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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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This paper investigates the impacts of climate change on US returns to research investments on agricultural productivity. We examine this using a historical data set in a panel time-series econometric model of state agricultural productivity. The fitted model allows derivation of the rate of return to research investments and the effects of climate change thereon. We find climate change is altering the rate of return to public agricultural research in a spatially heterogeneous manner. Increases in precipitation raise returns to research, while the impact of higher temperatures varies by region, are negative in Southern areas, particularly the Southern Plains, and positive in northern areas. We simulate the impact of projected climate change and find cases where agricultural productivity is reduced, for example in the Southern Plains. Finally, we consider the amount of research investment that is needed to adapt to overcome the impacts of climate change on agricultural productivity. Under the 2100 scenario, a 7–17 % increase in total US research investment is needed to adapt, but effects by region differ greatly—some requiring little changes and the Southern Plain requiring an increase as high as 57 %.

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Xavier Villavicencio, Bruce A. McCarl & Ximing Wu

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Villavicencio, X., McCarl, B.A., Wu, X. et al. Climate change influences on agricultural research productivity. Climatic Change 119 , 815–824 (2013). https://doi.org/10.1007/s10584-013-0768-6

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Received : 12 April 2011

Accepted : 06 April 2013

Published : 20 April 2013

Issue Date : August 2013

DOI : https://doi.org/10.1007/s10584-013-0768-6

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Impact of climate change on agricultural production; issues, challenges, and opportunities in asia.

• 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
• 3 Asian Disaster Preparedness Center, Islamabad, Pakistan
• 4 Department of Agronomy, University of Agriculture Faisalabad, Faisalabad, Pakistan
• 5 Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
• 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
• 8 Institute of Plant Breeding and Biotechnology, MNS-University of Agriculture, Multan, Pakistan
• 9 Department of Agronomy, The Islamia University, Bahwalpur, Pakistan
• 10 Department of Environmental Science and Engineering, Government College University, Faisalabad, Pakistan
• 11 Department of Economics, Business and Economics Faculty, Siirt University, Siirt, Turkey
• 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 . 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 .

Figure 1 . Share of each Asian country in cumulative global carbon dioxide emission (1751–2019; Source: OWID based on CDIAC and Global Carbon Project).

Figure 2 . 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).

Figure 3 . 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 ).

Figure 4 . 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 .

Table 1 . Productivity shock due to climate change and variability on rice and wheat crop production by 2030.

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.

Figure 5 . 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.

Figure 6 . 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.

Figure 7 . Reduction in rice yield of APSIM and DSSAT models for 155 farms; variation with 5-GCMs in rice-wheat cropping system of Punjab-Pakistan.

Figure 8 . 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%.

Table 2 . Relative yield summary of crop models.

Table 3 . 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.

Table 4 . 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).

Table 5 . 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).

Figure 9 . 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.

Table 6 . 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 .

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).

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.

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

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Keywords: climate variability, yield reduction, livestock, elevated temperature, adaptation, climate and crop modeling, decision support system, sustainable production

Citation: Habib-ur-Rahman M, Ahmad A, Raza A, Hasnain MU, Alharby HF, Alzahrani YM, Bamagoos AA, Hakeem KR, Ahmad S, Nasim W, Ali S, Mansour F and EL Sabagh A (2022) Impact of climate change on agricultural production; Issues, challenges, and opportunities in Asia. Front. Plant Sci. 13:925548. doi: 10.3389/fpls.2022.925548

Received: 21 April 2022; Accepted: 08 August 2022; Published: 10 October 2022.

Reviewed by:

Copyright © 2022 Habib-ur-Rahman, Ahmad, Raza, Hasnain, Alharby, Alzahrani, Bamagoos, Hakeem, Ahmad, Nasim, Ali, Mansour and EL Sabagh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Muhammad Habib-ur-Rahman, mhabibur@uni-bonn.de ; habibagri@hotmail.com ; Hesham F. Alharby, halharby@kau.edu.sa ; Ayman EL Sabagh, ayman.sabagh@agr.kfs.edu.eg

Disclaimer: 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.

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• v.365(1554); 2010 Sep 27

Implications of climate change for agricultural productivity in the early twenty-first century

This paper reviews recent literature concerning a wide range of processes through which climate change could potentially impact global-scale agricultural productivity, and presents projections of changes in relevant meteorological, hydrological and plant physiological quantities from a climate model ensemble to illustrate key areas of uncertainty. Few global-scale assessments have been carried out, and these are limited in their ability to capture the uncertainty in climate projections, and omit potentially important aspects such as extreme events and changes in pests and diseases. There is a lack of clarity on how climate change impacts on drought are best quantified from an agricultural perspective, with different metrics giving very different impressions of future risk. The dependence of some regional agriculture on remote rainfall, snowmelt and glaciers adds to the complexity. Indirect impacts via sea-level rise, storms and diseases have not been quantified. Perhaps most seriously, there is high uncertainty in the extent to which the direct effects of CO 2 rise on plant physiology will interact with climate change in affecting productivity. At present, the aggregate impacts of climate change on global-scale agricultural productivity cannot be reliably quantified.

1. Introduction

Agriculture is strongly influenced by weather and climate. While farmers are often flexible in dealing with weather and year-to-year variability, there is nevertheless a high degree of adaptation to the local climate in the form of established infrastructure, local farming practice and individual experience. Climate change can therefore be expected to impact on agriculture, potentially threatening established aspects of farming systems but also providing opportunities for improvements.

This paper reviews recent literature relevant to the impacts of climate change on global agricultural productivity through a wide range of processes. The aim is to provide a global-scale overview of all relevant impacts, rather than focusing on specific regions or processes, as the purpose of this review is to inform a wider assessment of the risks to global food security. Although there are a large number of studies which focus on the impact of a particular aspect of climate change in a specific location, there are relatively few studies which provide a global assessment. Moreover, these studies tend to focus more on the direct effect of changes in the mean climate state on crop growth and do not consider changes in extremes or in indirect effects of climate change such as sea-level rise or pests and diseases. A comprehensive, internally consistent assessment of all potential direct and indirect effects of climate change on agricultural productivity has not yet been carried out. As a step towards such a full-system assessment, we complement each stage of our review of the literature with presentation of projected changes in relevant climate-related quantities from the Met Office Hadley Centre (MOHC) models. This allows a comparison of the different aspects of climate change relevant to agricultural productivity, so that the relative importance of the different potential causes of impacts can be assessed. This provides some context to decision making in an area of high uncertainty, and also informs future research directions.

Most previous assessments of the impacts of climate change on agriculture (and indeed on other sectors) have focused on time horizons towards the end of the twenty-first century, illustrating the impacts of anthropogenic climate change that could be avoided by reducing greenhouse gas emissions. However, there is also a need to assess the impacts of climate change over the next few decades, which may now be largely unavoidable owing to inertia in the physical climate system and the time scales over which large-scale change in human social, economic and political influences on greenhouse gas emissions could be brought about. Even if greenhouse gas emissions began to be reduced immediately, there would still be some level of ongoing warming for decades and some sea-level rise continuing for centuries, as the climate system is slow to respond fully to imposed changes. There is relatively little information in the literature available on climate change impacts over these time horizons, so we present MOHC climate projections for approximately 2020 and 2050 in order to put the existing literature into context on these time scales.

This paper focuses on impacts on crop productivity, but many of the processes and impacts discussed may also apply to livestock. Some discussion of this is provided in the electronic supplementary material.

2. Direct impacts of climate change on agriculture

(a) changes in mean climate.

The nature of agriculture and farming practices in any particular location are strongly influenced by the long-term mean climate state—the experience and infrastructure of local farming communities are generally appropriate to particular types of farming and to a particular group of crops which are known to be productive under the current climate. Changes in the mean climate away from current states may require adjustments to current practices in order to maintain productivity, and in some cases the optimum type of farming may change.

Higher growing season temperatures can significantly impact agricultural productivity, farm incomes and food security ( Battisti & Naylor 2009 ). In mid and high latitudes, the suitability and productivity of crops are projected to increase and extend northwards, especially for cereals and cool season seed crops ( Maracchi et al . 2005 ; Tuck et al . 2006 ; Olesen et al . 2007 ). Crops prevalent in southern Europe such as maize, sunflower and soya beans could also become viable further north and at higher altitudes ( Hildén et al . 2005 ; Audsley et al . 2006 ; Olesen et al . 2007 ). Here, yields could increase by as much as 30 per cent by the 2050s, dependent on crop ( Alexandrov et al . 2002 ; Ewert et al . 2005 ; Richter & Semenov 2005 ; Audsley et al . 2006 ; Olesen et al . 2007 ). For the coming century, Fisher et al . (2005) simulated large gains in potential agricultural land for the regions such as the Russian Federation, owing to longer planting windows and generally more favourable growing conditions under warming, amounting to a 64 per cent increase over 245 million hectares by the 2080s. However, technological development could outweigh these effects, resulting in combined wheat yield increases of 37–101% by the 2050s ( Ewert et al . 2005 ).

Even moderate levels of climate change may not necessarily confer benefits to agriculture without adaptation by producers, as an increase in the mean seasonal temperature can bring forward the harvest time of current varieties of many crops and hence reduce final yield without adaptation to a longer growing season.

In areas where temperatures are already close to the physiological maxima for crops, such as seasonally arid and tropical regions, higher temperatures may be more immediately detrimental, increasing the heat stress on crops and water loss by evaporation. A 2°C local warming in the mid-latitudes could increase wheat production by nearly 10 per cent whereas at low latitudes the same amount of warming may decrease yields by nearly the same amount ( figure 1 ). Different crops show different sensitivities to warming. It is important to note the large uncertainties in crop yield changes for a given level of warming ( figure 1 ). By fitting statistical relationships between growing season temperature, precipitation and global average yield for six major crops, Lobell & Field (2007) estimated that warming since 1981 has resulted in annual combined losses of 40 million tonne or US$5 billion (negative relationships between wheat, maize & barley with temperature).

Sensitivity of cereal (( a , b ) maize (mid- to high-latitude and low latitude), ( c , d ) wheat (mid- to high-latitude and low latitude) and ( e , f ) rice (mid- to high-latitude)) to climate change as determined from the results of 69 studies, against temperature change. Results with (green), and without (red) adaptation are shown. Reproduced from Easterling et al . (2007) , fig. 5.2.

Figure 2 and table 1 show two scenarios for changes in mean annual temperature at 2020 and 2050 relative to present day. All areas of cropland are projected to experience some degree of warming, but the largest change in warming is projected in the high latitudes. However, small increases in temperature in low latitudes may have a greater impact than in high latitudes ( figure 1 ), possibly because agriculture in parts of these regions is already marginal.

Table 1.

Scenarios of future change in meteorological, hydrological and plant physiological variables relevant to agricultural productivity, selected from an ensemble of 17 scenarios with variants of the HadCM3 climate model. Results are presented as means over global cropland areas for 30-year periods centred on 2020 and 2050, relative to 1970–2000 (except for extreme temperature which is relative to 2000). Two scenarios are presented for each variable, spanning the range of results for each variable to illustrate uncertainties in the projections. For further details see the electronic supplementary material.

Two projections of change in annual mean temperature (°C) over global croplands for 30-year means centred around 2020 and 2050, relative to 1970–2000. The two projections are the members of the ensemble with the greatest and least change in annual mean temperature averaged over all global croplands. See the electronic supplementary material for further details.

Water is vital to plant growth, so varying precipitation patterns have a significant impact on agriculture. As over 80 per cent of total agriculture is rain-fed, projections of future precipitation changes often influence the magnitude and direction of climate impacts on crop production ( Olesen & Bindi 2002 ; Tubiello et al . 2002 ; Reilly et al . 2003 ). The impact of global warming on regional precipitation is difficult to predict owing to strong dependencies on changes in atmospheric circulation, although there is increasing confidence in projections of a general increase in high-latitude precipitation, especially in winter, and an overall decrease in many parts of the tropics and sub-tropics ( IPCC 2007 ). These uncertainties are reflected in two scenarios shown in figure 3 and table 1 , which project different signs of precipitation change averaged over all croplands, even though there is agreement in the sign of change in some regions. One scenario which predicts an overall increase in precipitation, shows large increases in southern USA and India but also significant decreases in the tropics and sub-tropics. The other scenario also shows the decreases in the low latitudes but without significant increases in India.

Two projections of change in annual mean precipitation (mm d −1 ) over global croplands for 30-year means centred around 2020 and 2050, relative to 1970–2000. The two projections are the members of the ensemble with the most positive and negative changes in annual mean precipitation averaged over all global croplands. See the electronic supplementary material for further details.

This reflects the wide range of projections of precipitation change from different climate models ( Christensen et al . 2007 ). The differences in precipitation projections arise for a number of reasons. A key factor is the strong dependence on changes in atmospheric circulation which itself depends on the relative rates of warming in different regions, but there are often a number of factors influencing precipitation change projections in a given location. For example, the uncertainty in precipitation change over India arises partly from the expected weakening of the dynamical monsoon circulation (decreasing the Indian monsoon precipitation) versus the increase in atmospheric water content associated with warming (increasing the Indian monsoon precipitation; Meehl et al . 2007 ).

However, changes in seasonal precipitation may be more relevant to agriculture than annual mean changes. In India, climate models generally project a decrease in dry season precipitation and an increase during the rest of the year including the monsoon season, but still with a large inter-model spread ( Christensen et al . 2007 ).

Precipitation is not the only influence on water availability. Increasing evaporative demand owing to rising temperatures and longer growing seasons could increase crop irrigation requirements globally by between 5 and 20 per cent, or possibly more, by the 2070s or 2080s ( Döll 2002 ; Fisher et al . 2006 ), but with large regional variations—South-East Asian irrigation requirements could increase by 15 per cent ( Döll 2002) . Regional studies project increasing irrigation demand in the Middle East and North Africa ( Abou-Hadid et al . 2003 ) and potentially 15 per cent increases in irrigation demand in South-East Asia ( Arnell et al . 2004 ; Fisher et al . 2006 ). However, decreased requirements are projected in China ( Tao et al . 2003 ). Clearly these projections also depend on uncertain changes in precipitation.

(b) Climate variability and extreme weather events

While change in long-term mean climate will have significance for global food production and may require ongoing adaptation, greater risks to food security may be posed by changes in year-to-year variability and extreme weather events. Historically, many of the largest falls in crop productivity have been attributed to anomalously low precipitation events ( Kumar et al . 2004 ; Sivakumar et al . 2005 ). However, even small changes in mean annual rainfall can impact on productivity. Lobell & Burke (2008) report that a change in growing season precipitation by one standard deviation can be associated with as much as a 10 per cent change in production (e.g. millet in South Asia).

For example, Indian agriculture is highly dependent on the spatial and temporal distribution of monsoon rainfall ( Kumar et al . 2004 ). Asada & Matsumoto (2009) analysed the relationship between district-level crop yield data (rainy season ‘kharif’ rice) and precipitation for 1960–2000. It was shown that different regions were sensitive to precipitation extremes in different ways. Crop yield in the upper Ganges basin is linked to total precipitation during the relatively short growing season and is thus sensitive to drought. Conversely, the lower Ganges basin was sensitive to pluvial flooding and the Brahmaputra basin demonstrated an increasing effect of precipitation variability on crop yield, in particular drought. These relationships were not consistent through time, in part owing to precipitation trends. Variation between districts implied the importance of social factors and the introduction of irrigation techniques.

Meteorological records suggest that heatwaves became more frequent over the twentieth century, and while individual events cannot be attributed to climate change, the change in probability of a heatwave can be attributed. Europe experienced a particularly extreme climate event during the summer of 2003, with average temperatures 6°C above normal and precipitation deficits of up to 300 mm. A record crop yield loss of 36 per cent occurred in Italy for corn grown in the Po valley where extremely high temperatures prevailed ( Ciais et al . 2005 ). It is estimated that such summer temperatures in Europe are now 50 per cent more likely to occur as a result of anthropogenic climate change ( Stott et al . 2004 ).

As current farming systems are highly adapted to local climate, growing suitable crops and varieties, the definition of what constitutes extreme weather depends on geographical location. For example, temperatures considered extreme for grain growers in the UK would be considered normal for cereal growers in central France. In many regions, farming may adapt to increases in extreme temperature events by moving to practices already used in warmer climate, for example by growing more tolerant crops. However, in regions where farming exists at the edge of key thresholds increases in extreme temperatures or drought may move the local climate into a state outside historical human experience. In these cases it is difficult to assess the extent to which adaptation will be possible.

(i) Extreme temperatures

Recent increases in climate variability may have affected crop yields in countries across Europe since around the mid-1980s ( Porter & Semenov 2005 ) causing higher inter-annual variability in wheat yields. This study suggested that such changes in annual yield variability would make wheat a high-risk crop in Spain. Even mid-latitude crops could suffer at very high temperatures in the absence of adaptation. In 1972, extremely high summer averaged temperature in the former Soviet Union (USSR) contributed to widespread disruptions in world cereal markets and food security ( Battisti & Naylor 2009 ).

Changes in short-term temperature extremes can be critical, especially if they coincide with key stages of development. Only a few days of extreme temperature (greater that 32°C) at the flowering stage of many crops can drastically reduce yield ( Wheeler et al . 2000 ). Crop responses to changes in growing conditions can be nonlinear, exhibit threshold responses and are subject to combinations of stress factors that affect their growth, development and eventual yield. Crop physiological processes related to growth such as photosynthesis and respiration show continuous and nonlinear responses to temperature, while rates of crop development often show a linear response to temperature to a certain level. Both growth and developmental processes, however, exhibit temperature optima. In the short-term high temperatures can affect enzyme reactions and gene expression. In the longer term these will impact on carbon assimilation and thus growth rates and eventual yield. The impact of high temperatures on final yield can depend on the stage of crop development. Wollenweber et al . (2003) found that the plants experience warming periods as independent events and that critical temperatures of 35°C for a short-period around anthesis had severe yield reducing effects. However, high temperatures during the vegetative stage did not seem to have significant effects on growth and development. Reviews of the literature ( Porter & Gawith 1999 ; Wheeler et al . 2000 ) suggest that temperature thresholds are well defined and highly conserved between species, especially for processes such as anthesis and grain filling.

Although groundnut is grown in semi-arid regions which regularly experience temperatures of 40°C, if after flowering the plants are exposed to temperatures exceeding 42°C, even for short periods, yield can be drastically reduced ( Vara Prasad et al . 2003 ). Maize exhibits reduced pollen viability for temperatures above 36°C. Rice grain sterility is brought on by temperatures in the mid-30s and similar temperatures can lead to the reverse of the vernalizing effects of cold temperatures in wheat. Increases in temperature above 29°C for corn, 30°C for soya bean and 32°C for cotton negatively impact on yields in the USA.

Figure 4 and table 1 show that in all cases and all regions, one in 20-year extreme temperature events is projected to be hotter. Events which today are considered extreme would be less unusual in the future. The impacts of extreme temperature events can be difficult to separate from those of drought. However, key temperature thresholds exist beyond which crop physiology is altered, potentially devastating yields.

Two projections of change in one in 20-year extreme temperature level (°C) over global croplands for 2020 and 2050, relative to 2000. The two projections are the members of the ensemble with the greatest and least change averaged over all global croplands. See the electronic supplementary material for further details.

(ii) Drought

There are a number of definitions of drought, which generally reflect different perspectives. Holton et al . (2003) point out that ‘the importance of drought lies in its impacts. Thus definitions should be region-specific and impact- or application-specific in order to be used in an operational mode by decision makers.’ It is common to distinguish between meteorological drought (broadly defined by low precipitation), agricultural drought (deficiency in soil moisture, increased plant water stress), hydrological drought (reduced streamflow) and socio-economic drought (balance of supply and demand of water to society; Holton et al . 2003 ). Globally, the areas sown for the major crops of barley, maize, rice, sorghum, soya bean and wheat have all seen an increase in the percentage of area affected by drought as defined in terms of the Palmer Drought Severity Index (PDSI; Palmer 1965 ) since the 1960s, from approximately 5–10% to approximately 15–25% ( Li et al . 2009 ). Global mean PDSI has also increased ( IPCC 2007 ), and a comparison of climate model simulations with observed data suggests that anthropogenic increases in greenhouse gas and aerosol concentrations have made a detectable contribution to the observed drying trend in PDSI ( Burke et al . 2006 ).

In climate-modelling studies, Burke et al . (2006) define drought as the 20th percentile of the PDSI distribution over time, for pre-industrial conditions; this definition is therefore regionally specific. Therefore at any given time, approximately 20 per cent of the land surface will be defined as being in drought, but the conditions in a normally wet area under drought may still be less dry than those in another region which is dry under normal conditions. Using this definition, the MOHC climate model simulates the proportion of the land surface under drought to have increased from 20 to 28 per cent over the twentieth century ( Burke et al . 2006 ).

Li et al . (2009) define a yield reduction rate (YRR) which takes a baseline of the long-term trend in yield (assumed to be owing to technological progress and infrastructure improvement) and compares this with actual annual yields to define a YRR owing to climate variability. Using national-scale data for the four major grains (barley, maize, rice and wheat), Li et al . (2009) suggested that 60–75% of observed YRRs can be explained by a linear relationship between YRR and a drought risk index based on the PDSI. Present-day mean YRR values are diagnosed as ranging from 5.82 per cent (rice) to 11.98 per cent (maize). By assuming the linear relationship between the drought risk index and YRR holds into the future, Li et al . (2009) estimated that drought related yield reductions would increase by more than 50 per cent by 2050 for the major crops.

The impacts of drought may offset benefits of increased temperature and season length observed at mid to high latitudes. Using models of global climate, crop production and water resources, Alcamo et al . (2007) suggested that decreased crop production in some Russian regions could be compensated by increased production in others, resulting in relatively small average changes. However, their results indicate that the frequency of food production shortfalls could double in many of the main crop growing areas in the 2020s, and triple in the 2070s ( Alcamo et al . 2007 ). Although water availability in Russia is increasing on average, the water resources model predicted more frequent low run-off events in the already dry crop growing regions in the south, and a significantly increased frequency of high run-off events in much of central Russia ( Alcamo et al . 2007 ).

(iii) Heavy rainfall and flooding

Food production can also be impacted by too much water. Heavy rainfall events leading to flooding can wipe out entire crops over wide areas, and excess water can also lead to other impacts including soil water logging, anaerobicity and reduced plant growth. Indirect impacts include delayed farming operations ( Falloon & Betts in press ). Agricultural machinery may simply not be adapted to wet soil conditions. In a study looking at the impacts of current climate variability, Kettlewell et al . (1999) showed that heavy rainfall in August was linked to lower grain quality which leads to sprouting of the grain in the ear and fungal disease infections of the grain. This was shown to affect the quality of the subsequent products such that it influenced the amount of milling wheat that was exported from the UK. The proportion of total rain falling in heavy rainfall events appears to be increasing, and this trend is expected to continue as the climate continues to warm. A doubling of CO 2 is projected to lead to an increase in intense rainfall over much of Europe. In the higher end projections, rainfall intensity increases by over 25 per cent in many areas important for agriculture ( figure 5 ).

( a ) Lower and ( b ) upper estimates covering the central 80% range of changes in precipitation intensity on wet days with a 1 year return period for a doubling of CO 2 .

(iv) Tropical storms

A tropical cyclone is the generic term for a non-frontal synoptic scale low-pressure system over tropical or sub-tropical waters with organized convection (i.e. thunderstorm activity) and definite cyclonic surface wind circulation ( Holland 1993 ). Severe tropical cyclones, with maximum sustained wind speeds of at least 74 mph, are known as ‘hurricanes’ in the eastern North Pacific and North Atlantic and ‘typhoons’ in the western North Pacific. The strongest tropical cyclones can reach wind speeds as large as 190 mph, as recorded in Typhoon Tip in the western North Pacific in October 1979. Tropical cyclones usually occur during the summer and early autumn: around May–November in the Northern Hemisphere and November–April in the Southern Hemisphere, although tropical cyclones are observed all year round in the western North Pacific. The North Indian Ocean is the only basin to have a two-part tropical cyclone season: before and after the onset of the South Asian monsoon, from April to May and October to November, respectively.

Figure 6 shows observed tropical cyclone tracks for all known storms over the period 1945–2008. In this context, the most vulnerable agricultural regions are found, among others, in the USA, China, Vietnam, India, Bangladesh, Myanmar and Madagascar.

Observed tropical cyclone tracks and intensity for all known storms over the period 1947–2008. Tracks are produced from the IBTrACS dataset of NOAA/NCDC ( Knapp et al . 2010 ).

Both societal and economic implications of tropical cyclones can be high, particularly in developing countries with high population growth rates in vulnerable tropical and subtropical regions. This is particularly the case in the North Indian Ocean, where the most vulnerable people live in the river deltas of Myanmar, Bangladesh, India and Pakistan; here population growth has resulted in increased farming in coastal regions most at risk from flooding ( Webster 2008 ). In 2007, cyclone Sidr hit Bangladesh costing 3500 lives ( United Nations 2007 ), and in 2008 cyclone Nargis caused 130 000 deaths in Myanmar. The agricultural impacts of these and other recent cyclones are shown in table 2 .

Table 2.

Selected tropical cyclones of the past decade, and their agricultural impacts.

Although many studies focus on the negative impacts, tropical cyclones can also bring benefits. In many arid regions in the tropics, a large portion of the annual rain comes from cyclones. It is estimated that tropical cyclones contribute to 15–20% of South Florida's annual rainfall ( Walther & Abtew 2006 ), which can temporarily end severe regional droughts. Examples of such storms are hurricane Gabrielle (2001) and tropical storm Fay (2008), which provided temporary relief from the 2000–2001 and 2006–2009 droughts, respectively. As much as 15 inches of rainfall was recorded in some regions from tropical storm Fay, without which, regions would have faced extreme water shortage, wildfires and potential saltwater intrusion into coastal freshwater aquifers ( Abtew et al . 2009 ). Tropical cyclones can also help replenish water supplies to inland regions: cyclone Eline, which devastated agriculture in Madagascar in February 2000, later made landfall in southern Africa and contributed significantly to the rainfall in the semi-desert region of southern Namibia.

There is much debate on the global change in tropical cyclone frequency and intensity under a warming climate. Climate modelling studies contributing to the IPCC's Fourth Assessment Report (AR4) suggest tropical cyclones may become more intense in the future with stronger winds and heavier precipitation ( Meehl et al . 2007 ). This is in agreement with more recent studies using high resolution models, which also indicate a possible decrease in future global tropical cyclone frequency ( McDonald et al . 2005 ; Bengtsson et al . 2007 ; Gualdi et al . 2008 ). However, there is limited consensus among the models on the regional variations in tropical cyclone frequency.

3. Indirect impacts of climate change on agricultural productivity

(a) pests and diseases.

Rising atmospheric CO 2 and climate change may also impact indirectly on crops through effects on pests and disease. These interactions are complex and as yet the full implications in terms of crop yield are uncertain. Indications suggest that pests, such as aphids ( Newman 2004 ) and weevil larvae ( Staley & Johnson 2008 ), respond positively to elevated CO 2 . Increased temperatures also reduced the overwintering mortality of aphids enabling earlier and potentially more widespread dispersion ( Zhou et al . 1995 ). Evidence suggests that in sub-Saharan Africa migration patterns of locusts may be influenced by rainfall patterns ( Cheke & Tratalos 2007 ) and thus potential exists for climate change to shape the impacts of this devastating pest. Pathogens and disease may also be affected by a changing climate. This may be through impacts of warming or drought on the resistance of crops to specific diseases and through the increased pathogenicity of organisms by mutation induced by environmental stress ( Gregory et al . 2009 ). Over the next 10–20 years, disease affecting oilseed rape could increase in severity within its existing range as well as spread to more northern regions where at present it is not observed ( Evans et al . 2008 ). Changes in climate variability may also be significant, affecting the predictability and amplitude of outbreaks.

(b) Changes in water availability owing to remote climate changes

Climate changes remote from production areas may also be critical. Irrigated agricultural land comprises less than one-fifth of all cropped area but produces between 40 and 45 per cent of the world's food ( Döll & Siebert 2002 ), and water for irrigation is often extracted from rivers which depend upon distant climatic conditions. For example, agriculture along the Nile in Egypt depends on rainfall in the upper reaches of the Nile such as the Ethiopian Highlands.

Figure 7 shows the projected changes in monthly river-flow for the 2020s and 2050s for selected key rivers of interest in this context. In some rivers such as the Nile, climate change increases flow throughout the year which could confer benefits to agriculture. However, in other catchments, e.g. the Ganges, the increase in run-off comes as an increase in peak flow around the monsoon. However, dry season river-flow is still very low. Without sufficient storage of peak season flow, water scarcity may affect agricultural productivity despite overall increases in annual water availability. Increases at peak flow may also cause damage to crop lands through flooding.

Projected mean monthly river flow (kg s −1 ) for 30 year means centred on 2000 (black), 2020 (green) and 2050 (blue) for the ( a ) Nile, ( b ) Ganges and ( c ) Volga. Projections are bias corrected ensemble means from the Hadley Centre models. See the electronic supplementary material for further details.

Figure 8 shows areas in the world where river flow is dominated by snow melt. These areas are mostly at mid to high latitudes where predictions for warming are greatest. Warming in winter means that less precipitation falls as snow and that which accumulates melts earlier in the year. Changing patterns of snow cover fundamentally alter how such systems store and release water. Changes in the amount of precipitation affect the volume of run-off, particularly near the end of the winter at the onset of snow melt. Temperature changes mostly affect the timing of run-off with earlier peak flow in the spring. Although additional river-flow can be considered beneficial to agriculture this is only true if there is an ability to store run-off during times of excess to use later in the growing season. Globally, only a few rivers currently have adequate storage to cope with large shifts in seasonality of run-off ( Barnett et al . 2005 ). Where storage capacities are not sufficient, much of the winter run-off will immediately be lost to the oceans. Figure 7 c shows the monthly river-flow from the Volga catchment in Russia. It shows an earlier and increased peak flow around snow melt with subsequently lower flow later in the year.

The fraction of run-off originating as snowfall. The red lines indicate the regions where streamflow is snowmelt-dominated, and where there is not adequate reservoir storage capacity to buffer shifts in the seasonal hydrograph. The black lines indicate additional areas where water availability is predominantly influenced by snowmelt generated upstream (but run-off generated within these areas is not snowmelt-dominated). Reproduced from Barnett et al . (2005) with permission from Macmillan Publishers Ltd: Nature.

Some major rivers, such as the Indus and Ganges, are fed by mountain glaciers, with approximately one-sixth of the world's population currently living in glacier-fed river basins ( Stern 2007 ). Populations are projected to rise significantly in major glacier-fed river basins such as the Indo-Gangetic plain. As such, changes in remote precipitation and the magnitude and seasonality of glacial melt waters could therefore potentially impact food production for many people.

The majority of observed glaciers around the globe are undergoing shrinkage ( Zemp et al. 2008 ). Formerly attributing this retreat to recent warming is not currently possible. However, there is a broad consensus that warming is a primary cause of retreat, although changes in atmospheric moisture particularly in the tropics may be contributing ( Bates et al . 2008 ). Melting glaciers will initially increase river-flow although the seasonality of flow will be enhanced ( Juen et al . 2007 ) bringing with it an increased flood risk. In the long term, glacial retreat is expected to be enhanced further leading to eventual decline in run-off, although the greater time scale of this decline is uncertain. The Chinese Glacier Inventory catalogued 46 377 glaciers in western China, with approximately 15 000 glaciers in the Himalayas. In total these glaciers store an estimated 12 000 km 3 of fresh water ( Ding et al . 2006 ; Cruz et al . 2007 ). Analysis of glaciers in the western Himalayas demonstrates evidence of glacial thinning ( Berthier et al . 2007 ), and radioactive bomb deposits from one high altitude glacier show no net accumulation since 1950 ( Kehrwald et al . 2008 ). The limited number of direct observations also supports evidence of a glacial retreat in the Himalayas ( Zemp et al. 2008 ). The water from these glaciers feeds large rivers such as the Indus, Ganges and Brahmaputra and is likely to be contributing a significant proportion of seasonal river flow although the exact magnitude is unknown. Currently nearly 500 million people are reliant on these rivers for domestic and agricultural water resources. Climate change may mean the Indus and Ganges become increasingly seasonal rivers, ceasing to flow during the dry season ( Kehrwald et al . 2008 ). Combined with a rising population this means that water scarcity in the region would be expected to increase in the future.

(c) Mean sea-level rise

Sea-level rise is an inevitable consequence of a warming climate owing to a combination of thermal expansion of the existing mass of ocean water and addition of extra water owing to the melting of land ice. This can be expected to eventually cause inundation of coastal land, especially where the capacity for introduction or modification of sea defences is relatively low or non-existent. Regarding crop productivity, vulnerability is clearly greatest where large sea-level rise occurs in conjunction with low-lying coastal agriculture. Many major river deltas provide important agricultural land owing to the fertility of fluvial soils, and many small island states are also low-lying. Increases in mean sea level threaten to inundate agricultural lands and salinize groundwater in the coming decades to centuries, although the largest impacts may not be seen for many centuries owing to the time required to melt large ice sheets and for warming to penetrate into the deep ocean.

The potential sea-level rise associated with melting of the main ice sheets would be 5 m for West Antarctic Ice Sheet (WAIS), 60 m for East Antarctic Ice Sheet (EAIS), and 7 m for Greenland Ice Sheet (GIS), with both the GIS and WAIS considered vulnerable. Due to the possible rate of discharge of these ice sheets, and past maximal sea-level rise (under similar climatic conditions) a maximum eustatic sea-level rise of approximately 2 m by 2100 is considered physically plausible, but very unlikely ( Pfeffer et al . 2008 ; Rohling et al . 2008 ; Lowe et al . 2009 ).

Short-lived storm surges can also cause great devastation, even if land is not permanently lost. There has been relatively little work assessing the impacts of either mean sea-level rise or storm surges on agriculture.

4. Non-climate impacts related to greenhouse gas emissions: impacts of changes in atmospheric composition

(a) co 2 fertilization.

As well as influencing climate through radiative forcing, increasing atmospheric CO 2 concentrations can also directly affect plant physiological processes of photosynthesis and transpiration ( Field et al . 1995 ). Therefore any assessment of the impacts of CO 2 -induced climate change on crop productivity should account for the modification of the climate impact by the CO 2 physiological impact. The CO 2 physiological response varies between species, and in particular, two different pathways of photosynthesis (named C 3 and C 4 ) have evolved and these affect the overall response. The difference lies in whether ribulose-1,5-bisphosphate carboxylase–oxygenase (RuBisCO) within the plant cells is saturated by CO 2 or not. In C 3 plants, RuBisCO is not CO 2 -saturated in present day atmospheric conditions, so rising CO 2 concentrations increase net uptake of carbon and thus growth. The RuBisCO enzyme is highly conserved in plants and as such it is thought that the response of all C 3 crops including wheat and soya beans will be comparable. Theoretical estimates suggest that increasing atmospheric CO 2 concentrations to 550 ppm, could increase photosynthesis in such C 3 crops by nearly 40 per cent ( Long et al . 2004 ). The physiology of C 4 crops, such as maize, millet, sorghum and sugarcane is different. In these plants CO 2 is concentrated to three to six times atmospheric concentrations and thus RuBisCO is already saturated ( von Caemmerer & Furbank 2003 ). Thus, rising CO 2 concentrations confer no additional physiological benefits. These crops may, however, become more water-use efficient at elevated CO 2 concentrations as stomata do not need to stay open as long for the plant to receive the required CO 2. Thus yields may increase marginally as a result ( Long et al . 2004 ).

Many studies suggest that yield rises owing to this CO 2 -fertilization effect and these results are consistent across a range of experimental approaches including controlled environment closed chambers, greenhouse, open and closed field top chambers, and free-air carbon dioxide enrichment (FACE) experiments ( Tubiello et al . 2007 ). Experiments under idealized conditions show that a doubling of atmospheric CO 2 concentration increases photosynthesis by 30–50% in C 3 plant species and 10–25% in C 4 species ( Ainsworth & Long 2005 ). Crop yield increase is lower than the photosynthetic response; increases of atmospheric CO 2 to 550 ppm would on average increase C 3 crop yields by 10–20% and C 4 crop yields by 0–10% ( Gifford 2004 ; Long et al . 2004 ; Ainsworth & Long 2005 ).

Some authors argue that crop response to elevated CO 2 may be lower than previously thought, with consequences for crop modelling and projections of food supply (Long et al . 2004 , 2009 ). Plant physiologists and modellers alike recognize that the effects of elevated CO 2 , as measured in experimental settings and subsequently implemented in models, may overestimate actual field and farm level responses. This is because of many limiting factors such as pests and weeds, nutrients, competition for resources, soil water and air quality which are neither well understood at large scales, nor well implemented in leading models.

Despite the potential positive effects on yield quantities, elevated CO 2 may, however, be detrimental to yield quality of certain crops. For example, elevated CO 2 is detrimental to wheat flour quality through reductions in protein content ( Sinclair et al . 2000 ).

Figure 9 and table 1 show the impact of including CO 2 physiological effects in projections of plant productivity in agricultural regions. Without CO 2 fertilization, many regions, especially in the low latitudes, suffer a decrease in productivity by 2050. In contrast, by including CO 2 fertilization all but the very driest regions show increases in productivity.

Two projections of future change in net primary productivity (kg C m −2 yr −1 ) over global croplands for 30-year means centred around 2020 and 2050, relative to 1970–2000. The two projections show the impact of including CO 2 physiological effects and are the members of the ensemble with the most positive and negative changes in productivity averaged over all global croplands. See the electronic supplementary material for further details.

Global-scale comparisons of the impacts of CO 2 fertilization with those of changes in mean climate ( Parry et al . 2004 ; Nelson et al . 2009 ) show that the strength of CO 2 fertilization effects is a critical factor in determining whether global-scale yields are projected to increase or decrease. If CO 2 fertilization is strong, North America and Europe may benefit from climate change at least in the short term ( figure 10 ). However, regions such as Africa and India are nevertheless still projected to experience up to 5 per cent losses by 2050, even with strong CO 2 fertilization. These losses increase to up to 30 per cent if the effects of CO 2 fertilization are omitted. In fact without CO 2 fertilization all regions are projected to experience a loss in productivity owing to climate change by 2050. However, existing global scale studies ( Parry et al . 2004 ; Nelson et al . 2009 ) have only used a limited sample of available climate model projections.

Potential changes (%) in national cereal yields for the 2020s and 2050s relative to 1990, with climate change projected by the HadCM3 model under the A1FI scenario ( a ) with and ( b ) without CO 2 fertilization. Reproduced from Parry et al . (2004) with permission from Elsevier.

A reduction in CO 2 emissions would be expected to reduce the positive effect of CO 2 fertilization on crop yields more rapidly than it would mitigate the negative impacts of climate change. Even if GHG concentrations rose no further, there is a commitment to a certain amount of further global warming ( IPCC 2007 ). Stabilization of CO 2 concentrations would therefore halt any increase in the impacts of CO 2 fertilization, while the impacts of climate change could still continue to grow. Therefore in the short term the impacts on global food production could be negative. However, estimates suggest that stabilizing CO 2 concentrations at 550 ppm would significantly reduce production losses by the end of the century ( Arnell et al . 2002 ; Tubiello & Fisher 2006 ).

For all species higher water-use efficiencies and greater root densities under elevated CO 2 in field systems may, in some cases, alleviate drought pressures, yet their large-scale implications are not well understood ( Wullschleger et al . 2002 ; Norby et al . 2004 ; Centritto 2005 ). This could offset some of the expected warming-induced increase in evaporative demand, thus easing the pressure for more irrigation water. This may also alter the relationship between meteorological drought and agricultural/hydrological drought; an increase in meteorological drought may result in a smaller increase in agricultural or hydrological drought owing to increased water-use efficiency of plants ( Betts et al . 2007 ).

Soil moisture and run-off may be more relevant than precipitation and meteorological drought indices as metrics of water resource availability, as these represent the water actually available for agricultural use. These quantities are routinely simulated by physically based climate models as a necessary component of the hydrological cycle. Figure 11 and table 1 show two scenarios of projected changes in soil moisture as a fraction of that required to prevent plant stress. The available soil moisture fraction is projected to increase on average across global croplands ( table 1 ), with increases in some regions, particularly the mid-latitudes, but decrease in others, particularly in the tropics. Similarly, run-off increases in some regions and decreases in others ( figure 12 ), but the mean change across global croplands varies in sign between scenarios ( table 1 ). Importantly, the scenarios with an increase in mean run-off and the greatest increase in available soil moisture included the effects of CO 2 fertilization in the model, while those with a decrease in mean run-off and the smallest increase in soil moisture availability did not include this effect ( Betts et al . 2007 ).

Two projections of future change in soil moisture as a fraction of that required to prevent plant water stress over global croplands for 30-year means centred around 2020 and 2050, relative to 1970–2000. Positive values indicate increased water availability. The two projections are the members of the ensemble with the greatest and least change averaged over all global croplands. See the electronic supplementary material for further details.

Two projections of future change in annual mean run-off (mm d −1 ) over global croplands for 30-year means centred around 2020 and 2050, relative to 1970–2000. The two projections are the members of the ensemble with the most positive and negative changes in annual mean run-off averaged over all global croplands. See the electronic supplementary material for further details.

However, as discussed in §2 b , changes in extremes are also important, and agricultural drought may be more critical than annual mean soil moisture availability. With drought defined as the driest 20th percentile of the distribution in soil moisture over time in any given location, the model ensemble used here consistently projects an increase in the time spent under drought in most regions for the first half of the twenty-first century ( figure 13 and table 1 ).

Two projections of percentage change in time spent under meteorological drought as defined in terms of soil moisture in global croplands for 30-year means centred around 2020 and 2050, relative to 2000. The two projections are the members of the ensemble with the greatest and least percentage change averaged over all global croplands. See the electronic supplementary material for further details.

Ozone is a major secondary air-pollutant, which at current concentrations has been shown to have significant negative impacts on crop yields ( Van Dingenen et al . 2009 ). Whereas in North America and Europe, emissions of ozone precursors are decreasing, in other regions of the world, especially Asia, they are increasing rapidly ( Van Dingenen et al . 2009 ).

Ozone reduces agricultural yield through several mechanisms. Firstly, acute and visible injury to products such as horticultural crops reduces market value. Secondly, ozone reduces photosynthetic rates and accelerates leaf senescence which in turn impacts on final yield. In Europe and North America many studies have investigated such yield reductions (e.g. Morgan et al . 2003 ). However, in other regions, such as Asia, little evidence currently exists. Thus, our understanding of the impacts in such regions is limited.

5. Conclusions

Anthropogenic greenhouse gas emissions and climate change have a number of implications for agricultural productivity, but the aggregate impact of these is not yet known and indeed many such impacts and their interactions have not yet been reliably quantified, especially at the global scale. An increase in mean temperature can be confidently expected, but the impacts on productivity may depend more on the magnitude and timing of extreme temperatures. Mean sea-level rise can also be confidently expected, which could eventually result in the loss of agricultural land through permanent inundation, but the impacts of temporary flooding through storm surges may be large although less predictable.

Freshwater availability is critical, but predictability of precipitation is highly uncertain and there is an added problem of lack of clarity on the relevant metric for drought—some studies including IPCC consider metrics based on local precipitation and temperature such as the Palmer Drought Severity Index, but this does not include all relevant factors. Agricultural impacts in some regions may arise from climate changes in other regions, owing to the dependency on rivers fed by precipitation, snowmelt and glaciers some distance away. Drought may also be offset to some extent by an increased efficiency of water use by plants under higher CO 2 concentrations, although the impact of this again is uncertain especially at large scales. The climate models used here project an increase in annual mean soil moisture availability and run-off in many regions, but nevertheless across most agricultural areas there is a projected increase in the time spent under drought as defined in terms of soil moisture.

Moreover, even the sign of crop yield projections is uncertain as this depends critically on the strength of CO 2 fertilization and also O 3 damage. Few studies have assessed the response of crop yields to CO 2 fertilization and O 3 pollution under actual growing conditions, and consequently model projections are poorly constrained. Indirect effects of climate change through pests and diseases have been studied locally but a global assessment is not yet available. Overall, it does not appear to be possible at the present time to provide a robust assessment of the impacts of anthropogenic climate change on global-scale agricultural productivity.

Acknowledgements

We are grateful to Simon Brown, Ian Crute, Diogo de Gusmão, Keith Jaggard, Doug McNeall, Erika Palin, Doug Smith and Jonathan Tinker for useful discussions.

One contribution of 23 to a Theme Issue ‘ Food security: feeding the world in 2050 '.

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• Published: 01 April 2021

Anthropogenic climate change has slowed global agricultural productivity growth

• Ariel Ortiz-Bobea   ORCID: orcid.org/0000-0003-4482-6843 1 ,
• Toby R. Ault 2 ,
• Carlos M. Carrillo   ORCID: orcid.org/0000-0002-0045-1595 2 ,
• Robert G. Chambers 3 &
• David B. Lobell   ORCID: orcid.org/0000-0002-5969-3476 4  

Nature Climate Change volume  11 ,  pages 306–312 ( 2021 ) Cite this article

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• Climate-change impacts
• Sustainability

Agricultural research has fostered productivity growth, but the historical influence of anthropogenic climate change (ACC) on that growth has not been quantified. We develop a robust econometric model of weather effects on global agricultural total factor productivity (TFP) and combine this model with counterfactual climate scenarios to evaluate impacts of past climate trends on TFP. Our baseline model indicates that ACC has reduced global agricultural TFP by about 21% since 1961, a slowdown that is equivalent to losing the last 7 years of productivity growth. The effect is substantially more severe (a reduction of ~26–34%) in warmer regions such as Africa and Latin America and the Caribbean. We also find that global agriculture has grown more vulnerable to ongoing climate change.

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Data availability.

Data and code necessary to fully reproduce results in this study are deposited in a permanent online repository at the Cornell Institute for Social and Economic Research (CISER): https://doi.org/10.6077/pfsd-0v93 .

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Acknowledgements

The authors thank C.B. Barrett and participants at the AERE and EAAE summer meetings, the Southern Economic Association meeting, the AGU Fall meeting, Giannini Foundation’s Big Ag Data Conference and seminars at Cornell University, Arizona State University, University of Arizona, North Carolina State University, Duke University, Michigan State University, University of Connecticut, Virginia Tech, UC Berkeley and Oregon State University and three anonymous referees for useful comments. A.O.B. was partially supported by the USDA National Institute of Food and Agriculture, Hatch/Multi State project 1011555. T.R.A. and C.M.C. were partially supported by NSF grants 1602564 and 1751535, as well as the Cornell Atkinson Center for Sustainability, the Cornell Initiative for Digital Agriculture and the Braudy Foundation.

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A.O.B. conceived the study and conducted and led research and the writing of the manuscript. C.M.C. obtained and downscaled modelled climate data. T.R.A., R.G.C. and D.B.L. provided detailed guidance and advice throughout the project. All authors contributed to writing the manuscript.

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Extended data

Extended data fig. 1 composition of global agricultural production..

Share of net production value of cereal crops, non-cereal crops and livestock. Source: FAOSTAT ( http://www.fao.org/faostat/en/#data/QV , accessed 6/29/2020).

Extended Data Fig. 2 The response of agricultural productivity to weather without 10% of coldest countries.

a , Response function of changes in country-level TFP to changes in green-season average T. Response functions are centered vertically so that the exposure-weighted marginal effect is zero. The baseline response function with all countries is shown in dashed lines. The coloured bands represent 90 and 95% confidence bands based on 500 year-by-region block bootstraps. The blue bars represent the country-level distribution of green-season average T over the sample period. The average green-season T is indicated for a select number of large countries. b , Panel shows the result of a placebo check whereby TFP and weather data are randomly mismatched or reshuffled by years. The distribution represents the linear and quadratic T coefficients based on 10,000 reshuffled datasets. c , Same as previous panel but based on datasets reshuffled by country. d , Response function of changes in country-level TFP to changes in green-season total P. e , Same as panel B but for P coefficients. f , Same as panel c but for P coefficients.

Extended Data Fig. 3 The response of agricultural productivity to weather without 10% of hottest countries.

Extended data fig. 4 the response of agricultural productivity to weather for 1962–1988..

a , Response function of changes in country-level TFP to changes in green-season average T. Response functions are centered vertically so that the exposure-weighted marginal effect is zero. The baseline response function for 1962–2015 is shown in dashed lines. The coloured bands represent 90 and 95% confidence bands based on 500 year-by-region block bootstraps. The blue bars represent the country-level distribution of green-season average T over the sample period. The average green-season T is indicated for a select number of large countries. b , Panel shows the result of a placebo check whereby TFP and weather data are randomly mismatched or reshuffled by years. The distribution represents the linear and quadratic T coefficients based on 10,000 reshuffled datasets. c , Same as previous panel but based on datasets reshuffled by country. d , Response function of changes in country-level TFP to changes in green-season total P. e , Same as panel B but for P coefficients. f , Same as panel c but for P coefficients.

Extended Data Fig. 5 The response of agricultural productivity to weather for 1989–2015.

a , Response function of changes in country-level TFP to changes in green-season average T. Response functions are centered vertically so that the exposure-weighted marginal effect is zero. The baseline response function for 1962–2015 is shown in dashed lines. The coloured bands represent 90 and 95% confidence bands based on 500 year-by-region block bootstraps. The blue bars represent the country-level distribution of green-season average T over the sample period. The average green-season T is indicated for a select number of large countries. b , Panel shows the result of a placebo check whereby TFP and weather data are randomly mismatched or reshuffled by years. The distribution represents the linear and quadratic T coefficients based on 10,000 reshuffled datasets. c , Same as previous panel but based on datasets reshuffled by country. d , Response function of chanfcentges in country-level TFP to changes in green-season total P. e , Same as panel B but for P coefficients. f , Same as panel c but for P coefficients.

Extended Data Fig. 6 The response of agricultural productivity to weather for 1962–1988 without 10% of coldest countries.

a , Response function of changes in country-level TFP to changes in green-season average T. Response functions are centered vertically so that the exposure-weighted marginal effect is zero. The baseline response function with all countries and years is shown in dashed lines. The coloured bands represent 90 and 95% confidence bands based on 500 year-by-region block bootstraps. The blue bars represent the country-level distribution of green-season average T over the sample period. The average green-season T is indicated for a select number of large countries. b , Panel shows the result of a placebo check whereby TFP and weather data are randomly mismatched or reshuffled by years. The distribution represents the linear and quadratic T coefficients based on 10,000 reshuffled datasets. c , Same as previous panel but based on datasets reshuffled by country. d , Response function of changes in country-level TFP to changes in green-season total P. e , Same as panel B but for P coefficients. f , Same as panel c but for P coefficients.

Extended Data Fig. 7 The response of agricultural productivity to weather for 1989–2015 without 10% of coldest countries.

Extended data fig. 8 the response of agricultural productivity to weather for 1962–1988 without 10% of hottest countries., extended data fig. 9 the response of agricultural productivity to weather for 1989–2015 without 10% of hottest countries., extended data fig. 10 global impact of anthropogenic climate change under a wide range of econometric models..

The upper part of the figure shows the impact estimates for 298 model variations. The vertical lines around each estimate represent the 90 and 95% confidence intervals (in light and dark colour, respectively) around the ensemble mean estimate for a particular model. ACC impacts for the baseline model, also shown in Extended Data Fig. 3a , is highlighted in blue whereas alternative models are shown in grey. The red horizontal line and band represent the average mean impact of the 288 models out of the 298 that do not exclude observations, plus and minus a standard deviation (−16.9 ± 5.9%). The vertical bars directly below the impact estimates represent the reduction in out-of-sample MSE of a 10-fold cross-validation (whereby years of data are sampled together) relative to a model that excludes weather variables. Thus, higher bars indicate better model fit. The dotted table on the bottom part of the figure provides information about the characteristics of each econometric model shown in the upper part of the figure.

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Supplementary Figs. 1–5 and Tables 1–4.

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Ortiz-Bobea, A., Ault, T.R., Carrillo, C.M. et al. Anthropogenic climate change has slowed global agricultural productivity growth. Nat. Clim. Chang. 11 , 306–312 (2021). https://doi.org/10.1038/s41558-021-01000-1

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Unravelling the missing link: climate risk, esg performance and debt capital cost in china.

1. Introduction

2. literature review and hypotheses development, 2.1. direct impact of esg performance on debt capital cost, 2.2. mediating effect based on debt default risk, 2.3. impact of esg performance on debt capital cost from a corporate pollution perspective, 2.4. impact of corporate esg performance on debt capital cost from an equity perspective, 3. study design, 3.1. sample selection and data sources, 3.2. definition of variables, 3.2.1. explained variables: debt capital cost, 3.2.2. explanatory variables: corporate esg performance, 3.2.3. control variables, 3.3. equation construction, 4. analysis of empirical results, 4.1. descriptive statistics, 4.2. correlation analysis, 4.3. regression analysis, 4.3.1. analysis of principal regression results, 4.3.2. robustness test, 4.3.3. mechanism analysis, 4.3.4. heterogeneity analysis, 5. research conclusions and suggestions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Yan, Y.; Cheng, X.; Ong, T. Unravelling the Missing Link: Climate Risk, ESG Performance and Debt Capital Cost in China. Sustainability 2024 , 16 , 7137. https://doi.org/10.3390/su16167137

Yan Y, Cheng X, Ong T. Unravelling the Missing Link: Climate Risk, ESG Performance and Debt Capital Cost in China. Sustainability . 2024; 16(16):7137. https://doi.org/10.3390/su16167137

Yan, Yu, Xinman Cheng, and Tricia Ong. 2024. "Unravelling the Missing Link: Climate Risk, ESG Performance and Debt Capital Cost in China" Sustainability 16, no. 16: 7137. https://doi.org/10.3390/su16167137

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