Sustainability & Environment (Agri-Food Systems) · Climate Science & Agriculture · also Climate Change and Agriculture, Climate Change and Food Security, Agricultural Impacts of Climate Change
What a warmer, more variable climate does to crops, livestock, fisheries, pests, water and food security, and how impacts are studied with experiments and crop models.
In the exams: ICAR AICE subjects 60 and 71 (climate variability, adaptation and mitigation); ICAR AIEEA-PG Agronomy Unit II (climate shift); UGC NET Unit 10
Climate change impacts on agriculture and food systems is the study of what a warmer, more variable climate with more carbon dioxide does to crops, livestock, fisheries and aquaculture, pests and diseases, water and land, and ultimately to food security and nutrition. It sits between climate science, which supplies the hazards, and agronomy, animal science, plant protection and economics, which explain how farms and food chains respond. It also teaches the methods used to estimate impacts: field and chamber experiments, free-air carbon dioxide enrichment (FACE), statistical models built on historical yields, and process-based crop simulation models.
The subject matters because agriculture is the most climate-exposed major economic sector and the livelihood of a large share of the world's poor. The Working Group II contribution to the IPCC Sixth Assessment Report (AR6) found that climate change has already slowed agricultural productivity growth globally, that extremes have exposed millions of people to acute food insecurity, and that risks rise steeply with every increment of warming, most in regions that already have low incomes and high dependence on rainfed farming. A plant breeder choosing traits, an extension officer advising on sowing dates, a crop insurer, a ministry setting food reserves and a food company securing supply all need the knowledge in this subject.
Exams worldwide treat the subject in three ways. Indian PhD and research-service papers (ICAR AICE Environmental Sciences and Agricultural Meteorology, ASRB ARS/NET) and UGC NET Unit 10 ask for mechanisms (C3 and C4 responses, heat stress at flowering, the temperature–humidity index), methods (FACE, crop models) and calculations (growing degree days, salinity yield loss, water-stress yield response). Civil-service papers such as UPSC test food security, regional vulnerability and policy in applied form. Professional credentials such as GARP Sustainability and Climate Risk and the Certified Crop Adviser Sustainability specialty test whether the practitioner can turn impact science into risk assessments and farm advice. This manual serves all three; where numbers vary by crop, breed, region or study it says so.
A food system includes everything from inputs and production through storage, processing, transport, trade and retail to consumption and waste, together with the people, institutions and environment involved. Climate change affects every stage. The most direct pathways act on production: higher mean temperatures and heat extremes shorten crop development and damage flowering; changed rainfall amounts, timing and intensity bring drought and flooding; rising carbon dioxide alters photosynthesis and water use; warmer water changes fish distribution and aquaculture conditions; and heat stresses livestock. Indirect pathways act through pests, diseases and weeds, through soil and water resources such as groundwater recharge and glacier-fed river flow, through sea-level rise and salinisation of coastal land, and through damage to roads, storage and markets. Beyond the farm gate, heat and floods disrupt transport and storage, raise spoilage and mycotoxin contamination, and affect the health and productivity of farm and food workers. Price effects transmit impacts globally: a harvest shock in a major exporting region raises world prices and hurts poor net food buyers far away, as the food price spikes of the late 2000s showed. Policy responses such as export bans can amplify these shocks. Impacts combine. Compound events, such as heat and drought at the same time or flood followed by disease, cause more damage than either alone. Simultaneous failures in several breadbaskets, linked through large-scale circulation patterns such as ENSO, are a systemic risk for global supply. Impacts also depend on vulnerability, so the same hazard produces different outcomes in a well-insured irrigated farm and a smallholder rainfed plot.
AR6 Working Group II frames climate risk as arising from the interaction of hazard, exposure and vulnerability, with vulnerability including sensitivity and the lack of capacity to cope and adapt. This replaced the older AR4 framing in which vulnerability itself was a function of exposure, sensitivity and adaptive capacity; both appear in exam questions, so students should know which report a definition comes from. Among AR6 findings relevant here, stated with high confidence: climate change has reduced food and water security; it has slowed the growth of agricultural productivity over the past 50 years globally; increasing weather and climate extremes have exposed millions of people to acute food insecurity and reduced water security, with the largest impacts in many locations in Africa, Asia, Central and South America, small islands and the Arctic; and sudden losses of food production and access, compounded by reduced diet diversity, have increased malnutrition in many communities. Ocean warming has decreased sustainable yields of some wild fish populations, and acidification and warming have harmed shellfish aquaculture in some regions. Not all impacts are negative everywhere. Some cool high-latitude and high-altitude regions gain longer growing seasons, and carbon dioxide stimulates the growth of many crops. But AR6 assesses that negative impacts on yields have outweighed positive ones overall, and projects that risks to food security rise with warming, with adaptation becoming harder and more costly beyond about 1.5 °C to 2 °C. Limits to adaptation are already being reached in some systems, for example where heat exceeds what crops or workers tolerate.
Every crop process has cardinal temperatures: a base temperature below which development stops, an optimum range in which it is fastest, and a maximum above which it stops or damage begins. Base temperatures differ between crops: cool-season crops such as wheat and barley are often modelled with a base near 0 °C, while warm-season crops such as maize, sorghum and rice use a base of about 8–10 °C. Published values vary by cultivar and by the model used. Crop development, the progress through stages such as emergence, flowering and maturity, is driven mainly by temperature, measured as thermal time or growing degree days (GDD). The simplest form is GDD = Σ[(Tmax + Tmin)/2 − Tbase], counting zero on days when the mean is below the base. Common variants cap Tmax at an upper threshold (30 °C for maize in the widely used 86/50 °F method) and raise Tmin to the base before averaging. Because a crop needs a roughly fixed thermal time to reach maturity, warming makes it develop faster. A shorter season means less time to intercept sunlight and fill grain, so warming reduces yield even without any heat damage, unless farmers switch to longer-duration cultivars or shift sowing dates. Photoperiod and vernalisation (the cold requirement of winter wheat) modify this in sensitive crops. The broad effect of warming on yields has been estimated by combining methods. A widely cited synthesis by Zhao and colleagues (2017) estimated that, without carbon dioxide fertilisation, effective adaptation or genetic improvement, each 1 °C rise in global mean temperature reduces global yields of wheat by about 6.0 per cent, rice 3.2 per cent, maize 7.4 per cent and soybean 3.1 per cent, with large regional differences.
Heat damage is most severe at a few sensitive stages. Around flowering (anthesis), high temperatures cause pollen sterility and failed fertilisation. In rice, spikelet sterility rises sharply when temperatures at anthesis exceed about 35 °C, even for an hour or two; in maize, pollen viability and silk receptivity fall above about 35 °C, and drought widens the gap between pollen shed and silking. In wheat, heat during grain filling, often above about 30–34 °C, shortens the filling period and reduces grain weight; in South Asia this 'terminal heat stress' is a main yield limiter for late-sown wheat in the Indo-Gangetic Plains. Statistical studies show that yields respond non-linearly: moderate warmth is harmless or helpful, but each degree-day above a threshold is very damaging. Schlenker and Roberts (2009), using United States county yields, found that yields increase with temperature up to about 29 °C for maize, 30 °C for soybean and 32 °C for cotton, and decline steeply above. Accumulated exposure above such thresholds, called extreme or killing degree days, is often a better predictor of yield loss than mean temperature. Night temperature matters too. High night temperatures raise respiration and can reduce grain filling; a field study at the International Rice Research Institute by Peng and colleagues (2004) found dry-season rice yield fell about 10 per cent for each 1 °C rise in growing-season minimum temperature. Minimum temperatures have risen faster than maximum temperatures in many regions. Heat also interacts with water. Canopies cool themselves by transpiration; when water is short, stomata close, leaves heat up, and heat and drought damage compound. That is why irrigated crops often tolerate air temperatures that would harm rainfed ones, and why canopy temperature is used as a screening trait for heat and drought tolerance in breeding.
In C3 plants, such as wheat, rice, barley, soybean, potato and most vegetables and trees, carbon dioxide is fixed directly by the enzyme Rubisco, which also reacts with oxygen in the wasteful process of photorespiration. At present atmospheric concentrations Rubisco is not saturated with carbon dioxide, so raising the concentration increases carboxylation and suppresses photorespiration; photosynthesis rises. In C4 plants, such as maize, sorghum, sugarcane and millets, the enzyme PEP carboxylase first fixes carbon in the mesophyll and concentrates carbon dioxide around Rubisco in the bundle-sheath cells. Their photosynthesis is already close to saturation, so extra carbon dioxide adds little directly. Both groups benefit through water use. Higher carbon dioxide lets stomata partly close while still taking up enough carbon, which reduces transpiration and raises water-use efficiency. This indirect benefit means C4 crops can gain under drought, when conserving soil water matters most. The size of the fertilisation effect in the field has been measured by free-air carbon dioxide enrichment (FACE) experiments, which raise the concentration over open plots without chambers. Syntheses of FACE results at about 550 ppm found yield increases for C3 grain crops of the order of 10–15 per cent and little or no yield increase in C4 crops under well-watered conditions. These gains were smaller than earlier enclosure studies had suggested. The benefit is reduced where nitrogen or other nutrients are limiting, varies among cultivars, and can be offset by the warming and ozone that accompany rising carbon dioxide. Crop models used for impact studies must therefore represent carbon dioxide effects carefully, and the projected net effect of climate change on a crop can switch sign depending on whether carbon dioxide fertilisation is included.
Faster carbon gain under high carbon dioxide does not raise all grain constituents in step. FACE experiments analysed by Myers and colleagues (2014) found that, at carbon dioxide concentrations expected around the middle to later part of this century, C3 cereals and legumes had lower concentrations of zinc and iron, and C3 cereals had lower protein, with reductions typically of a few to around ten per cent; C4 crops showed smaller changes. The mechanisms are thought to include dilution by extra carbohydrate and reduced transpiration-driven mineral uptake, though the explanation is not settled. Because hundreds of millions of people already lack enough zinc and iron, and depend on wheat and rice for them, these declines are a nutrition concern even where yields rise. Breeding for higher micronutrient content (biofortification) is one response. Ground-level ozone is a separate atmospheric threat linked to the same emissions. Tropospheric ozone forms from nitrogen oxides, volatile organic compounds and methane in sunlight; it enters leaves through stomata and damages cells, reducing photosynthesis and yield. Wheat, soybean and rice are relatively sensitive, maize less so. Ozone damage is greatest in regions with high precursor emissions and strong sunlight, notably parts of South and East Asia. Exposure is measured with indices such as AOT40, the accumulated hourly ozone over a threshold of 40 ppb during daylight hours, or with flux-based indices that account for stomatal uptake. High carbon dioxide partly protects plants against ozone by narrowing stomata, while drought does the same at the cost of growth.
Drought is a prolonged deficit of water relative to normal. Four types are distinguished: meteorological drought (below-normal precipitation), agricultural drought (soil moisture too low for crops), hydrological drought (low streamflow, reservoir and groundwater levels) and socioeconomic drought (when water shortage affects the supply of goods). They develop in sequence and recover at different speeds. Common indices include the Standardized Precipitation Index (SPI), which expresses precipitation over a chosen period as a standard normal deviate; the Standardized Precipitation–Evapotranspiration Index (SPEI), which uses precipitation minus potential evapotranspiration and so captures the effect of warming on water demand; and the Palmer Drought Severity Index. The aridity index used by UNEP is precipitation divided by potential evapotranspiration: below 0.05 is hyper-arid, 0.05–0.20 arid, 0.20–0.50 semi-arid and 0.50–0.65 dry sub-humid. Warming raises atmospheric evaporative demand, so even with unchanged rainfall soils dry faster. AR6 assessed that human-induced climate change has contributed to increases in agricultural and ecological droughts in some regions and projects further increases in many regions, including the Mediterranean, southern Africa and parts of the Americas. Crop yield response to water deficit is described in FAO Irrigation and Drainage Paper 33 by the relation (1 − Ya/Ym) = Ky(1 − ETa/ETm), where Ya and Ym are actual and maximum yield, ETa and ETm are actual and maximum evapotranspiration, and Ky is the yield response factor. Ky above 1 means yield falls proportionally faster than water use; values differ by crop and by growth stage, and are highest at flowering for most grain crops.
A warmer atmosphere holds about 7 per cent more water vapour per degree, so the heaviest rainfall events intensify, and AR6 found that heavy precipitation has increased in frequency and intensity over most land regions with good observations. For farms this means more erosion, nutrient loss, waterlogging and flooding, sometimes in the same seasons and places that also face longer dry spells. Rainfall variability, not just its total, governs rainfed farming: a delayed monsoon onset, long breaks within the season or early withdrawal can cut yields even if the seasonal total is normal. Waterlogging deprives roots of oxygen, causing reduced growth, nitrogen loss by denitrification and death of sensitive crops within days. Rice tolerates standing water but not complete submergence for long; the SUB1A gene, bred into popular varieties, lets rice survive about two weeks of complete submergence by suppressing elongation and conserving energy. Flood-tolerant and drought-tolerant varieties, raised beds, drainage and adjusted sowing dates are standard responses. Monsoon systems are of special concern because billions of people depend on them. AR6 projects that global monsoon precipitation is likely to increase over the long term, particularly in South and Southeast Asia, but with greater variability, while aerosol emissions have influenced past monsoon trends. In the Sahel, rainfall has partly recovered from the severe droughts of the 1970s and 1980s, with more intense and variable rains. Changes in snow and glacier melt affect irrigation water in the Indus, Ganges, Amu Darya and Andean basins: glacier melt can first increase and then decline, a pattern called 'peak water'.
Insects are ectotherms, so their development rate, like that of crops, follows thermal time. Warmer conditions shorten generation time, allowing more generations per season (higher voltinism), improve overwinter survival where frost was limiting, and let species expand to higher latitudes and altitudes. Bebber and colleagues (2013) estimated that, in records since 1960, crop pests and pathogens have shifted poleward at an average of about 3 km per year. Warming can also reduce pest pressure where temperatures exceed the pest's own optimum, so effects are not uniformly negative. Deutsch and colleagues (2018) projected that global yield losses of wheat, rice and maize to insect pests increase by about 10–25 per cent for each degree of global mean warming, mainly through higher insect metabolism and population growth in temperate regions. Plant disease follows the disease triangle: disease needs a susceptible host, a virulent pathogen and a favourable environment. Climate acts mainly on the environment side through temperature, leaf wetness and humidity. Fungal diseases such as rusts and blights need specific temperature and moisture windows; warmer, wetter conditions favour many, while drought favours others. Wheat stripe rust, for example, was long a cool-climate disease, but strains adapted to higher temperatures have spread widely. Vector-borne plant viruses spread with their insect vectors, such as aphids and whiteflies, whose ranges and numbers respond to temperature. Climate also affects mycotoxins: aflatoxin from Aspergillus flavus is favoured by heat and drought stress during grain filling and poor post-harvest drying, and the risk zone is projected to expand into warmer temperate regions such as southern Europe. Global trade and travel spread pests regardless of climate. An invasion should not be attributed to climate change without evidence; the role of climate is usually in whether the invader can establish and how fast it spreads.
Weeds compete with crops for light, water and nutrients, and their response to climate change depends on photosynthetic pathway and life history. Many of the world's worst weeds are C4 plants, while most crops are C3; warming favours C4 weeds in C3 crops, while rising carbon dioxide favours C3 weeds in C4 crops such as maize and sugarcane. Weeds generally have wide genetic diversity and rapid adaptation, which may let them respond to change faster than crops. Range expansion of invasive weeds such as parthenium in South Asia and Africa is linked to climate suitability as well as to disturbance and trade. Climate change can alter how well control measures work. Experiments by Ziska and others found that some perennial weeds grown at elevated carbon dioxide became less sensitive to the herbicide glyphosate, because larger root systems dilute the dose. Heat and drought change herbicide uptake and persistence; intense rainfall washes off pesticides and increases runoff to water bodies; and milder winters reduce the natural die-back that used to reset pest populations. Biological control can fail when warming desynchronises the life cycles of pests and their natural enemies. For integrated pest management (IPM), these changes mean that thresholds, spray calendars and degree-day forecasting models calibrated on past climate need recalibration. Monitoring networks, early-warning systems linked to weather forecasts, resistant varieties, crop rotation and diversity, and preserving natural enemies become more valuable. Pest risk analysis in plant health regulation increasingly includes climate suitability modelling to anticipate establishment of new pests.
Mammals and birds keep body temperature within narrow limits. Within the thermoneutral zone an animal needs no extra energy to stay warm or cool; above the upper critical temperature it must shed heat by panting, sweating, seeking shade and reducing activity, and it reduces feed intake to cut the metabolic heat of digestion. High-producing animals generate more metabolic heat and are more susceptible: a high-yielding dairy cow suffers at temperatures a low-yielding one tolerates. Pigs have few functional sweat glands and poultry have none, so both rely on panting and behaviour. Zebu cattle (Bos indicus) and many local breeds tolerate heat better than European breeds (Bos taurus), thanks to a lower metabolic rate, sweat gland density, short hair and loose skin. Effects of heat stress include reduced dry matter intake, lower milk yield and growth, poorer milk composition, reduced fertility (lower conception rates and embryo survival), weakened immunity and, in extreme events, death. Effects of a hot spell carry over: fertility can stay depressed for weeks. Because humidity limits evaporative cooling, heat stress is measured with a temperature–humidity index (THI). Several formulas exist; a widely used one (NRC 1971) is THI = (1.8T + 32) − (0.55 − 0.0055RH)(1.8T − 26), with T in °C and RH in per cent. Another common form is THI = 0.8T + (RH/100)(T − 14.4) + 46.4. They give similar but not identical values, so thresholds must be read with the formula they were derived for. For dairy cattle a THI of about 72 has long been treated as the onset of stress, and research on high-yielding cows suggests effects begin near 68. Indices that add solar radiation and wind speed, and the black globe–humidity index, are used for animals in the open.
Climate change affects livestock through feed and water as much as directly. Rangeland and pasture production depends on rainfall and temperature, so drought reduces forage quantity, and repeated drought has caused large herd losses in pastoral regions of the Horn of Africa and the Sahel. Warming also tends to reduce forage quality: plants grown at higher temperatures develop more lignified cell walls and lower digestibility, and higher carbon dioxide lowers the nitrogen (protein) content of C3 forages. Lower digestibility lengthens rumination and raises methane emissions per unit of product. Feed-grain prices transmit crop shocks to intensive pig, poultry and dairy systems. Water demand rises steeply with temperature: drinking water intake of cattle increases substantially in hot weather, at the same time that water sources may dry. Water quality also declines, with higher concentrations of salts and harmful algal blooms in stock ponds. Animal diseases respond to climate through vectors, pathogens and host condition. Bluetongue, a viral disease of ruminants spread by Culicoides biting midges, spread northward into much of Europe from the late 1990s onward, a change linked in part to warmer conditions that favoured the vector and virus replication. Rift Valley fever outbreaks in East Africa follow heavy rainfall that floods breeding sites of mosquito vectors, often in El Niño years. Ticks and the diseases they carry have expanded their altitudinal and latitudinal ranges in several regions. Heat-stressed animals also have weaker immunity. Responses include shade and cooling (sprinklers, fans), feeding at cooler times and adjusting rations, ensuring water supply, breeding for heat tolerance (including the 'slick' hair gene introduced into dairy breeds), changing species mix (for example toward camels or goats in drylands) and index-based livestock insurance.
Fish provide a large share of animal protein, especially in coastal and island countries, and are rich in micronutrients and omega-3 fatty acids. Fish and other aquatic animals are ectotherms whose physiology is tightly linked to water temperature and oxygen. As the ocean warms, many marine species shift their distributions poleward and to deeper water, tracking their preferred temperatures, and the timing of spawning and migrations changes. These shifts change which species are caught where, create conflicts over transboundary stocks, and favour fleets that can follow fish. The IPCC Special Report on the Ocean and Cryosphere (SROCC) and AR6 projected that the maximum catch potential of marine fisheries will decline globally under continued warming, with the largest declines in tropical regions and some increases at high latitudes. Warming, deoxygenation and acidification act together. Warmer water holds less oxygen while raising fishes' oxygen demand, and theory and observations suggest that average body sizes tend to shrink with warming. Coral reef fisheries, on which many small-island and tropical coastal communities depend, are threatened by bleaching from marine heatwaves; SR1.5 projected warm-water coral reefs to decline by 70–90 per cent at 1.5 °C of warming. Changes to upwelling systems, which support some of the world's largest fisheries, are uncertain. Inland fisheries in rivers, lakes and floodplains, which are important for food security in parts of Africa and Asia, respond to warming, altered river flows, drought and dams. Overfishing, habitat loss and pollution remain major stressors, and AR6 emphasises that sustainable management that avoids overfishing improves the resilience of stocks to climate change.
Aquaculture now supplies about half of the aquatic animals eaten by people, and it faces its own climate risks. Temperature sets the growth and survival of cultured species; cold-water species such as salmon suffer when summer temperatures exceed their tolerance, while warm-water species such as tilapia and carp may benefit in some cooler regions. Higher temperatures increase metabolic oxygen demand while dissolved oxygen falls, raising the risk of fish kills in ponds and cages, especially during calm, hot nights. Disease and parasite outbreaks, including sea lice in salmon farming, become more frequent or severe in warmer water. Harmful algal blooms can poison or suffocate stock and close shellfish harvesting. Ocean acidification threatens shellfish aquaculture. Larval oysters and other bivalves build their first shells from aragonite and are sensitive to low carbonate saturation; hatcheries on upwelling coasts of the north-east Pacific suffered larval die-offs when corrosive upwelled water entered their intakes, and they now monitor and buffer intake water. Coastal and pond aquaculture is also exposed to storms, flooding, sea-level rise and salinity change, which can destroy infrastructure or release farmed fish. In deltas such as the Mekong and Ganges–Brahmaputra, rising salinity has pushed some farmers from rice to brackish-water shrimp, with mixed social and environmental results. Feed is a further link: many aquaculture feeds rely on fishmeal and fish oil from small pelagic fish, whose stocks fluctuate with climate variability such as ENSO, and on crop-based ingredients exposed to crop shocks. Adaptation options include selective breeding for temperature and disease tolerance, site selection, deeper or offshore cages, diversification of species, monitoring and early warning of algal blooms, and better biosecurity.
Global mean sea level rose by about 0.20 m between 1901 and 2018 and is rising faster now, at several millimetres per year. For coastal farmland the relevant quantity is relative sea level, which adds local land subsidence. Many of the world's most productive rice-growing deltas, including the Ganges–Brahmaputra–Meghna, Mekong, Irrawaddy and Nile, are low-lying and subsiding through groundwater pumping, sediment trapping by upstream dams and compaction, so their relative sea-level rise can be several times the global mean. Salt reaches farmland in several ways. Storm surges and tidal flooding deposit seawater directly on fields; after a cyclone, salt left in the soil can depress yields for one or more seasons. Saltwater moves further up rivers and canals during the dry season when river flow is low, so irrigation water becomes saline. Seawater intrudes into coastal aquifers, especially where pumping lowers the freshwater table. The Ghyben–Herzberg relation, derived from the density difference between fresh water (about 1.000 g cm⁻³) and seawater (about 1.025 g cm⁻³), states that under hydrostatic conditions the freshwater–saltwater interface lies about 40 times as far below sea level as the water table stands above it. Lowering the water table by 1 m can therefore raise the interface by about 40 m, which is why over-pumping is often more damaging to coastal aquifers than sea-level rise itself. Salinity also rises where evaporation concentrates salts and drainage is poor, a process accelerated by warming and by irrigation without leaching, so salinisation is not only a coastal problem.
Soil salinity is measured as the electrical conductivity of a saturated soil paste extract, ECe, in decisiemens per metre (dS m⁻¹). The United States Salinity Laboratory classification treats a soil with ECe above 4 dS m⁻¹ as saline; a soil with exchangeable sodium percentage above 15 is sodic. Salts harm crops by osmotic stress, which makes water harder to extract, and by specific ion toxicity of sodium and chloride. Crop tolerance is described by the Maas–Hoffman threshold–slope model, given in FAO Irrigation and Drainage Paper 29: relative yield Yr (per cent) = 100 − b(ECe − a) for ECe above the threshold a, where b is the per cent yield loss per dS m⁻¹ above the threshold. Values tabulated in that paper include rice with a threshold of about 3.0 dS m⁻¹ and a slope of 12 per cent; wheat 6.0 dS m⁻¹ and 7.1 per cent; barley 8.0 dS m⁻¹ and 5.0 per cent; and maize 1.7 dS m⁻¹ and 12 per cent. These are averages; tolerance varies with cultivar, growth stage (rice is more sensitive at the seedling and reproductive stages), climate and soil. Responses include salt-tolerant varieties (such as rice carrying the Saltol region), changes of crop or land use (for example to brackish-water aquaculture or salt-tolerant crops), leaching and drainage where fresh water allows, embankments and sluice gates with careful management, rainwater harvesting for dry-season irrigation, and in the long run planned retreat from land that cannot be protected. Each option has trade-offs: embankments can trap sediment outside and worsen subsidence inside, and shrimp farming can salinise neighbouring land.
The 1996 World Food Summit defined food security as existing 'when all people, at all times, have physical and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life'. Four dimensions follow: availability (enough food produced, stored or imported), access (people can obtain it, which depends mainly on income, prices and social protection), utilisation (the body can make good use of it, which depends on diet quality, clean water, sanitation and health) and stability (all three hold over time, through shocks). Later work adds agency and sustainability as further dimensions. Climate change affects all four. Availability falls when yields drop or herds die. Access falls when food prices rise or incomes from farming and farm labour decline; poor households spend a large share of income on food and are hurt most by price spikes. Utilisation falls when floods contaminate water supplies and spread diarrhoeal disease, when mycotoxins contaminate staple grains, and when high carbon dioxide reduces grain zinc, iron and protein. Stability falls because extremes cause sudden shocks and variability makes planning hard. AR6 assessed that climate-related extremes have caused acute food insecurity for millions of people, that the impacts fall hardest on low-income households, women, children, Indigenous peoples, small-scale producers and the urban poor, and that conflict and economic shocks combine with climate shocks in many food crises. It projected that, compared with a future without climate change, millions more people will be at risk of hunger and malnutrition, with the numbers strongly dependent on the socioeconomic pathway: development that reduces poverty and inequality reduces the climate-related rise in hunger far more than any change in climate within the range studied.
Undernutrition has several forms: chronic hunger (insufficient energy), stunting and wasting in children, and micronutrient deficiency, called 'hidden hunger', which affects many more people than calorie deficiency. Climate change touches each. Droughts and floods are associated with higher child stunting and wasting in exposed regions. Diet diversity falls when fruit, vegetables, pulses and animal-source foods become scarcer or more expensive after shocks. Heat directly harms the health and work capacity of farm workers, which reduces both income and labour available for production; AR6 projects that losses of outdoor labour capacity to heat will rise steeply in tropical regions with further warming. Food safety risks rise too. Warmer temperatures favour Salmonella and other foodborne pathogens and increase spoilage where cold chains are lacking; harmful algal blooms contaminate shellfish; and aflatoxin, a potent liver carcinogen also linked to stunting, becomes more common in heat- and drought-stressed maize and groundnuts. At the same time the food system is a major source of greenhouse gas emissions, so the subject connects impacts to mitigation. Diets heavy in ruminant meat have large emission and land footprints, and shifting toward plant-rich diets brings both health and climate benefits in high-consuming populations. But in undernourished populations, animal-source foods supply vital micronutrients and protein; the right recommendation depends on the starting point. Food loss and waste reduction eases pressure on both supply and emissions. For practitioners, the key analytical habit is to separate hazards from vulnerability: the same drought causes famine in one place and a manageable shortfall in another, depending on income, markets, governance, reserves, insurance and social protection.
Experiments isolate causes. Controlled-environment growth chambers and glasshouses allow precise control of temperature and carbon dioxide but differ from fields in light, wind, rooting volume and canopy structure. Open-top chambers (OTCs), transparent cylinders open at the top and placed over field plots, were used widely for carbon dioxide and ozone studies; they warm the air inside and alter humidity and wind, the 'chamber effect'. Temperature gradient tunnels and field infrared heaters warm crops in the open; heaters warm canopies by radiation but can dry the soil surface more than real warming would. Rain-out shelters and irrigation treatments impose drought; late or staggered sowing exposes crops to higher temperatures as a cheap natural experiment, though it changes photoperiod and other factors too. Free-air carbon dioxide enrichment (FACE) releases carbon dioxide from pipes arranged in a ring around plots, typically 8–30 m across, with computer control that adjusts release to wind speed and direction to hold a target concentration, often about 550–600 ppm. FACE avoids chamber effects, lets crops grow in real field conditions and allows large plots for yield and quality measurements. Its limitations are cost, a step change rather than gradual rise in concentration, edge effects and a limited number of sites, mainly in temperate regions, with few in the tropics. Long and colleagues (2006) pointed out that FACE yield responses were about half those assumed from enclosure studies in many models, prompting revisions. Temperature FACE (T-FACE) adds infrared heating to the same design.
Statistical models relate observed yields to weather or climate. Time-series models examine one place over many years; panel models use many places over many years with fixed effects that remove time-invariant differences such as soil; cross-sectional models compare places with different climates. Their strength is that they are built on real farm outcomes, including effects that process models miss. Their weaknesses are that they assume past relationships hold in a future with different carbon dioxide, technology and adaptation, cannot extrapolate beyond the observed climate range reliably, and can confuse correlated variables such as temperature and rainfall. The Ricardian approach of Mendelsohn, Nordhaus and Shaw (1994) regresses farmland values on climate across places; it implicitly includes farmers' adaptation but is vulnerable to omitted variables such as irrigation and to the assumption that prices are constant. Process-based crop models simulate crop growth day by day from weather, soil, cultivar and management, representing phenology, light interception, photosynthesis or radiation use efficiency, partitioning, water and nitrogen balances. They can explore future conditions and adaptation options, such as new sowing dates or cultivars, that have never been observed. Their weaknesses are parameter uncertainty, simplified or missing processes (such as pests, diseases, ozone and extreme heat at flowering in older versions) and the need for calibration data. The two approaches are complementary. Global syntheses such as Zhao and colleagues (2017) combined grid-based and point-based process models, statistical regressions and field-warming experiments, and found broadly consistent temperature sensitivities. Impact assessments increasingly use ensembles of climate and crop models and report the spread as part of the uncertainty.
A crop simulation model is a set of equations, run on a daily or shorter time step, that turns weather, soil, cultivar and management inputs into growth, development and yield. Minimum weather inputs are daily maximum and minimum temperature, rainfall and solar radiation; humidity and wind are needed for evapotranspiration methods such as Penman–Monteith. Soil inputs include layer depths, water-holding limits and organic carbon; management covers sowing date, density, irrigation and fertiliser; and cultivar coefficients describe phenology and growth traits. Many models follow a radiation-use-efficiency logic. The fraction of incoming photosynthetically active radiation (PAR) intercepted by the canopy follows Beer's law, f = 1 − exp(−k × LAI), where LAI is leaf area index and k the extinction coefficient (often 0.4–0.7, depending on leaf angle). Daily biomass growth is RUE × f × PAR, where RUE, radiation use efficiency, is higher for C4 than C3 crops. Yield is biomass multiplied by harvest index or simulated by explicit partitioning to grains. Phenology is computed from thermal time, modified by photoperiod and vernalisation. Stress factors for water, nitrogen and temperature scale growth down from its potential. Widely used families include DSSAT (with the CERES cereal and CROPGRO legume models), APSIM, WOFOST, STICS, the rice model ORYZA, FAO's water-driven AquaCrop, and InfoCrop developed in India. These are public research models, and they differ in structure and in how they treat carbon dioxide, heat and water stress.
Before use, a model must be calibrated, adjusting cultivar coefficients to match observed phenology and yield in experiments, and then evaluated on independent data. Common statistics are the root mean square error (RMSE), normalised RMSE (RMSE divided by the observed mean), mean bias, and Willmott's index of agreement d, which ranges from 0 to 1. A model that matches the calibration data well but has not been tested on independent seasons and sites is not validated. For climate impact assessment, crop models are driven by climate model output. Global climate model grids are coarse and biased, so their output is downscaled, statistically or with regional climate models, and bias-corrected against observations. The simplest approach, the delta (change factor) method, adds the projected change in temperature, or multiplies the projected change in rainfall, onto an observed weather record; it preserves observed variability, which may itself change. Simulations are run for a baseline and a future period, with and without carbon dioxide effects and adaptation options, so that the contribution of each can be seen. Model intercomparison projects, notably AgMIP (the Agricultural Model Intercomparison and Improvement Project) and the agricultural sector of ISIMIP, run many crop models with the same inputs. They found large differences among models, especially in their response to high temperature and carbon dioxide, and that the median of a multi-model ensemble often predicted observations better than any single model. Uncertainty in an impact estimate therefore comes from emissions scenarios, climate models, downscaling, crop models and their parameters, and should be reported as a range. Good practice is to state which processes the model omits, such as pests, diseases, ozone, waterlogging or nutritional quality, because these omissions usually make impact estimates optimistic.
A climate hotspot is a region where strong climate hazards coincide with high exposure and high vulnerability. Vulnerability assessment combines indicators of exposure (people, crops and assets in harm's way), sensitivity (for example the share of rainfed farming or dependence on a single staple) and adaptive capacity (income, education, infrastructure, institutions, access to credit, insurance and information). Index-based assessments aggregate such indicators but depend heavily on the choice and weighting of indicators, so they should be read as screening tools. AR6 Working Group II assessed that approximately 3.3 to 3.6 billion people live in contexts that are highly vulnerable to climate change, and that vulnerability is higher where there is poverty, governance challenges, limited access to basic services, violent conflict and high levels of climate-sensitive livelihoods such as smallholder farming, pastoralism and fishing. Vulnerability is not fixed. It is shaped by development, inequality, gender, land tenure and history, which is why the same drought affects groups within one village differently. It can also be raised by maladaptation, such as groundwater pumping that relieves drought today but depletes the aquifer for tomorrow.
AR6 Working Group II gives regional assessments; in general terms they show the following. In Africa, where agriculture is mostly rainfed and employs a large share of the workforce, agricultural productivity growth has already been reduced, and further warming is projected to reduce yields of staple crops in many regions and to stress pastoral systems; the Sahel, the Horn of Africa and southern Africa face drought and variability risks. In South Asia, heat stress on people, wheat and livestock, monsoon variability, floods, and groundwater depletion in irrigated breadbaskets such as the Indo-Gangetic Plains are leading concerns, with sea-level rise and salinity in the Bengal delta. In Southeast Asia, rice deltas face sea-level rise, salinisation and flooding, and fisheries face reef and mangrove loss. In Central and South America, the Central American 'dry corridor' faces recurrent drought affecting smallholder maize and bean farmers, and Andean agriculture depends on shrinking glaciers. Small island developing states face sea-level rise, salinisation of freshwater lenses, cyclones and fisheries change. In the Mediterranean, drying and heat threaten rainfed cereals, olives and water supply. Some regions gain growing-season length, notably at high northern latitudes, but these gains are limited by soils, infrastructure, day length and extreme events, and they do not offset losses for global food security. Mountain regions face changing water supply as glaciers retreat. These are broad patterns; local impacts vary with crops, soils, irrigation and adaptation, and detailed numbers should be taken from current regional chapters and national assessments. For a practitioner, the lesson is to pair hazard projections with local vulnerability data and to look for compound and cascading risks, such as heat plus drought plus price spikes, rather than single hazards.
A student uses this subject to master the agrometeorology and impact arithmetic that ICAR, ASRB and UGC NET papers test: growing degree days with and without upper caps, the temperature–humidity index with the correct formula, Beer's law light interception, the FAO-33 yield response factor and the Maas–Hoffman salinity equation. The student should also be able to explain why C3 and C4 crops respond differently to carbon dioxide, why FACE gave smaller gains than chambers, and the strengths and weaknesses of statistical versus process models, because examiners set distractors from these confusions.
A five-year practitioner, such as an agrometeorologist issuing district advisories, an extension specialist, a crop insurance analyst or a CGIAR project officer, uses the subject to time sowing to avoid terminal heat, recommend submergence- or salt-tolerant varieties, adjust IPM thresholds for new pest pressures, set heat-stress alerts for dairy farmers using THI, and run or interpret crop models for a district. Typical decisions are which cultivar duration to recommend under a warmer season, whether an index-insurance trigger matches real crop damage, and how to communicate model uncertainty honestly.
A senior professional with 25 years' experience, such as a national agricultural research director, a ministry adviser, a food company's head of sourcing or a development-bank sector lead, uses the subject to set priorities and allocate money: which crops and regions to target for breeding, where to invest in irrigation versus diversification, when an area is approaching adaptation limits that call for transformation, and how to build food reserves and social protection against simultaneous shocks. Such a person judges whether impact studies omitted pests, ozone or nutrition and adjusts decisions accordingly.
A researcher with a PhD and 25 years' experience, such as a principal scientist or professor, works on improving crop models for heat at flowering, carbon dioxide–nitrogen interactions and pests; designs FACE and field-warming experiments in under-studied tropical systems; builds multi-model ensembles for regional assessments; links impacts to nutrition and economics; and contributes to IPCC and national assessments. Such a researcher also trains the next generation to combine experimental, statistical and process approaches and to communicate uncertainty without overstating or understating risk.
This manual describes regional impacts only in general terms; detailed regional numbers should be taken from the AR6 WGII regional chapters and national assessments, which differ by method and scenario. It gives no projected yield changes for specific countries or years, no projected percentages for fisheries catch potential, and no counts of people at risk of hunger, because these vary strongly with scenario, model and study. Cardinal temperatures, Ky values and salinity thresholds are typical tabulated values that vary by cultivar and site. Adaptation options are mentioned only briefly because they belong to the climate-adaptation-resilience subject, and greenhouse gas mitigation in agriculture belongs to agricultural-ghg-mitigation. Forestry impacts, agricultural economics models (CGE and partial equilibrium) and detailed crop-model equations for individual model families are not covered.
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