Loading metrics

Open Access

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

Affiliation South Asian Forum for Environment, India

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

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

ORCID logo

  • Amitava Aich, 
  • Dipayan Dey, 
  • Arindam Roy

PLOS

Published: July 28, 2022

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

Fig 1

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/ ).

thumbnail

  • 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 .

thumbnail

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 .

thumbnail

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.

  • View Article
  • Google Scholar
  • 2. Nori M, Switzer J, Crawford A. Herding on the brink: towards a global survey of pastoral communities and conflict. An Occasional Working Paper from the International Union for Conservation of Nature (IUCN) Commission on Environmental. Economic and Social Policy. Gland: IUCN; 2005.
  • 3. Howard P, Puri R, Smith L. Globally important agricultural heritage systems: a scientific conceptual framework and strategic principles. Rome: FAO; 2009.
  • 6. Adger WN, Brooks N, Bentham G, Agnew M, Eriksen S. New indicators of vulnerability and adaptive capacity. Norwich: Tyndall Centre for Climate Change Research; 2005.
  • PubMed/NCBI
  • 9. IAASTD (International Assessment of Agricultural Knowledge, Science and Technology for Development). Agriculture at a crossroads, international assessment of agricultural knowledge, science and technology for development global report. Washington, DC: Island Press; 2009.
  • 10. Salick J, Byg A. Indigenous peoples and climate change. Report of Symposium, 12–13 April 2007. University of Oxford and Missouri Botanical Garden. Oxford: Tyndall Centre Publication; 2007.
  • 12. Westley F, Zimmerman B, Patton M. Getting to maybe. Toronto, Ontario, Canada: Random House of Canada; 2006.
  • 25. PAR (Platform for Agrobiodiversity Research). Workshop report: experiences, knowledge gaps and opportunities for collaboration. The use of agrobiodiversity by indigenous peoples and rural communities in adapting to climate change [online]. Rome: Platform for Agrobiodiversity Research. 2009. Available from: https://satoyama-initiative.org/case_studies/the-use-of-agrobiodiversity-by-indigenous-and-traditional-agricultural-communities-in-adapting-to-climate-change/ PAR Chiang Mai Technical Report.doc [cited 2011 May 11].
  • 32. Browder JO. Fragile lands in Latin America: strategies for sustainable development. Boulder: Westview Press; 1989.
  • 36. FAO. “Climate-smart” agriculture: policies, practices and financing for food security, adaptation and mitigation. Rome. 2010.
  • 45. Haimendorf CVF. The Apatanis and their neighbours. London: Oxford University Press; 1962.
  • 65. Krishnan R, Shrestha AB, Ren G, Rajbhandari R, Saeed S, Sanjay J, et al. Unravelling climate change in the Hindu Kush Himalaya: rapid warming in the mountains and increasing extremes. In: The Hindu Kush Himalaya Assessment. Cham: Springer; 2019. p. 57–97.
  • 69. TNSAPCC (Tamil Nadu State Action Plan for Climate Change reports). 2013. Available from: https://cag.gov.in/uploads/media/tamil-nadu-climate-change-action-plan-20200726073516.pdf .
  • Reference Manager
  • Simple TEXT file

People also looked at

Review article, a method review of the climate change impact on crop yield.

research paper on climate change and agriculture

  • College of Resources and Environmental Sciences, China Agricultural University, Beijing, China

Climate change significantly impacts global agricultural production, giving rise to considerable uncertainties. To explore these climate impacts, three independent methods have been employed: manipulated experiments, process-based crop models, and empirical statistical models. However, the uncertainty stemming from the use of different methods has received insufficient attention, and its implications remain unclear, necessitating a systematic review. In this study, we conducted a comprehensive review of numerous previous studies to summarize the historic development and current status of each method. Through a method comparison, we identified their respective strengths, limitations, and ideal areas of application. Additionally, we outlined potential prospects and suggested directions for future improvements, including clarifying the response mechanisms, updating simulation technologies, and developing multi-method ensembles. By addressing the knowledge gap regarding method differences, this review could contribute to a more accurate assessment of climate impacts on agriculture.

1. Introduction

The impact of global climate change on agricultural production in the 21st century has been significant, many countries and regions worldwide have observed reduced yields in crops such as wheat, maize, rice, and oilseed rape ( Luo et al., 2005 ; Arora, 2019 ; Ray et al., 2019 ; Sultan et al., 2019 ; Ortiz-Bobea et al., 2021 ; Lachaud et al., 2022 ; Chandio et al., 2023 ). It is expected that temperatures will continue to rise, leading to an increase in extreme weather events. This trend adds to the agricultural production uncertainty, particularly for major crops such as maize, rice, and soybeans ( Vogel et al., 2019 ; Pörtner et al., 2022 ). Without adaptation measures, global yields of important food crops could decline by 12–20% by the end of this century ( Lobell and Gourdji, 2012 ; Wheeler and Von Braun, 2013 ; Challinor et al., 2014 ; Aggarwal et al., 2019 ). Therefore, accurately assessing the impact of climate change on crop yields is crucial for ensuring global food security.

There are several methods are used for climate change’s effects on agriculture research, such as manipulated experiments, process-based crop models, and empirical statistical models. Field experiments was the earliest commonly used to expose crops to different climatic conditions, either through natural variations or controlled climate factors, to study their impact on crop growth and yield. With technological advancements, process-based crop models and empirical statistical models have become more prominent. Process-based crop models utilize computer simulations to quantitatively analyze the physiological mechanisms and dynamic processes of crop growth and yield. Empirical statistical models establish mathematical relationships between climate change and crop yield. Over time, significant progress have been made in developing these methodologies. For example, Shi et al. (2013) identified uncertainties in statistical models related to research scale, collinearity of variables, and detrending. Similarly, White et al. (2011) and Rötter et al. (2018) evaluated existing process based crop models, assessing their simulation effects and research standards while highlighting sources of error and limitations. However, many current studies tend to focus on specific research methods, which may introduce biases and uncertainties into climate change impact studies. This limitation reduces the comprehensiveness and reliability of individual approaches. For example, empirical statistical models, based on limited historical observations, face challenges in accurately predicting future yield-climate relationships ( Lobell et al., 2006 ). Conversely, process-based crop models rely on empirical formulas to approximate internal crop growth processes ( Wang et al., 2022 ). Therefore, it is crucial to understand the advantages and disadvantages of each method and explore avenues for future improvement.

This paper presents a comprehensive review of recent advancements in research methods used to study the impacts of climate change on agriculture and adaptation strategies. Its primary aim is to provide researchers with a deeper understanding of existing methods and serve as a reference for future methodological innovations and interdisciplinary collaborations. To achieve this goal, the paper systematically examines three major methods: manipulated experiments, process-based crop models, and empirical statistical models. It critically evaluates the advantages, disadvantages, and directions for future improvement for each method. Additionally, the paper explores the interconnectedness of multiple methodological approaches and their relevance to current research. It also discusses the challenges associated with current methods and highlights potential future research prospects.

2. Manipulated experiments

Manipulated experiments involve setting different environmental conditions during crop growth to simulate the impact of climate change on crop yield. As an early research method, manipulated experiments has evolved from utilizing natural climate variations to artificial control. This method is simple and straightforward, with a high level of operability. Initially, artificial manipulated experiments were conducted in growth chambers or greenhouse, where temperature, light, water, and gas control experiments were carried out in adjustable but fully enclosed environments. Modern large-scale artificial greenhouse relies on facilities such as strip lights, removable platforms, and exhaust systems to achieve uniform distribution of meteorological factors such as light, temperature, and water. Real-time monitoring and precise control are achieved with the support of computers. Hatfield and Prueger (2015) set up warmer conditions in an artificial climate chamber and found maize yield was significantly reduced. Besides, temperature effects are increased by water deficits and excess.

However, artificial greenhouse block the exchange of water and gases between crops and the external environment, lacking the comprehensive effects of the natural environment. To avoid abnormal increases in humidity and temperature caused by fully enclosed environments, open-top chambers (OTCs) and open-air CO 2 enrichment systems (Free-Air CO 2 Enrichment, FACE) have been used. OTCs is a gas-enriched greenhouse without a cover on the top, this design ensures sufficient exchange of water and gases between crops and the external environment, significantly enhancing the ability to simulate realistic growth conditions. OTCs have been widely used in experiments investigating the effects of gases such as CO 2 and O 2 on crop growth ( Rogers et al., 1994 ; Ziska et al., 1997 ; Ewert et al., 2002 ; Kakani et al., 2003 ; Ainsworth et al., 2012 ). However, there are still some differences in conditions such as wind speed and light within the OTCs compared to natural conditions. Over time, the outer film of the OTCs may undergo oxidation, yellowing, and dirt accumulation, leading to shading of solar radiation and affecting the experimental outcomes ( Leadley and Drake, 1993 ).

To further enhance simulation realism, researchers have increasingly turned to FACE systems. FACE systems release CO 2 or CO 2 -rich air from above the ground onto plant canopies and adjust CO 2 flow rates through feedback mechanisms ( Long et al., 2004 ), enabling studies on the effects of elevated CO 2 and O 3 concentrations on crops’ productivity ( Long et al., 2006 ; Myers et al., 2014 ). Kimball et al. (2002) conducted experiments in different countries using the FACE system and found elevated CO 2 increased crop yield substantially in C3 species, but little in C4. Combining an infrared heater with the FACE system to conduct experiments, known as T-FACE (Temperature-FACE), allows for the research of the combined effects of temperature and CO 2 concentration on crops ( Ruiz-Vera et al., 2013 ). Compared to OTC, FACE systems effectively reduce edge effects and cause minimal disturbance to farmland microclimates ( Ainsworth et al., 2008 ). Nonetheless, the vertical gradient of CO 2 concentration gradually decreases, which is also influenced by wind speed ( Long et al., 2004 ). Currently, FACE systems are evolving toward genetic variation and transgenic technology research aimed at adapting agricultural planting systems to future climates ( Ainsworth et al., 2020 ).

3. Process-based crop models

Process-based crop models are a type of models based on the internal physiological mechanisms of crops, which can comprehensively consider the relationships among the soil-crop-atmosphere system. They describe various physiological processes of crop growth as equations and incorporate various environmental factors (meteorological modules such as temperature, water, and light, as well as soil parameters, cultivars, and agronomic management). The earliest crop models can be traced back to the model on corn canopy photosynthetic rate developed by de Wit (1965) . Subsequently, scientists from different countries have developed series of models, such as the DSSAT series ( Jones et al., 2003 ), and the APSIM series ( Keating et al., 2003 ), WOFOST ( Van Diepen et al., 1989 ), CropSyst ( Stöckle et al., 2003 ), SIMPLE ( Zhao et al., 2019 ), etc. In recent years, through setting different climate inputs, crop models have been widely applied in studies on the impacts of climate change on crop yields, whether in long-term changes ( Leng and Hall, 2019 ; Shahid et al., 2021 ) or in quantifying extreme weather events ( Xiao et al., 2022a ; Júnior et al., 2023 ). Researchers have integrated and compared multiple crop models, consistently reached the conclusion that global warming has a significant negative impact on crop yields ( Asseng et al., 2013 ; Bassu et al., 2014 ; Sultan et al., 2019 ; Zhao et al., 2022 ).

Process-based crop models based on site scale consider only the small-scale climate of individual locations. To incorporate large-scale regions, countries, or global scales, it is necessary to utilize crop models at a larger scale. One commonly approach is the grid-scale crop model. It involves dividing the geographic region into grids of specific resolutions and inputs gridded meteorological data, soil data, management data. For example, Deryng et al. (2011) used the PEGASUS model to simulate the response of major cereal crops to future climate change under different agronomic management. Rosenzweig et al. (2014) combined multiple global crop models to simulate the yield impact under historical climate conditions and found that the results were in good agreement with observed values, confirming the applicability of global grid-scale crop models. In recent years, the application of machine learning methods has made remarkable progress in regional or global-scale research, complementing traditional modeling methods through data-driven approaches ( Reichstein et al., 2019 ). Studies have shown that machine learning-based crop modeling systems can accurately and rapidly predict crop yields in large regions at different spatial resolutions ( Xiao et al., 2022b ).

4. Empirical statistical models

Empirical statistical models is an approach used by establishing mathematical models that describe the relationship between climate factors and crop production. These models rely on both crop yield data and climate data to establish this correlation. Prior to developing empirical statistical models, it is necessary to separate the climate yield from trend yield and error terms. This is crucial because variations in crop yield are influenced not only by climate change but also by factors like technological advancements. Common methods employed to detrend the original yield data include differencing, multi-year moving averages, linear regression, and filtering analysis ( Meza and Silva, 2009 ; Osborne and Wheeler, 2013 ; Troy et al., 2015 ; Kukal and Irmak, 2018 ). Additionally, considering the nonlinear relationship between economic factors and natural factors in grain production ( Xu et al., 2021 ), econometric models have been introduced. These models include production functions ( Just and Pope, 1978 ; Isik and Devadoss, 2006 ), economic-climate models (C-D-C models) ( Chou and Ye, 2006 ), and the Ricardo model ( Deressa and Hassan, 2009 ).

Empirical statistical methods have evolved from simple univariate linear regression models to more complex multivariate regression models, incorporating multiple influencing factors. The development has further advanced with the integration of machine learning and deep learning techniques, enabling the transition from univariate to multivariate and from linear to nonlinear modelling. The univariate linear regression model establishes a straightforward linear relationship between crop yield and a single climate factor ( Parry and Martens, 1999 ). For example, Peng et al. (2004) constructed a simple univariate linear model using rice yield data and seasonal temperature data from observation stations in the Philippines. They found a significant negative correlation between rice yield and minimum temperature, with approximately a 10% yield reduction for each 1°C increases in minimum temperature. Univariate regression models can only consider the influence of a specific climate condition, such as temperature alone, and cannot account for all factors affecting yield ( Carter et al., 1992 ). However, climate change is complex and often involves multiple simultaneous climate conditions impacting crop yield. Hence, the application of multivariate regression models has emerged. Multivariate regression models establish climate-yield correlation models with multiple climate conditions as independent variables and yield as the dependent variable. These models can be categorized based on temporal variations (time series models), spatial variations (cross-sectional models), or both (panel models) ( Lobell and Burke, 2010 ). They have demonstrated good performance in simulating the impacts of climate change on crops like maize, wheat, and soybeans ( Malikov et al., 2020 ; Ranjan et al., 2020 ). For instance, Lobell and Field (2007) established a multivariate linear regression model between temperature, precipitation, and yield, revealing a clear negative response of global wheat, maize, and barley yields to temperature increases. Schlenker and Lobell (2010) developed panel models and projected varying degrees of decline in crop yields such as maize, sorghum, and millet in Sub-Saharan Africa by the mid-century as climate change progresses.

While regression models excel at capturing linear relationships, they face limitations when it comes to simulating nonlinear relationships. To overcome this limitation, machine learning techniques have gained widespread use in climate change impact assessment ( Cao et al., 2021 ; Guo et al., 2021 ; Lischeid et al., 2022 ). Machine learning approaches employ semi-parametric variables based on deep neural networks ( Crane-Droesch, 2018 ) or decision systems like support vector machines and fuzzy logic for yield modelling ( Palanivel and Surianarayanan, 2019 ). They leverage algorithms such as decision trees and random forests for prediction ( Jeong et al., 2016 ). Machine learning demonstrates significant potential in yield assessment, surpassing traditional regression models. Unlike traditional empirical statistical models that rely on specific-shaped response functions, machine learning compensates for their limitations by effectively capturing complex nonlinear relationships in high-dimensional datasets. For example, Leng and Hall (2020) compared the performance of machine learning and multivariate regression models and found that machine learning explained 93% of the yield variability, significantly higher than the 51% explained by multivariate regression. Moreover, under a global warming scenario of 2°C, the maize yield in the United States is projected to decrease by 13.5% ( Leng and Hall, 2020 ).

5. Methods comparison

To date, these methods have been widely used. However, due to differences in their underlying principles, these methods have both advantages and disadvantages in practical applications ( Table 1 ).

www.frontiersin.org

Table 1. Advantages and disadvantages of each method.

5.1. Manipulated experiments

Using manipulated experiments to precisely regulate the thresholds of climate factors can simulate the actual environmental conditions of crop growth and development, resulting in high reliability of the obtained results. Therefore, the results obtained from controlled experiments are often used to calibrate crop models ( Asseng et al., 2004 ). However, the experimental period of manipulated experiments is dependent on the crop’s growth cycle, and it involves complex operations, a long experimental cycle, and substantial human and material resources. Consequently, it is challenging to conduct long-term studies on future climate changes spanning several decades to centuries. Furthermore, due to the heterogeneity of the climate terrain and soil in the experimental area, the site-based experimental results have poor representativeness to larger areas. To upscale to the regional scale, a large amount of experimental data might be needed. A plausible solution is to establish a unified research framework. For example, Coordinated Distributed Experiments (CDE) offers the advantage of addressing global problems while ensuring the inherent accuracy of control experiments ( Fraser et al., 2013 ).

5.2. Process-based crop models

Process-based crop models can effectively simulate the physical mechanisms of crop growth and strictly control the impact of single variables, avoiding the need for long-term field experiments. However, operating such models requires a significant amount of parameter calibration work, especially for large scale and long term simulations, and the process is complex. Within the model, crop growth processes and growing environments are approximately described using empirical or descriptive formulas, which results in some deviations in the response of crop physiological processes. Moreover, this uncertainty varies among different models ( Wang et al., 2022 ).

5.3. Empirical statistical models

Empirical statistical models evaluate and predict crop yields by establishing a correlation between climate factors and historical yields, avoiding the complex tuning required by crop models and the need for inputting soil properties and management practices measured in the field. These models are relatively easy to apply and suitable for regional and global-scale studies. However, the climate factors used as input for these models are often monthly or seasonal averages, smoothing the impact of climate variability during the growing season and neglecting the effects of extreme weather events ( Chen et al., 2004 ). Additionally, the accuracy of detrending methods used in empirical statistical models is difficult to evaluate. Moreover, empirical statistical models are based on limited historical observations, which introduce sampling uncertainty and simulation bias ( Lobell et al., 2006 ). Because these models lack a reliable physical mechanism for extrapolation, they exhibit uncertainty in predicting yields beyond the historical range, and are more suitable for historical or near future studies.

6. Perspectives

6.1. clarifying crop response mechanisms.

The majority of existing yield prediction models operate as “one-way” mechanisms, failing to account for the fact that crops can alter their morphology and physiological functions to adapt to climate change. Moreover, current models are inadequate in assessing the chain reaction of crops to a series of weather events, their ability to adapt to the combined effects of multiple factors, and their response to the precursors of impact factors ( Suzuki et al., 2014 ). In the future, more attention should be paid to the comprehensive influence of soil conditions, hydrological cycle, pest problems, and other factors in crop models ( Newbery et al., 2016 ; Basso et al., 2018 ; Deutsch et al., 2018 ; Tomaz et al., 2020 ; Wei et al., 2021 ; Denissen et al., 2022 ). Additionally, the potential impact of climate change on crop production is not limited to the growth period, as environmental changes during non-growth periods can also indirectly affect crop production. Therefore, it is necessary to establish a model that comprehensively considers the basic knowledge of plant physiology and atmospheric science, including feedback mechanisms ( Tonnang et al., 2022 ), in order to achieve a balance between the authenticity and controllability of the simulation.

6.2. Introducing emerging simulation technologies

The incorporation of emerging technologies can significantly enhance the research capabilities of traditional methods during the process of methodological development. For instance, the integration of remote sensing, big data, and artificial intelligence into existing approaches ( Jiang et al., 2020 ) can address more complex data acquisition and processing requirements, enabling large-scale simulation and regulation. The utilization of remote sensing technology allows for weather and crop data with multiple spatial and temporal resolutions, enabling the assimilation of dynamic crop phenotype data provided by satellites to achieve closer real-time monitoring. This, in turn, enhances the ability of crop models to monitor and predict large-scale crop yields ( Huang et al., 2019 ). However, the current stage of development of data assimilation technology and its application in this field is still in its early stages. As such, machine learning technologies may be key to enhancing the maturity of this approach ( Cai et al., 2019 ).

6.3. Application of method ensemble

Many researchers have assembled multiple crop models to optimize simulation effectiveness and avoid systematic errors associated with a single model ( Bassu et al., 2014 ; Martre et al., 2015 ), which have been shown to provide more reliable results ( Asseng et al., 2013 ). However, integrating models does not fundamentally improve the underlying mechanisms, combining physiological principles and basic science can be essential ( Yin et al., 2021 ). What is more remarkable is that combining multiple methods can effectively reduce uncertainty ( Zhao et al., 2017 ). For instance, the integration of manipulated experiments with process-based crop models can supplement missing modules within existing models or establish more targeted new models by observing various physiological processes throughout the entire growth period of crops under particular conditions. In addition, developing a joint model that combines process-based crop models with statistical models ( Roberts et al., 2017 ) can be beneficial. Such models use simple statistical models to summarize and statistically analyze simulation results generated by process-based crop models. By using polynomials and limited weather variables, these models can accurately replicate process-based crop model results in global grid cells, avoiding the complex parameter adjustment process associated with process-based crop models while also predicting long-term trends ( Blanc and Sultan, 2015 ).

Author contributions

CZ motivated the conception and review design. XF and HT wrote the draft of the manuscript. CZ and JC contributed to the manuscript revision. All authors contributed to the article and approved the submitted version.

This work was supported by the National Natural Science Youth Foundation of China (42201032), the National Natural Science Fund for Excellent Young Scholars (Overseas), and the Fundamental Research Funds for the Central Universities (15053348).

Conflict of interest

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

Publisher’s note

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

Aggarwal, P., Vyas, S., Thornton, P., Campbell, B. M., and Kropff, M. (2019). Importance of considering technology growth in impact assessments of climate change on agriculture. Glob Food Sec. 23, 41–48. doi: 10.1016/j.gfs.2019.04.002

CrossRef Full Text | Google Scholar

Ainsworth, E. A., Leakey, A. D., Ort, D. R., and Long, S. P. (2008). FACE-ing the facts: Inconsistencies and interdependence among field, chamber and modeling studies of elevated [CO 2 ] impacts on crop yield and food supply. New Phytol. 179, 5–9. doi: 10.1111/j.1469-8137.2008.02500.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Ainsworth, E. A., Lemonnier, P., and Wedow, J. (2020). The influence of rising tropospheric carbon dioxide and ozone on plant productivity. Plant Biol. 22, 5–11. doi: 10.1111/plb.12973

Ainsworth, E. A., Yendrek, C. R., Sitch, S., Collins, W. J., and Emberson, L. D. (2012). The effects of tropospheric ozone on net primary productivity and implications for climate change. Annu. Rev. Plant Biol. 63, 637–661. doi: 10.1146/annurev-arplant-042110-103829

Arora, N. K. (2019). Impact of climate change on agriculture production and its sustainable solutions. Environ. Sustain. 2, 95–96. doi: 10.1007/s42398-019-00078-w

Asseng, S., Ewert, F., Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., et al. (2013). Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change 3, 827–832. doi: 10.1038/nclimate1916

Asseng, S., Jamieson, P., Kimball, B., Pinter, P., Sayre, K., Bowden, J., et al. (2004). Simulated wheat growth affected by rising temperature, increased water deficit and elevated atmospheric CO 2 . Field Crops Res . 85, 85–102.

Google Scholar

Basso, B., Dumont, B., Maestrini, B., Shcherbak, I., Robertson, G. P., Porter, J. R., et al. (2018). Soil organic carbon and nitrogen feedbacks on crop yields under climate change. Agric. Environ. Lett. 3:180026. doi: 10.2134/ael2018.05.0026

Bassu, S., Brisson, N., Durand, J. L., Boote, K., Lizaso, J., Jones, J. W., et al. (2014). How do various maize crop models vary in their responses to climate change factors? Glob. Change Biol. 20, 2301–2320. doi: 10.1111/gcb.12520

Blanc, E., and Sultan, B. (2015). Emulating maize yields from global gridded crop models using statistical estimates. Agric. For. Meteorol. 214, 134–147. doi: 10.1016/j.agrformet.2015.08.256

Cai, Y., Guan, K., Lobell, D., Potgieter, A. B., Wang, S., Peng, J., et al. (2019). Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agric. For. Meteorol. 274, 144–159. doi: 10.1016/j.agrformet.2019.03.010

Cao, J., Zhang, Z., Tao, F., Zhang, L., Luo, Y., Zhang, J., et al. (2021). Integrating multi-source data for rice yield prediction across China using machine learning and deep learning approaches. Agric. For. Meteorol. 297:108275. doi: 10.1016/j.agrformet.2020.108275

Carter, T. R., Parry, M., Nishioka, S., and Harasawa, H. (1992). Preliminary guidelines for assessing impacts of climate change. Geneva: IPCC.

Challinor, A. J., Watson, J., Lobell, D. B., Howden, S., Smith, D., and Chhetri, N. (2014). A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Change 4, 287–291. doi: 10.1038/nclimate2153

Chandio, A. A., Jiang, Y., Amin, A., Ahmad, M., Akram, W., and Ahmad, F. (2023). Climate change and food security of South Asia: Fresh evidence from a policy perspective using novel empirical analysis. J. Environ. Plan. Manag. 66, 169–190. doi: 10.1080/09640568.2021.1980378

Chen, C. C., McCarl, B. A., and Schimmelpfennig, D. E. (2004). Yield variability as influenced by climate: A statistical investigation. Clim. Change 66, 239–261. doi: 10.1023/B:CLIM.0000043159.33816.e5

Chou, J. M., and Ye, D. Z. (2006). Assessing the effect of climate changes on grains yields with a new economy-climate model. Clim. Environ. Res. 11, 347–353.

Crane-Droesch, A. (2018). Machine learning methods for crop yield prediction and climate change impact assessment in agriculture. Environ. Res. Lett . 13:114003.

de Wit C. T. (1965). Photosynthesis of leaf canopies . Wageningen: PUDOC.

Denissen, J. M., Teuling, A. J., Pitman, A. J., Koirala, S., Migliavacca, M., Li, W., et al. (2022). Widespread shift from ecosystem energy to water limitation with climate change. Nat. Clim. Change 12, 677–684. doi: 10.1038/s41558-022-01403-8

Deressa, T. T., and Hassan, R. M. (2009). Economic impact of climate change on crop production in Ethiopia: Evidence from cross-section measures. J. Afr. Econ. 18, 529–554. doi: 10.1093/jae/ejp002

Deryng, D., Sacks, W., Barford, C., and Ramankutty, N. (2011). Simulating the effects of climate and agricultural management practices on global crop yield. Glob. Biogeochem. Cycles 25.

Deutsch, C. A., Tewksbury, J. J., Tigchelaar, M., Battisti, D. S., Merrill, S. C., Huey, R. B., et al. (2018). Increase in crop losses to insect pests in a warming climate. Science 361, 916–919. doi: 10.1126/science.aat3466

Ewert, F., Rodriguez, D., Jamieson, P., Semenov, M., Mitchell, R., Goudriaan, J., et al. (2002). Effects of elevated CO2 and drought on wheat: Testing crop simulation models for different experimental and climatic conditions. Agric. Ecosyst. Environ. 93, 249–266. doi: 10.1016/S0167-8809(01)00352-8

Fraser, L. H., Henry, H. A., Carlyle, C. N., White, S. R., Beierkuhnlein, C., Cahill, J. F., et al. (2013). Coordinated distributed experiments: An emerging tool for testing global hypotheses in ecology and environmental science. Front. Ecol. Environ. 11:147–155. doi: 10.1890/110279

Guo, Y., Fu, Y., Hao, F., Zhang, X., Wu, W., Jin, X., et al. (2021). Integrated phenology and climate in rice yields prediction using machine learning methods. Ecol. Indicat. 120:106935. doi: 10.1016/j.ecolind.2020.106935

Hatfield, J. L., and Prueger, J. H. (2015). Temperature extremes: Effect on plant growth and development. Weather Clim. Extremes 10, 4–10.

Huang, J., Gómez-Dans, J. L., Huang, H., Ma, H., Wu, Q., Lewis, P. E., et al. (2019). Assimilation of remote sensing into crop growth models: Current status and perspectives. Agric. For. Meteorol. 276:107609. doi: 10.1016/j.agrformet.2019.06.008

Isik, M., and Devadoss, S. (2006). An analysis of the impact of climate change on crop yields and yield variability. Appl. Econ. 38, 835–844. doi: 10.1080/00036840500193682

Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., et al. (2016). Random forests for global and regional crop yield predictions. PLoS One 11:e0156571. doi: 10.1371/journal.pone.0156571

Jiang, H., Hu, H., Zhong, R., Xu, J., Xu, J., Huang, J., et al. (2020). A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level. Glob. Change Biol. 26, 1754–1766. doi: 10.1111/gcb.14885

Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L., et al. (2003). The DSSAT cropping system model. Eur. J. Agron. 18, 235–265. doi: 10.1016/S1161-0301(02)00107-7

Júnior, R. D. S. N., Deswarte, J. C., Cohan, J. P., Martre, P., van der Velde, M., Lecerf, R., et al. (2023). The extreme 2016 wheat yield failure in France. Glob. Change Biol. 29, 3130–3146. doi: 10.1111/gcb.16662

Just, R. E., and Pope, R. D. (1978). Stochastic specification of production functions and economic implications. J. Econ. 7, 67–86. doi: 10.1016/0304-4076(78)90006-4

Kakani, V., Reddy, K., Zhao, D., and Sailaja, K. (2003). Field crop responses to ultraviolet-B radiation: A review. Agric. For. Meteorol. 120, 191–218. doi: 10.1016/j.agrformet.2003.08.015

Keating, B. A., Carberry, P. S., Hammer, G. L., Probert, M. E., Robertson, M. J., Holzworth, D., et al. (2003). An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron. 18, 267–288. doi: 10.1016/S1161-0301(02)00108-9

Kimball, B., Kobayashi, K., and Bindi, M. (2002). Responses of agricultural crops to free-air CO 2 enrichment. Adv. Agron . 77, 293–368.

Kukal, M. S., and Irmak, S. (2018). Climate-driven crop yield and yield variability and climate change impacts on the US Great Plains agricultural production. Sci. Rep. 8:3450. doi: 10.1038/s41598-018-21848-2

Lachaud, M. A., Bravo-Ureta, B. E., and Ludena, C. E. (2022). Economic effects of climate change on agricultural production and productivity in Latin America and the Caribbean (LAC). Agric. Econ. 53, 321–332. doi: 10.1111/agec.12682

Leadley, P. W., and Drake, B. G. (1993). Open top chambers for exposing plant canopies to elevated CO 2 concentration and for measuring net gas exchange. Vegetatio 104, 3–15. doi: 10.1007/BF00048141

Leng, G., and Hall, J. (2019). Crop yield sensitivity of global major agricultural countries to droughts and the projected changes in the future. Sci. Total Environ . 654, 811–821.

Leng, G., and Hall, J. W. (2020). Predicting spatial and temporal variability in crop yields: An inter-comparison of machine learning, regression and process-based models. Environ. Res. Lett. 15:044027. doi: 10.1088/1748-9326/ab7b24

Lischeid, G., Webber, H., Sommer, M., Nendel, C., and Ewert, F. (2022). Machine learning in crop yield modelling: A powerful tool, but no surrogate for science. Agric. For. Meteorol. 312:108698. doi: 10.1016/j.agrformet.2021.108698

Lobell, D. B., and Burke, M. B. (2010). On the use of statistical models to predict crop yield responses to climate change. Agric. For. Meteorol. 150, 1443–1452. doi: 10.1016/j.agrformet.2010.07.008

Lobell, D. B., and Field, C. B. (2007). Global scale climate–crop yield relationships and the impacts of recent warming. Environ. Res. Lett . 2:014002.

Lobell, D. B., and Gourdji, S. M. (2012). The influence of climate change on global crop productivity. Plant Physiol. 160, 1686–1697. doi: 10.1104/pp.112.208298

Lobell, D. B., Field, C. B., Cahill, K. N., and Bonfils, C. (2006). Impacts of future climate change on California perennial crop yields: Model projections with climate and crop uncertainties. Agric. For. Meteorol. 141, 208–218. doi: 10.1016/j.agrformet.2006.10.006

Long, S. P., Ainsworth, E. A., Leakey, A. D., Nosberger, J., and Ort, D. R. (2006). Food for thought: Lower-than-expected crop yield stimulation with rising CO2 concentrations. Science 312, 1918–1921. doi: 10.1126/science.1114722

Long, S. P., Ainsworth, E. A., Rogers, A., and Ort, D. R. (2004). Rising atmospheric carbon dioxide: Plants FACE the future. Annu. Rev. Plant Biol. 55, 591–628. doi: 10.1146/annurev.arplant.55.031903.141610

Luo, Q., Bellotti, W., Williams, M., and Bryan, B. (2005). Potential impact of climate change on wheat yield in South Australia. Agric. For. Meteorol. 132, 273–285. doi: 10.1016/j.agrformet.2005.08.003

Malikov, E., Miao, R., and Zhang, J. (2020). Distributional and temporal heterogeneity in the climate change effects on US agriculture. J. Environ. Econ. Manag. 104:102386. doi: 10.1016/j.jeem.2020.102386

Martre, P., Wallach, D., Asseng, S., Ewert, F., Jones, J. W., Rötter, R. P., et al. (2015). Multimodel ensembles of wheat growth: Many models are better than one. Glob. Change Biol. 21, 911–925. doi: 10.1111/gcb.12768

Meza, F. J., and Silva, D. (2009). Dynamic adaptation of maize and wheat production to climate change. Clim. Change 94, 143–156. doi: 10.1007/s10584-009-9544-z

Myers, S. S., Zanobetti, A., Kloog, I., Huybers, P., Leakey, A. D., Bloom, A. J., et al. (2014). Increasing CO2 threatens human nutrition. Nature 510, 139–142. doi: 10.1038/nature13179

Newbery, F., Qi, A., and Fitt, B. D. (2016). Modelling impacts of climate change on arable crop diseases: Progress, challenges and applications. Curr. Opin. Plant Biol. 32, 101–109. doi: 10.1016/j.pbi.2016.07.002

Ortiz-Bobea, A., Ault, T. R., Carrillo, C. M., Chambers, R. G., and Lobell, D. B. (2021). Anthropogenic climate change has slowed global agricultural productivity growth. Nat. Clim. Change 11, 306–312. doi: 10.1038/s41558-021-01000-1

Osborne, T. M., and Wheeler, T. R. (2013). Evidence for a climate signal in trends of global crop yield variability over the past 50 years. Environ. Res. Lett. 8:024001. doi: 10.1088/1748-9326/8/2/024001

Palanivel, K., and Surianarayanan, C. (2019). An approach for prediction of crop yield using machine learning and big data techniques. Int. J. Comput. Eng. Technol. 10, 110–118. doi: 10.34218/IJCET.10.3.2019.013

Parry, M., and Martens, P. (1999). “Impact Assessment of Climate Change,” in Climate change: An integrated perspective. advances in global change research , eds P. Martens, J. Rotmans, D. Jansen, and K. Vrieze (Dordrecht: Springer), 201–238. doi: 10.1007/0-306-47982-6_6

Peng, S., Huang, J., Sheehy, J. E., Laza, R. C., Visperas, R. M., Zhong, X., et al. (2004). Rice yields decline with higher night temperature from global warming. Proc. Natl. Acad. Sci. U.S.A . 101, 9971–9975.

Pörtner, H.-O., Roberts, D. C., Adams, H., Adler, C., Aldunce, P., Ali, E., et al. (2022). Climate change 2022: Impacts, adaptation and vulnerability. Geneva: IPCC.

Ranjan, R. K., Kumari, M., Rahman, M., Panda, C., and Homa, F. (2020). Effect of climate variables on yield of major crop in Samastipur district of Bihar: A time series analysis. Econ. Affairs 65, 637–644. doi: 10.46852/0424-2513.4.2020.21

Ray, D. K., West, P. C., Clark, M., Gerber, J. S., Prishchepov, A. V., and Chatterjee, S. (2019). Climate change has likely already affected global food production. PLoS One 14:e0217148. doi: 10.1371/journal.pone.0217148

Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., and Carvalhais, N. (2019). Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204. doi: 10.1038/s41586-019-0912-1

Roberts, M. J., Braun, N. O., Sinclair, T. R., Lobell, D. B., and Schlenker, W. (2017). Comparing and combining process-based crop models and statistical models with some implications for climate change. Environ. Res. Lett. 12:095010. doi: 10.1088/1748-9326/aa7f33

Rogers, H. H., Runion, G. B., and Krupa, S. V. (1994). Plant responses to atmospheric CO2 enrichment with emphasis on roots and the rhizosphere. Environ. Pollut. 83, 155–189. doi: 10.1016/0269-7491(94)90034-5

Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C., Arneth, A., et al. (2014). Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl. Acad. Sci. U. S. A. 111, 3268–3273. doi: 10.1073/pnas.1222463110

Rötter, R. P., Hoffmann, M., Koch, M., and Müller, C. (2018). Progress in modelling agricultural impacts of and adaptations to climate change. Curr. Opin. Plant Biol. 45, 255–261. doi: 10.1016/j.pbi.2018.05.009

Ruiz-Vera, U. M., Siebers, M., Gray, S. B., Drag, D. W., Rosenthal, D. M., Kimball, B. A., et al. (2013). Global warming can negate the expected CO2 stimulation in photosynthesis and productivity for soybean grown in the Midwestern United States. Plant Physiol. 162, 410–423. doi: 10.1104/pp.112.211938

Shahid, M. R., Wakeel, A., Ishaque, W., Ali, S., Soomro, K. B., and Awais, M. (2021). Optimizing different adaptive strategies by using crop growth modeling under IPCC climate change scenarios for sustainable wheat production. Environ. Dev. Sustain . 23, 11310–11334.

Schlenker, W., and Lobell, D. B. (2010). Robust negative impacts of climate change on African agriculture. Environ. Res. Lett . 5:014010.

Shi, W., Tao, F., and Zhang, Z. (2013). A review on statistical models for identifying climate contributions to crop yields. J. Geogr. Sci. 23, 567–576. doi: 10.1007/s11442-013-1029-3

Stöckle, C. O., Donatelli, M., and Nelson, R. (2003). CropSyst, a cropping systems simulation model. Eur. J. Agron . 18, 289–307.

Sultan, B., Defrance, D., and Iizumi, T. (2019). Evidence of crop production losses in West Africa due to historical global warming in two crop models. Sci. Rep. 9:12834. doi: 10.1038/s41598-019-49167-0

Suzuki, N., Rivero, R. M., Shulaev, V., Blumwald, E., and Mittler, R. (2014). Abiotic and biotic stress combinations. New Phytol. 203, 32–43. doi: 10.1111/nph.12797

Tomaz, A., Palma, P., Alvarenga, P., and Gonçalves, M. C. (2020). “Soil salinity risk in a climate change scenario and its effect on crop yield,” in Climate change and soil interactions , eds M. N. Prasad and M. Pietrzykowski (Amsterdam: Elsevier), 351–396. doi: 10.1016/B978-0-12-818032-7.00013-8

Tonnang, H. E., Sokame, B. M., Abdel-Rahman, E. M., and Dubois, T. (2022). Measuring and modelling crop yield losses due to invasive insect pests under climate change. Curr. Opin. Insect Sci. 50:100873. doi: 10.1016/j.cois.2022.100873

Troy, T. J., Kipgen, C., and Pal, I. (2015). The impact of climate extremes and irrigation on US crop yields. Environ. Res. Lett. 10:054013. doi: 10.1088/1748-9326/10/5/054013

Van Diepen, C. V., Wolf, J. V., Van Keulen, H., and Rappoldt, C. (1989). WOFOST: A simulation model of crop production. Soil Use Manag. 5, 16–24. doi: 10.1111/j.1475-2743.1989.tb00755.x

Vogel, E., Donat, M. G., Alexander, L. V., Meinshausen, M., Ray, D. K., Karoly, D., et al. (2019). The effects of climate extremes on global agricultural yields. Environ. Res. Lett. 14:054010. doi: 10.1088/1748-9326/ab154b

Wang, E., He, D., Wang, J., Lilley, J. M., Christy, B., Hoffmann, M. P., et al. (2022). How reliable are current crop models for simulating growth and seed yield of canola across global sites and under future climate change? Clim. Change 172:20. doi: 10.1007/s10584-022-03375-2

Wei, S., Peng, A., Huang, X., Deng, A., Chen, C., and Zhang, W. (2021). Contributions of climate and soil properties to wheat and maize yield based on long-term fertilization experiments. Plants 10:2002. doi: 10.3390/plants10102002

Wheeler, T., and Von Braun, J. (2013). Climate change impacts on global food security. Science 341, 508–513. doi: 10.1126/science.1239402

White, J. W., Hoogenboom, G., Kimball, B. A., and Wall, G. W. (2011). Methodologies for simulating impacts of climate change on crop production. Field Crops Res. 124, 357–368. doi: 10.1016/j.fcr.2011.07.001

Xiao, L., Asseng, S., Wang, X., Xia, J., Zhang, P., Liu, L., et al. (2022a). Simulating the effects of low-temperature stress on wheat biomass growth and yield. Agric. For. Meteorol. 326:109191. doi: 10.1016/j.agrformet.2022.109191

Xiao, L., Wang, G., Zhou, H., Jin, X., and Luo, Z. (2022b). Coupling agricultural system models with machine learning to facilitate regional predictions of management practices and crop production. Environ. Res. Lett. 17:114027. doi: 10.1088/1748-9326/ac9c71

Xu, Y., Chou, J., Yang, F., Sun, M., Zhao, W., and Li, J. (2021). Assessing the sensitivity of main crop yields to climate change impacts in China. Atmosphere 12:172. doi: 10.3390/atmos12020172

Yin, X., Struik, P. C., and Goudriaan, J. (2021). On the needs for combining physiological principles and mathematics to improve crop models. Field Crops Res. 271:108254. doi: 10.1016/j.fcr.2021.108254

Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D. B., Huang, Y., et al. (2017). Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl. Acad. Sci. U.S.A . 114, 9326–9331.

Zhao, C., Liu, B., Xiao, L., Hoogenboom, G., Boote, K. J., Kassie, B. T., et al. (2019). A SIMPLE crop model. Eur. J. Agron. 104, 97–106. doi: 10.1016/j.eja.2019.01.009

Zhao, C., Stockle, C. O., Karimi, T., Nelson, R. L., van Evert, F. K., Pronk, A. A., et al. (2022). Potential benefits of climate change for potatoes in the United States. Environ. Res. Lett . 17:104034.

Ziska, L. H., Namuco, O., Moya, T., and Quilang, J. (1997). Growth and yield response of field-grown tropical rice to increasing carbon dioxide and air temperature. Agron. J. 89, 45–53. doi: 10.2134/agronj1997.00021962008900010007x

Keywords : crop yield, climate change, research methods, manipulated experiment, process-based crop model, empirical statistical model

Citation: Feng X, Tian H, Cong J and Zhao C (2023) A method review of the climate change impact on crop yield. Front. For. Glob. Change 6:1198186. doi: 10.3389/ffgc.2023.1198186

Received: 31 March 2023; Accepted: 01 June 2023; Published: 06 July 2023.

Reviewed by:

Copyright © 2023 Feng, Tian, Cong and Zhao. 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: Chuang Zhao, [email protected]

† These authors have contributed equally to this work and share first authorship

This article is part of the Research Topic

The Varying Responses of Vegetation Activity to Climate Change in Northern Hemisphere Forests

U.S. flag

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

JavaScript appears to be disabled on this computer. Please click here to see any active alerts .

Climate Change Impacts on Agriculture and Food Supply

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

Low water levels at Lake Mead

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

Firefighting helicopter putting out a fire

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

Corn crops in a field

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

Dairy cows in field

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

Flooded crop field

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

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

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

Learn more about climate change and agriculture:

Top Climate Impacts on Agriculture

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

Cow in front of barn grazing

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

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

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

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

1. Changes in Agricultural Productivity 

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

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

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

2. Impacts to Soil and Water Resources

Oyster

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

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

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

3. Health Challenges to Agricultural Workers and Livestock

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

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

Pie chart

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

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

research paper on climate change and agriculture

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

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

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

research paper on climate change and agriculture

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

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

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

Related Climate Indicators

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  • Climate Change Impacts Home
  • Agriculture and Food Supply
  • Air Quality
  • Built Environment
  • Freshwater Resources
  • Ocean and Marine Resources
  • Transportation
  • Human Health
  • State and Regional Impacts
  • Climate Equity

Unlocking Agricultural Innovation: A Roadmap for Growth and Sustainability

  • Published: 22 February 2024

Cite this article

  • Elahe Davoodi Farsani 1 ,
  • Shahla Choobchian   ORCID: orcid.org/0000-0003-2750-1094 1 &
  • Moslem Shirvani Naghani 2  

19 Accesses

Explore all metrics

Agricultural innovation is crucial for navigating the dynamic market landscape and overcoming the diverse challenges posed by the current economic climate. It serves as a primary driver for both social advancement and economic prosperity, embodying the transformative force necessary for sustainable progress. Specifically, eco-friendly innovation not only boosts productivity but also promotes the responsible use of natural resources, marking a significant stride towards environmental stewardship. Identifying innovation indicators in agriculture is essential for achieving sustainable development and providing a benchmark for assessing progress in this pivotal industry. Recognizing the existing gap in this area, this paper aims to formulate and evaluate innovation indicators within the agricultural sector. Using the qualitative content analysis method with a deductive approach, the study meticulously examines a comprehensive research sample comprising 32 articles, three theses, and one book. The findings reveal eight primary factors that serve as key indicators of innovation in agriculture, covering aspects such as human capital and research, institutional frameworks, market sophistication, infrastructure, knowledge and technology outputs, and creative and innovative endeavors. This research represents a critical step in providing immediate insights into the effectiveness of political strategies and initiatives aimed at fostering innovation in the agricultural industry. By equipping policymakers with a thorough understanding of the current state of innovation in this sector, these insights have the potential to significantly alleviate food insecurity and stimulate economic growth. Thus, empowering policymakers with actionable insights derived from this study can pave the way for transformative change, driving the agricultural sector towards enhanced resilience and prosperity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

research paper on climate change and agriculture

Data availability

Data will be made available on request.

Research and development.

Organisation for Economic Co-operation and Development.

Abbreviations

Research and development

Organisation for Economic Co-operation and Development

Alizadeh, H. R., & Tabatabaeian, H. A. (2015). Innovation measurement framework in nanotechnology: A case study in the field of nanotechnology of Iran. Quarterly Journal of Public Policy, 1 (2), 89–105. https://doi.org/10.22059/PPOLICY.2015.57161

Article   CAS   Google Scholar  

Andrade, D., Pasini, F., & Scarano, F. R. (2020). Syntropy and innovation in agriculture. Current Opinion in Environmental Sustainability, 45 , 20–24. https://doi.org/10.1016/j.cosust.2020.08.003

Article   Google Scholar  

Ariza, C., Rugeles, L., Saavedra, D., & Guaitero, B. (2013). Measuring innovation in agricultural firms: A methodological approach. file:///C:/Users/narin%20pardaz%20co/Downloads/ejkm-volume11-issue3-article3963.pdf.

Bakhshayesh, M., Keshavarz, A., & Shariatmadar, M. H. (2020). Agricultural economics report. In National Center for Strategic Studies in Agriculture and Water http://awnrc.com . (In Persian)

Google Scholar  

Bjerke, L., & Johansson, S. (2022). Innovation in agriculture: An analysis of Swedish agricultural and non-agricultural firms. Food Policy, 109 , 1–14. https://doi.org/10.1016/j.foodpol.2022.102269

Buchana, Y., & Sithole, M. (2022). Toward a conceptual framework for measuring innovation in the agricultural sector in sub-Saharan developing countries. African Journal of Science, Technology, Innovation, and Development, 15 (2), 272–282. https://doi.org/10.1080/20421338.2022.2072794

Campos, H. (2020). The innovation revolution in agriculture (p. 234). https://doi.org/10.1007/978-3-030-50991-0

Book   Google Scholar  

Cavdar, S. C., & Aydin, A. D. (2015). An empirical analysis of technological development and innovation indicators. Procedia - Social and Behavioral Sciences, 195 , 1486–1495. https://doi.org/10.1016/j.sbspro.2015.06.449

Chang-Munoz, E., Mercado-Caruso, N., Ovallos Gazabon, D., Segarra-Ona, M., & Noguera Osorio, S. (2022). Product or process innovation? The dilemma for exporting SMEs in emerging economies: the case of the Colombian Caribbean. Procedia Computer Science, 198 , 620–625. https://doi.org/10.1016/j.procs.2021.12.296

Cirera, X., & Muzi, S. (2020). Measuring innovation using firm-level surveys: Evidence from developing countries. Research Policy, 49 , 225–237. https://doi.org/10.1016/j.respol.2019.103912

Cruz-Cázaresa, C., Bayona-Sáezb, C., & García-Marcob, T. (2013). You can’t manage right what you can’t measure well: Technological innovation efficiency. Research Policy, 42 , 1239–1250. https://doi.org/10.1016/j.respol.2013.03.012

Dutta, S., Lanvin, B., & Wunsch-Vincent, S. (2019). The Global Innovation Index 2017. Cornell University, INSEAD, & WIPO (Eds.), Global innovation index, 1–39.

Dziallas, M., & Blinda, K. (2019). Innovation indicators throughout the innovation process: An extensive literature analysis. Technovation, 80–81 , 3–29. https://doi.org/10.1016/j.technovation.2018.05.005

El Bakali, I., Brouziyne, Y., Ait El Mekki, A., Maatala, N., & Harbouze, R. (2023). The impact of policies on the diffusion of agricultural innovations: Systematic review on evaluation approaches. Outlook on Agriculture . https://doi.org/10.1177/00307270231215837)

FAO. (2018). FAO’s work on agricultural innovation: Sowing the seeds of transformation to achieve the SDGs . http://www.fao.org/3/CA2460EN/ca2460en.pdf .

Florian, K., Hoellen, M., & Konrad, E. (2023). Innovation in the creative industries: Linking the founder’s creative and business orientation to innovation outcomes. Creativity and Innovation Management, 32 (2), 281–297. https://doi.org/10.1111/caim.12554

Goudarzi, M., Mostofi, M. J., & Naghizadeh, M. (2020). A framework for evaluating innovation in the creative industries with a focus on handicrafts. Journal of Technology Development Management, 8 (4), 143–167. https://doi.org/10.22104/JTDM.2021.4172.2509

Graneheim, U. H., Lindgren, B. M., & Lundman, B. (2017). Methodological challenges in qualitative content analysis: A discussion paper. Nurse Education Today, 56 , 29–34. https://doi.org/10.1016/j.nedt.2017.06.002

Article   PubMed   Google Scholar  

Hu, R., Skea, J., & Hannon, M. J. (2017). Measuring the energy innovation process: An indicator framework and a case study of wind energy in China. Technological Forecasting & Social Change, 127 , 227–244. https://doi.org/10.1016/j.techfore.2017.09.025

Islam, N., Wang, O., Marinakis, Y., & Walsh, S. (2022). Family enterprise and technological innovation. Journal of Business Research, 147 , 208–221. https://doi.org/10.1016/j.jbusres.2022.04.004

Jafari Titkanlo, S., & Raees Al-Sadati, F. (2019). Presenting a model for measuring innovation in agriculture using a combination of multi-ground method of theory and analytical hierarchy (unpublished master’s thesis) . Imam Reza International University, Faculty of Literature and Humanities (Department of Management).

Janger, J., Schubert, T., Andries, P., Rammer, C., & Hoskens, M. (2017). The EU 2020 innovation indicator: A step forward in measuring innovation outputs and outcomes. Research Policy, 46 , 30–42. https://doi.org/10.1016/j.respol.2016.10.001

Janvi, A. (2020). Analysis of measurement and evaluation indicators in the fields of technology and innovation in the country’s upstream documents. Rahyaft, 77 , 43–25. https://doi.org/10.22034/RAHYAFT.2020.13818

Kallerud, E., Amanatidou, E., Upham, P., Nieminen, M., Klitkou, A., Olsen, D. S., Toivanen, M. L., Oksanen, J., & Scordato, L. (2013). Dimensions of research and innovation policies to address grand and global challenges. Working Paper 13/2013 . NIFU.

Karafillis, C., & Papanagiotou, E. (2011). Innovation and total factor productivity in organic farming. Applied Economics, 43 , 3075–3087. https://doi.org/10.1080/00036840903427240

Keshavarz, A., Fakari Sardhayi, B., Beikie, A., Khosravi, A. A., Farsi, M. M., Malekian, R., & Nejhandali, A. (2020). Report the challenges of the agricultural sector of the country (Vol. 44). National Center for Strategic Studies in Agriculture and Water.

Kleinheksel, A. J., Rockich-Winston, N., Tawfik, H., & Wyatt, T. R. (2020). Demystifying content analysis. American journal of pharmaceutical education, 84 (1), 7113. https://doi.org/10.5688/ajpe7113

Article   CAS   PubMed   PubMed Central   Google Scholar  

Korzun, M., Adekunle, B., & Filson, G. (2014). Innovation and agricultural exports: the case of sub-Saharan Africa. African Journal of Science, Technology, Innovation, and Development, 6 (6), 499–510. https://doi.org/10.1080/20421338.2014.976970

Krishnan, A., & Foster, C. (2017). A quantitative approach to innovation in agricultural value chains: Evidence from Kenyan horticulture. The European Journal of Development Research, 30 , 108–135. https://doi.org/10.1057/s41287-017-0117-0

Lainez, M., Manuel Gonzalezb, J., Aguilarc, A., & Velad, C. (2017). Spanish strategy on bioeconomy: Toward a knowledge-based sustainable innovation. New Biotechnology, 40 , 87–95. https://doi.org/10.1016/j.nbt.2017.05.006

Article   CAS   PubMed   Google Scholar  

Lapple, D., Renwick, A., & Thorne, F. (2016). Measuring and understanding the drivers of agricultural innovation: Evidence from Ireland. Food Policy, 51 , 1–8. https://doi.org/10.1016/j.foodpol.2014.11.003

Luo, Q., Miao, C., Sun, L., Meng, X., & Duan, M. (2019). Efficiency evaluation of green technology innovation of China’s strategic emerging industries: An empirical analysis based on Malmquist-data envelopment analysis index. Journal of Cleaner Production, 238 , 117782. https://doi.org/10.1016/j.jclepro.2019.117782

OECD. (2018). OECD science, technology and innovation outlook 2018 . OECD Publishing.

Olatomide Waheed, O., Omowumi Ayodele, O., & Issah, U. J. (2020). Innovation and creativity in agriculture for sustainable development. World Rural Observation, 12 (4), 41–46. https://doi.org/10.7537/marswro120420.05

Oliveira, M. F., Silva, F. G., Ferreira, S., Teixeira, M., Damásio, H., Ferreira, A. D., & Gonçalves, J. M. (2019). Innovations in sustainable agriculture: Case study of Lis Valley Irrigation District, Portugal. Sustainability, 11 (331), 2–20. https://doi.org/10.3390/su11020331

Ramayah, T., Soto-Acosta, P., Kheng, K., & Mahmud, I. (2020). Developing process and product innovation through internal and external knowledge sources in manufacturing Malaysian firms: The role of absorptive capacity. Business Process Management Journal, 26 (5), 1021–1039. https://doi.org/10.1108/BPMJ-11-2019-0453

Reddy, T. K., & Dutta, M. (2018). Impact of agricultural inputs on agricultural GDP in Indian economy. Theoretical Economics Letters, 8 , 1840–1853. https://doi.org/10.4236/tel.2018.81012

Rezvani, H. R., Aghajani, H. A., & Moghimi Darounkolayi, N. A. (2009). Determination of local indicators for measuring innovation in Iran using fuzzy assumption test (Case Study: Biotechnology Field). Entrepreneurship Development, 1 (4), 37–11.

Saavedra, D., Rugeles, L., & Guaitero, B. (2012). How to ask Colombian farmers for innovation in a methodological approach. Conference: 10th Globelics International Conference: Innovation and development, opportunities and challenges in globalization, Hangzhou, China.

Sertoğlu, K., Ugural, S., & Victor Bekun, F. (2017). The contribution of agricultural sector on economic growth of Nigeria. International Journal of Economics and Financial Issues, 7 (1), 547–552.

Shahmirzadi, T., Hariri, N., Fahimnia, F., Bab al-Havaeji, F., & Motallebi, D. (2019). Analysis of indicators of measurement and evaluation of science, technology, and innovation in Agricultural Research, Education, and Extension Organization. Biannual Scientific Journals of Shahed University, 5 (1), 66–47. https://doi.org/10.22070/RSCI.2018.639

Taques, F. H., Lopez, M., Basso, L. F., & Areal, N. (2021). Indicators are used to measure service innovation and manufacturing innovation. Journal of Innovation & Knowledge, 6 , 11–26. https://doi.org/10.1016/j.jik.2019.12.001

van Rijn, F., Bulte, E., & Adekunle, A. (2012). Social capital and agricultural innovation in Sub-Saharan Africa. Agricultural Systems, 108 , 112–122. https://doi.org/10.1016/j.agsy.2011.12.003

VanGalen, M., & Poppe, K. (2013). Innovation monitoring in the agri-food business is in its infancy. Eurochoices, 12 , 28–29. https://doi.org/10.1111/1746-692X.12016

Walsh, P. P., Murphy, E., & Horan, D. (2020). The role of science, technology, and innovation in the UN 2030 Agenda. Technological Forecasting and Social Change, 154 , 119957. https://doi.org/10.1016/j.techfore.2020.119957

Download references

Author information

Authors and affiliations.

Department of Agricultural Extension and Education, College of Agriculture, Tarbiat Modares University (TMU), Tehran, 1497713111, Iran

Elahe Davoodi Farsani & Shahla Choobchian

School of Social Sciences, Department of Futures Study, Imam Khomeini International University, Qazvin, Iran

Moslem Shirvani Naghani

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Shahla Choobchian .

Ethics declarations

Conflict of interest.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Farsani, E.D., Choobchian, S. & Naghani, M.S. Unlocking Agricultural Innovation: A Roadmap for Growth and Sustainability. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-01860-w

Download citation

Received : 02 January 2024

Accepted : 14 February 2024

Published : 22 February 2024

DOI : https://doi.org/10.1007/s13132-024-01860-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Knowledge-driven economies
  • Creative outputs
  • Economic development
  • Technology integration
  • Find a journal
  • Publish with us
  • Track your research

research paper on climate change and agriculture

CGIAR Research Program on Climate Change, Agriculture and Food Security

research paper on climate change and agriculture

  • Columbia University
  • Ùniversity of Vermont

Bruce Campbell

The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) generates evidence and supports adoption of climate-smart agricultural policies, practices, and services that alleviate poverty, increase gender equity, and support sustainable landscapes.

CCAFS promotes climate-smart policies, practices, and services that enable agriculture to meet the triple goals of food security, climate change adaptation, and mitigation.

Agriculture and climate function hand in hand. Today, 32–39% of global crop yield variability is explained by climate; this translates to annual production fluctuations of 2–22 million tonnes for major crops such as maize, rice, wheat, and soybean. At the same time, agriculture and livestock directly contribute about 11% of global greenhouse gas emissions, and agriculturally-driven land use changes cause additional emissions.

By 2050, a growing global population with shifting consumption patterns will require agriculture to deliver 60% more food, yet every 1 °C of warming above historical levels is likely to cause a decrease of approximately 5% in crop productivity. Continuing uneven rural development and inattention to the resource gaps that women and youth are facing will exacerbate inequality. These trends and drivers present a global challenge that requires concerted action.

CCAFS proposes a climate-smart agriculture (CSA) solution that will transform and re-orient agricultural systems to support food security in the context of the new realities of climate change. CSA has three pillars: 1) sustainably increasing agricultural productivity to support equitable increases in incomes, food security, and development; 2) adapting and building resilience to climate change from farm to national levels; and 3) reducing greenhouse gas emissions and sequestering carbon where possible. Embedded in CSA are efforts to close the gender gap and engage youth.

While the CSA approach is closely aligned with on-farm practices related to sustainable intensification and agro-ecological approaches, CCAFS extends CSA to landscape-level interventions (e.g. management of farm-forest boundaries), services (particularly information and finance), institutions (e.g. around market governance, incentives for adoption) and the food system (particularly consumption patterns and wider climate-informed safety nets).

Despite growing global action and investment in CSA, the science is not yet fully developed. There is limited evidence on synergies and trade-offs in productivity, resilience, and mitigation resulting from different agricultural practices, technologies, and programs, and across agro-ecologies and social contexts. Science must also inform national and global climate policies that fully integrate food security concerns with the need for climate action.

Where We Work

CCAFS target countries include:

  • Latin America: Colombia, El Salvador, Guatemala, Honduras, Nicaragua, Honduras
  • West Africa: Burkina Faso, Ghana, Mali, Niger, Senegal
  • East Africa: Ethiopia, Kenya, Rwanda, Tanzania, Uganda
  • South Asia: India, Nepal, Bangladesh
  • Southeast Asia: Cambodia, Laos, Vietnam

Impacts by 2022

Ensuring a food-secure future in a changing climate requires engagement, from farmers’ fields to global processes, forging linkages between the global change and agricultural communities, and giving equal attention to technology, institutions, power, and process. Both incremental and transformative pathways are necessary. CCAFS and partners catalyse change towards climate-smart agriculture, food systems and landscapes, thereby contributing to:

  • Reducing poverty
  • Improving food and nutrition security for health
  • Conserving natural resources and ecosystem services

With these goals in mind, CCAFS and partners are committed to the following globally ambitious impacts by 2022:

  • 9 million people (50% women) assisted to exit poverty
  • 6 million less people (50% women) that experience nutritional deficiencies
  • 160 million tonnes of greenhouse gas emissions mitigated per year
  • 11 million farm households adopt climate-smart agriculture
  • 8 million households with improved access to capital, with increased benefits to women

Related Links

  • CCAFS Website
  • CCAFS Publications
  • CCAFS Tools, maps, models and data
  • CCAFS Program Management Unit at Wageningen University & Research (The Netherlands)  [email protected]
  • Bruce Campbell, Program Leader (The Netherlands)  [email protected]
  • Rhys Bucknall-Williams , Global Communications and Knowledge Manager (The Netherlands) [email protected]

Social media

research paper on climate change and agriculture

CGIAR Research Program on Climate Change, Agriculture and Food Security leaflet

Annual reports, annual report.

research paper on climate change and agriculture

CGIAR Research Program on Climate Change, Agriculture and Food Security Annual Report 2021

  • Visit online

News from CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS)

research paper on climate change and agriculture

An expert discussion on putting gender at the heart of climate security

Giulia Caroli, Carolina Sarzana and Alice Taylor   Climate change and related security risks, such…

research paper on climate change and agriculture

Are energy transitions a risk or opportunity for climate security? What the experts say

Alice Taylor and Stefan Boessner The global energy system is at the centre of two…

research paper on climate change and agriculture

The UN must get climate-smart for peace and security

Photo credit: U.S. Department of State Grazia Pacillo, CGIAR Climate Security FOCUS Senior Economist…

Publications from CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS)

research paper on climate change and agriculture

Actions to Transform Food Systems Under Climate Change

  • Climate adaptation & mitigation
  • Food security

research paper on climate change and agriculture

Climate change, food and nutrition policies in Uganda: Are they gender- and nutrition-sensitive?

This website uses cookies in order to improve the use experience and provide additional functionality Detail

Climate Change, Population Growth, and Population Pressure

We develop a novel method for assessing the effect of constraints imposed by spatially-fixed natural resources on aggregate economic output. We apply it to estimate and compare the projected effects of climate change and population growth over the course of the 21st century, by country and globally. We find that standard population growth projections imply larger reductions in income than even the most extreme widely-adopted climate change scenario (RCP8.5). Climate and population impacts are correlated across countries: climate change and population growth will have their most damaging effects in similar places. Relative to previous work on macro climate impacts, our approach has the advantages of being disciplined by a simple macro growth model that allows for adaptation and of assessing impacts via a large set of climate moments, not just annual average temperature and precipitation. Further, our estimated effects of climate are by construction independent of country-level factors such as institutions.

We are grateful to Lint Barrage, Greg Casey, Maureen Cropper, Eric Galbraith, and Zeina Hasna for helpful advice; to Lucy Li, Frankie Fan, William Yang, and Raymond Yeo for research assistance; to David Anthoff, Brian Prest, and Lisa Rennels for access to data and code; and to seminar audiences at the Bank of Italy, University of Bologna, Université Catholique de Louvain, University of Chicago, University of Chile, University of Connecticut, ETH Zurich, IIASA, Korea University, Lahore School of Economics, University of Manchester, NBER Summer Institute, NYU Abu Dhabi, Osaka University, Oxford University, RIDGE forum on Sustainable Growth, Schumpeter Seminar (Humboldt University), Sungkyunkwan University, University of Tokyo, and the World Bank for useful feedback. Research was supported by the Population Studies and Training Center at Brown University through the generosity of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2C HD041020 and T32 HD007338).}} The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

In the past three years I have received significant research funding from the World Bank, the International Growth Centre and the U.S. Department of Transportation.

MARC RIS BibTeΧ

Download Citation Data

More from NBER

In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship  — as well as online conference reports , video lectures , and interviews .

15th Annual Feldstein Lecture, Mario Draghi, "The Next Flight of the Bumblebee: The Path to Common Fiscal Policy in the Eurozone cover slide

Extreme weather is driving food prices higher. These 5 crops are facing the biggest impacts

Rice is one of the crops under threat from the effects of climate change.

Rice is one of the crops under threat from the effects of climate change. Image:  Unsplash/Sandy Ravaloniaina

.chakra .wef-1c7l3mo{-webkit-transition:all 0.15s ease-out;transition:all 0.15s ease-out;cursor:pointer;-webkit-text-decoration:none;text-decoration:none;outline:none;color:inherit;}.chakra .wef-1c7l3mo:hover,.chakra .wef-1c7l3mo[data-hover]{-webkit-text-decoration:underline;text-decoration:underline;}.chakra .wef-1c7l3mo:focus,.chakra .wef-1c7l3mo[data-focus]{box-shadow:0 0 0 3px rgba(168,203,251,0.5);} Charlotte Edmond

Rebecca geldard.

research paper on climate change and agriculture

.chakra .wef-9dduvl{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-9dduvl{font-size:1.125rem;}} Explore and monitor how .chakra .wef-15eoq1r{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;color:#F7DB5E;}@media screen and (min-width:56.5rem){.chakra .wef-15eoq1r{font-size:1.125rem;}} Climate Change is affecting economies, industries and global issues

A hand holding a looking glass by a lake

.chakra .wef-1nk5u5d{margin-top:16px;margin-bottom:16px;line-height:1.388;color:#2846F8;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-1nk5u5d{font-size:1.125rem;}} Get involved with our crowdsourced digital platform to deliver impact at scale

Stay up to date:, climate change.

Listen to the article

This article was originally published in August 2023. It was last updated in February 2024.

  • Over recent years food prices have been inflated by the pandemic and war in Ukraine, while extreme summer temperatures have exacerbated the problem.
  • Soybeans, olive oil, rice, potatoes and cocoa are just some of the crops that have been affected.
  • A World Economic Forum report, Green Returns: Unleashing the Power of Finance for Sustainable Food Systems , calls on the financial sector to direct more resources into helping the food and agriculture sector to become more sustainable.

Shortages and supply issues caused by events including the pandemic and war in Ukraine have been felt through food price inflation for some while now.

But for some foods, the impact of climate change is also making itself felt, through record high temperatures and extreme weather.

It’s entirely usual for food prices to fluctuate alongside the seasons, but the exceptionally hot and dry summer 2023 in Europe, the US, Asia and beyond caused poor harvests and many crops to fail.

The climate crisis is making extreme weather – from heatwaves and droughts to storms and floods – more common, and some crops are more susceptible to these changes than others.

Here are five examples of foods where we are already seeing an impact.

In February 2024, cocoa prices globally hit a record high , as crops in West Africa were impacted by dry weather, according to the BBC.

It meant the cost of the chocolate ingredient had doubled since the beginning of 2023.

The two biggest cocoa bean-growing countries - Ghana and Ivory Coast - have been affected by the El Niño weather phenomenon , which causes drier weather.

"Traders are worried about another short production year and these feelings have been enhanced by El Niño that is threatening West Africa crops with hot and dry weather," Jack Scoville, an analyst at Price Futures Group, told the BBC.

2. Olive oil

A long, hot, dry summer in much of the Mediterranean damaged olive trees and caused a poor crop because reduced soil moisture has stunted plants and crops during their crucial growing season.

As a result, prices of olive oil soared to an all-time high. Stock piles are already significantly down on previous years and are likely to run very low before we reach the next harvest.

Between April 2022 and May 2023 average temperatures were up to 2.5°C – and sometimes 4°C – higher than average in countries including Spain, which is one of the world’s most significant producers of olive oil.

This has combined with persistently low rainfall for more than a year to create severe drought. In Andalusia in southern Spain, water reservoirs are down to about 25% of their capacity.

Maps showing how parts of the Mediterranean have experienced increasing levels of drought over recent years.

From Italy to India, rice farmers have been feeling the effects of climate change on their crops for some years. And the problem is multi-faceted – sometimes it’s drought, sometimes it’s flooding. And rising salinity from water intrusion is also affecting crops.

Italy grows about 50% of the EU’s rice , and is the world’s only grower of many varieties suitable for risotto. But in March 2023, the country warned rice output would fall as it faced a second year of drought.

In July 2023, rice prices in Asia soared to their highest levels in more than two years on concerns the dry weather would damage crops . In India, late and particularly heavy monsoon rains damaged the country’s rice crop causing it to halt exports of some categories of rice .

Export bans were expected to continue into 2024, according to Bloomberg.

The Californian rice belt in the US was severely hit by drought in 2022, with rice growers only planting half as much rice as usual . The long-running drought was estimated to have cost the region $703 million in lost economic activity in 2022, as well as 5,300 lost rice-related jobs.

The start to the 2023 season was more typical , which brought growers some respite, although the effects of the drought continued to be felt further down the supply chain including by millers, dryers, storage facilities and trucking operations.

NASA satellite shots of falling rice yields in California.

4. Soybeans

It’s not just America’s west coast that has seen a shortage of rain. In 2023, the Midwest experienced its worst drought since 2012, which affected soybean production.

An estimated 4.16 billion bushels of soybeans were produced in the US last year, a drop of almost 106 million bushels compared to the previous year.

Production of soybeans in the US from 2001 to 2023.

Soybean production is also significantly down in other places. In Argentina, for example, the drought saw the volume of crushed soybeans fall 27% year-on-year in the period between January and August 2023 - to its lowest level since 2015.

It had to import a record amount of the crop from neighbouring countries Paraguay, Bolivia and Brazil, in order to keep its crushing factories open.

As of January 2023, damage to various crops, including wheat, soybean and corn in Argentina, have led to estimated losses of $10.4 billion .

Have you read?

What does the future of food security look like after the collapse of the black sea grain deal, it's time for an innovative new approach to agri-food. we need entrepreneurs to step up, how to mobilise climate-smart agriculture finance.

Although soy oil is used as an ingredient in a variety of products, the vast majority of the world’s soybean crop is used for livestock feed.

A shortage or poorer quality livestock feed can cause prices to spike, leading farmers to difficult decisions about reducing herd sizes or finding alternative food sources, for example. This in turn feeds into the price, availability and quality of meats or dairy products down the line.

5. Potatoes

Europe, however, has experienced the opposite problem. Heavy rains in Belgium, France, and the UK in autumn 2023 drenched potato fields , hindering collection and increasing the risk of crop spoilage, Bloomberg reports.

The Netherlands – Europe's fourth-largest grower in 2022 – and Belgium have been the worst affected. Harvesting on the continent is normally over by late autumn, but on 24 November, around 15% and 11% of potato crops in these countries, respectively, remained uncollected in waterlogged fields , exacerbating supply constraints.

Not surprisingly, European potato prices soared as a result, hitting a 14-year high ahead of the holiday season. December Mintec data showed Maris Piper potatoes – a Christmas dinner staple – were up 158% on last year, at £465 per tonne.

The wet weather also threatened Europe's sugar beet crop and slowed winter grain sowing in France, which accounts for 17% of all agricultural production in the European Union .

A sign of things to come?

As the impacts of the climate crisis intensify, bringing with them more extreme weather, the concern is that the devastating impacts seen on crops this summer are only the start. A study by NASA suggests maize crop yields could be down 24% by 2030 as a result of climate change.

Much research time and money is being devoted to mitigating the effects of a changing climate on crops. This includes more resilient and better adapted crops, better and more efficient water use, and more effective and targeted fertilizers, for example.

It also is important to note that the agricultural sector itself is a significant contributor to greenhouse gas emissions – food systems account for around a third of global emissions. However, comparatively little finance is being directed towards solving this. Just under 4% of climate finance is allocated to agriculture and food , according to The World Economic Forum’s paper Green Returns: Unleashing the Power of Finance for Sustainable Food Systems.

The paper calls on the finance community to reshape its strategies and highlights five pivotal financial vehicles that could be used to bring about equitable and sustainable change.

Don't miss any update on this topic

Create a free account and access your personalized content collection with our latest publications and analyses.

License and Republishing

World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

Related topics:

The agenda .chakra .wef-n7bacu{margin-top:16px;margin-bottom:16px;line-height:1.388;font-weight:400;} weekly.

A weekly update of the most important issues driving the global agenda

.chakra .wef-1dtnjt5{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;} More on Climate Change .chakra .wef-nr1rr4{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;white-space:normal;vertical-align:middle;text-transform:uppercase;font-size:0.75rem;border-radius:0.25rem;font-weight:700;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;line-height:1.2;-webkit-letter-spacing:1.25px;-moz-letter-spacing:1.25px;-ms-letter-spacing:1.25px;letter-spacing:1.25px;background:none;padding:0px;color:#B3B3B3;-webkit-box-decoration-break:clone;box-decoration-break:clone;-webkit-box-decoration-break:clone;}@media screen and (min-width:37.5rem){.chakra .wef-nr1rr4{font-size:0.875rem;}}@media screen and (min-width:56.5rem){.chakra .wef-nr1rr4{font-size:1rem;}} See all

research paper on climate change and agriculture

The people’s choice: Stunning images from the Wildlife Photographer of the Year 2023

February 22, 2024

research paper on climate change and agriculture

1 in 5 migratory species are at risk of extinction, says a new UN report

Simon Torkington

February 21, 2024

research paper on climate change and agriculture

Al Gore: 3 ways to scale green investment in 2024

Andrea Willige

research paper on climate change and agriculture

A fifth of protected migratory species threatened with extinction, and other nature and climate stories you need to read this week

Michael Purton

February 19, 2024

research paper on climate change and agriculture

The Atlantic Ocean is headed for a tipping point − once melting glaciers shut down the Gulf Stream, we would see extreme climate change within decades, study shows

René van Westen, Henk A. Dijkstra and Michael Kliphuis

February 15, 2024

research paper on climate change and agriculture

Why Public-Private-Philanthropic Partnerships are central to Asia-Pacific's climate action

Seok Hui Lim and Luis Alvarado

February 14, 2024

University of Hawaiʻi System News

Researcher points to holistic climate solutions at global conference

  • February 20, 2024

University of Hawaiʻi Mānoa professor Susan Crow, who is leading a $40-million U.S. Department of Agriculture ( USDA ) grant to assist Hawaiʻi farmers, ranchers and foresters in implementing sustainable and climate-smart practices was an invited speaker at the annual American Geophysical Union ( AGU ) in December 2023, which hosts more than 25,000 attendees.

Crow presented “Overcoming barriers to implementation through a holistic framework for characterizing place-based suites of practices that achieve meaningful climate benefits,” and AGU produced a video of her research that was shown at the conference.

Person watering crops

“The climate-smart agriculture session highlighted research around the world tracking holistic impacts of innovative practices on climate and communities and really showcased the intersection of basic research and solutions-oriented approaches,” said Crow, a professor with the UH Mānoa College of Tropical Agriculture and Human Resources . “I was honored to be an invited speaker and open the session.”

The conference connected researchers, students and community members from Hawaiʻi with the greater scientific community to network and share our views with others.

Crow and her team created the Hawaiʻi Partnership for Climate Smart Commodities to develop equitable practices, data systems and decision support tools to promote and actuate meaningful climate benefits in agroecosystems guided by Hawaiʻi -based producers and ancestral practitioners. The project will identify, implement and incentivize the continuation of innovative and Indigenous practices that improve or maintain soil health and generate climate benefits.

“Our current work through the Hawaiʻi Partnership for Climate-Smart commodities is focused on grounding in equity and addressing the many, complex barriers experienced by those in Hawaiʻi to better care for their productive lands,” said Crow. “We aim to support future markets for locally produced, climate-smart (think: ‘in Hawaiʻi , for Hawaiʻi ’) food and forest products, and in doing so propel necessary change in local food systems and resiliency of landscapes and communities.”

The AGU attendees come from around the globe to share and learn about the planet and environment, and ask questions surrounding climate change and increasing equity and justice in a changing world.

Related Posts:

  • UH leads $40M grant for climate-smart food production
  • Sustainable, climate-smart food production focus of…
  • Climate change initiatives, mitigation strategies…
  • previous post: UH Mānoa seeks community input for Hawaiʻi well-being survey
  • next post: Pandemic recovery ending, construction up, UHERO forecasts

University of Hawaii System seal and name

If required, information contained on this website can be made available in an alternative format upon request. Get Adobe Acrobat Reader

About Calendar COVID-19 Updates Directory Emergency Information For Media MyUH Work at UH

Gagana Samoa

Kapasen Chuuk

Kajin Majôl

ʻŌlelo Hawaiʻi

  • Administrative

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List
  • Plants (Basel)

Logo of plants

Impact of Climate Change on Crops Adaptation and Strategies to Tackle Its Outcome: A Review

1 Key Laboratory of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Wuhan 430062, China; [email protected] (S.S.M.); nc.saac@10nayvl (Y.L.); nc.saac@gnosgnijux (J.X.)

2 Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad 38040, Pakistan; [email protected]

Sundas Saher Mehmood

Xuekun zhang.

Agriculture and climate change are internally correlated with each other in various aspects, as climate change is the main cause of biotic and abiotic stresses, which have adverse effects on the agriculture of a region. The land and its agriculture are being affected by climate changes in different ways, e.g., variations in annual rainfall, average temperature, heat waves, modifications in weeds, pests or microbes, global change of atmospheric CO 2 or ozone level, and fluctuations in sea level. The threat of varying global climate has greatly driven the attention of scientists, as these variations are imparting negative impact on global crop production and compromising food security worldwide. According to some predicted reports, agriculture is considered the most endangered activity adversely affected by climate changes. To date, food security and ecosystem resilience are the most concerning subjects worldwide. Climate-smart agriculture is the only way to lower the negative impact of climate variations on crop adaptation, before it might affect global crop production drastically. In this review paper, we summarize the causes of climate change, stresses produced due to climate change, impacts on crops, modern breeding technologies, and biotechnological strategies to cope with climate change, in order to develop climate resilient crops. Revolutions in genetic engineering techniques can also aid in overcoming food security issues against extreme environmental conditions, by producing transgenic plants.

1. Introduction

Natural systems, human health, and agricultural production have been badly affected by devastating environmental changes [ 1 ]. With the rapid increase in the world’s population, there is a corresponding increase in food demand owing to concerns about the stability of the global environment. Water availability, air pollution, and soil fertility have a large impact on agriculture productivity [ 2 ]. With abrupt changes in environmental conditions, the harsh impacts on plant productivity are progressing in great intensities owing to direct and indirect effects of abiotic stresses. Because of the continuous deforestation and excessive utilization of fossil fuels, the concentration of CO 2 has escalated from 280 µmol −1 to 400 µmol −1 in the atmosphere. It is predicted that the CO 2 concentration will elevate two-fold, i.e., up to 800 µmol −1 at the end of this century. Emission of dangerous gases, especially CO 2, are the main factors for the greenhouse effect and warmer average global temperatures [ 3 ]. The effects of climate change and environmental variation are mainly estimated by the number of stress spells, their impact on daily life, and damage to agricultural crops [ 4 ]. In developing countries, agricultural yield is predominantly suffered due to adverse environmental conditions, therefore high temperature and excess of CO 2 accumulation forced scientists to devise new strategies to cope with less predictable challenges [ 5 ]. To tackle these limitations and guaranteed food security there is a need for production of new climate-smart crop cultivars [ 6 ]. Plant growth and yield are greatly influenced by abiotic stresses. Under natural climate conditions, plants often experience numerous stresses like waterlogging, drought, heat, cold, and salinity [ 7 , 8 ]. The abiotic factors also include UV-B, light intensities, flooding, gas emissions, and physical and chemical factors which induce more stresses [ 9 ]. In the 21st century, the Earth’s average temperature is expected to increase from 2 to 4.5 °C. According to IPCC-2014 ( http://www.ipcc.ch/ ), the time-span between the 19th and the 21st centuries is considered to be the period which experienced the most warming [ 10 ]. Extreme precipitation events might well cause destructions due to floods whereas the scarcity or the total absence of rainfall for a longer period of time leads to drought stresses [ 11 ]. The environment of the globe is continuously changing and industrialization is one of the main factors for temperature increase. Due to extreme weather events the frequency of global warming is expected to rise, which will ultimately disturb the ecosystem globally [ 12 ]. All living organisms such as plants, animals, fishes, and humans have been affected by the extreme environmental conditions around the globe. The danger to the world’s climate conditions has triggered anxiety among everyone because crop yield might be compromised by fluctuations in various environmental factors that can risk food security. Recent studies reported that the developed countries have more vulnerability towards climatic changes (8–11%) than developing states [ 13 , 14 ]. Climate change and food insecurity are the two major issues of the 21st century. Around 815 million people are affected by malnutrition, hindering sustainable development programs to achieve the universal goal of eliminating hunger by 2030 [ 15 ]. Food security and agricultural yield is considerably affected by the adverse weather. With elevation in temperature, the production of major crops has been reduced evidently around the world [ 16 ]. At the end of this century global production of crops is likely to decrease as climatic severity increases from 2.6 to 4 °C. Reduction in the productivity of these crops signifies the main threat to food security, particularly in the speedy increase in the world’s population [ 17 ]. The population is supposed to grow to about 9 billion in 2050 and food requirement are expected to escalate by about 85% [ 18 ]. Climatic influences are worsened by present cropping schemes with low variation and elevated concentration of inputs, and unstable productivity due to environmental changes in crops [ 19 ]. The increased frequency of drought and heavy rainfalls, temperature fluctuations, salinity, and insect pest attacks are anticipated to decrease crop productivity leading to higher threats of starvation [ 20 ]. Crop adaptability has suffered not only as a result of temperature variations, but also because of rainfall [ 21 ]. Currently, the main task is lessening the pressure on food security [ 22 ]. This review emphasizes the influence of weather variations on crop production. The next sections outline the overview of the climate change, stresses produced due to climate change, impacts on agricultural crops, strategies to cope with extreme environmental conditions, and some recent genetically engineered approaches to develop transgenic plants against abiotic stresses.

2. Plant Yield and Climate Change

Plant physiology has been greatly influenced by climate variability by several means. Environmental extremes and climate variability enhanced the chances of numerous stresses on plants [ 23 ]. Climate change affects crop production by means of direct, indirect, and socio-economic effects as described in Figure 1 . Furthermore, climate change (drought, flood, high temperature, storm etc.) events are increased dramatically as reported by Food and Agriculture Organization (FAO) and as shown in Figure 2 .

An external file that holds a picture, illustration, etc.
Object name is plants-08-00034-g001.jpg

Direct, indirect and socio-economic effects of climate change on agricultural production.

An external file that holds a picture, illustration, etc.
Object name is plants-08-00034-g002.jpg

Increasing number of extreme climate-related events occurred during 1990–2016. Source: Food and Agriculture Organization (FAO) based on data from Emergency Events Database (EM-DAT) ( https://www.emdat.be/ ) [ 24 , 25 ].

Boyer reported that the climate changes have reduced the crop yield up to 70% since 1982 [ 26 ]. According to the study of FAO 2007 ( http://www.fao.org/home/en/ ), all cultivated areas in the world are affected by climatic changes and only 3.5% of areas are safe from environmental limitations (for detail look table 3.7 in http://www.fao.org/docrep/010/a1075e/a1075e00.htm ) [ 27 ]. Whereas the outcomes of abiotic stresses on crop yield are hard to calculate accurately, it is believed that abiotic stresses have a substantial influence on crop production depending upon the extent of damage to the total area under cultivation. In future, the productivity of the major crops is estimated to drop in many countries of the world due to global warming, water shortage, and other environmental impacts [ 28 , 29 ].

Based on national crop yields and questionnaire surveys, large differences in vulnerabilities to current climate changes were detected across Europe. In Northern Europe, the short duration for crop development and cool temperature are the major concerns, while the temperature extremes and low rainfall limits the crop productivity in Southern Europe, although the most negative effects will be found for the continental climate in the Pannonian zone, which includes Hungary, Serbia, Bulgaria, and Romania [ 30 ]. It was predicted that the enhancement of greenhouse gas emissions and abrupt climatic changes will occur that may increase the crop yield in North-Western Europe and decrease the crop yield in the Mediterranean area [ 31 ]. Wheat production is heavily affected by the temperature extremes due to climate change in many countries, and may reduce the crop yield by 6% for each °C rise in temperature [ 32 ]. Drought and high temperatures are key stress factors with high impact on cereal yields [ 33 ], and Rubisco , the central enzyme of photosynthesis, is disrupted if the temperature increases from 35 °C, and stops the photosynthetic process [ 34 ]. Gong et al. (1997) reported the negative influence of heat stress on antioxidant enzymes in Zea mays [ 35 ]. The combined impact of heat and drought stresses on crop yield have been examined in sorghum, maize, and barley. It was revealed that the combined effect of heat and drought stress had more damaging outcomes as compared to individual stress [ 36 ]. Xu and Zhou (2006) subjected the Leymus Chinensis under the combined stresses of drought and heat and found that the function of Photosystem II (PSII) decreased [ 37 ].

Due to climate change, water deficit and temperature extremes influence the reproductive phase of plant growth. It was described that the flower initiation and inflorescence is badly affected by the water stress in cereals [ 38 ]. Similarly, if the temperature increase of about 30 °C during floret development it can cause sterility in cereals [ 39 ]. During the meiotic phase, wheat and rice suffered from the 35–75% reduction in grain set due to water deficit [ 40 , 41 ]. In rice, drought stress greatly disturbs the process of fertilization and anthesis. Due to water deficit, the harvest index is reduced to 60% and decreases the grain set [ 42 ]. The cocoa yield has been significantly reduced by the major drought spells in West Africa during the 1980s El Niño years [ 43 ]. It has been estimated that agricultural production could reduce to 25.7% by 2080 due to climate change and maize will be the most affected crop in Mexico [ 44 ]. A study based on ECHAM6 climate data was analyzed for North German Plains during two different time durations: 1981–2010 and 2041–2070. The results showed that if the yield for winter wheat is to be sustained, water availability must be guaranteed [ 45 ]. Zhao et al (2017) carried an experiment to analyze the climate change impact on major crop yields and showed considerable yield reductions of 6%, 3.2%, 3.1%, and 7.4% in wheat, rice, soybean, and maize respectively [ 46 ]. To tackle the climate change new discoveries in genomics are enabling climate-smart agriculture by developing climate resilient crops [ 47 ].

Drought stress influences wheat during all developmental stages, but grain formation and the reproductive stage are the most critical ones [ 48 ]. Wheat yield was decreased from 1% to 30% during the mild drought stress at post-anthesis while this reduction increased up to 92% in case of prolonged mild drought stress at flowering and grain formation [ 49 , 50 ]. Drought stress has greatly reduced the yield of important grain legumes. Mashbean ( Vigna mungo L.) yield has been reduced by drought stress from 31% to 57% during the flowering stage while a 26% reduction was reported by drought stress during the reproductive phase [ 51 ]. Maleki et al. (2013) reported that the soybean yield has been largely effected by drought stress and a 42% reduction was observed during the grain filling stage of soybean [ 52 ]. Schlenker and Roberts (2009) described that maize yield was increased at an optimum temperature of 29 °C but a further increase in temperature hampered the yield of maize [ 53 ]. Every 1 °C rise in temperature was found to negatively influence the maize yield [ 54 ]. Similarly, it was reported that yield in maize decreased by 8.3% with every 1 °C rise in temperature from the optimum growth temperature [ 55 ]. Brown (2009) reported that wheat yield decreased by 10% with every 1 °C increase in temperature [ 56 ]. In another report it was revealed that a 3–4% reduction in wheat yield takes place for every 1 °C increase in temperature [ 57 ]. Easterling et al. (2007) described that a 2 °C increase in temperature cause 7% reduction in yield while a further increase in temperature to 4 °C decreased the yield by up to 34% in wheat. Similarly, rice yield decreased by 2.6% for every 1 °C rise in temperature [ 58 ]. In sorghum, yield was reduced by 7.8% due to a 1 °C increase in temperature [ 59 ]. In sorghum, water shortage is another big issue reported in most of the world’s top producer countries [ 60 ]. Schlenker and Roberts (2009) revealed that the threshold temperature for soybean is 30 °C; a rise in temperature to the optimum level increased soybean yield but after that level, further rise in temperature reduced the yield abruptly [ 53 ]. Eastburn et al. (2010) reported that the rise in ozone and CO 2 concentration in the atmosphere influenced the disease type, and with a continuous rise in temperature, disease susceptibility in soybean was enhanced [ 61 ].

This rising concern was revealed in the growing quantity of research papers focused on abiotic problems after the crucial review by Kitano on systems biology [ 62 ]. The amount of research studies has increased dramatically related to biotic and abiotic stresses in plants by applying different strategies ( Figure 3 ).

An external file that holds a picture, illustration, etc.
Object name is plants-08-00034-g003.jpg

The number of publications per year related to abiotic and biotic stresses from Jan/1990–Nov/2018. Source: PubMed (Keywords (abiotic stresses, drought, cold, heat, salinity and water-logging), (biotic stresses, bacteria, virus, fungi, insects, parasites, and weeds) used to search the number of publications in PubMed).

Climate change influences food security in a very complicated manner. It hampers the agricultural yield directly by means of disturbing the agro-ecological environment and indirectly by putting pressure on growth and circulation of income and consequently increased the necessity of agricultural products. Impacts of climate change on food security have been calculated in several ways [ 63 ]. Here we briefly discuss the potential impact of climate change and food security.

In temperate regions and humid grassland zones, a slight elevation in temperature may raise the pasture productivity. These advances have to be established to tackle amplified rate of climate changes, for example, drought and temperature extremes in the Mediterranean zone or massive rainfall spells and in temperate areas increase the risk of flooding [ 64 ]. But in the case of arid and semiarid regions, it may cause a reduction in livestock growth and enhance their death rates [ 65 ]. The extensive rate of evapotranspiration and less moisture in the soil are predicted in drier regions by various climate models [ 63 ]. Consequently, due to climate changes, many regions of cultivated land may become unsuitable for cultivation, and other tropical regions may produce more crops. Temperature instability will also provide more favorable environmental conditions for insect pests of crops to boost their capacity to stay alive in cold temperatures and then emerge in outbreaks in spring. It is very crucial to observe that in case of food accessibility, all recent calculations for food security and safety have concentrated mainly on the effects of climate changes in ways that did not measure the probability of substantial alteration in the rate of climate extremes on crop productivity. They have also not considered the situations of sudden changes in socio-economic status and climate, so all these factors have been putting negative impacts on global food security and safety [ 64 ]. Around the globe, food security is remarkably significant for human beings. Because of climate change, food quality, supply, and safety are still the biggest problems for researchers. Future studies on food security will need to incorporate climate change, crop productivity, water supply, and population to estimate food security conditions entirely and scientifically [ 21 ].

3. Crop Adaptation to Overall Extreme Climate Stresses

With the increase of the Earth’s temperature, the climate undergoes severe alterations and becomes abiotically stressful. Environmental changes are very damaging and pose various threats to naturally prevailing crop species [ 66 ]. Under field circumstances, drought and heat are the most predominant stresses and have a significant influence on plants [ 67 ]. It is reported that plants require an optimum temperature for their normal growth and blooming. Plant physiology is heavily influenced by temperature fluctuations [ 68 ]. As heat stress affects the grain production and yield, cold stress results in sterility, and drought stress negatively influences the morpho-physiology of plants [ 69 , 70 ]. These climatic problems severely distress plant development and yield, produce enormous responses, comprising molecular, biochemical, physiological, and morphological modifications [ 71 ]. Overall, global warming and climate change both have some negative and positive effects on agricultural crops as well as on humans as explained in Figure 4 .

An external file that holds a picture, illustration, etc.
Object name is plants-08-00034-g004.jpg

Overall positive and negative effects of climate change and global warming on crops and humans.

In this context, understanding the stress-resistance processes in plants has emerged as a very difficult task for plant scientists in order to develop stress-resistant plants [ 72 ]. The chief cereal crops around the world, such as maize, rice, and wheat, are crucial to meet the daily food demand. Out of them, wheat was the leading staple crop which has been cultivated on a large scale [ 73 ]. Wheat is harvested on 38.8% of total agricultural land worldwide and provides a considerably high concentration of proteins: 15% per gram as compared to maize or rice which only supplies 2 to 3% [ 74 ]. Regardless of large growing land globally, its productivity has been predominantly less than the maize and rice [ 18 ]. Reasonable reduction was anticipated in wheat productivity with a 2 °C increase in temperature. Related research on environmental variability expected a 6% reduction in wheat yield [ 75 ]. Challinor and his colleagues described that due to the increase in temperature, the grain filling phase decrease is the major reason of crop productivity reduction in changing climatic conditions [ 76 ]. Therefore, sustaining crop yield is an important task in current agriculture, and to produce stress-tolerant crop plants [ 75 ].

4. Various Limiting Factors for Crop Development

For sustainable agriculture and food safety for an increasing population of the world, it is necessary to grow stress-tolerant plants and understand their responses under different stress conditions. In relation to various climatic stresses, the response of plants varies in the expression of genes, physiology, and metabolism. It was reported that plants have the ability to sense any variation in surrounding environmental signals but in spite of many studies, only some reputed sensors have been recognized [ 77 ]. Due to different stresses, the organs and tissues of the plants are damaged and they respond accordingly, for example, transcriptional responses against various stresses are different in specific cells or tissues of roots [ 78 ]. Stress-responsive protein creation, high levels of associated solutes, and more elevated antioxidant ratios are the cellular signals which are produced due to salinity, drought, and chemical effluence. These stresses are regarded as primary stresses and they generate secondary stresses like oxidative and osmotic stress [ 79 ].

Under drought conditions, elevated level of CO 2 in leaf causes the initiation of reactive oxygen species (ROS) which trigger the multiple stresses in crops. With locked stomata, movement of CO 2 inside the leaf is clogged, and ROS are produced due to enhanced levels of oxygen under drought conditions. The frequency of plant development, photosynthesis, and respiration are disturbed by membrane breakdown due to ROS production. Several cell building materials like carbohydrates, lipids, proteins, and nucleic acid are impaired by ROS in drought stress [ 80 ]. In recent studies, it was observed that Osmo-protectants have been produced under the combined stress conditions of heat and salinity in tomato plants, but do not appear in individual stresses. Another experiment demonstrated that the combined effect of heat and salt stress leads to diverse metabolomic profiling which was established with molecular and physiological statistics. For plant development, ROS has a significant role and it is considered as a crucial secondary signal for cellular metabolism: an elevated level of ROS prompts cell apoptosis. Therefore, a gentle equilibrium among ROS creation and their decontamination may occur in every oxygenated organism [ 81 ]. The adaptability of Arabidopsis to persistent water deficiency at the molecular and morpho-physiological levels was examined. Arabidopsis collected from various habitations presented alterations at the transcriptomic level [ 82 ].

Metabolic profiling of various crucial plants have been comprehensively completed under water stress, such as rice, soybean, maize, and tomato. In barley, numerous metabolomic analyses have also been conducted to understand the impact of water scarcity on the oxidative phase, abscisic acid, and free amino acids. Barley cultivars were subjected to water shortage to explore the genetic variation on the metabolomic level at grain formation phase [ 83 ]. Protein production inhibition is the initial metabolic signal against the abiotic factors [ 84 ]. Post-translational modifications and processing are also the primary responsibilities of abiotic stresses [ 85 ]. Drought stress in coffee has been studied from a wide viewpoint by assimilating the vital features of plant biochemistry and physiology. The plants subjected to multiple events of constant drought stresses have greater photosynthesis processes, in contrast to plants with only one event of drought stress imposed on them. Certainly, these plants showed advance RuBisCo control and several enzymes related to metabolism. Adaptability to various drought doses elaborated the gene expressions associated with drought resistance [ 86 ].

5. Impact on Plants’ Morpho-Biochemical and Physiological Processes

With great environmental variability, plants are suffering from unique climatic conditions that limit the plants’ ability to adapt successfully in a range of ways. Due to more spells of rainfall and warmth, plant relocation is not to be the solution to this problem. However, modifications in plant physiology have been beneficial in unique climatic conditions, but environmental variability can be risky for plants [ 87 ]. Morphological, biological, and biochemical mechanisms of plants have been severely affected by abiotic stresses. Although for expected weather conditions in the future, plant physiology reactions are predicted to propagate quickly, with minor variation in fruiting and flowering [ 88 , 89 ]. The ideal temperature for plant development is in the range of 10 to 35 °С. Elevation of the temperature to a specific point will permit plants to generate excess energy but a larger increase in temperature retards the plant growth and the photosynthesis rate abates to deadly levels [ 90 ].

Turgor pressure is limited by the drought stress and therefore delays cell development. Water shortage impacts the photosynthesis enzymes actions and decreases the metabolic competency and ultimately destroys photosynthetic machinery [ 91 ]. Because of environmental changes CO 2 levels proliferate and retard respiration in plants and enhance temperature level. Respiration rates of the plant were elevated when the temperature was raised from 15 to 40 °C, disturbing morphological features of some crops [ 92 , 93 ]. During the process of photosynthesis, the enzyme Rubisco is associated with carbon fixation and translation of CO 2 into a complex energy-rich compound [ 94 ]. Rubisco is activated by the Rubisco activase at an optimum temperature by abolishing secondary metabolites. A minor elevation in temperature resulted in the deactivation of Rubisco enzyme leading to the generation of xylulose-1,5-bisphosphate which is supposed to be an inhibitory compound. At an increased temperature, Rubisco did not work properly because of the Rubisco activase breakdown and was unable to activate Rubisco [ 95 ]. ROS containing OH, H 2 O 2 , and singlet oxygen are derivatives of metabolisms and are regulated by antioxidant defense mechanism. ROS are mostly formed in minimum amount under optimum conditions but with the increase in concentration environmental stress triggered [ 96 ].

6. Plant Hormone Responses in Abiotic Stresses

Under different abiotic stresses, hormones are very crucial for regulating many signaling pathways and responses such as salicylic acid (SA), abscisic acid (ABA), and ethylene [ 97 ]. The major role is played by ABA in the regulation of stress responses by the interactions with some other hormones as shown in crosstalk ( Figure 5 ). The most important and vital hormone for regulating the climatic stresses in the plant is ABA. ABA plays a major role in different stages of plant development particularly in stomata opening and closing, drought stress, seed germination, and dormancy. PYR/PYL/RCAR-PP2C- SnRK2 is recognized as a signaling cascade generated by ABA and controls the seed dormancy efficiently. Under drought conditions, the plant growth is severely retarded and it increases the ABA concentration in cells. ABA accumulation during drought stress controls transpiration and inhibit stomatal disclosure [ 98 ]. ABA also triggers many physiological mechanisms in plants such as water scarcity, regulates stomata to close down, and produces many stress-responsive genes in this period [ 99 ]. ABA signaling machinery have been investigated recently and their mechanism of operation was elucidated. The signaling cascade consisted of 3 units, SnRK2/OST1 (Protein kinase), PP2C (protein phosphatases) and PYR/PYL/RCAR proteins [ 100 ].

An external file that holds a picture, illustration, etc.
Object name is plants-08-00034-g005.jpg

Hormonal crosstalk related to different stresses.

Two different group of scientists found the ABA PYR/PYL/RCAR receptors [ 101 , 102 ]. PP2C was first observed in Arabidopsis knockouts of abi1-1 and abi2-1 and is regarded as the negative controller of ABA [ 103 ]. Similarly, the protein kinase was collected and separated as SnRK2 and it is the activator of ABA [ 104 ]. Salicylic acid also has regulated numerous physiological processes in plants under stress climatic condition. It was identified that acetylsalicylic acid can encourage protoplast cluster development in corn, controlling cell cycle regulation [ 105 ]. The role of SA was discovered by a group of scientists working on cell cultures of tobacco, they found that SA regulates the bud development and flowering initiation [ 106 ]. Malamy and his colleagues were pioneers in studying the Tobacco Mosaic Virus and established the part of SA in plant-pathogen interaction [ 107 ]. Recent studies on SA described its impacts on fruit productivity, legumes nodulation, temperature resistance, stomata closing, respiration, genes related to senescence, and cell growth [ 107 ]. Salicylic acid regulations in these events might be secondary because they control the production of further plant stress-responsive hormones [ 108 , 109 ].

Phytohormones play a vital part in stress response by modulating different signal transduction mechanisms under climatic variability. One of the most important members of phytohormones is ethylene. It is found in gaseous form and thus enables plant-to-plant connections. A century ago, ethylene was discovered and since then many research studies were carried out to reveal its biosynthesis. Ethylene has a function in the control of seed germination, ripening, leaf growth, and senescence under different abiotic and biotic climatic stresses. It is supposed that ethylene acts as signaling pathway among plant growth and weather variations. Abiotic stresses such as salinity, water logging, high temperature, frost, heavy metal contact, nutrient deficiency, and drought are the reasons which modulate the synthesis of ethylene [ 110 ]. Ethylene response factors (ERFs) in plant ethylene belong to a massive transcription factors (TFs) family and are activated during the different physiological and environmental stresses. There have been extensive investigations on ERF proposing its role in abiotic stresses but until now there is no evidence of any specific signaling pathway under abiotic stresses. In a recent study, ERFs in tomato were subjected to drought, salinity, heat, cold, and excessive water conditions for their expression profiling [ 111 ].

7. Approaches to Combat Climate Changes

Variation in the environment has a long-lasting influence on agriculture and food security globally. Food security and safety are threatened by the severe weather conditions and it is not a recent problem. But formerly, no consideration was adopted to tackle this problem. Therefore, to cope with these weather variations is the most urgent demand worldwide. For crops to adapt to changing environmental stresses subsequent approaches are required.

7.1. Cultural Methodologies

Recently some experiments reported investigations of the strategies trialled by farmers to tackle the climatic variation for plant adaptation. There are many useful approaches adopted by farmers, including abiotic factors such as altering planting and harvesting time, a collection of crops with short life cycles, crop rotation, irrigation techniques, and variation in cropping schemes. Under climatic stress conditions, all of these approaches are very beneficial for crop adaptability [ 112 , 113 , 114 , 115 ]. Modification in sowing time, application of drought resistant cultivars, and the cultivation of new crops are some important strategies to lessen the climatic variability danger and provide better adaptability to crop plants for assuring food safety and security [ 116 ]. Another plant adaptability approach is by means of crop-management techniques that have the ability to enhance crop development under various environmental stresses. The choice of sowing time, planting density, and optimum irrigation practices are crucial techniques to tackle weather stresses [ 117 ]. Fertilizers are also very vital to reduce the effect of global warming and support the plant for better adaptability. It provides substantial energy to plants and is beneficial to maintain the fertility of the soil and increase productivity. Hence, the importance of fertilizer in nourishing the world is undeniable [ 118 ].

7.2. Conventional Techniques

Under various environmental stresses, plant breeding shows dynamic techniques in crop development and betterment. It gives a way to potentially guarantee food security and safety under harsh weather variations and help plants escape from various stresses through a crucial phase of plant growth by developing stress resistant cultivars [ 119 ]. Genetic divergence analysis is used for polymorphism, inbreeding, assessment, assortment, and recombination to attain plant perfection, and is amongst the main aspects for defining accomplished inbreeding. Genetic divergence analysis is considered a very important method for the development of new cultivars based on genetic distance and similarities [ 120 , 121 ]. For genetic studies landraces are a significant source, for example, a wheat landrace kept in data bank comprises broader genetic variance and is a valuable basis for stress resistance as it contains cultivars adjustable to diverse environmental stress [ 122 ]. Figure 6 demonstrates how molecular and integrated plant breeding are useful to develop the biotic and abiotic stress tolerance cultivars using genomics approaches like marker-assisted selection (MAS) and genome wide associated studies (GWAS).

An external file that holds a picture, illustration, etc.
Object name is plants-08-00034-g006.jpg

A step-wise presentation of physiological, molecular breeding and genomics approaches to develop biotic and abiotic stress tolerance cultivars.

7.3. Genetics and Genomics Strategies

7.3.1. omics-led breeding and marker-assisted selection (mas).

Omics approaches provide beneficial resources to elucidate biological functions of any genetic information for crop upgrading and development [ 123 ]. Different molecular markers are studied in population genomics across the environment in many individuals to find out novel variation patterns and help to find if the genes have functions in significant ecological traits [ 124 ]. In many crops, the breeding program is coupled with genomic approaches to achieve great heights in molecular breeding and to screen elite germplasms with multi-trait assembly [ 125 ] For the identification of phenotypes under different environmental variation associations, genetics and transcriptomic analysis are used [ 126 ] Genomics also enables investigation of the molecular mechanisms underlying the abiotic stress resistance. These approaches aid in the development of climate smart crops for better yield and production under different climate changes [ 127 ]. With the advent of high throughput sequencing and phenotyping, genomic-led breeding paved the way for identifying different stresses that are expected to adversely affect crop yield. Furthermore, the data available on environmental extremes, DNA fingerprinting, and quantitative trait loci (QTL) mapping allows the screening of elite germplasm under abiotic stresses [ 128 ]. QTL dissection of yield-related traits in crops under stress conditions permits the development of novel cultivars with better adaptability in abiotic stress [ 129 ]. Molecular plant breeding is an essential approach to enhancing crop yield and production in the presence of various biotic and abiotic stresses [ 130 ]. For speedy breeding progression marker-assisted selection (MAS) presents a crucial part in the betterment of crop traits and yield. With the advancement in crop genomics, DNA markers have been identified which are valuable for marker-assisted breeding [ 131 ].

The introduction of novel sequencing tools greatly eased the difficulty in researching genomic variants and lead towards the identification of huge amounts of DNA polymorphism, particularly single nucleotide polymorphism (SNPs) markers [ 132 ] Precision of QTL mapping enhanced on average from 10–30 centimogran to <1 cM [ 133 , 134 ] with the advancement of linkage maps [ 135 ]. The high-throughput phenomics approach is also contributing to increasing the accuracy of QTL mapping [ 136 ]. The association among genotypic and phenotypic data is crucial for enlightening the genetic basis of multiple traits [ 137 ]. By applying QTL mapping Haley and his colleagues successfully developed a wheat variety called “Ripper” which has the ability to withstand the drought conditions of Colorado, without affecting its grain yield and quality [ 138 ]. In 2009 Badu-Aparku and Yallou performed QTL mapping to screen elite maize germplasm with high yield under drought stress [ 139 ]. Merchuk-Ovant et al. (2016) conducted marker assisted selection studies on bread wheat ( Triticum aestivum L.) and durum wheat ( Triticum turgidum L.) to identify the QTLs related to drought stress [ 140 ]. Barley is cultivated on a wide range of land across the world but it is severely affected by drought stresses globally. QTL mapping of two novel barley cultivars that have been totally different in their response to drought stresses were selected and QTL mapping study performed for malting characters in double haploid. QTL investigation showed that MSA are specific reliable genomic sections regulating the malting feature which can be helpful [ 141 ]. Similarly, a recent QTL study was carried out to explore the epistatic mechanism and physiology of QTL for the elucidation of the targeted gene. Under drought stresses, 3 QTL were recognized such as qDTY6.2, qDTY6.1, and qDTY3.1, which have a considerable impact on grain productivity [ 142 ]. To be resistant under hyper temperature conditions three vital points on the genome of bread wheat have been predicted: 7D, 7B, and 2B [ 47 ]. Tahmasebi et al. (2016) performed QTL mapping for a recombinant inbred lines (RILs) population of wheat under different stress conditions of flooding, drought, heat, and a combination of both heat and drought simultaneously. QTL mapping showed a 19.6% variation in grain yield under these stress conditions. The authors concluded from this research that the molecular markers could be exploited to explore the unique allelic variations in wheat to enhance the potential to screen drought tolerant cultivars [ 143 ].

7.3.2. Genome Wide Association Studies (GWAS) for Stress Tolerance

Genome wide association studies (GWAS) is a powerful tool for understanding the complete set of genetic variants in different crop cultivars to recognize allelic variant linked with any specific trait [ 144 ]. GWAS generally highlight linkage among SNPs and traits and based on GWAS design, genotyping tools, statistical models for examination, and results interpretation [ 145 ]. In many crops GWAS has been carried out to exploit the genetic process responsible for genetic resistance under climate change [ 146 ]. In plants, GWAS has widespread applications related to biotic and abiotic stresses. GWAS have been applied to describe drought tolerance [ 147 ], salt tolerance [ 148 ], and heat tolerance [ 149 ].

In Arabidopsis thaliana GWAS study was carried out by Verslues et al. (2013) aided by reverse genetic approaches to elucidate unique genes that accumulate proline under drought stress. The linkage among SNPs of both genotypic and phenotypic data were examined, and specific regions regulating proline accumulation were recognized. Similarly, different proteins controlling the pro-accumulation such as aMADS box protein, Universal Stress Protein A domain proteins, protein phosphatase 2A subunit A3, thioredoxins, ribosomal protein RPL24A, and mitochondrial protease LON1 were identified by using reverse genetics. This research gave insights for proline accumulation under drought stress conditions [ 150 ]. Aegilops tauschii is reported to have many resistance genes regulating the abiotic stresses [ 151 ]. A significant knowledge is required for the breeders to understand the genetic architecture of Aegilops tauschii to improve drought resilience. Qin et al. (2016) investigated 373 different varieties of A. tauschii to examine 13 traits controlling drought stress. For GWAS 7185 SNPs were designated to study the phenotypic behavior and carried out mixed linear model and general linear model to find the association between SNPs with phenotypic traits [ 152 ].

QTLs related to salinity resistance in plants were studied by using GWAS. Kumar et al. (2015) reported various genes regulating the salinity tolerance in rice by using infinium high-throughput SNPs arrays. Six thousand genotype-based SNP were constructed for genes related to stress and linkage among SNPs and phenotypic data were interpreted. QTLs for salt tolerance by genomic regions were mapped on chromosome numbers 1, 4, 6, and 7. A novel QTL present on chromosome number 1 was reported and was called “Saltol” which is associated with salt tolerance at seedling stage [ 153 ]. Lafarge et al. (2017) performed GWAS for genotyping 167 rice varieties for spikelet sterility (SPKST), and panicle micro-nutrient by applying 3 techniques of haplotype regression, single marker regression, and co-fitting of all markers to analyze the impact of heat during anthesis process. A significant association was present between SPKST, secondary traits, and 14 loci. These loci were investigated for functions related to heat shock proteins, controlling plant responses, development of gametophyte, cell division, and detecting abiotic stresses [ 149 ]. Chopra et al. (2017) reported various stress-tolerant genes in Sorghum bicolor associated with heat and cold stresses. GWAS was conducted for genotyping and phenotyping analysis. Thirty SNPs were identified for genes related to anthocyanin expression and carbohydrate metabolism, which are powerfully associated with cold stress at the seedling growth phase of sorghum. Similarly, 12 SNPs were discovered for heat stress at the seedling stage and controlled by the genes having functions in ion transport mechanism and sugar metabolism [ 154 ]. In another study Chen et al. (2017) examined Sorghum bicolor for heat tolerant traits such as leaf firing (LF) and leaf blotching (LB) at the vegetative phase of growth. To identify the association among SNPs with genotype and heat tolerance, GWAS was performed. Nine SNPs were closely linked with LF and five SNPs were identified for LB traits. Furthermore, 14 genes associated with SNPs were discovered that have stress-responsive expression to abiotic stresses [ 155 ].

7.3.3. Genome Selection (GS) for Crop Improvement

Genomic selection (GS) is the exciting tool to revolutionize the crop improvement by using high-throughput phenotyping and marker densities to screen the elite germplasm, improving the polygenic traits and economical breeding line development [ 156 ]. Currently, the prospective of genomic selection (GS) to fast-track the speed of genetic achievements in main crops has stimulated the development of multi-environment designs for genomic estimation. Burgueño et al. (2012) proposed the first statistical design by applying a linear mixed model to G × E model [ 157 ]. Jarquín et al. (2014) suggested a system of modeling connections among an elevated dimensional combination of markers and environment that integrates with each other (G × E) [ 158 ]. Another model (GBLUP-type model) was proposed by Lopez-Cruz et al. (2015) in which regression of phenotypes was used for the interaction of marker × environment (M × E) [ 159 ]. The modern multi-environment model for genomic prediction was proposed by Cuevas et al. (2017) based on Bayesian model. These methods are applied on 4 wheat and 1 maize cultivars and CIMMYT data bank revealed that the G × E model have high significance rates and better genomic predictions as compared to other models [ 160 ].

Around 40 research studies based on GS have been published so far. Wheat is the most studied crop with 29 genomic selection studies. Moreover barley, oat, and durum wheat have 5, 2, and 1 research paper published. Diversity Array Technology (DArT) was the most promising maker used in GS followed by single nucleotide polymorphism (SNP) and genotyping by sequencing (GBS). These experiments showed that GS could be magnificently used in cereal breeding [ 161 ]. Genomic Selection (GS) designs were extensively developed for wheat to reveal the germplasms that have better ability to adapt in climate changes [ 162 ]. Crain et al. (2018) studied the different GS techniques to detect phenotypic data from high throughput phenotyping. At CIMMYT, heat and drought stresses were examined in 1000 elite wheat cultivars by using a high throughput phenomics approach [ 163 ].

7.3.4. Genetic Engineered Plants for Stress Tolerance

Biotechnology is an influential approach for genetic manipulation of the genome for the betterment of human beings. The genetic modification through biotechnology is a powerful strategy. Encouraging data is collected from genetics which can be exploited significantly to various biotic and abiotic stresses such as salinity, drought, heat, and cold. Identification of stress-responsive TFs are powerful findings to develop stress-resistant crop cultivars. These TFs can control the phenotypes of genes in genetic engineered crops associated with various stresses [ 164 ]. There are numerous transgenic plants which have been established by genetic engineering to tackle the biotic and abiotic stresses. These genetically engineered plants demonstrate significant resistance against climatic variations compared to normal plants [ 165 , 166 ].

Various transcriptions factor (TFs) are recognized as plant-specific TFs which includes AP2/ERFBP group [ 167 ]. This family of AP2/ERFBP TFs is responsible for many plant growth pathways and has functions in biotic and abiotic stress responses [ 168 ]. AP2/EREBP TFs are categorized into 4 sub-groups based on their similarity and numbers. The subfamilies consist of ERF TFs, DREB (dehydration-responsive element-binding protein), AP2 (Apetala 2), TFs, and RAV (related to ABI3/VP1 ). DREB and ERF are two major subfamilies which have been widely examined due to their role in plant biotic and abiotic responses [ 169 ]. The DREB TFs have significant regulating ability in various water deficit and cold stress conditions [ 170 ]. DREB TFs have been investigated in response to stresses in various plants species such as wheat, barley, maize, soybean, rice, tomato, and Arabidopsis [ 171 , 172 , 173 ]. In numerous experiments DREB1 has been studied in rice and Arabidopsis for its controlling mechanism in cold stress while DREB2 functions in drought, salinity, and high temperature stresses [ 174 , 175 , 176 ]. The DREB1 TFs were over-expressed to develop transgenic Arabidopsis with a better ability to withstand salinity, drought, and freezing stresses [ 177 , 178 ]. Similarly, DREB1 genes were introduced into the rapeseed, rice, tomato, and tobacco for cold stress resistance [ 179 , 180 , 181 , 182 ]. Many of the DREB1 genes have also been purified from wheat, rye, maize, rice, and oilseed rape and have been transformed to develop transgenic crops against different abiotic stress [ 183 , 184 ]. Qin et al. (2007) isolated the ZmDREB2A gene from maize and over-expressed in Arabidopsis to developed transgenic Arabidopsis with improved resistance against drought stress [ 185 ]. Similarly Chen et al. (2007) revealed that the transgenic plants with over-expression of GmDREB2 gene extracted from soybean showed significant tolerance against salt and drought resistance [ 186 ]. Mallikarjuna et al. (2011) successfully developed transgenic rice with the improved resistance against salinity and drought stresses by over-expressing the OsDREB2A gene [ 187 ].

The larger subfamily of AP2/EREBP TFs are ERF [ 188 ] and are responsible to regulate stress-tolerance genes in plants [ 189 ]. Under abiotic stresses these ERF genes are induced to hyper-express [ 190 ] results in the better tolerance against stresses in transgenic plants. Additionally, some ERF TFs are also involved both in biotic and abiotic stress tolerance due to their ability to regulate numerous hormonal biosynthesis pathways [ 191 ]. Zhu et al. (2014) studied that the introduction of TaPIE1 in wheat has successfully improved the tolerance ability against chilling stress and resistance to pathogens [ 192 ]. Further study was carried out to develop transgenic tobacco by an over-expressed GmERF3 gene. This transgenic tobacco has increased resistance against TMV, dehydration, and also toleration of salinity stress [ 193 ].

MYB TFs family called the myeloblastosis oncogene is a huge group of TFs discovered in eukaryotes and is extensively distributed in plants [ 194 , 195 ]. Various MYB TFs have been identified to regulate numerous biochemical and physiological pathways such as the cell cycle, hormonal biosynthesis, and primary and secondary metabolism. These TFs are also known to have functions in biotic and abiotic stress responses [ 196 ]. Li et al. (2015) have summarized various MYB TFs related to abiotic stress tolerance in plants and Arabidopsis [ 195 ]. Some of the MYB TFs such as AtMYB61, AtMYB60, and AtMYB44 were identified to enhance the drought resistance in transgenic Arabidopsis by controlling the movement of stomata [ 197 , 198 , 199 ]. The AtMYB96 gene was expressed in Arabidopsis as a regulator, either by ABA signaling pathways to impart drought resistance [ 200 ] or by controlling the biosynthesis process of cuticle wax [ 201 ]. Yang et al. (2012) developed transgenic rice by over-expressing the OsMYB2 gene to improve the resistance of rice against chilling, salinity, and dehydration [ 202 ]. The GmMYB76 gene isolated from soybean was successfully transformed into Arabidopsis for salinity and freeze resistance [ 203 ]. MdMYB121 from apple was significantly transformed into apple and tomato to develop transgenic plants with enhanced drought and salt tolerance [ 204 ]. Chen et al. (2017) reported that the ZmMYB30 gene isolated from maize was transformed into Arabidopsis to enhanced tolerance against salinity [ 205 ]. Similarly, the MdSIMYB1 gene from apple was used to developed transgenic tobacco and apple with improved resistance against cold, drought, and salt stresses [ 206 ]. The TaPIMP1 gene was expressed to develop transgenic wheat and it showed remarkable tolerance against drought and fungal pathogen Bipolaris sorokiniana. Hyper-expression of TaPIMP1 was confirmed by microarray analysis [ 207 ]. In another experiment TaPIMP1 TF was investigated for its regulating ability to tackle biotic and abiotic stresses in transgenic tobacco. Transgenic tobacco showed resistance against Ralstonia solanacearum , salinity, and drought stresses [ 208 ].

Another important family of TFs are WRKY which are extensively distributed in relation to abiotic [ 209 ] and biotic stresses in plants [ 210 ]. In transgenic plants WRKY genes were over-expressed to increase the abiotic stress tolerance such as in transgenic rice OsWRKY11 gene was introduced to enhance its tolerance to heat and drought stresses [ 211 ]. Zhou et al. (2008) conducted an experiment on Arabidopsis to make it resistant against different stress conditions. GmWRKY21 and GmWRKY54 were over-expressed in transgenic Arabidopsis to improved resistance to cold and drought stress respectively [ 212 ]. Niu et al. (2012) investigated the TaWRKY19 gene over-expression in transgenic wheat to freezing, drought, and salinity stresses [ 213 ]. Similarly, TaWRKY1 and TaWRKY33 isolated from wheat was used to develop transgenic Arabidopsis against drought and heat tolerance [ 214 ]. ZmWRKY33 genes in maize have been induced for salinity, drought, freeze, and ABA stresses. Under salinity stress conditions transgenic Arabidopsis with over-expression of ZmWRKY33 genes showed significant tolerance [ 215 ]. Dehydration tolerance was increased in transgenic Chrysanthemum with the over-expression of CmWRKY1 [ 216 ].

NAC TFs have significant importance in many processes, such as flower growth, cell division, and stress-responsive regulation in the plant due to biotic and abiotic stresses [ 217 , 218 ]. Numerous NAC TFs have been discovered in a wide range of plants with sequenced genomes such as in rice with 151, in Arabidopsis 117 [ 219 ], in maize 152 [ 220 ], and in soybean 152 [ 221 ] NAC TFs have been identified. Additionally, a large number of NAC TFs have been reported to have direct association in abiotic stresses, such as in transgenic Arabidopsis 31 NAC genes, which have been identified for salinity tolerance [ 222 ], and in rice 40 NAC genes were identified against drought tolerance [ 223 ]. In sorghum the expression of SbSNAC1 gene was induced by salt and drought stress and over-expression of this gene in transgenic Arabidopsis showed drought resistance [ 224 ]. Zheng et al. (2009) developed transgenic rice by over-expression of OsNAC045 which conferred drought and salt tolerance [ 225 ]. Over-expression of OsNAC1 in transgenic rice was studied for salinity and drought tolerance [ 226 ].

7.4. Genome Editing Strategies

Genome editing (GE) is the most powerful strategy to manipulate the plant genome by means of sequence-specific nucleases. GE for crop improvement has the remarkable ability to tackle food insecurity and develop a climate-smart agriculture system globally [ 227 ]. In traditional plant breeding approaches, genes are discovered to be associated with various important traits by means of mutation and conventional breeding strategies, which has recognized as a significant technique for the development of elite and high yielding germplasm [ 228 ]. Genetic diversity of various elite varieties has been substantially decreased due to the exploitation of important crops extensively that has, in various circumstances, been associated with the enhancement of the susceptibility to several abiotic and biotic stresses [ 229 , 230 ]. Plant breeding approaches have been greatly influenced by the GE tools and exploring new strategies for fast and accurate manipulations in crop genomes to protect them against different stresses and improve crop yield [ 231 ]. Thus, developing the novel modifications in the gene pool of various plant germplasm is required under abiotic and biotic stresses for the improvement of elite crop varieties with great ability to produce high yielding crops [ 232 ]. In genome editing technology site specific endonucleases are used comprising of zinc-finger nucleases (ZFNs), transcription activator like effector nucleases (TALENs), and CRISPR-Cas9 [ 233 ]. In contrast to the ZENs and TALENs genome editing tools, the CRISPR/Cas9 system is emerging as the most powerful GE strategy because it is economical, rapid, accurate, and enables multiple site specific editing within the genome [ 234 ].

CRISPR/Cas9 System for Crop Advancement

CRISPR/Cas9 is a modern genome editing strategy based on the prokaryotic defense mechanism triggered by type II RNA organization that offers protection to prokaryotes against attacking viruses [ 146 , 235 , 236 ]. Genome editing has been modernized by CRISPR-Cas9 assembly, by producing candidate gene mutants and knock down single nucleotides in a genome [ 237 ]. As compared to other genome editing tools like TALENs/ZFNs, CRISPR-based strategies have been tremendously exploited in plant genomes [ 238 ]. Moreover, it has great potential to aid crop breeding to establish high yielding and stress-resistant varieties [ 234 ]. Most significantly, the CRISPR/Cas9 tool is converting into a comprehensible environmentally friendly technique for the establishment of genome edited non-transgenic plants to tackle environmental extremes and guarantee food security [ 239 ]. A model of CRISPR/Cas9 based genome engineering to develop transgenic plants or abiotic stress tolerance cultivars is explained in Figure 7 .

An external file that holds a picture, illustration, etc.
Object name is plants-08-00034-g007.jpg

A model of CRISPR/Cas9 based genome engineering to develop transgenic plants or abiotic stress tolerance cultivars.

CRISPR/Cas9 has been extensively carried out for plant genome editing to cope with abiotic and biotic stresses [ 240 ]. A study was conducted to disrupt the gene TaERF3 and TaDREB2 to produce abiotic stress resistance by using the CRISPR genome editing tool [ 241 ]. Similarly, 21 KUP genes have been identified in cassava which were hyper-expressed under abiotic stresses. Differential expression analysis of KUP genes revealed that they induced drought resistance [ 242 ]. For drought tolerance studies MAPKKK genes have been investigated by means of genome-wide analysis [ 243 ]. CRISPR/Cas9 was applied in rice for producing triplet mutants. Genes TGW6, GW5, and GW2 have a function in regulating the seed size. By mutating this gene, the size of the seed was increased by 30% [ 244 ]. CRISPR technology has been adopted for a mutation in Brassica napus by knocking down the gene CLVTA3 which resulted in more seed production. A similar strategy was applied to increase the wheat seed size by knocking down the TaGW2 gene which has the ability to limit the seed size to increase [ 245 ]. Without any transformation of a gene, wheat has been developed with a low gluten level by using CRISPR/Cas9 technique [ 246 ]. Wang et al. (2017) studied the protein kinases 3 ( slmapk3 ) gene to investigate its regulating mechanism to confer drought resistance in tomato. The CRISPR/Cas9 strategy was used to develop tomato mutant lines which showed a considerably enhanced concentration of proline, malondialdehyde, and H 2 O 2 . The tomato mutant lines were susceptible to more oxidative stress in drought. The outcomes of this study revealed the importance of the SlMAPK3 gene in drought tolerance mechanisms and over-expression of this gene by genetic engineering provide improved drought tolerance [ 247 ]. The SlAGL6 gene was knocked down by Klap et al. (2017) by using the CRISPR/Cas9 system to develop parthenocarpic fruits in response to heat stress. The resulting mutated tomato lines were the same as the normal plants having same shape and weight [ 248 ]. Shen et al. (2017) studied japonica rice by knocking out the Osann3 gene. CRISPR/Cas9 technology was applied for mutant rice lines, resulting in the enhancement of tolerance under cold stress. This study showed the ability of the OsANN3 gene in cold tolerance and that it could be a potential gene for transgenic rice with increased cold tolerance [ 249 ]. Herbicide tolerance was developed by knocking down PmCDA1 gene in mutant rice lines with the help of CRISPR/Cas9 [ 250 ].

8. Conclusions

Climate changes are alarming the world by hampering agriculture and its products. Industrialization and poisonous gases cause global warming, which ultimately disturbs the world’s environment. Climate change has devastating effects on plant growth and yield. Abiotic stresses are the major type of stresses that plants suffer. To understand the plant responses under different abiotic conditions the most pressing current need is to explore the genetic basis underlying these mechanisms. Some bottleneck molecular and physiological challenges present in plants need to be resolved for better plant adaptation under abiotic conditions. Temperature fluctuations and variations in rainfall spells are a very crucial indicators of environmental stresses. Weather variations collectively have positive and negative outcomes but the negative effects are more thought-provoking. It is very difficult to overcome the imbalance in agriculture by climate change. How to tackle this problem and what strategies we should apply are still ambiguous. Hence, researchers need to focus on optimizing plant growth and development in abiotic stresses. For crop resistance against biotic and abiotic stresses, propagating novel cultural methods, implementing various cropping schemes, and different conventional and non-conventional approaches will be adopted to save agriculture in the future. Breeding approaches will help to develop climate resilient crops with better adaptability under drought and heat. Genome wide association studies (GWAS), genomic selection (GS) with high throughput phenotyping, and genotyping strategies are significant in identifying the different genes for crop improvement under climate change. Genetic engineering approaches have been significantly applied to develop transgenic plants with enhanced resistance against different biotic and abiotic stress responses. In future, we have to make eco-friendly genome edited crops through a CRISPR/Cas9 mediated genome editing to battle against climate change.

Acknowledgments

Authors are grateful to all members of Oil Crops Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Wuhan, China for their support and encouragement.

Author Contributions

A.R. (Ali Raza) and A.R. (Ali Razzaq) wrote the manuscript, S.S.M. and X.Z. (Xiling Zou) helped in literature; Y.L. helped in making figures; J.X. and X.Z. (Xuekun Zhang) proofread the manuscript. All authors have read and approved the manuscript.

This research was funded by the National Basic Research Program of China (2017YFD0101701) and Agricultural Science and Technology Innovation Program of CAAS and the APC was funded by the National Basic Research Program of China (2017YFD0101701) and Agricultural Science and Technology Innovation Program of CAAS.

Prolonged drought and record temperatures have critical impact in the Mediterranean

Severe and prolonged drought events have affected Europe for more than two years and northern Africa for six years, causing water shortages and hampering vegetation growth.

Satellite image Casablanca and surroundings in 2023 and 2024

Today the Commission’s Joint Research Centre (JRC) publishes its report Drought in the Mediterranean – January 2024 on the persisting droughts and their impact across the wider region.

Long-lasting, above-average temperatures, warm spells and poor precipitation have led to severe drought conditions in the Mediterranean region, affecting numerous areas across southern Italy, southern Spain, Malta, Morocco , Algeria, and Tunisia. In the midst of winter, the ongoing drought is already having critical impacts, according to the report compiled by the JRC-run European Drought Observatory (EDO) .

It also points to seasonal forecast predicting a warmer spring in southern Italy, Greece, the Mediterranean islands, and northern Africa. As the drought’s severity is expected to persist, concerns rise about its impacts on agriculture, ecosystems, drinking water availability and energy production.

The report shows the importance of climate mitigation – every tenth of a degree of global warming will increase the risks of prolonged droughts – as well as adaption measures for water management and for sectors depending on fresh water like agriculture and drinking water production in face of worsening impacts of global warming.

Record drought hits the Mediterranean region  

From 1 to 20 January the Mediterranean region experienced critical drought conditions, particularly affecting southern Italy, southern Spain and Malta. The situation has even been more severe and prolonged in Morocco, Algeria and Tunisia.

Map showing the Mediterannean and drought areas

As a consequence, water use restrictions have already been implemented in Morocco, Spain, and Sicily (Italy) in response to decreasing water availability.

On 1 February, drought emergency with strict water restrictions was declared in the Spanish region of Catalonia, as water reserves fell to below 16%. Water reservoirs in the southern Portugal region of Algarve were found to be at their lowest level and water use restrictions were ordered.

Reservoirs in Italy’s Sicily region are below the alert level and water rationing may be necessary to guarantee minimal services. In Sardinia, water reservoirs were estimated to hold less than 50% of their capacity in December 2023.

In Morocco, six consecutive years of drought have resulted in critically low level of water in reservoirs, with average dam filling at about 23%. Water use for cleaning roads, irrigation of parks and some farming areas has been banned.

Satellite images of Casablanca wider area in 2023 and 2024

Drought conditions and impact on agriculture

The researchers used the combined drought indicator (CDI) – based on precipitation, soil moisture, and vegetation stress – to assess agricultural drought. It estimates alert and warning conditions in many areas of the Mediterranean region. The analysis shows the situation was still alarming by the end of January.

Rain deficit and record-high temperatures in January 2024 affected winter crops and fruit trees along the coast in Spain, Italy, Greece, and the Mediterranean islands, while Morocco and Algeria saw a reduced crop growth, according to the latest JRC MARS bulletin .

Moreover, warm spells between September and December 2023 led to above-average temperatures, with January 2024 having been recently declared as the warmest January on record .

Drought forecasts to follow closely

Looking ahead, seasonal forecasts predict a warmer than average spring in 2024. The current lower-than-usual Alpine snowpack is expected to give a reduced snowmelt contribution to river flows in the region in the upcoming spring and summer seasons.

Prolonged droughts also increase the risk of wildfires due to reduced soil moisture, with the previous year - also marked by drought conditions in Europe - witnessing the largest wildfire ever recorded in the EU.

Need for adaptation and mitigation strategies

The UN's Intergovernmental Panel on Climate Change (IPCC) has predicted that heatwaves and droughts will become more frequent and severe in many regions in the coming decades. The Mediterranean basin is one of the few regions expected to experience a severe reduction in precipitation.

The situation is thus expected to continue to impact the region, highlighting the need for adaptation strategies to reduce the effects of the drought. Investments in drought early warning systems, increasing the water efficiency of existing and new technologies, changing to more drought resistant crops and improving access to water resources are crucial for improving community preparedness and resilience.

However, managing droughts is complex. A pragmatic approach to drought management and adaptation planning requires an impact-based risk assessment, which in turn should rely on drought impact observations. To this end, the JRC and European Drought Observatory for Resilience and Adaptation (EDORA) partners have collaborated on the development of the first European Drought Risk Atlas , aimed at assessing drought risk using innovative technologies.

Related content

Drought in the Mediterranean region - January 2024

European Drought Observatory’s drought dashboard for Europe

Current drought conditions in Europe

More news on a similar topic

A polar bear walking on a melting snow

  • News announcement
  • 9 February 2024

EU climate target recommendation: what does science say about getting there?

Satellite image Amazon river

  • General publications
  • 20 December 2023

Record temperatures and heatwaves bring unprecedented drought to the Amazon basin

aerial view of a forest merged with the view of a city

  • 18 December 2023

Decrypting the financial risks of climate change and biodiversity loss

a satellite in orbit around the Earth

  • 15 December 2023

How to measure the European space economy

Share this page

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • Published: 01 November 2021

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

  • Jonas Jägermeyr   ORCID: orcid.org/0000-0002-8368-0018 1 , 2 , 3 ,
  • Christoph Müller   ORCID: orcid.org/0000-0002-9491-3550 3 ,
  • Alex C. Ruane   ORCID: orcid.org/0000-0002-5582-9217 1 ,
  • Joshua Elliott 4 ,
  • Juraj Balkovic 5 , 6 ,
  • Oscar Castillo 7 ,
  • Babacar Faye 8 ,
  • Ian Foster   ORCID: orcid.org/0000-0003-2129-5269 9 ,
  • Christian Folberth   ORCID: orcid.org/0000-0002-6738-5238 5 ,
  • James A. Franke 4 , 10 ,
  • Kathrin Fuchs   ORCID: orcid.org/0000-0003-1776-283X 11 ,
  • Jose R. Guarin   ORCID: orcid.org/0000-0002-3167-4329 1 , 2 ,
  • Jens Heinke 3 ,
  • Gerrit Hoogenboom   ORCID: orcid.org/0000-0002-1555-0537 7 , 12 ,
  • Toshichika Iizumi   ORCID: orcid.org/0000-0002-0611-4637 13 ,
  • Atul K. Jain   ORCID: orcid.org/0000-0002-4051-3228 14 ,
  • David Kelly 9 ,
  • Nikolay Khabarov   ORCID: orcid.org/0000-0001-5372-4668 5 ,
  • Stefan Lange   ORCID: orcid.org/0000-0003-2102-8873 3 ,
  • Tzu-Shun Lin   ORCID: orcid.org/0000-0002-5741-8585 14 ,
  • Wenfeng Liu 15 ,
  • Oleksandr Mialyk   ORCID: orcid.org/0000-0002-7495-2325 16 ,
  • Sara Minoli 3 ,
  • Elisabeth J. Moyer   ORCID: orcid.org/0000-0003-1829-5196 4 , 10 ,
  • Masashi Okada 17 ,
  • Meridel Phillips 1 , 2 ,
  • Cheryl Porter   ORCID: orcid.org/0000-0001-7269-6543 7 ,
  • Sam S. Rabin 11 , 18 ,
  • Clemens Scheer 11 ,
  • Julia M. Schneider   ORCID: orcid.org/0000-0001-9588-3157 19 ,
  • Joep F. Schyns   ORCID: orcid.org/0000-0001-5058-353X 16 ,
  • Rastislav Skalsky   ORCID: orcid.org/0000-0002-0983-6897 5 , 20 ,
  • Andrew Smerald   ORCID: orcid.org/0000-0003-2026-273X 11 ,
  • Tommaso Stella   ORCID: orcid.org/0000-0002-3018-6585 21 ,
  • Haynes Stephens   ORCID: orcid.org/0000-0002-2258-5244 4 , 10 ,
  • Heidi Webber   ORCID: orcid.org/0000-0001-8301-5424 21 ,
  • Florian Zabel   ORCID: orcid.org/0000-0002-2923-4412 19 &
  • Cynthia Rosenzweig   ORCID: orcid.org/0000-0002-8541-2201 1  

Nature Food volume  2 ,  pages 873–885 ( 2021 ) Cite this article

13k Accesses

232 Citations

1632 Altmetric

Metrics details

  • Climate-change impacts
  • Environmental impact

Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project’s Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to −6% (SSP126) and from +1% to −24% (SSP585)—explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO 2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts consistently occurs earlier in the new projections—before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 digital issues and online access to articles

111,21 € per year

only 9,27 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

research paper on climate change and agriculture

Data availability

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Information . Model inputs are publicly available via https://www.isimip.org/ or from the corresponding author. The GGCMI crop calendar is accessible at https://doi.org/10.5281/zenodo.5062513 ; fertilizer inputs are available at https://doi.org/10.5281/zenodo.4954582 . Crop model simulations will be made publicly available under the CC0 license pending publication.

Code availability

Details and code for each crop model can be requested from the contact persons listed in Supplementary Table 3 . Code developed for data analysis and figures is available from the corresponding author upon request.

Mbow, C. et al. in Special Report on Climate Change and Land (eds Shukla, P. R. et al.) 437–550 (IPCC, 2019).

Asseng, S. et al. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change 3 , 827–832 (2013).

Article   ADS   CAS   Google Scholar  

Wang, E. et al. The uncertainty of crop yield projections is reduced by improved temperature response functions. Nat. Plants 3 , 17102 (2017).

Article   PubMed   Google Scholar  

Rosenzweig, C. et al. The agricultural model intercomparison and improvement project (AgMIP): protocols and pilot studies. Agric. For. Meteorol. 170 , 166–182 (2013).

Article   ADS   Google Scholar  

The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP, 2021); https://www.isimip.org/

Eyring, V. et al. Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9 , 1937–1958 (2016).

Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a Global Gridded Crop Model intercomparison. Proc. Natl Acad. Sci. USA 111 , 3268–3273 (2014).

Article   ADS   CAS   PubMed   Google Scholar  

Meehl, G. A. et al. Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Sci. Adv. 6 , eaba1981 (2020).

Article   ADS   PubMed   PubMed Central   Google Scholar  

O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9 , 3461–3482 (2016).

Lange, S. Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). Geosci. Model Dev. 12 , 3055–3070 (2019).

Hawkins, E. et al. Observed emergence of the climate change signal: from the familiar to the unknown. Geophys. Res. Lett. 47 , e2019GL086259 (2020).

Hawkins, E. & Sutton, R. Time of emergence of climate signals. Geophys. Res. Lett. 39 , L01702 (2012).

Kirtman, B. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 953–1028 (IPCC, Cambridge Univ. Press, 2013).

Rojas, M., Lambert, F., Ramirez-Villegas, J. & Challinor, A. J. Emergence of robust precipitation changes across crop production areas in the 21st century. Proc. Natl Acad. Sci. USA 116 , 6673–6678 (2019).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Raymond, C., Matthews, T. & Horton, R. M. The emergence of heat and humidity too severe for human tolerance. Sci. Adv. 6 , eaaw1838 (2020).

Park, C. E. et al. Keeping global warming within 1.5 °C constrains emergence of aridification. Nat. Clim. Change https://doi.org/10.1038/s41558-017-0034-4 (2018).

Liu, B. et al. Similar estimates of temperature impacts on global wheat yield by three independent methods. Nat. Clim. Change 6 , 1130–1136 (2016).

Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci . USA https://doi.org/10.1073/pnas.1701762114 (2017).

Asseng, S. et al. Rising temperatures reduce global wheat production. Nat. Clim. Change 5 , 143–147 (2014).

Toreti, A. et al. Narrowing uncertainties in the effects of elevated CO 2 on crops. Nat. Food 1 , 775–782 (2020).

Article   Google Scholar  

Ahmed, M. et al. Novel multimodel ensemble approach to evaluate the sole effect of elevated CO 2 on winter wheat productivity. Sci. Rep. 9 , 7813 (2019).

Leakey, A. D. B., Bishop, K. A. & Ainsworth, E. A. A multi-biome gap in understanding of crop and ecosystem responses to elevated CO 2 . Curr. Opin. Plant Biol. https://doi.org/10.1016/j.pbi.2012.01.009 (2012).

Kimball, B. A. Crop responses to elevated CO 2 and interactions with H 2 O, N, and temperature. Curr. Opin. Plant Biol. https://doi.org/10.1016/j.pbi.2016.03.006 (2016).

Zabel, F. et al. Large potential for crop production adaptation depends on available future varieties. Glob. Change Biol . https://doi.org/10.1111/gcb.15649 (2021).

Ray, D. K. et al. Climate change has likely already affected global food production. PLoS ONE 14 , e0217148 (2019).

Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333 , 616–620 (2011).

Ahmad, S. et al. Climate warming and management impact on the change of phenology of the rice–wheat cropping system in Punjab, Pakistan. Field Crops Res. 230 , 46–61 (2019).

Porter, J. R. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) 485–533 (IPCC, Cambridge Univ. Press, 2014).

Levis, S., Badger, A., Drewniak, B., Nevison, C. & Ren, X. CLMcrop yields and water requirements: avoided impacts by choosing RCP 4.5 over 8.5. Clim. Change 146 , 501–515 (2018).

Falconnier, G. N. et al. Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa. Glob. Change Biol. 26 , 5942–5964 (2020).

O’Neill, B. C. et al. IPCC reasons for concern regarding climate change risks. Nat. Clim. Change 7 , 28–37 (2017).

Li, Y., Guan, K., Schnitkey, G. D., DeLucia, E. & Peng, B. Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Glob. Change Biol. 25 , 2325–2337 (2019).

Zhu, P., Zhuang, Q., Archontoulis, S. V., Bernacchi, C. & Müller, C. Dissecting the nonlinear response of maize yield to high temperature stress with model-data integration. Glob. Change Biol. 25 , 2470–2484 (2019).

Iizumi, T. et al. Responses of crop yield growth to global temperature and socioeconomic changes. Sci. Rep. 7 , 7800 (2017).

Sherwood, S. C. et al. An assessment of Earth’s climate sensitivity using multiple lines of evidence. Rev. Geophys. 58 , e2019RG000678 (2020).

Nijsse, F. J. M. M., Cox, P. M. & Williamson, M. S. Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models. Earth Syst. Dyn. 11 , 737–750 (2020).

Zelinka, M. D. et al. Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett. 47 , e2019GL085782 (2020).

Tokarska, K. B. et al. Past warming trend constrains future warming in CMIP6 models. Sci. Adv. 6 , eaaz9549 (2020).

Fan, X., Miao, C., Duan, Q., Shen, C. & Wu, Y. The performance of CMIP6 versus CMIP5 in simulating temperature extremes over the global land surface. J. Geophys. Res. Atmos. 125 , e2020JD033031 (2020).

Xin, X., Wu, T., Zhang, J., Yao, J. & Fang, Y. Comparison of CMIP6 and CMIP5 simulations of precipitation in China and the East Asian summer monsoon. Int. J. Climatol. 40 , 6423–6440 (2020).

Ridder, N. N., Pitman, A. J. & Ukkola, A. M. Do CMIP6 climate models simulate global or regional compound events skilfully? Geophys. Res. Lett . https://doi.org/10.1029/2020gl091152 (2020).

Meinshausen, M. et al. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geosci. Model Dev. 13 , 3571–3605 (2020).

Von Bloh, W. et al. Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0). Geosci. Model Dev. 11 , 2789–2812 (2018).

Jägermeyr, J. & Frieler, K. Spatial variations in crop growing seasons pivotal to reproduce global fluctuations in maize and wheat yields. Sci. Adv. 4 , eaat4517 (2018).

Müller, C. et al. Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios. Environ. Res. Lett. 16 , 034040 (2021).

Franke, J. A. et al. The GGCMI Phase 2 emulators: Global Gridded Crop Model responses to changes in CO2, temperature, water, and nitrogen (version 1.0). Geosci. Model Dev. 13 , 2315–2336 (2020).

Allen, L. H. et al. Fluctuations of CO 2 in free-air CO 2 enrichment (FACE) depress plant photosynthesis, growth, and yield. Agric. For. Meteorol. 284 , 107899 (2020).

Durand, J. L. et al. How accurately do maize crop models simulate the interactions of atmospheric CO 2 concentration levels with limited water supply on water use and yield? Eur. J. Agron . https://doi.org/10.1016/j.eja.2017.01.002 (2018).

Myers, S. S. et al. Increasing CO 2 threatens human nutrition. Nature 510 , 139–142 (2014).

Zhu, C. et al. Carbon dioxide (CO 2 ) levels this century will alter the protein, micronutrients, and vitamin content of rice grains with potential health consequences for the poorest rice-dependent countries. Sci. Adv. 4 , eaaq1012 (2018).

Rising, J. & Devineni, N. Crop switching reduces agricultural losses from climate change in the United States by half under RCP 8.5. Nat. Commun. 11 , 4991 (2020).

Asseng, S. et al. Climate Change impact and adaptation for wheat protein. Glob. Change Biol. 25 , 155–173 (2019).

Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90 , 1095–1107 (2009).

Giorgi, F. & Bi, X. Time of emergence (TOE) of GHG-forced precipitation change hot-spots. Geophys. Res. Lett. 36 , L06709 (2009).

Lange, S. WFDE5 Over Land Merged with ERA5 Over the Ocean (W5E5). V. 1.0 (GFZ Data Services, 2019); https://doi.org/10.5880/pik.2019.023

Cucchi, M. et al. WFDE5: bias-adjusted ERA5 reanalysis data for impact studies. Earth Syst. Sci. Data 12 , 2097–2120 (2020).

Ruane, A. C. et al. Strong regional influence of climatic forcing datasets on global crop model ensembles. Agric. For. Meteorol. 300 , 108313 (2021).

FAOSTAT (United Nation’s Food and Agricultural Organization, 2019); http://www.fao.org/faostat/

Portmann, F. T., Siebert, S. & Döll, P. MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Global Biogeochem. Cycles 24 , GB1011 (2010).

Siebert, S. et al. A global data set of the extent of irrigated land from 1900 to 2005. Hydrol. Earth Syst. Sci. 19 , 1521–1545 (2015).

Heinke, J., Müller, C., Mueller, N. D. & Jägermeyr, J. N application rates from mineral fertiliser and manure Zenodo https://doi.org/10.5281/zenodo.4954582 (2021).

Zhang, B. et al. Global manure nitrogen production and application in cropland during 1860–2014: a 5 arcmin gridded global dataset for Earth system modeling. Earth Syst. Sci. Data 9 , 667–678 (2017).

Tian, H. et al. The global N 2 O model intercomparison project. Bull. Am. Meteorol. Soc. 99 , 1231–1251 (2018).

Nachtergaele, F. et al. Harmonized World Soil Database (version 1.2) (FAO and IIASA, 2012).

Shangguan, W., Dai, Y., Duan, Q., Liu, B. & Yuan, H. A global soil data set for Earth system modeling. J. Adv. Model. Earth Syst. 6 , 249–263 (2014).

Hengl, T. et al. SoilGrids1km—global soil information based on automated mapping. PLoS ONE 9 , e114788 (2014).

Müller, C. et al. Global Gridded Crop Model evaluation: benchmarking, skills, deficiencies and implications. Geosci. Model Dev. 10 , 1403–1422 (2017).

Franke, J. A. et al. The GGCMI Phase 2 experiment: Global Gridded Crop Model simulations under uniform changes in CO 2 , temperature, water, and nitrogen levels (protocol version 1.0). Geosci. Model Dev. 13 , 2315–2336 (2020).

Elliott, J. et al. The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8 , 261–277 (2015).

Ruane, A. C. et al. Multi-wheat-model ensemble responses to interannual climate variability. Environ. Model. Softw. 81 , 86–101 (2016).

Wang, R., Bowling, L. C. & Cherkauer, K. A. Estimation of the effects of climate variability on crop yield in the Midwest USA. Agric. For. Meteorol. 216 , 141–156 (2016).

Folberth, C., Gaiser, T., Abbaspour, K. C., Schulin, R. & Yang, H. Regionalization of a large-scale crop growth model for sub-Saharan Africa: model setup, evaluation, and estimation of maize yields. Agric. Ecosyst. Environ. 151 , 21–33 (2012).

Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.0. Harvard Dataverse, V1 (International Food Policy Research Institute, 2019); https://doi.org/10.7910/DVN/PRFF8V

Jägermeyr, J. et al. A regional nuclear conflict would compromise global food security. Proc. Natl Acad. Sci. USA 117 , 7071–7081 (2020).

Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3 , 1293 (2012).

Article   ADS   PubMed   Google Scholar  

Download references

Acknowledgements

J.J., A.C.R., C.R. and M.P. were supported by NASA GISS Climate Impacts Group and Indicators for the National Climate Assessment funding from the NASA Earth Sciences Division. J.J. and J.R.G. received support from the Open Philanthropy Project and thank the University of Chicago Research Computing Center for supercomputer allocations to run the pDSSAT model. Ludwig-Maximilians-Universität München thanks the Leibniz Supercomputing Center of the Bavarian Academy of Sciences and Humanities for providing capacity on the Cloud computing infrastructure to run the PROMET model. J.M.S. was supported by the German Federal Ministry of Education and Research (grant number 031B0230A: BioNex—The Future of the Biomass Nexus). O.M. and J.F.S. were supported by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Earth@lternatives project, grant agreement number 834716). J.A.F. and H.S. were supported by the NSF NRT programme (grant number DGE-1735359). J.A.F was supported by the NSF Graduate Research Fellowship Program (grant number DGE-1746045). RDCEP is funded by NSF through the Decision Making Under Uncertainty programme (grant number SES-1463644). T.I. was partly supported by the Environment Research and Technology Development Fund (2-2005) of the Environmental Restoration and Conservation Agency and Grant-in-Aid for Scientific Research B (18H02317) of the Japan Society for the Promotion of Science. A.K.J and T.-S.L. were supported by the US National Science Foundation (NSF - 831361857). M.O. was supported by the Climate Change Adaptation Research Program of NIES, Japan. S.L. was supported by the German Federal Office for Agriculture and Food (BLE) in the framework of OptAKlim (grant number 281B203316). S.S.R. acknowledges funding from the German Federal Ministry of Education and Research (BMBF) via the ISIpedia project.

Author information

Authors and affiliations.

NASA Goddard Institute for Space Studies, New York, NY, USA

Jonas Jägermeyr, Alex C. Ruane, Jose R. Guarin, Meridel Phillips & Cynthia Rosenzweig

Columbia University, Center for Climate Systems Research, New York, NY, USA

Jonas Jägermeyr, Jose R. Guarin & Meridel Phillips

Potsdam Institute for Climate Impacts Research (PIK), Member of the Leibniz Association, Potsdam, Germany

Jonas Jägermeyr, Christoph Müller, Jens Heinke, Stefan Lange & Sara Minoli

Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA

Joshua Elliott, James A. Franke, Elisabeth J. Moyer & Haynes Stephens

International Institute for Applied Systems Analysis, Laxenburg, Austria

Juraj Balkovic, Christian Folberth, Nikolay Khabarov & Rastislav Skalsky

Faculty of Natural Sciences, Comenius University in Bratislava, Bratislava, Slovak Republic

Juraj Balkovic

Agricultural & Biological Engineering Department, University of Florida, Gainesville, FL, USA

Oscar Castillo, Gerrit Hoogenboom & Cheryl Porter

Institut de recherche pour le développement (IRD) ESPACE-DEV, Montpellier, France

Babacar Faye

Department of Computer Science, University of Chicago, Chicago, IL, USA

Ian Foster & David Kelly

Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA

James A. Franke, Elisabeth J. Moyer & Haynes Stephens

Institute of Meteorology and Climate Research, Atmospheric Environmental Research, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany

Kathrin Fuchs, Sam S. Rabin, Clemens Scheer & Andrew Smerald

Institute for Sustainable Food Systems, University of Florida, Gainesville, FL, USA

Gerrit Hoogenboom

Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, Tsukuba, Japan

Toshichika Iizumi

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

Atul K. Jain & Tzu-Shun Lin

Center for Agricultural Water Research in China, College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China

Wenfeng Liu

Multidisciplinary Water Management group, University of Twente, Enschede, Netherlands

Oleksandr Mialyk & Joep F. Schyns

Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, Japan

Masashi Okada

Department of Environmental Sciences, Rutgers University, New Brunswick, NJ, USA

Sam S. Rabin

Ludwig-Maximilians-Universität München (LMU), Munich, Germany

Julia M. Schneider & Florian Zabel

Soil Science and Conservation Research Institute, National Agricultural and Food Centre, Bratislava, Slovak Republic

Rastislav Skalsky

Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany

Tommaso Stella & Heidi Webber

You can also search for this author in PubMed   Google Scholar

Contributions

J.J. and C.M. conceived the paper and coordinated GGCMI. J.J., C.M. and S.S.R. developed the simulation protocol. A.C.R. and C.R. coordinated AgMIP integration. C.M., J.J., J.B., O.C., B.F., C.F., K.F., G.H., T.I., A.K.J., N.K., T.-S.L., W.L., S.M., M.O., O.M., C.P., S.S.R., J.M.S., J.F.S., R.S., A.S., T.S. and F.Z. conducted crop model simulations. S.L. prepared climate data inputs. J.J. conducted the data analysis, and developed the manuscript and figures. All coauthors supported manuscript writing.

Corresponding author

Correspondence to Jonas Jägermeyr .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Peer review information Nature Food thanks Bin Peng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information.

Supplementary Figs. 1–14, Tables S1–S4 and text (‘Winter and spring wheat separation’, ‘Koeppen–Geiger climate class aggregation’, ‘GGCMI crop calendar’).

Reporting Summary

Rights and permissions.

Reprints and permissions

About this article

Cite this article.

Jägermeyr, J., Müller, C., Ruane, A.C. et al. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat Food 2 , 873–885 (2021). https://doi.org/10.1038/s43016-021-00400-y

Download citation

Received : 05 November 2020

Accepted : 29 September 2021

Published : 01 November 2021

Issue Date : November 2021

DOI : https://doi.org/10.1038/s43016-021-00400-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Spatiotemporal co-optimization of agricultural management practices towards climate-smart crop production.

  • Liujun Xiao
  • Guocheng Wang
  • Zhongkui Luo

Nature Food (2024)

Production vulnerability to wheat blast disease under climate change

  • Diego N. L. Pequeno
  • Thiago B. Ferreira
  • Senthold Asseng

Nature Climate Change (2024)

Unequal impact of climate warming on meat yields of global cattle farming

  • Weihang Liu
  • Junxiong Zhou
  • Yuchuan Luo

Communications Earth & Environment (2024)

Assessing and addressing the global state of food production data scarcity

  • Endalkachew Abebe Kebede
  • Hanan Abou Ali
  • Kyle Frankel Davis

Nature Reviews Earth & Environment (2024)

Water footprints and crop water use of 175 individual crops for 1990–2019 simulated with a global crop model

  • Oleksandr Mialyk
  • Joep F. Schyns
  • Markus Berger

Scientific Data (2024)

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

research paper on climate change and agriculture

IMAGES

  1. The Effects of Climate Change on Agriculture, Land Resources, Water

    research paper on climate change and agriculture

  2. Climate change and agriculture: introductory editorial

    research paper on climate change and agriculture

  3. Climate Change Impacts on Agriculture

    research paper on climate change and agriculture

  4. Plants

    research paper on climate change and agriculture

  5. With Climate Change, What's Better For The Farm Is Better For The Planet

    research paper on climate change and agriculture

  6. Agriculture and Climate Change

    research paper on climate change and agriculture

COMMENTS

  1. Climate change upsets agriculture

    Now, research quantifies how climate change has slowed agricultural productivity growth around the world. Writing in 1798, Thomas Malthus 1 saw population growth inevitably outstripping the...

  2. Climate change and Indian agriculture: A systematic review of farmers

    Perception and adaptation to climate change in agricultural system: meaning and definition. ... The geographical distribution of the research papers by states/regions and the publication trends are shown in Table S1 and Fig. S1, respectively. Maximum study sites are located in the Indian state of Uttarakhand, followed by Himachal Pradesh ...

  3. Climate change resilient agricultural practices: A learning ...

    1 Introduction 2 Methodology 4. Conclusions References Reader Comments Figures Abstract 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.

  4. New science of climate change impacts on agriculture implies higher

    Article Open access Published: 20 November 2017 New science of climate change impacts on agriculture implies higher social cost of carbon Frances C. Moore, Uris Baldos, Thomas Hertel &...

  5. PDF Climate Change Impacts on Agriculture: Challenges, Opportunities, and

    Climate change impacts on agriculture must be understood in the context of the intertwined systems that affect food security and agricultural trade, including biological, socioeconomic, and political processes.

  6. PDF Agricultural Climate Change Adaptation: A review of recent approaches

    This report reviews two recent approaches to studying climate change adaptation in agriculture: panel data methods and spatial general equilibrium models. Keywords Climate change, agriculture, adaptation, spatial general equilibrium models, trade models. JEL Classification Numbers F18, Q15, Q17, Q51, Q54, Q56 Acknowledgments

  7. Frontiers

    Agriculture is a significant contributor to anthropogenic global warming, and reducing agricultural emissions—largely methane and nitrous oxide—could play a significant role in climate change mitigation.

  8. A scoping review of adoption of climate-resilient crops by ...

    Article Open access Published: 12 October 2020 A scoping review of adoption of climate-resilient crops by small-scale producers in low- and middle-income countries Maricelis Acevedo, Kevin...

  9. A bibliometric analysis of research for climate impact on agriculture

    Climate anomalies and changes have complex and critical impacts on agriculture. Given global warming, the scientific community has dramatically increased research on these impacts. During 1996-2022, over 3,000 peer-reviewed papers in the Web of Science Core Collection database have investigated the fields. This study conducted a bibliometric analysis of these papers for systematic mapping ...

  10. A review of the global climate change impacts, adaptation, and

    Abstract Climate change is a long-lasting change in the weather arrays across tropics to polls. It is a global threat that has embarked on to put stress on various sectors. This study is aimed to conceptually engineer how climate variability is deteriorating the sustainability of diverse sectors worldwide.

  11. Impact of climate change on agricultural production; Issues, challenges

    Abstract 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.

  12. A method review of the climate change impact on crop yield

    This paper presents a comprehensive review of recent advancements in research methods used to study the impacts of climate change on agriculture and adaptation strategies. Its primary aim is to provide researchers with a deeper understanding of existing methods and serve as a reference for future methodological innovations and interdisciplinary ...

  13. Climate Change and its Impact on Agriculture

    Climate Change and its Impact on Agriculture Anupama Mahato Published 2014 Agricultural and Food Sciences, Environmental Science Global climate change is a change in the long-term weather patterns that characterize the regions of the world.

  14. Call for Papers on Climate-Smart Agriculture: Adoption, Impacts, and

    The Asian Development Bank Institute (ADBI) is seeking papers on climate-smart agriculture, with a focus on its adoption, impacts, and sustainable development implications in Asia and globally. The aim is to identify keys to boosting sustainable agricultural production and rural development to guide policy design and promote the farm sector's ...

  15. PDF CLIMATE CHANGE AND AGRICULTURE RESEARCH PAPER Assessing the impact of

    INTRODUCTION Recent climate change projections for Central Europe suggest an increase in mean temperature of 0·7 -2 °C, a decrease in precipitation, and an increase in carbon dioxide (CO2) concentration (up to 500 -700 ppm) by 2050 (EEA 2012; IPCC 2013).

  16. Climate Change Impacts on Agricultural Production and Crop Disaster

    1.1.1. Research on the Effect of Climate on Agricultural Production. Elias et al. [] studied the changes in agricultural production caused by extreme temperature in the southwest of the United States, and described the agricultural pressure and adaptive response of the United States by analyzing the changes in variable elements.The results showed that the water shortage in the semi-arid areas ...

  17. Harnessing AI for Climate-Resilient Agriculture: Opportunities and

    The paper concludes that AI technology offers promising solutions to the agricultural challenges posed by climate change and that while there are challenges to overcome, the urgency of adopting AI in agriculture cannot be overstated. Climate change is presenting a formidable challenge to global agriculture, with rising temperatures, shifting precipitation patterns, and increasing extreme ...

  18. PDF CLIMATE CHANGE AND AGRICULTURE RESEARCH PAPER Climate change impacts on

    Two global climate models (GCMs), CSIRO-Mk3·0 and MIROC-H under the A2 emission scenario for 2050 and 2100, were used to assess the impacts of climate change. A sensitivity analysis was conducted to identify which model parameters had the most effect on date palm distribution. Further refinements of the potential distributions were performed ...

  19. Climate Change Impacts on Agriculture and Food Supply

    Heat stress. Dairy cows are especially sensitive to heat stress, which can affect their appetite and milk production. In 2010, heat stress lowered annual U.S. dairy production by an estimated $1.2 billion. 40 Soil erosion. Heavy rainfalls can lead to more soil erosion, which is a major environmental threat to sustainable crop production. 41

  20. (PDF) A Review on Climate Change and its Impact on Agriculture in India

    Climate change is taking a toll on India's agricultural production and productivity. Intergovernmental panel on climate change (IPCC) has projected that by the end of 21st century temperature in ...

  21. Potential impacts of climate change on agriculture and fisheries

    … Richard Pollnac Show authors Nature Communications 13, Article number: 3530 ( 2022 ) Cite this article 13k Accesses 13 Citations 142 Altmetric Metrics Abstract Climate change is expected to...

  22. Unlocking Agricultural Innovation: A Roadmap for Growth and ...

    Agricultural innovation is crucial for navigating the dynamic market landscape and overcoming the diverse challenges posed by the current economic climate. It serves as a primary driver for both social advancement and economic prosperity, embodying the transformative force necessary for sustainable progress. Specifically, eco-friendly innovation not only boosts productivity but also promotes ...

  23. CGIAR Research Program on Climate Change, Agriculture and Food Security

    The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) generates evidence and supports adoption of climate-smart agricultural policies, practices, and services that alleviate poverty, increase gender equity, and support sustainable landscapes.

  24. Climate Change, Population Growth, and Population Pressure

    We find that standard population growth projections imply larger reductions in income than even the most extreme widely-adopted climate change scenario (RCP8.5). Climate and population impacts are correlated across countries: climate change and population growth will have their most damaging effects in similar places.

  25. Prices for these crops are most impacted by climate change

    Climate change events like droughts around the world are causing crops to fail, leading to shortages and high prices. ... Much research time and money is being devoted to mitigating the effects of a changing climate on crops. This includes more resilient and better adapted crops, better and more efficient water use, and more effective and ...

  26. Researcher points to holistic climate solutions at global conference

    "The climate-smart agriculture session highlighted research around the world tracking holistic impacts of innovative practices on climate and communities and really showcased the intersection of basic research and solutions-oriented approaches," said Crow, a professor with the UH Mānoa College of Tropical Agriculture and Human Resources ...

  27. Impact of Climate Change on Crops Adaptation and Strategies to Tackle

    Agriculture and climate change are internally correlated with each other in various aspects, as climate change is the main cause of biotic and abiotic stresses, which have adverse effects on the agriculture of a region. ... Moreover barley, oat, and durum wheat have 5, 2, and 1 research paper published. Diversity Array Technology (DArT) was the ...

  28. Prolonged drought and record temperatures have critical impact in the

    Today the Commission's Joint Research Centre (JRC) publishes its report Drought in the Mediterranean - January 2024 on the persisting droughts and their impact across the wider region.. Long-lasting, above-average temperatures, warm spells and poor precipitation have led to severe drought conditions in the Mediterranean region, affecting numerous areas across southern Italy, southern Spain ...

  29. The Role of Climate Change in the Proliferation of ...

    This paper synthesizes research on algal blooms in inland freshwater systems of the United States. This review examines how climate change contributes to trends in bloom frequency or severity and outlines approaches that states and tribes may use to monitor, report, and respond to harmful algae and cyanobacteria blooms.

  30. Climate impacts on global agriculture emerge earlier in new generation

    Climate impacts on global agriculture emerge earlier in new generation of climate and crop models | Nature Food Article Published: 01 November 2021 Climate impacts on global agriculture...