effortless solution for the study of soil spatial variability. Data fusion and handling of such heterogeneous “big data” may be important drawbacks, when traditional data analysis methods are used. ML techniques can serve as a trustworthy, low-cost solution for such a task.

2.2.4. Livestock Management

It is widely accepted that livestock production systems have been intensified in the context of productivity per animal. This intensification involves social concerns that can influence consumer perception of food safety, security, and sustainability, based on animal welfare and human health. In particular, monitoring both the welfare of animals and overall production is a key aspect so as to improve production systems [ 70 ]. The above fields take place in the framework of precision livestock farming, aiming at applying engineering techniques to monitor animal health in real time and recognizing warning messages, as well as improving the production at the initial stages. The role of precision livestock farming is getting more and more significant by supporting the decision-making processes of livestock owners and changing their role. It can also facilitate the products’ traceability, in addition to monitoring their quality and the living conditions of animals, as required by policy-makers [ 71 ]. Precision livestock farming relies on non-invasive sensors, such as cameras, accelerometers, gyroscopes, radio-frequency identification systems, pedometers, and optical and temperature sensors [ 25 ]. IoT sensors leverage variable physical quantities (VPQs) as a means of sensing temperature, sound, humidity, etc. For instance, IoT sensors can warn if a VPQ falls out of regular limits in real-time, giving valuable information regarding individual animals. As a result, the cost of repetitively and arduously checking each animal can be reduced [ 72 ]. In order to take advantage of the large amounts of data, ML methodologies have become an integral part of modern livestock farming. Models can be developed that have the capability of defining the manner a biological system operates, relying on causal relationships and exploiting this biological awareness towards generating predictions and suggestions.

Animal Welfare

There is an ongoing concern for animal welfare, since the health of animals is strongly associated with product quality and, as a consequence, predominantly with the health of consumers and, secondarily, with the improvement of economic efficiency [ 73 ]. There exist several indexes for animal welfare evaluation, including physiological stress and behavioral indicators. The most commonly used indicator is animal behavior, which can be affected by diseases, emotions, and living conditions, which have the potential to demonstrate physiological conditions [ 25 ]. Sensors, commonly used to detect behavioral changes (for example, changes in water or food consumption, reduced animal activity), include microphone systems, cameras, accelerometers, etc.

Livestock Production

The use of sensor technology, along with advanced ML techniques, can increase livestock production efficiency. Given the impact of practices of animal management on productive elements, livestock owners are getting cautious of their asset. However, as the livestock holdings get larger, the proper consideration of every single animal is very difficult. From this perspective, the support to farmers via precision livestock farming, mentioned above, is an auspicious step for aspects associated with economic efficiency and establishment of sustainable workplaces with reduced environmental footprint [ 74 ]. Generally, several models have been used in animal production, with their intentions normally revolving around growing and feeding animals in the best way. However, the large volumes of data being involved, again, call for ML approaches.

3.1. Screening of the Relative Literature

In order to identify the relevant studies concerning ML in respect to different aspects of management in agriculture, the search engines of Scopus, Google Scholar, ScienceDirect, PubMed, Web of Science, and MDPI were utilized. In addition, keywords’ combinations of “machine learning” in conjunction with each of the following: “crop management”, “water management”, “soil management”, and “livestock management” were used. Our intention was to filter the literature on the same framework as [ 12 ]; however, focusing solely within the period 2018–2020. Once a relevant study was being identified, the references of the paper at hand were being scanned to find studies that had not been found throughout the initial searching procedure. This process was being iterated until no relevant studies occurred. In this stage, only journal papers were considered eligible. Thus, non-English studies, conferences papers, chapters, reviews, as well as Master and Doctoral Theses were excluded. The latest search was conducted on 15 December 2020. Subsequently, the abstract of each paper was being reviewed, while, at a next stage, the full text was being read to decide its appropriateness. After a discussion between all co-authors with reference to the appropriateness of the selected papers, some of them were excluded, in the case they did not meet the two main inclusion criteria, namely: (a) the paper was published within 2018–2020 and (b) the paper referred to one of the categories and sub-categories, which were summarized in Figure 1 . Finally, the papers were classified in these sub-categories. Overall, 338 journal papers were identified. The flowchart of the present review methodology is depicted in Figure 3 , based on the PRISMA guidelines [ 75 ], along with information about at which stage each exclusive criterion was imposed similarly to recent systematic review studies such as [ 72 , 76 , 77 , 78 ].

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The flowchart of the methodology of the present systematic review along with the flow of information regarding the exclusive criteria, based on PRISMA guidelines [ 75 ].

3.2. Definition of the Performance Metrics Commonly Used in the Reviewed Studies

In this subsection, the most commonly used performance metrics of the reviewed papers are briefly described. In general, these metrics are utilized in an effort to provide a common measure to evaluate the ML algorithms. The selection of the appropriate metrics is very important, since: (a) how the algorithm’s performance is measured relies on these metrics and (b) the metric itself can influence the way the significance of several characteristics is weighted.

Confusion matrix constitutes one of the most intuitive metrics towards finding the correctness of a model. It is used for classification problems, where the result can be of at least two types of classes. Let us consider a simple example, by giving a label to a target variable: for example, “1” when a plant has been infected with a disease and “0” otherwise. In this simplified case, the confusion matrix ( Figure 4 ) is a 2 × 2 table having two dimensions, namely “Actual” and “Predicted”, while its dimensions have the outcome of the comparison between the predictions with the actual class label. Concerning the above simplified example, this outcome can acquire the following values:

  • True Positive (TP): The plant has a disease (1) and the model classifies this case as diseased (1);
  • True Negative (TN): The plant does not have a disease (0) and the model classifies this case as a healthy plant (0);
  • False Positive (FP): The plant does not have a disease (0), but the model classifies this case as diseased (1);
  • False Negative (FN): The plant has a disease (1), but the model classifies this case as a healthy plant (0).

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Representative illustration of a simplified confusion matrix.

As can be shown in Table 1 , the aforementioned values can be implemented in order to estimate the performance metrics, typically observed in classification problems [ 79 ].

Summary of the most commonly used evaluation metrics of the reviewed studies.

Other common evaluation metrics were the coefficient of correlation ( R ), coefficient of determination ( R 2 ; basically, the square of the correlation coefficient), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE), which can be given via the following relationships [ 80 , 81 ]:

where X t and Z t correspond to the predicted and real value, respectively, t stands for the iteration at each point, while T for the testing records number. Accordingly, low values of MAE, MAPE, and MSE values denote a small error and, hence, better performance. In contrast, R 2 near 1 is desired, which demonstrates better model performance and also that the regression curve efficiently fits the data.

4.1. Preliminary Data Visualization Analysis

Graphical representation of data related to the reviewed studies, by using maps, bar or pie charts, for example, can provide an efficient approach to demonstrate and interpret the patterns of data. The data visualization analysis, as it usually refers to, can be vital in the context of analyzing large amounts of data and has gained remarkable attention in the past few years, including review studies. Indicatively, significant results can be deduced in an effort to identify: (a) the most contributing authors and organizations, (b) the most contributing international journals (or equivalently which research fields are interested in this topic), and (c) the current trends in this field [ 82 ].

4.1.1. Classification of the Studies in Terms of Application Domain

As can be seen in the flowchart of the present methodology ( Figure 3 ), the literature survey on ML in agriculture resulted in 338 journal papers. Subsequently, these studies were classified into the four generic categories as well as into their sub-categories, as already mentioned above. Figure 5 depicts the aforementioned papers’ distribution. In particular, the majority of the studies were intended for crop management (68%), while soil management (10%), water management (10%), and livestock management (12% in total; animal welfare: 7% and livestock production: 5%) had almost equal contribution in the present bibliographic survey. Focusing on crop management, the most contributing sub-categories were yield prediction (20%) and disease detection (19%). The former research field arises as a consequence of the increasing interest of farmers in taking decisions based on efficient management that can lead to the desired yield. Disease detection, on the other hand, is also very important, as diseases constitute a primary menace for food security and quality assurance. Equal percentages (13%) were observed for weed detection and crop recognition, both of which are essential in crop management at farm and agricultural policy making level. Finally, examination of crop quality was relatively scarce corresponding to 3% of all studies. This can be attributed to the complexity of monitoring and modeling the quality-related parameters.

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The classification of the reviewed studies according to the field of application.

In this fashion, it should be mentioned again that all the selected journal papers are summarized in Table A1 , Table A2 , Table A3 , Table A4 , Table A5 , Table A6 , Table A7 , Table A8 and Table A9 , depending on their field of application, and presented in the Appendix A . The columns of the tables correspond (from left to right) to the “Reference number” (Ref), “Input Data”, “Functionality”, “Models/Algorithms”, and “Best Output”. One additional column exists for the sub-categories belonging in crop management, namely “Crop”, whereas the corresponding column in the sub-categories pertaining to livestock management refers to “Animal”. The present systematic review deals with a plethora of different ML models and algorithms. For the sake of brevity, the commonly used abbreviations are used instead of the entire names, which are summarized in Table A10 and Table A11 (presented also in the Appendix A ). The list of the aforementioned Tables, along with their content, is listed in Table 2 .

List of the tables appearing in the Appendix A related to: (a) the categories and sub-categories of the machine learning applications in agriculture ( Table A1 , Table A2 , Table A3 , Table A4 , Table A5 , Table A6 , Table A7 , Table A8 and Table A9 ) and (b) the abbreviations of machine learning models and algorithms ( Table A10 and Table A11 , respectively).

4.1.2. Geographical Distribution of the Contributing Organizations

The subject of this sub-section is to find out the geographical distribution of all the contributing organizations in ML applications in agriculture. To that end, the author’s affiliation was taken into account. In case a paper included more than one author, which was the most frequent scenario, each country could contribute only once in the final map chart ( Figure 6 ), similarly to [ 83 , 84 ]. As can be gleaned from Figure 6 , investigating ML in agriculture is distributed worldwide, including both developed and developing economies. Remarkably, out of the 55 contributing countries, the least contribution originated from African countries (3%), whereas the major contribution came from Asian countries (55%). The latter result is attributed mainly to the considerable contribution of Chinese (24.9%) as well as Indian organizations (10.1%). USA appeared to be the second most contributing country with 20.7% percentage, while Australia (9.5%), Spain (6.8%), Germany (5.9%), Brazil, UK, and Iran (5.62%) seem to be particularly interested in ML in agriculture. It should be stressed that livestock management, which is a relatively different sub-field comparing to crop, water, and soil management, was primary examined from studies coming from Australia, USA, China, and UK, while all the papers regarding Ireland were focused on animals. Finally, another noteworthy observation is that a large number of articles were a result of international collaboration, with the synergy of China and USA standing out.

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Geographical distribution of the contribution of each country to the research field focusing on machine learning in agriculture.

4.1.3. Distribution of the Most Contributing Journal Papers

For the purpose of identifying the research areas that are mostly interested in ML in agriculture, the most frequently appeared international journal papers are depicted in Figure 7 . In total, there were 129 relevant journals. However, in this bar chart, only the journals contributing with at least 4 papers are presented for brevity. As a general remark, remote sensing was of particular importance, since reliable data from satellites and UAV, for instance, constitute valuable input data for the ML algorithms. In addition, smart farming, environment, and agricultural sustainability were of central interest. Journals associated with computational techniques were also presented with considerable frequency. A typical example of such type of journals, which was presented in the majority of the studies with a percentage of 19.8%, was “ Computers and Electronics in Agriculture ”. This journal aims at providing the advances in relation to the application of computers and electronic systems for solving problems in plant and animal production.

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Distribution of the most contributing international journals (published at least four articles) concerning applications of machine learning in agriculture.

The “ Remote Sensing ” and “ Sensors ” journals followed with approximately 11.8% and 6.5% of the total number of publications, respectively. These are cross-sectoral journals that are concentrated on applications of science and sensing technologies in various fields, including agriculture. Other journals, covering this research field, were also “ IEEE Access ” and “ International Journal of Remote Sensing ” with approximately 2.1% and 1.2% contribution, respectively. Moreover, agriculture-oriented journals were also presented in Figure 7 , including “ Precision Agriculture ”, “ Frontiers in Plant Science ”, “ Agricultural and Forest Meteorology ”, and “ Agricultural Water Management ” with 1–3% percentage. These journals deal with several aspects of agriculture ranging from management strategies (so as to incorporate spatial and temporal data as a means of optimizing productivity, resource use efficiency, sustainability and profitability of agricultural production) up to crop molecular genetics and plant pathogens. An interdisciplinary journal concentrating on soil functions and processes also appeared with 2.1%, namely “ Geoderma ”, plausibly covering the soil management generic category. Finally, several journals focusing on physics and applied natural sciences, such as “ Applied Sciences ” (2.7%), “ Scientific Reports ” (1.8%), “ Biosystems Engineering ” (1.5%), and “ PLOS ONE ” (1.5%), had a notable contribution to ML studies. As a consequence, ML in agriculture concerns several disciplines and constitutes a fundamental area for developing various techniques, which can be beneficial to other fields as well.

4.2. Synopsis of the Main Features Associated with the Relative Literature

4.2.1. machine learning models providing the best results.

A wide range of ML algorithms was implemented in the selected studies; their abbreviations are given in Table A11 . The ML algorithms that were used by each study as well as those that provided the best output have been listed in the last two columns of Table A1 , Table A2 , Table A3 , Table A4 , Table A5 , Table A6 , Table A7 , Table A8 and Table A9 . These algorithms can be classified into the eight broad families of ML models, which are summarized in Table A10 . Figure 8 focuses on the best performed ML models as a means of capturing a broad picture of the current situation and demonstrating advancement similarly to [ 12 ].

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Machine Learning models giving the best output.

As can be demonstrated in Figure 8 , the most frequent ML model providing the best output was, by far, Artificial Neural Networks (ANNs), which appeared in almost half of the reviewed studies (namely, 51.8%). More specifically, ANN models provided the best results in the majority of the studies concerning all sub-categories. ANNs have been inspired by the biological neural networks that comprise human brains [ 85 ], while they allow for learning via examples from representative data describing a physical phenomenon. A distinct characteristic of ANNs is that they can develop relationships between dependent and independent variables, and thus extract useful information from representative datasets. ANN models can offer several benefits, such as their ability to handle noisy data [ 86 ], a situation that is very common in agricultural measurements. Among the most popular ANNs are the Deep Neural Networks (DNNs), which utilize multiple hidden layers between input and output layers. DNNs can be unsupervised, semi-supervised, or supervised. A usual kind of DNNs are the Convolutional Neural Networks (CNNs), whose layers, unlike common neural networks, can set up neurons in three dimensions [ 87 ]. In fact, CNNs were presented as the algorithms that provide the best output in all sub-categories, with an almost 50% of the individual percentage of ANNs. As stressed in recent studies, such as that of Yang et al. [ 88 ], CNNs are receiving more and more attention because of their efficient results when it comes to detection through images’ processing.

Recurrent Neural Networks (RNNs) followed, representing approximately 10% of ANNs, with Long Short-Term Memory (LSTM) standing out. They are called “recurrent” as they carry out the same process for every element, with the previous computations determining the current output, while they have a “memory” that stores information pertaining to what has been calculated so far. RNNs can face problems concerning vanishing gradients and inability to “memorize” many sequential data. Towards addressing these issues, the cell structures of LSTM can control which part of information will be either stored in long memory or discarded, resulting in optimization of the memorizing process [ 51 ]. Moreover, Multi-Layer Perceptron (MLP), Fully Convolutional Networks (FCNs), and Radial Basis Function Networks (RBFNs) appeared to have the best performance in almost 3–5% of ANNs. Finally, ML algorithms, belonging to ANNs with low frequency, were Back-Propagation Neural Networks (BPNNs), Modular Artificial Neural Networks (MANNs), Deep Belief Networks (DBNs), Adaptive-Neuro Fuzzy Inference System (ANFIS), Subtractive Clustering Fuzzy Inference System (SCFIS), Takagi-Sugeno Fuzzy Neural Networks (TS-FNN), and Feed Forward Neural Networks (FFNNs).

The second most accurate ML model was Ensemble Learning (EL), contributing to the ML models used in agricultural systems with approximately 22.2%. EL is a concise term for methods that integrate multiple inducers for the purpose of making a decision, normally in supervised ML tasks. An inducer is an algorithm, which gets as an input a number of labeled examples and creates a model that can generalize these examples. Thus, predictions can be made for a set of new unlabeled examples. The key feature of EL is that via combining various models, the errors coming from a single inducer is likely to be compensated from other inducers. Accordingly, the prediction of the overall performance would be superior comparing to a single inducer [ 89 ]. This type of ML model was presented in all sub-categories, apart from crop quality, perhaps owing to the small number of papers belonging in this subcategory. Support Vector Machine (SVM) followed, contributing in approximately 11.5% of the studies. The strength of the SVM stems from its capability to accurately learn data patterns while showing reproducibility. Despite the fact that it can also be applied for regression applications, SVM is a commonly used methodology for classification extending across numerous data science settings [ 90 ], including agricultural research.

Decision Trees (DT) and Regression models came next with equal percentage, namely 4.7%. Both these ML models were presented in all generic categories. As far as DT are concerned, they are either regression or classification models structured in a tree-like architecture. Interestingly, handling missing data in DT is a well-established problem. By implementing DT, the dataset can be gradually organized into smaller subsets, whereas, in parallel, a tree graph is created. In particular, each tree’s node denotes a dissimilar pairwise comparison regarding a certain feature, while each branch corresponds to the result of this comparison. As regards leaf nodes, they stand for the final decision/prediction provided after following a certain rule [ 91 , 92 ]. As for Regression, it is used for supervised learning models intending to model a target value on the basis of independent predictors. In particular, the output can be any number based on what it predicts. Regression is typically applied for time series modeling, prediction, and defining the relationships between the variables.

Finally, the ML models, leading to optimal performance (although with lower contribution to literature), were those of Instance Based Models (IBM) (2.7%), Dimensionality Reduction (DR) (1.5%), Bayesian Models (BM) (0.9%), and Clustering (0.3%). IBM appeared only in crop, water, and livestock management, whereas BM only in crop and soil management. On the other hand, DR and Clustering appeared as the best solution only in crop management. In brief, IBM are memory-based ML models that can learn through comparison of the new instances with examples within the training database. DR can be executed both in unsupervised and supervised learning types, while it is typically carried out in advance of classification/regression so as to prevent dimensionality effects. Concerning the case of BM, they are a family of probabilistic models whose analysis is performed within the Bayesian inference framework. BM can be implemented in both classification and regression problems and belong to the broad category of supervised learning. Finally, Clustering belongs to unsupervised ML models. It contains automatically discovering of natural grouping of data [ 12 ].

4.2.2. Most Studied Crops and Animals

In this sub-section, the most examined crops and animals that were used in the ML models are discussed as a result of our searching within the four sub-categories of crop management similarly to [ 12 ]. These sub-categories refer to yield prediction, disease detection, crop recognition, and crop quality. Overall, approximately 80 different crop species were investigated. The 10 most utilized crops are summarized in Figure 9 . Specifically, the remarkable interest on maize (also known as corn) can be attributed to the fact that it is cultivated in many parts across the globe as well as its versatile usage (for example, direct consumption by humans, animal feed, producing ethanol, and other biofuels). Wheat and rice follow, which are two of the most widely consumed cereal grains. According to the Food and Agriculture Organization (FAO) [ 93 ], the trade in wheat worldwide is more than the summation of all other crops. Concerning rice, it is the cereal grain with the third-highest production and constitutes the most consumed staple food in Asia [ 94 ]. The large contribution of Asian countries presented in Figure 6 , like China and India, justifies the interest in this crop. In the same vein, soybeans, which are broadly distributed in East Asia, USA, Africa, and Australia [ 95 ], were presented in many studies. Finally, tomato, grape, canola/rapeseed (cultivated primarily for its oil-rich seed), potato, cotton, and barley complete the top 10 examined crops. All these species are widely cultivated all over the world. Some other indicative species, which were investigated at least five times in the present reviewed studies, were also alfalfa, citrus, sunflower, pepper, pea, apple, squash, sugarcane, and rye.

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The 10 most investigated crops using machine learning models; the results refer to crop management.

As far as livestock management is concerned, the examined animal species can be classified, in descending order of frequency, into the categories of cattle (58.5%), sheep and goats (26.8%), swine (14.6%), poultry (4.9%), and sheepdog (2.4%). As can be depicted in Figure 10 , the last animal, which is historically utilized with regard to the raising of sheep, was investigated only in one study belonging to animal welfare, whereas all the other animals were examined in both categories of livestock management. In particular, the most investigated animal in both animal welfare and livestock production was cattle. Sheep and goats came next, which included nine studies for sheep and two studies for goats. Cattles are usually raised as livestock aimed at meat, milk, and hide used for leather. Similarly, sheep are raised for meat and milk as well as fleece. Finally, swine (often called domestic pigs) and poultry (for example, chicken, turkey, and duck), which are used mainly for their meat or eggs (poultry), had equal contribution from the two livestock sub-categories.

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Frequency of animal species in studies concerning livestock management by using machine learning models.

4.2.3. Most Studied Features and Technologies

As mentioned in the beginning of this study, modern agriculture has to incorporate large amounts of heterogeneous data, which have originated from a variety of sensors over large areas at various spatial scale and resolution. Subsequently, such data are used as input into ML algorithms for their iterative learning up until modeling of the process in the most effective way possible. Figure 11 shows the features and technologies that were used in the reviewed studies, separately for each category, for the sake of better comprehending the results of the analysis.

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Distribution of the most usual features implemented as input data in the machine learning algorithms for each category/sub-category.

Data coming from remote sensing were the most common in the yield prediction sub-category. Remote sensing, in turn, was primarily based on data derived from satellites (40.6% of the total studies published in this sub-category) and, secondarily, from UAVs (23.2% of the total studies published in this sub-category). A remarkable observation is the rapid increase of the usage of UAVs versus satellites from the year 2018 towards 2020, as UAVs seem to be a reliable alternative that can give faster and cheaper results, usually in higher resolution and independent of the weather conditions. Therefore, UAVs allow for discriminating details of localized circumscribed regions that the satellites’ lowest resolution may miss, especially under cloudy conditions. This explosion in the use of UAV systems in agriculture is a result of the developing market of drones and sensing solutions attached to them, rendering them economically affordable. In addition, the establishment of formal regulations for UAV operations and the simplification and automatization of the operational and analysis processes had a significant contribution on the increasing popularity of these systems. Data pertaining to the weather conditions of the investigated area were also of great importance as well as soil parameters of the farm at hand. An additional way of getting the data was via in situ manual measurements, involving measurements such as crop height, plant growth, and crop maturity. Finally, data concerning topographic, irrigation, and fertilization aspects were presented with approximately equal frequency.

As far as disease detection is concerned, Red-Green-Blue (RGB) images appear to be the most usual input data for the ML algorithms (in 62% of the publications). Normally, deep learning methods like CNNs are implemented with the intention of training a classifier to discriminate images depicting healthy leaves, for example, from infected ones. CNNs use some particular operations to transform the RGB images so that the desired features are enhanced. Subsequently, higher weights are given to the images having the most suitable features. This characteristic constitutes a significant advantage of CNNs as compared to other ML algorithms, when it comes to image classification [ 79 ]. The second most common input data came from either multispectral or hyperspectral measurements originated from spectroradiometers, UAVs, and satellites. Concerning the investigated diseases, fungal diseases were the most common ones with diseases from bacteria following, as is illustrated in Figure 12 a. This kind of disease can cause major problems in agriculture with detrimental economic consequences [ 96 ]. Other examined origins of crop diseases were, in descending order of frequency, pests, viruses, toxicity, and deficiencies.

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Distribution of the most usual output features of the machine learning algorithms regarding: ( a ) Disease detection and ( b ) Crop quality.

Images were also the most used input data for weed detection purposes. These images were RGB images that originated mainly from in situ measurements as well as from UGVs and UAVs and, secondarily, multispectral images from the aforementioned sources. Finally, other parameters that were observed, although with lower frequency, were satellite multispectral images, mainly due to the considerably low resolution they provide, video recordings, and hyperspectral and greyscale images. Concerning crop recognition, the majority of the studies used data coming mostly from satellites and, secondarily, from in situ manual measurements. This is attributed to the fact that most of the studies in this category concern crop classification, a sector where satellite imaging is the most widely used data source owing to its potential for analysis of time series of extremely large surfaces of cultivated land. Laboratory measurements followed, while RGB and greyscale images as well as hyperspectral and multispectral measurements from UAVs were observed with lower incidence.

The input data pertaining to crop quality consisted mainly of RGB images, while X-ray images were also utilized (for seed germination monitoring). Additionally, quality parameters, such as color, mass, and flesh firmness, were used. There were also two studies using spectral data either from satellites or spectroradiometers. In general, the studies belonging in this sub-category dealt with either crop quality (80%) or seed germination potential (20%) ( Figure 12 b). The latter refers to the seed quality assessment that is essential for the seed production industry. Two studies were found about germination that both combined X-ray images analysis and ML.

Concerning soil management, various soil properties were taken into account in 65.7% of the studies. These properties included salinity, organic matter content, and electrical conductivity of soil and soil organic carbon. Usage of weather data was also very common (in 48.6% of the studies), while topographic and data pertaining to the soil moisture content (namely the ratio of the water mass over the dry soil) and crop properties were presented with lower frequency. Additionally, remote sensing, including satellite and UAV multispectral and hyperspectral data, as well as proximal sensing, to a lesser extent, were very frequent choices (in 40% of the studies). Finally, properties associated with soil temperature, land type, land cover, root microbial dynamics, and groundwater salinity make up the rest of data, which are labeled as “other” in the corresponding graph of Figure 11 .

In water management, weather data stood for the most common input data (appeared in the 75% of the studies), with ET being used in the vast majority of them. In many cases, accurate estimation of ET (the summation of the transpiration via the plant canopy and the evaporation from plant, soil, and open water surface) is among the most central elements of hydrologic cycle for optimal management of water resources [ 97 ]. Data from remote sensors and measurements of soil water content were also broadly used in this category. Soil water availability has a central impact on crops’ root growth by affecting soil aeration and nutrient availability [ 98 ]. Stem water potential, appearing in three studies, is actually a measure of water tension within the xylem of the plant, therefore functioning as an indicator of the crop’s water status. Furthermore, in situ measurements, soil, and other parameters related to cumulative water infiltration, soil and water quality, field topography, and crop yield were also used, as can be seen in Figure 11 .

Finally, in what concerns livestock management, motion capture sensors, including accelerometers, gyroscopes, and pedometers, were the most common devices giving information about the daily activities of animals. This kind of sensors was used solely in the studies investigating animal welfare. Images, audio, and video recordings came next, however, appearing in both animal welfare and livestock production sub-categories. Physical and growth characteristics followed, with slightly less incidence, by appearing mainly in livestock production sub-category. These characteristics included the animal’s weight, gender, age, metabolites, biometric traits, backfat and muscle thickness, and heat stress. The final characteristic may have detrimental consequences in livestock health and product quality [ 99 ], while through the measurement of backfat and muscle thickness, estimations of the carcass lean yield can be made [ 100 ].

5. Discussion and Main Conclusions

The present systematic review study deals with ML in agriculture, an ever-increasing topic worldwide. To that end, a comprehensive analysis of the present status was conducted concerning the four generic categories that had been identified in the previous review by Liakos et al. [ 12 ]. These categories pertain to crop, water, soil, and livestock management. Thus, by reviewing the relative literature of the last three years (2018–2020), several aspects were analyzed on the basis of an integrated approach. In summary, the following main conclusions can be drawn:

  • The majority of the journal papers focused on crop management, whereas the other three generic categories contributed almost with equal percentage. Considering the review paper of [ 12 ] as a reference study, it can be deduced that the above picture remains, more or less, the same, with the only difference being the decrease of the percentage of the articles regarding livestock from 19% to 12% in favor of those referring to crop management. Nonetheless, this reveals just one side of the coin. Taking into account the tremendous increase in the number of relative papers published within the last three years (in particular, 40 articles were identified in [ 12 ] comparing to the 338 of the present literature survey), approximately 400% more publications were found on livestock management. Another important finding was the increasing research interest on crop recognition.
  • Several ML algorithms have been developed for the purpose of handling the heterogeneous data coming from agricultural fields. These algorithms can be classified in families of ML models. Similar to [ 12 ], the most efficient ML models proved to be ANNs. Nevertheless, in contrast to [ 12 ], the interest also been shifted towards EL, which can combine the predictions that originated from more than one model. SVM completes the group with the three most accurate ML models in agriculture, due to some advantages, such as its high performance when it works with image data [ 101 ].
  • As far as the most investigated crops are concerned, mainly maize and, secondarily, wheat, rice, and soybean were widely studied by using ML. In livestock management, cattle along with sheep and goats stood out constituting almost 85% of the studies. Comparing to [ 12 ], more species have been included, while wheat and rice as well as cattle, remain important specimens for ML applications.
  • A very important result of the present review study was the demonstration of the input data used in the ML algorithms and the corresponding sensors. RGB images constituted the most common choice, thus, justifying the broad usage of CNNs due to their ability to handle this type of data more efficiently. Moreover, a wide range of parameters pertaining to weather as well as soil, water, and crop quality was used. The most common means of acquiring measurements for ML applications was remote sensing, including imaging from satellites, UAVs and UGVs, while in situ and laboratory measurements were also used. As highlighted above, UAVs are constantly gaining ground against satellites mainly because of their flexibility and ability to provide images with high resolution under any weather conditions. Satellites, on the other hand, can supply time-series over large areas [ 102 ]. Finally, animal welfare-related studies used mainly devices such as accelerometers for activity recognition, whereas those ones referring to livestock production utilized primary physical and growth characteristics of the animal.

As can be inferred from the geographical distribution (illustrated in Figure 6 ) in tandem with the broad spectrum of research fields, ML applications for facilitating various aspects of management in the agricultural sector is an important issue on an international scale. As a matter of fact, its versatile nature favors convergence research. Convergence research is a relatively recently introduced approach that is based on shared knowledge between different research fields and can have a positive impact on the society. This can refer to several aspects, including improvement of the environmental footprint and assuring human’s health. Towards this direction, ML in agriculture has a considerable potential to create value.

Another noteworthy finding of the present analysis is the capturing of the increasing interest on topics concerning ML analyses in agricultural applications. More specifically, as can be shown in Figure 13 , an approximately 26% increase was presented in the total number of the relevant studies, if a comparison is made between 2018 and 2019. The next year (i.e., 2020), the corresponding increase jumped to 109% against 2019 findings; thus, resulting in an overall 164% rise comparing with 2018. The accelerating rate of the research interest on ML in agriculture is a consequence of various factors, following the considerable advancements of ICT systems in agriculture. Moreover, there exists a vital need for increasing the efficiency of agricultural practices while reducing the environmental burden. This calls for both reliable measurements and handling of large volumes of data as a means of providing a wide overview of the processes taking place in agriculture. The currently observed technological outbreak has a great potential to strengthen agriculture in the direction of enhancing food security and responding to the rising consumers’ demands.

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Temporal distribution of the reviewed studies focusing on machine learning in agriculture, which were published within 2018–2020.

In a nutshell, ICT in combination with ML, seem to constitute one of our best hopes to meet the emerging challenges. Taking into account the rate of today’s data accumulation along with the advancement of various technologies, farms will certainly need to advance their management practices by adopting Decision Support Systems (DSSs) tailored to the needs of each cultivation system. These DSSs use algorithms, which have the ability to work on a wider set of cases by considering a vast amount of data and parameters that the farmers would be impossible to handle. However, the majority of ICT necessitates upfront costs to be paid, namely the high infrastructure investment costs that frequently prevent farmers from adopting these technologies. This is going to be a pressing issue, mainly in developing economies, where agriculture is an essential economic factor. Nevertheless, having a tangible impact is a long-haul game. A different mentality is required by all stakeholders so as to learn new skills, be aware of the potential profits of handling big data, and assert sufficient funding. Overall, considering the constantly increasing recognition of the value of artificial intelligence in agriculture, ML will definitely become a behind-the-scenes enabler for the establishment of a sustainable and more productive agriculture. It is anticipated that the present systematic effort is going to constitute a beneficial guide to researchers, manufacturers, engineers, ICT system developers, policymakers, and farmers and, consequently, contribute towards a more systematic research on ML in agriculture.

In this section, the reviewed articles are summarized within the corresponding Tables as described in Table 2 .

Crop Management: Yield Prediction.

Acc: Accuracy: CA: Conservation Agriculture; CI: Crop Indices; CEC: Cation Exchange Capacity; CCC: Concordance Correlation Coefficient; DOY: Day Of Year; EC: Electrical Conductivity; HD: Heading Date; HDM: Heading Date to Maturity; K: Potassium; Mg: Magnesium; N: Nitrogen; OLI: Operational Land Imager; P: Phosphorus; RGB: Red-Green-Blue; S: Sulphur; SOM: Soil Organic Matter; SPAD: Soil and Plant Analyzer Development; STI: Soil Texture Information; STD: Standard Deviation; UAV: Unmanned Aerial Vehicle; UGV: Unmanned Ground Vehicle.

Crop Management: Disease Detection.

Acc: Accuracy; AUC: Area Under Curve; CR: Cedar Rust; ExGR: Excess Green Minus Excess Red; FS: Frogeye Spot; H: Healthy; mAP: mean Average Precision; RGB: Red-Green-Blue; S: Scab; TYLC: Tomato Yellow Leaf Curl; UAV: Unmanned Aerial Vehicle; VddNet: Vine Disease Detection Network.

Crop Management: Weed Detection.

Acc: Accuracy; AUC: Area under Curve; IoU: Intersection over Union; mAP: mean Average Precision; RGB: Red-Green-Blue; UAV: Unmanned Aerial Vehicle; UGV: Unmanned Ground Vehicle.

Crop Management: Crop Recognition.

Acc: Accuracy; IoU: Intersection over Union; RGB: Red-Green-Blue; UAV: Unmanned Aerial Vehicle.

Crop Management: Crop Quality.

Acc: Accuracy; DSM: Detection and Segmentation Module; EDG: Estimated Dimensions Geometry; IVTD: In Vitro True Digestibility; RGB; Red-Green-Blue; MMD: Manually Measured Dimensions; mAP: mean Average Precision; PSO: Particle Swarm Optimization; RGB; Red-Green-Blue; SAE: Stacked AutoEncoder; VI: Vegetation Indices; WF: Wavelet Features.

Water management.

Acc: Accuracy; CC: Coefficient of Correlation; ET: Evapotranspiration; ET o : reference EvapoTranspiration; ROC: Receiver Operating Characteristic; ME: Model Efficiency; NSE: Nash-Sutcliffe model efficiency Coefficient; POD: Probability Of Detection.

Soil management.

ACCA: Aminoyclopropane-1-carboxylate; AUC: Area Under Curve; BP: Bacterial Population; CC: Coefficient of Correlation; CCC: Concordance Correlation Coefficient; CCE: Calcium Carbonate Equivalent; ET: EvaporoTransporation; MIR: Mid InfraRed; NSE: Nash-Sutcliffe model efficiency Coefficient; NIR: Near-InfraRed; PS: Phosphate Solubilization; PWP: Permanent Wilting Point; RPIQ: Ratio of Performance to Interquartile Range; RPD: Relative Percent Deviation; SOC: Soil Organic Carbon; WI: Willmott’s Index.

Livestock Management: Animal Welfare.

AUC: Area Under Curve; Cont: Contagious; DE: Digestible Energy; ED: Energy Digestibility; ENV: Environmental; DWT: Discrete Wavelet Transform; MFCCs: Mel-Frequency Cepstral Coefficients; NIR: Near InfraRed; NPV: Negative Predictive Value; PTZ: Pan-Tilt-Zoom; PPV: Positive Predictive Value; RGB: Red-Green-Blue; RR: Respiration Rate; ST: Skin Temperature.

Livestock Management: Livestock Production.

ACFW: Adult Clean Fleece Weight; ADG: Average Daily Gain; AFD: Adult Fibre Diameter; AGFW: Adult Greasy Fleece Weight; ASL: Adult Staple Length; ASS: Adult Staple Strength; BBFT: Bacon/BackFat Thickness; BCS: Body Condition Score; CCW: Cold Carcass Weights; CTLEAN: Computed Tomography Lean Meat Yield; DBT: Deep Body Temperature; EMA: Eye Muscle Area; GWAS: Genome-Wide Association Studies; GRFAT: Greville Rule Fat Depth; HER: Human Error Range; IMF: IntraMuscular Fat; HCW: Hot Carcass Weight; LW: Loin Weight; MS: Marbling Score; MT: Muscle Thickness; REIMS: Rapid Evaporative Ionization Mass Spectrometry; RGB: Red-Green-Blue; SMY: Saleable Meat Yield.

Abbreviations for machine learning models.

Abbreviations for machine learning algorithms.

Author Contributions

Conceptualization, D.B.; methodology, L.B., G.D., R.B., D.K. and A.C.T.; investigation, L.B. and G.D.; writing—original draft preparation, L.B. and A.C.T.; writing—review and editing, L.B., G.D., D.K., A.C.T., R.B. and D.B.; visualization, L.B.; supervision, D.B. All authors have read and agreed to the published version of the manuscript.

This work has been partly supported by the Project “BioCircular: Bio-production System for Circular Precision Farming” (project code: T1EDK- 03987) co-financed by the European Union and the Greek national funds through the Operational Programme Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH—CREATE—INNOVATE.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

National Academies Press: OpenBook

Sustainable Agriculture Research and Education in the Field: A Proceedings (1991)

Chapter: introduction, introduction.

Charles M. Benbrook

These proceedings are based on a workshop that brought together scientists, farmer-innovators, policymakers, and interested members of the public for a progress report on sustainable agriculture research and education efforts across the United States. The workshop, which was held on April 3 and 4, 1990, in Washington, D.C., was sponsored by the Office of Science and Education of the U.S. Department of Agriculture and the Board on Agriculture of the National Research Council. The encouraging new science discussed there should convince nearly everyone of two facts.

First, the natural resource, economic, and food safety problems facing U.S. agriculture are diverse, dynamic, and often complex. Second, a common set of biological and ecological principles—when systematically embodied in cropping and livestock management systems—can bring improved economic and environmental performance within the reach of innovative farmers. Some people contend that this result is not a realistic expectation for U.S. agriculture. The evidence presented here does not support such a pessimistic assessment.

The report of the Board on Agriculture entitled Alternative Agriculture (National Research Council, 1989a) challenged everyone to rethink key components of conventional wisdom and contemporary scientific dogma. That report has provided encouragement and direction to those individuals and organizations striving toward more sustainable production systems, and it has provoked skeptics to articulate why they feel U.S. agriculture cannot—some even say should not—seriously contemplate the need for such change. The debate has been spirited and generally constructive.

Scholars, activists, professional critics, and analysts have participated in

this debate by writing papers and books, conducting research, and offering opinions about alternative and sustainable agriculture for over 10 years. Over the past decade, many terms and concepts have come and gone. Most people—and unfortunately, many farmers—have not gone very far beyond the confusion, frustration, and occasional demagoguery that swirls around the different definitions of alternative, low-input, organic, and sustainable agriculture.

Fortunately, though, beginning in late 1989, a broad cross-section of people has grown comfortable with the term sustainable agriculture. The May 21, 1990, issue of Time magazine, in an article on sustainable agriculture entitled “It's Ugly, But It Works” includes the following passage:

[A] growing corps of experts [are] urging farmers to adopt a new approach called sustainable agriculture. Once the term was synonymous with the dreaded O word—a farm-belt euphemism for trendy organic farming that uses no synthetic chemicals. But sustainable agriculture has blossomed into an effort to curb erosion by modifying plowing techniques and to protect water supplies by minimizing, if not eliminating, artificial fertilizers and pest controls.

Concern and ridicule in farm publications and during agribusiness meetings over the philosophical roots of low-input, sustainable, or organic farming have given way to more thoughtful appraisals of the ecological and biological foundations of practical, profitable, and sustainable farming systems. While consensus clearly does not yet exist on how to “fix” agriculture's contemporary problems, a constructive dialogue is now under way among a broad cross-section of individuals, both practitioners and technicians involved in a wide variety of specialties.

This new dialogue is powerful because of the people and ideas it is connecting. Change will come slowly, however. Critical comments in some farm magazines will persist, and research and on-farm experimentation will not always lead to the hoped for insights or breakthroughs. Some systems that now appear to be sustainable will encounter unexpected production problems. Nonetheless, progress will be made.

The Board on Agriculture believes that over the next several decades significant progress can and will be made toward more profitable, resource-conserving, and environmentally prudent farming systems. Rural areas of the United States could become safer, more diverse, and aesthetically pleasing places to live. Farming could, as a result, become a more rewarding profession, both economically and through stewardship of the nation's soil and water resources. Change will be made possible; and it will be driven by new scientific knowledge, novel on-farm management tools and approaches, and economic necessity. The policy reforms adopted in the 1990 farm bill, and ongoing efforts to incorporate environmental objectives

into farm policy, may also in time make a significant difference in reshaping the economic environment in which on-farm management decisions are made.

This volume presents an array of new knowledge and insight about the functioning of agricultural systems that will provide the managerial and technological foundations for improved farming practices and systems. Examples of the research projects under way around the country are described. Through exploration of the practical experiences, recent findings, and insights of these researchers, the papers and discussions presented in this volume should demonstrate the value of field- and farm-level systems-based research that is designed and conducted with ongoing input from farmer-innovators.

Some discussion of the basic concepts that guide sustainable agriculture research and education activities may be useful. Definitions of key terms, such as sustainable agriculture, alternative agriculture, and low-input sustainable agriculture, are drawn from Alternative Agriculture and a recent paper (Benbrook and Cook, 1990).


Basic concepts.

Sustainable agriculture, which is a goal rather than a distinct set of practices, is a system of food and fiber production that

improves the underlying productivity of natural resources and cropping systems so that farmers can meet increasing levels of demand in concert with population and economic growth;

produces food that is safe, wholesome, and nutritious and that promotes human well-being;

ensures an adequate net farm income to support an acceptable standard of living for farmers while also underwriting the annual investments needed to improve progressively the productivity of soil, water, and other resources; and

complies with community norms and meets social expectations.

Other similar definitions could be cited, but there is now a general consensus regarding the essential elements of sustainable agriculture. Various definitions place differing degrees of emphasis on certain aspects, but a common set of core features is now found in nearly all definitions.

While sustainable agriculture is an inherently dynamic concept, alternative agriculture is the process of on-farm innovation that strives toward the goal of sustainable agriculture. Alternative agriculture encompasses efforts by farmers to develop more efficient production systems, as well as

efforts by researchers to explore the biological and ecological foundations of agricultural productivity.

The challenges inherent in striving toward sustainability are clearly dynamic. The production of adequate food on a sustainable basis will become more difficult if demographers are correct in their estimates that the global population will not stabilize before it reaches 11 billion or 12 billion in the middle of the twenty-first century. The sustainability challenge and what must be done to meet it range in nature from a single farm field, to the scale of an individual farm as an enterprise, to the food and fiber needs of a region or country, and finally to the world as a whole.

A comprehensive definition of sustainability must include physical, biological, and socioeconomic components. The continued viability of a farming system can be threatened by problems that arise within any one of these components. Farmers are often confronted with choices and sacrifices because of seemingly unavoidable trade-offs—an investment in a conservation system may improve soil and water quality but may sacrifice near-term economic performance. Diversification may increase the efficiency of resource use and bring within reach certain biological benefits, yet it may require additional machinery and a more stable and versatile labor supply. Indeed, agricultural researchers and those who design and administer farm policy must seek ways to alleviate seemingly unwelcome trade-offs by developing new knowledge and technology and, when warranted, new policies.

Operational Definitions

Sustainable agriculture is the production of food and fiber using a system that increases the inherent productive capacity of natural and biological resources in step with demand. At the same time, it must allow farmers to earn adequate profits, provide consumers with wholesome, safe food, and minimize adverse impacts on the environment.

As defined in our report, alternative agriculture is any system of food or fiber production that systematically pursues the following goals (National Research Council, 1989a):

more thorough incorporation of natural processes such as nutrient cycling, nitrogen fixation, and beneficial pest-predator relationships into the agricultural production process;

reduction in the use of off-farm inputs with the greatest potential to harm the environment or the health of farmers and consumers;

productive use of the biological and genetic potential of plant and animal species;

improvement in the match between cropping patterns and the productive potential and physical limitations of agricultural lands; and

profitable and efficient production with emphasis on improved farm management, prevention of animal disease, optimal integration of livestock and cropping enterprises, and conservation of soil, water, energy, and biological resources.

Conventional agriculture is the predominant farming practices, methods, and systems used in a region. Conventional agriculture varies over time and according to soil, climatic, and other environmental factors. Moreover, many conventional practices and methods are fully sustainable when pursued or applied properly and will continue to play integral roles in future farming systems.

Low-input sustainable agriculture (LISA) systems strive to achieve sustainability by incorporating biologically based practices that indirectly result in lessened reliance on purchased agrichemical inputs. The goal of LISA systems is improved profitability and environmental performance through systems that reduce pest pressure, efficiently manage nutrients, and comprehensively conserve resources.

Successful LISA systems are founded on practices that enhance the efficiency of resource use and limit pest pressures in a sustainable way. The operational goal of LISA should not, as a matter of first principles, be viewed as a reduction in the use of pesticides and fertilizers. Higher yields, lower per unit production costs, and lessened reliance on agrichemicals in intensive agricultural systems are, however, often among the positive outcomes of the successful adoption of LISA systems. But in much of the Third World an increased level of certain agrichemical and fertilizer inputs will be very helpful if not essential to achieve sustainability. For example, the phosphorous-starved pastures in the humid tropics will continue to suffer severe erosion and degradation in soil physical properties until soil fertility levels are restored and more vigorous plant growth provides protection from rain and sun.

Farmers are continuously modifying farming systems whenever opportunities arise for increasing productivity or profits. Management decisions are not made just in the context of one goal or concern but in the context of the overall performance of the farm and take into account many variables: prices, policy, available resources, climatic conditions, and implications for risk and uncertainty.

A necessary step in carrying out comparative assessments of conventional and alternative farming systems is to understand the differences between farming practices, farming methods, and farming systems. It is somewhat easier, then, to determine what a conventional practice, method, or system is and how an alternative or sustainable practice, method, or system might or should differ from a conventional one. The following definitions are drawn from the Glossary of Alternative Agriculture (National Research Council, 1989a).

A farming practice is a way of carrying out a discrete farming task such as a tillage operation, particular pesticide application technology, or single conservation practice. Most important farming operations—preparing a seedbed, controlling weeds and erosion, or maintaining soil fertility, for example—require a combination of practices, or a method. Most farming operations can be carried out by different methods, each of which can be accomplished by several unique combinations of different practices. The manner in which a practice is carried out—the speed and depth of a tillage operation, for example—can markedly alter its consequences.

A farming method is a systematic way to accomplish a specific farming objective by integrating a number of practices. A discrete method is needed for each essential farming task, such as preparing a seedbed and planting a crop, sustaining soil fertility, managing irrigation, collecting and disposing of manure, controlling pests, and preventing animal diseases.

A farming system is the overall approach used in crop or livestock production, often derived from a farmer's goals, values, knowledge, available technologies, and economic opportunities. A farming system influences, and is in turn defined by, the choice of methods and practices used to produce a crop or care for animals.

In practice, farmers are constantly adjusting cropping systems in an effort to improve a farm's performance. Changes in management practices generally lead to a complex set of results—some positive, others negative—all of which occur over different time scales.

The transition to more sustainable agriculture systems may, for many farmers, require some short-term sacrifices in economic performance in order to prepare the physical resource and biological ecosystem base needed for long-term improvement in both economic and environmental performance. As a result, some say that practices essential to progress toward sustainable agriculture are not economically viable and are unlikely to take hold on the farm (Marten, 1989). Their contention may prove correct, given current farm policies and the contemporary inclination to accept contemporary, short-term economic challenges as inviolate. Nonetheless, one question lingers: What is the alternative to sustainable agriculture?


Farmers, conservationists, consumers, and political leaders share an intense interest in the sustainability of agricultural production systems. This interest is heightened by growing recognition of the successes achieved by innovative farmers across the country who are discovering alternative agriculture practices and methods that improve a farm's economic and environmental performance. Ongoing experimental efforts on the farm, by no

means universally successful, are being subjected to rigorous scientific investigation. New insights should help farmers become even more effective stewards of natural resources and produce food that is consistently free of man-made or natural contaminants that may pose health risks.

The major challenge for U.S. agriculture in the 1990s will be to strike a balance between near-term economic performance and long-term ecological and food safety imperatives. As recommended in Alternative Agriculture (National Research Council, 1989a), public policies in the 1990s should, at a minimum, no longer penalize farmers who are committed to resource protection or those who are trying to make progress toward sustainability. Sustainability will always remain a goal to strive toward, and alternative agriculture systems will continuously evolve as a means to this end. Policy can and must play an integral role in this process.

If sustainability emerges as a principal farm and environmental policy goal, the design and assessment of agricultural policies will become more complex. Trade-offs, and hence choices, will become more explicit between near-term economic performance and enhancement of the long-term biological and physical factors that can contribute to soil and water resource productivity.

Drawing on expertise in several disciplines, policy analysts will be compelled to assess more insightfully the complex interactions that link a farm's economic, ecological, and environmental performance. It is hoped that political leaders will, as a result, recognize the importance of unraveling conflicts among policy goals and more aggressively seizing opportunities to advance the productivity and sustainability of U.S. agriculture.

A few examples may help clarify how adopting the concept of sustainability as a policy goal complicates the identification of cause-and-effect relationships and, hence, the design of remedial policies.

When a farmer is pushed toward bankruptcy by falling crop prices, a farm operation can become financially unsustainable. When crop losses mount because of pest pressure or a lack of soil nutrients, however, the farming system still becomes unsustainable financially, but for a different reason. In the former example, economic forces beyond any individual farmer's control are the clear cause; in the latter case the underlying cause is rooted in the biological management and performance of the farming system.

The biological and economic performance of a farming system can, in turn, unravel for several different reasons. Consider an example involving a particular farm that is enrolled each year in the U.S. Department of Agriculture's commodity price support programs. To maintain eligibility for government subsidies on a continuing basis, the farmer understands the importance of growing a certain minimum (base) acreage of the same crop each year. Hence, the cropping pattern on this farm is likely to lead to a

buildup in soilborne pathogens that attack plant roots and reduce yields. As a result, the farmer might resort to the use of a fumigant to control the pathogens, but the pesticide might become ineffective because of steadily worsening microbial degradation of the fumigant, or a pesticide-resistant pathogen may emerge.

A solution to these new problems might be to speed up the registration of another pesticide that could be used, or relax regulatory standards so more new products can get registered, or both. Consider another possibility. A regulatory agency may cancel use of a fumigant a farmer has been relying upon because of food safety, water quality, or concerns about it effect on wildlife. The farmer might then seek a change in grading standards or an increase in commodity prices or program benefits if alternative pesticides are more costly.

Each of these problems is distinctive when viewed in isolation and could be attacked through a number of changes in policy. The most cost-effective solution, however, will prove elusive unless the biology of the whole system is perceptively evaluated. For this reason, in the policy arena, just as on the farm, it is critical to know what the problem is that warrants intervention and what the root causes of the problem really are.

Research Challenges

In thinking through agricultural research priorities, it should be acknowledged that the crossroads where the sciences of agriculture and ecology meet remain largely undefined, yet clearly promising. There is too little information to specify in detail the features of a truly sustainable agriculture system, yet there is enough information to recognize the merit in striving toward sustainability in a more systematic way.

The capacity of current research programs and institutions to carry out such work is suspect (see Investing in Research [National Research Council, 1989b]). It also remains uncertain whether current policies and programs that were designed in the 1930s or earlier to serve a different set of farmer needs can effectively bring about the types of changes needed to improve ecological management on the modern farm.

In the 1980s, the research community reached consensus on the diagnosis of many of agriculture's contemporary ills; it may take most of the 1990s to agree on cures, and it will take at least another decade to get them into place. Those who are eager for a quick fix or who are just impatient are bound to be chronically frustrated by the slow rate of change.

Another important caution deserves emphasis. The “silver bullet” approach to solving agricultural production problems offers little promise for providing an understanding of the ecological and biological bases of sustainable agriculture. The one-on-one syndrome seeks to discover a new

pesticide for each pest, a new plant variety when a new strain of rust evolves, or a new nitrogen management method when nitrate contamination of drinking water becomes a pressing social concern. This reductionist approach reflects the inclination in the past to focus scientific and technological attention on products and outcomes rather than processes and on overcoming symptoms rather than eliminating causes. This must be changed if research aimed at making agriculture more sustainable is to move ahead at the rate possible given the new tools available to agricultural scientists.

One area of research in particular—biotechnology—will benefit from a shift in focus toward understanding the biology and ecology underlying agricultural systems. Biotechnology research tools make possible powerful new approaches in unraveling biological interactions and other natural processes at the molecular and cellular levels, thus shedding vital new light on ecological interactions with a degree of precision previously unimagined in the biological sciences. However, rather than using these new tools to advance knowledge about the functioning of systems as a first order of priority, emphasis is increasingly placed on discovering products to solve specific production problems or elucidating the mode of action of specific products.

This is regrettable for several reasons. A chance to decipher the physiological basis of sustainable agriculture systems is being put off. The payoff from focusing on products is also likely to be disappointing. The current widespread pattern of failure and consolidation within the agricultural biotechnology industry suggests that biotechnology is not yet mature enough as a science to reliably discover, refine, and commercialize product-based technologies. Products from biotechnology are inevitable, but a necessary first step must be to generate more in-depth understanding of biological processes, cycles, and interactions.

Perhaps the greatest potential of biotechnology lies in the design and on-farm application of more efficient, stable, and profitable cropping and livestock management systems. For farmers to use such systems successfully, they will need access to a range of new information and diagnostic and analytical techniques that can be used on a real-time basis to make agronomic and animal husbandry judgments about how to optimize the efficiencies of the processes and interactions that underlie plant and animal growth.

Knowledge, in combination with both conventional and novel inputs, will be deployed much more systematically to avoid soil nutrient or animal nutrition-related limits on growth; to ensure that diseases and pests do not become serious enough to warrant the excessive use of costly or hazardous pesticides; to increase the realistically attainable annual level of energy flows independent of purchased inputs within agroecosystems; and to maximize a range of functional symbiotic relationships between soil micro-

and macrofauna, plants, and animals. Discrete goals will include pathogen-suppressive soils, enhanced rotation effects, pest suppression by populations of plant-associated microorganisms, nutrient cycling and renewal, the optimization of general resistance mechanisms in plants by cultural practices, and much more effective soil and water conservation systems that benefit from changes in the stability of soil aggregates and the capacity of soils to absorb and hold moisture.

Because of the profound changes needed to create and instill this new knowledge and skills on the farm, the recommendations in Alternative Agriculture (National Research Council, 1989a) emphasize the need to expand systems-based applied research, on-farm experimentation utilizing farmers as research collaborators, and novel extension education strategies—the very goals of the U.S. Department of Agriculture's LISA program.

Future research efforts—and not just those funded through LISA—should place a premium on the application of ecological principles in the multidisciplinary study of farming system performance. A diversity of approaches in researching and designing innovative farming systems will ensure broad-based progress, particularly if farmers are actively engaged in the research enterprise.

Benbrook, C., and J. Cook. 1990. Striving toward sustainability: A framework to guide on-farm innovation, research, and policy analysis. Speech presented at the 1990 Pacific Northwest Symposium on Sustainable Agriculture, March 2.

Marten, J. 1989. Commentary: Will low-input rotations sustain your income? Farm Journal, Dec. 6.

National Research Council. 1989a. Alternative Agriculture. Washington, D.C.: National Academy Press.

National Research Council. 1989b. Investing in Research: A Proposal to Strengthen the Agricultural, Food, and Environmental System. Washington, D.C.: National Academy Press.

Interest is growing in sustainable agriculture, which involves the use of productive and profitable farming practices that take advantage of natural biological processes to conserve resources, reduce inputs, protect the environment, and enhance public health. Continuing research is helping to demonstrate the ways that many factors—economics, biology, policy, and tradition—interact in sustainable agriculture systems.

This book contains the proceedings of a workshop on the findings of a broad range of research projects funded by the U.S. Department of Agriculture. The areas of study, such as integrated pest management, alternative cropping and tillage systems, and comparisons with more conventional approaches, are essential to developing and adopting profitable and sustainable farming systems.


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Scientists are on a quest for drought-resistant wheat, agriculture's 'Holy Grail'

It would be one of the biggest victories in agricultural research if achieved.

research for agriculture

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Plant biologist Marcus Samuel has been working for more than a decade to improve the climate resilience of crops.

At his research greenhouse at the University of Calgary, he uses cutting-edge gene editing techniques to produce hardier varieties of plants able to withstand temperature fluctuations, floods and frosts.

But while he has worked on canola, peas and other crops, perhaps the most elusive and exciting part of his work is the quest for drought-resistant wheat.

"It is definitely the Holy Grail. I think this has been one of the hardest things to crack," Samuel said.

Samuel is just one of many scientists in Canada and around the world pursuing the development of a drought-resistant wheat strain.

It would be one of the biggest victories in agricultural research, if achieved.

Wheat is the most widely grown cereal grain, occupying 17 per cent of the total cultivated land in the world, according to the International Development Research Centre, a federal Crown corporation.

It is a staple food for 35 per cent of the world's population, and provides more calories and protein in the world's diet than any other crop.

Yet wheat is a "thirstier" plant than other staple crops like maize, rice and soy, making it more vulnerable to water shortages.

A head of wheat is pictured in a silhouette against an orange and yellow sun and sky.

The Washington, D.C.-based World Resources Institute estimates that by 2040, nearly three-quarters of global wheat production will be under threat due to drought and climate change-induced water supply stress.

Santosh Kumar, a wheat breeder working on drought resistance for Agriculture and Agri-Food Canada in Brandon, Man., said he sometimes feels like he is racing against time.

"When our world population is projected to be doubled by 2050, we need to feed people," Kumar said.

"If we don't grow enough wheat, there will be food shortages."

Complex genetic profile

While no wheat is ever going to survive in zero-water conditions, scientists have found that wheat plants with certain traits — such as longer, deeper roots — have a better chance of surviving in low-water conditions.

It's possible, using traditional plant breeding methods, to isolate plants with these desirable traits and cross them with other selected plants to create new, more drought-resistant varieties.

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Gains have been made — the wheat Canadian farmers plant today is tougher and hardier than the wheat of 100 years ago. But the process remains painstakingly slow, requiring years of field trials.

And truly drought-tolerant wheat remains elusive, even as the need for it becomes more urgent due to climate change. Canada, for example, saw its total wheat production decline almost 40 per cent year-over-year in 2021 due to extreme heat and drought on the prairies.

Drought walloped Canadian wheat production again last year, when farmers saw yields decline 12 per cent from 2022 levels, according to Statistics Canada.

One reason why science has yet to crack the problem is the sheer complexity of the wheat plant itself. The wheat genome is huge, containing five times more DNA than the human genome. Hunting for better wheat traits is infinitely more difficult than working with a crop that has a simpler genetic profile.

"It's like doing a puzzle of 50 pieces versus 10,000 pieces," Kumar said.

A wheat crop is pictured.

International scientists finally fully mapped the wheat genome in 2018, a breakthrough that has led to recent advancements using genetic research.

The most dramatic of these was a 2020 announcement that Argentine scientists had developed the first genetically engineered wheat, which incorporates a drought-resistant gene from the sunflower plant.

The Argentine wheat has not been approved for growing or eating in Canada, and many markets around the world remain hostile to genetically engineered crops.

But gene editing is less controversial than full-scale genetic modification, and it's in this realm where Canadian scientists — such as the U of C's Samuel — are making strides.

Unlike full-scale genetic modification, gene editing does not involve splicing genetic material from different species together. Instead, it's a precision method that allows scientists to make small, targeted changes to DNA sequences.

In 2021, the Canadian government relaxed its rules around gene-edited crops, saying seeds that have been produced using the technology are safe and do not require special assessments by Health Canada and the Canadian Food Inspection Agency.

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  • A pipeline to send water to southern Alberta? Ideas float to the surface in times of drought

Ellen Sparry, president of the industry group Seeds Canada, said that decision was a milestone that should speed up the quest for drought-resistant wheat.

But she said a promising strain discovered in a research lab tomorrow would still require several years of testing and regulatory work before it could end up in farmers' hands.

She added that's why it's vital that scientists receive the public and private funding they need to work as quickly as possible, so that agriculture's Holy Grail can be discovered before the climate crisis takes a heavier toll.

"It's not a question of 'Can we do it?' It's a question of how fast we can do it in order to face the challenges we're facing," Sparry said.


research for agriculture

Amanda Stephenson, Canadian Press reporter

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  • Open access
  • Published: 27 May 2024

Current status of community resources and priorities for weed genomics research

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Genome Biology volume  25 , Article number:  139 ( 2024 ) Cite this article

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Weeds are attractive models for basic and applied research due to their impacts on agricultural systems and capacity to swiftly adapt in response to anthropogenic selection pressures. Currently, a lack of genomic information precludes research to elucidate the genetic basis of rapid adaptation for important traits like herbicide resistance and stress tolerance and the effect of evolutionary mechanisms on wild populations. The International Weed Genomics Consortium is a collaborative group of scientists focused on developing genomic resources to impact research into sustainable, effective weed control methods and to provide insights about stress tolerance and adaptation to assist crop breeding.

Each year globally, agricultural producers and landscape managers spend billions of US dollars [ 1 , 2 ] and countless hours attempting to control weedy plants and reduce their adverse effects. These management methods range from low-tech (e.g., pulling plants from the soil by hand) to extremely high-tech (e.g., computer vision-controlled spraying of herbicides). Regardless of technology level, effective control methods serve as strong selection pressures on weedy plants and often result in rapid evolution of weed populations resistant to such methods [ 3 , 4 , 5 , 6 , 7 ]. Thus, humans and weeds have been locked in an arms race, where humans develop new or improved control methods and weeds adapt and evolve to circumvent such methods.

Applying genomics to weed science offers a unique opportunity to study rapid adaptation, epigenetic responses, and examples of evolutionary rescue of diverse weedy species in the face of widespread and powerful selective pressures. Furthermore, lessons learned from these studies may also help to develop more sustainable control methods and to improve crop breeding efforts in the face of our ever-changing climate. While other research fields have used genetics and genomics to uncover the basis of many biological traits [ 8 , 9 , 10 , 11 ] and to understand how ecological factors affect evolution [ 12 , 13 ], the field of weed science has lagged behind in the development of genomic tools essential for such studies [ 14 ]. As research in human and crop genetics pushes into the era of pangenomics (i.e., multiple chromosome scale genome assemblies for a single species [ 15 , 16 ]), publicly available genomic information is still lacking or severely limited for the majority of weed species. Recent reviews of current weed genomes identified 26 [ 17 ] and 32 weed species with sequenced genomes [ 18 ]—many assembled to a sub-chromosome level.

Here, we summarize the current state of weed genomics, highlighting cases where genomics approaches have successfully provided insights on topics such as population genetic dynamics, genome evolution, and the genetic basis of herbicide resistance, rapid adaptation, and crop dedomestication. These highlighted investigations all relied upon genomic resources that are relatively rare for weedy species. Throughout, we identify additional resources that would advance the field of weed science and enable further progress in weed genomics. We then introduce the International Weed Genomics Consortium (IWGC), an open collaboration among researchers, and describe current efforts to generate these additional resources.

Evolution of weediness: potential research utilizing weed genomics tools

Weeds can evolve from non-weed progenitors through wild colonization, crop de-domestication, or crop-wild hybridization [ 19 ]. Because the time span in which weeds have evolved is necessarily limited by the origins of agriculture, these non-weed relatives often still exist and can be leveraged through population genomic and comparative genomic approaches to identify the adaptive changes that have driven the evolution of weediness. The ability to rapidly adapt, persist, and spread in agroecosystems are defining features of weedy plants, leading many to advocate agricultural weeds as ideal candidates for studying rapid plant adaptation [ 20 , 21 , 22 , 23 ]. The insights gained from applying plant ecological approaches to the study of rapid weed adaptation will move us towards the ultimate goals of mitigating such adaptation and increasing the efficacy of crop breeding and biotechnology [ 14 ].

Biology and ecological genomics of weeds

The impressive community effort to create and maintain resources for Arabidopsis thaliana ecological genomics provides a motivating example for the emerging study of weed genomics [ 24 , 25 , 26 , 27 ]. Arabidopsis thaliana was the first flowering plant species to have its genome fully sequenced [ 28 ] and rapidly became a model organism for plant molecular biology. As weedy genomes become available, collection, maintenance, and resequencing of globally distributed accessions of these species will help to replicate the success found in ecological studies of A. thaliana [ 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. Evaluation of these accessions for traits of interest to produce large phenomics data sets (as in [ 36 , 37 , 38 , 39 , 40 ]) enables genome-wide association studies and population genomics analyses aimed at dissecting the genetic basis of variation in such traits [ 41 ]. Increasingly, these resources (e.g. the 1001 genomes project [ 29 ]) have enabled A. thaliana to be utilized as a model species to explore the eco-evolutionary basis of plant adaptation in a more realistic ecological context. Weedy species should supplement lessons in eco-evolutionary genomics learned from these experiments in A. thaliana .

Untargeted genomic approaches for understanding the evolutionary trajectories of populations and the genetic basis of traits as described above rely on the collection of genotypic information from across the genome of many individuals. While whole-genome resequencing accomplishes this requirement and requires no custom methodology, this approach provides more information than is necessary and is prohibitively expensive in species with large genomes. Development and optimization of genotype-by-sequencing methods for capturing reduced representations of newly sequence genomes like those described by [ 42 , 43 , 44 ] will reduce the cost and computational requirements of genetic mapping and population genetic experiments. Most major weed species do not currently have protocols for stable transformation, a key development in the popularity of A. thaliana as a model organism and a requirement for many functional genomic approaches. Functional validation of genes/variants believed to be responsible for traits of interest in weeds has thus far relied on transiently manipulating endogenous gene expression [ 45 , 46 ] or ectopic expression of a transgene in a model system [ 47 , 48 , 49 ]. While these methods have been successful, few weed species have well-studied viral vectors to adapt for use in virus induced gene silencing. Spray induced gene silencing is another potential option for functional investigation of candidate genes in weeds, but more research is needed to establish reliable delivery and gene knockdown [ 50 ]. Furthermore, traits with complex genetic architecture divergent between the researched and model species may not be amenable to functional genomic approaches using transgenesis techniques in model systems. Developing protocols for reduced representation sequencing, stable transformation, and gene editing/silencing in weeds will allow for more thorough characterization of candidate genetic variants underlying traits of interest.

Beyond rapid adaptation, some weedy species offer an opportunity to better understand co-evolution, like that between plants and pollinators and how their interaction leads to the spread of weedy alleles (Additional File 1 : Table S1). A suite of plant–insect traits has co-evolved to maximize the attraction of the insect pollinator community and the efficiency of pollen deposition between flowers ensuring fruit and seed production in many weeds [ 51 , 52 ]. Genetic mapping experiments have identified genes and genetic variants responsible for many floral traits affecting pollinator interaction including petal color [ 53 , 54 , 55 , 56 ], flower symmetry and size [ 57 , 58 , 59 ], and production of volatile organic compounds [ 60 , 61 , 62 ] and nectar [ 63 , 64 , 65 ]. While these studies reveal candidate genes for selection under co-evolution, herbicide resistance alleles may also have pleiotropic effects on the ecology of weeds [ 66 ], altering plant-pollinator interactions [ 67 ]. Discovery of genes and genetic variants involved in weed-pollinator interaction and their molecular and environmental control may create opportunities for better management of weeds with insect-mediated pollination. For example, if management can disrupt pollinator attraction/interaction with these weeds, the efficiency of reproduction may be reduced.

A more complete understanding of weed ecological genomics will undoubtedly elucidate many unresolved questions regarding the genetic basis of various aspects of weediness. For instance, when comparing populations of a species from agricultural and non-agricultural environments, is there evidence for contemporary evolution of weedy traits selected by agricultural management or were “natural” populations pre-adapted to agroecosystems? Where there is differentiation between weedy and natural populations, which traits are under selection and what is the genetic basis of variation in those traits? When comparing between weedy populations, is there evidence for parallel versus non-parallel evolution of weediness at the phenotypic and genotypic levels? Such studies may uncover fundamental truths about weediness. For example, is there a common phenotypic and/or genotypic basis for aspects of weediness among diverse weed species? The availability of characterized accessions and reference genomes for species of interest are required for such studies but only a few weedy species have these resources developed.

Population genomics

Weed species are certainly fierce competitors, able to outcompete crops and endemic species in their native environment, but they are also remarkable colonizers of perturbed habitats. Weeds achieve this through high fecundity, often producing tens of thousands of seeds per individual plant [ 68 , 69 , 70 ]. These large numbers in terms of demographic population size often combine with outcrossing reproduction to generate high levels of diversity with local effective population sizes in the hundreds of thousands [ 71 , 72 ]. This has two important consequences: weed populations retain standing genetic variation and generate many new mutations, supporting weed success in the face of harsh control. The generation of genomic tools to monitor weed populations at the molecular level is a game-changer to understanding weed dynamics and precisely testing the effect of artificial selection (i.e., management) and other evolutionary mechanisms on the genetic make-up of populations.

Population genomic data, without any environmental or phenotypic information, can be used to scan the genomes of weed and non-weed relatives to identify selective sweeps, pointing at loci supporting weed adaptation on micro- or macro-evolutionary scales. Two recent within-species examples include weedy rice, where population differentiation between weedy and domesticated populations was used to identify the genetic basis of weedy de-domestication [ 73 ], and common waterhemp, where consistent allelic differences among natural and agricultural collections resolved a complex set of agriculturally adaptive alleles [ 74 , 75 ]. A recent comparative population genomic study of weedy barnyardgrass and crop millet species has demonstrated how inter-specific investigations can resolve the signatures of crop and weed evolution [ 76 ] (also see [ 77 ] for a non-weed climate adaptation example). Multiple sequence alignments across numerous species provide complementary insight into adaptive convergence over deeper timescales, even with just one genomic sample per species (e.g., [ 78 , 79 ]). Thus, newly sequenced weed genomes combined with genomes available for closely related crops (outlined by [ 14 , 80 ]) and an effort to identify other non-weed wild relatives will be invaluable in characterizing the genetic architecture of weed adaptation and evolution across diverse species.

Weeds experience high levels of genetic selection, both artificial in response to agricultural practices and particularly herbicides, and natural in response to the environmental conditions they encounter [ 81 , 82 ]. Using genomic analysis to identify loci that are the targets of selection, whether natural or artificial, would point at vulnerabilities that could be leveraged against weeds to develop new and more sustainable management strategies [ 83 ]. This is a key motivation to develop genotype-by-environment association (GEA) and selective sweep scan approaches, which allow researchers to resolve the molecular basis of multi-dimensional adaptation [ 84 , 85 ]. GEA approaches, in particular, have been widely used on landscape-wide resequencing collections to determine the genetic basis of climate adaptation (e.g., [ 27 , 86 , 87 ]), but have yet to be fully exploited to diagnose the genetic basis of the various aspects of weediness [ 88 ]. Armed with data on environmental dimensions of agricultural settings, such as focal crop, soil quality, herbicide use, and climate, GEA approaches can help disentangle how discrete farming practices have influenced the evolution of weediness and resolve broader patterns of local adaptation across a weed’s range. Although non-weedy relatives are not technically required for GEA analyses, inclusion of environmental and genomic data from weed progenitors can further distinguish genetic variants underpinning weed origins from those involved in local adaptation.

New weeds emerge frequently [ 89 ], either through hybridization between species as documented for sea beet ( Beta vulgaris ssp. maritima) hybridizing with crop beet to produce progeny that are well adapted to agricultural conditions [ 90 , 91 , 92 ], or through the invasion of alien species that find a new range to colonize. Biosecurity measures are often in place to stop the introduction of new weeds; however, the vast scale of global agricultural commodity trade precludes the possibility of total control. Population genomic analysis is now able to measure gene flow between populations [ 74 , 93 , 94 , 95 ] and identify populations of origin for invasive species including weeds [ 96 , 97 , 98 ]. For example, the invasion route of the pest fruitfly Drosophila suzukii from Eastern Asia to North America and Europe through Hawaii was deciphered using Approximate Bayesian Computation on high-throughput sequencing data from a global sample of multiple populations [ 99 ]. Genomics can also be leveraged to predict invasion rather than explain it. The resequencing of a global sample of common ragweed ( Ambrosia artemisiifolia L.) elucidated a complex invasion route whereby Europe was invaded by multiple introductions of American ragweed that hybridized in Europe prior to a subsequent introduction to Australia [ 100 , 101 ]. In this context, the use of genomically informed species distribution models helps assess the risk associated with different source populations, which in the case of common ragweed, suggests that a source population from Florida would allow ragweed to invade most of northern Australia [ 102 ]. Globally coordinated research efforts to understand potential distribution models could support the transformation of biosecurity from perspective analysis towards predictive risk assessment.

Herbicide resistance and weed management

Herbicide resistance is among the numerous weedy traits that can evolve in plant populations exposed to agricultural selection pressures. Over-reliance on herbicides to control weeds, along with low diversity and lack of redundancy in weed management strategies, has resulted in globally widespread herbicide resistance [ 103 ]. To date, 272 herbicide-resistant weed species have been reported worldwide, and at least one resistance case exists for 21 of the 31 existing herbicide sites of action [ 104 ]—significantly limiting chemical weed control options available to agriculturalists. This limitation of control options is exacerbated by the recent lack of discovery of herbicides with new sites of action [ 105 ].

Herbicide resistance may result from several different physiological mechanisms. Such mechanisms have been classified into two main groups, target-site resistance (TSR) [ 4 , 106 ] and non-target-site resistance (NTSR) [ 4 , 107 ]. The first group encompasses changes that reduce binding affinity between a herbicide and its target [ 108 ]. These changes may provide resistance to multiple herbicides that have a common biochemical target [ 109 ] and can be effectively managed through mixture and/or rotation of herbicides targeting different sites of action [ 110 ]. The second group (NTSR), includes alterations in herbicide absorption, translocation, sequestration, and/or metabolism that may lead to unpredictable pleotropic cross-resistance profiles where structurally and functionally diverse herbicides are rendered ineffective by one or more genetic variant(s) [ 47 ]. This mechanism of resistance threatens not only the efficacy of existing herbicidal chemistries, but also ones yet to be discovered. While TSR is well understood because of the ease of identification and molecular characterization of target site variants, NTSR mechanisms are significantly more challenging to research because they are often polygenic, and the resistance causing element(s) are not well understood [ 111 ].

Improving the current understanding of metabolic NTSR mechanisms is not an easy task, since genes of diverse biochemical functions are involved, many of which exist as extensive gene families [ 109 , 112 ]. Expression changes of NTSR genes have been implicated in several resistance cases where the protein products of the genes are functionally equivalent across sensitive and resistant plants, but their relative abundance leads to resistance. Thus, regulatory elements of NTSR genes have been scrutinized to understand their role in NTSR mechanisms [ 113 ]. Similarly, epigenetic modifications have been hypothesized to play a role in NTSR, with much remaining to be explored [ 114 , 115 , 116 ]. Untargeted approaches such as genome-wide association, selective sweep scans, linkage mapping, RNA-sequencing, and metabolomic profiling have proven helpful to complement more specific biochemical- and chemo-characterization studies towards the elucidation of NTSR mechanisms as well as their regulation and evolution [ 47 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 ]. Even in cases where resistance has been attributed to TSR, genetic mapping approaches can detect other NTSR loci contributing to resistance (as shown by [ 123 ]) and provide further evidence for the role of TSR mutations across populations. Knowledge of the genetic basis of NTSR will aid the rational design of herbicides by screening new compounds for interaction with newly discovered NTSR proteins during early research phases and by identifying conserved chemical structures that interact with these proteins that should be avoided in small molecule design.

Genomic resources can also be used to predict the protein structure for novel herbicide target site and metabolism genes. This will allow for prediction of efficacy and selectivity for new candidate herbicides in silico to increase herbicide discovery throughput as well as aid in the design and development of next-generation technologies for sustainable weed management. Proteolysis targeting chimeras (PROTACs) have the potential to bind desired targets with great selectivity and degrade proteins by utilizing natural protein ubiquitination and degradation pathways within plants [ 125 ]. Spray-induced gene silencing in weeds using oligonucleotides has potential as a new, innovative, and sustainable method for weed management, but improved methods for design and delivery of oligonucleotides are needed to make this technique a viable management option [ 50 ]. Additionally, success in the field of pharmaceutical drug discovery in the development of molecules modulating protein–protein interactions offers another potential avenue towards the development of herbicides with novel targets [ 126 , 127 ]. High-quality reference genomes allow for the design of new weed management technologies like the ones listed here that are specific to—and effective across—weed species but have a null effect on non-target organisms.

Comparative genomics and genome biology

The genomes of weed species are as diverse as weed species themselves. Weeds are found across highly diverged plant families and often have no phylogenetically close model or crop species relatives for comparison. On all measurable metrics, weed genomes run the gamut. Some have smaller genomes like Cyperus spp. (~ 0.26 Gb) while others are larger, such as Avena fatua (~ 11.1 Gb) (Table  1 ). Some have high heterozygosity in terms of single-nucleotide polymorphisms, such as the Amaranthus spp., while others are primarily self-pollinated and quite homozygous, such as Poa annua [ 128 , 129 ]. Some are diploid such as Conyza canadensis and Echinochloa haploclada while others are polyploid such as C. sumetrensis , E. crus-galli , and E. colona [ 76 ]. The availability of genomic resources in these diverse, unexplored branches of the tree of life allows us to identify consistencies and anomalies in the field of genome biology.

The weed genomes published so far have focused mainly on weeds of agronomic crops, and studies have revolved around their ability to resist key herbicides. For example, genomic resources were vital in the elucidation of herbicide resistance cases involving target site gene copy number variants (CNVs). Gene CNVs of 5-enolpyruvylshikimate-3-phosphate synthase ( EPSPS ) have been found to confer resistance to the herbicide glyphosate in diverse weed species. To date, nine species have independently evolved EPSPS CNVs, and species achieve increased EPSPS copy number via different mechanisms [ 153 ]. For instance, the EPSPS CNV in Bassia scoparia is caused by tandem duplication, which is accredited to transposable element insertions flanking EPSPS and subsequent unequal crossing over events [ 154 , 155 ]. In Eleusine indica , a EPSPS CNV was caused by translocation of the EPSPS locus into the subtelomere followed by telomeric sequence exchange [ 156 ]. One of the most fascinating genome biology discoveries in weed science has been that of extra-chromosomal circular DNAs (eccDNAs) that harbor the EPSPS gene in the weed species Amaranthus palmeri [ 157 , 158 ]. In this case, the eccDNAs autonomously replicate separately from the nuclear genome and do not reintegrate into chromosomes, which has implications for inheritance, fitness, and genome structure [ 159 ]. These discoveries would not have been possible without reference assemblies of weed genomes, next-generation sequencing, and collaboration with experts in plant genomics and bioinformatics.

Another question that is often explored with weedy genomes is the nature and composition of gene families that are associated with NTSR. Gene families under consideration often include cytochrome P450s (CYPs), glutathione- S -transferases (GSTs), ABC transporters, etc. Some questions commonly considered with new weed genomes include how many genes are in each of these gene families, where are they located, and which weed accessions and species have an over-abundance of them that might explain their ability to evolve resistance so rapidly [ 76 , 146 , 160 , 161 ]? Weed genome resources are necessary to answer questions about gene family expansion or contraction during the evolution of weediness, including the role of polyploidy in NTSR gene family expansion as explored by [ 162 ].

Translational research and communication with weed management stakeholders

Whereas genomics of model plants is typically aimed at addressing fundamental questions in plant biology, and genomics of crop species has the obvious goal of crop improvement, goals of genomics of weedy plants also include the development of more effective and sustainable strategies for their management. Weed genomic resources assist with these objectives by providing novel molecular ecological and evolutionary insights from the context of intensive anthropogenic management (which is lacking in model plants), and offer knowledge and resources for trait discovery for crop improvement, especially given that many wild crop relatives are also important agronomic weeds (e.g., [ 163 ]). For instance, crop-wild relatives are valuable for improving crop breeding for marginal environments [ 164 ]. Thus, weed genomics presents unique opportunities and challenges relative to plant genomics more broadly. It should also be noted that although weed science at its core is an applied discipline, it draws broadly from many scientific disciplines such as, plant physiology, chemistry, ecology, and evolutionary biology, to name a few. The successful integration of weed-management strategies, therefore, requires extensive collaboration among individuals collectively possessing the necessary expertise [ 165 ].

With the growing complexity of herbicide resistance management, practitioners are beginning to recognize the importance of understanding resistance mechanisms to inform appropriate management tactics [ 14 ]. Although weed science practitioners do not need to understand the technical details of weed genomics, their appreciation of the power of weed genomics—together with their unique insights from field observations—will yield novel opportunities for applications of weed genomics to weed management. In particular, combining field management history with information on weed resistance mechanisms is expected to provide novel insights into evolutionary trajectories (e.g. [ 6 , 166 ]), which can be utilized for disrupting evolutionary adaptation. It can be difficult to obtain field history information from practitioners, but developing an understanding among them of the importance of such information can be invaluable.

Development of weed genomics resources by the IWGC

Weed genomics is a fast-growing field of research with many recent breakthroughs and many unexplored areas of study. The International Weed Genomics Consortium (IWGC) started in 2021 to address the roadblocks listed above and to promote the study of weedy plants. The IWGC is an open collaboration among academic, government, and industry researchers focused on producing genomic tools for weedy species from around the world. Through this collaboration, our initial aim is to provide chromosome-level reference genome assemblies for at least 50 important weedy species from across the globe that are chosen based on member input, economic impact, and global prevalence (Fig.  1 ). Each genome will include annotation of gene models and repetitive elements and will be freely available through public databases with no intellectual property restrictions. Additionally, future funding of the IWGC will focus on improving gene annotations and supplementing these reference genomes with tools that increase their utility.

figure 1

The International Weed Genomics Consortium (IWGC) collected input from the weed genomics community to develop plans for weed genome sequencing, annotation, user-friendly genome analysis tools, and community engagement

Reference genomes and data analysis tools

The first objective of the IWGC is to provide high-quality genomic resources for agriculturally important weeds. The IWGC therefore created two main resources for information about, access to, or analysis of weed genomic data (Fig.  1 ). The IWGC website (available at [ 167 ]) communicates the status and results of genome sequencing projects, information on training and funding opportunities, upcoming events, and news in weed genomics. It also contains details of all sequenced species including genome size, ploidy, chromosome number, herbicide resistance status, and reference genome assembly statistics. The IWGC either compiles existing data on genome size, ploidy, and chromosome number, or obtains the data using flow cytometry and cytogenetics (Fig.  1 ; Additional File 2 : Fig S1-S4). Through this website, users can request an account to access our second main resource, an online genome database called WeedPedia (accessible at [ 168 ]), with an account that is created within 3–5 working days of an account request submission. WeedPedia hosts IWGC-generated and other relevant publicly accessible genomic data as well as a suite of bioinformatic tools. Unlike what is available for other fields, weed science did not have a centralized hub for genomics information, data, and analysis prior to the IWGC. Our intention in creating WeedPedia is to encourage collaboration and equity of access to information across the research community. Importantly, all genome assemblies and annotations from the IWGC (Table  1 ), along with the raw data used to produce them, will be made available through NCBI GenBank. Upon completion of a 1-year sponsoring member data confidentiality period for each species (dates listed in Table  1 ), scientific teams within the IWGC produce the first genome-wide investigation to submit for publication including whole genome level analyses on genes, gene families, and repetitive sequences as well as comparative analysis with other species. Genome assemblies and data will be publicly available through NCBI as part of these initial publications for each species.

WeedPedia is a cloud-based omics database management platform built from the software “CropPedia” and licensed from KeyGene (Wageningen, The Netherlands). The interface allows users to access, visualize, and download genome assemblies along with structural and functional annotation. The platform includes a genome browser, comparative map viewer, pangenome tools, RNA-sequencing data visualization tools, genetic mapping and marker analysis tools, and alignment capabilities that allow searches by keyword or sequence. Additionally, genes encoding known target sites of herbicides have been specially annotated, allowing users to quickly identify and compare these genes of interest. The platform is flexible, making it compatible with future integration of other data types such as epigenetic or proteomic information. As an online platform with a graphical user interface, WeedPedia provides user-friendly, intuitive tools that encourage users to integrate genomics into their research while also allowing more advanced users to download genomic data to be used in custom analysis pipelines. We aspire for WeedPedia to mimic the success of other public genomic databases such as NCBI, CoGe, Phytozome, InsectBase, and Mycocosm to name a few. WeedPedia currently hosts reference genomes for 40 species (some of which are currently in their 1-year confidentiality period) with additional genomes in the pipeline to reach a currently planned total of 55 species (Table  1 ). These genomes include both de novo reference genomes generated or in progress by the IWGC (31 species; Table  1 ), and publicly available genome assemblies of 24 weedy or related species that were generated by independent research groups (Table  2 ). As of May 2024, WeedPedia has over 370 registered users from more than 27 countries spread across 6 continents.

The IWGC reference genomes are generated in partnership with the Corteva Agriscience Genome Center of Excellence (Johnston, Iowa) using a combination of single-molecule long-read sequencing, optical genome maps, and chromosome conformation mapping. This strategy has already yielded highly contiguous, phased, chromosome-level assemblies for 26 weed species, with additional assemblies currently in progress (Table  1 ). The IWGC assemblies have been completed as single or haplotype-resolved double-haplotype pseudomolecules in inbreeding and outbreeding species, respectively, with multiple genomes being near gapless. For example, the de novo assemblies of the allohexaploids Conyza sumatrensis and Chenopodium album have all chromosomes captured in single scaffolds and most chromosomes being gapless from telomere to telomere. Complementary full-length isoform (IsoSeq) sequencing of RNA collected from diverse tissue types and developmental stages assists in the development of gene models during annotation.

As with accessibility of data, a core objective of the IWGC is to facilitate open access to sequenced germplasm when possible for featured species. Historically, the weed science community has rarely shared or adopted standard germplasm (e.g., specific weed accessions). The IWGC has selected a specific accession of each species for reference genome assembly (typically susceptible to herbicides). In collaboration with a parallel effort by the Herbicide Resistant Plants committee of the Weed Science Society of America, seeds of the sequenced weed accessions will be deposited in the United States Department of Agriculture Germplasm Resources Information Network [ 186 ] for broad access by the scientific community and their accession numbers will be listed on the IWGC website. In some cases, it is not possible to generate enough seed to deposit into a public repository (e.g., plants that typically reproduce vegetatively, that are self-incompatible, or that produce very few seeds from a single individual). In these cases, the location of collection for sequenced accessions will at least inform the community where the sequenced individual came from and where they may expect to collect individuals with similar genotypes. The IWGC ensures that sequenced accessions are collected and documented to comply with the Nagoya Protocol on access to genetic resources and the fair and equitable sharing of benefits arising from their utilization under the Convention on Biological Diversity and related Access and Benefit Sharing Legislation [ 187 ]. As additional accessions of weed species are sequenced (e.g., pangenomes are obtained), the IWGC will facilitate germplasm sharing protocols to support collaboration. Further, to simplify the investigation of herbicide resistance, the IWGC will link WeedPedia with the International Herbicide-Resistant Weed Database [ 104 ], an already widely known and utilized database for weed scientists.

Training and collaboration in weed genomics

Beyond producing genomic tools and resources, a priority of the IWGC is to enable the utilization of these resources across a wide range of stakeholders. A holistic approach to training is required for weed science generally [ 188 ], and we would argue even more so for weed genomics. To accomplish our training goals, the IWGC is developing and delivering programs aimed at the full range of IWGC stakeholders and covering a breadth of relevant topics. We have taken care to ensure our approaches are diverse as to provide training to researchers with all levels of existing experience and differing reasons for engaging with these tools. Throughout, the focus is on ensuring that our training and outreach result in impacts that benefit a wide range of stakeholders.

Although recently developed tools are incredibly enabling and have great potential to replace antiquated methodology [ 189 ] and to solve pressing weed science problems [ 14 ], specialized computational skills are required to fully explore and unlock meaning from these highly complex datasets. Collaboration with, or training of, computational biologists equipped with these skills and resources developed by the IWGC will enable weed scientists to expand research programs and better understand the genetic underpinnings of weed evolution and herbicide resistance. To fill existing skill gaps, the IWGC is developing summer bootcamps and online modules directed specifically at weed scientists that will provide training on computational skills (Fig.  1 ). Because successful utilization of the IWGC resources requires more than general computational skills, we have created three targeted workshops that teach practical skills related to genomics databases, molecular biology, and population genomics (available at [ 190 ]). The IWGC has also hosted two official conference meetings, one in September of 2021 and one in January of 2023, with more conferences planned. These conferences have included invited speakers to present successful implementations of weed genomics, educational workshops to build computational skills, and networking opportunities for research to connect and collaborate.

Engagement opportunities during undergraduate degrees have been shown to improve academic outcomes [ 191 , 192 ]. As one activity to help achieve this goal, the IWGC has sponsored opportunities for US undergraduates to undertake a 10-week research experience, which includes an introduction to bioinformatics, a plant genomics research project that results in a presentation, and access to career building opportunities in diverse workplace environments. To increase equitable access to conferences and professional communities, we supported early career researchers to attend the first two IWGC conferences in the USA as well as workshops and bootcamps in Europe, South America, and Australia. These hybrid or in-person travel grants are intentionally designed to remove barriers and increase participation of individuals from backgrounds and experiences currently underrepresented within weed/plant science or genomics [ 193 ]. Recipients of these travel awards gave presentations and gained the measurable benefits that come from either virtual or in-person participation in conferences [ 194 ]. Moving forward, weed scientists must amass skills associated with genomic analyses and collaborate with other area experts to fully leverage resources developed by the IWGC.

The tools generated through the IWGC will enable many new research projects with diverse objectives like those listed above. In summary, contiguous genome assemblies and complete annotation information will allow weed scientists to join plant breeders in the use of genetic mapping for many traits including stress tolerance, plant architecture, and herbicide resistance (especially important for cases of NTSR). These assemblies will also allow for investigations of population structure, gene flow, and responses to evolutionary mechanisms like genetic bottlenecking and artificial selection. Understanding gene sequences across diverse weed species will be vital in modeling new herbicide target site proteins and designing novel effective herbicides with minimal off-target effects. The IWGC website will improve accessibility to weed genomics data by providing a single hub for reference genomes as well as phenotypic and genotypic information for accessions shared with the IWGC. Deposition of sequenced germplasm into public repositories will ensure that researchers are able to access and utilize these accessions in their own research to make the field more standardized and equitable. WeedPedia allows users of all backgrounds to quickly access information of interest such as herbicide target site gene sequence or subcellular localization of protein products for different genes. Users can also utilize server-based tools such as BLAST and genome browsing similar to other public genomic databases. Finally, the IWGC is committed to training and connecting weed genomicists through hosting trainings, workshops, and conferences.


Weeds are unique and fascinating plants, having significant impacts on agriculture and ecosystems; and yet, aspects of their biology, ecology, and genetics remain poorly understood. Weeds represent a unique area within plant biology, given their repeated rapid adaptation to sudden and severe shifts in the selective landscape of anthropogenic management practices. The production of a public genomics database with reference genomes and annotations for over 50 weed species represents a substantial step forward towards research goals that improve our understanding of the biology and evolution of weeds. Future work is needed to improve annotations, particularly for complex gene families involved in herbicide detoxification, structural variants, and mobile genetic elements. As reference genome assemblies become available; standard, affordable methods for gathering genotype information will allow for the identification of genetic variants underlying traits of interest. Further, methods for functional validation and hypothesis testing are needed in weeds to validate the effect of genetic variants detected through such experiments, including systems for transformation, gene editing, and transient gene silencing and expression. Future research should focus on utilizing weed genomes to investigate questions about evolutionary biology, ecology, genetics of weedy traits, and weed population dynamics. The IWGC plans to continue the public–private partnership model to host the WeedPedia database over time, integrate new datasets such as genome resequencing and transcriptomes, conduct trainings, and serve as a research coordination network to ensure that advances in weed science from around the world are shared across the research community (Fig.  1 ). Bridging basic plant genomics with translational applications in weeds is needed to deliver on the potential of weed genomics to improve weed management and crop breeding.

Availability of data and materials

All genome assemblies and related sequencing data produced by the IWGC will be available through NCBI as part of publications reporting the first genome-wide analysis for each species.

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Peer review information

Wenjing She was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

The International Weed Genomics Consortium is supported by BASF SE, Bayer AG, Syngenta Ltd, Corteva Agriscience, CropLife International (Global Herbicide Resistance Action Committee), the Foundation for Food and Agriculture Research (Award DSnew-0000000024), and two conference grants from USDA-NIFA (Award numbers 2021–67013-33570 and 2023-67013-38785).

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Department of Agricultural Biology, Colorado State University, 1177 Campus Delivery, Fort Collins, CO, 80523, USA

Jacob Montgomery, Sarah Morran & Todd A. Gaines

Protecting Crops and the Environment, Rothamsted Research, Harpenden, Hertfordshire, UK

Dana R. MacGregor

Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, AL, USA

J. Scott McElroy

Department of Plant and Environmental Sciences, University of Copenhagen, Taastrup, Denmark

Paul Neve & Célia Neto

IFEVA-Conicet-Department of Ecology, University of Buenos Aires, Buenos Aires, Argentina

Martin M. Vila-Aiub & Maria Victoria Sandoval

Department of Ecology, Faculty of Agronomy, University of Buenos Aires, Buenos Aires, Argentina

Analia I. Menéndez

Department of Botany, The University of British Columbia, Vancouver, BC, Canada

Julia M. Kreiner

Institute of Crop Sciences, Zhejiang University, Hangzhou, China

Longjiang Fan

Department of Biology, University of Massachusetts Amherst, Amherst, MA, USA

Ana L. Caicedo

Department of Plant and Wildlife Sciences, Brigham Young University, Provo, UT, USA

Peter J. Maughan

Bayer AG, Weed Control Research, Frankfurt, Germany

Bianca Assis Barbosa Martins, Jagoda Mika, Alberto Collavo & Bodo Peters

Department of Crop Sciences, Federal University of Rio Grande Do Sul, Porto Alegre, Rio Grande Do Sul, Brazil

Aldo Merotto Jr.

Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, USA

Nithya K. Subramanian & Muthukumar V. Bagavathiannan

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Department of Plant and Soil Sciences, University of Pretoria, Pretoria, South Africa

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Department of Crop Sciences, University of Illinois, Urbana, IL, USA

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JMo and TG conceived and outlined the article. TG, DM, EP, RB, JSM, PJT, MJ wrote grants to obtain funding. MMI, BSG, and MJ performed mitotic chromosome visualization. VL performed sequencing. VL and KF assembled the genomes. LC and ELP annotated the genomes. JMo, SM, DRM, JSM, PN, CN, MV, MVS, AIM, JMK, LF, ALC, PJM, BABM, JMi, AC, MVB, LC, AFL, and ELP wrote the first draft of the article. All authors edited the article and improved the final version.

Corresponding author

Correspondence to Todd A. Gaines .

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Some authors work for commercial agricultural companies (BASF, Bayer, Corteva Agriscience, or Syngenta) that develop and sell weed control products.

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Supplementary Information


Additional file 1. List of completed and in-progress genome assemblies of weed species pollinated by insects (Table S1).


Additional file 2. Methods and results for visualizing and counting the metaphase chromosomes of hexaploid Avena fatua (Fig S1); diploid Lolium rigidum  (Fig S2); tetraploid Phalaris minor (Fig S3); and tetraploid Salsola tragus (Fig S4).

Additional file 3. Review history.

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Montgomery, J., Morran, S., MacGregor, D.R. et al. Current status of community resources and priorities for weed genomics research. Genome Biol 25 , 139 (2024).

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We won’t decarbonize agriculture without more government support, says Breakthrough Institute

June 3, 2024

Elaine Watson

While agriculture is a major driver of greenhouse gas emissions , companies seeking to help the sector decarbonize “receive scant support from the federal government,” according to a new report from environmental think tank The Breakthrough Institute , which makes the case for “a far more interventionist approach.”

The report—which you can download here —argues that the US Department of Agriculture (USDA) “ought to mirror the Department of Energy (DOE)” which it says offers support to companies at the research, development, demonstration, and deployment stages, with initiatives explicitly targeting the technological and commercialization ‘valleys of death.’

“Increasing agricultural research and development funding — which has been diminishing for decades — and investing in the innovations needed to bring about climate-friendly, low land-use, and affordable food abundance will be crucial for the next generation of US food and farming.”

Inflation Reduction Act

The Inflation Reduction Act (IRA) set aside nearly $20 billion for agriculture, notes the report. However, the funding is primarily for programs aimed at incentivizing the adoption of measures such as no-till and cover crops, which “have limited and variable potential to achieve long-term climate mitigation,” with the bulk of the funding going towards energy production, transportation, and infrastructure.

On the plus side, notes the report, as part of its new  CDR (carbon dioxide removal) Purchase Pilot Prize , the DOE is providing up to $35 million in the form of offtake agreements from the federal government in carbon dioxide removal pathways including enhanced weathering and mineralization technologies, which are highly applicable to agricultural lands.

“But making agriculture a sub-priority of DOE programs is not enough.”

An underinvested industry

Governments may not have a great track record for picking winners (remember Solyndra ), concede the authors. But private seed, angel, and venture capital investors typically “fund early-stage research and development but lack the funds or will to support firms through scale-up of manufacturing activities,” leaving many companies floundering in the valley of death.

“Even for climate-focused investors, food and agricultural technologies play second fiddle. In 2022, only 4.3% of total climate finance — which includes both private and public sources — went to food and agriculture. Similar to US public funding’s focus on energy and transportation, global climate funders have typically ignored the food system.”

Policy recommendations

The report concludes with a series of policy recommendations that would inject what it claims is desperately needed capital into the agrifoodtech space at each innovation stage:

  • R&D stage: Double federal research funding for major agricultural R&D agencies and programs including the National Institute of Food and Agriculture ( NIFA ) and the Agricultural Research Service ( ARS ) and fully establish the Department of Agriculture’s Advanced Research Authority ( AgARDA ) to develop innovative technologies at USDA.
  • Demonstration stage : Fund testbeds through ARS and NIFA to evaluate and measure the environmental impacts of agricultural technologies and practices. Establish an Office of Food and Agriculture Technology Demonstration at USDA to focus on bridging the gap from R&D to market adoption for emerging food and ag technologies and products with decarbonization potential.
  • Deployment stage : Support and prioritize innovation under USDA direct and guaranteed loan programs such as the Food Supply Chain Guaranteed Loan Program and the Business & Industry Loan Guarantee Program to enable the expansion of commercial scale production of lower-carbon food products and technologies. Establish a Loan Programs Office at USDA ( mirroring that of the DOE ) to explore ways to provide larger loans to catalyze innovations with decarbonization potential for the food and ag sector. Establish a sustainable agriculture investment tax credit to encourage investments in sustainable agriculture technologies and systems, especially those with high upfront costs.

Further reading:

Letter from an underinvested category: agrifoodtech
Khosla, Friedberg strike positive note amid agrifoodtech funding winter: ‘Exceptional founders have nothing to worry about’
‘Incremental’ innovation, a warning on the push for profitability and investors ‘scared shitless’ about portfolios: overheard at World Agri-Tech
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Short course and research spotlight successes for the Plant Protein Innovation Center

Ppic 2024 short course slideshow.

PPIC post-doc and grad students explain structural and functional assays used in PPIC labs

PPIC post-doc and grad students explain structural and functional assays used in PPIC labs

PPIC post-doc and grad students explain structural and functional assays used in PPIC labs

Protein Basics Short Course | May 20-21, 2024

During PPIC's spring short course geared toward anyone with an interest in learning the basics of plant protein, attendees heard from speakers within both industry and academia as they discussed current and upcoming market trends, gave an overview on food proteins, taught basic protein chemistry and production of protein ingredients, introduced protein modification for enhanced functionality, highlighted challenges and opportunities when formulating with plant proteins, and more. The course featured live demonstrations and experiences in PPIC's analytical laboratories, test kitchen, and the UMN Pilot Plant.

50 attendees participated from 36 food companies, from different R&D areas such as technical sales, product development, and category leaders.

PPIC Research Spotlight 2024

PPIC Annual Research Spotlight Meeting 2024

PPIC Annual Research Spotlight Meeting 2024

PPIC Annual Research Spotlight Meeting 2024

PPIC Founder and Director Pam Ismail

PPIC Annual Research Spotlight Meeting 2024

Keynote speaker Stephan Drusch, Professor, for Food Technology and Food Material Science at Technische Universität Berlin

5th Annual Research Spotlight Meeting | May 22-23, 2024 at the University of Minnesota

130 attendees from 66 companies, mostly those in R&D, joined for the Center's Research Spotlight Meeting to stay updated on the latest in plant proteins research from expert researchers who are part of the first plant-based research center of its kind in the nation. PPIC founder and director Pam Ismail hosted the event, as well as speaking on the topic, "Plant proteins are a foe or friend? Extraction and modification remedies."

26 students from different universities across the U.S. showcased their plant protein based research in poster presentations. 

“These poster sessions are a great benefit to both the student researchers and industry,” notes PPIC Director Prof. B. Pam Ismail. “Students get the opportunity to showcase their work, while industry participants get to ask questions about the research and connect with future research talent for hire.” 

The keynote speaker was Stephan Drusch, Professor, Food Technology and Food Material Science at Technische Universität Berlin. 

PPIC's research efforts are headquartered within the Food Science and Nutrition Building on the University of Minnesota campus in Saint Paul. Learn more about PPIC research, membership benefits and future event opportunities .

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New method makes hydrogen from solar power and agricultural waste

University of Illinois Chicago engineers have helped design a new method to make hydrogen gas from water using only solar power and agricultural waste, such as manure or husks. The method reduces the energy needed to extract hydrogen from water by 600%, creating new opportunities for sustainable, climate-friendly chemical production.

Hydrogen-based fuels are one of the most promising sources of clean energy. But producing pure hydrogen gas is an energy-intensive process that often requires coal or natural gas and large amounts of electricity.

In a paper for Cell Reports Physical Science , a multi-institutional team led by UIC engineer Meenesh Singh unveils the new process for green hydrogen production.

The method uses a carbon-rich substance called biochar to decrease the amount of electricity needed to convert water to hydrogen. By using renewable energy sources such as solar power or wind and capturing byproducts for other uses, the process can reduce greenhouse gas emissions to net zero.

"We are the first group to show that you can produce hydrogen utilizing biomass at a fraction of a volt," said Singh, associate professor in the department of chemical engineering. "This is a transformative technology."

Electrolysis, the process of splitting water into hydrogen and oxygen, requires an electric current. At an industrial scale, fossil fuels are typically required to generate this electricity.

Recently, scientists have decreased the voltage required for water splitting by introducing a carbon source to the reaction. But this process also uses coal or expensive chemicals and releases carbon dioxide as a byproduct.

Singh and colleagues modified this process to instead use biomass from common waste products. By mixing sulfuric acid with agricultural waste, animal waste or sewage, they create a slurry-like substance called biochar, which is rich in carbon.

The team experimented with different kinds of biochar made from sugarcane husks, hemp waste, paper waste and cow manure. When added to the electrolysis chamber, all five biochar varieties reduced the power needed to convert water to hydrogen. The best performer, cow dung, decreased the electrical requirement sixfold to roughly a fifth of a volt.

The energy requirements were low enough that the researchers could power the reaction with one standard silicon solar cell generating roughly 15 milliamps of current at 0.5 volt. That's less than the amount of power produced by an AA battery.

"It's very efficient, with almost 35% conversion of the biochar and solar energy into hydrogen" said Rohit Chauhan, a co-author and postdoctoral scholar in Singh's lab. "These are world record numbers; it's the highest anyone has demonstrated."

To make the process net-zero, it must capture the carbon dioxide generated by the reaction. But Singh said this too could have environmental and economic benefits, such as producing pure carbon dioxide to carbonate beverages or converting it into ethylene and other chemicals used in plastic manufacturing.

"It not only diversifies the utilization of biowaste but enables the clean production of different chemicals beyond hydrogen," said UIC graduate Nishithan Kani, co-lead author on the paper. "This cheap way of making hydrogen could allow farmers to become self-sustainable for their energy needs or create new streams of revenue."

Orochem Technologies Inc., who sponsored the research, has filed for patents on their processes for producing biochar and hydrogen, and the UIC team plans to test the methods on a large scale.

In addition to Singh, Kani and Chauhan, the paper was co-authored by UIC graduate student Rajan Bhawnani. Other co-authors come from Stanford University, Texas Tech University, Indian Institute of Technology Roorkee, Korea University and Orochem Technologies Inc.

  • Energy and Resources
  • Alternative Fuels
  • Energy Technology
  • Solar Energy
  • Energy and the Environment
  • Renewable Energy
  • Environmental Science
  • Sustainability
  • Alternative fuel vehicle
  • Hydrogen vehicle
  • Hydroelectricity
  • Water resources
  • Renewable energy

Story Source:

Materials provided by University of Illinois Chicago . Original written by Rob Mitchum. Note: Content may be edited for style and length.

Journal Reference :

  • Nishithan C. Kani, Rohit Chauhan, Samuel A. Olusegun, Ishwar Sharan, Anag Katiyar, David W. House, Sang-Won Lee, Alena Jairamsingh, Rajan R. Bhawnani, Dongjin Choi, Adam C. Nielander, Thomas F. Jaramillo, Hae-Seok Lee, Anil Oroskar, Vimal C. Srivastava, Shishir Sinha, Joseph A. Gauthier, Meenesh R. Singh. Sub-volt conversion of activated biochar and water for H2 production near equilibrium via biochar-assisted water electrolysis . Cell Reports Physical Science , 2024; 102013 DOI: 10.1016/j.xcrp.2024.102013

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    Definition. Agricultural research can be broadly defined as any research activity aimed at improving productivity and quality of crops by their genetic improvement, better plant protection, irrigation, storage methods, farm mechanization, efficient marketing, and a better management of resources (Loebenstein and Thottappilly 2007 ).

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    By helping farmers to adopt practices that reduce chemical use and conserve scarce resources, sustainable agriculture research and education can play a key role in building public support for agricultural land preservation. Educating land use planners and decision-makers about sustainable agriculture is an important priority. Labor

  7. Outlook on Agriculture: Sage Journals

    Outlook on Agriculture is a peer reviewed journal, published quarterly, which welcomes original research papers, reviews and perspectives on current developments in agricultural science and associated disciplines for an international and … | View full journal description. This journal is a member of the Committee on Publication Ethics (COPE).

  8. Journal of Agriculture and Food Research

    The Journal of Agriculture and Food Research is a peer-reviewed open access journal focusing on research in the agricultural and food sciences. The journal welcomes full length research articles, reviews, short communications, perspectives, and commentaries from researchers in academic …. View full aims & scope. $1890. Article publishing charge.

  9. Agricultural research for development

    Agricultural research is essential for sustainable and inclusive agricultural development. Research generates new technologies and improved policies which are essential for small-scale farmers who face the interconnected challenges of climate change, land degradation, gender biases, hunger and exploitation. Despite this, the connection between ...

  10. Agricultural Research and Development

    Global volatility of public agricultural R&D expenditure. Stuti Rawat, in Advances in Food Security and Sustainability, 2020. Abstract. Public investment in agricultural research and development (R&D) is important for global food security and environmental sustainability. Although public agricultural R&D projects are associated with high economic returns, they are characterized by long time ...

  11. AGORA: Global Online Research in Agriculture

    Research4Life provides institutions in lower and middle-income countries with online access to academic and professional peer-reviewed content. We aim to improve teaching, research and policy-making in health, agriculture, the environment and other life, physical and social sciences.National universities, professional schools, research institutes, teaching hospitals, government offices ...

  12. USDA ERS

    Advances in agricultural productivity have led to abundant and affordable food and fiber throughout most of the developed world. Public and private agricultural research has been the foundation and basis for much of this growth and development. ERS data, research, and analyses quantify agricultural productivity improvements and the sources of improvement, in the U.S. and globally.

  13. Articles

    Development and Application of a Tractor-Operated Side Dispensing Type Farmyard Manure Applicator for Organic Fertilizer Application in Vineyards. Agricultural Research is a multi-disciplinary journal covering all disciplines of agricultural sciences to promote global research.

  14. Agriculture Overview: Development news, research, data

    Agriculture can help reduce poverty for 75% of the world's poor, who live in rural areas and work mainly in farming. It can raise incomes, improve food security and benefit the environment. The World Bank Group is a leading financier of agriculture, with $8.1 billion in new commitments in 2013.

  15. CGIAR: Science for humanity's greatest challenges

    CGIAR's global network of 15 Research Centers contributes to an unrivalled mix of knowledge, skills and research facilities able to respond to emerging development issues. Unequalled partnerships network of more than 3,000 partners from national governments, academic institutions, global policy bodies, private companies and NGOs.

  16. Fieldwork Ready: An Introductory Guide to Field Research for

    An Introductory Guide to Field Research for Agriculture, Environment, and Soil Scientists ffirs.indd 1 2/8/2021 8:19:20 AM. Fieldwork Ready An Introductory Guide to Field Research for Agriculture, Environment, and Soil Scientists Sara E. Vero ffirs.indd 3 2/8/2021 8:19:21 AM.

  17. Agriculture

    Agriculture is an international, scientific peer-reviewed open access journal published monthly online by MDPI.. Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.; High Visibility: indexed within Scopus, SCIE (Web of Science), PubAg, AGRIS, RePEc, and other databases. Journal Rank: JCR - Q1 (Agronomy) / CiteScore - Q2 (Plant Science)

  18. Foundation for Food & Agriculture Research

    The Consortium for Innovation in Post-Harvest Loss & Food Waste Reduction is a team of global experts that aim to preserve nutrients, improve livelihoods and realize an efficient food system. The International Consortium for Antimicrobial Stewardship in Agriculture (ICASA) advances research on antimicrobial stewardship in animal agriculture.

  19. Machine Learning in Agriculture: A Comprehensive Updated Review

    The accelerating rate of the research interest on ML in agriculture is a consequence of various factors, following the considerable advancements of ICT systems in agriculture. Moreover, there exists a vital need for increasing the efficiency of agricultural practices while reducing the environmental burden. This calls for both reliable ...


    In thinking through agricultural research priorities, it should be acknowledged that the crossroads where the sciences of agriculture and ecology meet remain largely undefined, yet clearly promising. There is too little information to specify in detail the features of a truly sustainable agriculture system, yet there is enough information to ...

  21. Scientists are on a quest for drought-resistant wheat, agriculture's

    It would be one of the biggest victories in agricultural research, if achieved. Wheat is the most widely grown cereal grain, occupying 17 per cent of the total cultivated land in the world ...

  22. House Agriculture Committee Completes Markup of 2024 Farm Bill

    The U.S. House of Representatives Committee on Agriculture completed its markup of the Farm, Food and National Security Act of 2024 (Farm Bill) on May 24, 2024, and passed the proposed legislation 33-21, with four Democrats - Reps. Don Davis (D-N.C.), Sanford Bishop (D-Ga.), Eric Sorensen (D-Ill.) and Yadira Caraveo (D-Colo.) - voting in support of the legislation.

  23. Current status of community resources and priorities for weed genomics

    Weeds are attractive models for basic and applied research due to their impacts on agricultural systems and capacity to swiftly adapt in response to anthropogenic selection pressures. Currently, a lack of genomic information precludes research to elucidate the genetic basis of rapid adaptation for important traits like herbicide resistance and stress tolerance and the effect of evolutionary ...

  24. Underfunded & overlooked? Ag startups need govt support

    While agriculture is a major driver of greenhouse gas emissions, companies seeking to help the sector decarbonize "receive scant support from the federal government," according to a new report from environmental think tank The Breakthrough Institute, which makes the case for "a far more interventionist approach.". The report—which you can download here—argues that the US Department ...

  25. Review and outlook on reinforcement learning: Its application in

    This framework provides a clear pathway for the application of reinforcement learning in AEI. This research acts as a conduit, merging agricultural and energy domains to promote a cohesive progression that markedly aids in the enhancement of rural electrification and the adoption of sustainable energy methodologies through reinforcement learning.

  26. Short course and research spotlight successes for the Plant Protein

    5th Annual Research Spotlight Meeting | May 22-23, 2024 at the University of Minnesota. 130 attendees from 66 companies, mostly those in R&D, joined for the Center's Research Spotlight Meeting to stay updated on the latest in plant proteins research from expert researchers who are part of the first plant-based research center of its kind in the nation.

  27. New method makes hydrogen from solar power and agricultural waste

    New method makes hydrogen from solar power and agricultural waste. ScienceDaily . Retrieved June 3, 2024 from / releases / 2024 / 05 / 240530132723.htm