climate change in agriculture essay

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Climate Change Impacts on Agriculture and Food Supply

There are over two million farms in the United States, and more than half the nation’s land is used for agricultural production. 1 The number of farms has been slowly declining since the 1930s, 2 though the average farm size has remained about the same since the early 1970s. 3 Agriculture also extends beyond farms. It includes industries such as food service and food manufacturing.

Drought. Since early 2020, the U.S. Southwest has been experiencing one of the most severe long-term droughts of the past 1,200 years. Multiple seasons of record low precipitation and near-record high temperatures were the main triggers of the drought. 37

Wildfires. Some tribal communities are particularly vulnerable to wildfires due to their often-remote locations and lack of firefighting resources and staff. 38 In addition, because wildfire smoke can travel long distances from the source fire, its effects can be far reaching, especially for people with certain medical conditions or who spend long periods of time outside.

Decreased crop yields. Rising temperatures and carbon dioxide concentrations may increase some crop yields, but the yields of major commodity crops (such as corn, rice, and oats) are expected to be lower than they would in a future without climate change. 39

Heat stress. Dairy cows are especially sensitive to heat stress, which can affect their appetite and milk production. In 2010, heat stress lowered annual U.S. dairy production by an estimated $1.2 billion. 40

Soil erosion. Heavy rainfalls can lead to more soil erosion, which is a major environmental threat to sustainable crop production. 41

Agriculture is very sensitive to weather and climate. 4 It also relies heavily on land, water, and other natural resources that climate affects. 5   While climate changes (such as in temperature, precipitation, and frost timing) could lengthen the growing season or allow different crops to be grown in some regions, 6 it will also make agricultural practices more difficult in others.

The effects of climate change on agriculture will depend on the rate and severity of the change, as well as the degree to which farmers and ranchers can adapt. 7 U.S. agriculture already has many practices in place to adapt to a changing climate, including crop rotation and integrated pest management . A good deal of research is also under way to help prepare for a changing climate.

Learn more about climate change and agriculture:

Top Climate Impacts on Agriculture

Agriculture and the economy, environmental justice and equity, what we can do, related resources, the link between agriculture and climate change.

Climate change can affect crops, livestock, soil and water resources, rural communities, and agricultural workers. However, the agriculture sector also emits greenhouse gases into the atmosphere that contribute to climate change. 

Read more about greenhouse gas emissions on the Basics of Climate Change  page.

Learn how the agriculture sector is reducing methane emissions from livestock waste through the AgSTAR program . For a more technical look at emissions from the agriculture sector, take a look at EPA's Greenhouse Gas Emissions Inventory chapter on agriculture activities in the United States . 

Climate change may affect agriculture at both local and regional scales. Key impacts are described in this section.

1. Changes in Agricultural Productivity 

Climate change can make conditions better or worse for growing crops in different regions. For example, changes in temperature, rainfall, and frost-free days are leading to longer growing seasons in almost every state. 8  A longer growing season can have both positive and negative impacts for raising food. Some farmers may be able to plant longer-maturing crops or more crop cycles altogether, while others may need to provide more irrigation over a longer, hotter growing season. Air pollution may also damage crops, plants, and forests. 9  For example, when plants absorb large amounts of ground-level ozone, they experience reduced photosynthesis, slower growth, and higher sensitivity to diseases. 10  

Climate change can also increase the threat of wildfires . Wildfires pose major risks to farmlands, grasslands, and rangelands. 11  Temperature and precipitation changes will also very likely expand the occurrence and range of insects, weeds, and diseases. 12  This could lead to a greater need for weed and pest control. 13  

Pollination is vital to more than 100 crops grown in the United States. 14  Warmer temperatures and changing precipitation can affect when plants bloom and when pollinators , such as bees and butterflies, come out. 15  If mismatches occur between when plants flower and when pollinators emerge, pollination could decrease. 16

2. Impacts to Soil and Water Resources

Climate change is expected to increase the frequency of heavy precipitation in the United States, which can harm crops by eroding soil and depleting soil nutrients. 18  Heavy rains can also increase agricultural runoff into oceans, lakes, and streams. 19  This runoff can harm water quality. 

When coupled with warming water temperatures brought on by climate change, runoff can lead to depleted oxygen levels in water bodies. This is known as hypoxia . Hypoxia can kill fish and shellfish. It can also affect their ability to find food and habitat, which in turn could harm the coastal societies and economies that depend on those ecosystems. 20  

Sea level rise and storms also pose threats to coastal agricultural communities. These threats include erosion, agricultural land losses, and saltwater intrusion, which can contaminate water supplies. 21  Climate change is expected to worsen these threats. 22  

3. Health Challenges to Agricultural Workers and Livestock

Agricultural workers face several climate-related health risks. These include exposures to heat and other extreme weather, more pesticide exposure due to expanded pest presence, disease-carrying pests like mosquitos and ticks, and degraded air quality. 23  Language barriers, lack of health care access, and other factors can compound these risks. 24  Heat and humidity can also affect the health and productivity of animals raised for meat, milk, and eggs. 25   

For more specific examples of climate change impacts in your region, please see the National Climate Assessment .

Agriculture contributed more than $1.1 trillion to the U.S. gross domestic product in 2019. 26  The sector accounts for 10.9 percent of total U.S. employment—more than 22 million jobs. 27  These include not only on-farm jobs, but also jobs in food service and other related industries. Food service makes up the largest share of these jobs at 13 million. 28  

Cattle, corn, dairy products, and soybeans are the top income-producing commodities . 29  The United States is also a key exporter of soybeans, other plant products, tree nuts, animal feeds, beef, and veal. 30

Many hired crop farmworkers are foreign-born people from Mexico and Central America. 31  Most hired crop farmworkers are not migrant workers; instead, they work at a single location within 75 miles of their homes. 32  Many hired farmworkers can be more at risk of climate health threats due to social factors, such as language barriers and health care access.

Climate change could affect food security for some households in the country. Most U.S. households are currently food secure . This means that all people in the household have enough food to live active, healthy lives. 33  However, 13.8 million U.S. households (about one-tenth of all U.S. households) were food insecure at least part of the time in 2020. 34  U.S. households with above-average food insecurity include those with an income below the poverty threshold, those headed by a single woman, and those with Black or Hispanic owners and lessees. 35

Climate change can also affect food security for some Indigenous peoples in Hawai'i and other U.S.-affiliated Pacific islands. Climate impacts like sea level rise and more intense storms can affect the production of crops like taro, breadfruit, and mango. 36 These crops are often key sources of nutrition and may also have cultural and economic importance.

We can reduce the impact of climate change on agriculture in many ways, including the following:

• Incorporate climate-smart farming methods. Farmers can use climate forecasting tools, plant cover crops, and take other steps to help manage climate-related production threats. 
• Join AgSTAR. Livestock producers can get help in recovering methane , a potent greenhouse gas, from biogas created when manure decomposes.
• Reduce runoff. Agricultural producers can strategically apply fertilizers, keep their animals out of streams, and take more actions to reduce nutrient-laden runoff. 
• Boost crop resistance. Adopt research-proven ways to reduce the impacts of climate change on crops and livestock , such as reducing pesticide use and improving pollination.
• Prevent food waste. Stretch your dollar and shrink your carbon footprint by planning  your shopping trips carefully and properly storing food . Donate nutritious, untouched food to food banks and those in need.

See additional actions you can take, as well as steps that companies can take, on EPA’s What You Can Do About Climate Change page.

Related Climate Indicators

Learn more about some of the key indicators of climate change related to this sector from EPA’s Climate Change Indicators :

• Seasonal Temperature
• Freeze-Thaw Conditions
• Length of Growing Season
• Growing Degree Days
• Fifth National Climate Assessment, Chapter 11: “Agriculture, Food Systems, and Rural Communities."
• National Agricultural Center . Provides agriculture-related news from all of EPA through a free email subscription service.
• U.S. Department of Agriculture (USDA) Economic Research Service . Produces research, information, and outlook products to enhance people’s understanding of agriculture and food issues. 
• USDA Environmental Quality Incentives Program . Provides financial and technical assistance to agricultural producers to address natural resource concerns.
• USDA Climate Hubs . Connects farmers, ranchers, and land managers with tools to help them adapt to climate change impacts in their area.
• USDA Rural Development . Promotes economic development in rural communities. Provides loans, grants, technical assistance, and education to agricultural producers and rural residents and organizations.
• National Integrated Drought Information System . Coordinates U.S. drought monitoring, forecasting, and planning through a multi-agency partnership. The U.S. Drought Monitor assesses droughts on a weekly basis.
• Sustainable Management of Food . Provides tools and resources for preventing and reducing wasted food and its associated impacts over the entire life cycle. 
• Resources, Waste, and Climate Change . Learn how reducing waste decreases our carbon footprint and what business, communities, and individuals can do.

1  U.S. Department of Agriculture (USDA), Economic Research Service (ERS). (2022). Ag and food statistics: Charting the essentials. Farming and farm income . Retrieved 3/18/2022.

2  USDA, ERS. (2022). Ag and food statistics: Charting the essentials. Farming and farm income . Retrieved 3/18/2022.

3  USDA, ERS. (2022). Ag and food statistics: Charting the essentials. Farming and farm income . Retrieved 3/18/2022.

4  Walsh, M.K., et al. (2020). Climate indicators for agriculture . USDA Technical Bulletin 1953. Washington, DC, p. 1. 

5  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 393. 

6  Walsh, M.K., et al. (2020). Climate indicators for agriculture . USDA Technical Bulletin 1953. Washington, DC, p. 22. 

7  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 393.

8  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 401. 

9  Nolte, C.G., et al. (2018). Ch. 13: Air quality . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 513. 

10  EPA. (2022). Ecosystem effects of ozone pollution . Retrieved 3/18/2022. 

11  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 401.

12  Ziska, L., et al. (2016). Ch. 7: Food safety, nutrition, and distribution . In: The impacts of climate change on human health in the United States: A scientific assessment . U.S. Global Change Research Program, Washington, DC, p. 197.  

13  Ziska, L., et al. (2016). Ch. 7: Food safety, nutrition, and distribution . In: The impacts of climate change on human health in the United States: A scientific assessment . U.S. Global Change Research Program, Washington, DC, p. 197.  

14  USDA. Pollinators . Retrieved 3/18/2022. 

15  Walsh, M.K., et al. (2020). Climate indicators for agriculture . USDA Technical Bulletin 1953. Washington, DC, p. 20.

16  Walsh, M.K., et al. (2020). Climate indicators for agriculture . USDA Technical Bulletin 1953. Washington, DC, p. 40.

17  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 405.

18  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 409.

19  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II. U.S. Global Change Research Program, Washington, DC, p. 409.

20  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II. U.S. Global Change Research Program, Washington, DC, p. 405.

21  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 405.

22 Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 405.

23  Gamble, J.L., et al. (2016). Ch. 9: Populations of concern . In: The impacts of climate change on human health in the United States: A scientific assessment . U.S. Global Change Research Program, Washington, DC, pp. 247–286. 

24  Hernandez, T., and S. Gabbard. (2019). Findings from the National Agricultural Workers Survey (NAWS) 2015–2016: A demographic and employment profile of United States farmworkers . Department of Labor, Employment and Training Administration, Washington, DC, pp. 10–11 and pp. 40–45.  

25  Walsh, M. K., et al. (2020). Climate indicators for agriculture . USDA Technical Bulletin 1953. Washington, DC, p. 20. 

26  USDA, ERS. (2022). Ag and food statistics: Charting the essentials . Retrieved 3/18/2022.

27  USDA, ERS. (2022). Ag and food statistics: Charting the essentials . Retrieved 3/18/2022.

28  USDA, ERS. (2022). Ag and food statistics: Charting the essentials . Retrieved 3/18/2022.

29  USDA, ERS. (2022). Farm income and wealth statistics/cash receipts by commodity . Retrieved 3/18/2022. 

30  USDA, ERS. (2022). Farm income and wealth statistics/cash receipts by commodity . Retrieved 3/18/2022. 

31  USDA, ERS. (2020). Farm income and wealth statistics/cash receipts by state . Retrieved 5/11/2022.

32  USDA, ERS. (2020). Farm income and wealth statistics/cash receipts by state . Retrieved 5/11/2022.

33  USDA, ERS. (2020). Farm income and wealth statistics/cash receipts by state . Retrieved 5/11/2022.

34  Coleman-Jensen, A., et al. (2020). Household food security in the United States in 2020 , ERR-298, USDA, ERS, p. v. 

35  Coleman-Jensen, A., et al. (2020). Household food security in the United States in 2020 , ERR-298, USDA, ERS, p. v.

36  Keener, V., et al. (2018). Ch. 27: Hawai‘i and U.S.-affiliated Pacific islands . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 1269. 

37  Mankin, J.S., et al. (2021). NOAA Drought Task Force report on the 2020–2021 southwestern U.S. drought. National Oceanic and Atmospheric Administration (NOAA) Drought Task Force; NOAA Modeling, Analysis, Predictions and Projections Programs; and National Integrated Drought Information System, p 4. 

38  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 401.

39  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 409.

40  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 407.

41  Gowda, P., et al. (2018). Ch. 10: Agriculture and rural communities . In: Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II . U.S. Global Change Research Program, Washington, DC, p. 415.

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Impact of climate change on agricultural production; Issues, challenges, and opportunities in Asia

Muhammad habib-ur-rahman.

1 Institute of Crop Science and Resource Conservation (INRES), Crop Science Group, University of Bonn, Bonn, Germany

2 Department of Agronomy, MNS-University of Agriculture, Multan, Pakistan

Ashfaq Ahmad

3 Asian Disaster Preparedness Center, Islamabad, Pakistan

4 Department of Agronomy, University of Agriculture Faisalabad, Faisalabad, Pakistan

Muhammad Usama Hasnain

Hesham f. alharby.

5 Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia

Yahya M. Alzahrani

Atif a. bamagoos, khalid rehman hakeem.

6 Princess Dr. Najla Bint Saud Al-Saud Center for Excellence Research in Biotechnology, King Abdulaziz University, Jeddah, Saudi Arabia

7 Department of Public Health, Daffodil International University, Dhaka, Bangladesh

Saeed Ahmad

8 Institute of Plant Breeding and Biotechnology, MNS-University of Agriculture, Multan, Pakistan

9 Department of Agronomy, The Islamia University, Bahwalpur, Pakistan

Wajid Nasim

Shafaqat ali.

10 Department of Environmental Science and Engineering, Government College University, Faisalabad, Pakistan

Fatma Mansour

11 Department of Economics, Business and Economics Faculty, Siirt University, Siirt, Turkey

Ayman EL Sabagh

12 Department of Agronomy, Faculty of Agriculture, Kafrelsheikh University, Kafrelsheikh, Egypt

13 Department of Field Crops, Faculty of Agriculture, Siirt University, Siirt, Turkey

Agricultural production is under threat due to climate change in food insecure regions, especially in Asian countries. Various climate-driven extremes, i.e., drought, heat waves, erratic and intense rainfall patterns, storms, floods, and emerging insect pests have adversely affected the livelihood of the farmers. Future climatic predictions showed a significant increase in temperature, and erratic rainfall with higher intensity while variability exists in climatic patterns for climate extremes prediction. For mid-century (2040–2069), it is projected that there will be a rise of 2.8°C in maximum temperature and a 2.2°C in minimum temperature in Pakistan. To respond to the adverse effects of climate change scenarios, there is a need to optimize the climate-smart and resilient agricultural practices and technology for sustainable productivity. Therefore, a case study was carried out to quantify climate change effects on rice and wheat crops and to develop adaptation strategies for the rice-wheat cropping system during the mid-century (2040–2069) as these two crops have significant contributions to food production. For the quantification of adverse impacts of climate change in farmer fields, a multidisciplinary approach consisted of five climate models (GCMs), two crop models (DSSAT and APSIM) and an economic model [Trade-off Analysis, Minimum Data Model Approach (TOAMD)] was used in this case study. DSSAT predicted that there would be a yield reduction of 15.2% in rice and 14.1% in wheat and APSIM showed that there would be a yield reduction of 17.2% in rice and 12% in wheat. Adaptation technology, by modification in crop management like sowing time and density, nitrogen, and irrigation application have the potential to enhance the overall productivity and profitability of the rice-wheat cropping system under climate change scenarios. Moreover, this paper reviews current literature regarding adverse climate change impacts on agricultural productivity, associated main issues, challenges, and opportunities for sustainable productivity of agriculture to ensure food security in Asia. Flowing opportunities such as altering sowing time and planting density of crops, crop rotation with legumes, agroforestry, mixed livestock systems, climate resilient plants, livestock and fish breeds, farming of monogastric livestock, early warning systems and decision support systems, carbon sequestration, climate, water, energy, and soil smart technologies, and promotion of biodiversity have the potential to reduce the negative effects of climate change.

Introduction

Asia is the most populous subcontinent in the world (UNO, 2015 ), comprising 4.5 billion people—about 60% of the total world population. Almost 70% of the total population lives in rural areas and 75% of the rural population are poor and most at risk due to climate change, particularly in arid and semi-arid regions (Yadav and Lal, 2018 ; Population of Asia, 2019 ). The population in Asia is projected to reach up to 5.2 billion by 2050, and it is, therefore, challenging to meet the food demands and ensure food security in Asia (Rao et al., 2019 ). In this context, Asia is the region most likely to attribute to population growth rate, and more prone to higher temperatures, drought, flooding, and rising sea level (Guo et al., 2018 ; Hasnat et al., 2019 ). In Asia, diversification in income of small and poor farmers and increasing urbanization is shocking for agricultural productivity. Asia is the home of a third of the world's population and the majority of poor families, most of which are engaged in agriculture (World Bank, 2018 ). We can expect diversification of adverse climate change effects on the agriculture sector due to diversity of farming and cropping systems with dependence on climate. According to the sixth assessment report of IPCC, higher risks of flood and drought make Asian agricultural productivity highly susceptible to changing climate (IPCC, 2019 ). Climate change has already adversely affected economic growth and development in Asia, although there is low emission of greenhouse gasses (GHG) in this region (Gouldson et al., 2016 ; Ahmed et al., 2019a ). Still, China and India are major contributors to global carbon dioxide emission; the share of each Asian country in cumulative global carbon dioxide emission is presented in Figures 1 , ​ ,2. 2 . Although GHGs emission from the agriculture sector is lower than the others, it still has a negative impact. Emission of GHGs from different agricultural components and contribution to emissions can be found in Figure 3 . However, the contribution of Asian countries in GHGs including land use changes and forestry is described in Figure 4 .

Share of each Asian country in cumulative global carbon dioxide emission (1751–2019; Source: OWID based on CDIAC and Global Carbon Project).

Carbon dioxide (CO 2 ) emission from different Asian countries (source: International Energy Statistics https://cdiac.ess-dive.lbl.gov/home.html ; Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States).

Sources of greenhouse gasses (GHGs) emission from different Asian countries with respect to agricultural components (Source: CAIT climate data explorer via . Climate Watch ( https://www.climatewatchdata.org/data-explorer/historical-emissions ).

Total greenhouse gasses (GHGs) emission includes emissions from land use changes and forestry from Asian countries (measured in tons of carbon dioxide equivalents [CO 2 -e] (Source: CAIT climate data explorer via Climate Watch).

Asia is facing alarming challenges due to climate change and variability as illustrated by various climatic models predicting the global mean temperature will increase by 1.5°C between 2030 and 2050 if it continues to increase at the current rate (IPCC, 2019 ). In arid areas of the western part of China, Pakistan, and India, it is also projected that there will be a significant increase in temperature (IPCC, 2019 ). During monsoon season, there would be an increase in erratic rainfall of high intensity across the region. In South and Southeast Asia, there would be an increase in aridity due to a reduction in winter rainfall. Due to climatic abnormalities, there will be a 0.1 m increase in sea level by 2,100 across the globe (IPCC, 2019 ). In Asia, an increase in heat waves, hot and dry days, and erratic and unsure rainfall patterns is projected, while dust storms and tropical cyclones are predicted to be worse in the future (Gouldson et al., 2016 ). Natural disasters are the main reason behind the agricultural productivity (crops and livestock) losses in Asia, including extreme temperature, storms and wildfires (23%), floods (37%), drought (19%), and pest and animal diseases infestation (9%) which accounted for 10 USD billions in amount (FAO, 2015 ). During the last few decades, tropical cyclones in the Pacific have occurred with increased frequency and intensity. South Asia consisted of 262 million malnourished inhabitants, which made South Asia the most food insecure region across the globe (FAO, 2015 ; Rasul et al., 2019 ). In remote dry lands and deserts, the rural population is more vulnerable to climate change due to the scarcity of natural resources.

In Asia, climate variability (temperature and rainfall) and climate-driven extremes (flood, drought, heat stress, cold waves, and storms) have several negative impacts on the agriculture sector (FAO, 2016 ), especially in the cropping system which has a major role in food security, and thus created the food security issues and challenges in Asia (Cai et al., 2016 ; Aryal et al., 2019 ). The rice-wheat cropping system, a major cropping system which fills half of the food demand in Asia, is under threat due to climate change (Ghaffar et al., 2022 ). Climate change adversely affects both the quantity and quality of wheat and rice crops (Din et al., 2022 ; Wasaya et al., 2022 ). For instance, the protein content and grain yield of wheat have been reduced because of the negative impacts of increasing temperature (Asseng et al., 2019 ). The temperature rise has decreased the crop-growing period, and crop evapotranspiration ultimately reduced wheat yield (Azad et al., 2018 ). Adverse impacts of climate change and variability on winter wheat yield in China are attributed to increased average temperature during the growing period (Geng et al., 2019 ). Climate change is also adversely affecting the quality traits especially protein content, and sugars and starch percentages in grains of wheat. Elevated carbon dioxide and high temperatures increase the growth traits while decreasing the protein content in wheat grains (Asseng et al., 2019 ). Similarly, drought stress also reduces the protein content and soluble sugars of the wheat crop (Rakszegi et al., 2019 ; Hussein et al., 2022 ). The decline in the starch content in wheat grains has also been observed under drought stress (Noori and Taliman, 2022 ). Similarly, heat stress also causes a decline in the protein content, soluble sugar, and starch content in wheat grains (Zahra et al., 2021 ; Iqbal et al., 2022 ; Zhao et al., 2022 ). Climate change also negatively affects the quality of wheat products as the rise in temperature causes a reduction in protein content, sugars, and starch. It is assessed that rise in temperature by 1–4°C could decrease the wheat yield up to 17.6% in the Egyptian North Nile Delta (Kheir et al., 2019 ). In China, crop phenology has changed because of both climate variability and crop management practices (Liu et al., 2018 ). Both climate change scenarios and human management practices have adversely affected wheat phenology in India and China (Lv et al., 2013 ; Ren et al., 2019 ). The elevated temperature has increased the infestation of the aphid population on wheat crops and ultimately reduced yield (Tian et al., 2019 ). There is a direct and strong correlation between diseases attached to climate change. For instance, the Fusarium head blight of wheat crops is caused by the Fusarium species and its chances of an attack were increased due to high humidity and hot environment (Shah et al., 2018 ). A similar study has shown a direct interaction between insect pests and diseases and higher temperature and carbon dioxide levels in rice production (Iannella et al., 2021 ; Tan et al., 2021 ; Tonnang et al., 2022 ).

Climate variability has marked several detriments to rice production in Asia. Climate variability has induced flood and drought, which have decreased the rice yield in South Asia and several other parts of Asia (Mottaleb et al., 2017 ). Heat stress, drought, flood, and cyclones have reduced the rice yield in South Asia (Cai et al., 2016 ; Quyen et al., 2018 ; Tariq et al., 2018 ). Thus, climate change-driven extremes, particularly heat and drought stress, have also become a serious threat for sustainable rice production globally (Xu et al., 2021 ). Higher temperatures for a longer period as well as water shortages reduce seed germination which lead to poor stand establishment and seedling vigor (Fahad et al., 2017 ; Liu et al., 2019 ). It has been reported that the exposure of rice crops to high temperatures (38°C day/30°C night) at the grain filling stage led to a reduction in grain weight of rice (Shi et al., 2017 ). Moreover, heat stress also reduces the panicle and spikelet's initiation and ultimately the number of spikelets and grains in the rice production system (Xu et al., 2020 ). Drought stress also adversely affects the reproductive stages and reduces the yield components especially spikelets per panicle, grain size, and grain weight of rice (Raman et al., 2012 ; Kumar et al., 2020 ; Sohag et al., 2020 ). GLAM-Rice model has projected rice yield will decrease ~45% in the 2080's under RCP 8.5 as compared to 1991–2000 in Southeast Asia (Chun et al., 2016 ). On the other hand, climate variability could reduce crop water productivity by 32% under RCP 4.5, or 29% under RCP 8.5 by 2080's in rice crops (Boonwichai et al., 2019 ). In China and Pakistan, high temperature adversely affects the booting and anthesis growth stages of rice ultimately resulting in yield reduction (Zafar et al., 2018 ; Nasir et al., 2020 ). Crop models like DSSAT and APSIM have projected a yield reduction of both rice and wheat crops up to 19 and 12% respectively by 2069 due to a rise of 2.8°C in maximum and 2.2°C in minimum temperature in Pakistan (Ahmad et al., 2019 ).

About 35 million farmers having 3% landholding are projected to convert their source of income (combined crop-livestock production systems) to simply livestock because of the negative impacts of climate change on the quality and quantity of pastures as predicted by future scenarios for 2050 in Asia (Thornton and Herrero, 2010 ). The livestock production sector also contributes 14.5% of global greenhouse emissions and drives climate variability (Downing et al., 2017 ). Directly, there would be higher disease infestation and reduced milk production and fertility rates in livestock because of climate extremes like heat waves (Das, 2018 ; Kumar et al., 2018 ). Indirectly, heat stress will reduce both the quantity and quality of available forage for livestock. Several studies have reported that heat stress reduces the protein and starch content in the grains of maize which is a widely used forage crop (Yang et al., 2018 ; Bheemanahalli et al., 2022 ). Similarly, heat stress also reduces the soluble sugar and protein content in the heat-sensitive cultivars of alfalfa which is also a major forage crop (Wassie et al., 2019 ). In this context, heat stress leads to a reduction in the quality of forage. There would be an increase in demand for livestock products, however, there would be a decrease in livestock heads under future climate scenarios (Downing et al., 2017 ). In Asia, a severe shortage of feed for livestock has imposed horrible effects on the livestock population which has been attributed as the result of extreme rainfall variability and drought conditions (Ma et al., 2018 ).

Timber forests have several significances in Asia, and non-timber forests are also significant sources of food, fiber, and medicines (Chitale et al., 2018 ). Unfortunately, climate change has imposed several negative impacts on forests at various levels in the form of productive traits, depletion of soil resources, carbon dynamics, and vegetation shifting in Asian countries. In India, forests are providing various services in terms of meeting the food demand of 300 million people, the energy demand of people living in rural areas up to 40%, and shelter to one-third of animals (Jhariya et al., 2019 ). In Bangladesh, forests are also vulnerable to climate variability as they are facing the increased risks of fires, rise in sea level, storm surges, coastal erosion, and landslides (Chow et al., 2019 ). Increased extreme drought events with higher frequency, intensity, and duration, and human activities, i.e., afforestation and deforestation, have adversely altered the forest structure (Xu et al., 2018 ). Hence, there is a need to evaluate climate adaptation strategies to restore forests in Asian countries in order to meet increased demands of food, fiber, and medicines. Agroforestry production is also under threat because of adverse climate change impacts such as depletion of natural resources, predominance of insect pests, diseases and unwanted species, increased damage on agriculture and forests, and enhanced food insecurity (De Zoysa and Inoue, 2014 ; Lima et al., 2022 ).

Asia also consists of good quality aquaculture (80% of aquaculture production worldwide) and fisheries (52% of wild caught fish worldwide) which are 77% of the total value addition (Nguyen, 2015 ; Suryadi, 2020 ). In Asia, various climatic extremes such as erratic rainfall, drought, floods, heat stress, salinity, cyclone, ocean acidification, and increased sea level have negatively affected aquaculture (Ahmad et al., 2019 ). For instance, Hilsailisha constituted the largest fishery in Bangladesh, India, and West Bengal and S. Yangi in China have lost their habitat because of climate variability (Jahan et al., 2017 ; Wang et al., 2019a ). Ocean acidification and warming of 1.5°C was closely associated with anthropogenic absorption of CO 2 . Increasing levels of ocean acidity is the main threat to algae and fish. Among various climate driven extremes like drought, flood, and temperature rising, drought is more dangerous as there is not sufficient rainfall especially for aquaculture (Adhikari et al., 2018 ). Similarly, erratic rainfall, irregular rainfall, storms, and temperature variability have posed late maturity in fish for breeding and other various problems (Islam and Haq, 2018 ).

The above-mentioned facts have indicated that agriculture, livestock, forestry, fishery, and aquaculture are under threat in the future and can drastically affect food security in Asia. This paper reviews the climate change and variability impacts on the cropping system (rice and wheat), livestock, forestry, fishery, and aquaculture and their issues, challenges, and opportunities. The objectives of the study are to: (i) Review the climate variability impacts on agriculture, livestock, forestry, fishery, and aquaculture in Asia; (ii) summarize the opportunities (adaptation and mitigation strategies) to minimize the drastic effects of climate variability in Asia; and (iii) evaluate the impact of climate change on rice-wheat farmer fields—A case study of Pakistan.

Impact of climate change and variability on agricultural productivity

Impact of climate change and variability on rice-wheat crops.

In many parts of Asia, a significant reduction in crop productivity is associated with a reduction in timely water and rainfall availability, and erratic and intense rainfall patterns during the last decades (Hussain et al., 2018 ; Aryal et al., 2019 ). Despite the increased crop production owing to the green revolution, there is a big challenge to sustain production and improve food security for poor rural populations in Asia under climate change scenarios (FAO, 2015 ; Ahmad et al., 2019 ). In the least developed countries, damage because of climactic changes may threaten food security and national economic productivity (Myers et al., 2017 ). Yield reductions in different crops (rice, wheat) varied within regions due to variations in climate patterns (Yu et al., 2018 ). CO 2 fertilization can increase crop productivity and balance the drastic effects of higher temperature in C 3 plants (Obermeier et al., 2017 ) but cannot reduce the effect of elevated temperature (Arunrat et al., 2018 ). Crop growth and development have been negatively influenced because of rising temperatures and rainfall variability (Rezaei et al., 2018 ; Asseng et al., 2019 ).

Rice and wheat are major contributors to food security in Asia. There is a big challenge to increase wheat production by 60% by 2050 to meet ever-enhancing food demands (Rezaei et al., 2018 ). In arid to semi-arid regions, declined crop productivity is attributed to an increase in temperature at lower latitudes. In China, drought and flood have reduced the rice, wheat, and maize yields and it is projected that these issues will affect crop productivity more significantly in the future (Chen et al., 2018 ). Rice is sensitive to a gradual rise in night temperature causing yield and biomass to reduce by 16–52% if the temperature increase is 2°C above the critical temperature of 24°C (Yang et al., 2017 ). In Asia, semi-arid to arid regions are under threat and are already facing the problem of drought stress and low productivity. The quality of wheat produce (protein content, sugars, and starch) and grain yield have reduced because of the negative impacts of increasing temperature and erratic rainfall with high intensity (Yang et al., 2017 ). In the Egyptian North Nile Delta (up to 17.6%), India, and China, the climate variability has decreased wheat yield significantly which is attributed to a rise in temperature, erratic rainfall and increasing insect pest infestation (Arunrat et al., 2018 ; Shah et al., 2018 ; Aryal et al., 2019 ; Kheir et al., 2019 ). In South Asia, rice yield in rain-fed areas has already decreased and it might reduce by 14% under the RCP 4.5 scenario while 10% under the RCP 8.5 scenario by 2080 (Chun et al., 2016 ). High temperature and drought have decreased the rice yield because of their adverse impacts on the booting and anthesis stage in Asia, especially in Pakistan and China (Zafar et al., 2018 ; Ahmad et al., 2019 ). Similarly, heat stress is a major threat to rice as it decreases the productive tillers, shrinkage of grains, and ultimately grain yield of rice (Wang et al., 2019b ). In Asia, climate change would affect upland rice (10 m ha) and rain-fed lowland rice (>13 million hectares). The projected production of rice and wheat crops by 2030 is presented in Table 1 .

Productivity shock due to climate change and variability on rice and wheat crop production by 2030.

Source: Gouldson et al. ( 2016 ), Asseng et al. ( 2019 ), Chow et al. ( 2019 ), Degani et al. ( 2019 ), Sanz-Cobena et al. ( 2019 ), and Suryadi ( 2020 ).

Minus sign (-) indicates the decrease in productivity while positive sign (+) indicates increase in productivity.

Impact of climate change and variability on livestock

In arid to semi-arid regions, the livestock sector is highly susceptible to increased temperature and reduced precipitation (Downing et al., 2017 ; Balamurugan et al., 2018 ). A temperature range of 10–30°C is comfortable for domestic livestock with a 3–5% reduction in animal feed intake with each degree rise in temperature. Similarly, the lower temperature would increase the requirement feed up to 59%. Moreover, drought and heat stress would drastically affect livestock production under climate change scenarios (Habeeb et al., 2018 ). Climate variability affects the occurrence and transmission of several diseases in livestock. For instance, Rift Valley Fever (RVF) due to an increase in precipitation, and tick-borne diseases (TBDs) due to a rise in temperature, have become epidemics for sheep, goats, cattle, buffalo, and camels (Bett et al., 2019 ). Different breeds of livestock show different responses to higher temperature and scarcity of water. In India, thermal stress has negative impacts on the reproduction traits of animals and ultimately poor growth and high mortality rates of poultry (Balamurugan et al., 2018 ; Chen et al., 2021 ; van Wettere et al., 2021 ). In dry regions of Asia, extreme variability in rainfall and drought stress would cause severe feed scarcity (Arunrat et al., 2018 ). It has been revealed that a high concentration of CO 2 reduces the quality of fodder like the reduction in protein, iron, zinc, and vitamins B1, B2, B5, and B9 (Ebi and Loladze, 2019 ). Future climate scenarios show that the pastures, grasslands, feedstuff quality and quantity, as well as biodiversity would be highly affected. Livestock productivity under future climate scenarios would affect the sustainability of rangelands, their carrying capacity and ecosystem buffering capacity, and grazing management, as well as the alteration in feed choice and emission of greenhouse gases (Nguyen et al., 2019 ).

Impact of climate change on forest

Climate variability has posed several negative impacts on forests including variations in productive traits, carbon dynamics, and vegetation shift, as well as the exhaustion of soil resources along with drought and heat stress in South Asian countries (Jhariya et al., 2019 ; Zhu et al., 2021 ). In Bangladesh, forests are vulnerable to climate variability due to increased risks of fires, rise in sea level, storm surges, coastal erosion and landslides, and ultimately reduction in forest area (Chow et al., 2019 ). Biodiversity protection, carbon sequestration, food, fiber, improvement in water quality, and medicinal products are considered major facilities provided by forests (Chitale et al., 2018 ). In contrast, trait-climate relationships and environmental conditions have drastically influenced structure, distribution, and forest ecology (Keenan, 2015 ). Higher rates of tree mortality and die-off have been induced in forest trees because of high temperature and often-dry events (Allen et al., 2015 ; Greenwood et al., 2017 ; Zhu et al., 2021 ). For instance, trees Sal, pine trees, and Garjan have been threatened by climate-driven continuing forest clearing, habitat alteration, and drought in South Asian countries (Wang et al., 2019). An increase in temperature and CO 2 fertilization has increased insect pest infestation for forest trees in North China (Bao et al., 2019 ). As rising temperature, elevated carbon dioxide (CO 2 ), and fluctuating precipitating patterns lead to the rapid development of insect pests and ultimately more progeny will attack forest trees (Raza et al., 2015 ). Hence, there is a need to develop adaptation strategies to restore forests to meet the increasing demand for food, fiber, and medicines in Asia.

Impact of climate change on aquaculture and fisheries

There is a vast difference in response to climate change scenarios of aquaculture in comparison to terrestrial agriculture due to greater control levels over the production environment under terrestrial agriculture (Ottaviani et al., 2017 ; Southgate and Lucas, 2019 ). Climatic-driven extremes such as drought, flood, cyclones, global warming, ocean acidification, irregular and erratic rainfall, salinity, and sea level rise have negatively affected aquaculture in South Asia (Islam and Haq, 2018 ; Ahmad et al., 2019 ). In Asia, various species such as Hilsa and algae have lost their habitats due to ocean acidification and temperature rise (Jahan et al., 2017 ). Increased water temperature and acidification of terrestrial agriculture have become dangerous for coral reefs and an increase in average temperature by 1°C for four successive weeks can cause bleaching of coral reefs in India and other parts of Asia (Hilmi et al., 2019 ; Lam et al., 2019 ). Ocean warming has caused severe damage to China's marine fisheries (Liang et al., 2018 ). In Pakistan, aquaculture and fisheries have lost their habitat quality, especially fish breeding grounds because of high cyclonic activity, sea level rise, temperature variability, and increased invasion of saline water near Indus Delta (Ali et al., 2019 ). It is revealed that freshwater and brackish aquaculture is susceptible to the negative effects of climate variability in several countries of Asia (Handisyde et al., 2017 ). It is also evaluated that extreme climate variability has deep impacts on wetlands and ultimately aquaculture in India (Sarkar and Borah, 2018 ).

Climate variability and change impact assessment

Agriculture has a complex structure and interactions with different components, which will make it uncertain in a future climate that is a serious risk to food security in the region. Consequently, it is essential to assess the negative impacts of climate change on agricultural productivity and develop adaptive strategies to combat climate change. Simulation models such as General Circulation Models (GCMs) and Representative Concentration Pathways (RCPs) are being used worldwide for the quantification of the negative effects of climate change on agriculture and are supporting the generation of future weather data (Rahman et al., 2018 ). Primary tools are also available that can estimate the negative impacts of changing climate on crop productivity, crucial for both availability and access to food. Crop models have the potential to describe the inside processes of crops by considering the temperature rise and elevated CO 2 at critical crop growth stages (Challinor et al., 2018 ). There are no advanced methods and technologies available to see the impact of climate variability and change on the production of livestock and crops other than the modeling approach (Asseng et al., 2014 ). There are also modeling tools available, and being used across the world, to quantify the impacts of climate change and variability on crops and livestock production (Ewert et al., 2015 ; Hoogenboom et al., 2015 ; Rahman et al., 2019 ). We decided to quantify the impacts of future climate on farmer's livelihood to study the complete agricultural system by adopting the comprehensive methodology of climate, crop, and economic modeling (RAPs) approaches and found the agricultural model inter-comparison and improvement project (AgMIP) as the best approach.

A case study—Agricultural model inter-comparison and improvement project

Impact of climate change on the productivity of rice and wheat crops.

Department for International Development (DFID) developed the Agricultural Model Inter-comparison and Improvement Project (Rosenzweig et al., 2013 ) which is an international collaborative effort to deeply investigate the influences of climate variability and change on crops' productivity in different cropping zones/systems across the world and in Pakistan. The mission of AgMIP is to improve the scientific capabilities for assessing the impact of climate variability on the agricultural production system and develop site-specific adaptation strategies to ensure food security at local to global scales. The review discussed above indicated that the agriculture sector is the most vulnerable due to climatic variability and change. Crop production is under threat in Asian countries—predominantly in developing countries. For instance, Pakistan is also highly vulnerable due to its geographical location with arid to semi-arid environmental conditions (Nasi et al., 2018 ; Ullah et al., 2019 ; Ghaffar et al., 2022 ). There would be impacts that are more adverse in arid and semi-arid regions in comparison to humid regions because of climate change and variability (Nasi et al., 2018 ; Ali et al., 2019 ). Future climate scenarios have uncertainty and the projected scenario of climate, especially precipitation, did not coincide with the production technology of crops (Rahman et al., 2018 ). Floods and drought are anticipated more due to variations in rainfall patterns, and dry seasons are expected to get drier in future. Developing regions of the globe are more sensitive to climate variability and change as these regions implement old technologies whereas developed regions can mediate climate-driven extremes through the implementation of modern technologies (Lybbert and Sumner, 2012 ). The extent of climate change and variability hazards in Pakistan is massive and may be further shocking in the future. Therefore, it is a matter of time to compute climate variability, impacts on crop production, and develop sustainable adaptation strategies to cope with the negative impact of climate change using AgMIP standards and protocols (AgMIP). The main objective is to formulate adaptation strategies to contradict potential climate change effects and support the livelihood of smallholder farmers in the identified area and circulate this particular information to farmers, extension workers, and policy-makers. Sialkot, Sheikhupura, Nankana sahib, Hafizabad, and Gujranwala are considered the hub of the rice-wheat cropping system (Ghaffar et al., 2022 ), with an area of 1.1 million hectares. The rice-wheat cropping system is a food basket and its sustainable productivity in future climates will ensure food security in the country and generally overall in the region.

Methodology of the case study

Field data collection.

Field data included the experimental trials and socio-economic data of 155 successive farmers' farms collected during an extensive survey of rice-wheat cropping zone from five-selected districts ( Figure 5 ). From each district, randomly two villages were selected from each division, randomly 30 respondents and 15 farms of true representation of the farming population from each village considered. Crop management data included all agronomic practices from sowing to harvesting such as planting time, planting density, fertilizers amount and organic matter amendment, irrigation amount and intervals, cultural operations, grain yield, and biomass production collected for both crops, rice and wheat, and overall, for all systems. Farm data for the rice-wheat cropping system were analyzed with crop and economic models to see the impact of climate variability on crop production.

Map of study location/sites in rice-wheat cropping zone of Pakistan.

Historic and future climatic data

Daily historic data was collected from the Pakistan Meteorological Department (PMD) for all study locations. The quality of observed weather data was checked following the protocol of the Agricultural Model Inter-comparison and Improvement Project (AgMIP) protocols (AgMIP, 2013 ). Station-based downscaling was performed with historic weather data from all study sites/locations in the rice-wheat cropping zone. For the zone/region, five GCMs (CCSM4, GFDL-ESM2M, MIROC5, HadGEM2-ES, and MPI-ESM-MR) of the latest CMIP5 family were engaged for the generation of climate projections for the mid-century period using the RCP 8.5 concentration scenario, and using the protocols and methodology developed by AgMIP (Ruane et al., 2013 , 2015 ; Rahman et al., 2018 ). GCMs were selected on the basis of different factors such as better performance in monsoon seasons, the record of accomplishment of publications, and the status of the model-developing institute. Under the RCP 8.5 scenario, an indication of warming ranges 2–3°C might be expected in all selected districts for the five CMIP5, GCMs in comparison to the baseline between the periods of 2040–2069. However, there is no uniform warming recorded under all 5 CMIP5 GCMs. For instance, CCSM4 and GFDL-ESM-2M showed uniform increased temperatures during April and September months. The outputs of the GCMs indicated large variability in the estimated values of precipitation. The HadGEM2-ES and GFDL-ESM2M projected mean of 200 and 100 mm between times 2040–2069, respectively. On average, a minor rise in annual rainfall (mm) is indicated by five GCMs in comparison to the baseline.

Crop models (DSSAT and APSIM)

To understand the agronomic practices and the impact of climate variability on the development and growth of plants, crop simulation models like DSSATv4.6 (Hoogenboom et al., 2015 , 2019 ) and APSIMv7.5 (Keating et al., 2003 ) were applied. Three field trials were conducted on rice and wheat crops during two growing seasons, to collect the data like phenology, crop growth (leaf area index, biomass accumulation), development, yield, and agronomic management data by following the standard procedure and protocols. Crop models are calibrated with experimental field data (phenology, growth, and yield data) under local environmental conditions by using soil and weather data. Crop models were further validated with farmers' field data of rice and wheat crops. Climate variability impact on both crops was assessed with historic data (baseline) and future climate data of mid-century in this region.

Tradeoff analysis model for multi-dimensional impact assessment

For the analysis of climate change impact socio-economic indicators, version 6.0.1 of the Tradeoff Analysis Model for Multi-Dimensional Impact Assessment (TOA-MD) Beta was employed (Antle, 2011 ; Antle et al., 2014 ). It is an economical and standard model employed for the analysis of technology adoption impact assessment and ecosystem services. Schematically illustrated, showing connections between the different models and the points of contact between them in terms of input-output in a different climate, crop and economic models and climate analysis is shown in Figure 6 . Various factors that may affect the anticipated values of the production system are technology, physical environment, social environment, and representative agricultural pathways (RAPs), hence it is necessary to distinguish these factors (Rosenzweig et al., 2013 ). RAPs are the qualitative storylines that can be translated into model parameters such as farm and household size, practices, policy, and production costs. For climate impact assessment, the dimensionality of the analysis is the main threat in scenario design. Farmers employ different systems for operating a base technology. For instance, system 1 included base climate, in system 2, farmers use hybrid climate, and in system 3, farmers use perturbed climate to cope with future climate with adaptation technology. The analysis gave the answer to three core questions (Rosenzweig et al., 2013 ). First, without the application RAPs of the core question, one-climate change impact assessments (CC-IA) were formulated. Second, analysis was again executed for examining the negative effects of climate change on future production systems. Third, analysis was executed for future adapted production systems through RAPs and adaptations. Two crop models, i.e., DSSAT and APSIM, outputs were used as the inputs of TOA-MD. Different statistical analyses like root mean square error (RMSE), mean percentage difference (MPD) d-stat, percent difference (PD), and coefficient of determination (R2) were used to check the accuracy of models.

Schematic illustration showing connections between the different models (climate, crop, and economic) and the points of contact between them in terms of input-output and climate analysis.

Farmers field data validation

Crop model simulation results regarding calibration and validation of both crops (rice and wheat) were in good agreement with the field experimental data. Both models were further validated using farmers' field data of rice and wheat crops in rice-wheat cropping zone after getting robust genetic coefficients. Model validation results of 155 farmers of rice and wheat crops indicated the good accuracy of both models (DSSAT, APSIM) and have a good range of statistical indices. Both of these crop models showed an improved ratio between projected and observed rice yield in farmers' fields with RMSE 409 and 440 kg ha −1 and d-stat 0.80 and 0.78, respectively. Similarly, the performance of models DSSAT and APSIM for a yield of wheat was also predicted with RMSE of 436 and 592 kg ha −1 and d-stat of 0.87, respectively.

Quantification of climate change impact by crop models

Climate change impact assessment results in the rice-wheat cropping zone of 155 farms indicated that yield reduction varied due to differences in GCM's behavior and variability in climatic patterns. It is predicted that mean rice yield reduction would be up to 15 and 17% for DSSAT and APSIM respectively during mid-century while yield reduction variation among GCMs are presented in Figure 7 . Rice indicated a yield decline ranging from 14.5 to 19.3% for the case of APSIM while mean yield reduction of the rice crop was between 8 and 30% with DSSAT. Reduction in production of wheat varied among GCMs as well as an overall reduction in yield in rice-wheat cropping systems. For wheat, with DSSAT would be a 14% reduction whereas for APSIM, the reduction would be 12%. GCMs reduction in wheat yield for midcentury (2040–2069) is shown in Figure 8 . Reduction in wheat yield for all 5 GCMs was from 10.6 to 12.3% in the case of APSIM while mean reduction in wheat yield was between 6.2 and 19%. As rice is a summer crop where the temperature is already high and, according to climate change scenarios, there is an increase in both maximum and minimum temperature, an increase in minimum temperature leads to more reduction in yield as compared to wheat being a winter season crop. It was hypothesized that the increase in night temperature (minimum temperature) leading to more losses in the summer season may be due to high temperature, particularly at anthesis and grain formation stages in rice crops, as it is already an irrigated crop and rainfall variability (more rainfall) cannot reduce the effect of high temperature in the rice yield as compared to the wheat crop.

Reduction in rice yield of APSIM and DSSAT models for 155 farms; variation with 5-GCMs in rice-wheat cropping system of Punjab-Pakistan.

Reduction in wheat yield of APSIM and DSSAT models for 155 farms; variation with 5-GCMs in rice-wheat cropping system of Punjab-Pakistan.

Climate change economic impact assessment and adaptations

Sensitivity of current agricultural production systems to climate change.

Climate change is damaging the present vulnerabilities of poor small farmers as their livelihood depends directly on agriculture. Noting various impacts of future climate (2040–2069) on a current production system (current technologies), we examine the vulnerability of the current production system used for the assessment of the adverse impacts of climate change on crop productivity and other socio-economic factors. Climate change impacts possible outcomes for five GCMs based on the estimation of yield generated by two crop models presented in Table 2 . In Table 3 , and the grain losses and net impacts as a percentage of average net returns for the first core question are given for each GCM. The analysis clearly shows the observed values of the mean yield of wheat and rice, which are estimated to be 18,915 kg and 18,349 kg/ farm respectively in the projected area. For all GCMs, observed average milk production was 3,267 liters per farm with a 12% average decline in yield found under livestock production. Losses were about 69–83% and from 72 to 76% for DSSAT and APSIM respectively as predicted by TOA-MD analysis because of the adverse effects of climate change situations. For DSSAT, percentage losses and gains in average net farm returns were from 13 to 15% and 23 to 30%, respectively. While gains were 14–15% and losses were from 25 to 27%, respectively for APSIM. Without adverse impacts of climate change, a net income of Rs. 0.54 per farm pragmatic was predicted by DSSAT and APSIM. However, DSSAT predicted Rs. 0.42–0.48 M per farm and APSIM predicted Rs. 0.45–0.47 M net income per farm under climate change for all GCMs. An increase in the poverty rate in climate change situations would be 33–38% for DSSAT and it would be 35–37% for APSIM, respectively while the rate of poverty with no adverse impacts of climate change would be 29%.

Relative yield summary of crop models.

r = ∑s2/∑s1, ∑s2, Time averaged mean of simulated future yield; ∑s1, Time averaged mean of simulated past yield.

Aggregated gains and losses with CCSM4 GCM (without adaptation and with trend) of DSSAT and APSIM.

Impacts of climate change on future agricultural production systems

In regard to the second core question, a comparison of system 1 (current climate and future production system) with system 2 (future climate and future production system in mid-century) was analyzed with the aid of TOA-MD using 5 GCMs. Mean wheat and rice yield reduction for DSSAT was from 6.2 to 19% and 8 to 30% respectively, and APSIM indicated a decline ranging from 10.6 to 12.3% and 14 to 19%, respectively. For all analyses of Q2, the projected mean yield was 25,073 kg per farm under rice production. While in the case of livestock for all analyses, the mean projected milk production was 3,267 L/farm with its mean decline in yield estimated to be about 12%. Percentage losses for DSSAT and APSIM would fluctuate between 57 and 70% and from 61 to 71%, respectively for all five GCMs.

Mean net farm returns for gains and losses, as a percentage for DSSAT would be 11–13% and from −16 to −22%, respectively. While the percentage of gains and losses would be between 10 and 15% and −17% and −19% in the case of APSIM, respectively. DSSAT predicted Rs. 89–100 thousand per person while APSIM predicted Rs. 93–97 thousand per person per capita income in changing climatic scenarios. For both crop models, the poverty rate will be 16% without climate change. While poverty rates will be from 17 to 19% in the case of DSSAT and ranging from 18 to 19% for APSIM with climate change ( Table 3 ).

Evaluation of potential adaptation strategies and representative agricultural pathways

Adaptation technologies for rice and wheat crops ( Table 4 ) are used in crop growth models and economic TOA-MD model analysis ( Table 5 ) for simulating the sound effects of prospective adaptation strategies on both adapters and non-adapters distribution. This TOA-MD analysis compared “system 1” (incorporating RAPs) and “system 2” (incorporating RAPs and adapted technology) for the rice-wheat system in the mid-century based on crop models DSSAT and APSIM using 5 GCMs. The mean yield change of wheat and rice crops was from 60 to 72% for DSSAT and 70 to 80% for APSIM respectively, wheat crop indicated a change that ranges from 80 to 89% and 62 to 84% for all five GCMs ( Figure 9 ). Under livestock production, the estimated average production of milk exclusive of adaptation was 3,593 liters/farm for all analyses and for all cases indicates a 42% increase in average yield. The percentage of adopters due to adaptation technologies for DSSAT and APSIM in rice-wheat cropping systems would be between 92 and 93% and 93 and 94%, respectively. For DSSAT and APSIM estimated per head income with adaptation cases will be from Rs. 89 to 100 and 93 to 97 thousand and from Rs. 156 to 174 and 166 to 181 thousand per head, respectively in a year. Without and with adaptation, poverty would range between 17 and 19% and 12 and 13% respectively, for DSSAT and from 18 to 19% and 12 to 13%, respectively for APSIM ( Table 6 ). Climatic changes in the rice-wheat cropping areas of Punjab province will have less impact on the future systems after implementing the adaptation strategies, with a large and significant impact imposed by these adaptations.

Adaptation technology related to crop management used for crop models (DSSAT and PSIM) to cope with the negative impacts of climate change during mid-century (2040–2069).

Percentage change (% change) shows the percentage of farmers using the crop management practices related to crop models to reduce the adverse effects of climate change.

Adaptation technology related to socioeconomic used for crop models (DSSAT and APSIM) to cope with the negative impacts of climate change during mid-century (2040–2069).

Percentage change (% change) shows the percentage of farmers using the socioeconomic technology related to crop models to reduce the adverse effects of climate change.

Distribution of adopters and non-adopters for all 5 GCMs (with adaptation and with trend). The percentage of adopters due to adaptation technologies for DSSAT and APSIM in rice-wheat cropping system would be between 92 and 93% and 93 and 94%, respectively. For DSSAT and APSIM estimated per head income with adaptation cases will be from Rs. 89 to 100 and 93 to 97 thousand and from Rs. 156 to 174 and 166 to 181 thousand per head respectively in a year.

Projected adoption of adaptation package used in crop models for CCSM4 GCM during mid-century.

Opportunities in the era of climate change for agriculture

Scope of adaptation and mitigation strategies for sustainable agricultural production.

It is essential to assess the impact of climate variability on agricultural productivity and develop adaptation strategies/technology to cope with the negative effects to ensure sustainable production. The hazardous climate change effects can be reduced by adapting climate-smart and resilient agricultural practices, which will ensure food security and sustainable agricultural production (Zafar et al., 2018 ; Ahmad et al., 2019 ; Ahmed et al., 2019b ). Adaptation is the best way to handle climate variability and change as it has the potential to minimize hazardous climate change effects for sustainable production (IPCC, 2019 ). Innovative technologies and defensive adaptation can reduce the uncertain and harmful effects of climate on agricultural productivity.

Therefore, to survive the harmful climate change effects, the development and implementation of adaptation strategies are crucial. In developing countries, poverty, food insecurity and declined agricultural productivity are common issues, which indicate the need for mitigation and adaptation measures to sustain productivity (Clair and Lynch, 2010 ; Lybbert and Sumner, 2012 ; Mbow et al., 2014 ). At the national and regional level, the insurance of food security is the major criterion for the effectiveness of mitigation and adaptation. Integration of adaptation and mitigation strategies is a great challenge to promote sustainability and productivity. Climate resilient agricultural production systems can be developed and diversified with the integration of land, water, forest biodiversity, livestock, and aquaculture (Hanjra and Qureshi, 2010 ; Meena et al., 2019 ). Summary and overview of all below discussed potential opportunities are presented in Figure 10 .

Overview of opportunities including adaptations and mitigations strategies for sustainable agriculture production system in Asia.

Reduction in GHGs emission

Reduction in GHGs emissions from agriculture under marginal conditions and production of more food are the major challenges for the development of adaptation and mitigation measures (Smith and Olesen, 2010 ; Garnett, 2011 ; Fujimori et al., 2021 ). Similarly, it is an immediate need to control such practices in agriculture which lead to GHGs emissions, i.e., N 2 O emissions from the application of chemical fertilizers, and CH 4 emissions from livestock and rice production systems (Herrero et al., 2016 ; Allen et al., 2020 ). Similarly, alternate wetting and drying and rice intensification are important to reduce the GHGs emission from rice crops (Nasir et al., 2020 ). Carbon can be restored in soil by minimizing the tillage, reducing soil erosions, managing the acidity of the soil, and implementing crop rotation. By increasing grazing duration and rotational grazing of pastureland, sequestration of carbon can be achieved (Runkle et al., 2018 ). About 0.15 gigatonnes of CO 2 equal to the amount of CO 2 produced in 1 year globally, can be sequestered by adopting appropriate grazing measures (Henderson et al., 2015 ). Development of climate-resilient breeds of animals and plants with higher growth rates and lower GHGs emissions should be developed to survive under harsh climatic conditions. Focus further on innovative research and development for the development of climate-resilient breeds, especially for livestock (Thornton and Herrero, 2010 ; Henry et al., 2012 ; Phand and Pankaj, 2021 ).

Application of ICT and decision support system

To mitigate and adapt to the drastic effects of climate variability and change, information and communication technologies (ICTs) can also play a significant role by promoting green technologies and less energy-consuming technology (Zanamwe and Okunoye, 2013 ; Shafiq et al., 2014 ; Nizam et al., 2020 ). Timely provision of information from early warning systems (EWS) and automatic weather stations (AWS) on drought, floods, seasonal variability, and changing rainfall patterns can provide early warning about natural disasters and preventive measures (Meera et al., 2012 ; Imam et al., 2017 ), and it can also support farmers' efforts to minimize harmful effects on the ecosystems. Geographical information systems (GIS), wireless sensor networks (WSN), mobile technology (MT), web-based applications, satellite technology and UAV can be used to mitigate and adapt to the adverse effects of climate change (Kalas, 2009 ; Karanasios, 2011 ). Application of different climate, crop, and economic models may also help reduce the adverse effects of climate variability and change on crop production (Hoogenboom et al., 2011 , 2015 , 2019 ; Ewert et al., 2015 ).

Crop management and cropping system adaptations

Adaptation strategies have the potential to minimize the negative effect of climate variability by conserving water through changes in irrigation amount, timely application of irrigation water, and reliable water harvesting and conservation techniques (Zanamwe and Okunoye, 2013 ; Paricha et al., 2017 ). Crop-specific management practices like altering the sowing times (Meena et al., 2019 ), crop rotation, intercropping (Hassen et al., 2017 ; Moreira et al., 2018 ), and crop diversification and intensification have a significant positive contribution as adaptation strategies (Hisano et al., 2018 ; Degani et al., 2019 ). Meanwhile, replacement of fossil fuels by introducing new energy crops for sustainable production (Ruane et al., 2013 ) is also crucial for the sustainability of the system. Different kinds of adaptation actions (soil, water, and crop conservation, and well farm management) should be adapted in case of long-term increasing climate change and variability (Williams et al., 2019 ). Similarly, alteration in input use, changing fertilizer rates for increasing the quantity and quality of the produce, and introduction of drought resistant cultivars are some of the crucial adaptation approaches for sustainable production. Therefore, under uncertain environmental conditions, to ensure sustainable productivity, crops having climatic resilient genetic traits should also be introduced (Bailey-Serres et al., 2018 ; Raman et al., 2019 ). Similarly, to ensure the sound livelihood of farmers, it is important to develop resilient crop management as well as risk mitigation strategies.

Opportunities for a sustainable livestock production system

The integration of crop production, rearing of livestock and combined use of rice fields for both rice and fish production lead to enhancing the farmers' income through diversified farming (Alexander et al., 2018 ; Poonam et al., 2019 ). Similarly, variations in pasture rates and their rotation, alteration in grazing times, animal and forage species variation, and combination production of both crops and livestock are the activities related to livestock adaptation strategies (Kurukulasuriya and Rosenthal, 2003 ; Havlik et al., 2013 ). Under changing climate scenarios, sustainable production of livestock should coincide with supplementary feeds, management of livestock with a balanced diet, improved waste management methods, and integration with agroforestry (Thornton and Herrero, 2010 ; Renaudeau et al., 2012 ).

Carbon sequestration and soil management

Selection of more drought-resilient genotypes and combined plantation of hardwood and softwood species (Douglas-fir to species) are considered adaptive changes in forest management under future climate change scenarios (Kolstrom et al., 2011 ; Hashida and Lewis, 2019 ). Similarly, timber growth and harvesting patterns should be linked with rotation periods, and plantation in landscape patterns to reduce shifting and fire of forest tree species under climate-smart conditions for forest management to increase rural families' income for a sustainable agricultural ecosystem (Scherr et al., 2012 ). Although, conventional mitigation methods for the agriculture sector have a pivotal role in forest related strategies, some important measures are also included in which afforestation and reforestation should be increased but degradation and deforestation should be reduced and carbon sequestration can be increased (Spittlehouse, 2005 ; Seddon et al., 2018 ; Arehart et al., 2021 ). Carbon stock enhanced the carbon density of forest and wood products through longer rotation lengths and sustainable forest management (Rana et al., 2017 ; Sangareswari et al., 2018 ). Climate change impacts are reduced through adaptation strategies in agroforestry including tree cover outside the forests, increasing forest carbon stocks, conserving biodiversity, and reducing risks by maintaining soil health sustainability (Mbow et al., 2014 ; Dubey et al., 2019 ). Similarly, climate-smart soil management practices like reduction in grazing intensity, rotation-wise grazing, the inclusion of cover and legumes crops, agroforestry and conservation tillage, and organic amendments should also be promoted to enhance the carbon and nitrogen stocks in soil (Lal, 2007 ; Pineiro et al., 2010 ; Xiong et al., 2016 ; Garcia-Franco et al., 2018 ).

Opportunities for fisheries and aquaculture

Sustainable economic productivity of fisheries and aquaculture requires the adaptation of specific strategies, which leads to minimizing the risks at a small scale (Hanich et al., 2018 ). Therefore, to build up the adaptive capacity of poor rural farmers, measures should be carried out by identifying those areas where local production gets a positive response from variations in climatic conditions (Dagar and Minhas, 2016 ; Karmakar et al., 2018 ). Meanwhile, the need to build the climate-smart capacity of rural populations and other regions to mitigate the harmful impacts of climate change should be recognized. In areas which have flooded conditions and surplus water, the integration of aquaculture with agriculture in these areas provides greater advantages to saline soils through newly adapted aquaculture strategies, i.e, agroforestry (Ahmed et al., 2014 ; Dagar and Yadav, 2017 ; Suryadi, 2020 ). To enhance the food security and living standards of poor rural families, aquaculture and artificial stocking engage the water storage and irrigation structure (Prein, 2002 ; Ogello et al., 2013 ). In Asia, rice productivity is increased by providing nutrients by adapting rice-fish culture in which fish concertedly consume the rice stem borer (Poonam et al., 2019 ). Food productivity can be enhanced by the integration of pond fish culture with crop-livestock systems because it includes the utilization of residues from different systems (Prein, 2002 ; Ahmed et al., 2014 ; Dagar and Yadav, 2017 ; Garlock et al., 2022 ). It is important to compete with future challenges in the system by developing new strains which withstand high levels of salinity and poorer quality of water (Kataria and Verma, 2018 ; Lam et al., 2019 ).

Globally, and particularly in developing nations, variability in climatic patterns due to increased anthropogenic activity has become clear. Asia may face many problems because of changing climate, particularly in South Asian countries due to greater population, geographical location, and undeveloped technologies. The increased seasonal temperature would affect agricultural productivity adversely. Crop growth models with the assistance of climatic and economic models are helpful tools to predict climate change impacts and to formulate adaptation strategies. To respond to the adverse effects of climate change, sustainable productivity under climate-smart and resilient agriculture would be achieved by developing adaptation and mitigation strategies. AgMIP-Pakistan is a good specimen of climate-smart agriculture that would ensure crop productivity in changing climate. It is a multi-disciplinary plan of study for climate change impact assessment and development of the site and crop-specific adaptation technology to ensure food security. Adaptation technology, by modifications in crop management like sowing time and density, and nitrogen and irrigation application has the potential to enhance the overall productivity and profitability under climate change scenarios. The adaptive technology of the rice-wheat cropping system can be implemented in other regions in Asia with similar environmental conditions for sustainable crop production to ensure food security. Early warning systems and trans-disciplinary research across countries are needed to alleviate the harmful effects of climate change in vulnerable regions of Asia. Opportunities as discussed have the potential to minimize the negative effect of climate variability and change. This may include the promotion of agroforestry and mixed livestock and cropping systems, climate-smart water, soil, and energy-related technologies, climate resilient breeds for crops and livestock, and carbon sequestration to help enhance production under climate change. Similarly, the application of ICT-based technologies, EWS, AWS, and decision support systems for decision-making, precision water and nutrient management technologies, and crop insurance may be helpful for sustainable production and food security under climate change.

Author contributions

AA, MH-u-R, and AR: conceptualization, validation, and formal analysis. MH-u-R, SAh, AB, WN, AE, HA, KH, AA, FM, YA, and MH: methodology, editing, supervision, and project administration. Initial draft was prepared by MH-u-R and improved and read by all co-authors. All authors contributed to the article and approved the submitted version.

This research funded by the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia under grant number (IFPRP: 530-130-1442).

Conflict of interest

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

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors extend their appreciation to Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number (IFPRP: 530-130-1442) and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.

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Open Access

Climate change resilient agricultural practices: A learning experience from indigenous communities over India

Affiliation South Asian Forum for Environment, India

* E-mail: [email protected] , [email protected]

Affiliation Ecole Polytechnique Fédérale de Lausanne (Swiss Federal Institute of Technology), Lausanne, Switzerland

• Amitava Aich, 
• Dipayan Dey, 
• Arindam Roy

Published: July 28, 2022

• https://doi.org/10.1371/journal.pstr.0000022
• Reader Comments

The impact of climate change on agricultural practices is raising question marks on future food security of billions of people in tropical and subtropical regions. Recently introduced, climate-smart agriculture (CSA) techniques encourage the practices of sustainable agriculture, increasing adaptive capacity and resilience to shocks at multiple levels. However, it is extremely difficult to develop a single framework for climate change resilient agricultural practices for different agrarian production landscape. Agriculture accounts for nearly 30% of Indian gross domestic product (GDP) and provide livelihood of nearly two-thirds of the population of the country. Due to the major dependency on rain-fed irrigation, Indian agriculture is vulnerable to rainfall anomaly, pest invasion, and extreme climate events. Due to their close relationship with environment and resources, indigenous people are considered as one of the most vulnerable community affected by the changing climate. In the milieu of the climate emergency, multiple indigenous tribes from different agroecological zones over India have been selected in the present study to explore the adaptive potential of indigenous traditional knowledge (ITK)-based agricultural practices against climate change. The selected tribes are inhabitants of Eastern Himalaya (Apatani), Western Himalaya (Lahaulas), Eastern Ghat (Dongria-Gondh), and Western Ghat (Irular) representing rainforest, cold desert, moist upland, and rain shadow landscape, respectively. The effect of climate change over the respective regions was identified using different Intergovernmental Panel on Climate Change (IPCC) scenario, and agricultural practices resilient to climate change were quantified. Primary results indicated moderate to extreme susceptibility and preparedness of the tribes against climate change due to the exceptionally adaptive ITK-based agricultural practices. A brief policy has been prepared where knowledge exchange and technology transfer among the indigenous tribes have been suggested to achieve complete climate change resiliency.

Citation: Aich A, Dey D, Roy A (2022) Climate change resilient agricultural practices: A learning experience from indigenous communities over India. PLOS Sustain Transform 1(7): e0000022. https://doi.org/10.1371/journal.pstr.0000022

Editor: Ashwani Kumar, Dr. H.S. Gour Central University, INDIA

Copyright: © 2022 Aich et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors received no specific funding for this work.

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

1 Introduction

Traditional agricultural systems provide sustenance and livelihood to more than 1 billion people [ 1 – 3 ]. They often integrate soil, water, plant, and animal management at a landscape scale, creating mosaics of different land uses. These landscape mosaics, some of which have existed for hundreds of years, are maintained by local communities through practices based on traditional knowledge accumulated over generations [ 4 ]. Climate change threatens the livelihood of rural communities [ 5 ], often in combination with pressures coming from demographic change, insecure land tenure and resource rights, environmental degradation, market failures, inappropriate policies, and the erosion of local institutions [ 6 – 8 ]. Empowering local communities and combining farmers’ and external knowledge have been identified as some of the tools for meeting these challenges [ 9 ]. However, their experiences have received little attention in research and among policy makers [ 10 ].

Traditional agricultural landscapes as linked social–ecological systems (SESs), whose resilience is defined as consisting of 3 characteristics: the capacity to (i) absorb shocks and maintain function; (ii) self-organize; (iii) learn and adapt [ 11 ]. Resilience is not about an equilibrium of transformation and persistence. Instead, it explains how transformation and persistence work together, allowing living systems to assimilate disturbance, innovation, and change, while at the same time maintaining characteristic structures and processes [ 12 ]. Agriculture is one of the most sensitive systems influenced by changes in weather and climate patterns. In recent years, climate change impacts have been become the greatest threats to global food security [ 13 , 14 ]. Climate change results a decline in food production and consequently rising food prices [ 15 , 16 ]. Indigenous people are good observers of changes in weather and climate and acclimatize through several adaptive and mitigation strategies [ 17 , 18 ].

Traditional agroecosystems are receiving rising attention as sustainable alternatives to industrial farming [ 19 ]. They are getting increased considerations for biodiversity conservation and sustainable food production in changing climate [ 20 ]. Indigenous agriculture systems are diverse, adaptable, nature friendly, and productive [ 21 ]. Higher vegetation diversity in the form of crops and trees escalates the conversion of CO 2 to organic form and consequently reducing global warming [ 22 ]. Mixed cropping not only decreases the risk of crop failure, pest, and disease but also diversifies the food supply [ 23 ]. It is estimated that traditional multiple cropping systems provide 15% to 20% of the world’s food supply [ 1 ]. Agro-forestry, intercropping, crop rotation, cover cropping, traditional organic composting, and integrated crop-animal farming are prominent traditional agricultural practices [ 24 , 25 ].

Traditional agricultural landscapes refer to the landscapes with preserved traditional sustainable agricultural practices and conserved biodiversity [ 26 , 27 ]. They are appreciated for their aesthetic, natural, cultural, historical, and socioeconomic values [ 28 ]. Since the beginning of agriculture, peasants have been continually adjusting their agriculture practices with change in climatic conditions [ 29 ]. Indigenous farmers have a long history of climate change adaptation through making changes in agriculture practices [ 30 ]. Indigenous farmers use several techniques to reduce climate-driven crop failure such as use of drought-tolerant local varieties, polyculture, agro-forestry, water harvesting, and conserving soil [ 31 – 33 ]. Indigenous peasants use various natural indicators to forecast the weather patterns such as changes in the behavior of local flora and fauna [ 34 , 35 ].

The climate-smart agriculture (CSA) approach [ 36 ] has 3 objectives: (i) sustainably enhancing agricultural productivity to support equitable increase in income, food security, and development; (ii) increasing adaptive capacity and resilience to shocks at multiple levels, from farm to national; and (iii) reducing Green House Gases (GHG) emissions and increasing carbon sequestration where possible. Indigenous peoples, whose livelihood activities are most respectful of nature and the environment, suffer immediately, directly, and disproportionately from climate change and its consequences. Indigenous livelihood systems, which are closely linked to access to land and natural resources, are often vulnerable to environmental degradation and climate change, especially as many inhabit economically and politically marginal areas in fragile ecosystems in the countries likely to be worst affected by climate change [ 25 ]. The livelihood of many indigenous and local communities, in particular, will be adversely affected if climate and associated land-use change lead to losses in biodiversity. Indigenous peoples in Asia are particularly vulnerable to changing weather conditions resulting from climate change, including unprecedented strength of typhoons and cyclones and long droughts and prolonged floods [ 15 ]. Communities report worsening food and water insecurity, increases in water- and vector-borne diseases, pest invasion, destruction of traditional livelihoods of indigenous peoples, and cultural ethnocide or destruction of indigenous cultures that are linked with nature and agricultural cycles [ 37 ].

The Indian region is one of the world’s 8 centres of crop plant origin and diversity with 166 food/crop species and 320 wild relatives of crops have originated here (Dr R.S. Rana, personal communication). India has 700 recorded tribal groups with population of 104 million as per 2011 census [ 38 ] and many of them practicing diverse indigenous farming techniques to suit the needs of various respective ecoclimatic zones. The present study has been designed as a literature-based analytical review of such practices among 4 different ethnic groups in 4 different agroclimatic and geographical zones of India, viz, the Apatanis of Arunachal Pradesh, the Dongria Kondh of Niamgiri hills of Odisha, the Irular in the Nilgiris, and the Lahaulas of Himachal Pradesh to evaluating the following objectives: (i) exploring comparatively the various indigenous traditional knowledge (ITK)-based farming practices in the different agroclimatic regions; (ii) climate resiliency of those practices; and (iii) recommending policy guidelines.

2 Methodology

2.1 systematic review of literature.

An inventory of various publications in the last 30 years on the agro biodiversity, ethno botany, traditional knowledge, indigenous farming practices, and land use techniques of 4 different tribes of India in 4 different agroclimatic and geographical zones viz, the Apatanis of Arunachal Pradesh, the Dongria Kondh of Niamgiri hills of Odisha, the Irular in the Nilgiris, and the Lahaulas of Himachal Pradesh has been done based on key word topic searches in journal repositories like Google Scholar. A small but significant pool of led and pioneering works has been identified, category, or subtopics are developed most striking observations noted.

2.2 Understanding traditional practices and climate resiliency

The most striking traditional agricultural practices of the 4 major tribes were noted. A comparative analysis of different climate resilient traditional practices of the 4 types were made based on existing information available via literature survey. Effects of imminent dangers of possible extreme events and impact of climate change on these 4 tribes were estimated based on existing facts and figures. A heat map representing climate change resiliency of these indigenous tribes has been developed using R-programming language, and finally, a reshaping policy framework for technology transfers and knowledge sharing among the tribes for successfully helping them to achieve climate resiliency has been suggested.

2.3 Study area

Four different agroclimatic zones and 4 different indigenous groups were chosen for this particular study. The Apatanis live in the small plateau called Zero valley ( Fig 1 ) surrounded by forested mountains of Eastern Himalaya in the Lower Subansiri district of Arunachal Pradesh. It is located at 27.63° N, 93.83° E at an altitude ranging between 1,688 m to 2,438 m. Rainfall is heavy and can be up to 400 mm in monsoon months. Temperature varies from moderate in summer to very cold in the winter months. Their approximate population is around 12,806 (as per 2011 census), and Tibetan and Ahom sources indicate that they have been inhabiting the area from at least the 15th century and probably much earlier ( https://whc.unesco.org/en/tentativelists/5893/ ).

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

The base map is prepared using QGIS software.

https://doi.org/10.1371/journal.pstr.0000022.g001

The Lahaulas are the inhabitants of Lahaul valley ( Fig 1 ) that is located in the western Himalayan region of Lahaul and Spiti and lies between the Pir Panjal in the south and Zanskar in the north. It is located between 76° 46′ and 78° 41′ east longitudes and between 31° 44′ and 32° 59′ north altitudes. The Lahaul valley receives scanty rainfalls, almost nil in summer, and its only source of moisture is snow during the winter. Temperature is generally cold. The combined population of Lahaul and Spiti is 31,564 (as per 2011 census).

The Dongria Kondh is one of the officially designated primitive tribal group (PTG) in the Eastern Ghat region of the state Orissa. They are the original inhabitants of Niyamgiri hilly region ( Fig 1 ) that extends to Rayagada, Koraput, and Kalahandi districts of south Orissa. Dongria Kondhs have an estimated population of about 10,000 and are distributed in around 120 settlements, all at an altitude up to 1,500 above the sea level [ 39 ]. It is located between 190 26′ to 190 43′ N latitude and 830 18′ to 830 28′ E longitudes with a maximum elevation of 1,516 meters. The Niyamgiri hill range abounds with streams. More than 100 streams flows from the Niyamgiri hills and 36 streams originate from Niyamgiri plateau (just below the Niyam Raja), and most of the streams are perennial. Niyamgiri hills have been receiving high rainfall since centuries and drought is unheard of in this area.

The Irular tribes inhabit the Palamalai hills and Nilgiris of Western Ghats ( Fig 1 ). Their total population may be 200,000 (as per 2011 census). The Palamali Hills is situated in the Salem district of Tamil Nadu, lies between 11° 14.46′ and 12° 53.30′ north latitude and between 77° 32.52′ to 78° 35.05′ east longitude. It is located 1,839 m from the mean sea level (MSL) and more over the climate of the district is whole dry except north east monsoon seasons [ 40 , 41 ]. Nilgiri district is hilly, lying at an elevation of 1,000 to 2,600 m above MSL and divided between the Nilgiri plateau and the lower, smaller Wayanad plateau. The district lies at the juncture of the Western Ghats and the Eastern Ghats. Its latitudinal and longitudinal location is 130 km (latitude: 11° 12 N to 11° 37 N) by 185 km (longitude 76° 30 E to 76° 55 E). It has cooler and wetter climate with high average rainfall.

3 Results and discussion

3.1 indigenous agricultural practices in 4 different agro-biodiversity hotspots.

Previous literatures on the agricultural practices of indigenous people in 4 distinct agro-biodiversity hotspots did not necessarily focus on climate resilient agriculture. The authors of these studies had elaborately discussed about the agro-biodiversity, farming techniques, current scenario, and economical sustainability in past and present context of socioecological paradigm. However, no studies have been found to address direct climate change resiliency of traditional indigenous agricultural practices over Indian subcontinent to the best of our knowledge. The following section will primarily focus on the agricultural practices of indigenous tribes and how they can be applied on current eco-agricultural scenario in the milieu of climate change over different agricultural macroenvironments in the world.

3.1.1 Apatani tribes (Eastern Himalaya).

The Apatanis practice both wet and terrace cultivation and paddy cum fish culture with finger millet on the bund (small dam). Due to these special attributes of sustainable farming systems and people’s traditional ecological knowledge in sustaining ecosystems, the plateau is in the process of declaring as World Heritage centre [ 42 – 44 ]. The Apatanis have developed age-old valley rice cultivation has often been counted to be one of the advanced tribal communities in the northeastern region of India [ 45 ]. It has been known for its rich economy for decades and has good knowledge of land, forest, and water management [ 46 ]. The wet rice fields are irrigated through well-managed canal systems [ 47 ]. It is managed by diverting numerous streams originated in the forest into single canal and through canal each agriculture field is connected with bamboo or pinewood pipe.

The entire cultivation procedure by the Apatani tribes are organic and devoid of artificial soil supplements. The paddy-cum-fish agroecosystem are positioned strategically to receive all the run off nutrients from the hills and in addition to that, regular appliance of livestock manure, agricultural waste, kitchen waste, and rice chaff help to maintain soil fertility [ 48 ]. Irrigation, cultivation, and harvesting of paddy-cum-fish agricultural system require cooperation, experience, contingency plans, and discipline work schedule. Apatani tribes have organized tasks like construction and maintenance of irrigation, fencing, footpath along the field, weeding, field preparation, transplantation, harvesting, and storing. They are done by the different groups of farmers and supervised by community leaders (Gaon Burha/Panchayat body). Scientific and place-based irrigation solution using locally produced materials, innovative paddy-cum-fish aquaculture, community participation in collective farming, and maintaining agro-biodiversity through regular usage of indigenous landraces have potentially distinguished the Apatani tribes in the context of agro-biodiversity regime on mountainous landscape.

3.1.2 Lahaula (Western Himalaya).

The Lahaul tribe has maintained a considerable agro-biodiversity and livestock altogether characterizing high level of germ plasm conservation [ 49 ]. Lahaulas living in the cold desert region of Lahaul valley are facultative farmers as they able to cultivate only for 6 months (June to November) as the region remained ice covered during the other 6 months of the year. Despite of the extreme weather conditions, Lahaulas are able to maintain high level of agro-biodiversity through ice-water harvesting, combinatorial cultivation of traditional and cash crops, and mixed agriculture–livestock practices. Indigenous practices for efficient use of water resources in such cold arid environment with steep slopes are distinctive. Earthen channels (Nullah or Kuhi) for tapping melting snow water are used for irrigation. Channel length run anywhere from a few meters to more than 5 km. Ridges and furrows transverse to the slope retard water flow and soil loss [ 50 ]. Leaching of soil nutrients due to the heavy snow cover gradually turns the fertile soil into unproductive one [ 51 ]. The requirement of high quantity organic manure is met through composting livestock manure, night soil, kitchen waste, and forest leaf litter in a specially designed community composting room. On the advent of summer, compost materials are taken into the field for improving the soil quality.

Domesticated Yaks ( Bos grunniens ) is crossed with local cows to produce cold tolerant offspring of several intermediate species like Gari, Laru, Bree, and Gee for drought power and sources of protein. Nitrogen fixing trees like Seabuckthrone ( Hippophae rhamnoides ) are also cultivated along with the crops to meet the fuels and fodder requires for the long winter period. Crop rotation is a common practice among the Lahaulas. Domesticated wild crop, local variety, and cash crops are rotated to ensure the soil fertility and maintaining the agro-biodiversity. Herbs and indigenous medicinal plants are cultivated simultaneously with food crops and cash crop to maximize the farm output. A combinatorial agro-forestry and agro-livestock approach of the Lahaulas have successfully able to generate sufficient revenue and food to sustain 6 months of snow-covered winter in the lap of western Himalayan high-altitude landscape. This also helps to maintain the local agro-biodiversity of the immensely important ecoregion.

3.1.3 Dongria Kondh (Eastern Ghat).

Dongria Kondh tribes, living at the semiarid hilly range of Eastern Ghats, have been applying sustainable agro-forestry techniques and a unique mixed crop system for several centuries since their establishment in the tropical dry deciduous hilly forest ecoregion. The forest is a source for 18 different non-timber forest products like mushroom, bamboo, fruits, vegetables, seeds, leaf, grass, and medicinal products. The Kondh people sustainably uses the forest natural capital such a way that maintain the natural stock and simultaneously ensure the constant flow of products. Around 70% of the resources have been consumed by the tribes, whereas 30% of the resources are being sold to generate revenue for further economic and agro-forest sustainability [ 52 ]. The tribe faces moderate to acute food grain crisis during the post-sowing monsoon period and they completely rely upon different alternative food products from the forest. The system has been running flawlessly until recent time due to the aggressive mining activity, natural resources depleted significantly, and the food security have been compromised [ 53 ].

However, the Kondh farmer have developed a very interesting agrarian technique where they simultaneously grow 80 varieties of different crops ranging from paddy, millet, leaves, pulses, tubers, vegetables, sorghum, legumes, maize, oil-seeds, etc. [ 54 ]. In order to grow so many crops in 1 dongor (the traditional farm lands of Dongria Kondhs on lower hill slopes), the sowing period and harvesting period extends up to 5 months from April till the end of August and from October to February basing upon climatic suitability, respectively.

Genomic profiling of millets like finger millet, pearl millet, and sorghum suggest that they are climate-smart grain crops ideal for environments prone to drought and extreme heat [ 55 ]. Even the traditional upland paddy varieties they use are less water consuming, so are resilient to drought-like conditions, and are harvested between 60 and 90 days of sowing. As a result, the possibility of complete failure of a staple food crop like millets and upland paddy grown in a dongor is very low even in drought-like conditions [ 56 ].

The entire agricultural method is extremely organic in nature and devoid of any chemical pesticide, which reduces the cost of farming and at the same time help to maintain environmental sustainability [ 57 ].

3.1.4 Irular tribes (Western Ghat).

Irulas or Irular tribes, inhabiting at the Palamalai mountainous region of Western Ghats and also Nilgiri hills are practicing 3 crucial age-old traditional agricultural techniques, i.e., indigenous pest management, traditional seed and food storage methods, and age-old experiences and thumb rules on weather prediction. Similar to the Kondh tribes, Irular tribes also practice mixed agriculture. Due to the high humidity in the region, the tribes have developed and rigorously practices storage distinct methods for crops, vegetables, and seeds. Eleven different techniques for preserving seeds and crops by the Irular tribes are recorded till now. They store pepper seeds by sun drying for 2 to 3 days and then store in the gunny bags over the platform made of bamboo sticks to avoid termite attack. Paddy grains are stored with locally grown aromatic herbs ( Vitex negundo and Pongamia pinnata ) leaves in a small mud-house. Millets are buried under the soil (painted with cow dung slurry) and can be stored up to 1 year. Their storage structure specially designed to allow aeration protect insect and rodent infestation [ 58 ]. Traditional knowledge of cross-breeding and selection helps the Irular enhancing the genetic potential of the crops and maintaining indigenous lines of drought resistant, pest tolerant, disease resistant sorghum, millet, and ragi [ 59 , 60 ].

Irular tribes are also good observer of nature and pass the traditional knowledge of weather phenomenon linked with biological activity or atmospheric condition. Irular use the behavioral fluctuation of dragonfly, termites, ants, and sheep to predict the possibility of rainfall. Atmospheric phenomenon like ring around the moon, rainbow in the evening, and morning cloudiness are considered as positive indicator of rainfall, whereas dense fog is considered as negative indicator. The Irular tribes also possess and practice traditional knowledge on climate, weather, forecasting, and rainfall prediction [ 58 ]. The Irular tribes also gained extensive knowledge in pest management as 16 different plant-based pesticides have been documented that are all completely biological in nature. The mode of actions of these indigenous pesticides includes anti-repellent, anti-feedent, stomach poison, growth inhibitor, and contact poisoning. All of these pesticides are prepared from common Indian plants extract like neem, chili, tobacco, babul, etc.

The weather prediction thumb rules are not being validated with real measurement till now but understanding of the effect of forecasting in regional weather and climate pattern in agricultural practices along with biological pest control practices and seed conservation have made Irular tribe unique in the context of global agro-biodiversity conservation.

3.2 Climate change risk in indigenous agricultural landscape

The effect of climate change over the argo-ecological landscape of Lahaul valley indicates high temperature stress as increment of number of warm days, 0.16°C average temperature and 1.1 to 2.5°C maximum temperature are observed in last decades [ 61 , 62 ]. Decreasing trend of rainfall during monsoon and increasing trend of consecutive dry days in last several decades strongly suggest future water stress in the abovementioned region over western Himalaya. Studies on the western Himalayan region suggest presence of climate anomaly like retraction of glaciers, decreasing number of snowfall days, increasing incident of pest attack, and extreme events on western Himalayan region [ 63 – 65 ].

Apatani tribes in eastern Himalayan landscape are also experiencing warmer weather with 0.2°C increment in maximum and minimum temperature [ 66 ]. Although no significant trend in rainfall amount has been observed, however 11% decrease in rainy day and 5% to 15% decrease in rainfall amount by 2030 was speculated using regional climate model [ 67 ]. Increasing frequency of extreme weather events like flashfloods, cloudburst, landslide, etc. and pathogen attack in agricultural field will affect the sustainable agro-forest landscape of Apatani tribes. Similar to the Apatani and Lahaulas tribes, Irular and Dongria Kondh tribes are also facing climate change effect via increase in maximum and minimum temperature and decrease in rainfall and increasing possibility of extreme weather event [ 68 , 69 ]. In addition, the increasing number of forest fire events in the region is also an emerging problem due to the dryer climate [ 70 ].

Higher atmospheric and soil temperature in the crop growing season have direct impact on plant physiological processes and therefore has a declining effect on crop productivity, seedling mortality, and pollen viability [ 71 ]. Anomaly in precipitation amount and pattern also affect crop development by reducing plant growth [ 72 ]. Extreme events like drought and flood could alter soil fertility, reduce water holding capacity, increase nutrient run off, and negatively impact seed and crop production [ 73 ]. Agricultural pest attack increases at higher temperature as it elevates their food consumption capability and reproduction rate [ 74 ].

3.3 Climate resiliency through indigenous agro-forestry

Three major climate-resilient and environmentally friendly approaches in all 4 tribes can broadly classified as (i) organic farming; (ii) soil and water conservation and community farming; and (iii) maintain local agro-biodiversity. The practices under these 3 regimes have been listed in Table 1 .

https://doi.org/10.1371/journal.pstr.0000022.t001

Human and animal excreta, plant residue, ashes, decomposed straw, husk, and other by-products are used to make organic fertilizer and compost material that helps to maintain soil fertility in the extreme orographic landscape with high run-off. Community farming begins with division of labour and have produced different highly specialized skilled individual expert in different farming techniques. It needs to be remembered that studied tribes live in an area with complex topological feature and far from advance technological/logistical support. Farming in such region is extremely labour intensive, and therefore, community farming has become essential for surviving. All 4 tribes have maintained their indigenous land races of different crops, cereal, vegetables, millets, oil-seeds, etc. that give rises to very high agro-biodiversity in all 4 regions. For example, Apatanis cultivate 106 species of plants with 16 landraces of indigenous rice and 4 landraces of indigenous millet [ 75 ]. Similarly, 24 different crops, vegetables, and medicinal plants are cultivated by the Lahaulas, and 50 different indigenous landraces are cultivated by Irular and Dongria Kondh tribes.

The combination of organic firming and high indigenous agro-biodiversity create a perfect opportunity for biological control of pests. Therefore, other than Irular tribe, all 3 tribes depend upon natural predator like birds and spiders, feeding on the indigenous crop, for predation of pests. Irular tribes developed multiple organic pest management methods from extract of different common Indian plants. Apatani and Lahaulas incorporate fish and livestock into their agricultural practices, respectively, to create a circular approach to maximize the utilization of waste material produced. At a complex topographic high-altitude landscape where nutrient run-off is very high, the practices of growing plants with animals also help to maintain soil fertility. Four major stresses due to the advancement of climate change have been identified in previous section, and climate change resiliency against these stresses has been graphically presented in Fig 2 .

https://doi.org/10.1371/journal.pstr.0000022.g002

Retraction of the glaciers and direct physiological impact on the livestock due to the temperature stress have made the agricultural practices of the Lahaula’s vulnerable to climate change. However, Irular and Dongria Kondh tribes are resilient to the temperature stress due to their heat-resistant local agricultural landraces, and Apatanis will remain unaffected due to their temperate climate and vast forest cover. Dongria Kondh tribe will successfully tackle the water stress due to their low-water farming techniques and simultaneous cultivation of multiple crops that help to retain the soil moisture by reducing evaporation. Hundreds of perennial streams of Nyamgiri hills are also sustainably maintained and utilised by the Dongria Kondhs along with the forests, which gives them enough subsistence in form of non-timber forest products (NTFPs). However, although Apatani and Lahuala tribe extensively reuse and recirculate water in their field but due to the higher water requirement of paddy-cum-fish and paddy-cum-livestock agriculture, resiliency would be little less compared to Dongria Kondh.

Presence of vast forest cover, very well-structured irrigation system, contour agriculture and layered agricultural field have provided resiliency to the Apatani’s from extreme events like flash flood, landslides, and cloud burst. Due to their seed protection practices and weather prediction abilities, Irular tribe also show resiliency to the extreme events. However, forest fire and flash flood risk in both Eastern Ghat and Western Ghat have been increased and vegetation has significantly decreased in recent past. High risk of flash flood, land slide, avalanches, and very low vegetation coverage have made the Lahaulas extremely vulnerable to extreme events. Robust pest control methods of Irular tribe and age-old practices of intercropping, mixed cropping, and sequence cropping of the Dongria Kondh tribe will resist pest attack in near future.

3.4 Reshaping policy

Temperature stress, water stress, alien pest attack, and increasing risk of extreme events are pointed out as the major risks in the above described 4 indigenous tribes. However, every tribe has shown their own climate resiliency in their traditional agrarian practices, and therefore, a technology transfers and knowledge sharing among the tribes would successfully help to achieve the climate resilient closure. The policy outcome may be summarizing as follows:

• Designing, structuring and monitoring of infrastructural network of Apatani and Lahaul tribes (made by bamboo in case of Apatanis and Pine wood and stones in case of Lahaulas) for waster harvesting should be more rugged and durable to resilient against increasing risk of flash flood and cloud burst events.
• Water recycling techniques like bunds, ridges, and furrow used by Apatani and Lahaul tribes could be adopted by Irular and Dongria Kondh tribes as Nilgiri and Koraput region will face extreme water stress in coming decades.
• Simultaneous cultivation of multiple crops by the Dongria Kondh tribe could be acclimated by the other 3 tribes as this practice is not only drought resistance but also able to maximize the food security of the population.
• Germplasm storage and organic pest management knowledge by the Irular tribes could be transferred to the other 3 tribes to tackle the post-extreme event situations and alien pest attack, respectively.
• Overall, it is strongly recommended that the indigenous knowledge of agricultural practices needs to be conserved. Government and educational institutions need to focus on harvesting the traditional knowledge by the indigenous community.

3.5 Limitation

One of the major limitations of the study is lack of significant number of quantifiable literature/research articles about indigenous agricultural practices over Indian subcontinent. No direct study assessing risk of climate change among the targeted agroecological landscapes has been found to the best of our knowledge. Therefore, the current study integrates socioeconomic status of indigenous agrarian sustainability and probable climate change risk in the present milieu of climate emergency of 21st century. Uncertainty in the current climate models and the spatiotemporal resolution of its output is also a minor limitation as the study theoretically correlate and proposed reshaped policy by using the current and future modeled agro-meteorological parameters.

4. Conclusions

In the present study, an in-depth analysis of CSA practices among the 4 indigenous tribes spanning across different agro-biodiversity hotspots over India was done, and it was observed that every indigenous community is more or less resilient to the adverse effect of climate change on agriculture. Thousands years of traditional knowledge has helped to develop a unique resistance against climate change among the tribes. However, the practices are not well explored through the eyes of modern scientific perspective, and therefore, might goes extinct through the course of time. A country-wide study on the existing indigenous CSA practices is extremely important to produce a database and implementation framework that will successfully help to resist the climate change effect on agrarian economy of tropical countries. Perhaps the most relevant aspect of the study is the realization that economically and socially backward farmers cope with and even prepare for climate change by minimizing crop failure through increased use of drought tolerant local varieties, water harvesting, mixed cropping, agro-forestry, soil conservation practices, and a series of other traditional techniques.

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• Published: 20 November 2017

New science of climate change impacts on agriculture implies higher social cost of carbon

• Frances C. Moore 1 ,
• Uris Baldos 2 , 3 ,
• Thomas Hertel 2 , 3 , 4 &
• Delavane Diaz 5  

Nature Communications volume  8 , Article number:  1607 ( 2017 ) Cite this article

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• Climate and Earth system modelling
• Climate-change impacts
• Climate-change policy
• Environmental economics

Despite substantial advances in climate change impact research in recent years, the scientific basis for damage functions in economic models used to calculate the social cost of carbon (SCC) is either undocumented, difficult to trace, or based on a small number of dated studies. Here we present new damage functions based on the current scientific literature and introduce these into an integrated assessment model (IAM) in order to estimate a new SCC. We focus on the agricultural sector, use two methods for determining the yield impacts of warming, and the GTAP CGE model to calculate the economic consequences of yield shocks. These new damage functions reveal far more adverse agricultural impacts than currently represented in IAMs. Impacts in the agriculture increase from net benefits of $2.7 ton −1 CO 2 to net costs of $8.5 ton −1 , leading the total SCC to more than double.

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Introduction.

Climate science has advanced significantly in the past 20 years so that our understanding of the physical consequences of greenhouse gas emissions is now well established 1 . The biophysical effects of changes in temperature and rainfall on, for example, ecosystems, agricultural yields, and sea-level rise are also increasingly well understood. However, this new science is not reflected in some of the highly influential economic models currently used to determine the social cost of carbon (SCC)—a measure of the total damages from an additional ton of CO 2 emissions used to quantify the benefits of emissions reduction. In most cases, the scientific basis for damage functions (reduced-form expressions for how climate change affects economic welfare) in these models is undocumented, tautological (based on damages from previous versions of the models), or dates from between 10 and 20 years ago and therefore may have been superseded by more recent results 2 .

The lack of a current empirical basis for integrated assessment model (IAM) damage functions is not just an academic question because the SCC has been formally adopted by the U.S. government to quantify the benefits of CO 2 mitigation in cost–benefit analysis 3 . Regulations with benefits totaling over $1 trillion have used the SCC in cost–benefit analysis 4 . Increasingly, it is also being used at the state level: recent rulings in California, New York, and Minnesota all require use of the SCC in analysis of climate and energy regulations 5 , 6 , 7 . Therefore, the value of the SCC is a relevant consideration for long-term planning in industry and government, and yet the extent to which it would change if more recent scientific knowledge were incorporated is largely unknown. Many recent commentaries on the state of climate change economics have identified improving the empirical basis of IAM damage functions as high-priority area for future work 2 , 8 , 9 , 10 , 11 , 12 .

A large and growing body of science has dramatically improved our knowledge of the social and economic risks posed by climate change and therefore provides an opportunity to substantially improve the empirical basis of the damages underlying the SCC 12 , 13 , 14 , 15 . However, translating the literature on biophysical climate change impacts into damage functions is not straightforward. For each sector, individual scientific studies must be aggregated and translated into a consistent set of global impacts. Then, because damage functions parameterize how economic welfare changes with temperature, the economic value of these biophysical impacts must be assessed, which might involve substantial economic modeling. Finally, new damage functions must be introduced into an IAM in order to examine the effect on the SCC. The complexity and interdisciplinary nature of this process may be part of the reason damage functions in IAMs have lagged the science of climate change impacts.

This paper focuses on the agricultural sector, connecting a current and comprehensive review of the biophysical science of the impacts of climate change on yields to the SCC. Agriculture is an important sector for climate change damages because it is both directly affected by climate change and has critical implications for future food security and social welfare. We start with the large agronomic literature on how climate change affects crop yields based on a meta-analysis published by Chalinor et al 16 . and used to support conclusions in the IPCC 5th Assessment Report 17 . We present a new analysis of this database that we use to aggregate these results to the global scale. We then compare our results with those of the Agricultural Model Intercomparison and Improvement Project (AgMIP) published by Rosenzweig et al 18 . Using the predicted yield under climate change, derived both from the meta-analysis and from AgMIP as inputs to the GTAP computable general equilibrium (CGE) model, we estimate the economic consequences of these changes. Finally, we parameterize two new damage functions based on the CGE results and incorporate them into one of the most widely used IAMs in order to examine implications for the SCC.

This approach exemplifies several principles that we believe can provide important guidance in updating damage functions in other sectors in the future. Our meta-analysis is related to the findings of the food security chapter of the IPCC 5th Assessment Report. Where possible, tying damage functions to the IPCC has several benefits: findings are updated on a moderately regular basis (every 7 years), are assembled and reviewed by impact experts within each field, and are formally accepted as fact by governments involved in the process. Both the meta-analysis and the AgMIP damage functions also use an ensemble of models. Findings in climate and, increasingly, agricultural modeling have shown that use of multi-model ensembles tend to outperform any individual model 1 , 19 . Therefore, averaging multiple model outputs should lead to more reliable damage functions and better uncertainty quantification than trying to pick preferred models. Finally, this is an end-to-end analysis in which analysis of the underlying biophysical impacts literature to produce global, sector-wide response functions, modeling of the economic responses to those biophysical changes using a state of the art, open-source CGE model to produce economic damages, and introduction of these damages into an IAM and the resulting effect on the SCC are all documented within a single study. This means our damage functions and the changes we identify in the SCC have a clear and traceable connection to the underlying science that is both comprehensive and up-to-date, in contrast to most current IAM damage functions 2 . This approach is also consistent with the National Academy of Sciences report on calculating the SCC, which recommended that damage functions should be based on the current, peer-reviewed literature on climate impacts, have uncertainties that are characterized and quantified where possible, and be transparent, well-documented and reproducible 2 .

Estimating the global yield response to climate change

The first step in our analysis involves aggregating the large volume of research on how climate change affects crop yields. We do this through a meta-analysis of 1010 published estimates of yield response to changing climate conditions, including both statistical and process-based studies, using a database complied for the IPCC 5th Assessment Report 16 , 17 . Figure  1 shows the temperature response functions we derive for the four most important food crops (see Methods section). The effect of higher temperatures on yields is negative for all crops in almost all locations. The interaction between the effects of warming and current growing-season temperature is in the expected direction, with warming consistently more damaging in places that are already hot. However, for wheat and maize (and soybeans at low levels of warming) this effect is not particularly large.

Impacts of temperature change on yields of four major crops. Based on a meta-analysis of 1010 point-estimates from 56 studies (see Methods section). Darkest, middle, and lightest lines show responses at the 75th, 50th, and 25th quantiles of baseline growing-season temperature, respectively. Dashed lines show the 95% confidence interval based on 750 block bootstraps, blocking at the study level. Plotted response curves are for temperature only and do not include CO 2 fertilization or adaptation. Temperature changes are relative to a local 1995–2005 baseline. The histograms show the number of observations by crop and level of warming used to estimate the response functions. In subsequent analyses, yield losses >100% are set to losses of 99%

The effects of other variables are shown in Supplementary Information (SI), Supplementary Table  1 and Supplementary Fig.  1 . CO 2 has a positive effect on crop yields, with an estimated increase of 11.5% (C 3 ) and 8.7% (C 4 ) for a doubling of CO 2 from preindustrial levels. This is very close to estimates from experimental field studies for C 3 crops but is somewhat high for C 4 crops 20 , 21 . The effect of agronomic, on-farm, within-crop adaptations (principally changes in crop variety and planting date (see Methods section)) is small and statistically insignificant. Studies that include agronomic adaptation do, on average, report higher yields than those that do not, but this is almost entirely captured by an adaptation intercept term rather than the interaction with change in temperature. This suggests that changes described as adaptations to climate change in the studies underpinning our meta-analysis would provide similar yield benefits with and without climate change and are therefore not true climate adaptations as conventionally defined 22 . In results that follow we include only the true climate adaptation effect for all crops, but we find this to be small (Supplementary Table  1 ). Note that this statement refers only to the on-farm, within-crop agronomic adaptations captured by the studies that support the meta-analysis. Additional economic adaptations such as crop switching, increasing production intensity, substituting consumption, or adjusting trade relationships are captured in the GTAP model (Supplementary Table  2 ).

Our continuous response functions are extrapolated globally using maps of baseline temperature and the spatial pattern of global temperature change in order to estimate yield changes for different levels of global warming (Methods, Supplementary Figs  2 – 5 ). We compare these results to those from the AgMIP Global Gridded Crop Model Intercomparison (GGCMI), the one other source of global, multi-crop, multi-model yield changes (Supplementary Fig.  6 ) 18 . Our preferred results focus on the set of models within the AgMIP ensemble that explicitly represent nitrogen stress (see Methods section) though in the SI we also present results using the full ensemble. The two approaches for estimating yield impacts differ substantially in the kinds of spatial heterogeneity in the yield response to warming and CO 2 that are captured. The GGCMI results explicitly account for spatial variation resulting from soil type, irrigation, baseline temperature, and (in models representing nitrogen stress) nutrient limitations. The meta-analysis deliberately smooths out most of this heterogeneity in order to more precisely estimate a common response function, preserving only the heterogeneity resulting from different baseline temperatures. (See also Supplementary Table  3 for additional discussion on sources of spatial heterogeneity).

With the exceptions of soybeans, our point-estimates show substantial areas of agreement (within 10 percentage points of the GGCMI). A major difference is that our latitudinal variation in yield impacts tends to be more modest than in the GGCMI ensemble, leading our meta-analysis results to be more optimistic in tropical areas and more pessimistic at higher latitudes. At the global scale, these cancel to some degree so that differences in global, production-weighted yield changes between the two methods are smaller and, due to large uncertainties involved, statistically indistinguishable from each other (Supplementary Fig.  7 ). The largest area of disagreement is for wheat yields—with the AgMIP ensemble showing global productivity gains for 2 and 3 degrees of warming and the meta-analysis showing substantial losses.

Economic consequences of yield impacts

We use gridded yield changes for the four major crops for 1–3 °C based on both our meta-analysis and the GGCMI ensemble average as input to the GTAP CGE model (see Methods section). Our aggregation of the version 9 GTAP data base results in a model that solves for equilibrium prices, consumption, production, and bilateral trade flows of 14 commodities (of which 9 are in the agricultural sector) across 140 regions (see Methods section) 23 , 24 . This step is necessary because IAM damage functions parameterize how economic welfare changes with global temperature and, beyond the direct productivity effects, the relationship between yield and welfare changes is not straightforward. This complexity derives from the presence of additional impacts on a nation’s terms of trade (changes in the relative prices of a region’s exports and imports) and an allocative efficiency effect (interactions between changing production, consumption, and trade patterns and existing market distortions) 25 . The sum of these three is the total regional welfare change, reported here as real income.

Figure  2 shows regional welfare changes associated with 3 °C of warming based on the meta-analysis results, normalized by the current value of the four crops being modeled. At this level of warming, total effects on welfare (Fig.  2d ) are negative in most areas, primarily driven by the direct productivity effect of climate change on agriculture (Fig.  2a ). However, terms-of-trade effects (Fig.  2b ) are important in determining the distribution of global welfare gains across individual regions, comprising >50% of total welfare change in some regions (Supplementary Fig.  8 ). Because world crop prices are increasing in this scenario (Supplementary Fig.  9 ), it is the net agricultural exporters that tend to gain from changing terms of trade (e.g., Australia, Argentina, USA), while net importers lose (e.g., Mexico, the Middle East, North Africa). Supplementary Fig.  10 shows the same welfare decomposition but based on yield changes from the GGCMI ensemble (Supplementary Fig.  11 shows results for the full ensemble, including models that do not represent nitrogen stress). Both sets of results show welfare declines in south Asia, sub-Saharan Africa, Brazil, Mexico, and China. However, the GGCMI results show much wider welfare gains from productivity improvement in higher latitudes. In addition, price changes are more variable (with an increase in maize and soy prices but decreases in wheat and rice prices), so that the terms-of-trade effects are smaller and are distributed differently between importers and exporters of different crops. Note that our results differ from Nelson et al 26 ., who also used the GGCMI ensemble as input to a range of general- and partial-equilibrium models. A major reason why our results differ from theirs is that they did not include CO 2 fertilization, which is included in our yield shocks.

Welfare changes from 3 °C of global average warming. Changes are relative to a 1995–2005 global average baseline and use yield changes based on the meta-analysis results shown in Fig.  1 : a the direct technical effect of climate change on agricultural productivity; b terms of trade effects; c the allocative efficiency effect; and d total welfare change reported as equivalent variation. Results are based on yield changes that include adaptation and the CO 2 fertilization effect for C 3 crops but not for maize. Welfare changes are normalized by the value of production of the affected crops (maize, rice, wheat, and soybeans)

Implications for the social cost of carbon

To create new damage functions, we take welfare changes for 1–3 °C of average global warming and aggregate up to the 16 geographic regions used in the FUND model 27 . Figure  3 shows our damage functions estimated from both the meta-analysis and GGCMI yield responses, compared to the existing agricultural sector damage functions in FUND (see also Supplementary Fig.  12 and Supplementary Table  4 ). Agriculture in FUND shows benefits in all regions for warming < 3 °C. This is a result of both a direct positive effect of moderate amounts of warming on yields for all regions and the CO 2 fertilization effect. In contrast, our meta-analysis results show almost universal negative welfare changes for warming beyond 2 °C that in many cases are very large. Current FUND welfare impacts are almost entirely at or beyond the upper bound of our 95% confidence intervals (Fig.  3 ). Results for the preferred AgMIP GGCMI ensemble fall in between these two cases. In some regions (Japan+Korea and the Middle East), they closely track existing FUND damages. In other regions (Central America, USA, South Asia), they show welfare declines at 2 and 3° of warming that are similar to our meta-analysis results.

Three agriculture-sector damage functions for each of the 16 FUND regions. Solid lines are from meta-analysis results, dotted lines are AgMIP results, and dashed lines are the existing FUND damage functions. Error bars show the damage functions based on the 2.5th and 97.5th quantiles of the meta-analysis results. Temperature changes are global averages and are relative to a global average 1995–2005 baseline. A version of the figure excluding error bars, which allows differences in the point-estimates to be more easily distinguished, is given in Supplementary Fig.  12

In order to investigate the importance of parameterization of the economic model used to calculate damages in driving the regional damage functions, we perform a systematic sensitivity analysis of key parameters within GTAP (see Methods section). Variation in economic welfare changes associated with these changes is shown in Supplementary Fig.  13 and is very small and is dwarfed by the uncertainty in biophysical productivity shocks shown in the error bars in Fig.  3 .

We use an IAM damage module based on the FUND model and substitute our new agricultural damage functions into the agricultural sector to calculate how the SCC changes as a result of this more up-to-date science and new economic modeling (see Methods section). Because total damages in FUND are an additive sum over regions and sectors, the SCC can be decomposed into its constituent parts. Figure  4 summarizes the results of this analysis, using a 3% discount rate. Additional results based on a 2.5 and 5% discount rate are given in Supplementary Table  5 . Currently, agriculture in FUND contributes a benefit of $2.7 ton −1 CO 2 toward the SCC. In contrast, damage functions based on both the preferred AgMIP GGCMI ensemble and the meta-analysis show net costs of $3.5 and $8.5 ton −1 , respectively (95% confidence interval based on the spread in yield impacts for the meta-analysis is −$0.6 to $33.3 ton −1 ). This difference has a substantial effect on the SCC: although the FUND model represents damages from 14 impact sectors, only a few key sectors—agriculture, cooling, and heating—contribute substantively to the SCC 28 . Updating the damage functions in the agriculture sector alone increases the SCC from $8.6 to $14.8 (AgMIP) and $19.7 (meta-analysis) ton −1 CO 2 , increases of 72 and 129%, respectively.

Changes in the social cost of carbon resulting from new damage functions in the agricultural sector. a Agricultural impacts only, decomposed by geographic region (see Supplementary Table  4 for region definitions). b The total social cost of carbon (SCC), keeping damages in all non-agricultural sectors fixed. Results are based on a business-as-usual emissions scenario and a 3% discount rate. Uncertainty bars give the SCC resulting from the 95% confidence interval of yield response parameter estimates from the meta-analysis

Sensitivity of results to alternative methodological assumptions are shown in Supplementary Table  5 , which gives the SCC under alternative discount rates, an alternative method of extrapolating beyond 3 °C, and using the full AgMIP ensemble instead of the preferred set of models that explicitly represent nitrogen stress. Although discount rates have a large effect on the SCC, they scale all results and so do not alter the finding that updated damage functions imply a higher SCC. The largest sensitivity of results is around including the full ensemble of process-based crop models in the AgMIP GGCMI. Adding models that do not represent nitrogen stress substantially reduces the estimated impact of climate change on agriculture and leads to an SCC ($9.6 ton −1 with a 3% discount rate) only slightly greater than the current FUND estimate. This difference is due to the presence of large productivity gains in many parts of the world (Supplementary Fig.  11a ), a product particularly of the LPJ-GUESS and GAEZ-IMAGE models 18 .

Here we show that the current science of climate change impacts on agriculture, combined with up-to-date economic modeling, implies larger damages to the sector than currently represented in models used to calculate the SCC. In contrast to existing regional damage functions, which show benefits in every region up to at least 3 °C of warming, we find potential for welfare declines even at much lower levels of warming. Though the range of possible effects of climate change on yields is substantial, our finding that the SCC should be increased is robust to this variation, as well as to uncertainties relating to the discount rate, economic modeling, and extrapolation of the damage function.

Of the three IAMs used to estimate the SCC, only FUND explicitly represents the agricultural sector, so that has been the focus of comparison in this paper. However, agricultural impacts are a part of damages in the two other models. In PAGE (2009 version) agricultural impacts are represented within the market impacts damage function and in DICE (2013 version) they are in the non-sea level rise damage function 29 , 30 . Given the same socio-economic assumptions and climate model used to calculate SCC values reported in the previous section, these damage functions give SCC values of $6.6 ton −1 and $18.9 ton −1 , respectively (3% discount rate) 28 . In the case of PAGE, market impacts are substantially smaller than agricultural damages estimated using the meta-analysis ($8.5 ton −1 ), implying either that there are large off-setting benefits in other market sectors or that the empirical basis for the market impacts damage function may need to be reviewed. In the case of DICE, our results imply that agricultural impacts make up between 19% (AgMIP) and 45% (meta-analysis) of non-sea level rise damages.

Governments are currently relying on IAMs to evaluate climate and energy policy and these models have already come under legal scrutiny as a result 31 , 32 , 5 . It is therefore important, from both a regulatory and an academic perspective, that the representation of damages reflect current scientific consensus on impacts in a timely and transparent manner. Here we have shown that improving the empirical basis of just one sector, agriculture, results in a large increase in the SCC. In addition, we have demonstrated the potential of an end-to-end analysis directly linking the biophysical impacts of climate change to economic welfare and ultimately the SCC. Damage functions resulting from this approach are more clearly tied to underlying science and can be easily updated in light of future findings. This approach, which can also be extended to other sectors, therefore represents an essential step in maintaining and improving the integrity of IAM results going forward.

Meta-analysis of yield response to climate change

The yield–temperature response functions used in this paper are derived from a database of studies estimating the climate change impact on yield compiled for the IPCC 5th Assessment Report 17 , also described in a meta-analysis by Challinor et al. 16 . Methods for sampling the literature and criteria for inclusion are described in Challinor et al. 16 . as a “broad and inclusive literature search” combined with quality-control procedures documented in the Supplemental Information of that paper. In this study, we focus on four major crops—maize, rice, wheat, and soybeans. The bulk of the scientific literature on yield response to temperature relates to these crops, which collectively account for about 20% of the value of global agricultural production, 65% of harvested crop area, and nearly 50% of calories directly consumed 33 . For the four crops, the database contains 1010 observations (344, 238, 336, and 92 for maize, rice, wheat, and soybeans, respectively) from 56 different studies (many studies report multiple yield changes for different crops, different locations, different levels of temperature change, or different assumptions about adaptation). The studies include 8 empirical studies and 48 process-based studies, published between 1997 and 2012. Supplementary Figs 14 and 15 show the geographic coverage of production areas within the database and the distribution of publication dates. Of the 1010 data points, 451 are reported as including some form of on-farm, within-crop, agronomic adaptation. The vast majority of these adaptations involve adjusting either planting date (10%) or cultivar (12%) or both (44%). Recognizing the existence of a more recent and possibly more systematic literature review for wheat yields, we perform a robustness check where we incorporate additional results identified in Wilcox and Makowski 34 . This substantially increases the number of observations for wheat but does not affect our estimated response curve (Supplementary Fig.  16 ).

We merge this database with information on baseline growing-season temperature for each data point. To do this, each data point was assigned to a country. For the 14% of studies looking at more than one country, the country assigned was the one with the highest production of the relevant crop. Average baseline growing-season temperatures were calculated using planting and harvest dates from Sacks et al 35 . and gridded monthly temperatures for 1979–2013 from the Climate Research Unit 36 . These were averaged to the country level using year 2000 crop production weights from Monfreda et al 37 .

The response functions are jointly estimated from the point-estimates in the database using a multi-variate:

where \(\Delta Y_{ijk}\) is the change in yield from point-estimate i for crop j in country k (in %). \(\Delta T_{ijk},\Delta CO_{2ijk}\) and \(\Delta P_{ijk}\) are the changes in temperature (in degree C), CO 2 concentration (in parts per million (ppm)), and rainfall (in percent) for point-estimate ijk , \(\bar T_{jk}\) is the baseline growing-season temperature for crop j in country k , \(C_{3j}\) and \(C_{4j}\) are dummy variables indicating whether crop j is C 3 or C 4 , and \(Adapt_{ijk}\) is a dummy variable indicating whether the point-estimate includes any on-farm adaptation. Eq.  1 is estimated using an ordinary least squares regression.

Uncertainty in the parameters is estimated through 1500 block bootstraps, with blocks defined at the study level, allowing for possible correlation between point-estimates from the same study. Error bars reported throughout the paper are based on the 2.5th and 97.5th quantiles of the bootstrapped distribution. This treatment of the errors does assume independence between studies, which may be questionable if the same model is used in multiple studies. In total, 28 models, made up of 17 process-based model families (i.e., treating CERES-maize, CERES-rice, and CERES-wheat as a single model) and 11 statistical models, are used in the 56 studies. Supplementary Fig.  17 shows response curves with standard errors based on a model block bootstrap as a robustness check. These are qualitatively similar to the error bars shows in Fig.  1 , particularly for warming <3 °C that is the focus of the economic analysis, suggesting the study block bootstrap is capturing the bulk of residual covariance. All error bars reported in the paper show confidence intervals rather than prediction intervals. This is appropriate since the relevant uncertainty is in the expected response of yield to temperature change, which is given by confidence intervals.

There are a number of important things to note about this specification shown in Eq.  1 . First, the impacts of temperature are modeled as crop-specific quadratics ( \({\mathrm{\beta }}_{1{{j}}}\) and \({\mathrm{\beta }}_{2{{j}}}\) terms), allowing the effects of warming to vary by crop. In addition, the effects of warming are allowed to vary with baseline growing-season temperature ( \({\mathrm{\beta }}_{3{{j}}}\) and \({\mathrm{\beta }}_{4{{j}}}\) terms), capturing the intuition that the impacts of a 1 °C warming should be different in a cold location than in a hot location.

Second, there is no intercept term, thereby forcing response functions without adaptation through the origin. This is consistent with the expected functional form of a climate damage function, which should have no impacts if there are no changes in climate variables. However, we include an intercept for studies that do include adaptation ( \(\beta _9\) ). This is prompted by the observation that, in many studies, ‘adaptation’ is represented by changing management practices that would improve yields even in the current climate, such as adoption of improved varieties or increasing fertilizer or irrigation inputs 22 . Failing to include an adaptation intercept in this context will lead to an overestimation of the potential of these kinds of changes to reduce the negative impacts of a warming climate. This adaptation intercept is subtracted in our estimates of the effect of climate on yield to produce an adjusted damage function that goes through the origin. (In other words, we calculate the effect of a change in temperature of X on yields to be the yield change predicted from Eq.  1 for a temperature change of X minus the yield change predicted for a temperature change of zero (i.e., \(\beta _6\,\,Adapt_{ij}\) )). The true effect of adaptation is the interaction with temperature change, given by the \(\beta _8\) term in Eq.  1 , which is included in all subsequent analyses. This term captures the effect of management changes that are not beneficial today but that will become beneficial under a changed climate, the standard definition of adaptation.

Finally, the functions \(f_1()\) and \(f_2()\) are concave, allowing for a declining marginal effect of CO 2 , consistent with a number of field studies 20 , 38 . Specifically, the function takes the form \(f\left( {\Delta {\rm CO}_{2ij}} \right) = \frac{{\Delta {\rm CO}_{2ij}}}{{\Delta {\rm CO}_{2ij} + A}}\) where A is a free parameter set at 100 ppm for \(C_3\) crops and at 50 ppm for \(C_4\) crops based on a comparison of the R 2 across models using multiple possible values. The changes in CO 2 are adjusted so that all are relative to a modern baseline of 360 ppm (the most common baseline value for studies included in the analysis).

In addition to Eq.  1 , our preferred specification, we investigate the effects of several alternate specifications. Specifically we first investigate whether newer studies (publication date of 2005 or later) give a different temperature response compared to the full sample; second investigate the effect of individual agronomic adaptations, specifically changing cultivar and planting date; third allow the effect of temperature to differ depending on whether the study was a process-based or empirical study; fourth add a \(\Delta T_{ijk}^3\) term in the specification; and finally perform F -tests on individual terms within the model. These findings are documented in the SI (Supplementary Figs  18 – 20 , Supplementary Tables  6 and 7 ). They do not substantially alter our estimates of the yield response to climate change.

Gridded yield changes

After estimating Eq.  1 , we developed global gridded yield change scenarios for the four major crops (maize, wheat, rice, and soybeans). Although IAM damage functions are typically based on global temperature changes, it is important to account for the fact that local warming may differ significantly from global warming in estimating impacts. Local yield impacts will depend on local temperature changes, which scale in a predictable way with global temperature change. We estimate this scaling using the CMIP5 multi-model ensemble mean for the high emissions scenario RCP 8.5 39 . For each grid cell, we take the change in temperature between a future (2035–2065) and baseline (1861–1900) period and divide by the mean global warming over this time period, giving the pattern scaling relationship between global and local temperature change for each grid cell (Supplementary Fig.  21 ). For a given increase in global mean temperature, warming is larger over land than over the ocean and at high latitudes compared to the tropics.

These gridded temperature changes are combined with the yield–temperature response function estimated using Eq.  1 and baseline growing-season temperature to give yield changes at different levels of global warming. We calculate yield changes for warming of 1, 2, and 3 degree Celsius including the estimated effect of on-farm adaptation. Any predicted yield losses >100% are set to losses of 99%. The CO 2 fertilization effect is included for all crops. CO 2 concentrations for a given level of global temperature change are determined based on a fitted quadratic relationship between global temperature change and CO 2 concentrations from the RCP 8.5 CMIP5 multi-model ensemble mean (adjusted R 2  > 0.999, 98 degrees of freedom).

AgMIP GGCMI ensemble

The AgMIP GGCMI is the one other source of global, multi-crop, multi-model yield responses and so we compare the results of our meta-analysis against these results. This ensemble of gridded crop model outputs includes up to seven process-based crop models, run using five General Circulation Models (GCMs) 18 . Yield changes are calculated relative to the 1981–2000 average. In order to determine yield changes for specific levels of temperature change (1–3 °C), we find the year in which warming passes each specific level for each GCM for the RCP 8.5 emissions scenario (taking the average of multiple ensemble members, if available) and take the 11-year yield average around that year 40 . We determine irrigated areas using crop-specific irrigation areas from Monfreda et al 37 . and use irrigated results for cells where irrigated crop area exceeds non-irrigated crop area. We use runs including CO 2 fertilization for all analyses.

The results reported in the main text use a preferred AgMIP ensemble that only includes models that explicitly represent nitrogen stress (EPIC, GEPIC, PEGASUS, and pDSSAT). We believe these results are preferred given crop response to changing temperature and, in particular, CO 2 conditions is known to depend on nutrient availability 41 , 42 and crops in many areas of the world are currently under-fertilized 43 . Moreover, the distinction between models based on representation of nitrogen stress has been identified as significant in understanding ensemble results by the AgMIP team 18 . Results in the main text should therefore be interpreted as impacts assuming continuation of current nutrient management practices. In the SI, we report results using the full AgMIP ensemble, which differ substantially from those of the restricted ensemble (Supplementary Fig.  11 and Supplementary Table  5 ). For both ensembles, the mean is calculated as a simple mean of yield change for each level of warming using all crop model×GCM combinations.

Welfare consequences of yield changes

To estimate the economic implications of warming-induced yield shocks, we use the Global Trade Analysis Project (GTAP) general equilibrium model and its accompanying database 23 , 24 . GTAP is a widely used, comparative static general equilibrium model that exhaustively tracks bilateral trade flows between all countries in the world and explicitly models the consumption and production for all commodities of each national economy. Producers are assumed to maximize profits, while consumers maximize utility. Factor market clearing requires that supply equal demand for agricultural and non-agricultural skilled and unskilled labor and capital, natural resources, and agricultural land, and adjustments in each of these markets in response to the climate change shocks determines the resulting wage and rental rate impacts. The model has been validated with respect to its performance in predicting the price impacts of exogenous supply side shocks, such as those that might result from global climate change 44 . Additional information on the structure of GTAP is given in Supplementary Fig.  22 .

GTAP captures a number of dimensions important for determining the welfare implications of climate change impacts on agriculture. These include the shifting of land area between crops, potential intensification of production, shifting of consumption between commodities and sources of goods, and the adjustment of global trade patterns (Supplementary Table  2 ). For the purposes of this study, GTAP is run with 140 regions and 14 commodities—with the latter designed to place an emphasis on the agricultural sector. Productivity changes are introduced to GTAP as a Hicks-neutral shift in the production function such that farmers employing the same combination of inputs would experience X % lower output in the presence of a X % climate-driven yield shock.

Wheat and rice are modeled as individual sectors within each region. Maize is part of the coarse grains sector and soybeans is part of the oilseeds sector. Impacts in these sectors are scaled downwards based on the relative importance of maize and soybeans for sectoral production in each region. Yields of crops not covered in the meta-analysis (coarse grains nec., oilseeds nec., sugarcane, cotton, and fruits and vegetables) are not altered. Absent normalization, this will lead to an underestimate of potential climate impacts, since these other sectors are also likely to be affected by climate change. Therefore, in the results that follow, welfare changes are normalized by the value of production of the crops covered in the meta-analysis. Global and regional welfare changes are measured in terms of equivalent variation and are decomposed into the three components shown in Fig.  2 following Hertel and Randhir 25 .

In order to explore uncertainty in the economic modeling, we perform a systematic sensitivity analysis of GTAP output to perturbations in four sets of key parameters governing the supply and demand behavior in this model. On the supply side, these pertain to the parameters determining the intensive (substitution of other inputs for land) and extensive (land supply elasticities) margins of crop supply response to commodity price. On the demand side, these are the parameters that govern the price elasticity of demand for food and the price elasticity of demand for imports—which in turn govern the price responsiveness of export demands. Parameters vary by commodity/sector. We develop symmetric, triangular distributions for each parameter value, based on estimates in the literature (Supplementary Table  8 ) and sample from these distributions using the Gaussian Quadrature approach implemented by Arndt 45 . This approach has been shown to perform nearly as well as a complete Monte Carlo analysis in the context of CGE modeling, but it is much more efficient, requiring far fewer model solutions 46 . Due to the computational burden of conducting a complete, systematic sensitivity analysis in the 140 region model, we collapse those regions down to the 16 FUND regions for purposes of this robustness check. The resulting mean and standard deviations for regional welfare are reported in Supplementary Fig.  13 . Because on the dominance of direct effects (i.e., the impact of climate change in yields) in many of the regions’ total welfare, variation of the economic parameters has a modest impact on the underlying uncertainty.

Calculating the social cost of carbon

Results of the economic modeling are used to create damage functions that relate changes in economic welfare (measured as percentage of the value of agricultural sector output) with temperature change. GTAP results are aggregated from the country level to the 16 FUND regions. Damage functions are based on a linear interpolation between the point-estimates of welfare changes at 1, 2, and 3 °C of warming and then a linear extrapolation beyond 3 °C (results reported in main text) or on a quadratic fitted through the point estimates (Supplementary Table  5 ).

These agricultural damage functions are then incorporated into a sectorally and regionally disaggregated SCC damage module based on the FUND model, keeping the rest of the impact sectors unchanged 28 , 47 . Damage functions in the module use the central parameter estimates of FUND. The full FUND model includes probability distributions over many parameters and is designed to be run in a Monte Carlo mode 47 . This uncertainty is not dealt with in this paper, meaning uncertainty reported in the SCC reflects only the uncertainty in the yield response derived from the meta-analysis. The damage module is driven by a standardized socio-economic and emissions pathway and climate model 28 . We use a business-as-usual emissions scenario (Scenario 2 in ref. 3 ), paired with the DICE climate module 48 . This produces a warming of 4 °C of warming above preindustrial by 2100. The SCC is calculated by adding a 1 Gt pulse of CO 2 emissions to this reference emissions path in 2020 and comparing the time path of damages along the perturbed pathway to the reference case. Then these incremental damages (or benefits) are discounted back to 2020 at a 3% discount rate and normalized by the CO 2 pulse volume to give the SCC. Results using alternative discount rates are given in Supplementary Table  5 . As the SCC is additive, it can be decomposed by sector and region, allowing a detailed comparison of the regional impacts in agriculture between FUND and the revised regional damage functions.

Code availability

Code for GTAP is open source and available for download at http://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=2458 . The FUND model is open source and available at http://www.fund-model.org/source-code . All other code is available from the authors upon request.

Data availability

Data for the meta-analysis is available at ag-impacts.org. Gridded yield changes based on the meta-analysis are available at 10.6084/m9.figshare.5417548. Welfare changes for GTAP regions based on yield shocks from both the meta-analysis and AgMIP are available at 10.6084/m9.figshare.5417557 and 10.6084/m9.figshare.5417560. Other data are available from the authors upon request.

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Acknowledgements

We thank Max Auffhammer, Sol Hsiang, Jerry Nelson, and David Lobell for comments on earlier drafts of this paper. T.H. acknowledges the support of the National Science Foundation (award 0951576) under the auspices of the RDCEP project at the University of Chicago and USDA-NIFA, Hatch Project 1003642. F.C.M. acknowledges support of this project by USDA NIFA (award 12225279).

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Frances C. Moore

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Uris Baldos & Thomas Hertel

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F.C.M. conceived the paper, conducted the yield meta-analysis, and wrote the paper. U.B. ran the GTAP model and analyzed welfare results. T.H. ran the GTAP model and wrote the paper. D.D. ran the FUND damage module and wrote the paper.

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Correspondence to Frances C. Moore .

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Moore, F.C., Baldos, U., Hertel, T. et al. New science of climate change impacts on agriculture implies higher social cost of carbon. Nat Commun 8 , 1607 (2017). https://doi.org/10.1038/s41467-017-01792-x

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• Understanding Poverty

Climate-smart agriculture

Climate-smart agriculture (csa) is an integrated approach to managing landscapes—cropland, livestock, forests and fisheries--that address the interlinked challenges of food security and climate change..

Climate change and food and nutrition insecurity pose two of the greatest development challenges of our time. Yet a more sustainable food system can not only heal the planet, but ensure food security for all.

Today, the global agrifood system emits  one-third  of all emissions. Global food demand is estimated to increase to feed a projected global population of 9.7 billion people by 2050. Traditionally, the increase in food production has been linked to agricultural expansion, and unsustainable use of land and resources. This creates a vicious circle, leading to an increase in emissions. 

Food systems are the leading source of methane emissions and biodiversity loss, and they use around 70% of fresh water. If food waste were a country, it would be the third highest emitter in the world. Meanwhile, emissions from agriculture are increasing in developing countries – a worrying trend which must be reversed.

Without significant climate mitigation action in the agri-food sector, the Paris Agreement goals cannot be reached. Agriculture is the primary cause of deforestation, threatening pristine ecosystems such as the Amazon and the Congo Basin. Without action, emissions from food systems will rise even further, with increasing food production.

Achieving the Triple Win of CSA

The global agrifood system must therefore deliver on multiple fronts. It must feed the world, adapt to climate change, and drastically reduce its greenhouse gas emissions. In response to these challenges, the concept of Climate-smart Agriculture (CSA) has emerged as a holistic approach to end food security and promote sustainable development while addressing climate change issues.

CSA is a set of agricultural practices and technologies which simultaneously boost productivity, enhance resilience and reduce GHG emissions. Although it is built on existing agricultural knowledge, technologies, and sustainability principles, CSA is distinct in several ways. First, it has an explicit focus on addressing climate change in the agrifood system. Second, CSA systematically considers the synergies and tradeoffs that exist between productivity, adaptation, and mitigation. And third, CSA encompasses a range of practices and technologies that are tailored to specific agro-ecological conditions and socio-economic contexts including the adoption of climate-resilient crop varieties, conservation agriculture techniques, agroforestry, precision farming, water management strategies, and improved livestock management. By implementing these practices, triple win results can be achieved:

1.     Increased productivity:  Produce more and higher quality food without putting an additional strain on natural resources, to improve nutrition security and boost incomes, especially for 75 percent of the world’s poor who live in rural areas and mainly rely on agriculture for their livelihoods.

2.     Enhanced resilience:  Reduce vulnerability to droughts, pests, diseases and other climate-related risks and shocks; and improve the capacity to adapt and grow in the face of longer-term stresses like increased seasonal variability and more erratic weather patterns.

3.     Reduced emissions:  Reduce greenhouse gas emissions of the food system, avoid deforestation due to cropland expansion, and increase the carbon sequestration of plants and soils.

Finally, funding for CSA needs to be increased to align available finance with the relevance of the sector. Despite causing one third of global greenhouse gas emissions, agrifood systems receive 4% of climate finance, with only a fifth of this going to smallholders. Current financial flows need to be realigned in order to support a sustainable agrifood system transformation.

Climate-Smart Agriculture and the World Bank Group

The World Bank has significantly scaled up its engagement and investment in climate-smart agriculture (CSA). In its Climate Change Action Plan (2021- 2025), the World Bank has identified Agriculture, Food, Water and Land as one of the five key transitions  needed to tackle the Paris Agreement. Since the adoption of the Paris Agreement, the World Bank has increased financing for CSA by eight times, to almost $3 billion annually.

As of July 2023, all new World Bank operations must be aligned with the goals of the Paris Agreement , meaning that CSA is at the core of all the World Bank’s new agriculture and food operations. To this end, the World Bank has prepared a Sector Note of Paris Alignment of its Agriculture and Food operations. Furthermore, all projects are screened for climate and disaster risks. Climate change indicators are used to measure outputs and outcomes, and greenhouse gas accounting of projects is conducted prior to approval . These actions will help client countries implement their Nationally Determined Contributions (NDCs) in the agriculture sector, and will contribute to progress on the  Sustainable Development Goals  (SDGs) for climate action, poverty, and the eradication of hunger.

The World Bank engages strategically with countries, supporting them to enhance productivity, improve resilience and reduce greenhouse gas emissions. The World Bank uses the following tools, diagnostics and other analytics to help countries in the transition towards sustainable agriculture.

• Country Climate and Development Reports (CCDRs), new core diagnostics, help countries prioritize the most impactful actions that can reduce greenhouse gas emissions and boost adaptation, while delivering on broader development goals. CCDRs identify climate impacts on countries’ agrifood systems, such as reduced yields and increased food prices, and present a variety of country-specific technology options as well as policy reforms under the umbrella of CSA.
• Climate-Smart Agriculture (CSA) Country Profiles developed by the World Bank and  partners,  give an overview of the agricultural challenges in countries around the world, and how CSA can help them adapt to and mitigate climate change. They bridge knowledge gaps by providing clarity on CSA terminology, components, relevant issues, and how to contextualize them under different country conditions.
• Climate-Smart Agriculture Investment Plans  (CSAIPs) developed for a subset of client countries aim to mainstream CSA into national agricultural policies and to identify investment opportunities in CSA. The World Bank provides technical assistance and financial support to help countries develop and implement CSAIPs. These plans prioritize investments in climate-resilient infrastructure, capacity building, and knowledge sharing to promote sustainable agricultural practices. CSAIPs are available, or currently under preparation, for  Bangladesh , Belize,  Burkina Faso, Cote D’Ivoire , Cameroon, the Republic of Congo, Ethiopia, Ghana , Iraq, Jordan, Kenya, Lesotho , Madagascar, Mali , Morocco , Nepal , Senegal, Zambia , and  Zimbabwe .
• The World Bank also supports research programs such as with the  CGIAR , which develops and supports climate-smart technologies and management methods, early warning systems, risk insurance, and other innovations that promote resilience and combat climate change.”

Working Toward Resilience and Food and Nutrition Security, while Curbing GHG Emissions

The Bank’s support of CSA is making a difference across the globe, for example:

• A new US$345 million loan for the China Green Agricultural and Rural Revitalization Program for Results will support China’s global public goods agenda by promoting the greening of agriculture and rural development in Hubei and Hunan provinces in central China. The program will reduce greenhouse gas (GHG) emissions from crop and livestock farming, increase carbon sequestration in farmlands, and improve biodiversity protection and restoration in agricultural ecosystems, while strengthening the institutional capacity of local governments to integrate environmental and decarbonization objectives in government rural revitalization plans and investments. World Bank financing will complement a US$4.1 billion commitment by the Government of China (GoC).
• The US$621 million  Food Systems Resilience Program for Eastern and Southern Africa (Phase 3) FSRP Project in Kenya, Comoros, Malawi, Somalia aims to increase the resilience of food systems and the recipients’ preparedness for food insecurity. The project has six components, including building resilient agricultural production capacity to strengthen the productivity and resilience of domestic food production to shocks and stressors, by supporting the development and adoption of improved agricultural inputs and services and climate-smart and gender-sensitive farming technologies in the crops, livestock, and fisheries sectors.
• A US$200 million credit for the Punjab Resilient and Inclusive Agriculture Transformation Project (PRIAT) will help Pakistan enhance access to, and productivity of, agricultural water, and improve incomes of farmers supported by the project. PRIAT will notably reduce the differences in water availability among head, middle, and tail end users of watercourses, increase agricultural output per unit of water used at farm level for selected crops, increase the share of area under high-value crops cultivation, and increase agriculture incomes of households participating in project activities, yielding important climate change adaptation and mitigation co-benefits.
• The US$125 million  Agriculture Resilience, Value Chain Development and Innovation (ARDI) program will play a pivotal role in strengthening the transition Jordan’s agri-food sector. It supports Jordan's National Sustainable Agriculture Plan and aims to enhance climate resilience, competitiveness, and inclusivity of the agri-food sector. Over the next five years, it will support 30,000 farming household with the adoption of climate-smart and water-efficient agricultural practices, provide needs-based training, create about 12,000 employment opportunities, and promote value chain and export promotion through advanced market diagnostics. A particular focus will be on strengthening the participation of women, youth and refugees. 

Last Updated: Feb 26, 2024

Agriculture and Food

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• 'Recipe for a Livable Planet' report launch event, May 22 - Watch live stream
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• Future of Food: Shaping a Climate-Smart Global Food System
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The impact of climate change on US agriculture has been debated for more than two decades, but the estimates ranged from no damage at the lower end to 80 % losses of grain yields at the higher end. This essay aims to help understand such divergent predictions by clarifying the concepts of weather and climate. First, the widely-read panel fixed effects models capture only the impacts of weather fluctuations but not of climate normals. Random weather fluctuations and climatic shifts are two different meteorological events and they have distinct implications on farming decisions. The former is perceived as random while the latter is perceived as non-random by the farmers. Using the historical corn yield data in the US, I explain the differences between the impact of random weather and that of climate change. Second, adaptation strategies to climatic changes and increased climate risks cannot be accounted for by the panel fixed effects models. Using the farm household data collected in sub-Saharan Africa and Latin America, I discuss quantitative significance of modeling adaptation strategies in the estimates of climate damage. Distinction between random weather fluctuations and climatic shifts is critical in modeling farming decisions, as they are fundamental to climate science, but is poorly understood by the impact researchers.

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Acknowledgements

I thank the editors of the Journal and three anonymous reviewers who provided insightful comments. I am grateful to Richard Adams for the exchanges we had on climate change and agriculture broadly.

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Seo, S.N. An essay on the impact of climate change on US agriculture: weather fluctuations, climatic shifts, and adaptation strategies. Climatic Change 121 , 115–124 (2013). https://doi.org/10.1007/s10584-013-0839-8

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DOI : https://doi.org/10.1007/s10584-013-0839-8

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Table of Contents

Measuring the Effect of Climate Change on Developing Country Agriculture

Towards a framework for the implementation of the clean development mechanism in the agricultural sector of developing countries.

2. THE KYOTO PROTOCOL, AGRICULTURE SECTOR AND OTHER UN CONVENTIONS

Editing, design, graphics and desktop publishing: Editorial Group, FAO Information Division

The designations employed and the presentation of material in this publication do not imply the expression of any opinion whatsoever on the part of the Food and Agriculture Organization of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying or otherwise, without the prior permission of the copyright owner. Applications for such permission, with a statement of the purpose and extent of the reproduction, should be addressed to the Director, Information Division, Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla, 00100 Rome, Italy.

Climate Change and Agriculture

A Perfect Storm in Farm Country

Published Mar 20, 2019

As our climate continues to heat up and the impacts of that warming grow more frequent and severe, farmers and farm communities around the world will be increasingly challenged. And US farmers won’t be spared the damage that climate change is already beginning to inflict.

In fact, the industrial model that dominates our nation’s agriculture—a model that neglects soils, reduces diversity, and relies too heavily on fertilizers and pesticides—makes US farms susceptible to climate impacts in several ways.

The combination of advancing climate change and an already-vulnerable industrial system is a “perfect storm” that threatens farmers’ livelihoods and our food supply. The good news is that there are tools—in the form of science-based farming practices—that can buffer farmers from climate damage and help make their operations more resilient and sustainable for the long term. But farmers face many obstacles to changing practices, so it’s critical that policymakers shift federal agriculture investments to support and accelerate this transition.

How climate change will challenge farmers

Various authoritative reports, most notably the multiagency 2018 Fourth National Climate Assessment, have reviewed what science is telling US farmers to expect in coming decades—and it’s not pretty.

Climate change trends

Changing precipitation patterns. Rainfall patterns have already begun shifting across the country, and such changes are expected to intensify over the coming years. This is likely to mean more intense periods of heavy rain and longer dry periods, even within the same regions.

Changing temperature patterns. Rising average temperatures, more extreme heat throughout the year, fewer sufficiently cool days during the winter, and more frequent cold-season thaws will likely affect farmers in all regions.

Climate change impacts

Floods. We’ve already seen an increase in flooding in many agricultural regions of the country, including the Midwest, the Southern Plains, and California. Sea level rise is also ratcheting up the frequency and intensity of flooding on farms in coastal regions. These costly floods devastate crops and livestock, accelerate soil erosion, pollute water, and damage roads, bridges, schools, and other infrastructure.

Droughts. Too little water can be just as damaging as too much. Severe droughts have taken a heavy toll on crops, livestock, and farmers in many parts of the country, most notably California, the Great Plains, and the Midwest, over the past decade—and science tells us that rising temperatures will likely make such droughts even worse, depleting water supplies and, in some cases, spurring destructive wildfires.

Changes in crop and livestock viability. Farmers choose crop varieties and animal breeds that are well suited to local conditions. As those conditions shift rapidly over the coming decades, many farmers will be forced to rethink some of their choices—which can mean making new capital investments, finding new markets, and learning new practices.

New pests, pathogens, and weed problems. Just as farmers will need to find new crops, livestock, and practices, they will have to cope with new threats. An insect or weed that couldn’t thrive north of Texas in decades past may find Iowa a perfect fit going forward—and farmers will have to adapt.

Industrial amplifiers

Degraded soils. Typical monoculture cropping systems leave soil bare for much of the year, rely on synthetic fertilizer, and plow fields regularly. These practices leave soils low in organic matter and prevent formation of deep, complex root systems. Among the results: reduced water-holding capacity (which worsens drought impacts), and increased vulnerability to erosion and water pollution (which worsens flood impacts).

Simplified landscapes. Industrial agriculture treats the farm as a crop factory rather than a managed ecosystem, with minimal biodiversity over wide areas of land. This lack of diversity in farming operations exposes farmers to greater risk and amplifies climate impacts such as changes in crop viability and encroaching pests.

Intensive inputs. The industrial farm’s heavy reliance on fertilizers and pesticides may become even more costly to struggling farmers as climate impacts accelerate soil erosion and increase pest problems. Heavy use of such chemicals will also increase the pollution burden faced by downstream communities as flooding increases. Farmers may also increase irrigation in response to rising temperature extremes and drought, further depleting precious water supplies.

What it will mean

How will these industrially amplified climate change impacts affect people—farmers, residents of rural communities, and all of us who rely on the food farmers produce? In a variety of ways:

• As summer heat intensifies , farmers and farm workers will face increasingly grueling and potentially unsafe working conditions.
• Accelerating crop failures and livestock losses will make farmers with access to insurance or disaster relief programs more reliant on those taxpayer-funded supports, while those without sufficient safety nets will face additional challenges. Failing farms and stagnating farm profits will also increase suffering in many rural communities.
• Farming communities will be among the first to feel the ways extreme weather exacerbates agriculture’s impacts on water resources—with nearby water supplies polluted or depleted before the damage extends to drinking water and fisheries far downstream.

Nationwide, reductions to agricultural productivity or sudden losses of crops or livestock will likely have ripple effects, including increased food prices and greater food insecurity.

Meeting the challenge

Business as usual won’t protect the future of our food supply—or the well-being of the farmers and communities that produce it. We need to take concrete steps to prepare for climate impacts on agriculture and to reduce both their severity and our vulnerability to them.

We also need to remember that climate change risks aren’t distributed equally—and neither are the pathways to climate adaptation. Public policies and institutional practices have long denied communities of color, low-income groups, and tribal communities access to critical resources and decision-making processes, leaving them with fewer options and more risk in the face of climate impacts. So it’s crucial to ensure that these communities have a voice in shaping our adaptation strategies.

Helping farm communities manage severe impacts

When climate impacts strike, support systems need to be in place to help communities cope and recover:

Shelters and other facilities to provide housing, food, first aid, and other immediate needs for people whose lives have been disrupted or displaced by floods, droughts, fires, or storms.

Investment in local capacity and infrastructure to support people harmed by climate impacts as they rethink or rebuild their lives and businesses. This includes not only infrastructure for communication, transportation, water and sanitation, but also training in new practices and opportunities that build adaptive capacity.

Reducing damage by making farms more resilient

Our farms and farm communities don’t have to be sitting ducks for climate impacts. Forward-looking farmers and scientists are finding new, climate-resilient ways to produce our food:

Build healthier, “spongier” soils through practices—such as planting cover crops and deep-rooted perennials—that increase soil’s capacity to soak up heavy rainfall and hold water for dry periods;

Make farms stronger by redesigning them as diverse agroecosystems —incorporating trees and native perennials, reducing dependence on fertilizers and pesticides, and reintegrating crops and livestock;

Develop new crop varieties, livestock breeds, and farm practices specifically designed to help farmers adapt to evolving climate realities.

Addressing the root of the problem

Finally, whatever we do to help farmers adapt to climate change, we still face the urgent need and obligation to reduce the source of the problem as far and as fast as we can. This means bringing net emissions of heat-trapping gases down to zero , and doing it soon.

Fortunately, our farm and food system can be an important part of the solution, both by reducing emissions at every stage of the food production and distribution process, and by building agroecosystems that can sequester (store) more carbon .

Policy recommendations

What policy levers can we pull to help get these solutions off the ground?

Invest in public research. Publicly funded research provides farmers with the tools and information they need to maximize efficiency and productivity. With climate change, farmers need science more than ever, yet public funding for research that can help them cope has been in short supply. Agroecology research—which produces the kind of long-term, literally root-deep solutions that can help farms stay viable for generations— has been particularly underfunded .

Expand conservation programs in the federal farm bill that make it easier for farmers to adopt sustainable practices that will make their farms more climate-resilient. We need to boost their funding and their impact .

Strengthen safety nets (and make them drivers of resilience) . Regardless of what science and forward-looking policy can do, farms across the country will be challenged—and some more than others. It’s essential that we provide farm families and communities with the support they need to survive the climate crisis and become more resilient. This includes better crop insurance programs , health care access for farmers and farm workers, and effective, responsive disaster relief programs.

Achieve net zero emissions . We need to prioritize policies to drastically reduce our climate emissions and give us a chance of getting to net zero ASAP. Reversing the Trump administration’s withdrawal from the Paris Agreement would be a good start, but there is much more we can do .

Related resources

How Long Has The Farm Bill Been Expired?

What’s Wrong with Fossil Fuel–Based Fertilizer?

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Agriculture and Changing Climate

• 06 Oct 2020
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• Environmental Pollution & Degradation
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The article is based on ‘Climate Change and Agriculture: Way Ahead for Low-Emission Growth’ published in Down To Earth on 25/09/20. It focuses upon how the Agriculture and Changing Climate are affecting each other and what steps can be taken to curb the worse.

As the climate continues to heat up and the impacts of global warming grow more frequent and severe, farmers and farm communities around the world have been increasingly challenged.

While the agriculture sector is responsible for climate change due to Greenhouse Gas (GHG) emissions, it is also severely impacted by the effects of changing climate.

Climate change is also threatening India’s agricultural growth with frequent dry spells, heat waves and erratic rainfall. Besides, the changing rainfall patterns in the form of delayed onset or early withdrawal has adversely affected the cropping cycle and farm operations.

With increasing population and the need to enhance food production, one has to address the challenge of meeting the growing demand for food production while controlling and reducing the GHG emissions from agriculture.

Agriculture Affecting Climate

• It can also escape from stored manure and organic waste in landfills.
• Livestock is alone responsible for 44% of methane emissions.
• 53% of Nitrous oxide emissions are an indirect product of organic and mineral nitrogen fertilisers.
• Fertilisers rich in nitrogen pollute water and threaten the aquatic ecosystem.
• Monoculture cropping systems leave soil bare for much of the year, rely on synthetic fertilizer, and plow fields regularly.
• These practices leave soils low in organic matter and prevent formation of deep, complex root systems leading to reduced water holding capacity.
• Clearing uncultivated land for farming can lead to the destruction of natural ecosystems, which may have a devastating effect on the local wildlife and biodiversity and the micro-climate.
• Many agricultural sectors need large amounts of water, which may cause water scarcity and drought.

Changing Climate Affecting Agriculture

• Extreme heat: Crops need suitable soil, water, sunlight, and heat to grow. However, extreme heat events and reductions in precipitation and water availability have hampered the crop productivity.
• This is likely to mean more intense periods of heavy rain and longer dry periods, even within the same regions.
• Floods: Flooding in many agricultural regions of the country have been witnessed and these floods have devastated crops and livestock, accelerated soil erosion and have polluted water.

The Scenario of India

• India is the third-largest emitter of greenhouse gases after China and the United States.
• This accounts for 7% of global GHG emissions.
• Agriculture and livestock account for 18% of gross national emissions.
• Efficient use of fertiliser
• Adoption of zero-tillage
• Management of water used to irrigate paddy.

Zero Tillage also called No-till Agriculture , is a cultivation technique in which the soil is disturbed only along the slit or in the hole into which the seeds are planted, the reserved detritus from previous crops covers and protects the seedbed.

• Convincing the farmers for switching to the alternate package of practices; changing their socio-economic perspective.
• Pursuing people to change their mindsets for agricultural areas; treating them as whole of an ecosystem rather than treating them as a crop producing factory.
• Ensuring that all the farmers are provided with better market linkages so that they are able to get higher returns for their produce.

Measures That Can Be Taken

• A combination of tools and techniques covering capacity building, field demonstration, extension and outreach will enable faster adoption.
• Concepts such as Low External Input Sustainable Agriculture (LEISA) are receiving increased attention as a sustainable alternative to chemical farming.
• Zero Budget Natural Farming (ZBNF) : It encourages farmers to use low-cost locally sourced input and should be promoted to minimise the use of chemical fertilisers and pesticides.
• For example, Cotton farmers in Maharashtra’s Yavatmal district are making the shift to a package of practices that lower the use of water (through in-situ soil moisture conservation and other demand management measures), promote the use of biofertilizers and biopesticides as a means to reduce the cost of cultivation and lower the environmental footprint of cotton cultivation.

Low External Input Sustainable Agriculture (LEISA)

• The term low-input agriculture has been defined as a production activity that uses synthetic fertilizers or pesticides below rates commonly recommended by the Extension Service. However, it does not mean elimination of these materials.
• Yields are maintained through greater emphasis on cultural practices, Integrated Pest Management , and utilization of on-farm resources and management.
• The LEISA concept seeks to optimize the use of locally available resources by maximizing the complementary and synergistic effects of different components of the farming systems. External inputs are used in a complementary way.
• The efforts to combat climate change will have to focus on mitigation and adaptation efforts across all sectors.
• For agrarian countries, the task will be to ensure increased production without increasing the environmental footprint of agriculture by enhancing the knowledge and skills of our farmers.

This editorial is based on ‘China drives QUAD’ published in ‘The Indian Express’ on October 10, 2020. Now watch this on our youtube channel.

Essays on Climate Change Impacts and Adaptation for Agriculture

Publication or External Link

Over the past twenty years economists have developed econometric approaches for estimating the impacts of climate change on agriculture by accounting for farmer adaptation implicitly. These reduced-form approaches are simple to implement but provide little insights into impact mechanisms, limiting their usefulness for adaptation policy. Recently, conflicting estimates for US agriculture have led to research with greater emphasis on mechanisms including renewed interest in statistical crop yield models. Findings suggest US agriculture will be mainly and severely affected by an increased frequency of high temperatures with crop yield suggested as a major driver.

This dissertation is comprised of three essays highlighting methodological aspects in this literature. It contributes to the ongoing debate and shows the preeminent role of extreme temperature is overestimated while the role of soil moisture is seriously underestimated. This stems from issues related to weather data quality, the presence of time-varying omitted weather variables, as well as from modeling assumptions that inadvertently underestimate farmers' ability to adapt to seasonal aspects of climate change. My work illustrates how econometric models of climate change impacts on crop production can be improved by structuring them to admit some basic principles of agronomic science.

The first essay shows that nonlinear temperature effects on corn yields are not robust to alternative weather datasets. The leading econometric studies in the current literature are based on a weather dataset that involves considerable interpolation. I introduce the use of a new dataset to agricultural climate change research that has been carefully developed with scientific methods to represent weather variation with one-hour and 14 kilometer accuracy. Detrimental effects of extreme temperature crucially hinge upon the recorded frequency at the highest temperatures. My research suggests that measurement error in short amounts of time spent at extreme temperature levels has disproportionate effects on estimated parameters associated with the right tail of the temperature distribution. My alternative dataset suggests detrimental temperature effects of climate change over the next 50-100 years will be half as much as in leading econometric studies in the current literature.

The second essay relaxes the prevalent assumption in the literature that weather is additive. This has been the practice in most empirical models. Weather regressors are typically aggregated over the months that include the growing season. Using a simple model I show that this assumption imposes implausible characteristics on the technology. I test this assumption empirically using a crop yield model for US corn that accounts for differences in intra-day temperature variation in different stages of the growing season. Results strongly reject additivity and suggest that weather shocks such as extreme temperatures are particularly detrimental toward the middle of the season around flowering time, which corrects a disagreement of empirical yield models with the natural sciences. I discuss how this assumption tends to underestimate the range of adaptation possibilities available to farmers, thus overstating projected climate change impacts on the sector.

The third essay introduces an improved measure of water availability for crops that accounts for time variation of soil moisture rather than season-long rainfall totals, as has been common practice in the literature. Leading studies in the literature are based on season-long rainfall. My alternative dataset based on scientific models that track soil moisture variation during the growing season includes variables that are more relevant for tracking crop development. Results show that models in the literature attribute too much variation in yields to temperature variation because rainfall variables are a crude and inaccurate measure of the moisture that determined crop growth. Consequently, I find that third of damages to corn yields previously attributed to extreme temperature are explained by drought, which is far more consistent with agronomic science. This highlights the potential adaptive role for water management in addressing climate change, unlike the literature now suggests.

The fourth essay proposes a general structural framework for analyzing the mechanisms of climate change impacts on the sector. An empirical example incorporates some of the flexibilities highlighted in the previous essay to assess how farmer adaptation can reduce projected impacts on corn yields substantially. Global warming increases the length of the growing season in northern states. This gives farmers the flexibility to change planting dates that can reduce exposure of crops during the most sensitive flowering stage of the crop growth cycle. These research results identify another important type of farmer adaptation that can reduce vulnerability to climate change, which has been overlooked in the literature but which becomes evident only by incorporating the principles of agronomic science into econometric modeling of climate change impact analysis.

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Organic Agriculture Helps Solve Climate Change

As farmers grapple with everything from extreme weather events to heat stress to wildfires, and agriculture becomes less predictable in the face of a changing climate, it is essential for governments to help farmers transition to practices that increase resilience and dramatically decrease reliance on fossil-fuel based chemicals.

New and beginning farmers on the 100-acre Agricultural Land Based Training Association (ALBA) organic farm in Salinas, CA

USDA Photo by Lance Cheung

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For the past year, the California Air Resources Board (CARB) has been developing its 2022 Draft Climate Change Scoping Plan , intended to carve out a path to carbon neutrality for California by mid-century. After months of advocacy from NRDC and its allies calling on CARB to include incentives for organic farming as well as pesticide use reduction in the Natural Working Lands section, the agency released its proposed approach in May. In this draft, the agency recommends converting 20% of California’s agricultural lands to organic agriculture by 2045 as a way to mitigate climate change. While this recommendation is not nearly ambitious enough (California’s organic acreage grew by 44% from 2014 to 2019 according to a report from the state’s Department of Agriculture), it is nonetheless an important milestone because it recognizes and affirms the essential role that organic farming systems can play in climate-smart agriculture.

Organic agriculture is an important lever in moving the needle on climate change. Here’s why:

Organic Farming Reduces Greenhouse Gases

Because fossil fuel-based fertilizers and most synthetic pesticides are prohibited in organic farming, it has a significantly lower carbon footprint. The production of these farm chemicals are energy intensive. Studies show that the elimination of synthetic nitrogen fertilizers alone, as is required in organic systems, could lower direct global agricultural greenhouse gas emissions by about 20%. A forty-year study conducted by the Rodale Institute also showed that organic farms use 45% less energy compared to conventional farms (while maintaining or even exceeding yields after a 5-year transition period.) Meanwhile, fumigant pesticides - commonly used on crops like strawberries and injected into soil - emit nitrous oxide (N2O), the most potent greenhouse gas . Research indicates that one commonly used fumigant pesticides, chloropicrin, can increase N2O emissions by 700-800%. Two other fumigants (metam sodium and dazomet) are also known to significantly increase N2O output.

Organic Farming Improves Soil Carbon Sequestration

Beth Hoinacki shows an aspect of her crop rotation and cover crop plan on the certified organic Goodfoot Farm, Philomath, OR.

U.S. Department of Agriculture

Soil-boosting practices that are the foundation of organic agriculture also help sequester more carbon in soil compared to non-organic systems. Multiple meta-analyses comparing thousands of farms nationwide have shown that organic agriculture results in higher stable soil organic carbon and reduced nitrous oxide (N2O) emissions when compared to conventional farming. A recent review of almost 400 studies showed pesticide use was associated with damage to soil invertebrates in more than 70% of the studies. Soil invertebrates are critical to carbon sequestration, because they are responsible for the formation of soil components that are essential to building soil organic carbon. In fact, estimates indicate that with worldwide adoption of agroecological best management practices like diversified organic farming, soils could actually absorb more carbon than the farming sector emits between 2020 and 2100.

Organic Farming Increases Resilience

A welcome sign for Huerta del Valle (HdV), a 4-acre organic farm in a low-income urban community in Ontario, CA that faces severe drought. HdV grows over 100 different crops.

USDA Photo by Lance Cheung.

Organic farms are required to build healthy soil and crops that make them better able to adapt in a changing climate. First and foremost, organic farmers rely on composting, crop rotation, and natural rather than fossil fuel-based inputs in order to maintain or improve soil health. As stewards of healthy soil, organic farmers and ranchers can be a major force for climate mitigation (U.S. Department of Agriculture Secretary Vilsack confirmed as much during the recent announcement of the new USDA framework for resilient food and farming systems). Organic farming promotes resiliency by boosting soil’s ability to retain water and the natural nutrients found in healthy soils. By increasing organic matter in soil continuously over time, organic agriculture improves water percolation by 15-20%, replenishing groundwater and helping crops perform well in extreme weather like drought and flooding. A decades-long organic farming trial found that organic yields can be up to 40% higher than nonorganic farms in drought years. By foregoing most fossil fuel-based inputs, organic farmers are also more resilient and adaptable not only to stressors related to climate change but also other disruptive global stressors.

As farmers grapple with everything from extreme weather events to heat stress and wildfires, and agriculture becomes even less predictable in the face of a changing climate, it is essential for governments to help farmers transition to practices that increase resilience and dramatically decrease reliance on fossil-fuel based chemicals. Setting ambitious goals—as the European Union has done with its 2020 Farm to Fork Strategy —is a critical first step. The California Air Resources Board has moved in the right direction by recognizing that organic agriculture can play an important role in our state’s climate plan. However, CARB ought to stretch its ambitions as it develops its final plan to maximize the climate potential of California’s organic agriculture sector.

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Climate-Resilient Crops and Animal Production Strategy for Curbing Food Insecurity in Arid-and Semi-Arid Regions of Kenya: A Systematic Literature Review of 10 Counties

4 Pages Posted: 4 Jun 2024

Martin Otundo Richard

Jomo Kenyatta University of Agriculture and Technology

Date Written: June 01, 2024

Food insecurity is a persistent problem in Kenya's arid and semi-arid regions, exacerbated by climate change. This systematic literature review aimed to investigate the effectiveness of climate-resilient crops and animal production strategies in curbing food insecurity in 10 counties. A comprehensive search of electronic databases yielded 25 relevant studies, which were analyzed using a meta-analysis and narrative synthesis. The findings suggest that climate-resilient crops such as sorghum, pearl millet, and cowpeas can improve crop yields by up to 30% in arid and semi-arid regions. Animal production strategies, such as integrated livestock-farming systems and agro-pastoralism, can also enhance food security by increasing meat and dairy production. However, the adoption of these strategies is hindered by limited access to climate information, lack of extension services, and limited market access. The study recommends that policymakers and practitioners prioritize climate-resilient agriculture and animal production strategies, while addressing the identified barriers to adoption.

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Martin Otundo Richard (Contact Author)

Jomo kenyatta university of agriculture and technology ( email ), do you have a job opening that you would like to promote on ssrn, paper statistics, related ejournals, development economics: agriculture, natural resources, & environmental impact ejournal.

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• DOI: 10.1029/2024ef004577
• Corpus ID: 270406003

The Essential Role of Local Context in Shaping Risk and Risk Reduction Strategies for Snowmelt‐Dependent Irrigated Agriculture

• B. Gordon , G. Boisramé , +8 authors Adrian Harpold
• Published in Earth's Future 1 June 2024
• Environmental Science, Agricultural and Food Sciences

11 References

Assessment of irrigated agriculture vulnerability under climate change in southern italy, field scale quantification indicates potential for variability in return flows from flood irrigation in the high altitude western us, the controversial debate on the role of water reservoirs in reducing water scarcity, changes in snowpack and snowmelt runoff for key mountain regions, potential impacts of a warming climate on water availability in snow-dominated regions, humidity determines snowpack ablation under a warming climate, adaptable and comprehensive vulnerability assessments for water resources systems in a rapidly changing world., ecological services to and from rangelands of the united states, measuring land-use and land-cover change using the u.s. department of agriculture's cropland data layer: cautions and recommendations, uncertainty of downscaling method in quantifying the impact of climate change on hydrology, related papers.

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USDA Releases Updated Climate Adaptation and Resilience Plan

Biden-Harris Administration expands agency efforts to ensure operations are increasingly resilient to climate change impacts

WASHINGTON, June 20, 2024 – Today, the U.S. Department of Agriculture (USDA) joined more than 20 federal agencies to release its updated Climate Adaptation Plan and expand the Biden-Harris Administration’s efforts to ensure federal operations are increasingly resilient to climate change impacts. The updated adaptation plans advance the Biden-Harris Administration’s National Climate Resilience Framework , which helps to align climate resilience investments across the public and private sector through common principles and opportunities for action to build a climate resilient nation.

Communities from coast to coast are experiencing the impacts of climate change firsthand through crop yields depleted by droughts; businesses, homes, and roadways washed away by floods; and entire communities destroyed by deadly wildfires. In light of these impacts, the Biden-Harris Administration is taking action to assess, manage, and reduce the risks that climate change poses to the nation. USDA is developing a mission-wide approach to climate adaptation, establishing protocols to promote climate resilience in agricultural production, natural resource and land management, rural development, food security and safety, and science and innovation. For example, USDA’s Forest Service is seeking to reduce climate-driven wildfire risk through the implementation of the Wildfire Crisis Strategy (WCS) and support post-wildfire recovery through climate-informed actions in its Reforestation Strategy .

“USDA has taken a Department-wide approach to considering the impacts of climate change on our mission delivery and those we serve,” said Agriculture Secretary Tom Vilsack. “From USDA headquarters to field offices nationwide, these efforts enable USDA to support the agriculture and forestry sectors and diverse communities across the country as they confront the impacts of climate change.”

“As communities face extreme heat, natural disasters and severe weather from the impacts of climate change, President Biden is delivering record resources to build climate resilience across the country,” said Brenda Mallory, Chair of the White House Council on Environmental Quality. “Through his Investing in America agenda and an all-of-government approach to tackling the climate crisis, the Biden-Harris Administration is delivering more than $50 billion to help communities increase their resilience and bolster protections for those who need it most. By updating our own adaptation strategies, the federal government is leading by example to build a more resilient future for all.”

At the beginning of his Administration, President Biden tasked federal agencies with leading whole-of-government efforts to address climate change through Executive Order 14008, Tackling the Climate Crisis at Home and Abroad . Following the magnitude of challenges posed by the climate crisis underscored last year when the nation endured a record 28 individual billion-dollar extreme weather and climate disasters that caused more than $90 billion in aggregate damage, USDA continues to be a leader and partner in adaptation and resilience.

USDA released its initial Climate Adaptation Plan in 2021 and progress reports outlining advancements toward achieving their adaptation goals in 2022 . In coordination with the White House Council on Environmental Quality and the Office of Management and Budget, agencies updated their Climate Adaptation Plans for 2024 to 2027 to better integrate climate risk across their mission, operations, and asset management, including:

• Combining historical data and projections to assess exposure of assets to climate-related hazards including extreme heat and precipitation, sea level rise, flooding, and wildfire;
• Expanding the operational focus on managing climate risk to facilities and supply chains to include federal employees and federal lands and waters;
• Broadening the mission focus to describe mainstreaming adaptation into agency policies, programs, planning, budget formulation, and external funding;
• Linking climate adaptation actions with other Biden-Harris Administration priorities , including advancing environmental justice and the President’s Justice40 Initiative , strengthening engagement with Tribal Nations, supporting the America the Beautiful initiative , scaling up nature-based solutions , and addressing the causes of climate change through climate mitigation; and
• Adopting common progress indicators across agencies to assess the progress of agency climate adaptation efforts.

All plans from each of the 20+ agencies and more information are available at www.sustainability.gov/adaptation . For more information on USDA climate adaptation efforts, visit www.usda.gov/oce/energy-and-environment/climate/adaptation .

USDA touches the lives of all Americans each day in so many positive ways. In the Biden-Harris Administration, USDA is transforming America’s food system with a greater focus on more resilient local and regional food production, fairer markets for all producers, ensuring access to safe, healthy and nutritious food in all communities, building new markets and streams of income for farmers and producers using climate smart food and forestry practices, making historic investments in infrastructure and clean energy capabilities in rural America, and committing to equity across the Department by removing systemic barriers and building a workforce more representative of America. To learn more, visit www.usda.gov .

USDA is an equal opportunity provider, employer, and lender.

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Causes and Effects of Climate Change

Fossil fuels – coal, oil and gas – are by far the largest contributor to global climate change, accounting for over 75 per cent of global greenhouse gas emissions and nearly 90 per cent of all carbon dioxide emissions.

As greenhouse gas emissions blanket the Earth, they trap the sun’s heat. This leads to global warming and climate change. The world is now warming faster than at any point in recorded history. Warmer temperatures over time are changing weather patterns and disrupting the usual balance of nature. This poses many risks to human beings and all other forms of life on Earth.

Causes of Climate Change

Generating power

Generating electricity and heat by burning fossil fuels causes a large chunk of global emissions. Most electricity is still generated by burning coal, oil, or gas, which produces carbon dioxide and nitrous oxide – powerful greenhouse gases that blanket the Earth and trap the sun’s heat. Globally, a bit more than a quarter of electricity comes from wind, solar and other renewable sources which, as opposed to fossil fuels, emit little to no greenhouse gases or pollutants into the air.

Manufacturing goods

Manufacturing and industry produce emissions, mostly from burning fossil fuels to produce energy for making things like cement, iron, steel, electronics, plastics, clothes, and other goods. Mining and other industrial processes also release gases, as does the construction industry. Machines used in the manufacturing process often run on coal, oil, or gas; and some materials, like plastics, are made from chemicals sourced from fossil fuels. The manufacturing industry is one of the largest contributors to greenhouse gas emissions worldwide.

Cutting down forests

Cutting down forests to create farms or pastures, or for other reasons, causes emissions, since trees, when they are cut, release the carbon they have been storing. Each year approximately 12 million hectares of forest are destroyed. Since forests absorb carbon dioxide, destroying them also limits nature’s ability to keep emissions out of the atmosphere. Deforestation, together with agriculture and other land use changes, is responsible for roughly a quarter of global greenhouse gas emissions.

Using transportation

Most cars, trucks, ships, and planes run on fossil fuels. That makes transportation a major contributor of greenhouse gases, especially carbon-dioxide emissions. Road vehicles account for the largest part, due to the combustion of petroleum-based products, like gasoline, in internal combustion engines. But emissions from ships and planes continue to grow. Transport accounts for nearly one quarter of global energy-related carbon-dioxide emissions. And trends point to a significant increase in energy use for transport over the coming years.

Producing food

Producing food causes emissions of carbon dioxide, methane, and other greenhouse gases in various ways, including through deforestation and clearing of land for agriculture and grazing, digestion by cows and sheep, the production and use of fertilizers and manure for growing crops, and the use of energy to run farm equipment or fishing boats, usually with fossil fuels. All this makes food production a major contributor to climate change. And greenhouse gas emissions also come from packaging and distributing food.

Powering buildings

Globally, residential and commercial buildings consume over half of all electricity. As they continue to draw on coal, oil, and natural gas for heating and cooling, they emit significant quantities of greenhouse gas emissions. Growing energy demand for heating and cooling, with rising air-conditioner ownership, as well as increased electricity consumption for lighting, appliances, and connected devices, has contributed to a rise in energy-related carbon-dioxide emissions from buildings in recent years.

Consuming too much

Your home and use of power, how you move around, what you eat and how much you throw away all contribute to greenhouse gas emissions. So does the consumption of goods such as clothing, electronics, and plastics. A large chunk of global greenhouse gas emissions are linked to private households. Our lifestyles have a profound impact on our planet. The wealthiest bear the greatest responsibility: the richest 1 per cent of the global population combined account for more greenhouse gas emissions than the poorest 50 per cent.

Based on various UN sources

Effects of Climate Change

Hotter temperatures

As greenhouse gas concentrations rise, so does the global surface temperature. The last decade, 2011-2020, is the warmest on record. Since the 1980s, each decade has been warmer than the previous one. Nearly all land areas are seeing more hot days and heat waves. Higher temperatures increase heat-related illnesses and make working outdoors more difficult. Wildfires start more easily and spread more rapidly when conditions are hotter. Temperatures in the Arctic have warmed at least twice as fast as the global average.

More severe storms

Destructive storms have become more intense and more frequent in many regions. As temperatures rise, more moisture evaporates, which exacerbates extreme rainfall and flooding, causing more destructive storms. The frequency and extent of tropical storms is also affected by the warming ocean. Cyclones, hurricanes, and typhoons feed on warm waters at the ocean surface. Such storms often destroy homes and communities, causing deaths and huge economic losses.

Increased drought

Climate change is changing water availability, making it scarcer in more regions. Global warming exacerbates water shortages in already water-stressed regions and is leading to an increased risk of agricultural droughts affecting crops, and ecological droughts increasing the vulnerability of ecosystems. Droughts can also stir destructive sand and dust storms that can move billions of tons of sand across continents. Deserts are expanding, reducing land for growing food. Many people now face the threat of not having enough water on a regular basis.

A warming, rising ocean

The ocean soaks up most of the heat from global warming. The rate at which the ocean is warming strongly increased over the past two decades, across all depths of the ocean. As the ocean warms, its volume increases since water expands as it gets warmer. Melting ice sheets also cause sea levels to rise, threatening coastal and island communities. In addition, the ocean absorbs carbon dioxide, keeping it from the atmosphere. But more carbon dioxide makes the ocean more acidic, which endangers marine life and coral reefs.

Loss of species

Climate change poses risks to the survival of species on land and in the ocean. These risks increase as temperatures climb. Exacerbated by climate change, the world is losing species at a rate 1,000 times greater than at any other time in recorded human history. One million species are at risk of becoming extinct within the next few decades. Forest fires, extreme weather, and invasive pests and diseases are among many threats related to climate change. Some species will be able to relocate and survive, but others will not.

Not enough food

Changes in the climate and increases in extreme weather events are among the reasons behind a global rise in hunger and poor nutrition. Fisheries, crops, and livestock may be destroyed or become less productive. With the ocean becoming more acidic, marine resources that feed billions of people are at risk. Changes in snow and ice cover in many Arctic regions have disrupted food supplies from herding, hunting, and fishing. Heat stress can diminish water and grasslands for grazing, causing declining crop yields and affecting livestock.

More health risks

Climate change is the single biggest health threat facing humanity. Climate impacts are already harming health, through air pollution, disease, extreme weather events, forced displacement, pressures on mental health, and increased hunger and poor nutrition in places where people cannot grow or find sufficient food. Every year, environmental factors take the lives of around 13 million people. Changing weather patterns are expanding diseases, and extreme weather events increase deaths and make it difficult for health care systems to keep up.

Poverty and displacement

Climate change increases the factors that put and keep people in poverty. Floods may sweep away urban slums, destroying homes and livelihoods. Heat can make it difficult to work in outdoor jobs. Water scarcity may affect crops. Over the past decade (2010–2019), weather-related events displaced an estimated 23.1 million people on average each year, leaving many more vulnerable to poverty. Most refugees come from countries that are most vulnerable and least ready to adapt to the impacts of climate change.

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Fostering Resilient Communities and Sustainable Tourism against Climate-Driven Disaster Risk in Gili Islands

• Handayani, A. P.
• Muhammad, M. F.
• Sujatmiko, K. A.

Climate change exacerbates natural conditions and disasters. It increased the frequency and severity of droughts, rising sea levels leading to coastal erosion and flooding, more intense and unpredictable weather patterns, and the potential for stronger storm surges and tropical cyclones. These changes have significant impacts on the local ecosystems, water resources, and agriculture and ultimately affect the lives and livelihoods of the island's residents. Additionally, the changes in ocean temperature and acidity due to climate change can disrupt marine ecosystems, affecting fisheries and coral reefs, which are vital for the island's tourism industry. This research investigates the consequences of climate change-induced disaster risks on the Gili Islands, particularly drought and their implications for local tourism. Utilizing qualitative methods, the study focuses on developing strategies for sustainable tourism and bolstering community capacity across the islands. Prioritizing the establishment of a strong nexus and resilient community, the research underscores the essential role of collaboration and education. It highlights the importance of active engagement from well-resourced hotels and resorts in addressing these challenges. The study also proposes innovative business processes to enhance connections between hotels, resorts, the government, and the local community. Ultimately, the research aims to provide a roadmap for sustainable tourism practices, fostering a resilient community and facilitating effective cooperation among stakeholders to mitigate the impacts of climate change in the Gili Islands.

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Heavy rains and climate change challenge Minnesota agriculture, farmers of color

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After two years of drought-dried fields, Minnesota farmers are facing the opposite problem — extremely soggy soil and flooding following several inches of rainfall that washed out roads and continue to push up river levels this week.

“All I’ll say is uffdah,” Minnesota Agriculture Commissioner Thom Petersen said.

“A lot of the crop in Minnesota didn’t get planted [yet]. We’ll get some of the final acreage here later this month … this week is going to kind of put a nail in the coffin for some of the farmers who are trying to get in,” Petersen said Thursday.

Marcus Carpenter, founder of Route 1 — an organization working for greater racial and ethnic diversity in farming — agrees.

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“It has been a tough season,” Carpenter said. Among the several hundred farmers involved in Route 1, many have had their crops washed out.

“When you have farmers of color who have very little acreage to deal with in the beginning, having an entire washout can be detrimental for them, both economically … and from a community perspective.”

Overall, the median Minnesota net income for farms was $44,719 last year — down more than 76 percent from 2022, according to data and analysis from the farm financial database FINBIN and the University of Minnesota Extension.

Carpenter said farmers of color in the state make somewhere around $20,000 annually and are challenged by limited access to finances and market entry.

• Listen The changing face of Minnesota farming

Delayed planting also contributes to food access and availability and health equity, according to Carpenter.

One in four Black Minnesota households experiences food insecurity, according to Second Harvest Heartland — that’s compared to 4 percent of white households.

“Farmers of color most of the time are not only growing for their families, but they’re growing for their communities,” he said.

Farming and climate change

Addressing climate change, Petersen says, has been a top priority for the Walz administration.

“As we see these extremes … really, a lot of it comes down to soil and so we’ve been working very hard on soil health,” he said.

To support cover crop usage, conservative tillage equipment and other methods of cultivating and maintaining rich soil, the state Legislature has prioritized funding loans for farmers.

State grants, Petersen says, are popular too. The state also partners with the USDA for outreach.

“We see farmers adapting quickly to soil health practices and also showing good profitability on those,” Petersen explained. “There’s a lot going on, but it almost has to” with a changing landscape.

Route 1, too, prioritizes education, especially around soil health, Carpenter said. The organization also supports green infrastructure like rainwater collection and cover cropping and is actively finding ways to feed communities despite climate change.

• ‘Farmers are the largest gamblers ever’ Scientists and ag representatives plan for climate uncertainty
• Listen Farming on the frontlines of the climate crisis

“As we’re dealing with the elements outside, we’re also teaching practices of sustainable farming on the inside that can have an impact on these emerging farming communities, Black and brown communities,” he said.

Earlier this year, Route 1 acquired the first Black-owned freight farm in Minnesota, KARE 11 reported . The modular, hydroponic farm inside a shipping container can grow more than 200 pounds of produce per week, year-round.

Learn more about Route 1’s community-supported agriculture, hyperlocal produce production, emerging farmer programs and more on their website, route1mn.org .

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