thesis on climate change and agriculture

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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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Impacts of climate change and agricultural diversification on agricultural production value of Thai farm households

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• Published: 28 June 2024
• Volume 177 , article number  112 , ( 2024 )

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• Benjapon Prommawin 1 , 2 ,
• Nattanun Svavasu 3 , 8 ,
• Spol Tanpraphan 3 , 9 ,
• Voravee Saengavut 4 ,
• Theepakorn Jithitikulchai   ORCID: orcid.org/0000-0002-1725-9567 5 ,
• Witsanu Attavanich 6 &
• Bruce A. McCarl 7  

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A Correction to this article was published on 18 July 2024

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This paper examines how rising temperatures impact the agricultural production value of Thai farmers, compares potential adaptation strategies like agricultural diversification, and analyzes future projections based on IPCC AR6 scenarios. We analyze nationally representative socioeconomic survey data from farm households alongside ERA5 weather data, utilizing econometric regression analysis. Our analysis reveals that higher temperatures lead to a reduction in agricultural output value, with the situation expected to worsen as global warming progresses. Furthermore, we find that households with diversified production practices, such as a variety of agricultural activities or multicropping, exhibit a greater capacity to adapt to rising temperatures. These findings substantiate the importance of the country’s policies promoting integrated farming and diversified crop-mix strategies.

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1 Introduction

Farm households in developing countries frequently confront production risk and income fluctuations due to climatic shocks, worsened by the absence of well-developed farm income support systems and limited financial and agricultural markets. This lack of resources forces households to deprive means to insure themselves, leading to costly coping mechanisms like selling assets or relying on informal borrowing (Dercon and Krishnan  2000 ; Gertler and Gruber  2002 ; Kazianga and Udry  2006 ; among several others). Regional climate shocks can trigger disruptions affecting households in an area. Climate change is altering probability distributions, intensifying coping challenges (McCarl et al. 2008 ).

Global average temperature has been increasing since the 1970s and is projected to continue (IPCC 2021 ), intensifying climate change implications on farm households worldwide. Previous research confirms significant agricultural sector losses due to climate change, with projections of future damage, especially for developing countries (Mendelsohn et al. 1994 ; Attavanich and McCarl  2014 ; Brown et al. 2017 ). Studies consistently highlight lower adaptation capacity of impoverished farmers (Mano and Nhemachena  2007 ; Skoufias  2012 ; Hallegatte et al. 2016 ; Nikoloski et al. 2018 ; Sesmero et al.  2018 ). However, few existing studies specifically examine household responses to climate shocks at the country level. Among these, Seo ( 2012 ) finds that integrated crop-livestock farms in Africa adapt better than specialized crop farms. Bellora et al. ( 2018 ) show that crop biodiversity enhances agricultural production in South Africa. But none exists on a national scale for Thailand.

This paper quantifies the effects of rising temperature on the agricultural production value of Thai farm households and explores agricultural diversification as an adaptation strategy for climate change. We assess diversification’s impact by examining diversification across various multiple enterprises: crops, livestock, fisheries, and crop diversification strategies within farms. Thailand is chosen due to its large agricultural workforce, role as a major food exporter, and vulnerability to climate events (Eckstein et al. 2021 ).

To fulfill our objectives, we utilize a mix of survey and climate data. Specifically, we use the Agricultural Household Socioeconomic and Labor Survey (2006–2020) by the Office of Agricultural Economics, chosen to capture data over a substantial period. This dataset is matched with sub-district level climate re-analysis data derived from satellite and weather station data. We assess temperature impacts on production output value, exploring whether diversification can mitigate these impacts for Thai farm households. We then project the agricultural outcomes under five climate scenarios from the IPCC ( 2021 ) Shared Socioeconomic Pathways (SSPs).

Our analyses show that higher temperatures damage Thai agricultural production. However, diversification across enterprises, including crops and livestock, is an effective adaptation strategy. Moreover, multicropping or planting a variety of crops reduces climate change sensitivity.  It’s important to note that even if we succeed in limiting global warming to 1.5 degrees Celsius as per the Paris Agreement, the value of agricultural production will continue to be impacted by rising temperatures in any IPCC scenarios.

2.1 Agricultural household survey data

The Annual Agricultural Farm Household Socio-economic Survey, administered by the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives, Thailand, were collected over 2006 to 2020 in 14 annual survey rounds. Each survey year starts from 1 May and ends on 30 April of the following year. The data encompass all 77 provinces in Thailand and provide detailed information on household characteristics, income, land usage, and agricultural activities such as crops, livestock and fisheries.

Our outcome variable is agricultural output value, which includes monetary value from both home consumption and products sold from agricultural activities. The survey does not directly report data on the total harvested crop value of each household. Therefore, we estimate the output value using reported price and quantity produced. For households that do not report selling price, we calculate harvested crop value using regional average prices (Golan et al. 2001 ; Jenkins et al. 2011 ). We remove outliers exceeding the top 0.5% of our outcome variables. This trimming process does not significantly affect our constructed revenue compared to the reported revenue (see Appendix D for untrimmed results and robustness checks). Additionally, we exclude households with no agricultural output value.

To ensure comparability across time, all monetary variables are expressed in real terms, using 2019 Thai Baht as the base year. Nominal agricultural variables are deflated using the agricultural price index compiled by Thailand’s Office of Agricultural Economics.

Table  1 (Panels A-C) presents descriptive statistics for outcome variables, household characteristics, and plot characteristics. Agricultural output value and revenue are skewed, with averages of agricultural output value and revenue roughly double the medians. Half of Thai farmers produce agricultural products for both for their own consumption and commercial sale. These products are valued below 89,000 baht annually, and their revenue from sales is typically under 76,000 Baht. This aligns with the small average and median farm size (23.9 Rai or 9.4 acres, and 16 Rai or 6.3 acres, respectively). Notably, 90% of households less than 30 rai (11.9 acres), and most land is dedicated to cropping. Additionally, less than half of the farmers have access to irrigation. Table  1 also shows that the average Thai farm household consists of 4 people with the average age of household head of 56 years old. Several factors, such as aging households, limited labor, and the relatively higher costs associated with small landholdings compared to larger ones, might discourage farm households from adopting diversification strategies. This aligns with the low diversification rates observed in Table  2 .

We explore two types of diversification: (i) across agricultural enterprises, i.e., cropping, livestock, and fisheries (Seo  2010 ; Chonabayashi et al. 2020 ; Chonabayashi  2021 ; Jithitikulchai 2023 ); and (ii) across crop mix (Attavanich et al. 2019 ). Table 2 indicates that only 39% of Thai farm households are engaged in more than one type of agricultural activity, and more than half of those who engage in cropping do not diversify their farm activities into livestock or fisheries. Within crops, the majority of Thai farm households grow around 2 different crops per year. Focusing on farm households engaged only in cropping, we find that 30% of them practice monoculture.

2.2 Climate data

2.2.1 re-analysis satellite remote sensing data, the era5 database.

We utilize historical reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF). This reanalysis incorporates past observations and information from many sources to generate consistent, complete time series of climate variables (Hersbach et al. 2020 ).

The data contains hourly average weather data at a spatial resolution of 0.10 degrees (approximately 9 km). For each grid cell, we obtained hourly precipitation data in millimeters (mm) and average daily temperature in degrees Kelvin measured at 2 m above the earth’s surface. We then calculated total daily precipitation (mm) and average daily temperatures ( \(^\circ C\) ) within each grid.

We generated daily precipitation and temperature for each sub-district (tambon) in Thailand by combining data from all 0.10-degree pixels within each sub-district’s boundary. This process yielded daily average temperature and total precipitation at the sub-district level. In cases of adjacent sub-districts sharing the same pixel, we assigned identical precipitation and temperature values. We then used these sub-district-level daily weather data to construct the necessary annual weather variables for our analyses, such as average temperature, total precipitation, number of hot days, and number of wet days.

Weather variables

For each variable, we aggregated the gridded daily data to annual measures corresponding to the survey period (May 1st to April 30th of the following year) for each survey round. We follow previous studies in choice of weather variables (Attavanich  2011 ; Attavanich and McCarl  2014 ; Chen et al. 2001 ,  2004 ; Jithitikulchai 2014 ; Jithitikulchai et al. 2019 ; McCarl et al.  2008 ,  2014 ; Rhodes and McCarl 2020a , b ; Yu and McCarl  2018 ). Their descriptive statistics are presented in Panel D of Table  1 . Specifically, we constructed the following weather variables:

Annual average temperature : Sub-district-level average temperature, calculated across all days (365 or 366) within each survey year.

Annual total precipitation : Cumulative amount of rainfall measured at the sub-district level for the entire survey period due to potential storage of precipitation in soil or tanks.

Number of hot days per year : Number of days within a survey round where the maximum temperature exceeds 32.22 \(^\circ C\) .

Number of wet days per year : Number of days within a survey round where total precipitation exceeds an inch (25 mm).

Validity of the ERA5 data

To validate our climate data, we compared it with the monthly ground-station data recorded by Thailand’s Meteorological Department during 1981–2020. We find strong positive correlations between ERA5 and the ground-station data, as evidenced by high Pearson’s correlation coefficients (see Appendix A for details). Appendix A also presents additional test results that demonstrate the close correspondence between ERA5 data and observed data.

2.2.2 IPCC temperature projections

To assess long-term climate change impacts, we rely on projections from the 2021 IPCC report. We specifically focus on the projected ensemble mean global surface temperature changes under five different Shared Socio-economic Pathway(SSP)/Representative Concentration Pathway (RCP) scenarios. These scenarios describe alternative socio-economic trends and the approximate level of radiative forcing and greenhouse gas (GHG) emissions resulting from each pathway by the year 2100 (Arias et al. 2021 ; IPCC 2021 ).

3 Methodology

3.1 baseline model specification.

To estimate climate effects on agricultural output, we use the household survey data for the 2006/2007–2019/2020 crop years, matched with the sub-district-level weather data. For estimation we use the following base specification:

where \({Y}_{st}^{i}\) is the value of agricultural output for household i in sub-district s in survey year t . \(f\left({w}_{st}\right)\)  is a flexible functional form depicting the effects of climate on the outcome variable. \({X}_{t}^{i}\) is a set of household-level characteristics which include household size, whether the household head is female, age of the household head, whether the household head completed secondary education (9 years), whether the household has membership in cooperatives or agricultural banks (BAAC), the share of irrigated land, the share of rented land, and the size of agricultural land farmed.

To specifically examine diversification within crop production, we focus on a sub-sample of households engaged only in cultivation. For these crop-producing households, we use the size of land used for cultivation in place of that used for agriculture. Note that we do not include the value of household assets and its squared term due to possible endogeneity issues (correlation between the variable and the error term). Because over half of the households in our sample grow rice and the Thai government often intervenes the rice market, we also include the dummy variable for whether the households grow rice to control for the effect of market price intervention by the government.

To capture country-wide effects of the 2011 major flood and 2015 severe drought, we include dummy variables for cropping years 2011/2012 and 2014/2015, \({\delta }_{t=2012}\) and \({\delta }_{t=2015}\) . We also include the region dummies ( \({R}_{r}\) ) which control for region-specific time-invariant effects on outcomes. Thailand’s Meteorological Department divides the country into six regions deemed to have similar climates. A quadratic time trend is included as a proxy of agricultural technology progress (McCarl et al. 2008 ; Attavanich and McCarl  2014 ; Ding and McCarl  2014 ; Jithitikulchai et al. 2019 ) among many others). The error term ( \({\epsilon}_{st}^{i}\) ) captures unobservable factors, measurement errors, and random fluctuations. Finally, we report robust standard errors that account for heteroskedasticity (unequal variance of errors across observations).

Our primary focus is on the effects of temperature on the real value of agricultural output. However, since variations in temperature are likely correlated with precipitation, precipitation is included in the model. We define the dependent variable, real value of agricultural output, in three different specifications \(f\left({w}_{st}\right)\) described as follow:

Model 1: Linear weather

Mean annual temperature (in \(^\circ C\) ) and annual total rainfall (in mm) enter the baseline specification linearly and separately:

Model 2: Quadratic weather

Since the effects of weather, especially temperature, can exhibit non-linear relationships (Dell et al. 2014 ; Deschénes and Greenstone  2011 ), quadratic terms of both temperature and rainfall are included:

Model 3: Including extreme weather variables

We further add indicators which capture extreme weather conditions:

We focus on the real value of total output, including both on-farm consumption and sales, to comprehensively analyze household impacts. Almost a third of household output is consumed on-farm, highlighting the importance of considering this aspect. We then transform the outcome variables using the inverse hyperbolic sine function as described below.

3.1.1 Inverse hyperbolic sine transformation and temperature elasticity of output value

We apply the inverse hyperbolic sine (IHS) transformation, a well-established approach in the literature (Pence  2006 ), to the outcome variable. This allows interpretation of the regression coefficients as an approximation of the logarithm transformation while retaining non-positive valued observations (Bellemare and Wichman 2020 ). In our case, the ‘temperature elasticity of output’ derived from the estimated coefficients of the temperature and its squared terms is useful in that it measures the sensitivity of output (percentage change) with respect to a one-percentage change in temperature. Thus, we can compare impacts of temperature across different household cohorts.

Following Bellemare and Wichman ( 2020 ), we derive the elasticity for Models 2–3 with quadratic terms [Eqs. ( 3 )-( 4 )] as follows, and using subscript \(st\) to represent sub-district \(s\) in year \(t\) :

In turn taking the hyperbolic sine transformation:

Then taking partial derivative with respect to temperature and rearrange:

By the definition of elasticity, we have:

where \({\xi }_{{Y}_{st}^{i}temp}\) is the temperature point elasticity of output at a given temperature calculated from the nonlinear transformation of the estimated parameters.

Both temperature and temperature-squared coefficients affect the elasticity magnitude [as shown in Eq. ( 5 )]. For calculating of point elasticity, we use the fitted values from the regressions as a corresponding value \({Z}_{st}^{i}\) for each value of temperature; and for summary measure we do the calculation holding the value of all other control variables constant.

3.2 Assessing the impact of agricultural diversification

We investigate the role of agricultural diversification by determining whether it can help attenuate the global warming impacts. To do this we estimate models for separate cohorts of two levels of diversification: (i) types of enterprises, i.e., cropping, livestock, and fisheries; and (ii) the mix of crops grown.

Firstly, we run models 1–3 using the sub-sample of farm households that state they pursue multiple agricultural enterprises diversification strategies and those that do not. Then we compare the estimated coefficients on temperature and the resulting temperature elasticities as similar to Lien et al. ( 2006 ), Birthal et al. ( 2013 ), Chonabayashi et al. ( 2020 ), Chonabayashi ( 2021 ), and Jithitikulchai ( 2023 ).

One approach to address the above problem is by running a pooled regression with an additional interaction term between a diversification dummy variable and weather variables. Specifically, we use the specification:

where \({D}_{st}^{i}\) is the dummy variable indicating agricultural diversification in some forms, and \(f\left(tem{p}_{st}\right)\) is the function of temperature variables. The \({D}_{st}^{i}\) diversification dummy is included to account for the mean difference in the output value between households that do and do not diversify. The estimated coefficient \(\beta\) of the interaction term(s) indicates whether diversification helps reduce the impact of temperature changes on output value. Note, however, that the point estimate of \(\beta\) could be subject to potential selection bias in that the decision whether household adopt a diversification strategy is not random. With both approaches potentially having their own threats to identification, we present the results for both for robustness checks.

3.3 Threats to identification and the use of pseudo-panel settings

One main concern with cross-sectional regression analysis is that the estimates may be biased due to unobserved heterogeneity that are not included in the model but can influence the results (Arellano and Honoré  2001 ; Arellano  2003 ; Glenn 2005 ; Warunsiri and McNown  2010  among others). To address this concern and leverage the additional time dimension in our data, we use a pseudo-panel approach (Deaton  1985 ). This approach leverages the repeated observations across time for groups of individuals with similar characteristics, allowing us to control unobserved heterogeneity to some extent. We defined the cohorts based on the province of residence of farm households as farm location is a time-invariant characteristic that likely influences agricultural practices and outcomes (Attavanich et al. 2019 ). Our pseudo-panel data consists of 77 province cohorts and covers a span of 14 years of survey data. Following recommendations by Deaton ( 1985 ) and Verbeek and Nijman ( 1992 ) to mitigate potential bias from sampling errors of small cohort sizes, we exclude groups with less than 10 cohort-year observations from the analysis.

We apply Moffitt ( 1993 )’s estimator which is equivalent to a within cohort estimator. Specifically, we start by averaging Eq. ( 1 ) with individual fixed effects over cohort \(c\) at time \(t\) :

Then, we apply within transformation and estimate the following specification:

We also apply a similar within transformation to Eq. ( 6 ) as the pooled regression with the diversification dummy variable to examine the role of agricultural diversification.

3.4 Predicting value of agricultural output under different temperature projections

Given the estimated coefficients obtained from the regression analysis, we can simulate the impact of climate change using the IPCC scenarios. To do this, we use five SSP scenarios (Attavanich et al. 2019 ; Arias et al. 2021 ; IPCC 2021 ) that capture a range of low and high climate impacts:

SSP1-1.9: a very low GHG emissions scenario.

SSP1-2.6: a low GHG emissions scenario.

SSP2-4.5: an intermediate GHG emissions scenario.

SSP3-7.0: a high GHG emissions scenario.

SSP5-8.5: a very high GHG emissions scenario.

In reporting the scenarios are denoted as ‘SSPx-y’, and this stands for the socio-economic trend for SSP scenario ‘x’, with a ‘y’ radiative forcing level (IPCC 2021 ). We use historical sub-district-level temperature from the re-analysis data to combine with the predicted changes in temperature under different IPCC’s scenarios for global surface temperature change to generate subregional temperature projections for Thailand from 2021 to 2050.

With the realizations and projections of the mean temperature during 1981–2020, we can then predict the value of total output in each year and under different scenarios from 2021 onward ceteris paribus . That is, we are holding household and plot characteristics (control variables) at their levels in 2020 and then vary temperature to reflect the climate scenarios. Since our fitted value of the outcome variable is expressed in an inverse hyperbolic sine function format, we transform it back by taking the hyperbolic sine function thereby acquiring the predicted output value. We did not consider alterations in other climate variables such as precipitation and extreme weather, because we only had projection data on temperature at the time of analysis.

4 Estimation results

In this section, we report and discuss the estimated impacts of climate change, particularly focusing on temperature, on real farm output value. We begin by presenting the coefficients that quantify the overall impact of temperature changes. Next, we explore the role of agricultural diversification as a potential strategy to alleviate these negative impacts. Finally, we leverage the estimated coefficients to project the future value of output under various climate scenarios.

4.1 The effects of temperature on agricultural output

Columns (1)–(3) of Table 3  report the point estimates of the coefficients from cross-sectional analysis for the three model specifications with varying climate variables. The results show that a higher temperature leads to a reduction in the value of agricultural output. We can interpret the coefficient as an elasticity, since the IHS transformation approximates a log transformation in linear models (Bellemare and Wichman 2020 ). In other words, a one-degree Celsius rise in temperature leads to a roughly 3.4% decrease in average real output value.

The significant coefficients on both the linear and squared terms of the temperature and precipitation variables in Model 2 indicate non-linear effects of climate on output value, consistent with findings in the literature (Dell et al. 2014 ). This supports the use of the quadratic specification for a more accurate estimation of the climate impacts.

Our findings remain robust when including controls for extreme weather events (floods and droughts) in Model 3. While the coefficients on temperature terms become less significant (reflected in the wider confidence interval in Fig.  1 b). This aligns with the conclusion that the temperature elasticity of output weakens as temperature increases. Notably, the coefficients for extreme weather controls are negative and highly significant, indicating a negative impact of such events on output. However, their correlation with temperature reduces the explanatory power for our main temperature terms. Therefore, we adopt Model 2 as the baseline and move the full regression results for Models 1 and 3 to Appendix B .

Point elasticity of agricultural output value with respect to annual average temperature. Note: Panels ( a ) to ( e ) of Fig.  1  illustrate point elasticity of agricultural output value along with 95% confidence interval calculated from the nonlinear combination of estimated parameters

Given the nonlinear model, the effects of temperature on real output value are best interpreted as elasticity. We calculated elasticity using the estimated coefficients of Model 2 (Table  3 ). Our results suggest that a one-percent rise in surface temperature would lead to a fall in the value of output of approximately 1.5-2%.

We use estimates from Model 2 to calculate the point elasticity for each annual average temperature and its corresponding fitted output value. Figure  1 a illustrates these elasticities with their 95% confidence intervals. We observe that the elasticity becomes negative around the average temperature of 24–27 \(^\circ C\) and declines at an increasing rate thereafter. Farms with diversified activities, such as those combining cultivation and livestock (24 °C) or practicing multicropping (27 °C), show greater resilience to rising temperatures compared to non-diversified farms. Non-diversified farms, especially those focused solely on monoculture production (25 °C) or cultivation alone (26 °C), experience negative impacts on production value at lower temperature thresholds. This highlights the potential benefits of farm diversification as a strategy to adapt to climate change and maintain production value in a warming environment. Despite wider confidence intervals, the point elasticity reaches an estimated maximum of -10 at the highest observed annual average temperature (33 \(^\circ C\) ). This suggests that a 1% point increase in temperature could lead to a 10% decrease in the value of output.

Columns (4) to (6) of Table  3 present the estimates obtained from the pseudo-panel approach. These estimates are largely consistent with those from the pooled cross-sectional data analysis, despite potentially lower significance of some coefficients due to fewer observations in the pseudo-panel data. This reinforces the robustness of our findings and suggests that bias arising from unobserved heterogeneity should not be a major concern. Therefore, we primarily rely on pooled cross-sectional data for our main results.

Across all models (Table  3 , columns 1–3), the coefficients of the share of irrigated land are positive and significant, indicating that households with irrigation systems have a higher average output value. This finding, along with the relatively small effect of precipitation, suggests that irrigation is a crucial water source for Thai agriculture. Other noteworthy controls are the survey round dummy variables. The significant negative coefficients for the 2015 dummy variable reflect the severe drought that year. Interestingly, the 2011 dummy has a positive coefficient. While floods can damage crops, 2011’s floods might have been short-lived, and the year likely experienced higher overall rainfall which could benefit many agricultural activities. Additionally, positive pass-through effects of flood-induced higher prices for agricultural products might also play a role.

4.2 The role of agricultural diversification amid climate change

We now investigate whether agricultural diversification can mitigate the negative impacts of climate change. To capture diversification behavior across two levels - by types of agricultural activities and crop mix - we consider diversification strategies in three forms: (i) the number of agricultural enterprises, (ii) by types of enterprise (cropping, livestock, and fisheries), and (iii) within cropping activities.

Diversification by the number of agricultural activities

Table 4 (columns 1–2) reports the coefficients from a cross-sectional analysis using the model 2 specification. Column1 shows results for households with only one agricultural activity (cropping, livestock, or fisheries), and column 2 reports presents results for those with at least two activities (full results in Appendix B, Table B1 ). We use these parameter estimates to calculate point elasticities (output response to temperature change) displayed in Fig.  1 c. Crucially, households with diversified activities (two or more enterprises) exhibit significantly lower sensitivity to temperature changes (in absolute terms) compared to those with just one enterprise.

In fact, the elasticity value of diversified households is close to zero, implying minimal impact of temperature fluctuations on their total output at any given average annual temperature. The temperature elasticity for one-enterprise households evaluated at the mean temperature (26.4 \(^\circ C\) )is -2.04, while for diversified households it is less than half at -0.94. This suggests that, at the long-run average temperature, a one-percent temperature increase would lead to a nearly 2% decrease for non-diversified households. In other words, Thai farm households with higher diversification experience lower output losses due to rising temperatures.

Table 5 presents the estimates from our pseudo-panel regression (full results in Appendix C ). Consistent with the cross-sectional analysis, Table 5 (columns 1–2) confirms that households with more than one agricultural enterprise exhibit lower sensitive to temperature changes, compared to those with only one enterprise (Table 6 ).

Table B2 (columns 7–8) in Appendix B show that the negative impact of extreme rainfall is smaller for households engaged in more than one enterprise. This is consistent with findings in Chonabayashi et al. ( 2020 ) or Jithitikulchai ( 2023 ) that diversified households can better mitigate the adverse impact of droughts, floods, or a rise in temperature.

Diversification by type of agricultural activities

We now analyze a popular diversification strategy: households engaged in both cropping and livestock, compared to those solely engaged in cropping (Table 4 , columns 3–4) with full results in Appendix B, Table B2 . The temperature terms have significantly lower magnitudes for households with both enterprises. Figure  1 d confirmed this, illustrating that the absolute values of the temperature point elasticity are generally smaller for households practicing crop-livestock diversification. This implies reduced sensitivity to temperature changes for diversified households. Table 5  (columns 3–4) also confirmed this from pseudo-panel regression analysis.

While the focus of our study is not on extreme weather variables, it is noteworthy noting that their coefficients in Table B3 (columns 7–8) are also smaller for households engaged in both cropping and livestock activities compare to solely cropping households. This finding further supports agricultural diversification as a strategy to mitigate the adverse effects of extreme weather on household agricultural production.

Diversification within cropping activities

Since most households engage in cropping, we now focus on diversification strategies by crop choices and the number of crop(s) grown. Table 4 (columns 5–6) presents the estimated temperature impact, comparing monoculture with diversified crop mixes. We find suggestive evidence that growing more than one crop might help alleviate the negative effects of temperature changes. The coefficients for temperature terms in column 6 (diversified crops) are smaller in magnitude and statistically insignificant compared to column 5 (monoculture). This suggests that households practicing crop diversification experience, on average, a lesser impact from rising temperature. The findings from the pseudo-panel regression (Table 5 , columns 5–6) align with these results. Figure  1 e further this, as the absolute value of the temperature elasticity for diversified households is lower than for those growing just one crop type.

4.3 Predicted value of agricultural output under different temperature projections

Using the coefficients and fitted value obtained from our base case Model 2, we can predict the value of agricultural output under the ensemble SSP scenarios as discussed in Section 3.4 . Figure  2 a depicts the nation-wide annual average temperature projection in Thailand for 2020–2050. Under the intermediate GHG emission scenario (SSP2-4.5), the annual average temperature in Thailand will rise from 26.68 \(^\circ C\) in 2020 to 27.39 \(^\circ C\) by 2050. By contrast, under the worst-case scenario (SSP5-8.5), the surface temperature in Thailand will be 27.7 \(^\circ C\) in 2050.

Figure  2 b illustrates the projected total farm household output value by year. Consistent with Fig.  1 a, which shows a negative temperature elasticity above a certain temperature range (around 24–27 °C), the real output value exhibits a gradual decline (driven by rising temperatures) from around 44,000 Baht in the early 2000s to just below 40,000 Baht by 2020 (all else being equal).

Agricultural output value and temperature projection. Note: Panel ( a ) illustrates the temperature predictions from the latest Intergovernmental Panel on Climate Change (IPCC) Assessment Report (AR6). Panel ( b ) illustrates the projected annual average household’s real agricultural output value by different climate change scenarios. The average household and plot characteristics in 2020 were used to calculate the projected values

It is important to recognize that this projection assumes average characteristics for all households. While holding everything else constant allows us to isolate the temperature effect, it’s a strong assumption. Several factors, such as advancements in agricultural technology, infrastructure improvements, market changes, or better policies, could potentially raise output value despite rising temperatures. However, this exercise helps visualize the potential adverse impact of climate change on agricultural production value due to variations in annual average temperature under different emission scenarios. We see a drastic drop in the average annual output value to below 30,000 Baht in the worst-case scenario (SSP5-8.5) with very high greenhouse gas emissions. This projected decline could have significant negative consequences for the Thai economy and household well-being.

Figure  3 illustrates the projected average output value for households with different diversification levels, estimated using Model 2 in Table 5 (columns 4–5). Households engaging in 2 or 3 enterprises (diversified) show significantly higher projected average output value compared to those with only one enterprise (non-diversified). The difference in projected output between the best and worst-case scenarios is most pronounced in 2050, with the gap for non-diversified households being roughly twice as large. Furthermore, even in the worst-case scenario, the projected output for diversified households remains considerably higher than the best-case scenario for the non-diversified ones. These findings highlight the potential of agricultural diversification strategies in mitigating the negative impacts of climate change on farm output, particularly for poor farmers.

Output value projection by number of agricultural activities. Note: The figure illustrates the projected annual average household’s real agricultural output value by different climate change scenario and by number of agricultural activities. We use the average household and plot characteristics in 2020 to calculate the projected values

Figure  4 compares the projected output between households solely engaged in cropping and those practicing crop-livestock. The results reveal a consistent pattern, suggesting that diversification might play a vital role in buffering households against potential climate changes, particularly rising temperature.

Output value projection by type of agricultural activities. Note: The figure illustrates the projected annual average household’s real agricultural output value by different climate change scenario and type of agricultural activities. We use the average household and plot characteristics in 2020 to calculate the projected values

5 Discussion

In developing countries like Thailand, impoverished farm households face challenges due to unpredictable agricultural production and income resulting from climatic and economic shocks. These losses are further compounded by the lack of insurance, social securities, and institutional support. With climate change amplifying these issues, weather-related shocks are expected to increase in frequency and intensity, placing smallholder farm households at even greater vulnerability. While prior studies demonstrate agricultural diversification as a viable strategy for Thai agricultural households to adapt to climate change (Attavanich et al. 2019 ; Saengavut et al. 2019 ; Bellora et al. 2018 ; Forsyth and Evans 2013 ; Kasem and Thapa 2011 ; Rungruxsirivorn 2007 ; among others), there is a research gap in country-level analysis.

This paper investigates the impact of temperature changes on agricultural production in Thailand and the effectiveness of agricultural diversification as an adaptation strategy. We achieve this by integrating a nationally representative socioeconomic survey of Thai farm households with reanalyzed temperature and precipitation data.

Our analyses reveal that average annual temperatures exceeding a range of 24 °C to 27 °C negatively affect Thai farmers’ agricultural production. Our results suggest a potential decrease in agricultural production of up to 10% for every 1% point rise in average annual temperature. Diversification, defined as (a) engaging in two or more activities like cropping, livestock, or fisheries, or (b) mixed crops are more climate resilient. Importantly, our research highlights the critical role of effective irrigation systems. Regardless of farm size, households with a larger portion of irrigated land achieve higher output value. This underscores the reliable irrigation systems for farm households. The land holding characteristics in this study are predominantly small farms. This is an important consideration when interpreting the findings on the relationship between farm size and diversification strategies of Thai farm households.

This paper contributes to the literature on climate change adaptation. We explore how agricultural enterprise diversification and crop diversification impact farm output and climate vulnerability, using sub-district-level weather variations to identify climate impacts. Despite being nationally representative, our use of pooled cross-sectional data means some unobserved factors, like entrepreneurial ability or risk preferences might correlate with both weather and production decisions but remain unaccounted for in our model. However, our pseudo-panel regression results align with regression using pooled cross-sectional data, suggesting minimal bias from unobserved heterogeneity.

It is important to acknowledge the limitations of this study when interpreting its policy implications, that is the potential for over extrapolating the benefits observed in diversified farms to non-diversified ones. For farms within ecoregions or microclimates with unique soil compositions or specific climate patterns that support the growth of particular crops, specialization might be a more strategic choice (Kray et al. 2019 ). For example, evidence suggests that crop intensification is preferable for rubber farmers in some regions of Thailand (Amornratananukroh et al. 2023 ). However, if we can identify practices where changing from non-diversification to diversified activities can increase profitability while also offering greater climate resilience in the long run, then pre-emptive adaptation to climate change becomes a reasonable strategy.

For future research, incorporating analyses of soil quality’s role in climate change impacts on agriculture holds significant promise. To delve deeper, accessing richer panel-level soil quality data is essential. Currently, our household surveys lack this; limited two-year spatial soil data per subdistrict hinders its inclusion. Further analysis of the CO2 effect, which may have a counterbalancing effect on temperature, is also warranted as the SSPS series provides CO2 projections. While initial studies predicted CO2 fertilization mitigating temperature stress on rice yield, recent research highlights negative interactions between these factors (Ishigooka et al. 2021 ). This complexity suggests that CO2 enrichment can only partially offset yield decline from rising temperatures (Maniruzzaman et al. 2018 ; Yamaguchi et al. 2023 ), and rising night temperatures could diminish the potential gains from CO2 fertilization in rice production (Cheng et al. 2009 ). Analyzing the effects on rice yields (and other crops, if applicable) from interactions of temperature and rainfall effects, direct physiological effects of increased CO2, and the effectiveness and availability of adaptations is complex, but crucial. Therefore, further research on the intricate interactions between CO2 and temperature on crop yields is crucial for informing adaptation strategies in a changing climate. Another avenue is climate-smart agriculture, but our survey data lacks details. Focusing on smart farming and advanced technology could help farmers mitigate climate impact. Despite limitations, our study contributes nationally representative insights into agricultural diversification’s interplay with economic resilience of farm households, using historical and projected climate change impacts.

6 Conclusions and policy implications

Our analysis shows that climate change negatively affects Thai farm households’ agricultural production. Agricultural diversification, including multiple enterprises and crops, offers potential as an ex-ante adaptation strategy.

From a policy perspective, our main results support Thailand’s current national climate change strategic plan for agriculture, promoting integrated farming and crop diversification (Attavanich 2018 ). In 2020, about 70% of Thai farmers practiced only one agricultural activity, with about 40% focusing on one crop. Our insight highlights diversification’s benefits, urging support for farmers to diversify. To encourage the adoption of integrated farming for sustainable agriculture, incentives, financial support, and specific guidance on integrating livestock and crops or selecting profitable and drought-resistant crops are essential.

In practice, implementing a major diversification strategy, like an integrated crop-livestock system (ICLS), could be challenging, particularly in the short run, due to the small size of Thai farmers and the high implementation cost. Nevertheless, it is essential to consider a policy approach that prioritizes immediate actions (high-level policy approach) in the agricultural sector to adapt to observed climate conditions. Meanwhile, the national sectoral policy should encourage long-term, adaptable strategies through well-designed incentives (Kurukulasuriya and Rosenthal  2013 ; Makate et al. 2023 ). When devising incentives and support mechanisms for agricultural diversification, it is important to consider factors like poverty reduction, sustainable development, and climate resilience. These efforts also contribute to broader socio-economic and environmental objectives.

In conclusion, this study highlights the urgency of addressing climate change’s negative impact on Thai agriculture through diversified farming practices. While implementing large-scale diversification strategies may be challenging in the short-term, a multi-pronged approach is crucial. This approach should combine immediate actions focused on adapting to current climate conditions with long-term, dynamic adaptation strategies like diversified farming, supported by well-designed incentives and targeted policies. By prioritizing both short-term and long-term solutions while considering diverse objectives from the Sustainable Development Goals (SDGs), Thailand can ensure the long-term viability of its agricultural sector and the well-being of its farming households.

Data availability

The data is available upon request , with permission from the Office of Agricultural Economics.

Change history

18 july 2024.

A Correction to this paper has been published: https://doi.org/10.1007/s10584-024-03776-5

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Acknowledgements

We would like to thank Karnjanaporn Wilairat, Natakarn Boonarsa, Jirawan Kojansri, and Sorawit Yankittikul for their excellent research assistance.

This study was supported by a grant from the Puey Ungphakorn Institute for Economic Research (PIER) at the Bank of Thailand.

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Benjapon Prommawin

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Nattanun Svavasu & Spol Tanpraphan

Faculty of Economics, Khon Kaen University, Khon Kaen, Thailand

Voravee Saengavut

Faculty of Economics, Thammasat University, Bangkok, Thailand

Theepakorn Jithitikulchai

Faculty of Economics, Kasetsart University, Bangkok, Thailand

Witsanu Attavanich

Department of Agricultural Economics, Texas A&M University, College Station, TX, USA

Bruce A. McCarl

Department of Economics, Michigan State University, East Lansing, MI, USA

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Thailand Development Research Institute, Bangkok, Thailand

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TJ, VS, and WA conceptualized this study. NS, ST, and TJ compiled the survey microdata, while VS was responsible for preparing and validating the reanalyzed weather data. BP, NT, ST, and VS conducted the analyses, with BP, NS, ST, VS, TJ, WA, and BAM jointly analyzing and discussing the results and their implications. BP, NS, ST, VS, TJ, and BAM contributed to drafting the manuscript. TJ and VS shared the role of Co-PIs.

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The original article has been corrected. In this article the affiliation details for the lead author Benjapon Prommawin were incorrectly given and have now been corrected to 'Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand' and 'Faculty of Economics, University of Cambridge, Cambridge, UK'. Tables 3 and 4 were also adjusted to the right formatting, dividing the data analyzed and the 'No. of Observations' on each table.

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Prommawin, B., Svavasu, N., Tanpraphan, S. et al. Impacts of climate change and agricultural diversification on agricultural production value of Thai farm households. Climatic Change 177 , 112 (2024). https://doi.org/10.1007/s10584-024-03732-3

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• Data Descriptor
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• Published: 16 February 2022

A global dataset for the projected impacts of climate change on four major crops

• Toshihiro Hasegawa   ORCID: orcid.org/0000-0001-8501-5612 1 ,
• Hitomi Wakatsuki   ORCID: orcid.org/0000-0002-9861-5921 1 ,
• Shalika Vyas   ORCID: orcid.org/0000-0002-9933-1269 3 ,
• Gerald C. Nelson   ORCID: orcid.org/0000-0003-3626-1221 4 ,
• Aidan Farrell 5 ,
• Delphine Deryng   ORCID: orcid.org/0000-0001-6214-7241 6 ,
• Francisco Meza 7 &
• David Makowski   ORCID: orcid.org/0000-0001-6385-3703 8  

Scientific Data volume  9 , Article number:  58 ( 2022 ) Cite this article

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• Environmental impact

Reliable estimates of the impacts of climate change on crop production are critical for assessing the sustainability of food systems. Global, regional, and site-specific crop simulation studies have been conducted for nearly four decades, representing valuable sources of information for climate change impact assessments. However, the wealth of data produced by these studies has not been made publicly available. Here, we develop a global dataset by consolidating previously published meta-analyses and data collected through a new literature search covering recent crop simulations. The new global dataset builds on 8703 simulations from 202 studies published between 1984 and 2020. It contains projected yields of four major crops (maize, rice, soybean, and wheat) in 91 countries under major emission scenarios for the 21st century, with and without adaptation measures, along with geographical coordinates, current temperature and precipitation levels, projected temperature and precipitation changes. This dataset provides a solid basis for a quantitative assessment of the impacts of climate change on crop production and will facilitate the rapidly developing data-driven machine learning applications.

Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.17427674

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Climate impacts on global agriculture emerge earlier in new generation of climate and crop models

CROPGRIDS: a global geo-referenced dataset of 173 crops

Global gridded crop harvested area, production, yield, and monthly physical area data circa 2015

Background & summary.

Climate change affects many processes of food systems directly and indirectly 1 , but the primary effects often appear in crop production. Projections of crop production under future climate change have been studied since the early 1980s. From the 1990s onward, researchers have used future climate data and crop simulation models to project the impacts of climate change on crop yields under various scenarios 2 . Since then, crop simulation models have been used in hundreds of studies to simulate yields for different crops under a range of climate scenarios and growing conditions 3 . The results have been periodically reviewed and assessed by national and international organisations, in particular by the Intergovernmental Panel on Climate Change (IPCC) Working Group II, which provides policy-relevant scientific evidence for the impacts of and adaptation to climate change 3 . Review studies covering the last five IPCC assessment cycles confirm that the overall effects are negative but vary significantly among regions 4 , 5 .

Before 2010, simulation studies were conducted mainly by individual research groups using different climate models, target years, spatial resolution with local management and cultivar conditions. Since 2010, however, significant efforts have been made to coordinate modelling studies through Agricultural Model Intercomparison and Improvement Project (AgMIP) 6 , which compares results from multiple crop models using standardised inputs. Early AgMIP activities have disentangled sources of uncertainties in crop yield projections and revealed that yield projections are variable among crop models and that model ensemble mean or median often works better than a single model 7 , 8 , 9 , 10 , underpinning the importance of datasets based on multiple crop models.

Data sets including crop model simulations produced by AgMIP were subjected to statistical analysis and the results were used to quantify the impacts of climate change on major crops 11 , 12 . A versatile tool to aggregate simulated results is already available for global gridded studies 13 to facilitate access to the data. Besides these coordinated efforts, however, many simulation results are scattered and not readily available for meta-analysis. To deliver policy-relevant quantitative information, we need to develop a shared and well-documented database that can be used to assess the impacts of different climate and adaptation scenarios on crop yields.

Here, we have developed a global database for potential use for the IPCC Working Group II assessment, obtained through two methods. The first method draws on the dataset used in the meta-analysis of Aggarwal, et al . 5 , which includes studies considered in the previous five cycles of IPCC assessments 4 , 14 . The second method is based on a new literature search of studies published during the sixth IPCC assessment cycle (covering the period 2014–2020) reporting crop simulations produced for several contrasting climate change scenarios. The combined dataset covers all six cycles of the IPCC assessment and can serve as a solid basis for analyses from the sixth IPCC assessment onward.

The dataset contains the most relevant variables for evaluating climate change impacts on yields of maize, rice, soybean, and rice for the 21st century. They include geographical coordinates, crop species, CO 2 emission scenarios, CO 2 concentrations, current temperature and precipitation levels, local and global warming degrees, projected changes in precipitation, the relative changes in yield as a percentage of the baseline period obtained with or without CO 2 effects, and with or without implementation of different types of adaptation options.

Data collection

As shown in a PRISMA diagram (Fig.  1 ), we obtained data through two methods to develop this dataset. The first method is based on the previous meta-analysis by Aggarwal et al . 5 , which includes studies published before 2016 (Aggarwal-DS, hereafter). This meta-analysis builds on the dataset used for the 5 th IPCC assessment report 4 , 14 and an additional search through three types of databases: Scientific database (Scopus, Web of Science, CAB Direct, JSTOR, Agricola etc), journals and open access repositories, and institutional Websites (FAO database, AgMIP Database, World Bank, etc.) and Google Scholar. See Aggarwal et al . 5 for details. Briefly, the search terms used by Aggarwal et al . 5 include “agriculture” or “crop “or “farm” or “crop yield” or “crop yields” or “farm yields” or “crop productivity” or “agricultural productivity” or “maize” or “rice” or “wheat” and “climate change assessment” or “climate impacts” or “impact assessments” or “climate change impact” or “climate impact” or “effect of climate” or “impact of climate change”. The number of selected papers covering the four major crops is 166. We further screened them according to the availability of local temperature rise and geographical information, and traceability, resulting in 99 studies published between 1984 and 2016.

A diagram depicting paper collection and selection using the two search strategies. N is the number of studies.

The second method relies on a new recent literature review conducted using Scopus in March 2020 for four major crops (maize, rice, soybean, and wheat) for peer-review papers published from 2014 onward in line with the sixth assessment cycle of IPCC. In this method, we used several combinations of terms to retrieve relevant studies reporting simulations of the impacts of climate change on crop yields using recent climate change scenarios.

For maize, the following search equation was used: PUBYEAR > 2013 AND TITLE-ABS-KEY((maize OR corn) AND ((“greenhouse gas” OR “global warming” OR “climate change” OR “climate variability” OR “climate warming”)) AND NOT (emissions OR mitigation OR REDD OR MRV)).

Similar search equations were used for the other crops. Collectively, this search returned a total of 4703 references between 2014 and 2020: 1899 for maize, 1790 for wheat, 757 for rice, and 257 for soybean with some duplications because some papers studied multiple crops. Removing the duplicates, the number is down to 3816 studies.

To collect climate-scenario-based simulations, we then selected a subset of studies including the following terms related to climate scenarios in titles, abstracts, or authors’ keywords; “RCP”, “RCP2.6”, “RCP6.0”, “RCP4.5”, “RCP8.5”, “CMIP5”, and “CMIP6”. RCP stands for the Representative Concentration Pathways 15 , and each RCP corresponds to a greenhouse gas concentration trajectory describing different future greenhouse gas emission levels. The number followed by RCP is the level of radiative forcing (Wm −2 ) reached at the end of the 21 st century, which increases with the volume of greenhouse gas emitted to the atmosphere 16 . CMIP5 17 and CMIP6 18 are the Coupled Model Intercomparison Project Phase 5 and Phase 6, respectively, where groups of different earth system models (ESMs) provide global-scale climate projections based on different RCPs. Additionally, “process-based model” was used to search in the authors’ keywords to select for studies that use crop simulation models under CMIP5 or CMIP6 climate scenarios. As of March 2020, no results were found for CMIP6 in any search results.

This screening process resulted in a total of 207 references all together for four major crops. These studies were further evaluated for their variables and data availability; studies not reporting yield data were excluded. Projected yields with and without adaptations and yields of the baseline period were extracted, along with geographical coordinates, crop species, greenhouse gas emission scenarios, and adaptation options. We also tried to obtain local and global temperature changes and CO 2 concentrations as much as possible. In addition to extracting data from the literature, we contacted several authors of grid simulation studies to provide aggregated results for countries or regions. The authors of the three grid simulation studies responded and provided baseline and projected yields, annual temperature and precipitation data aggregated over for countries or regions 19 , 20 , 21 . The results from different ESMs were then averaged.

We removed duplicates between the datasets produced by the two methods and ultimatelly obtained a total of 202 unique studies. Both datasets include studies with different spatial scales: site-based, regional, and global. Among these, the results from the global gridded crop models were aggregated to country levels, and we focused on top-producing countries, which account for 95% of the world’s production of each commodity as of 2010 (FAOSTAT, http://www.fao.org/faostat/en/ , accessed on September 4, 2020). As a result, the dataset contains 8,703 sets of yield projections during the 21 st century from studies published between 1984 and 2020 (Online-only Table  1 ).

Relative yield impacts

Simulated grain mass per unit land area is used to derive the impact of climate change on yield (YI), which is defined as:

Where Y f is the future yield, and Y b is the baseline yield. One study 20 simulated yields separately under both climate change and counterfactual non-climate change scenarios from the pre-industrial era toward the end of the 21 st century, also accounting for yield increases due to non-climatic technological factors over time. In this case, YI obtained with the above equation under the climate change scenario was not fully relevant because it combines effects of both climate change and technological factors. Thus, for this study, YI was derived from the average yield in the 2001–2010 period under climate change and the average yield in the same period assuming no climate change, as follows:

Where Y f_cc and Y b_cc are the future and baseline average yields with climate change, Y f_ncc and Y b_ncc are the future and baseline average yields under counterfactual no climate change scenario.

Projected absolute grain yield (t/ha) is also included in the dataset, when available. These values should be used with caution because absolute grains yields are not always comparable due to the use of different yield definitions or assumptions. Different definitions include graded or non-graded yields, husked or unhusked, milled or non-milled yield. Moisture content correction factors can also be different, but these are not often explicitly indicated in the literature. Contrary to absolute yields, relative yields are unitless and rule out differences of yield defintions between studies.

Adaptation to climate changes

Various management or cultivar options are tested in the simulations. If the authors of the article consider these options as ways to adapt crops to climate change, we treat them as adaptation options, which are categorised into fertiliser, irrigation, cultivar, soil organic matter management, planting time, tillage, and others. Specifically, in the fertiliser option, if the amount and timing of fertiliser application are changed from the current conventional method, we treat them as adaptation. In the irrigation option, if the simulation program determines the irrigation scheduling based on the crop growth, climatic and soil moisture conditions, we treat this as adaptation because the management is adjusted to future climatic conditions. If rainfed and irrigated conditions are simulated separately, we do not consider irrigation as an adaptation. We define cultivar option as the use of cultivars of different maturity groups and/or higher heat tolerance than conventional cultivars. The planting time option corresponds to a shift of planting time from conventional timing. If multiple planting times are tested, we select the one that gives the best yield. The soil organic matter management option corresponds to application of compost and/or crop residue. The tillage option corresponds to reduced- or no-till cultivation compared to no conventional tillage. When studies consider adaptation options, we compute YI from the ratio of yield with adaptation under climate change to baseline yield without adaptation. To measure our capacity to adapt to climate change, we calculated adaptation potential - defined as the difference between yield impacts with and without adaptation - when a pair of yield values were available in the same study.

Temperature and precipitation changes

Both local temperature rise (ΔT l ) and global mean temperature rise (ΔT g ) from the baseline period have important implications. The former directly affects crop growth and yield, and the latter represents a global target associated with mitigation activities. We extracted both ΔT l and ΔT g from the literature as much as possible, but ΔT g is not available in many studies. In such cases, we estimated ΔT g using the Warming Attribution Calculator ( http://wlcalc.climateanalytics.org/choices ). In the dataset, we provide two estimates for ΔT g : one from the current baseline period (2001–2010) and the other from the preindustrial era (1850–1900). We also extracted precipitation changes (ΔPr) and baseline precipitation data reported in the selected studies. When only relative changes were available for precipitation data, we estimated ΔPr using the reported relative change and current precipitation levels described in the next section.

Current temperature and precipitation levels

Current annual mean temperatures and precipitation were obtained from the W5E5 dataset 22 , which was compiled to support the bias adjustment of climate input data for the impact assessments performed in Phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b, https://www.isimip.org/protocol/3/ ). The W5E5 dataset includes half-degree grid resolution daily mean temperature and precipitation data from 1979 to 2016, which we averaged for the period from 2001 and 2010. They were then extracted for each simulation site or region using the geographic information. For global simulations, which were aggregated to the country level, central coordinates were used to link gridded temperature and precipitation data with each country. As centroids may not represent the centre of the growing regions, particularly in large countries, growing-area weighted averages of annual temperature and precipitation were also provided using MIRCA 2000 23 , which contains half-degree grid harvested areas (a total of irrigated and rainfed) around the year 2000.

CO 2 concentrations

Several studies report two series of yield simulations obtained using two CO 2 levels to infer the CO 2 fertilization effects: one obtained with CO 2 concentrations fixed at the current levels and the other obtained with increased future CO 2 concentrations provided by the emission scenario considered. In the dataset, we make this explicit in the following two variables:

CO 2 : Binary variable equal to “Yes” if future CO 2 concentrations from the emission scenarios were used and “No” if the current CO 2 concentration was used for the yield simulations.

CO 2 ppm: if available, CO 2 concentration was extracted from the original paper. If not, we calculated it from projected changes in CO 2 concentrations based on the scenarios and periods studied. CO 2 concentration data were obtained from https://www.ipcc-data.org/observ/ddc_co2.html for CMIP3 and Meinshausen, et al . 16 ( http://www.pik-potsdam.de/~mmalte/rcps/ ) for CMIP5.

Baseline correction

Because baseline periods differed between studies, we corrected YI, ΔT l , ΔT g, ΔPr to the 2001–2010 baseline period by a linear interpolation method following Aggarwal et al . 5 . Namely, the impacts YI were first divided by the year gap between the future period midpoint year and the baseline period midpoint year of the original study. The impact per year was then multiplied by the year gap from our reference baseline period midpoint year (2005). The same method was applied to express ΔT l and ΔPr relatively to 2001–2010.

We made all data publicly available to increase accessibility (see Data Records section for access).

Data Records

All the data and R scripsts associated with the dataset are stored in the figshare repository 24 , where the following files are uploaded:

“Projected_impacts_datasheet_11.24.2021.xlsx” includes three worksheets. “Projected_impacts” worksheet contains the final dataset after screening, and “Adaptation_potential” is the extracted subset of the paired data comparing yield impacts with and without adaptation. “Excluded” has untraceable simulation results in the Aggarwal-DS.

“Meta-data_11.25.2021.xlsx” contains the summary of the dataset, such as the definition and unit of the variables used in “Projected_impacts_datatasheet.xlsx”.

“Online_only_summary_tables_11.18.2021.xlsx” contains data distribution, median, and mean impacts of climate change, presented in the online-only tables.

“Supplementary_materials_11.29.2021.pdf” contains methods for estimating local temperature rise and summary distribution of climate change impacts on four crop yields.

“Reference_11.24.2021.docx” provides a list of references that provided data.

“R_script_for_Hasegawa_et.al.11.26.2021.zip” contains R scripts used to estimate missing values of ΔT l ,ΔT l and ΔPr and draw Figs.  2 – 6 .

Data availability of crop yield simulations and its breakdown. (a) By global temperature rise from the preindustrial era and climate scenarios, (b) By projected time periods (midpoint years) and climate scenarios, (c) IPCC regions 29 and crop species, and (d) adaptation options and crop species. Note that n = 9812 in adaptation options (d) exceeds the total number of simulations (8703) because we collectively add all the options used in the simulations, including those that use multiple options. n is the number of simulation results.

Distribution of relative yield change due to climate change from the baseline period (2001–2010) with and without adaptation.

Climate change impacts (% of yield change from the baseline period) on four crops without adaptation under RCP8.5. ( a ) Mid-century; ( b ) End-Century. Maps with bluish symbols show positive effects (yield gain); Maps with reddish symbols show negative effects (yield loss). Projections under RCP2.6 and RCP4.5 are given in Supplementary Fig.  S3 .

Projected yield changes relative to the baseline period (2001–2010). (a) Mid-century (MC) projections without adaptation under RCP8.5 scenario, upper panels showing positive impacts and lower panels negative impacts, (b) End-century (EC) projections under three RCP scenarios by current annual temperature (T ave ), and (c) Yield change as a function of global temperature rise from the pre-industrial period by three T ave levels. The box is the interquartile range (IQR) and the middle line in the box represents the median. The upper- and lower-end of whiskers are median 1.5 × IQR ± median. Open circles are values outside the 1.5 × IQR.

Adaptation potential, defined as the difference between yield impacts with and without adaptation in projected yield impacts, for three RCPs by mid- and end-century (MC, EC). The box is the interquartile range (IQR) and the middle line in the box represents the median. The upper- and lower-end of whiskers are median 1.5 × IQR ± median. Open circles are values outside the 1.5 × IQR. (a) By adaptation options and (b) by IPCC regions.

Coverage of the data

A total of 8703 yield simulations are registered in the consolidated dataset. The number of simulations grows exponentially with publication year: 20 in the 1980s, 304 in 1990s, 830 in 2000s and 7549 in 2010s (Online-only Table  1 ). About 80% of the simulations use CMIP5 climate scenarios, and 11% use CMIP3. From CMIP5, RCP2.6, RCP4.5 and RCP8.5 are the most used concentration pathways (Online-only Table  2 , Fig.  2a ). ΔT g from the baseline period (2001–2010) ranges from 0 to 4.8 °C (0.8 to 5.6 °C from the preindustrial period). Almost all simulations with ΔT g  > 3 °C use RCP8.5, resulting in a greater ΔTg range under CMIP5 (RCPs) than under previous scenarios (SRES and others).

Projected time periods span widely in the 21 st century, but the midpoint years peak at 2020 for the near future, 2050 for mid-century, and 2080 for end-century (Fig.  2b ). Major emission scenarios such as RCP2.6, 4.5 and 8.5 are almost equally distributed across time periods. About 5% of the simulations assume no CO 2 fertilisation effects.

Relative frequency of the regions studied generally reflects harvested areas of the four crops in each region (Fig.  2c ). About 41% of the simulations were performed in Asia, which accounts for about 47% of the harvested area of the four major crops (mean of 2017–2019, FAOSTAT, http://www.fao.org/faostat/en/ , accessed on April 28, 2021). Europe is slightly overstudied (22%) for its world share of the harvested areas (12%). Central and South Americas is slightly under-researched (9%) for the regional share of harvested areas (15%), whereas Africa’s share (15%) is comparable to the area harvested (10%). Altogether global harvested areas for these four major crops is 7 × 10 8 ha: wheat represents 31% of this area, followed by maize (28%), rice (23%) and soybean (18%). Maize studies are over represented, accounting for about half of the simulations (52%), followed by wheat (26%) and rice (17%); soybean accounts only for 3% of the simulations (Fig.  2c ). Regionally, maize and wheat are harvested across almost all regions, and simulations follow the actual distribution of these crops. Rice is predominantly studied in Asia, reflecting actual distribution (85% of the harvested area is in Asia). Soybean remains understudied compared to the other three crops despite its large cultivated area (about 75% of the rice harvested area). Regionally, simulation sites or regions for soybean are mostly in the Americas, which account for 76% of the total soybean harvested area.

About 39% of the simulations (3376) use current management practices, and the rest (5327) consider different management or cultivars as adaptation options (Fig.  1d ). More than half of the simulations are run with multiple options. Among these options, fertiliser accounts for 32% followed by irrigation (29%), cultivar and planting date (17% each). There are 2005 pairs of yield simulations available for comparing results obtained with and without adaptation. These pairs of yield data can be used to compute the adaptation potentials of the different options considered.

Technical Validation

Data quality check.

We repeatedly checked the data with multiple authors for the new dataset. For the Aggarwal-DS, we reviewed the sources of references. In case of missing information such as climate scenarios, CO 2 concentration, or temperature increase, we came back to the original reference. Inconsistencies between the dataset and original papers were corrected when possible. Overall, corrections were made on 333 simulations from 10 studies, which we flag with “*” in the remark column of the dataset. We removed all data of the Aggarwal-DS that were untraceable in the original paper. This quality control excluded 47 simulations from 9 articles listed in the “Excluded” sheet.

We first examined the distribution of the climate change impacts on crop yields, which span from −100 to 136% (Fig.  3 ). This distribution is skewed to the left, as indicated by the negative skewness. The large kurtosis shows that distribution tails are longer than than those of the normal distribution. We tested the effects of potential outliers outside the 1.5-fold interquartile range (IQR) on the summary statistics of the climate change impacts on crop yields 25 . Removing values outside the 1.5-fold IQR decreases the number of simulations by 907(10.4%) and the negative effects of climate change on crop yields by 3.0% for the mean and 0.6% for the median, suggesting that the deletion affected the original distribution. We, therefore, keep all the simulation results in the dataset.

Methods to estimate local temperature and precipitation changes

Out of 8703 simulations, local temperature change (ΔT l ) and global temperature range (ΔT g ) were available in 4316 and 8109 simulations, respectively. To estimate ΔT l for 3793 simulations with missing ΔT l , we examined the relationship between ΔT l and the following six input variables in 4316 simulations where ΔT l was available: ΔT g , average temperature (area weighted), latitudes, longitudes, time periods, and emission scenarios. Values of ΔT l were estimated using random forest algorithms trained to establish a function relating local temperature rise to the six inputs considered. We tested and compared four models based on different combinations of the input variables. Among the four models, a reduced model with three variables (ΔT g , latitude, and longitude) showed the highest percentage of explained variance (97.1%), and led to a cross-validated RMSE as low as 0.18 °C (Supplementary Table  S1 and Fig.  S1 ). We, therefore, used the reduced model to impute ΔT l for the 4430 missing data. We also estimated ΔT g for 504 simulations with missing ΔT l from ΔT g , average temperature (area weighted), latitude, longitude, climate scenarios, future-midpoint year (Supplementary Table  S2 and Fig.  S2 ).

Likewise, we applied a random forest model to estimate ΔPr from current annual precipitation and average temperature (area weighted), latitude, longitude, local temperature change from 2005), climate scenario, future mid-point year, and climate change impact on yield relatively to 2005. Among eight models tested, a one with ΔT g , ΔT l , latitude, longitude, RCP, future-mid-point year and current annual precipitation perfomed best, which accounted for 96.9% of the out-of-bag variation of the data (n = 3560) and led to a cross-validated RMSE was 18 mm (Supplementary Table  S3 ). We then applied this model to estimate all missing ΔPr.

Comparison with previous studies

The overall effects of climate change on crop yields are negative, with the mean and median of −11% and −6.2% without adaptation and −4.6% and −1.6% with adaptation, respectively (Online-only Tables  3 and 4 ). The median per-decade yield impact without adaptation is −2.1% for maize, −1.2% for soybean, −0.7% for rice, and −1.2% for wheat (Table  1 ), which are consistent with previous IPCC assessments 14 . The median per-warming-degree impact is −7.1% for maize, −4.0% for soybean, −2.3% for rice, and −3.7% for wheat (Table  1 ). Per-degree yield impacts for each crop are generally within the range reported in the previous meta-analysis 11 . Among the four crops, soybean has the least number of simulations, resulting in a greater variation in both per-decade and per-degree impacts. Maize consistently shows the largest negative impacts, while rice shows the least.

The climate change impacts by IPCC regional groups reveals that Europe and North America are expected to be less affected by climate change in the mid-century (MC) and the end-century (EC) than Africa, Central and South America, particularly for maize and soybean. Both positive and negative effects are mixed in all regions (Fig.  4 , Supplementary Figs.  S3 , S4 ).

Regional differences in the impacts in MC and EC are associated with the current temperature level. In MC, positive or neutral effects are projected when current annual average temperatures (T ave ) are below 10–15 °C, but the effects become negative as T ave increases beyond these levels regardless of RCPs (Fig.  5a ). This accounts for the regional differences as a function of latitude reported in previous meta-analyses 4 , 5 . In EC, the threshold T ave shifts lower, and the negative effects become more severe, particularly under a high emission scenario (RCP8.5) (Fig.  5b ). The effect of ΔT g from the baseline period onYI differs depending on the T ave (Fig.  5c ); At T ave  < 10 °C, YI is generally neutral even where ΔT g  > 2 °C in most crops, but at T ave  > 20 °C, YI is negative even with small ΔT g, notably in maize. The difference in the YI dependence on ΔT g between regions is also consistent with the previous study 4 .

Adaptation potential averaged 7.3% in MC and 11.6% in EC (Fig.  6 , Supplementary Fig.  S5 ), which is not sufficient to offset the negative impacts, particularly in currently warmer regions. Residual damages will thus likely remain even with adaptation, which is also supported by other lines of evidence 26 , 27 .

Usage Notes

Crop yield simulation studies can provide a narrative of when, where, and what will happen to crop production under different GHG emissions and climate scenarios. They are also expected to provide quantitative information on the potential and limits to adaptation. However, robust estimates covering different temporal and spatial scales need to draw on multiple results obtained from various simulation studies. Nearly four decades have passed since the model projections based on future climate scenarios started. This dataset covers the entire period of simulation studies using climate scenarios, which can help update the quantitative review of climate change impacts on crops. The full list of references is provided in the reference file ( https://doi.org/10.6084/m9.figshare.14691579.v4 ).

Currently, studies are heavily biased towards major cereals such as maize, rice, and wheat, but this can be expanded to include other crops. As of 2020, our literature search failed to find published reports using CMIP6 climate scenarios, but this dataset can be easily updated when new simulations using new climate scenarios or other crop species become available. The next IPCC assessment cycle can fully utilise this dataset by adding the latest simulation results.

One of the caveats to the current dataset is that it only includes crop yield data, notwithstanding crop simulation studies are expected to produce other results than yield. Because of the recent progress in crop modelling, grain quality projections are emerging 28 . We have extensively included the temperature and precipitation levels to account for the impacts concerning the warming and current temperature, but there is a need to include other key climatic variables such as soil moisture. It will be useful to expand our dataset in the future to include this type of data.

Code availability

Script files were created using the R statistical programming to estimate missing values of ΔT l , ΔT l and ΔPr and draw Figs.  2 – 6 which are available in the figshare repository 24 .

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Acknowledgements

This study was performed by the Environment Research and Technology Development Fund (JPMEERF20S11820) of the Environmental Restoration and Conservation Agency of Japan. TH and DM would like to thank Joint-Linkage-Call between INRAE and NARO for supporting this collaborative study and the CLAND Institute of convergence (ANR 16-CONV-0003). We also thank Dr. T. Iizumi and Y. Ishigooka for providing the aggregated simulation results.

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Toshihiro Hasegawa and Hitomi Wakatsuki designed the dataset. Hitomi Wakatsuki and Hui Ju collected simulation results from the SCOPUS search. Shalika Vyas designed and collected the Aggarwal dataset. Gerald C. Nelson conducted literature search and provided global temperature dataset. David Makowski and Hitomi Wakatsuki developed a statistical imputation for missing data on the local temperature rise and precipitation change. All authors worked on data analysis and drafting the final version of the manuscript.

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Hasegawa, T., Wakatsuki, H., Ju, H. et al. A global dataset for the projected impacts of climate change on four major crops. Sci Data 9 , 58 (2022). https://doi.org/10.1038/s41597-022-01150-7

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Perceptions of climate change, environmental variability and the role of agricultural adaptation strategies by small-scale farmers in Africa: the case of Mwanga district in Northern Tanzania

Mngumi, Julius W. (2016) Perceptions of climate change, environmental variability and the role of agricultural adaptation strategies by small-scale farmers in Africa: the case of Mwanga district in Northern Tanzania. PhD thesis, University of Glasgow.

Abstract The potential impacts of climate change and environmental variability are already evident in most parts of the world, which is witnessing increasing temperature rates and prolonged flood or drought conditions that affect agriculture activities and nature-dependent livelihoods. This study was conducted in Mwanga District in the Kilimanjaro region of Tanzania to assess the nature and impacts of climate change and environmental variability on agriculture-dependent livelihoods and the adaptation strategies adopted by small-scale rural farmers. To attain its objective, the study employed a mixed methods approach in which both qualitative and quantitative techniques were used. The study shows that farmers are highly aware of their local environment and are conscious of the ways environmental changes affect their livelihoods. Farmers perceived that changes in climatic variables such as rainfall and temperature had occurred in their area over the period of three decades, and associated these changes with climate change and environmental variability. Farmers’ perceptions were confirmed by the evidence from rainfall and temperature data obtained from local and national weather stations, which showed that temperature and rainfall in the study area had become more variable over the past three decades. Farmers’ knowledge and perceptions of climate change vary depending on the location, age and gender of the respondents. The findings show that the farmers have limited understanding of the causes of climatic conditions and environmental variability, as some respondents associated climate change and environmental variability with social, cultural and religious factors. This study suggests that, despite the changing climatic conditions and environmental variability, farmers have developed and implemented a number of agriculture adaptation strategies that enable them to reduce their vulnerability to the changing conditions. The findings show that agriculture adaptation strategies employ both planned and autonomous adaptation strategies. However, the study shows that increasing drought conditions, rainfall variability, declining soil fertility and use of cheap farming technology are among the challenges that limit effective implementation of agriculture adaptation strategies. This study recommends further research on the varieties of drought-resilient crops, the development of small-scale irrigation schemes to reduce dependence on rain-fed agriculture, and the improvement of crop production in a given plot of land. In respect of the development of adaptation strategies, the study recommends the involvement of the local farmers and consideration of their knowledge and experience in the farming activities as well as the conditions of their local environment. Thus, the findings of this study may be helpful at various levels of decision making with regard to the development of climate change and environmental variability policies and strategies towards reducing farmers’ vulnerability to current and expected future changes.

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Effects of nitrogen deposition and precipitation patterns on nitrogen allocation of mongolian pine ( pinus sylvestris var. mongolica ) on sandy land using 15 n isotope.

1. Introduction

2. materials and methods, 2.1. experimental materials, 2.2. experimental design, 2.3. measurement and calculation of indicators, 2.3.1. sample collection and dry weight measurement, 2.3.2. n content and 15 n abundance measurement, 2.3.3. calculation, 2.4. data processing, 3.1. impact of nitrogen deposition and precipitation patterns on 15 n abundance in mongolian pine, 3.1.1. changes in 15 n abundance in seedlings, 3.1.2. changes in soil 15 n abundance, 3.2. correlation and variance analysis of 15 n abundance in different organs and soil of mongolian pine seedlings under various treatments of nitrogen deposition and precipitation patterns, 3.2.1. correlation analysis, 3.2.2. analysis of variance, 3.3. effects of nitrogen deposition and changes in precipitation patterns on 15 n uptake by mongolian pine, 3.3.1. n dff (%) in different organs of mongolian pine seedlings and soil, 3.3.2. nitrogen content and 15 n absorption, 3.4. effects of nitrogen deposition and precipitation patterns on the 15 n distribution ratio in organs of mongolian pine, 4. discussion, 5. conclusions, author contributions, institutional review board statement, data availability statement, acknowledgments, conflicts of interest.

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Zhao, T.; Cheng, S.; Gang, Q.; Zhuang, Y.; Zhu, X. Effects of Nitrogen Deposition and Precipitation Patterns on Nitrogen Allocation of Mongolian Pine ( Pinus sylvestris var. mongolica ) on Sandy Land Using 15 N Isotope. Agriculture 2024 , 14 , 1367. https://doi.org/10.3390/agriculture14081367

Zhao T, Cheng S, Gang Q, Zhuang Y, Zhu X. Effects of Nitrogen Deposition and Precipitation Patterns on Nitrogen Allocation of Mongolian Pine ( Pinus sylvestris var. mongolica ) on Sandy Land Using 15 N Isotope. Agriculture . 2024; 14(8):1367. https://doi.org/10.3390/agriculture14081367

Zhao, Tianhong, Shihao Cheng, Qun Gang, Yonghui Zhuang, and Xianjin Zhu. 2024. "Effects of Nitrogen Deposition and Precipitation Patterns on Nitrogen Allocation of Mongolian Pine ( Pinus sylvestris var. mongolica ) on Sandy Land Using 15 N Isotope" Agriculture 14, no. 8: 1367. https://doi.org/10.3390/agriculture14081367

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Paper Highlights How Climate Change Challenges, Transforms Agriculture

As the climate continues to change, the risks to farming are only going to increase.

That's the key takeaway from a recent paper published by a team that included UC Merced researchers. The paper dives into what those challenges are, how farmers are working to address them and what should come next.

"Climate Smart Agriculture: Assessing Needs and Perceptions of California's Farmers" was first authored by Samuel Ikendi, academic coordinator, with engineering research Professor Tapan Pathak  as a corresponding author. Pathak is also a project director of National Institute of Food and Agriculture-funded project "Multifaceted Pathways to Climate-Smart Agriculture through Participator Program Development and Delivery," which supported this study. The study appeared in the open access journal Frontiers in Sustainable Food Systems .

The needs assessment was designed to understand farmers' perceptions and experiences with climate change exposures; the risk management practices they currently use; and what tools and resources would assist them in making strategic decisions.

Of the farmers surveyed, roughly two-thirds agree climate change is occurring and requires action. Even more said they are interested in learning more about the impacts of climate change on the agricultural industry. Most respondents said they experience greater climate change impacts on their farms today compared with 10 years ago.

Farmers were very concerned with water-related issues, with those in the San Joaquin Valley, Central Coast and Inland Empire areas particularly worried about a reduction in the availability of groundwater. Increased drought severity was a very significant concern among farmers in the Inland Empire, Central Coast and Southern regions. Farmers in the North Coast and Southern regions were concerned about increased damage to crops due to wildfire.

Closely related were temperature-related issues, including crop damage due to extreme heat.

Those who farm vegetables were more concerned about water availability for irrigation, while fruit farmers were more concerned about increased crop/water stress and increased crop damage due to extreme heat.

Many respondents said they are implementing climate adaption practices including managing water resources, maintaining soil health and making more use of renewable energy sources. They are seeking insurance and government help to pay for these adaptations and increase their agricultural resilience, the researchers wrote.

The farmers expressed interest in learning more about measures they might take to mitigate climate change. But they cited significant barriers to this work, including government regulations, high implementation cost, labor access/cost, access to water and the availability of money to pay for it.

"Climate change is significantly altering California's highly diverse agricultural landscape, posing challenges such as increased water stress, heat stress, and shifting growing seasons," Pathak said. "Climate-smart agriculture practices can alleviate some of those stresses."

But, he said, research and UC Extension efforts only have value if they lead to enhanced climate-informed decision-making at the local level.

"Assessing their level of knowledge, perception and needs will help in tailoring research and extension activities that are most relevant to farmers on the ground," Pathak said. "Results from this study could also provide important policy insights on financial incentives and technical assistance."

Patty Guerra

Media Contact

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Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

FIGURE 1 Integrated assessment model

FIGURE 2 Likely carbon emissions, years 2000-2100

FIGURE 3 Predicted global temperature changes, years 2000-2100

Latin America

Eastern Europe includes the former Soviet Union.

TABLE 2 Cross-sectional results for Brazil

-47 300 (6.62)

Winter Temp

-12 000 (13.12)

Spring Temp

16 300 (14.82)

Summer Temp

-19 400 (11.19)

-309 (15.53)

10 100 (5.95

715 (11.10)

Winter Temp Squared

1 490 (12.05)

Winter Precip Squared

-0.1 (0.52)

Spring Temp Squared

-3 690 (31.99)

Spring Precip Squared

-5.1 (15.11)

Summer Temp Squared

Summer Precip Squared

Fall Temp Squared

Fall Precip Squared

-0.3 (3.67)

-2 600 (1.41)

-6 200 (1.62)

14 700 (7.53)

-45 000 (8.78)

-42 500 (14.50)

Dependent variable is pooled land values. T-statistics are in parentheses. Source: Sanghi and Mendelsohn, 1999.

TABLE 3 Cross-sectional results for India

4 660 (8.92)

-133 (3.38)

18.5 (6.11)

-372 (16.71)

-14.4 (8.00)

-103 (2.84)

-0.4 (2.11)

-39.3 (11.40)

-0.16 (1.57)

-80.3 (12.48)

0.28 (10.58)

35.0 (4.62)

0.01 (3.89)

-68.1 (6.77)

-0.04 (7.34)

Winter Temp x Precip

-3.62 (4.57)

-0.21 (1.97)

Spring Temp x Precip

8.21 (11.59)

3.01 (5.83)

-153 (4.39)

Cultivators

28 680 (8.98)

Pop. density

-174 (7.83)

Dependent variable is pooled net revenues. T-statistics are in parentheses. A set of dummy variables for each year is also included but not shown. Source: Sanghi and Mendelsohn, 1999.

TABLE 4 Agro-economic results: change in yields

Wheat Maize

Wheat Rice Maize

United States

Source: Reilly et al ., 1996.

TABLE 5 Ricardian results: percent reduction in net income

These estimates do not include carbon fertilization, which is expected to add 30% to crop productivity. Climate scenario assumes a 7% increase in precipitation.

TABLE 6 Agricultural impacts (000 million US$)

Negative numbers imply damages and positive numbers imply benefits. Effects are annual impacts in the year 2100. CO2 is assumed to be 700, 900, and 1000 ppmv in the three respective scenarios. Eastern Europe includes the former Soviet Union. Global agricultural GDP in 2100 is assumed to be 4759 000 million dollars.

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Can Dirt Clean the Climate?

An Australian start-up is hoping fungi can pull carbon dioxide from the air and stash it underground. It’s one of several ventures trying to deploy the superpowers of soil to slow global warming.

Supported by

By Somini Sengupta

Photographs and Video by Matthew Abbott

Somini Sengupta traveled to farms around New South Wales, Australia, to report this article.

• Published Aug. 10, 2024 Updated Aug. 13, 2024

Across 100,000 acres in the vast agricultural heartland of Australia, an unusual approach is taking root to slow down the wrecking ball of climate change. Farmers are trying to tap the superpowers of tiny subterranean tendrils of fungus to pull carbon dioxide out of the air and stash it underground.

It’s part of a big bet that entrepreneurs and investors around the world are making on whether dirt can clean up climate pollution. They are using a variety of technologies on farmland not just to grow food but to also eat the excess carbon dioxide produced by more than a century of fossil fuel burning and intensive agriculture.

Why fungus? Because fungi act as nature’s carbon traders. As they sow their crops, farmers are adding a pulverized dust of fungal spores. The fungus latches on to the crop roots, takes carbon that is absorbed by the plants from the air and locks it away in subterranean storage in a form that may keep it underground for much longer than the natural carbon cycle.

The fungal venture, the handiwork of an Australian company called Loam Bio, is among several start-ups to have mobilized hundreds of millions of dollars in investments in efforts to use soil to remove carbon dioxide from the atmosphere. Like Loam Bio, companies like Andes and Groundworks Bio Ag are also experimenting with microbes. Lithos and Mati offer farmers crushed volcanic rocks that absorb carbon to sprinkle on their fields. Silicate Carbon is milling leftover concrete into a fine powder, while several companies are scorching crop waste into charcoal.

The appeal of the Australian start-up is that it doesn’t demand too much of farmers.

“Pretty simple,” is how a fifth-generation Australian farmer named Stuart McDonald described his experience as he sowed a dusting of fungal spores with his wheat and canola seeds on his farm near Canowindra this year. “It’s not asking us to change too much. It’s not a big capital outlay.”

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