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The panel approach with fixed effects and nonlinear weather effects has become a popular method to uncover weather impacts on economic outcomes, but its ability to capture long‐run climatic adaptation remains unclear. Building upon a framework proposed by McIntosh and Schlenker (2006), this paper identifies empirical conditions under which the nonlinear panel approach can approximate a long‐run response to climate. When these conditions fail, the obtained relationship may still be interpretable as a weighted average of underlying short‐run and long‐run responses. We use this decomposition to revisit recently published climate impact estimates. For spatially large panels, the estimated temperature–outcome relationship mostly reflects the long‐run climatic response; this is not so for precipitation. We find some evidence of long‐run climatic adaptation for crop yield outcomes in the United States and France. [ABSTRACT FROM AUTHOR]

Northeast China (NEC) is the most prominent grain‐producing region in China. However, it is currently facing significant impacts from climate change. Since the climate‐related impacts on crop yield in this region are a major concern for society in the future, quantifying climate change impacts on crop yields in NEC is essential to ensure future food security. This study aimed to quantify the effects of future climate change on crop yields in NEC and explore adaptation strategies using the Crop Growth Model (PCSE) driven by downscaled CMIP6 climate projections under four Shared Socioeconomic Pathways (SSPs) scenarios during 2015–2100. Results showed that there could be average reductions in crop yields of 21.4% for maize and 4.2% for soybean by the year 2100 under SSP585 compared to the 2015 baseline. The increasing temperature was the dominant factor in reducing yields, although elevated CO2 and precipitation offered partial compensation. The optimized planting date brought noticeable benefits for rice and soybean but had limited effects on maize due to heat stress. Relocating rice expansion eastward and implementing earlier planting increased yields by up to 50% but adversely decreased soybean and maize due to competition. This study enriches our comprehension of climate change impacts on NEC agriculture, while also quantifying potential benefits and constraints of evaluated adaptations. The proposed adaptations may help mitigate projected yield declines in other key agricultural regions across the globe. Adjusting crop management practices to capitalize on changing climate factors shows promise as a strategy for sustaining production globally. Plain Language Summary: Northeast China (NEC) is essential for achieving food accessibility and food security in China, but its agricultural production is seriously threatened by climate change. This study investigated how climate change could impact maize, rice, and soybean crop yields in this region from 2015 to 2100, using the PCSE crop model and forcing from the international Coupled Model Intercomparison Project 6 (CMIP6) climate projections. Simulation results suggested rising temperatures would negatively affect crop yields, especially for maize. Increased carbon dioxide and rainfall partially offset these losses. By 2100, average declines were projected for maize (−21%) and soybeans (−4%), respectively. Altering planting dates to match crop needs was an effective adaptation to boost yields, with rice benefiting the most. Relocating rice production eastward and expanding its area substantially increased simulated rice yields (+62%) but decreased other crops due to competition (−32% maize, −28% soybeans). Even with adaptations, some areas still showed yield declines, indicating extra measures may be needed to maintain production. Overall, this study suggests significant risks to crop yields from climate change in Northeast China, with adaptations like optimized planting and strategic crop relocation able to mitigate some but not all of the projected impacts. Key Points: Rising temperatures negatively impacted simulated future crop yields, especially for maizeAdjusting planting dates boosted yields for rice and soybeans but had limited benefits for maizeRelocating and expanding the rice area increased yields substantially [ABSTRACT FROM AUTHOR]

Global warming has become one of the major challenges in maintaining global food security. This paper reviews the impacts of climate change on fourteen strategic crops for eight sub-Saharan Africa countries. Climate change is projected to increase median temperature by 1.4-5.5°C and median precipitation by −2% to 20% by the end of the 21st century. However, large levels of uncertainty exist with temporal and spatial variability of rainfall events. The impact of climate change on crop yields in the region is largely negative. Among the grain crops, wheat is reported as the most vulnerable crop, for which up to 72% of the current yield is projected to decline. For other grain crops, such as maize, rice and soybean, up to 45% yield reductions are expected by the end of this century. Two grain crops, millet and sorghum, are more resilient to climate change for which projected impacts on crop yields are <20%. Root crops, such as sweet potato, potato and cassava are projected to be less affected than the grain crops with changes to crop yields ranging from about −15% to 10%. For the two major export crops, tea and coffee, up to 40% yield loss is expected due to the reduction in suitable areas caused by temperature increase. Similar loss of suitable areas is also expected for banana and sugarcane production, however, this reduction is due to rainfall variability in lowland areas. Other crops such as cotton and sugarcane are projected to be more susceptible to precipitation variation that will vary significantly in the region. In order to mitigate the long-term impacts of climate change on agricultural sectors, the development of small-scale irrigation systems and water harvesting structures seems promising, however, affordability of such measures remains a key issue. [ABSTRACT FROM AUTHOR]

We investigate the nature of crop yield systemic risk and its implications for farmers' area yield insurance choices. A theory‐grounded, normalized measure of systemic risk, R2, is developed and made amenable to empirical analysis through the logit link. The measure is estimated on a large‐scale, unit‐level corn yield dataset. We find that systemic risk explains less than half of total unit yield variability on average, suggesting that the risk management effectiveness of area yield insurance is low. By relating natural resource endowments with systemic risk, we find that more excessive heat and drought events lead to larger while more excessive precipitation events lead to smaller systemic risk. We then study whether current area yield insurance subsidy rates provide farmers with sufficient subsidy transfers to compensate for the uncovered risk exposure associated with area yield insurance. A new concept, the threshold relative area subsidy rate (TRASR), quantifies a lower bound on the ratio of area yield insurance subsidy rate over individual yield insurance subsidy rate below which risk‐averse farmers should always prefer individual yield insurance to area yield insurance. The calibrated TRASR values indicate that current area yield insurance subsidy rates discourage farmers from choosing area yield insurance over individual yield insurance, especially at low area yield insurance coverage levels. We also find that TRASR is positively correlated with systemic risk at the unit‐level of analysis, suggesting that farmers who like area yield insurance's risk management features will dislike its transfer implications and vice versa. [ABSTRACT FROM AUTHOR]

Simulations of crop yield due to climate change vary widely between models, locations, species, management strategies, and Representative Concentration Pathways (RCPs). To understand how climate and adaptation affects yield change, we developed a meta‐model based on 8703 site‐level process‐model simulations of yield with different future adaptation strategies and climate scenarios for maize, rice, wheat and soybean. We tested 10 statistical models, including some machine learning models, to predict the percentage change in projected future yield relative to the baseline period (2000–2010) as a function of explanatory variables related to adaptation strategy and climate change. We used the best model to produce global maps of yield change for the RCP4.5 scenario and identify the most influential variables affecting yield change using Shapley additive explanations. For most locations, adaptation was the most influential factor determining the projected yield change for maize, rice and wheat. Without adaptation under RCP4.5, all crops are expected to experience average global yield losses of 6%–21%. Adaptation alleviates this average projected loss by 1–13 percentage points. Maize was most responsive to adaptive practices with a projected mean yield loss of −21% [range across locations: −63%, +3.7%] without adaptation and −7.5% [range: −46%, +13%] with adaptation. For maize and rice, irrigation method and cultivar choice were the adaptation types predicted to most prevent large yield losses, respectively. When adaptation practices are applied, some areas are predicted to experience yield gains, especially at northern high latitudes. These results reveal the critical importance of implementing adequate adaptation strategies to mitigate the impact of climate change on crop yields. Plain Language Summary: Computer simulations are commonly used to predict how crop yield may change under future climate conditions and land management practices. For four major crops (maize, rice, wheat and soybean), we tested different statistical methods to synthesize thousands of computer simulations of crop yield change under future climate into one meta‐model which can be used to predict crop yield at any location where that crop is grown. We then predicted the change in crop yield under a likely future climate scenario (Representative Concentration Pathway 4.5) and identified which variables most explained the crop yield change. Considering both adaptive management status (whether or not adaptation practices were applied) and climate factors (average temperature, change in temperature, average precipitation, change in precipitation, CO2 concentration), we found that adaptation status was the most influential factor determining the predicted yield change for most crops. Managing cropland adaptively in the future can reduce predicted yield losses by 1–13 percentage points relative to maintaining the same management practices. We discuss which types of management practices may be the most useful for different crops, as well as which areas of the world are expected to gain or lose crop yield in the future. Key Points: Under future climate scenario Representative Concentration Pathway 4.5, average global yields of maize, rice, wheat, and soybean are projected to decrease by 6%–21%Implementing adaptive management practices could reduce this loss by 1–13 percentage points relative to maintaining the same practicesSome areas of the world, such as northern high latitudes, may see future yield increases if adaptation practices are applied [ABSTRACT FROM AUTHOR]

Plant growth and crop harvest are impacted by both climate change and air pollution. However, their relative importance in crop yields remains elusive, especially in heavily polluted regions. Here we develop crop yield prediction models, based on a large volume of historical crop data, as well as climate and pollution records in China since 1980. A long‐term surface ozone concentration data set is developed from a machine‐learning model and various observations. An assessment of four climate and pollution factors reveals the critical role of particulate and ozone pollution in regulating interannual variations of crop yields in China. During 2010–2018, we find that the particulate pollution mitigation outweighs the negative impacts of concurrent climate change, resulting in 0.5%–1.9% net yield increases nationwide, despite of the ozone increases in the North China Plain. Looking to the future, the impacts of climate change, particularly from surface temperature increase, will dominate over pollution factors and profoundly reduce future maize and rice yields by 0.6 to 2.8% 10 yr−1 by 2050. Our findings call for attention on the threat to future global food security from the absence of pollution mitigation and the persistence of global warming. Plain Language Summary: We capitalize on 39‐year historical records of crop yields, climate variables, and pollution data to develop a robust crop yield prediction model. Utilizing this model, we show a critical role of particulate pollution in determining annual crop yields. Moreover, our analyses reveal that the recent abatement of anthropogenic emission in China results in a net crop gain, which masks the crop loss due to the increased temperature and precipitation. In the next 30 years, temperature dominant climate effects will emerge to steer the future trend of crop yields. The present study demonstrates the co‐benefit of the recent air pollution control policy from agriculture and food perspectives. However, this benefit will eventually be diminished after the air pollution becomes alleviated in the full scale, while persisting or even exacerbated global warming will pose larger threat on the future food security. Key Points: Robust crop yield prediction models are developed to assess climate change and air pollution impacts on agricultureDuring 2010–2018, the particulate pollution mitigation outweighs the negative impacts of concurrent climate change in ChinaExacerbated global warming will lead to crop yield reduction in spite of pollution alleviation in the future scenarios [ABSTRACT FROM AUTHOR]

The article investigates the nonlinear effects of climate change on the yields using the data set of corn yields and daily weather records covering the eastern U.S. from 1950-2004. The study focuses on the association between heat and crop yields which could have implications on food supply. The data show that the relationship between the weather outcomes and the yields implies that yields are increasing in temperature for moderate temperatures, but they become harmful when temperatures exceed 30 °Celsius. In fact, this relationship has been considered by the researchers as highly significant.

Heatwaves are increasing in frequency, duration, and intensity due to climate change. They are associated with high mortality rates and cross‐sectional impacts including a reduction in crop yield and power outages. Here we demonstrate that there are large deficiencies in reporting of heatwave impacts in international disasters databases, international organization reports, and climate bulletins. We characterize the distribution of heat stress across the world focusing on August in the Northern Hemisphere, when notably heatwaves have taken place (i.e., 2003, 2010, and 2020) for the last 20 years using the ERA5‐HEAT reanalysis of the Universal Thermal Comfort Index and establish heat stress has grown larger in extent, more so during a heatwave. Comparison of heat stress against the emergency events impacts database and climate reports reveals underreporting of heatwave‐related impacts. This work suggests an internationally agreed protocol should be put in place for impact reporting by organizations and national government, facilitating implementation of preparedness measures, and early warning systems. Plain Language Summary: Heat extremes are increasing in frequency, duration, and intensity due to climate change. Their impacts include a rise in death rates, a decrease in how much of a crop is produced and power outages. Here we show that there is a lack of reporting of impacts in international organization reports, international disaster databases. Further, heat stress, an impact of heat extremes is characterized using an index that is human‐centric. With a focus on August and the Northern Hemisphere, we show that heat stress has grown in extent and is presenting a growing risk to more of the population over this millennium. This work suggests that organizations and national governments should come together to agree a protocol for how to best report heat impacts, so that we can be better prepared for them. Key Points: In the Northern Hemisphere, heat stress during the month of August is growing in area and is larger during a heatwaveHeat stress area increase is greater over the populated land surface than the total land surface during AugustImpacts of Heatwaves are not sufficiency captured by international meteorological organization reports and emergency events impacts database [ABSTRACT FROM AUTHOR]

Pressures of population growth and climate change require the development of resilient higher yielding crops, particularly to drought. A spring wheat diversity panel was developed to combine high‐yield potential with resilience. To assess performance under drought, which in many environments is intermittent and dependent on plant development, 150 lines were grown with drought imposed for 10 days either at jointing or at anthesis stages in Obregon, Mexico. Both drought treatments strongly reduced grain numbers compared with the fully irrigated check. Best performers under drought at jointing had more grain than poor performers, while best performers under drought at anthesis had larger grain than poor performers. Most high‐yielding lines were high yielding in one drought environment only. However, some of the best‐performing lines displayed yield potential and resilience across two environments (28 lines), particularly for yield under well‐watered and drought at jointing, where yield was most related to grain numbers. Strikingly, only three lines were high yielding across all three environments, and interestingly, these lines had high grain numbers. Among parameters measured in leaves and grain, leaf relative water content did not correlate with yield, and proline was negatively correlated with yield; there were small but significant relationships between leaf sugars and yield. This study provides a valuable resource for further crosses and for elucidating genes and mechanisms that may contribute to grain number and grain filling conservation to combine yield potential and drought resilience. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Hazelnut is consumed worldwide and is of critical economic importance to the rural communities of Turkey's northern coast. A new disease outbreak has drastically decreased yields across Turkey and climate change is emerging as a new threat to cultivation. Our study is the first to provide a genomic perspective on diversity in this vulnerable crop, which will prove valuable for future breeding efforts. Such research into perennial crops like hazelnut can help to improve farmer livelihoods and ensure the sustainability of crop production in a changing world. SummaryAssessing and describing genetic diversity in crop plants is a crucial first step toward their improvement. The European hazelnut, Corylus avellana L., is one of the most economically important tree nut crops worldwide. It is primarily produced in Turkey where rural communities depend on it for their livelihoods. Despite this, we know little about its domestication history and the genetic diversity it holds.We use double digest restriction‐site associated DNA (ddRAD) sequencing to produce a genome‐wide dataset containing wild and domesticated hazelnut. We uncover patterns of population structure and diversity, determine levels of crop‐wild gene flow, and estimate the relationships among different genetic clusters.We use a dataset of over 60k single nucleotide polymorphisms to find that genetic clusters of cultivars do not reflect their given names and that there is limited evidence for a reduction in genetic diversity in domesticated individuals. We find evidence that hazelnut may have been domesticated more than once and that admixture has likely occurred multiple times between wild and domesticated hazelnut.We provide the first genomic assessment of Turkish hazelnut diversity and suggest that there has not been an extreme bottleneck during the domestication of this crop, leading to cultivars at different stages of domestication. Our study provides a platform for further research that will protect hazelnut from the threats of climate change and an emerging fungal disease. [ABSTRACT FROM AUTHOR]

Breeding for resilience to climate change is a daunting prospect. Crop and climate models tell us that global wheat yields are likely to decline as the climate warms, causing a significant risk to global food security. High temperatures are known to affect crop development yet breeding for tolerance to heat stress is difficult to achieve in field environments. We conducted an active warming study over two years to quantify the effects of heat stress on genetic variation of soft red winter (SRW) wheat (Triticum aestivum L.). Forty SRW cultivars and breeding lines were chosen based on marker genotypes at photoperiod sensitivity and reduced height loci. These genotypes were planted in a randomized complete block design replicated twice across two environments, ambient and artificially warmed. Average heading date occurred 5 days earlier in the warmed environment than in the ambient environment over both years (p ≤ 0.05). On average, grain yield was significantly reduced in the warmed environment by 211.41 kg/ha (p ≤ 0.05) or 4.84%, though we identified 13 genotypes with increased yield in response to warming in both years. Of these genotypes, eight had significantly increased N uptake while six showed significantly increased N utilization efficiency under warming. Under warming, genotypes with wild‐type alleles at the Rht‐D1 locus display significantly greater yields (p ≤ 0.01) and biomass (p ≤ 0.001) than genotypes with reduced height alleles. Of the 13 genotypes with higher (p ≤ 0.01) yields under warming, nine have the wild‐type allele at the Rht‐D1 locus in addition to being photoperiod insensitive. The next steps will be to validate these findings in other populations and to develop an efficient breeding/phenotyping scheme that will lead to more resilient cultivars. Increasing global temperatures will reduce wheat yield and productivity. To identify genetic variation for resilience to warming, we screened a set of diverse wheat genotypes in a warmed and a control environment. On average, grain yield was significantly (p < 0.05) reduced in the warmed environment though we identified genotypes which had higher yields in response to warming in both years. [ABSTRACT FROM AUTHOR]

Abstract: To accurately assess the roles of agriculture in securing food security and maintaining environmental sustainability, it is essential to improve the representation of crop growth, development, and yield formation in global land models that traditionally focus on energy, water, carbon, and nitrogen exchanges between land and the atmosphere. In this study, a process‐based agricultural module has been coupled with the Dynamic Land Ecosystem Model (DLEM‐AG2.0) for assessing how multiple environmental factors (climate change, atmospheric CO2 concentration, tropospheric O3, and nitrogen deposition) and human activities (land use/cover change, nitrogen fertilizer use, and irrigation) have affected the crop growth, development, yield, carbon (C), nitrogen (N), and water cycles in agroecosystems. Here we describe the model structure for simulating crop growth, development, and yield formation in the DLEM‐AG2.0, and then we validate the model using field observations and a national yield survey for three major crops (wheat, maize, and rice) in China during 1980–2012. Results show that the DLEM‐AG2.0 is capable of simulating the dynamic processes of phenological development, leaf growth expansion, biomass accumulation, biomass allocation, and yield formation for wheat, maize, and rice with normalized root mean square errors of the simulations of less than 20%. Our model‐based yield estimation for the three major crops at the national scale for the period 1980–2012 is generally consistent with the national yield survey in China. The crop representation in the DLEM‐AG2.0 is flexible for extrapolating to a global scale after rigorous testing with both site‐specific and regional observations. Further advancement of agricultural modeling within the global land modeling framework will require consideration of human perception and behavior for adapting and mitigating global change. [ABSTRACT FROM AUTHOR]

Abstract: Agricultural food production in China is deeply vulnerable to extreme droughts. Although there are many studies to evaluate this issue from different aspects, comprehensive assessments with full consideration of climate change, crop rotations, irrigation effects, and socioeconomic factors in broad scales have not been well addressed. Considering both the probability of drought occurrence and the consequential yield losses, here we propose an integrated approach for assessing past and future agricultural drought risks that relies on multimodel ensemble simulations calibrated for rice, maize, and wheat (RMW) in China. Our results show that irrigation has reduced drought‐related yield losses by 31 ± 2%; the largest reductions in food production were primarily attributable to socioeconomic factors rather than droughts during 1955–2014. Unsustainable water management, especially groundwater management, could potentially cause disastrous consequences in both food production and water supply in extreme events. Our simulations project a rise of 2.5~3.3% in average rice, maize, and wheat productivity before 2050 but decrease thereafter if climate warming continues. The frequency of extreme agricultural droughts in China is projected to increase under all examined Representative Concentration Pathway (RCP). A current 100‐year drought is projected to occur once every 30 years under RCP 2.6, once every 13 years under RCP 4.5, and once every 5 years under RCP 8.5. This increased occurrence of severe droughts would double the rate of drought‐induced yield losses in the largest warming scenario. Policies for future food security should prioritize sustainable intensification and conservation of groundwater, as well as geographically balanced water resource and food production. [ABSTRACT FROM AUTHOR]

We propose and empirically test a simple model to shed light on the nature of interactions between weather, land quality, and yield. The conceptual model posits substitution relations between water stress metrics and soil quality, as well as a soil-conditioned threshold water stress level beyond which soil cannot buffer crop yields. The model implies that yield-yield dependence should vary as growing conditions vary. In comparison with intermediate growing conditions, yield-yield dependence should strengthen when growing conditions are either very good or very poor. County yield data strongly support substitution between soil and benign water availability levels, but complementarity between soil and beneficial heat variables. Our estimated model provides qualified support for the hypothesis that better land is more resilient to water stress. We estimate a pseudo-copula that nests the Gaussian copula, finding strong evidence of left tail dependence among yields. Our formal model and empirical findings corroborate others' concerns about the appropriateness of current USDA rate-setting methodologies, which posit constant state-conditional rank correlations, implicitly assumed by use of the Gaussian copula. An application to aggregate crop yield rate setting suggests that current methods underprice area yield and whole farm premiums. Applying our empirical model to medium-range weather projections under a climate change scenario for the Northern Great Plains, we infer that systemic yield correlations will increase in future years. [ABSTRACT FROM AUTHOR]

Abstract: Climate extremes can negatively impact crop production, and climate change is expected to affect the frequency and severity of extremes. Using a combination of in situ station measurements (Global Historical Climatology Network's Daily data set) and multiple other gridded data products, a derived 1° data set of growing season climate indices and extremes is compiled over the major growing regions for maize, wheat, soybean, and rice for 1951–2006. This data set contains growing season climate indices that are agriculturally relevant, such as the number of hot days, duration of dry spells, and rainfall intensity. Before 1980, temperature‐related indices had few trends; after 1980, statistically significant warming trends exist for each crop in the majority of growing regions. In particular, crops have increasingly been exposed to extreme hot temperatures, above which yields have been shown to decline. Rainfall trends are less consistent compared to temperature, with some regions receiving more rainfall and others less. Anomalous temperature and precipitation conditions are shown to often occur concurrently, with dry growing seasons more likely to be hotter, have larger drought indices, and have larger vapor pressure deficits. This leads to the confluence of a variety of climate conditions that negatively impact crop yields. These results show a consistent increase in global agricultural exposure to negative climate conditions since 1980. [ABSTRACT FROM AUTHOR]

Abstract: During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water crisis, deforestation, and climate change threaten the global food security. An understanding of the variables that caused past changes in crop yields can help improve future crop prediction models. In this article, we present a comprehensive global analysis of the changes in the crop yields and how they relate to different large‐scale and regional climate variables, climate change variables and technology in a unified framework. A new multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Niño‐southern oscillation (ENSO), Palmer drought severity index (PDSI), geopotential height anomalies (GPH), historical carbon dioxide (CO2) concentration and country‐based time series of GDP per capita as an approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. Results indicate that these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out‐of‐sample verifications. While some countries were not generally affected by climatic factors, PDSI and GPH acted both positively and negatively in different regions for crop yields in many countries. [ABSTRACT FROM AUTHOR]

While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climate change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water-stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO2 fertilization effect compared to an unconstrained GHG emission scenario. [ABSTRACT FROM AUTHOR]

Contents50I.50II.52III.54IV.55V.57VI.57VII.5960References61 Summary: As a consequence of an increase in world population, food demand is expected to grow by up to 110% in the next 30–35 yr. The population of sub‐Saharan Africa is projected to increase by > 120%. In this region, cassava (Manihot esculenta) is the second most important source of calories and contributes c. 30% of the daily calorie requirements per person. Despite its importance, the average yield of cassava in Africa has not increased significantly since 1961. An evaluation of modern cultivars of cassava showed that the interception efficiency (ɛi) of photosynthetically active radiation (PAR) and the efficiency of conversion of that intercepted PAR (ɛc) are major opportunities for genetic improvement of the yield potential. This review examines what is known of the physiological processes underlying productivity in cassava and seeks to provide some strategies and directions toward yield improvement through genetic alterations to physiology to increase ɛi and ɛc. Possible physiological limitations, as well as environmental constraints, are discussed. [ABSTRACT FROM AUTHOR]

Climate change affects agriculture by altering not only output quantity, but also crop quality. We quantify the economic impacts of climate change on agriculture through changes in both quantity and quality, where quality is measured by crop grades. Our model controls for methodological issues regarding sample selection, aggregation, phenology, and nonlinearity. The empirical application to Japanese rice production indicates that temperature effects are asymmetric: quantity is especially vulnerable to cold, whereas quality is vulnerable to extremely high temperature. Using these results, we simulate the effect of global warming, and we find that warming (a 3°C increase) increases farm revenues by improving yield but decreases revenues as a result of deteriorating quality. The net effect is negative, suggesting that quality matters more than quantity. The negative effect, however, can be mitigated by shifting cultivation periods and/or regions. Overall, our results suggest that the estimated impacts of climate change and adaptation strategies could be severely misleading unless quality is considered. [ABSTRACT FROM AUTHOR]

Climate change poses a serious global challenge in the face of rapidly increasing human demand for energy and food. A recent phenomenon in which climate change may play an important role is the acquisition of large tracts of land in the developing world by governments and corporations. In the target countries, where land is relatively inexpensive, the potential to increase crop yields is generally high and property rights are often poorly defined. By acquiring land, investors can realize large profits and countries can substantially alter the land and water resources under their control, thereby changing their outlook for meeting future demand. While the drivers, actors, and impacts involved with land deals have received substantial attention in the literature, we propose that climate change plays an important yet underappreciated role, both through its direct effects on agricultural production and through its influence on mitigative or adaptive policy decisions. Drawing from various literature sources as well as a new global database on reported land deals, we trace the evolution of the global land rush and highlight prominent examples in which the role of climate change is evident. We find that climate change-both historical and anticipated-interacts substantially with drivers of land acquisitions, having important implications for the resilience of communities in targeted areas. As a result of this synthesis, we ultimately contend that considerations of climate change should be integrated into future policy decisions relating to the large-scale land acquisitions. [ABSTRACT FROM AUTHOR]

Many climate change mitigation strategies rely on strong projected growth in biomass energy, supported by literature estimating high future bioenergy potential. However, expectations to 2050 are highly divergent. Examining the most widely cited studies finds that some assumptions in these models are inconsistent with the best available evidence. By identifying literature-supported, up-to-date assumptions for parameters including crop yields, land availability, and costs, we revise upper-end estimates of potential biomass availability from dedicated energy crops. Even allowing for the conversion of virtually all 'unused' grassland and savannah, we find that the maximum plausible limit to sustainable energy crop production in 2050 would be 40-110 EJ yr−1. Combined with forestry, crop residues, and wastes, the maximum limit to long-term total biomass availability is 60-120 EJ yr−1 in primary energy. After accounting for current trends in bioenergy allocation and conversion losses, we estimate maximum potentials of 10-20 EJ yr−1 of biofuel, 20-40 EJ yr−1 of electricity, and 10-30 EJ yr−1 of heating in 2050. These findings suggest that many technical projections and aspirational goals for future bioenergy use could be difficult or impossible to achieve sustainably. [ABSTRACT FROM AUTHOR]

The article focuses on the assessment of climate change impact methods for agriculture, with emphasis on incorporating process-based crop simulation models into global economic analysis. It explains the biophysical effects of climate change on yields and assessing the impacts of climate change and economic development on food security.

The article focuses on the econometric models to calculate the implication of climate change on crop yield in the U.S. It presents explanation of vapor pressure deficit (VPD) model to calculate the crop yield. It provides detail information on three regression models to see the impact of climate change on crop yield.

Scientists predict that global agricultural lands will expand over the next few decades due to increasing demands for food production and an exponential increase in crop-based biofuel production. These changes in land use will greatly impact biogeochemical and biogeophysical cycles across the globe. It is therefore important to develop models that can accurately simulate the interactions between the atmosphere and important crops. In this study, we develop and validate a new process-based sugarcane model (included as a module within the Agro-IBIS dynamic agro-ecosystem model) which can be applied at multiple spatial scales. At site level, the model systematically under/overestimated the daily sensible/latent heat flux (by −10.5% and 14.8%, H and λE, respectively) when compared against the micrometeorological observations from southeast Brazil. The model underestimated ET (relative bias between −10.1% and -12.5%) when compared against an agro-meteorological field experiment from northeast Australia. At the regional level, the model accurately simulated average yield for the four largest mesoregions (clusters of municipalities) in the state of São Paulo, Brazil, over a period of 16 years, with a yield relative bias of −0.68% to 1.08%. Finally, the simulated annual average sugarcane yield over 31 years for the state of Louisiana (US) had a low relative bias (−2.67%), but exhibited a lower interannual variability than the observed yields. [ABSTRACT FROM AUTHOR]

Despite complex regional patterns of projected climate change, significant decreases in food crop yields have been predicted using the 'worst case' CO emission scenario (A1FI) of the Intergovernmental Panel on Climate Change. Overall, climate change is predicted to have a progressively negative effect on the yield of food crops, particularly in the absence of efforts to mitigate global CO emissions. As with all species, plant pathogens will have varying responses to climate change. Whilst the life cycle of some pathogens will be limited by increasing temperatures, e.g. Puccinia striiformis f.sp. tritici, other climatic factors such as increasing atmospheric CO, may provide more favourable conditions for pathogens such as Fusarium pseudograminearum. Based on published literature and unpublished work in progress, we have reviewed the qualitative effects of climate change on pathogens that cause disease of four major food crops: wheat, rice, soybean and potato. The limited data show that the influence will be positive, negative or neutral, depending on the host-pathogen interaction. Quantitative analysis of climate change on pathogens of these crops is largely lacking, either from field or laboratory studies or from modelling-based assessments. Systematic quantitative analysis of these effects will be necessary in developing future disease management plans, such as plant breeding, altered planting schedules, chemical and biological control methods and increased monitoring for new disease threats. [ABSTRACT FROM AUTHOR]

Summary 1I. What is FACE? 2II. Materials and methods 2III. Photosynthetic carbon uptake 3IV. Acclimation of photosynthesis 6V. Growth, above-ground production and yield 8VI. So, what have we learned? 10 Acknowledgements 11 References 11. References included in the database for meta-analyses 14. Results of the meta-analysis of FACE effects 18Free-air CO2 enrichment (FACE) experiments allow study of the effects of elevated[CO2] on plants and ecosystems grown under natural conditions without enclosure. Data from 120 primary, peer-reviewed articles describing physiology and production in the 12 large-scale FACE experiments (475–600 ppm) were collected and summarized using meta-analytic techniques. The results confirm some results from previous chamber experiments: light-saturated carbon uptake, diurnal C assimilation, growth and above-ground production increased, while specific leaf area and stomatal conductance decreased in elevated[CO2]. There were differences in FACE. Trees were more responsive than herbaceous species to elevated[CO2]. Grain crop yields increased far less than anticipated from prior enclosure studies. The broad direction of change in photosynthesis and production in elevated[CO2] may be similar in FACE and enclosure studies, but there are major quantitative differences: trees were more responsive than other functional types; C4 species showed little response; and the reduction in plant nitrogen was small and largely accounted for by decreased Rubisco. The results from this review may provide the most plausible estimates of how plants in their native environments and field-grown crops will respond to rising atmospheric[CO2]; but even with FACE there are limitations, which are also discussed.New Phytologist(2004)doi: 10.1111/j.1469-8137.2004.01224.x© New Phytologist(2004) [ABSTRACT FROM AUTHOR]

Simultaneous genetic improvements in grain yield and heat stress tolerance (HST) are necessary to avoid a fall in crop yields caused by global warming during the 21st century. Future food security depends on crop breeding solutions to this challenge, especially in developing countries where the need is greatest. We stochastically model a wheat breeding program during 60 years of rapid global warming based on rapid 2‐year cycles, with selection in early generations for HST, grain yield, disease resistance, and stem strength. In each cycle, breeding values were estimated by best linear unbiased prediction using all pedigree and phenotypic information (including selfing) back to the founders. We compared two methods of selection and mating design with similar costs. The first method was truncation selection for HST to match predicted increases in land temperatures followed by selection for an economic index composed of weighted estimated breeding values for each trait, followed by random pair‐wise mating among selections. The second method was optimal contributions selection (OCS) for the economic index with an overriding constraint to increase HST in each cycle to match global warming trends, and mating prescribed by OCS. Truncation selection caused a rapid loss of genetic diversity, and HST did not keep pace with global warming. Consequently, grain yield began to decline due to heat stress before 60 years. With OCS, HST matched global warming trends, the economic index almost tripled and grain yield nearly doubled during 60 years of global warming. OCS on an economic index, with a priority to meet HST, increased grain yields and avoided a major threat to global food security caused by global warming. [ABSTRACT FROM AUTHOR]