impact of climate change on agriculture essay

What You Need to Know About Food Security and Climate Change

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What is the state of global food security today, and what is the role of climate change?

The number of people suffering acute food insecurity increased from 135 million in 2019 to 345 million in 82 countries by June 2022, as the war in Ukraine, supply chain disruptions, and the continued economic fallout of the COVID-19 pandemic pushed food prices to all-time highs.

Global food insecurity had already been rising, due in large part to climate phenomena. Global warming is influencing weather patterns, causing heat waves, heavy rainfall, and droughts. Rising food commodity prices in 2021 were a major factor in pushing approximately 30 million additional people in low-income countries toward food insecurity.

At the same time, the way that food is often produced today is a big part of the problem. It’s recently been estimated that the global food system is responsible for about a third of greenhouse gas emissions—second only to the energy sector; it is the number one source of methane and biodiversity loss.

It’s recently been estimated that the global food system is responsible for about a third of greenhouse gas emissions—second only to the energy sector; it is the number one source of methane and biodiversity loss.

Who is most affected by climate impacts on food security?

About 80% of the global population most at risk from crop failures and hunger from climate change are in Sub-Saharan Africa, South Asia, and Southeast Asia, where farming families are disproportionally poor and vulnerable. A  severe drought caused by an El Nino weather pattern or climate change can push millions more people into poverty. This is true even in places like the Philippines and Vietnam, which have relatively high incomes, but where farmers often live at the edge of poverty and food price increases have an outsized impact on poor urban consumers.

How might climate change affect farming and food security in the future?

Up to a certain point, rising temperatures and CO2 can be beneficial for crops. But rising temperatures also accelerate evapotranspiration from plants and soils, and there must also be enough water for crops to thrive.  

For areas of the world that are already water-constrained, climate change will increasingly cause adverse impacts on agricultural production through diminishing water supplies, increases in extreme events like floods and severe storms, heat stress, and increased prevalence of pests and diseases.

Above a certain point of warming -- and particularly above an increase of 2 degrees Celsius in average global temperatures – it becomes increasingly more difficult to adapt and increasingly more expensive. In countries where temperatures are already extremely high, such as the Sahel belt of Africa or South Asia, rising temperatures could have a more immediate effect on crops such as wheat that are less heat tolerant.

Without solutions, falling crop yields, especially in the world's most food-insecure regions, will push more people into poverty – an estimated 43 million people in Africa alone could fall below the poverty line by 2030 as a result.

How can agriculture adapt to climate change?

It’s possible to reduce emissions and become more resilient, but doing so often requires major social, economic, and technological change. There are a few key strategies:

Use water more efficiently and effectively, combined with policies to manage demand . Building more irrigation infrastructure may not be a solution if future water supply proves to be inadequate to supply the irrigation systems—which our research has shown may indeed be the case for some countries. Other options include better management of water demand as well as the use of advanced water accounting systems and technologies to assess the amount of water available, including soil moisture sensors and satellite evapotranspiration measurements . Such measures can facilitate techniques such as alternate wetting and drying of rice paddies, which saves water and reduces methane emissions at the same time.

Switch to less-thirsty crops . For example, rice farmers could switch to crops that require less water such as maize or legumes. Doing so would also help reduce methane emissions, because rice is a major source of agri-food emissions. But a culture that has been growing and consuming rice for thousands of years may not so easily switch to another less thirsty, less emitting crop.

Improve soil health . This is hugely important. Increasing organic carbon in soil helps it better retain water and allows plants to access water more readily, increasing resilience to drought. It also provides more nutrients without requiring as much chemical fertilizer -- which is a major source of emissions. Farmers can restore carbon that has been lost by not tilling soil and by using cover crops, particularly with large roots, in the rotation cycle rather than leaving fields fallow. Such nature-based solutions to environmental challenges could deliver 37% of climate change mitigation necessary to meet the goals of the Paris Agreement. But getting farmers to adopt these practices will take time, awareness-raising and training. In places where farm plots are small and farmers can’t afford to let fields lie fallow or even rotate with leguminous crops, improving soil health could pose a challenge.  

What is the World Bank doing to help countries build food security in the face of climate change?

The World Bank Group’s Climate Change Action Plan (2021-2025) is stepping up support for climate-smart agriculture across the agriculture and food value chains and via policy and technological interventions to enhance productivity, improve resilience, and reduce GHG emissions. The Bank also helps countries tackle food loss and waste and manage flood and drought risks. For example, in Niger, a Bank-supported project aims to benefit 500,000 farmers and pastoralists in 44 communes through the distribution of improved, drought-tolerant seeds, more efficient irrigation, and expanded use of forestry for farming and conservation agriculture techniques. To date, the project has helped 336,518 farmers more sustainably manage their land and brought 79,938 hectares under more sustainable farming practices.

Website:  Climate Explainer Series

Website:  Climate Stories: How Countries and Communities Are Shaping A Sustainable Future

Website:  Food Security Update

Website:  World Bank - Climate Change

Website:  World Bank - Agriculture and Food

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Review article, the impact of climate change on food systems, diet quality, nutrition, and health outcomes: a narrative review.

• 1 International Atomic Energy Agency, Vienna, Austria
• 2 Department of Food Science and Nutrition, University of Zambia, Lusaka, Zambia
• 3 Alliance Bioversity International and CIAT (Kenya), Nairobi, Kenya
• 4 PATH, Seattle, WA, United States
• 5 Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark
• 6 Food and Agriculture Organization of the United Nations (Thailand), Bangkok, Thailand
• 7 PNCA, AgroParisTech Institut des Sciences et Industries du Vivant et de L'environnement, Paris, France

Many consequences of climate change undermine the stability of global food systems, decreasing food security and diet quality, and exposing vulnerable populations to multiple forms of malnutrition. The emergence of pandemics such as Covid-19 exacerbate the situation and make interactions even more complex. Climate change impacts food systems at different levels, including changes in soil fertility and crop yield, composition, and bioavailability of nutrients in foods, pest resistance, and risk of malnutrition. Sustainable and resilient food systems, coupled with climate-smart agriculture, are needed to ensure sustainable diets that are adequately diverse, nutritious, and better aligned with contextual ecosystem functions and environmental conservation. Robust tools and indicators are urgently needed to measure the reciprocal food systems-climate change interaction, that is further complicated by pandemics, and how it impacts human health.

Introduction

Many consequences of climate change threaten food security and diet quality, thereby exposing vulnerable populations across continents to multiple forms of malnutrition. Poor diet is a major cause of mortality and morbidity ( Afshin et al., 2019 ; Micha et al., 2020 ). Currently, about 690 million people are hungry and the number is expected to surpass 840 million by 2030 ( FAO, 2020 ). As of 2020, 149.2 million children under 5 years of age were stunted and 45.4 million were wasted, partially due to poor diets. Simultaneously, 38.9 million children below 5 years of age were overweight in 2020 ( WHO, 2021 ). These trends are partly driven by inequality and unsustainable foods systems which cannot satisfy food security and nutritional requirements for all. Apart from climate change, other external shocks that adversely impact foods system include pandemics, such as the on-going COVID-19 pandemic, that was projected to add an additional 83–132 million people into the undernourished bracket by 2020 ( WHO, 2020 ).

Climate change worsens unsustainable food systems by directly impacting soil fertility, rain patterns, crop yields and food production, food-nutrient and anti-nutrient composition, and nutrient bioavailability. These changes decrease macro- and micronutrients available in the global food supply. Further problems arise from indirect impacts such as pests that result in increased occurrence of spoilage and food safety hazards at various stages of the food chain from primary production to post-harvest protection through to consumption. Each of these factors may have deleterious impacts on human nutrition ( Parfitt et al., 2010 ; Tirado et al., 2010 ; Hodges et al., 2011 ). Measuring this complex and reciprocal food systems-climate change interaction, that is further complicated by pandemics, remains a major challenge especially in how it impacts human health.

This narrative review is based on the outcomes of a Technical Meeting organized by the International Atomic Energy Agency (IAEA) from 19 to 21 October 2020 with the aim of understanding the effectiveness of food-based approaches to improve diet quality under our rapidly changing food systems. This paper covers how food systems and dietary patterns have changed at the community level over time. The vicious and reciprocal cycle between food systems and climate change, in relation to food systems vulnerability and resilience, is discussed. The impact of these interactions on diet quality in terms of food-nutrient, nutrient deficiencies and ultimately the risk of malnutrition is also analyzed. Lastly, this paper highlights the need to develop appropriate measurement tools that can be used to monitor and evaluate the different components and levels (micro- and macro-) of the entire food system.

Climate change, food systems and biodiversity

Food systems comprise all activities from production, post-harvest storage, transportation, processing, distribution, trade and marketing, regulation, consumption of food, and the outcomes of nutrition and health, socio-economy, and the environment. Food systems constitute the first action track of the Decade of Action on Nutrition ( WHO, 2017 ; Demaio and Branca, 2018 ; Turner et al., 2020 ). The food environment is an integral part of food systems and consists of an external domain (food availability, product properties, prices, marketing, and regulation) and a personal domain (accessibility, affordability, convenience, and desirability), both of which influence food acquisition, consumption and ultimately nutrition and health outcomes ( Turner et al., 2018 , 2020 ; UNSCN, 2019 ).

Evolution of food systems

The advent of agriculture in the Neolithic revolution marked a shift to predominantly plant-based diets ( Leitzmann, 2014 ). Food systems further evolved with the development of city-states and governance, food storage and means of transportation, trading routes, and consumer demands. Science and technology revolutionized food production, processing, preservation, and transportation ( Hueston and McLeod, 2012 ) with a shift to the consumption of processed energy and macronutrient-dense foods ( Vermeulen et al., 2020 ).

Furthermore, the global meat demand is on the rise, especially within LMICs due to increasing urbanization, education, and affluence ( Bruinsma, 2003 ; Zhang et al., 2017 ; Headey et al., 2018 ; Adesogan et al., 2020 ). For example, per capita meat consumption increased by 20 kg from 1961 to 2014, mainly in Asia and Africa ( Vermeulen et al., 2020 ), while it decreased in many Western countries. High-income countries consume almost six times more milk products and nine times more eggs per capita than low-income countries ( Herforth et al., 2019 ). The global demand for livestock and dairy is projected to increase by 70 and 60%, respectively, between 2010 and 2050.

There is a reciprocal and cyclical interaction between foods systems and climate change. Within the past 40 years, agricultural production has doubled and food supply chains have been globalized ( Niles et al., 2017 ; Von Braun, 2018 ). Mass food production practices (e.g., fertilizer use, expanded crop and livestock production) and deforestation lead to increased amounts of greenhouse gases and attendant climate change, which in turn result in reduced food production ( Niles et al., 2017 ). Climate change has impacted food systems through weather events such as drought, flooding, and heat waves with attendant loss of life, livelihood, and reduced productivity related to lower soil fertility, disrupted rain patterns, and acid rain from heavy fertilizer use ( Niles et al., 2017 ; Von Braun, 2018 ). This vicious cycle leads to food insecurity and malnutrition in all its forms, environmental damage, water scarcity, and the emergence of new human, plant, and animal diseases ( Tirado et al., 2009 ; Niles et al., 2017 ; Von Braun, 2018 ; Popkin et al., 2020 ).

The popularization of dietary practices, such as vegetarianism and veganism, in recent decades has resulted in dietary pattern changes that reflect an increased awareness of the environmental footprint associated with the consumption of animal source foods ( Leitzmann, 2014 ). Increased interest in these diets extends beyond a focus on environmental health alone. Unprocessed, vegetarian diets have been associated with several health benefits including longevity and lower rates of diet-related, non-communicable diseases ( Burkitt and Trowell, 1977 ; Keys, 1980 ; Trowell and Burkitt, 1981 ; Leitzmann, 2014 ).

Nevertheless, recent attempts to assess shifts in dietary patterns focus more on the link to socioeconomic status without including how these dietary patterns link to nutrition and health outcomes. Da Costa and colleagues reported that animal source foods were consumed more in western contexts and with increasing income and urbanization ( da Costa et al., 2022 ). At the same time, the definition of a healthy diet has been confusing. O'Hearn et al., assessed dietary patterns in 185 countries and concluded that Western and Latin American regions had healthier dietary patterns compared to Asia and sub-Saharan Africa, but they failed to explain the global rise in the double burden of malnutrition ( O'Hearn et al., 2019 ). This shows that better and more sensitive metrics and tools are needed to better understand the complex dimensions of food systems. Further discussion of the nutritional impacts of dietary patterns occurs in section Climate change, nutrient adequacy, and nutrition and health outcomes.

Climate change and biodiversity loss

Sustainable agriculture, nutrition and the livelihoods of millions of people depend on the diversity of crops and livestock species, and intra-species genetic diversity ( Sunderland, 2011 ). The biodiversity of plants and animal species consumed is directly correlated with food security ( Sunderland, 2011 ). Genetic diversity is a critical factor for the continued improvement of crop varieties and livestock breeds, and determines the extent to which genetic resources are passed down to future generations ( Rosendal, 2013 ).

Unfortunately, there has been a dramatic loss of biodiversity, including the diversity of genes, species, and ecosystems due to habitat destruction (i.e., settlement, changing agricultural practices, deforestation, industrialization), global warming, and the uncontrolled spread of invasive species. Pollution, nitrogen deposition, and shifts in precipitation further exacerbate biodiversity loss ( Cramer et al., 2017 ). Over the past 50 years, agriculture has focused too heavily on conventional cereal and horticultural crops leading to the loss of indigenous and traditional food crops ( Akinola et al., 2020 ). While more than 6,000 plant species have been cultivated for food, just 9 account for 66% of total crop production, indicating widespread monoculture agriculture ( FAO, 2019 ). Today, 80–90% of the human diet relies on 12 to 20 species ( Chivenge et al., 2015 ), and only three, rice, maize and wheat contribute nearly 60% of calories and proteins obtained by humans from plants.

Only few terrestrial animal species, namely, cattle, sheep, pig and chicken are domesticated for food production ( Robinson and Pozzi, 2011 ). Almost 26% of livestock breeds are at risk of extinction. About 24% of wild food species are decreasing in abundance, while the status of another 61% is not reported or known ( FAO, 2019 ).

A general reliance on fewer species to feed the world, the resulting loss of biodiversity due to non-utilization and lack of conservation puts food security and human nutrition at great risk ( FAO, 2019 ). Agricultural production therefore must embrace strategies beyond exploiting the same “Green Revolution” technologies from the last half century, which were based on genetic improvement and higher inputs ( Kahane et al., 2013 ). Although such strategies were beneficial in preventing widespread famine, the inappropriate and excessive use of agrochemicals, wasteful water usage via inefficient irrigation systems, loss of beneficial biodiversity (pollinators, soil fauna, etc.) and significantly reduced crop and varietal diversity have had significant deleterious effects on our resulting food systems ( Kahane et al., 2013 ).

Mainstreaming biodiversity conservation is a key strategy to broaden food production to include locally adaptable, often underutilized, nutrient-rich species and ensure diversified, healthy diets and livelihoods among more resilient populations ( Bélanger and Pilling, 2019 ).

Climate change, nutrient adequacy, and nutrition and health outcomes

Climate change impacts food systems and thereby global food production via changes in yield, biomass food composition and nutritional quality which in turn directly influence human nutrition and health ( Glopan, 2020 ). Climate change can also disrupt food supply chains and transportation, hence food price volatility, and compromised food security, nutrition and human health ( FAO, 2020 ). It exacerbates inequities, and poorer, vulnerable groups tend to suffer more as they are less resilient to shocks ( Tirado et al., 2013 ; Niles et al., 2017 ; FAO, 2020 ). Efforts should be put in place to make food systems more climate-smart and nutrition-sensitive, from production to consumption ( Bryan et al., 2019 ; UNSCN, 2020b ). Food-based dietary guidelines that include sustainability criteria can help to promote those diets that are good for human and planetary health ( UNSCN, 2020a ; UN, 2021 ).

Impact of climate change on food nutrient content

There is a lack of evidence regarding the impact of climate change on human nutrition and health indicators. Climate change may affect human health by altering the food nutrient content via increasing concentrations of CO 2 in the atmosphere ( Dietterich et al., 2015 ). Elevated CO 2 results in more rapid growth rates but also reduces plant protein content and micronutrients such as calcium, iron, and zinc ( Taub et al., 2008 ; Taub, 2010 ; Fernando et al., 2012 ; Loladze, 2014 ; Myers et al., 2014 ; Ziska and McConnell, 2016 ; Medek et al., 2017 ; Myers, 2017 ; Smith et al., 2017 ; Uddling et al., 2018 ). Most crops grown under elevated CO 2 –except for legumes and C4 crops—systematically exhibit decreased concentrations of nitrogen and protein in the edible portion ( Cotrufo et al., 1998 ; Pleijel et al., 1999 ; Idso and Idso, 2001 ; Jablonski et al., 2002 ). C3 grains and tubers including rice, wheat, barley, and potatoes experience 7–15% reductions in protein content, whereas C3 legumes and C4 crops show either exceedingly small or insignificant reductions ( Myers et al., 2014 ). Elevated CO 2 concentrations of 550 ppm can lead to 3–11% declines in the zinc and iron concentrations of cereal grains and legumes ( Myers et al., 2014 ). Under more extreme conditions, CO2 concentrations of 690 ppm, lead to 5–10% reductions in the concentration of phosphorus, potassium, calcium, sulfur, magnesium, iron, zinc, copper, and manganese across a wide range of crops ( Loladze, 2014 ). The carbon nutrient penalty results in decreases in the global availability of dietary protein of 2.9 to 4.1%, iron 2.8 to 3.6%, and zinc 2.5 to 3.4% ( Beach et al., 2019 ). Overall, the combined effects of projected atmospheric CO 2 increases (i.e., carbon nutrient penalty, CO 2 fertilization, and climate effects on productivity) will decrease growth in the global availability of nutrients by 19.5% for protein, 13.6% for iron, and 14.6% for zinc relative to expected technology and market gains by 2050.

Climate change vs. animal source foods

As discussed in section Evolution of food systems, animal product consumption is on the rise, however an inverse relationship has been observed between animal product consumption and environmental health. This poses a challenge for improving global nutritional status, as the consumption of animal source foods has been linked to improved growth and development in young children, particularly in LMICs. Ecological studies indicate an inverse association between increasing meat consumption per capita and decreasing child stunting rates ( UNICEF and The World Bank Group, 2017 ; Headey et al., 2018 ). This is attributed to the greater bioavailability of nutrients such as protein and iron from this category of foods. Data from Demographic Health surveys in 49 countries indicates that across sub-Saharan Africa and Asia, dairy, egg, and meat consumption are low while fish consumption is relatively higher ( Headey et al., 2018 ). The urbanization of LMICs seems to be positively associated with egg and fish consumption, and negatively associated with meat consumption ( Headey et al., 2018 ).

Alternative dietary protein sources such as plant proteins, edible insects, seaweed, microalgae and cell-culture based proteins (e.g., cultured milk and eggs; lab-grown meats) ( Thavamani et al., 2020 ; Tso et al., 2020 ) may offer a suitable alternative to animal source foods with a lower environmental footprint. Edible insects have garnered renewed interest recently as a sustainable protein source. While more than 2,000 species of edible insects collected from wild sources have been identified in traditional diets around the world, a select few have emerged over the past decade as suitable for farming and mass production ( Halloran et al., 2018 ). These insect species convert organic feed substrates very efficiently into animal tissue (protein, fat, and other compounds). Edible insects can be produced on less space, using less water and feed, and they produce less greenhouse gas emission than traditional livestock making them attractive from an environmental sustainability standpoint ( Halloran et al., 2016 , 2017 ).

Further research is warranted to evaluate the nutritional quality of these alternative dietary protein sources, with specific focus on the composition and bioavailability of protein, amino acids, essential fatty acids, vitamin B12, zinc and iron and the concentration of anti-nutrient compounds that decrease bioavailability.

Emergence of diseases and impact on health

Access to safe water remains an extremely important global health issue. More than two billion people live in the dry regions of the world and suffer disproportionately from malnutrition and other health risks related to contaminated or insufficient safe water. The absence of safe water with poor sanitation systems, extreme precipitations with either excessive rainfall, or prolonged drought, all increase exposure to pathogenic microbes resulting in enteric infections and diarrheal diseases. These exacerbate infant and young child malnutrition, leading to retarded growth and development resulting in wasting and stunting ( Fink et al., 2011 ; Guerrant et al., 2013 ; Ngure et al., 2014 ). Increased flooding, precipitation, rising temperatures, and other extremes of climate change, are projected to increase the burden of diarrhoeal diseases in low-income regions. Climate change has also been shown to play a role in the spatial and temporal distribution of malaria and is expected to increase the risk of emerging zoonotic diseases. Changes in the survival of pathogens in the environment, changes in migration pathways, carriers and vectors, and changes in the natural ecosystems are all predicted to increase health risks to mankind. Human encroachment into wildlife habitat, including bushmeat hunting and agricultural expansion, has increased the risks of exposure to zoonotic diseases such as HIV, Ebola or SARS-CoV-2 ( Goldberg et al., 2012 ; McGrath, 2020 ). Furthermore, consumption of poorer quality diets as climate change alters to food system can not only increase the risk of non-communicable diseases as discussed in a prior section, but also increase an individual's susceptibility to infectious diseases ( Humphries et al., 2021 ).

Climate change and food systems: strengths and weaknesses, interventions, and metrics

Strengths, resilience, and vulnerability of food systems.

Food systems are faced with various dynamic shocks ( Meyer, 2020 ), which are mostly anthropogenic, including economic, trade, and public health issues such as disease outbreaks ( Hamilton et al., 2020 ). Although climate change is primarily considered an environmental factor, the predicted impacts of climate change, such as increased disease transmission, have wide-sweeping impacts. Food systems depend on the availability of human labor and are easily disrupted by both temporary and chronic shocks. Ebola outbreaks in West Africa ( Figuié, 2016 ) disrupted workers movement, thereby inhibiting food production and food supply chains in the region, while the on-going COVID-19 pandemic has had a similar impact globally ( Aday and Aday, 2020 ; Matthews, 2020 ; Belton et al., 2021 ; Weersink et al., 2021 ).

Many food systems rely on international trade ( Gephart et al., 2017 ; Kummu et al., 2020 ) and are prone to disruption when countries impose trade restrictions such as export bans on foods to safeguard domestic consumption ( Seekell et al., 2017 ; Aday and Aday, 2020 ). Food systems that significantly rely on local institutions, knowledge, and farmers are most resilient ( Pingali et al., 2005 ; Hamilton et al., 2020 ; Kummu et al., 2020 ; Love et al., 2021 ). Resilient food systems are based on diversification in production and distribution channels and reduced waste, which cumulatively increase food and nutrition security ( Schipanski et al., 2016 ; Seekell et al., 2017 ). Similarly, food systems that foster biodiversity have been shown to contribute toward more sustainable food production systems ( Snapp et al., 2010 ). Social factors are another crucial element in food system resiliency. Women are the major food systems stakeholders ( Nkengla-Asi et al., 2017 ), yet there are still large gender-based disparities in access to opportunities to guarantee sustainable and healthy diets for women ( Kusakabe, 2004 ). Food systems fostering gender equity have been shown to be more resilient to shocks, and examples from India and Malawi ( Schipanski et al., 2016 ) have demonstrated that efforts aimed at reducing social injustice, including gender inequity, can foster sustainable and resilient food systems.

Interventions to adapt to changes in food systems

As discussed in section Impact of climate change on food nutrient content, climate change is expected to lower the nutrient content of foods. This poses a significant threat because prior attempts to remedy malnutrition via agricultural improvements have focused on achieving caloric sufficiency, leaving hundreds of millions of people to still suffer from micronutrients and protein deficiencies ( Nelson et al., 2009 ; Kahane et al., 2013 ; Myers et al., 2013 , 2017 ; Springmann et al., 2016 ; Smith and Myers, 2018 ; Soares et al., 2019 ). Interventions such as biofortification (i.e., increase of the micronutrient content of crops by genetic selection), fortification (addition of micronutrients to common edible products that are manufactured in formal and centralized factories), and as a last resource supplementation (provision of pharmaceutical and concentrated forms of micronutrients), have led to significant improvements in population health ( Clune et al., 2017 ; Olson et al., 2021 ).

Biofortification is a process used to enhance the micronutrient content of staple crops via agronomic practices (i.e., micronutrient fertilizer), conventional plant breeding, and/or genetic modification, making it a food-based strategy with heightened nutritional status at harvest ( Khush et al., 2012 ). Since biofortification is a localized nutrition solution, it is less vulnerable to value-chain disruptions. Furthermore, it allows smallholder farmers across Africa and South Asia to own the solution, as these crops are grown in their fields and the seeds only have to be purchased once as a single investment ( Khush et al., 2012 ). The effects of biofortification on improving nutrition are likewise encouraging, and clinical studies have shown significant improvements in child iron deficiency ( Afolami et al., 2021 ), serum retinol ( Afolami et al., 2021 ), β-carotene concentration ( Talsma et al., 2016 ; Olson et al., 2021 ). Challenges do remain, however, since sustainability and population coverage are not guaranteed ( Bhutta et al., 2013 ). Biofortification is more sustainable among rural communities as they depend on agriculture for both food and income. Going forward, nutrient bioavailability and anti-nutrient composition of biofortified crops require further attention.

Food fortification, under a public health perspective, is a strategy that takes advantage of using existing food products as micronutrient carriers, manufactured by large, centralized food industries. Maize flour, oil, rice, salt, and wheat flour are the primary food fortification vehicles used to deliver micronutrients. The main advantage of food fortification is that it does not require behavior change modifications since common staples and condiments are used as the fortification vehicles; and social marketing is not needed in fortification programmes.

Iodine deficiency disorders have almost disappeared in most countries with the addition of iodine to salt. Cereal flours containing folic acid have decreased the prevalence of neural tube Defects in both industrialized and LMICs where their consumption is high and the dietary intake of folate is low. Vitamin A deficiency is currently being addressed with fortified sugars and oils in Central America and Eastern and Southern Africa, and iron deficiency can be best prevented via fortified rice, wheat and maize flours at the population level. Additional micronutrient deficiencies (e.g., zinc, vitamins B1, B2, B 12 , and D) may also be effectively targeted in the future via mainstream fortification vehicles ( Dary and Hurrell, 2006 ; Keats et al., 2019 ).

Populations most in need often do not receive the fortified commodities targeted for consumption. This is in part due to the fact most food fortification programs are private sector driven, and without adequate private-public sector partnerships, as is the current case ( Olson et al., 2021 ), populations with low purchasing power cannot afford commercialized fortified commodities ( Dary, 2007 ). Supplementation has the restrictions of high cost and exceptionally low coverage. Multiple interventions may be used to effectively target the same population group simultaneously, and this could potentially lead to unintended excess intakes ( Olson et al., 2021 ).

Metrics to measure the impact of food systems

Measuring the impact of changing food systems on diet quality and human health outcomes remains a challenge due to the multi-faceted nature of foods systems and the fact that the existing criterion for a healthy diet is universal instead of being country or context-specific; each country should have domesticated food-based dietary guidelines.

When evaluating diet quality, it is important to consider the data source and how it is processed, interpreted, and used. The source of dietary data can be at the level of the household or the individual. This data can then be processed in the form of metrics or benchmarks, food composition tables, or statistical analysis. Metrics and benchmarks for processing data include nutrient adequacy; consumption of specific health or disease promoting dietary components (e.g., fat, sugar, salt, fruits, and vegetables), and overall quality of diet. No single method is perfect, and all methods have their strengths and weaknesses.

Recent developments such as a resource guide in supporting countries to strengthen nutrition actions based on the policy recommendations of the Second International Conference on Nutrition (ICN2) ( WHO, 2018 ) and the endorsement of the CFS voluntary guidelines for food systems and nutrition are a first step in the right direction toward food systems transformation. However, tools to evaluate the impact of these recommendations and climate-smart solutions are needed. Such tools should be developed with due regard to trade-offs related to crop yield vs. nutrient content and bioavailability, nutritional benefits related to increased animal source food consumption vs. environmental footprint and biodiversity; win-win of moving toward more healthy dietary patterns that are also more sustainable; health consequences of altered food intake behavior; and cross-cutting issues such as gender, urbanization, and food wastage ( Figure 1 ). Future food systems must find ways to provide adequate nourishment without environmental trade-offs.

Figure 1 . A food systems continuum and value chain schema to address the link between climate change and diet quality and identify entry opportunities for stable isotope techniques relating to soil, water, and seed biodiversity, food production, nutrient retention and bioavailability, and nutrition, health, and cognitive outcomes.

Some such remaining questions are: (1) What is the impact of climate change on crop nutrient density and bioavailability; are there particularly sensitive nutrients and how does anti-nutrient content vary? (2) What is the role of alternative proteins such as edible insects in the food systems value chain; what is their implication on environmental footprint and food waste? (3) What is the linkage between climate change, sanitary conditions, diet quality and health; what is the role of environmental enteric dysfunction, diarrhea, and mycotoxins? (4) What are the most appropriate tools to holistically assess the foods systems value chain; what role can stable isotope techniques play? (5) What is the minimum set of indicators that can be used to measure the entire food systems continuum (from food production to health including functional outcomes)? Promising innovations in foods systems assessment tools could range from stable isotope techniques ( Owino et al., 2017 ; Owino and Mouratidou, 2019 ) to assess nutrient bioavailability, functional nutrition outcomes such as body composition, soil fertility, water use efficiency ( Wang et al., 2021 ); metabolomics to reveal the metabolic signature of changes in dietary behaviors and food composition ( Jin et al., 2019 ); to geospatial mapping ( Gashu et al., 2021 ; Giller and Zingore, 2021 ) to predict the risk of malnutrition based on soil and crop nutrient profiles; (6) What are the implications of climate change on diet quality in the context of population displacement, urbanization, and shifting consumer behavior? (7) How can we make nutrition and health-related research useful to policy makers? (8) What partnerships and collaborations are needed; how can other sectors and disciplines be brought in to comprehensively understand the food systems continuum?

Climate change and unsustainable food systems interact reciprocally with adverse impact on food and nutrition security. Climate change impacts food systems via multiple pathways, including soil fertility, water availability, reduced food yield, reduced food nutrient concentration and bioavailability, increased food anti-nutrient content and increased episodes of infectious diseases. Unsustainable food systems characterized by mass monocultural production with excess fertilizer use, mass livestock production, and deforestation lead to elevated greenhouse gas emissions and loss of biodiversity. Reducing environmental footprints linked to food systems may be achieved by reverting to more sustainable diets that meet nutrition requirements while safeguarding the environment. Dietary diversification, fortification, biofortification, and the inclusion of alternative protein sources (e.g., edible insects) are some of the available alternative options. All of these food systems-climate change-diet and nutrition outcomes are made even more complex by other dynamic factors, including rapid population growth, urbanization, evolving eating habits, and emergent pandemics such as COVID-19. Food systems have witnessed dramatic changes overtime. However, there is a limited ability to measure the multiple points at which these interactions occur. Recent developments at the global level, including ICN2 recommendations and the endorsement of the CFS voluntary guidelines for food systems and nutrition, are a first step in the right direction toward food systems transformation. However, tools to evaluate the impact of these recommendations and climate-smart solutions are needed. The development of these tools should consider crop yield vs. nutrient content and bioavailability, nutritional benefits related to increased animal source food consumption vs. environmental footprint and biodiversity; the win-win of moving toward more healthy, sustainable dietary patterns; unintended health consequences of altered food intake behavior; and cross-cutting issues such as gender, urbanization, and food wastage. Potential health consequences of multiple food-based interventions targeting micronutrient deficiencies must also be assessed. Robust tools and indicators for assessing the impacts and complexity of food systems are needed.

Author contributions

VO, CK, BE, MP, LE, NR, WL, and DT wrote sections of this manuscript. All authors also provided independent review.

All costs related to the virtual Technical Meeting on Leveraging of Stable Isotope Techniques in Evaluating Food-based Approaches to Improve Diet Quality and the Open Access fee for this review were contributed by the IAEA.

Acknowledgments

We are indebted to all participants of the virtual Technical Meeting on Leveraging of Stable Isotope Techniques in Evaluating Food-based Approaches to Improve Diet Quality (EVT1904254) organized by the IAEA from 19 to 21 October 2020 for their contribution to discussions that inspired the writing of this review. All internal reviewers [Stineke Oenema, Executive Secretary, UN Nutrition; Cornelia Loechl, Head, Nutritional and Health Related Environmental Studies Section, Division of Human Health, International Atomic Energy Agency, Omar Dary, Health Science Specialist, Bureau for Global Health, United States Agency for International Development, Maria Xipsiti, Food and Nutrition Officer, Nutrition and Food Systems Division, FAO] are appreciated for their editorial input.

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.

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Abbreviations

ASF, Animal Source Foods; CFS, Committee on Food Security; COVID, Coronavirus Disease; DIAAS, Digestible Indispensable Amino Acid Score; FAO, Food and Agriculture Organization of the United Nations; FZA, Fractional Zinc Absorption; HIV, Human Immunodeficiency Virus; IAEA, International Atomic Energy Agency; ICN2, Second International Conference on Nutrition; IRRI, International Rice Research Institute; LMIC, Low-and-middle-income countries; SarCOV2, Severe acute respiratory syndrome Coronavirus 2; UNDP, United Nations Development Programme; UNICEF, United Nations Children's Education Fund; UN SDGs, United Nations Sustainable Development Goals; WHO, World Health Organization.

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Keywords: climate change, sustainable food systems, COVID-19 pandemic, nutrient deficiencies, food composition

Citation: Owino V, Kumwenda C, Ekesa B, Parker ME, Ewoldt L, Roos N, Lee WT and Tome D (2022) The impact of climate change on food systems, diet quality, nutrition, and health outcomes: A narrative review. Front. Clim. 4:941842. doi: 10.3389/fclim.2022.941842

Received: 11 May 2022; Accepted: 21 July 2022; Published: 16 August 2022.

Reviewed by:

Copyright © 2022 Owino, Kumwenda, Ekesa, Parker, Ewoldt, Roos, Lee and Tome. 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: Megan E. Parker, mparker@path.org

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Climate Change and Health: From Data and Strategies to Real Actions

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A review of the global climate change impacts, adaptation, and sustainable mitigation measures

Kashif abbass.

1 School of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094 People’s Republic of China

Muhammad Zeeshan Qasim

2 Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094 People’s Republic of China

Huaming Song

Muntasir murshed.

3 School of Business and Economics, North South University, Dhaka, 1229 Bangladesh

4 Department of Journalism, Media and Communications, Daffodil International University, Dhaka, Bangladesh

Haider Mahmood

5 Department of Finance, College of Business Administration, Prince Sattam Bin Abdulaziz University, 173, Alkharj, 11942 Saudi Arabia

Ijaz Younis

Associated data.

Data sources and relevant links are provided in the paper to access data.

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. Specifically, the agricultural sector’s vulnerability is a globally concerning scenario, as sufficient production and food supplies are threatened due to irreversible weather fluctuations. In turn, it is challenging the global feeding patterns, particularly in countries with agriculture as an integral part of their economy and total productivity. Climate change has also put the integrity and survival of many species at stake due to shifts in optimum temperature ranges, thereby accelerating biodiversity loss by progressively changing the ecosystem structures. Climate variations increase the likelihood of particular food and waterborne and vector-borne diseases, and a recent example is a coronavirus pandemic. Climate change also accelerates the enigma of antimicrobial resistance, another threat to human health due to the increasing incidence of resistant pathogenic infections. Besides, the global tourism industry is devastated as climate change impacts unfavorable tourism spots. The methodology investigates hypothetical scenarios of climate variability and attempts to describe the quality of evidence to facilitate readers’ careful, critical engagement. Secondary data is used to identify sustainability issues such as environmental, social, and economic viability. To better understand the problem, gathered the information in this report from various media outlets, research agencies, policy papers, newspapers, and other sources. This review is a sectorial assessment of climate change mitigation and adaptation approaches worldwide in the aforementioned sectors and the associated economic costs. According to the findings, government involvement is necessary for the country’s long-term development through strict accountability of resources and regulations implemented in the past to generate cutting-edge climate policy. Therefore, mitigating the impacts of climate change must be of the utmost importance, and hence, this global threat requires global commitment to address its dreadful implications to ensure global sustenance.

Introduction

Worldwide observed and anticipated climatic changes for the twenty-first century and global warming are significant global changes that have been encountered during the past 65 years. Climate change (CC) is an inter-governmental complex challenge globally with its influence over various components of the ecological, environmental, socio-political, and socio-economic disciplines (Adger et al.  2005 ; Leal Filho et al.  2021 ; Feliciano et al.  2022 ). Climate change involves heightened temperatures across numerous worlds (Battisti and Naylor  2009 ; Schuurmans  2021 ; Weisheimer and Palmer  2005 ; Yadav et al.  2015 ). With the onset of the industrial revolution, the problem of earth climate was amplified manifold (Leppänen et al.  2014 ). It is reported that the immediate attention and due steps might increase the probability of overcoming its devastating impacts. It is not plausible to interpret the exact consequences of climate change (CC) on a sectoral basis (Izaguirre et al.  2021 ; Jurgilevich et al.  2017 ), which is evident by the emerging level of recognition plus the inclusion of climatic uncertainties at both local and national level of policymaking (Ayers et al.  2014 ).

Climate change is characterized based on the comprehensive long-haul temperature and precipitation trends and other components such as pressure and humidity level in the surrounding environment. Besides, the irregular weather patterns, retreating of global ice sheets, and the corresponding elevated sea level rise are among the most renowned international and domestic effects of climate change (Lipczynska-Kochany  2018 ; Michel et al.  2021 ; Murshed and Dao 2020 ). Before the industrial revolution, natural sources, including volcanoes, forest fires, and seismic activities, were regarded as the distinct sources of greenhouse gases (GHGs) such as CO 2 , CH 4 , N 2 O, and H 2 O into the atmosphere (Murshed et al. 2020 ; Hussain et al.  2020 ; Sovacool et al.  2021 ; Usman and Balsalobre-Lorente 2022 ; Murshed 2022 ). United Nations Framework Convention on Climate Change (UNFCCC) struck a major agreement to tackle climate change and accelerate and intensify the actions and investments required for a sustainable low-carbon future at Conference of the Parties (COP-21) in Paris on December 12, 2015. The Paris Agreement expands on the Convention by bringing all nations together for the first time in a single cause to undertake ambitious measures to prevent climate change and adapt to its impacts, with increased funding to assist developing countries in doing so. As so, it marks a turning point in the global climate fight. The core goal of the Paris Agreement is to improve the global response to the threat of climate change by keeping the global temperature rise this century well below 2 °C over pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5° C (Sharma et al. 2020 ; Sharif et al. 2020 ; Chien et al. 2021 .

Furthermore, the agreement aspires to strengthen nations’ ability to deal with the effects of climate change and align financing flows with low GHG emissions and climate-resilient paths (Shahbaz et al. 2019 ; Anwar et al. 2021 ; Usman et al. 2022a ). To achieve these lofty goals, adequate financial resources must be mobilized and provided, as well as a new technology framework and expanded capacity building, allowing developing countries and the most vulnerable countries to act under their respective national objectives. The agreement also establishes a more transparent action and support mechanism. All Parties are required by the Paris Agreement to do their best through “nationally determined contributions” (NDCs) and to strengthen these efforts in the coming years (Balsalobre-Lorente et al. 2020 ). It includes obligations that all Parties regularly report on their emissions and implementation activities. A global stock-take will be conducted every five years to review collective progress toward the agreement’s goal and inform the Parties’ future individual actions. The Paris Agreement became available for signature on April 22, 2016, Earth Day, at the United Nations Headquarters in New York. On November 4, 2016, it went into effect 30 days after the so-called double threshold was met (ratification by 55 nations accounting for at least 55% of world emissions). More countries have ratified and continue to ratify the agreement since then, bringing 125 Parties in early 2017. To fully operationalize the Paris Agreement, a work program was initiated in Paris to define mechanisms, processes, and recommendations on a wide range of concerns (Murshed et al. 2021 ). Since 2016, Parties have collaborated in subsidiary bodies (APA, SBSTA, and SBI) and numerous formed entities. The Conference of the Parties functioning as the meeting of the Parties to the Paris Agreement (CMA) convened for the first time in November 2016 in Marrakesh in conjunction with COP22 and made its first two resolutions. The work plan is scheduled to be finished by 2018. Some mitigation and adaptation strategies to reduce the emission in the prospective of Paris agreement are following firstly, a long-term goal of keeping the increase in global average temperature to well below 2 °C above pre-industrial levels, secondly, to aim to limit the rise to 1.5 °C, since this would significantly reduce risks and the impacts of climate change, thirdly, on the need for global emissions to peak as soon as possible, recognizing that this will take longer for developing countries, lastly, to undertake rapid reductions after that under the best available science, to achieve a balance between emissions and removals in the second half of the century. On the other side, some adaptation strategies are; strengthening societies’ ability to deal with the effects of climate change and to continue & expand international assistance for developing nations’ adaptation.

However, anthropogenic activities are currently regarded as most accountable for CC (Murshed et al. 2022 ). Apart from the industrial revolution, other anthropogenic activities include excessive agricultural operations, which further involve the high use of fuel-based mechanization, burning of agricultural residues, burning fossil fuels, deforestation, national and domestic transportation sectors, etc. (Huang et al.  2016 ). Consequently, these anthropogenic activities lead to climatic catastrophes, damaging local and global infrastructure, human health, and total productivity. Energy consumption has mounted GHGs levels concerning warming temperatures as most of the energy production in developing countries comes from fossil fuels (Balsalobre-Lorente et al. 2022 ; Usman et al. 2022b ; Abbass et al. 2021a ; Ishikawa-Ishiwata and Furuya  2022 ).

This review aims to highlight the effects of climate change in a socio-scientific aspect by analyzing the existing literature on various sectorial pieces of evidence globally that influence the environment. Although this review provides a thorough examination of climate change and its severe affected sectors that pose a grave danger for global agriculture, biodiversity, health, economy, forestry, and tourism, and to purpose some practical prophylactic measures and mitigation strategies to be adapted as sound substitutes to survive from climate change (CC) impacts. The societal implications of irregular weather patterns and other effects of climate changes are discussed in detail. Some numerous sustainable mitigation measures and adaptation practices and techniques at the global level are discussed in this review with an in-depth focus on its economic, social, and environmental aspects. Methods of data collection section are included in the supplementary information.

Review methodology

Related study and its objectives.

Today, we live an ordinary life in the beautiful digital, globalized world where climate change has a decisive role. What happens in one country has a massive influence on geographically far apart countries, which points to the current crisis known as COVID-19 (Sarkar et al.  2021 ). The most dangerous disease like COVID-19 has affected the world’s climate changes and economic conditions (Abbass et al. 2022 ; Pirasteh-Anosheh et al.  2021 ). The purpose of the present study is to review the status of research on the subject, which is based on “Global Climate Change Impacts, adaptation, and sustainable mitigation measures” by systematically reviewing past published and unpublished research work. Furthermore, the current study seeks to comment on research on the same topic and suggest future research on the same topic. Specifically, the present study aims: The first one is, organize publications to make them easy and quick to find. Secondly, to explore issues in this area, propose an outline of research for future work. The third aim of the study is to synthesize the previous literature on climate change, various sectors, and their mitigation measurement. Lastly , classify the articles according to the different methods and procedures that have been adopted.

Review methodology for reviewers

This review-based article followed systematic literature review techniques that have proved the literature review as a rigorous framework (Benita  2021 ; Tranfield et al.  2003 ). Moreover, we illustrate in Fig.  1 the search method that we have started for this research. First, finalized the research theme to search literature (Cooper et al.  2018 ). Second, used numerous research databases to search related articles and download from the database (Web of Science, Google Scholar, Scopus Index Journals, Emerald, Elsevier Science Direct, Springer, and Sciverse). We focused on various articles, with research articles, feedback pieces, short notes, debates, and review articles published in scholarly journals. Reports used to search for multiple keywords such as “Climate Change,” “Mitigation and Adaptation,” “Department of Agriculture and Human Health,” “Department of Biodiversity and Forestry,” etc.; in summary, keyword list and full text have been made. Initially, the search for keywords yielded a large amount of literature.

Methodology search for finalized articles for investigations.

Source : constructed by authors

Since 2020, it has been impossible to review all the articles found; some restrictions have been set for the literature exhibition. The study searched 95 articles on a different database mentioned above based on the nature of the study. It excluded 40 irrelevant papers due to copied from a previous search after readings tiles, abstract and full pieces. The criteria for inclusion were: (i) articles focused on “Global Climate Change Impacts, adaptation, and sustainable mitigation measures,” and (ii) the search key terms related to study requirements. The complete procedure yielded 55 articles for our study. We repeat our search on the “Web of Science and Google Scholars” database to enhance the search results and check the referenced articles.

In this study, 55 articles are reviewed systematically and analyzed for research topics and other aspects, such as the methods, contexts, and theories used in these studies. Furthermore, this study analyzes closely related areas to provide unique research opportunities in the future. The study also discussed future direction opportunities and research questions by understanding the research findings climate changes and other affected sectors. The reviewed paper framework analysis process is outlined in Fig.  2 .

Framework of the analysis Process.

Natural disasters and climate change’s socio-economic consequences

Natural and environmental disasters can be highly variable from year to year; some years pass with very few deaths before a significant disaster event claims many lives (Symanski et al.  2021 ). Approximately 60,000 people globally died from natural disasters each year on average over the past decade (Ritchie and Roser  2014 ; Wiranata and Simbolon  2021 ). So, according to the report, around 0.1% of global deaths. Annual variability in the number and share of deaths from natural disasters in recent decades are shown in Fig.  3 . The number of fatalities can be meager—sometimes less than 10,000, and as few as 0.01% of all deaths. But shock events have a devastating impact: the 1983–1985 famine and drought in Ethiopia; the 2004 Indian Ocean earthquake and tsunami; Cyclone Nargis, which struck Myanmar in 2008; and the 2010 Port-au-Prince earthquake in Haiti and now recent example is COVID-19 pandemic (Erman et al.  2021 ). These events pushed global disaster deaths to over 200,000—more than 0.4% of deaths in these years. Low-frequency, high-impact events such as earthquakes and tsunamis are not preventable, but such high losses of human life are. Historical evidence shows that earlier disaster detection, more robust infrastructure, emergency preparedness, and response programmers have substantially reduced disaster deaths worldwide. Low-income is also the most vulnerable to disasters; improving living conditions, facilities, and response services in these areas would be critical in reducing natural disaster deaths in the coming decades.

Global deaths from natural disasters, 1978 to 2020.

Source EMDAT ( 2020 )

The interior regions of the continent are likely to be impacted by rising temperatures (Dimri et al.  2018 ; Goes et al.  2020 ; Mannig et al.  2018 ; Schuurmans  2021 ). Weather patterns change due to the shortage of natural resources (water), increase in glacier melting, and rising mercury are likely to cause extinction to many planted species (Gampe et al.  2016 ; Mihiretu et al.  2021 ; Shaffril et al.  2018 ).On the other hand, the coastal ecosystem is on the verge of devastation (Perera et al.  2018 ; Phillips  2018 ). The temperature rises, insect disease outbreaks, health-related problems, and seasonal and lifestyle changes are persistent, with a strong probability of these patterns continuing in the future (Abbass et al. 2021c ; Hussain et al.  2018 ). At the global level, a shortage of good infrastructure and insufficient adaptive capacity are hammering the most (IPCC  2013 ). In addition to the above concerns, a lack of environmental education and knowledge, outdated consumer behavior, a scarcity of incentives, a lack of legislation, and the government’s lack of commitment to climate change contribute to the general public’s concerns. By 2050, a 2 to 3% rise in mercury and a drastic shift in rainfall patterns may have serious consequences (Huang et al. 2022 ; Gorst et al.  2018 ). Natural and environmental calamities caused huge losses globally, such as decreased agriculture outputs, rehabilitation of the system, and rebuilding necessary technologies (Ali and Erenstein  2017 ; Ramankutty et al.  2018 ; Yu et al.  2021 ) (Table ​ (Table1). 1 ). Furthermore, in the last 3 or 4 years, the world has been plagued by smog-related eye and skin diseases, as well as a rise in road accidents due to poor visibility.

Main natural danger statistics for 1985–2020 at the global level

Source: EM-DAT ( 2020 )

Climate change and agriculture

Global agriculture is the ultimate sector responsible for 30–40% of all greenhouse emissions, which makes it a leading industry predominantly contributing to climate warming and significantly impacted by it (Grieg; Mishra et al.  2021 ; Ortiz et al.  2021 ; Thornton and Lipper  2014 ). Numerous agro-environmental and climatic factors that have a dominant influence on agriculture productivity (Pautasso et al.  2012 ) are significantly impacted in response to precipitation extremes including floods, forest fires, and droughts (Huang  2004 ). Besides, the immense dependency on exhaustible resources also fuels the fire and leads global agriculture to become prone to devastation. Godfray et al. ( 2010 ) mentioned that decline in agriculture challenges the farmer’s quality of life and thus a significant factor to poverty as the food and water supplies are critically impacted by CC (Ortiz et al.  2021 ; Rosenzweig et al.  2014 ). As an essential part of the economic systems, especially in developing countries, agricultural systems affect the overall economy and potentially the well-being of households (Schlenker and Roberts  2009 ). According to the report published by the Intergovernmental Panel on Climate Change (IPCC), atmospheric concentrations of greenhouse gases, i.e., CH 4, CO 2 , and N 2 O, are increased in the air to extraordinary levels over the last few centuries (Usman and Makhdum 2021 ; Stocker et al.  2013 ). Climate change is the composite outcome of two different factors. The first is the natural causes, and the second is the anthropogenic actions (Karami 2012 ). It is also forecasted that the world may experience a typical rise in temperature stretching from 1 to 3.7 °C at the end of this century (Pachauri et al. 2014 ). The world’s crop production is also highly vulnerable to these global temperature-changing trends as raised temperatures will pose severe negative impacts on crop growth (Reidsma et al. 2009 ). Some of the recent modeling about the fate of global agriculture is briefly described below.

Decline in cereal productivity

Crop productivity will also be affected dramatically in the next few decades due to variations in integral abiotic factors such as temperature, solar radiation, precipitation, and CO 2 . These all factors are included in various regulatory instruments like progress and growth, weather-tempted changes, pest invasions (Cammell and Knight 1992 ), accompanying disease snags (Fand et al. 2012 ), water supplies (Panda et al. 2003 ), high prices of agro-products in world’s agriculture industry, and preeminent quantity of fertilizer consumption. Lobell and field ( 2007 ) claimed that from 1962 to 2002, wheat crop output had condensed significantly due to rising temperatures. Therefore, during 1980–2011, the common wheat productivity trends endorsed extreme temperature events confirmed by Gourdji et al. ( 2013 ) around South Asia, South America, and Central Asia. Various other studies (Asseng, Cao, Zhang, and Ludwig 2009 ; Asseng et al. 2013 ; García et al. 2015 ; Ortiz et al. 2021 ) also proved that wheat output is negatively affected by the rising temperatures and also caused adverse effects on biomass productivity (Calderini et al. 1999 ; Sadras and Slafer 2012 ). Hereafter, the rice crop is also influenced by the high temperatures at night. These difficulties will worsen because the temperature will be rising further in the future owing to CC (Tebaldi et al. 2006 ). Another research conducted in China revealed that a 4.6% of rice production per 1 °C has happened connected with the advancement in night temperatures (Tao et al. 2006 ). Moreover, the average night temperature growth also affected rice indicia cultivar’s output pragmatically during 25 years in the Philippines (Peng et al. 2004 ). It is anticipated that the increase in world average temperature will also cause a substantial reduction in yield (Hatfield et al. 2011 ; Lobell and Gourdji 2012 ). In the southern hemisphere, Parry et al. ( 2007 ) noted a rise of 1–4 °C in average daily temperatures at the end of spring season unti the middle of summers, and this raised temperature reduced crop output by cutting down the time length for phenophases eventually reduce the yield (Hatfield and Prueger 2015 ; R. Ortiz 2008 ). Also, world climate models have recommended that humid and subtropical regions expect to be plentiful prey to the upcoming heat strokes (Battisti and Naylor 2009 ). Grain production is the amalgamation of two constituents: the average weight and the grain output/m 2 , however, in crop production. Crop output is mainly accredited to the grain quantity (Araus et al. 2008 ; Gambín and Borrás 2010 ). In the times of grain set, yield resources are mainly strewn between hitherto defined components, i.e., grain usual weight and grain output, which presents a trade-off between them (Gambín and Borrás 2010 ) beside disparities in per grain integration (B. L. Gambín et al. 2006 ). In addition to this, the maize crop is also susceptible to raised temperatures, principally in the flowering stage (Edreira and Otegui 2013 ). In reality, the lower grain number is associated with insufficient acclimatization due to intense photosynthesis and higher respiration and the high-temperature effect on the reproduction phenomena (Edreira and Otegui 2013 ). During the flowering phase, maize visible to heat (30–36 °C) seemed less anthesis-silking intermissions (Edreira et al. 2011 ). Another research by Dupuis and Dumas ( 1990 ) proved that a drop in spikelet when directly visible to high temperatures above 35 °C in vitro pollination. Abnormalities in kernel number claimed by Vega et al. ( 2001 ) is related to conceded plant development during a flowering phase that is linked with the active ear growth phase and categorized as a critical phase for approximation of kernel number during silking (Otegui and Bonhomme 1998 ).

The retort of rice output to high temperature presents disparities in flowering patterns, and seed set lessens and lessens grain weight (Qasim et al. 2020 ; Qasim, Hammad, Maqsood, Tariq, & Chawla). During the daytime, heat directly impacts flowers which lessens the thesis period and quickens the earlier peak flowering (Tao et al. 2006 ). Antagonistic effect of higher daytime temperature d on pollen sprouting proposed seed set decay, whereas, seed set was lengthily reduced than could be explicated by pollen growing at high temperatures 40◦C (Matsui et al. 2001 ).

The decline in wheat output is linked with higher temperatures, confirmed in numerous studies (Semenov 2009 ; Stone and Nicolas 1994 ). High temperatures fast-track the arrangements of plant expansion (Blum et al. 2001 ), diminution photosynthetic process (Salvucci and Crafts‐Brandner 2004 ), and also considerably affect the reproductive operations (Farooq et al. 2011 ).

The destructive impacts of CC induced weather extremes to deteriorate the integrity of crops (Chaudhary et al. 2011 ), e.g., Spartan cold and extreme fog cause falling and discoloration of betel leaves (Rosenzweig et al. 2001 ), giving them a somehow reddish appearance, squeezing of lemon leaves (Pautasso et al. 2012 ), as well as root rot of pineapple, have reported (Vedwan and Rhoades 2001 ). Henceforth, in tackling the disruptive effects of CC, several short-term and long-term management approaches are the crucial need of time (Fig.  4 ). Moreover, various studies (Chaudhary et al. 2011 ; Patz et al. 2005 ; Pautasso et al. 2012 ) have demonstrated adapting trends such as ameliorating crop diversity can yield better adaptability towards CC.

Schematic description of potential impacts of climate change on the agriculture sector and the appropriate mitigation and adaptation measures to overcome its impact.

Climate change impacts on biodiversity

Global biodiversity is among the severe victims of CC because it is the fastest emerging cause of species loss. Studies demonstrated that the massive scale species dynamics are considerably associated with diverse climatic events (Abraham and Chain 1988 ; Manes et al. 2021 ; A. M. D. Ortiz et al. 2021 ). Both the pace and magnitude of CC are altering the compatible habitat ranges for living entities of marine, freshwater, and terrestrial regions. Alterations in general climate regimes influence the integrity of ecosystems in numerous ways, such as variation in the relative abundance of species, range shifts, changes in activity timing, and microhabitat use (Bates et al. 2014 ). The geographic distribution of any species often depends upon its ability to tolerate environmental stresses, biological interactions, and dispersal constraints. Hence, instead of the CC, the local species must only accept, adapt, move, or face extinction (Berg et al. 2010 ). So, the best performer species have a better survival capacity for adjusting to new ecosystems or a decreased perseverance to survive where they are already situated (Bates et al. 2014 ). An important aspect here is the inadequate habitat connectivity and access to microclimates, also crucial in raising the exposure to climate warming and extreme heatwave episodes. For example, the carbon sequestration rates are undergoing fluctuations due to climate-driven expansion in the range of global mangroves (Cavanaugh et al. 2014 ).

Similarly, the loss of kelp-forest ecosystems in various regions and its occupancy by the seaweed turfs has set the track for elevated herbivory by the high influx of tropical fish populations. Not only this, the increased water temperatures have exacerbated the conditions far away from the physiological tolerance level of the kelp communities (Vergés et al. 2016 ; Wernberg et al. 2016 ). Another pertinent danger is the devastation of keystone species, which even has more pervasive effects on the entire communities in that habitat (Zarnetske et al. 2012 ). It is particularly important as CC does not specify specific populations or communities. Eventually, this CC-induced redistribution of species may deteriorate carbon storage and the net ecosystem productivity (Weed et al. 2013 ). Among the typical disruptions, the prominent ones include impacts on marine and terrestrial productivity, marine community assembly, and the extended invasion of toxic cyanobacteria bloom (Fossheim et al. 2015 ).

The CC-impacted species extinction is widely reported in the literature (Beesley et al. 2019 ; Urban 2015 ), and the predictions of demise until the twenty-first century are dreadful (Abbass et al. 2019 ; Pereira et al. 2013 ). In a few cases, northward shifting of species may not be formidable as it allows mountain-dwelling species to find optimum climates. However, the migrant species may be trapped in isolated and incompatible habitats due to losing topography and range (Dullinger et al. 2012 ). For example, a study indicated that the American pika has been extirpated or intensely diminished in some regions, primarily attributed to the CC-impacted extinction or at least local extirpation (Stewart et al. 2015 ). Besides, the anticipation of persistent responses to the impacts of CC often requires data records of several decades to rigorously analyze the critical pre and post CC patterns at species and ecosystem levels (Manes et al. 2021 ; Testa et al. 2018 ).

Nonetheless, the availability of such long-term data records is rare; hence, attempts are needed to focus on these profound aspects. Biodiversity is also vulnerable to the other associated impacts of CC, such as rising temperatures, droughts, and certain invasive pest species. For instance, a study revealed the changes in the composition of plankton communities attributed to rising temperatures. Henceforth, alterations in such aquatic producer communities, i.e., diatoms and calcareous plants, can ultimately lead to variation in the recycling of biological carbon. Moreover, such changes are characterized as a potential contributor to CO 2 differences between the Pleistocene glacial and interglacial periods (Kohfeld et al. 2005 ).

Climate change implications on human health

It is an understood corporality that human health is a significant victim of CC (Costello et al. 2009 ). According to the WHO, CC might be responsible for 250,000 additional deaths per year during 2030–2050 (Watts et al. 2015 ). These deaths are attributed to extreme weather-induced mortality and morbidity and the global expansion of vector-borne diseases (Lemery et al. 2021; Yang and Usman 2021 ; Meierrieks 2021 ; UNEP 2017 ). Here, some of the emerging health issues pertinent to this global problem are briefly described.

Climate change and antimicrobial resistance with corresponding economic costs

Antimicrobial resistance (AMR) is an up-surging complex global health challenge (Garner et al. 2019 ; Lemery et al. 2021 ). Health professionals across the globe are extremely worried due to this phenomenon that has critical potential to reverse almost all the progress that has been achieved so far in the health discipline (Gosling and Arnell 2016 ). A massive amount of antibiotics is produced by many pharmaceutical industries worldwide, and the pathogenic microorganisms are gradually developing resistance to them, which can be comprehended how strongly this aspect can shake the foundations of national and global economies (UNEP 2017 ). This statement is supported by the fact that AMR is not developing in a particular region or country. Instead, it is flourishing in every continent of the world (WHO 2018 ). This plague is heavily pushing humanity to the post-antibiotic era, in which currently antibiotic-susceptible pathogens will once again lead to certain endemics and pandemics after being resistant(WHO 2018 ). Undesirably, if this statement would become a factuality, there might emerge certain risks in undertaking sophisticated interventions such as chemotherapy, joint replacement cases, and organ transplantation (Su et al. 2018 ). Presently, the amplification of drug resistance cases has made common illnesses like pneumonia, post-surgical infections, HIV/AIDS, tuberculosis, malaria, etc., too difficult and costly to be treated or cure well (WHO 2018 ). From a simple example, it can be assumed how easily antibiotic-resistant strains can be transmitted from one person to another and ultimately travel across the boundaries (Berendonk et al. 2015 ). Talking about the second- and third-generation classes of antibiotics, e.g., most renowned generations of cephalosporin antibiotics that are more expensive, broad-spectrum, more toxic, and usually require more extended periods whenever prescribed to patients (Lemery et al. 2021 ; Pärnänen et al. 2019 ). This scenario has also revealed that the abundance of resistant strains of pathogens was also higher in the Southern part (WHO 2018 ). As southern parts are generally warmer than their counterparts, it is evident from this example how CC-induced global warming can augment the spread of antibiotic-resistant strains within the biosphere, eventually putting additional economic burden in the face of developing new and costlier antibiotics. The ARG exchange to susceptible bacteria through one of the potential mechanisms, transformation, transduction, and conjugation; Selection pressure can be caused by certain antibiotics, metals or pesticides, etc., as shown in Fig.  5 .

A typical interaction between the susceptible and resistant strains.

Source: Elsayed et al. ( 2021 ); Karkman et al. ( 2018 )

Certain studies highlighted that conventional urban wastewater treatment plants are typical hotspots where most bacterial strains exchange genetic material through horizontal gene transfer (Fig.  5 ). Although at present, the extent of risks associated with the antibiotic resistance found in wastewater is complicated; environmental scientists and engineers have particular concerns about the potential impacts of these antibiotic resistance genes on human health (Ashbolt 2015 ). At most undesirable and worst case, these antibiotic-resistant genes containing bacteria can make their way to enter into the environment (Pruden et al. 2013 ), irrigation water used for crops and public water supplies and ultimately become a part of food chains and food webs (Ma et al. 2019 ; D. Wu et al. 2019 ). This problem has been reported manifold in several countries (Hendriksen et al. 2019 ), where wastewater as a means of irrigated water is quite common.

Climate change and vector borne-diseases

Temperature is a fundamental factor for the sustenance of living entities regardless of an ecosystem. So, a specific living being, especially a pathogen, requires a sophisticated temperature range to exist on earth. The second essential component of CC is precipitation, which also impacts numerous infectious agents’ transport and dissemination patterns. Global rising temperature is a significant cause of many species extinction. On the one hand, this changing environmental temperature may be causing species extinction, and on the other, this warming temperature might favor the thriving of some new organisms. Here, it was evident that some pathogens may also upraise once non-evident or reported (Patz et al. 2000 ). This concept can be exemplified through certain pathogenic strains of microorganisms that how the likelihood of various diseases increases in response to climate warming-induced environmental changes (Table ​ (Table2 2 ).

Examples of how various environmental changes affect various infectious diseases in humans

Source: Aron and Patz ( 2001 )

A recent example is an outburst of coronavirus (COVID-19) in the Republic of China, causing pneumonia and severe acute respiratory complications (Cui et al. 2021 ; Song et al. 2021 ). The large family of viruses is harbored in numerous animals, bats, and snakes in particular (livescience.com) with the subsequent transfer into human beings. Hence, it is worth noting that the thriving of numerous vectors involved in spreading various diseases is influenced by Climate change (Ogden 2018 ; Santos et al. 2021 ).

Psychological impacts of climate change

Climate change (CC) is responsible for the rapid dissemination and exaggeration of certain epidemics and pandemics. In addition to the vast apparent impacts of climate change on health, forestry, agriculture, etc., it may also have psychological implications on vulnerable societies. It can be exemplified through the recent outburst of (COVID-19) in various countries around the world (Pal 2021 ). Besides, the victims of this viral infection have made healthy beings scarier and terrified. In the wake of such epidemics, people with common colds or fever are also frightened and must pass specific regulatory protocols. Living in such situations continuously terrifies the public and makes the stress familiar, which eventually makes them psychologically weak (npr.org).

CC boosts the extent of anxiety, distress, and other issues in public, pushing them to develop various mental-related problems. Besides, frequent exposure to extreme climatic catastrophes such as geological disasters also imprints post-traumatic disorder, and their ubiquitous occurrence paves the way to developing chronic psychological dysfunction. Moreover, repetitive listening from media also causes an increase in the person’s stress level (Association 2020 ). Similarly, communities living in flood-prone areas constantly live in extreme fear of drowning and die by floods. In addition to human lives, the flood-induced destruction of physical infrastructure is a specific reason for putting pressure on these communities (Ogden 2018 ). For instance, Ogden ( 2018 ) comprehensively denoted that Katrina’s Hurricane augmented the mental health issues in the victim communities.

Climate change impacts on the forestry sector

Forests are the global regulators of the world’s climate (FAO 2018 ) and have an indispensable role in regulating global carbon and nitrogen cycles (Rehman et al. 2021 ; Reichstein and Carvalhais 2019 ). Hence, disturbances in forest ecology affect the micro and macro-climates (Ellison et al. 2017 ). Climate warming, in return, has profound impacts on the growth and productivity of transboundary forests by influencing the temperature and precipitation patterns, etc. As CC induces specific changes in the typical structure and functions of ecosystems (Zhang et al. 2017 ) as well impacts forest health, climate change also has several devastating consequences such as forest fires, droughts, pest outbreaks (EPA 2018 ), and last but not the least is the livelihoods of forest-dependent communities. The rising frequency and intensity of another CC product, i.e., droughts, pose plenty of challenges to the well-being of global forests (Diffenbaugh et al. 2017 ), which is further projected to increase soon (Hartmann et al. 2018 ; Lehner et al. 2017 ; Rehman et al. 2021 ). Hence, CC induces storms, with more significant impacts also put extra pressure on the survival of the global forests (Martínez-Alvarado et al. 2018 ), significantly since their influences are augmented during higher winter precipitations with corresponding wetter soils causing weak root anchorage of trees (Brázdil et al. 2018 ). Surging temperature regimes causes alterations in usual precipitation patterns, which is a significant hurdle for the survival of temperate forests (Allen et al. 2010 ; Flannigan et al. 2013 ), letting them encounter severe stress and disturbances which adversely affects the local tree species (Hubbart et al. 2016 ; Millar and Stephenson 2015 ; Rehman et al. 2021 ).

Climate change impacts on forest-dependent communities

Forests are the fundamental livelihood resource for about 1.6 billion people worldwide; out of them, 350 million are distinguished with relatively higher reliance (Bank 2008 ). Agro-forestry-dependent communities comprise 1.2 billion, and 60 million indigenous people solely rely on forests and their products to sustain their lives (Sunderlin et al. 2005 ). For example, in the entire African continent, more than 2/3rd of inhabitants depend on forest resources and woodlands for their alimonies, e.g., food, fuelwood and grazing (Wasiq and Ahmad 2004 ). The livings of these people are more intensely affected by the climatic disruptions making their lives harder (Brown et al. 2014 ). On the one hand, forest communities are incredibly vulnerable to CC due to their livelihoods, cultural and spiritual ties as well as socio-ecological connections, and on the other, they are not familiar with the term “climate change.” (Rahman and Alam 2016 ). Among the destructive impacts of temperature and rainfall, disruption of the agroforestry crops with resultant downscale growth and yield (Macchi et al. 2008 ). Cruz ( 2015 ) ascribed that forest-dependent smallholder farmers in the Philippines face the enigma of delayed fruiting, more severe damages by insect and pest incidences due to unfavorable temperature regimes, and changed rainfall patterns.

Among these series of challenges to forest communities, their well-being is also distinctly vulnerable to CC. Though the detailed climate change impacts on human health have been comprehensively mentioned in the previous section, some studies have listed a few more devastating effects on the prosperity of forest-dependent communities. For instance, the Himalayan people have been experiencing frequent skin-borne diseases such as malaria and other skin diseases due to increasing mosquitoes, wild boar as well, and new wasps species, particularly in higher altitudes that were almost non-existent before last 5–10 years (Xu et al. 2008 ). Similarly, people living at high altitudes in Bangladesh have experienced frequent mosquito-borne calamities (Fardous; Sharma 2012 ). In addition, the pace of other waterborne diseases such as infectious diarrhea, cholera, pathogenic induced abdominal complications and dengue has also been boosted in other distinguished regions of Bangladesh (Cell 2009 ; Gunter et al. 2008 ).

Pest outbreak

Upscaling hotter climate may positively affect the mobile organisms with shorter generation times because they can scurry from harsh conditions than the immobile species (Fettig et al. 2013 ; Schoene and Bernier 2012 ) and are also relatively more capable of adapting to new environments (Jactel et al. 2019 ). It reveals that insects adapt quickly to global warming due to their mobility advantages. Due to past outbreaks, the trees (forests) are relatively more susceptible victims (Kurz et al. 2008 ). Before CC, the influence of factors mentioned earlier, i.e., droughts and storms, was existent and made the forests susceptible to insect pest interventions; however, the global forests remain steadfast, assiduous, and green (Jactel et al. 2019 ). The typical reasons could be the insect herbivores were regulated by several tree defenses and pressures of predation (Wilkinson and Sherratt 2016 ). As climate greatly influences these phenomena, the global forests cannot be so sedulous against such challenges (Jactel et al. 2019 ). Table ​ Table3 3 demonstrates some of the particular considerations with practical examples that are essential while mitigating the impacts of CC in the forestry sector.

Essential considerations while mitigating the climate change impacts on the forestry sector

Source : Fischer ( 2019 )

Climate change impacts on tourism

Tourism is a commercial activity that has roots in multi-dimensions and an efficient tool with adequate job generation potential, revenue creation, earning of spectacular foreign exchange, enhancement in cross-cultural promulgation and cooperation, a business tool for entrepreneurs and eventually for the country’s national development (Arshad et al. 2018 ; Scott 2021 ). Among a plethora of other disciplines, the tourism industry is also a distinct victim of climate warming (Gössling et al. 2012 ; Hall et al. 2015 ) as the climate is among the essential resources that enable tourism in particular regions as most preferred locations. Different places at different times of the year attract tourists both within and across the countries depending upon the feasibility and compatibility of particular weather patterns. Hence, the massive variations in these weather patterns resulting from CC will eventually lead to monumental challenges to the local economy in that specific area’s particular and national economy (Bujosa et al. 2015 ). For instance, the Intergovernmental Panel on Climate Change (IPCC) report demonstrated that the global tourism industry had faced a considerable decline in the duration of ski season, including the loss of some ski areas and the dramatic shifts in tourist destinations’ climate warming.

Furthermore, different studies (Neuvonen et al. 2015 ; Scott et al. 2004 ) indicated that various currently perfect tourist spots, e.g., coastal areas, splendid islands, and ski resorts, will suffer consequences of CC. It is also worth noting that the quality and potential of administrative management potential to cope with the influence of CC on the tourism industry is of crucial significance, which renders specific strengths of resiliency to numerous destinations to withstand against it (Füssel and Hildén 2014 ). Similarly, in the partial or complete absence of adequate socio-economic and socio-political capital, the high-demanding tourist sites scurry towards the verge of vulnerability. The susceptibility of tourism is based on different components such as the extent of exposure, sensitivity, life-supporting sectors, and capacity assessment factors (Füssel and Hildén 2014 ). It is obvious corporality that sectors such as health, food, ecosystems, human habitat, infrastructure, water availability, and the accessibility of a particular region are prone to CC. Henceforth, the sensitivity of these critical sectors to CC and, in return, the adaptive measures are a hallmark in determining the composite vulnerability of climate warming (Ionescu et al. 2009 ).

Moreover, the dependence on imported food items, poor hygienic conditions, and inadequate health professionals are dominant aspects affecting the local terrestrial and aquatic biodiversity. Meanwhile, the greater dependency on ecosystem services and its products also makes a destination more fragile to become a prey of CC (Rizvi et al. 2015 ). Some significant non-climatic factors are important indicators of a particular ecosystem’s typical health and functioning, e.g., resource richness and abundance portray the picture of ecosystem stability. Similarly, the species abundance is also a productive tool that ensures that the ecosystem has a higher buffering capacity, which is terrific in terms of resiliency (Roscher et al. 2013 ).

Climate change impacts on the economic sector

Climate plays a significant role in overall productivity and economic growth. Due to its increasingly global existence and its effect on economic growth, CC has become one of the major concerns of both local and international environmental policymakers (Ferreira et al. 2020 ; Gleditsch 2021 ; Abbass et al. 2021b ; Lamperti et al. 2021 ). The adverse effects of CC on the overall productivity factor of the agricultural sector are therefore significant for understanding the creation of local adaptation policies and the composition of productive climate policy contracts. Previous studies on CC in the world have already forecasted its effects on the agricultural sector. Researchers have found that global CC will impact the agricultural sector in different world regions. The study of the impacts of CC on various agrarian activities in other demographic areas and the development of relative strategies to respond to effects has become a focal point for researchers (Chandioet al. 2020 ; Gleditsch 2021 ; Mosavi et al. 2020 ).

With the rapid growth of global warming since the 1980s, the temperature has started increasing globally, which resulted in the incredible transformation of rain and evaporation in the countries. The agricultural development of many countries has been reliant, delicate, and susceptible to CC for a long time, and it is on the development of agriculture total factor productivity (ATFP) influence different crops and yields of farmers (Alhassan 2021 ; Wu  2020 ).

Food security and natural disasters are increasing rapidly in the world. Several major climatic/natural disasters have impacted local crop production in the countries concerned. The effects of these natural disasters have been poorly controlled by the development of the economies and populations and may affect human life as well. One example is China, which is among the world’s most affected countries, vulnerable to natural disasters due to its large population, harsh environmental conditions, rapid CC, low environmental stability, and disaster power. According to the January 2016 statistical survey, China experienced an economic loss of 298.3 billion Yuan, and about 137 million Chinese people were severely affected by various natural disasters (Xie et al. 2018 ).

Mitigation and adaptation strategies of climate changes

Adaptation and mitigation are the crucial factors to address the response to CC (Jahanzad et al. 2020 ). Researchers define mitigation on climate changes, and on the other hand, adaptation directly impacts climate changes like floods. To some extent, mitigation reduces or moderates greenhouse gas emission, and it becomes a critical issue both economically and environmentally (Botzen et al. 2021 ; Jahanzad et al. 2020 ; Kongsager 2018 ; Smit et al. 2000 ; Vale et al. 2021 ; Usman et al. 2021 ; Verheyen 2005 ).

Researchers have deep concern about the adaptation and mitigation methodologies in sectoral and geographical contexts. Agriculture, industry, forestry, transport, and land use are the main sectors to adapt and mitigate policies(Kärkkäinen et al. 2020 ; Waheed et al. 2021 ). Adaptation and mitigation require particular concern both at the national and international levels. The world has faced a significant problem of climate change in the last decades, and adaptation to these effects is compulsory for economic and social development. To adapt and mitigate against CC, one should develop policies and strategies at the international level (Hussain et al. 2020 ). Figure  6 depicts the list of current studies on sectoral impacts of CC with adaptation and mitigation measures globally.

Sectoral impacts of climate change with adaptation and mitigation measures.

Conclusion and future perspectives

Specific socio-agricultural, socio-economic, and physical systems are the cornerstone of psychological well-being, and the alteration in these systems by CC will have disastrous impacts. Climate variability, alongside other anthropogenic and natural stressors, influences human and environmental health sustainability. Food security is another concerning scenario that may lead to compromised food quality, higher food prices, and inadequate food distribution systems. Global forests are challenged by different climatic factors such as storms, droughts, flash floods, and intense precipitation. On the other hand, their anthropogenic wiping is aggrandizing their existence. Undoubtedly, the vulnerability scale of the world’s regions differs; however, appropriate mitigation and adaptation measures can aid the decision-making bodies in developing effective policies to tackle its impacts. Presently, modern life on earth has tailored to consistent climatic patterns, and accordingly, adapting to such considerable variations is of paramount importance. Because the faster changes in climate will make it harder to survive and adjust, this globally-raising enigma calls for immediate attention at every scale ranging from elementary community level to international level. Still, much effort, research, and dedication are required, which is the most critical time. Some policy implications can help us to mitigate the consequences of climate change, especially the most affected sectors like the agriculture sector;

Warming might lengthen the season in frost-prone growing regions (temperate and arctic zones), allowing for longer-maturing seasonal cultivars with better yields (Pfadenhauer 2020 ; Bonacci 2019 ). Extending the planting season may allow additional crops each year; when warming leads to frequent warmer months highs over critical thresholds, a split season with a brief summer fallow may be conceivable for short-period crops such as wheat barley, cereals, and many other vegetable crops. The capacity to prolong the planting season in tropical and subtropical places where the harvest season is constrained by precipitation or agriculture farming occurs after the year may be more limited and dependent on how precipitation patterns vary (Wu et al. 2017 ).

The genetic component is comprehensive for many yields, but it is restricted like kiwi fruit for a few. Ali et al. ( 2017 ) investigated how new crops will react to climatic changes (also stated in Mall et al. 2017 ). Hot temperature, drought, insect resistance; salt tolerance; and overall crop production and product quality increases would all be advantageous (Akkari 2016 ). Genetic mapping and engineering can introduce a greater spectrum of features. The adoption of genetically altered cultivars has been slowed, particularly in the early forecasts owing to the complexity in ensuring features are expediently expressed throughout the entire plant, customer concerns, economic profitability, and regulatory impediments (Wirehn 2018 ; Davidson et al. 2016 ).

To get the full benefit of the CO 2 would certainly require additional nitrogen and other fertilizers. Nitrogen not consumed by the plants may be excreted into groundwater, discharged into water surface, or emitted from the land, soil nitrous oxide when large doses of fertilizer are sprayed. Increased nitrogen levels in groundwater sources have been related to human chronic illnesses and impact marine ecosystems. Cultivation, grain drying, and other field activities have all been examined in depth in the studies (Barua et al. 2018 ).

• The technological and socio-economic adaptation

The policy consequence of the causative conclusion is that as a source of alternative energy, biofuel production is one of the routes that explain oil price volatility separate from international macroeconomic factors. Even though biofuel production has just begun in a few sample nations, there is still a tremendous worldwide need for feedstock to satisfy industrial expansion in China and the USA, which explains the food price relationship to the global oil price. Essentially, oil-exporting countries may create incentives in their economies to increase food production. It may accomplish by giving farmers financing, seedlings, fertilizers, and farming equipment. Because of the declining global oil price and, as a result, their earnings from oil export, oil-producing nations may be unable to subsidize food imports even in the near term. As a result, these countries can boost the agricultural value chain for export. It may be accomplished through R&D and adding value to their food products to increase income by correcting exchange rate misalignment and adverse trade terms. These nations may also diversify their economies away from oil, as dependence on oil exports alone is no longer economically viable given the extreme volatility of global oil prices. Finally, resource-rich and oil-exporting countries can convert to non-food renewable energy sources such as solar, hydro, coal, wind, wave, and tidal energy. By doing so, both world food and oil supplies would be maintained rather than harmed.

IRENA’s modeling work shows that, if a comprehensive policy framework is in place, efforts toward decarbonizing the energy future will benefit economic activity, jobs (outweighing losses in the fossil fuel industry), and welfare. Countries with weak domestic supply chains and a large reliance on fossil fuel income, in particular, must undertake structural reforms to capitalize on the opportunities inherent in the energy transition. Governments continue to give major policy assistance to extract fossil fuels, including tax incentives, financing, direct infrastructure expenditures, exemptions from environmental regulations, and other measures. The majority of major oil and gas producing countries intend to increase output. Some countries intend to cut coal output, while others plan to maintain or expand it. While some nations are beginning to explore and execute policies aimed at a just and equitable transition away from fossil fuel production, these efforts have yet to impact major producing countries’ plans and goals. Verifiable and comparable data on fossil fuel output and assistance from governments and industries are critical to closing the production gap. Governments could increase openness by declaring their production intentions in their climate obligations under the Paris Agreement.

It is firmly believed that achieving the Paris Agreement commitments is doubtlful without undergoing renewable energy transition across the globe (Murshed 2020 ; Zhao et al. 2022 ). Policy instruments play the most important role in determining the degree of investment in renewable energy technology. This study examines the efficacy of various policy strategies in the renewable energy industry of multiple nations. Although its impact is more visible in established renewable energy markets, a renewable portfolio standard is also a useful policy instrument. The cost of producing renewable energy is still greater than other traditional energy sources. Furthermore, government incentives in the R&D sector can foster innovation in this field, resulting in cost reductions in the renewable energy industry. These nations may export their technologies and share their policy experiences by forming networks among their renewable energy-focused organizations. All policy measures aim to reduce production costs while increasing the proportion of renewables to a country’s energy system. Meanwhile, long-term contracts with renewable energy providers, government commitment and control, and the establishment of long-term goals can assist developing nations in deploying renewable energy technology in their energy sector.

Author contribution

KA: Writing the original manuscript, data collection, data analysis, Study design, Formal analysis, Visualization, Revised draft, Writing-review, and editing. MZQ: Writing the original manuscript, data collection, data analysis, Writing-review, and editing. HS: Contribution to the contextualization of the theme, Conceptualization, Validation, Supervision, literature review, Revised drapt, and writing review and editing. MM: Writing review and editing, compiling the literature review, language editing. HM: Writing review and editing, compiling the literature review, language editing. IY: Contribution to the contextualization of the theme, literature review, and writing review and editing.

Availability of data and material

Declarations.

Not applicable.

The authors declare no competing interests.

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Contributor Information

Kashif Abbass, Email: nc.ude.tsujn@ssabbafihsak .

Muhammad Zeeshan Qasim, Email: moc.kooltuo@888misaqnahseez .

Huaming Song, Email: nc.ude.tsujn@gnimauh .

Muntasir Murshed, Email: [email protected] .

Haider Mahmood, Email: moc.liamtoh@doomhamrediah .

Ijaz Younis, Email: nc.ude.tsujn@sinuoyzaji .

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Good and bad effects of climate change on agricultural production

Natashia qwabe, a research assistant at the agricultural research council, writes about the shifting patterns of agricultural production due to climate change..

Climate change is a global issue that is affecting the way people live and interact with the environment.

Among its numerous effects, climate change has a significant impact on agricultural production systems, leading to patterns that vary, with positive as well as negative effects.

READ Empowering communities to cope with climate change

In order to ensure food security, economic stability, and environmental sustainability, agricultural practices need to adapt as the Earth’s climate continues to change, resulting in changes to temperature and precipitation patterns.

Farmers are finding it challenging to maintain consistent and predictable crop yields as a result of the escalating effects of climate change on traditional agricultural landscapes.

Global agricultural systems are being affected by factors such as rising temperatures, shifting rainfall patterns and a rise in the frequency of extreme weather events including heatwaves, floods and droughts.

In order to maintain production and profitability, these shifts are requiring farmers to reconsider existing methods, adopt new technologies and investigate other strategies.

Modifications in agricultural production patterns may also present chances for creativity and adaptability.

Utilising drought-tolerant crop varieties, precision irrigation and improved soil management techniques are just a few examples of the climate-smart agricultural practices that farmers can use to adapt.

By improving resource efficiency, lowering greenhouse gas emissions and conserving water, these modifications can support agricultural sustainability system.

Shifting patterns in the maize belt

In South Africa, maize is an important staple crop, and the success of the maize belt greatly influences agricultural output.

The Free State, Mpumalanga and North West are home to most of the maize belt. Significant variations in yield have resulted from the impact of climate change on maize production in this region during the past few decades.

Extended frost-free periods, brought on by changing patterns of precipitation and higher temperatures, have enabled farmers to plant maize earlier in the growing season and prolong the harvesting period.

As a result of a longer growth period and more favourable conditions for maize farming, certain areas have experienced increased yields.

The maize belt region is experiencing severe and more frequent droughts due to climate change. Crops are under water stress due to irregular rainfall patterns and extended dry periods, particularly during crucial growth stages.

Heat waves and droughts can have an adverse effect on maize plants, lowering yields and reducing the harvest overall quality.

Prolonged dry periods can have detrimental consequences and small-scale farmers are especially vulnerable since they generally lack access to resources including irrigation.

An additional negative impact of climate change on the maize belt is the rising incidence of extreme weather events.

READ Climate-proof, no-till crop production in the maize triangle

Severe storms, tremendous amounts of rainfall and sudden flooding can destroy crops, wash away topsoil and create erosion, all of which could decrease yields and increase production costs.

Such occurrences can also interfere with planting and harvesting schedules, which makes it difficult for farmers to effectively plan and manage their crops.

South African farmers are implementing a variety of adaptation strategies to deal with the changing environment and minimise the negative impact on maize production.

Investing in better irrigation systems to replace the water supply during dry periods and drought periods is one crucial strategy.

Additionally, researchers and agricultural experts are developing and promoting drought-resistant maize varieties that can better withstand water scarcity and heat stress.

Water resources for industrial use, agricultural irrigation and water supply are under pressure due to decreased rainfall, higher evaporation rates and changing hydrological patterns.

The shortage has an impact on water-dependent enterprises, rural livelihoods, and agricultural output.

Variations in temperature and precipitation patterns influence agricultural pest and disease dynamics.

A farm’s profitability can be impacted and sustainable pest management is difficult due to increased pest pressure and changed disease patterns that lower crop yields and increase the need for pesticides.

Furthermore, by limiting the supply of staple crops and increasing food prices, climate change could threaten food security if it leads to reduced agricultural production.

It is crucial to emphasise that the possible advantages are greatly outweighed by the overall negative implications.

On the other hand, some farmers may discover new ways to adjust to the changing environment, like moving to crop varieties that are more tolerant to heat or drought, shifting planting and harvesting periods and putting water-saving irrigation techniques into practice.

READ Cost-effective irrigation

New cultivars for prolonged or out-of-season cultivation assist with all-year-round sales and income flow. This is due to technological advancements.

Certain areas that were previously too cold for some crops may become more suitable for agriculture as a result of warmer temperatures.

This could enable growers to cultivate new varieties of crops that were historically inappropriate and diversify their crop selection.

Good infrastructure construction can allow for the storage of some water during heavy rains for later use.

South Africa could uncover new markets for its agricultural products, such as new-season maize cultivars to adjust to the new environment and take advantage of the opportunities brought about by shifting production patterns.

By ensuring an adequate and varied food supply, effective climate change adaptation and the development of new crops can improve food security.

Sustainable methods such as improving soil health, effective water use, promoting agroforestry and developing varieties of crops that are resilient to shifting weather patterns should be the focus of efforts to mitigate the effects of climate change and adapt to it.

This strategy could maximise long-term sustainability and productivity while reducing any potential negative effects of climate change.

Investing in the development of new technologies and water-efficient systems to mitigate the effects of drought also help minimise carbon emissions, which cause climate change and environmental destruction.

Also, promote curriculum reform and prioritise native and/or underutilised crops that are adapted to the local region since they can endure adverse environmental circumstances.

Email Natashia Qwabe at [email protected] .

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The impact of climate change on agriculture

Editorial essay

• Published: May 1996
• Volume 33 , pages 1–6, ( 1996 )

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• Susan Helms 1 ,
• Robert Mendelsohn 2 &
• Jim Neumann 3  

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Studies that include moderate climate forecasts, farmer adaptation, carbon fertilization, and warm-loving crops tend to show that climate change will have only mild impacts on average global agricultural output and may even improve temperate agricultural production. On this point, recent studies yield strikingly consistent results. Of course, impact estimates still contain uncertainties. Key questions include how agriculture might change by 2060, how tropical and subtropical farming will be affected, and how effects will be distributed regionally. The most likely threat to agriculture from climate warming is regional damages in relatively poor areas that lack either the knowledge or the financial resources to adjust. Although it is not clear which regions will actually suffer, the ones that are most vulnerable lie predominantly in or near the tropics (IPCC, 1995). Nonetheless, on average, the factors discussed in this essay will help mitigate the impact of climate change on agriculture.

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Susan Helms

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The authors are grateful to the Electric Power Research Institute (EPRI) for financial support. We also wish to thank Richard Adams, Cynthia Rosenzweig, Kathleen Segerson, Joel Smith, Robert Unsworth and Thomas Wilson for their helpful comments. The authors alone are responsible for any remaining errors or omissions.

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Helms, S., Mendelsohn, R. & Neumann, J. The impact of climate change on agriculture. Climatic Change 33 , 1–6 (1996). https://doi.org/10.1007/BF00140510

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Received : 22 November 1994

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Issue Date : May 1996

DOI : https://doi.org/10.1007/BF00140510

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• Published: 16 February 2022

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

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

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

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

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

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

CROPGRIDS: a global geo-referenced dataset of 173 crops

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

Background & summary.

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

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

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

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

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

Data collection

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

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

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

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

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

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

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

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

Relative yield impacts

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

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

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

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

Adaptation to climate changes

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

Temperature and precipitation changes

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

Current temperature and precipitation levels

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

CO 2 concentrations

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

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

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

Baseline correction

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

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

Data Records

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

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

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

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

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

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

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

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

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

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

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

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

Coverage of the data

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

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

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

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

Technical Validation

Data quality check.

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

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

Methods to estimate local temperature and precipitation changes

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

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

Comparison with previous studies

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

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

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

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

Usage Notes

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

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

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

Code availability

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

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Acknowledgements

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

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Institute for Agro-Environmental Sciences, National Agricultural and Food Research Organization, Tsukuba, Ibaraki, 305-8604, Japan

Toshihiro Hasegawa & Hitomi Wakatsuki

Institute of Environment and sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences (IEDA,CAAS), Beijing, 100081, China

Alliance of Bioversity International and International Center for Tropical Agriculture (CIAT), Nairobi, Kenya

Shalika Vyas

University of Illinois, Urbana, IL, USA

Gerald C. Nelson

The University of the West Indies, St. Augustine, Trinidad

Aidan Farrell

IRI THESys, Humboldt-Universität zu Berlin, Berlin, 10099, Germany

Delphine Deryng

Pontificia Universidad Católica de Chile, Santiago, Chile

Francisco Meza

Applied mathematics and computer science (MIA 518), INRAE AgroParisTech, Université Paris-Saclay, 75231, Paris, France

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Contributions

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

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Correspondence to Toshihiro Hasegawa .

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

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DOI : https://doi.org/10.1038/s41597-022-01150-7

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The impacts of climate change on food production

New study explores long-term impacts of climate change on plant pollinators.

A new peer-reviewed study from researchers at The University of Texas at Arlington; the University of Nevada, Reno; and Virginia Tech shows that climate change has led to decreased pollen production from plants and less pollen more diversity than previously thought, which could have a significant impact on food production.

"This research is crucial as it examines the long-term impacts of climate change on plant-pollinator interactions," said Behnaz Balmaki, lead author of the study and an assistant professor of research in biology at UTA. "This study investigates how shifts in flowering times and extreme weather events affect the availability of critical food sources for insect pollinators."

The research team, which included UTA's Masoud A. Rostami, focused its study on the Great Basin and Sierra Nevada mountains. The Great Basin, which includes about 95% of Nevada as well as portions of California, Oregon, Idaho and Wyoming, is an ideal place for this type of research because the mountains shield the region from Pacific storms, rain and snow.

The Great Basin region is home to more than 200 butterfly species, many of which act as pollinators. Pollinators are important in agriculture because they carry pollen from the male to the female parts of flowers so they can become fertilized and produce fruit.

The research team created 19 sampling sites throughout the region, collecting a wide sample of butterflies to study how they distribute pollen to plants. In addition to the newly collected samples, the team also examined previously captured butterfly samples obtained between 2000 and 2021 that are stored at the University of Nevada, Reno Museum of Natural History.

"By analyzing 21 years of historical data, a very long period that provides clear views, the research offers detailed perspectives on the consequences of habitat loss, fragmented landscapes and changes in plant assemblages on pollination services," Balmaki said. "Our innovative use of museum specimens to track changes in pollen adds a new dimension to understanding these dynamics. These findings are vital for informing conservation efforts aimed at reducing biodiversity loss and preserving ecological balance, which are essential for sustaining natural ecosystems and human agriculture."

Another important aspect of this study is that it highlights the deep importance of pollinators in maintaining food production vital for human consumption and survival.

"Without effective pollination, many crops vital to the global food supply could fail," Balmaki said. "Our research underscores the necessity of developing targeted conservation policies to protect pollinators and maintain essential pollination services during global warming, thereby addressing some of the most significant environmental challenges of our time."

• Agriculture and Food
• Food and Agriculture
• Endangered Plants
• Environmental Issues
• Global Warming
• Paleoclimatology
• Seedless Fruit
• Energy development
• Phytopathology
• Temperature record of the past 1000 years

Story Source:

Materials provided by University of Texas at Arlington . Original written by Katherine Egan Bennett. Note: Content may be edited for style and length.

Journal Reference :

• Behnaz Balmaki, Masoud A. Rostami, Julie M. Allen, Lee A. Dyer. Effects of climate change on Lepidoptera pollen loads and their pollination services in space and time . Oecologia , 2024; 204 (4): 751 DOI: 10.1007/s00442-024-05533-y

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AI offers a new era in agricultural innovation: combating climate-driven crop pests

Crop pests are among the climate-related issues contributing to a 40% loss of crops annually. Image:  Unsplash/Ram Kishor

.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);} Tom Meade

.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 .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;}} with our crowdsourced digital platform to deliver impact at scale

• Climate change is creating conditions that favour the proliferation of weeds, insects and plant pathogens, leading to significant agricultural losses – as much as 40% loss of crops annually.
• Innovative solutions are urgently needed to protect crops without harming the environment, as high costs and lengthy timelines mean growers rely on outdated products.
• Artificial intelligence is a game-changer, which will shorten the timeline for new crop protection solutions and improve their efficiency and effectiveness.

Voracious beetles, mammoth weeds and mushrooming fungi. Climate change is delivering farm conditions that allow weeds, insects, and plant pathogens to thrive around the globe. Why does this matter?

Roughly 40% of crops are lost annually to new pests or stalwart species resistant to traditional crop protection treatments. That percentage loss translates to a tremendous amount of desperately needed food that never makes it off the farm and farmers who, simply put, lose money.

Innovation is needed to protect our global food chain and farmer livelihoods without harming the earth. Just like people, plants get sick. Eliminating the cause of the sickness – whether it be a fungus, beetle or weed – without harming anything around it is the next crucial step in the digital revolution that is already making a difference in agricultural productivity.

Plant pests have and continue to adapt to changes in their environments with devasting agricultural consequences. We talk a lot about plant pests that have developed resistance to crop protection treatments but we know that pests can adapt to any change that impacts their reproductive success.

Some examples of plant pests adapting to change include the adaptation of corn rootworm to crop rotation in North America, the elimination of cultivation of the Gros Michel banana due to the impact of Panama wilt disease in Central America and the adaptation of barnyard grass that mimics rice plants and therefore escapes detection in response to generations of hand-weeding, in Asia.

Climate change will impact the environments where crops and their pests live, and we can expect that pests will quickly adapt to these changes, creating new challenges for their management.

Have you read?

40% of global crop production is lost to pests. and it’s getting worse, no jobs, few crops: coronavirus and pests leave nepal fearing hunger, ai for agriculture: how indian farmers are harvesting innovation, climate change and the escalating threat of crop pests.

Warmer temperatures and higher CO2 concentrations will enhance the ability of some weeds to compete with crops and increase the threat posed by plant pathogens. Temperature increases will lead to more generations of insect pests and expansion into new geographical areas as environmental conditions become more favourable.

All of this is happening at a time when it is taking longer and costing more to bring new crop protection chemistries to the market. Barriers to innovation leave growers using products that have been on the market for decades when new, safe and effective products are desperately needed.

We must accelerate the discovery and development of new crop protection chemistries to bring affordable solutions to growers and meet the challenges that climate change will bring.

AI is revolutionizing drug discovery and can do the same for the discovery and development of crop protection chemistries .

AI can be viewed as an agricultural superhero of sorts. It provides scientists with the tools needed to explore the untapped, vast diversity of chemical space by rapidly and efficiently directing them to the most promising molecules.

When AI is applied to data, like that from screening of DNA-encoded chemical libraries, the most promising molecules can be rapidly identified from the billions of molecules contained within them. Training machine learning models on these data further expand the chemical diversity from which to select molecules to the billions of molecules in ultra-large, make-on-demand libraries.

Accelerating crop protection with AI-driven innovation

Scientists are already using these digital tools to rapidly discover and develop pre-screened novel molecules that effectively control crop pests and are earth-friendly, meaning safe for the crop, people and the environment.

Once tested and approved by regulatory bodies, new, safer products can be leveraged to fight the plant pests that emerge from our ever-changing climate. So why has there been so little innovation in crop protection over the years? Cost and time .

Traditional research methods involve guessing and testing possible products over the years in labs, greenhouses and fields. That takes millions of dollars in facilities and staff. It can take 12 years from start to finish (clearance for use by government regulatory agencies) and very few products get there.

Digital discovery and screening strip years from the discovery process and ensure that the products being advanced towards commercialization have the best chance of clearing every hurdle. Nimble, AI-powered techniques also cost far less – a winning combination against the most adaptable crop pest threats.

Researchers today are focused on providing solutions to some of the world’s largest threats to agriculture. My vision is that new AI technologies will accelerate the discovery of products to fight new pests and diseases while lowering the cost.

This acceleration would enable us to feed all pockets of the world, addressing unique concerns more rapidly as they arise and allowing us to provide solutions in the interest of food, even concerns with lower economic potential, a little like orphan drug research, to continue the drug discovery analogy.

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