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Background and Aims: Two main problems the globe currently facing are migration and weather variation. Weather change has a significant impact on the agricultural industry, which affects the majority of poor people. There is a dearth of adequate methodological documentation when examining the relationship between weather variation, agricultural output, and migration. We aimed to identify methodological reporting difficulties by reviewing the quantitative literature on weather‐related migration through agricultural channels. Methods: A systematic evaluation was conducted using papers published between January 2010 and June 2022, indexed in the SCOPUS, PUBMED, and Google Scholar databases. Using inclusion/exclusion criteria, we selected 22 original research articles out of 18,929 distinct articles for review, in accordance with the PRISMA guidelines. We extracted data from each study to understand how various concepts, research designs, and investigative techniques influence our understanding of migration patterns related to weather in the agricultural sector. Results: The majority (64%) of the study's data consisted of time series data. In 50% of the studies, secondary data were used. Additionally, 55% of these studies did not state the sample size. In 40% of the studies, model assumptions were fully adhered to, whereas in 36% of the studies, they were not followed at all. The majority of the articles used the Ordinary Least Squares technique, while about 41% applied the Two‐Stage Least Squares technique. Various tests were conducted across these studies, such as robustness checks (59.1%), endogeneity tests (31.8%), omitted variable bias tests (22.7%), sensitivity analyses (22.7%), and weak instrument tests (13.6%), to name a few. In the research we selected, the methodology section had various shortcomings and lacked organization. Furthermore, the justifications for deviations from model assumptions were unclear, potentially affecting the study outcomes. Conclusion: This study has important indications for researchers in studying climatic (weather) migration through agricultural channels besides for policymakers by giving a thorough review of the methods and techniques. Key points: Providing narrative insights on how various issues, research designs, and analytical techniques shape our understanding of the association between weather variation and migration via agricultural production.Providing a broad overview of the pertinent literature, covering 22 articles, as a complement to earlier studies.Our study has important ramifications for researchers studying climatic (weather) migration through agricultural channels as well as for policymakers by giving a thorough review of the methods and techniques currently used in this sector.Future studies on weather variation, agricultural production, and migration should use prudent and comparable models that capture whole climatic (weather) impacts on migration through mediating factors like agricultural production. [ABSTRACT FROM AUTHOR]

Agriculture is weather dependent, and changes in climate can have a drastic impact on our ability to feed, fuel, and clothe the world's population. Climate change is causing more frequent and unprecedented extreme weather events that are already negatively affecting agriculture. We need to assess the effects of extreme temperatures and rainfall on agriculture. Patterns of short‐term extreme weather events, such as elevated temperatures, flooding, and strong winds, are not predictable enough to design field experiments around. Process‐based crop and soil simulation models allow us to explore new management options and thus provide whole‐system‐based knowledge and management guides for different locations over variable climate conditions. By using crop simulation models, researchers can test different adaptation strategies and assess their effectiveness in reducing the impacts of climate change on agricultural production. In this paper, we discuss the development of crop models and how they have been used to assess the effects of a changing climate on agricultural productivity and propose methods for agriculture to adapt to those changes. We describe potential applications of crop models to assess regional issues such as irrigation demand, greenhouse gas emissions, and policy decisions. Better understanding of how weather and climate forecasts at various scales are provided and the reliability of these forecasts is important for using crop models as a planning tool. Different approaches for simulating long‐term climate change impacts on crop yield and seasonal yield forecasting are discussed. The use of ensemble models to better assess climate change impacts is also discussed. Core Ideas: Assessment of climate change impacts on agriculture requires a systems approach.Agriculture is weather dependent, and changes in climate can have a drastic impact on food production.Crop models can extend the results of physical experiments to better assess long‐term climate change effects on agriculture.Crop models are critical in assessing climate change impacts and developing adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

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Climate is an essential element in agricultural production, and climate change inevitably have an impact on agriculture. Assessing the economic consequences of climate change requires comprehensive assessments of the impact chain from climate to crops and the economy. In our previous study, we derived a dose‐response function to estimate the response of crop yields to climate variables through a systematic review. In this paper, a dynamic multiregional input‐output model is established to assess the economic consequences of changes in agricultural production on China's regional and sectoral levels. The results show that (1) the direct economic damage is equivalent to 1% of gross domestic product (GDP) which implies the resulting economic cascade effect (ECE) that amounts to 17.8% of China's GDP. At the end of 21st century, the ECE is −0.1% to 13.6% of GDP (negative values indicate economic gains) without considering CO2 fertilization effect, of which the ECE in the most pessimistic pathway are equivalent to the total agricultural output in China today. (2) Regional‐level results show an uneven distribution of economic impact in China, which is related to the regional economic development. The least developed region in China experiences 2.8 to 8.5 times more ECE caused by climate change than the most developed region. (3) Sector‐level results show that agriculture is still the main affected sector, but in developed regions, manufacturing and services also bear part of the ECE. Plain Language Summary: Evidence from numerous studies has confirmed the impact of climate change on agriculture. This paper assesses the economic consequences of changes in agricultural production under climate change in China. We find that the direct economic damage is equivalent to 1% of gross domestic product which implies the resulting economic cascade effect that amounts to 17.8% of total gross domestic product. The most pessimistic estimate of the economic impacts in the 2090s is equivalent to China's total agricultural output today without considering CO2 fertilization effect of climate change. The economic impacts suffered by different regions of China are related to regional economic development. The least developed region in China experiences more economic damage from climate change than the most developed region. In addition to the agricultural sector, manufacturing and services are expected to experience part of the impacts, especially in developed regions. This paper hopes to provide data support for a comprehensive understanding of climate change impacts in different regions of China by assessing the economic consequences. Key Points: The economic cascading effect of climate change is approximately 18 times larger than its direct damageThe least developed region in China experiences 2.8 to 8.5 times more economic impacts than the most developed regionThe decline in agricultural output also affects manufacturing and services through trade networks, especially in developed regions [ABSTRACT FROM AUTHOR]

This paper explores the interplay between the biophysical and economic geographies of climate change impacts on agriculture. It does so by bridging the extensive literature on climate impacts on yields and physical productivity in global crop production, with the literature on the role of adaptation through international trade in determining the consequences of climate change impacts. Unlike previous work in this area, instead of using a specific crop model or a set of models, we employ a statistical meta-analysis that encompasses all studies available to the IPCC-AR5 report. This permits us to isolate specific elements of the spatially heterogeneous biophysical geography of climate impacts, including the role of initial temperature, differential patterns of warming, and varying crop responses to warming across the globe. We combine these climate impact estimates with the Global Trade Analysis Project model of global trade in order to estimate the national welfare changes that are decomposed into three components: the direct (biophysical impact) contribution to welfare, the terms of trade effect, and the allocative efficiency effect. We find that when we remove the spatial variation in climate impacts, the terms of trade impacts are cut in half. Given the inherent heterogeneity of climate impacts in agriculture, this points to the important role of trade in distributing the associated welfare impacts. When we allow the biophysical impacts to vary across the empirically estimated uncertainty range taken from the meta-analysis, we find that the welfare consequences are highly asymmetric, with much larger losses at the low end of the yield distribution. This interaction between the magnitude and heterogeneity of biophysical climate shocks and their welfare effects highlight the need for detailed representation of both in projecting climate change impacts. [ABSTRACT FROM AUTHOR]

The issue of transboundary river management is highly pertinent to South Asia with conflicts emerging in water resource management and utilization which indeed affect the beneficial uses of downstream states. The paper considers the situation of the Teesta river basin to assess the water unavailability driven agricultural shock, cost of loss and damage of the downstream region, identify the mutual benefits of water collaboration, and suggest potential adaptation options for water unavailability. The study found during 1995–2010, the annual mean discharge in the upper Teesta floodplain was 707 cumec, where the downstream discharge was 140 cumec and during 2005–2010 the downstream region received less than 8% of the mean annual discharge which may be attributed to reduced flow from the upstream region due to the intense operation of dams and barrage. Consequently, the water reliant 5,000 villages and 12 sub‐districts of lower Teesta basin of Bangladesh were found to be experiencing immense hydro‐socioeconomic impairment and food insecurity. The study estimates that in the Rangpur region, total loss and damage of Boro rice production is 15,08,020 metric tons during 2006–2015, and in the Nilphamri total loss and damage is estimated 9,83,054 metric tons during the same period. Here, the total computed economic loss for both the region is $564 million from 2006–15 FY, which remarkably trims down the overall agricultural production and socio‐economic situation in the lower Teesta region of Bangladesh and fuel the hydro‐economic and political tension between the close riparian. [ABSTRACT FROM AUTHOR]

In this paper, we used a Bayesian model averaging (BMA) approach to analyse the changes in rainfall extremes in the periods 2041–2070 and 2071–2099 over northeast Bangladesh as a consequence of climate change. Climate change over this region could potentially impact agricultural production, water resources management, and the overall economy of the country. We used six regional climate models (RCMs) over the Coordinated Regional Downscaling Experiment South Asia domain. We used one medium stabilization scenario (RCP4.5) and one high‐emission scenario (RCP8.5) for projecting the extreme rainfall indices. A multi‐model ensemble mean was generated using the BMA approach. The BMA mean is a weighted average related to each RCM's predictive skill during the training period. Most of the rainfall extremes are expected to increase in both pre‐monsoon (March–May) and monsoon (June–September) seasons in the future compared with baseline (1976–2005). The average pre‐monsoon rainfall of the study area is projected to increase by 12.93 and 18.42% under RCP4.5 and 18.18 and 23.85% under RCP8.5 for the periods 2041–2070 and 2071–2099, respectively. The average monsoon rainfall of the study area is projected to increase by 4.96 and 2.27% under RCP4.5 and 6.56 and 6.40% under RCP8.5 for the periods 2041–2070 and 2071–2099, respectively. All the extreme indices except consecutive wet day are expected to change significantly at the 95% confidence level during the pre‐monsoon season. The study area will potentially be subjected to more frequent floods in the future both in pre‐monsoon and monsoon seasons as a consequence of climate change. Notably, the intensity and the magnitude of flash flooding in the pre‐monsoon season are expected to increase more in the future because the increase in extreme indices is more significant during that season. [ABSTRACT FROM AUTHOR]

This Address considers a range of issues relating to the contribution of meat consumption and livestock production to global warming, given the need highlighted by the Committee on Climate Change ( CCC) to reduce global GHG emissions by over 50% by 2050. The IPCC Climate Change 2014 report recognised that demand oriented measures may also contribute to GHG mitigation. The paper reviews a number of studies which examine demand-led mitigation potentials, and concludes that such estimates ignore the market effects of changes in meat consumption habits or demand oriented policies. A simple partial equilibrium model of beef, poultry, pig and ovine meats is developed for the major regions of the world to explore the impact of a range of scenarios which might reduce meat consumption and GHG emissions. These include emissions taxation, long-term trends in reduction of red meat consumption in developed economy regions, and supply side improvements in livestock emissions intensities. The paper discusses problems associated with many published demand elasticities suitable for incorporation into a market model, problems of selection from widely varying published estimates and their appropriateness for longer-run projections. The dearth of published supply elasticity estimates is also highlighted. The modelling concludes that economic and population growth to 2050 without any mitigation measures will lead to a 21% increase in per capita meat consumption and a 63% increase in total consumption and GHG emissions by 2050. However, the mitigation projections from the scenarios explored only generate a 14% reduction in cumulative emissions from the baseline 2050 projections, insufficient to meet the CCC target. [ABSTRACT FROM AUTHOR]

The main challenge facing agricultural crop production in the current global climate change scenario is sustainability. Drought, as a yield‐limiting factor, has become a major threat to international food security. Tolerance to drought is a complex trait and its response is carried out by various genes, transcription factors, microRNAs, hormones, proteins, cofactors, ions and metabolites. The complexity of the trait has limited the development of drought‐tolerant sweet potato cultivars by traditional breeding. Advances in sweet potato breeding to exploit the full potential of the crop to contribute to improved and higher performing sweet potato cultivars, adapted to increasingly risky rainfed conditions, mainly drought, are key to making food production systems more efficient and more tolerant to pressure from drought and other stressors. Genetic gain for yield potential in sweet potato has improved mostly in African countries, mainly as a result of an accelerated breeding scheme. The focus on maximising the utilisation of molecular tools for sweet potato improvement and yield has been recently explored in breeding programmes in sub‐Saharan Africa (SSA). This article provides an update on the trends and gaps in sweet potato breeding in SSA, reviewing the relevant strategies used to improve sweet potato in the region. Finally, the perspectives of using new advanced tools including genetic engineering and genome editing, and the challenges associated with climate change for further improvement of the crop are highlighted. Collaborative efforts in African countries are driving advances in breeding methods through the incorporation of molecular tools to develop drought‐tolerant sweet potato varieties that are important to global food security despite challenges posed by the drastic change in climate. [ABSTRACT FROM AUTHOR]

Quantifying and separating different sources of uncertainty helps to improve the understanding of the projected effects of climate change and can inform decision‐making in adaptation planning. This paper (1) evaluated four process‐based crop models; (2) assessed the effects of climate change on maize yield using climate change outputs from seven global climate models (GCMs) under three representative concentration pathways (RCPs); and (3) disaggregated the contributions of multiple crop models, GCMs and RCPs to overall uncertainty. All four models captured more than 80% of the variation in days to silking, maturity and yield, indicating reasonably reproduced observations. Similarly, the root mean square errors were moderate for days to silking and maturity (fewer than 4 days) and yield (0.5–0.7 t/ha). Overall, the results indicate that the models could assess grain yield at the study sites reasonably well. The results of the multiple models ensemble indicate that the maize yield will decrease by 9–11% with a probability of 72–80% on average during the period 2010–2039 relative to the baseline (1976–2005). The uncertainty in the maize‐yield simulations might arise mostly from the GCM models, followed by the crop models and RCPs, the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. The uncertainty in maize‐yield simulations might arise mostly from global climate models (GCMs), followed by crop models and representative concentration pathways (RCPs), the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. [ABSTRACT FROM AUTHOR]

Abstract: This paper makes use of a Computable General Equilibrium model to analyze the impact of two agricultural productivity trends on poor households’ food security in Burkina Faso: a negative trend that could arise from many different factors, including land degradation, climate change, and harmful agricultural practices; and a positive trend which may result from enhanced public investment in agriculture, notably in research and development, extension, irrigation, rural roads, rural electrification, and rural education. The results point to a high sensitivity of the poor's consumption to agricultural productivity as well as to stronger impacts on the urban poor than on the rural poor. The current situation is already characterized by severe food insecurity, such that a decline in agricultural productivity is likely to further plunge the urban poor into a deep food crisis. By contrast, positive agricultural productivity trends may help alleviate poverty and food insecurity. Agricultural productivity may indeed affect the poor's food consumption mainly through large changes in agricultural prices and real incomes. Cet article utilise un modèle d’Équilibre Général Calculable pour analyser l'impact des différentes tendances de la productivité agricole sur la sécurité alimentaire des ménages pauvres au Burkina Faso. D'une part, les tendances négatives peuvent résulter de plusieurs facteurs, y compris la dégradation des terres, le changement climatique, et les pratiques agricoles nuisibles. D'autre part, la tendance positive peut résulter de l'investissement public accru dans l'agriculture, notamment dans la recherche et développement, la vulgarisation, l'irrigation, les routes rurales, l′électrification rurale et l′éducation rurale. Les résultats montrent une sensibilité élevée de la consommation des pauvres à la productivité agricole, ainsi que des impacts plus forts sur les populations urbaines pauvres. La situation actuelle est déjà caractérisée par une insécurité alimentaire grave de sorte qu'une baisse de la productivité agricole est susceptible de plonger davantage les pauvres en milieu urbain dans une crise alimentaire profonde. En revanche, la tendance positive sur la productivité agricole peut contribuer à atténuer la pauvreté et l'insécurité alimentaire. La productivité agricole peut en effet affecter la consommation alimentaire des pauvres principalement par le biais de grandes variations des prix agricoles et des revenus réels. [ABSTRACT FROM AUTHOR]

This review was undertaken to draw together research on how climate change impacts sugarcane production, and to assess the implications of climate change for the sugarcane industry, as well as possible response options. Much of the extant research examines how changes in climate lead to changes in primary production; however, few studies consider how climate change translates into industry-wide impacts and economic consequences across the sugarcane value chain. Of the 90 studies we reviewed (published as journal articles, proceedings, and book chapters), 61 assess observed and/or projected impacts of climate change on sugarcane production. These studies reach largely different conclusions regarding how increases in air temperature or atmospheric carbon dioxide levels impact sugarcane production. These mixed results can be attributed to differences between the studies in terms of methods, time frames, and growing regions, which all limit cross-study comparability. A total of 17 studies focus on the adaptation to observed and/or projected impacts of climate change, such as changed management procedures or farming practices, but there is limited evidence regarding successful adaptation outcomes. In addition, a separate stream of papers discusses mitigating energy use and greenhouse gas emissions in the sugarcane production process, often with a view to reducing environmental impacts. Our review concludes by outlining the pathways for future research, highlighting that further insights are needed in particular regarding the economic consequences of climate change for the sugarcane industry. WIREs Clim Change 2018, 9:e498. doi: 10.1002/wcc.498 This article is categorized under: Future of Global Energy > Evaluating Future Impacts of Climate Change [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]

Nitrogen ( N) fertilizers are critical in today's agriculture especially in the United States. Leaching, methane and nitrous oxide emissions from N fertilizer use and the implications for global climate and water pollution raised serious concerns among environmentalists and agronomists. Prices of N fertilizers increased geometrically over the past few years. The projected increase in maize ( Zea mays L.) ethanol production is expected to increase N fertilizer demand and prices. Hay prices are, however, staggering and producers are looking for alternatives to N fertilizers. This review paper assessed trends in fertilizer use and prices as well as factors affecting fixation and transfer of dinitrogen ( N2) in forage production systems. Additionally, economic implications of using N fertilizers and grass-legume mixtures are discussed. From the review, it was apparent that legumes have the potential to replace N fertilizers or at least complement the use of N fertilizers in forage production systems. However, N transfer in forage production systems is low. A lot more research is needed to answer the question 'Why legumes fix so much N2 and transfer just a small proportion to other crops.' Similarly, identifying compatible grass-legume species that enhance N2 fixation will be a giant step towards reduced N fertilizer use and ensuring environmental sustainability. With the sharp increase in N fertilizer prices, the use of legumes in forage production systems seems promising. [ABSTRACT FROM AUTHOR]

Climate change is a constant threat to global food security. In Sub‐Saharan Africa (SSA), extended dry spells, heavy rainfall, flooding, and tropical storms have driven a large percentage of the population to a severe food crisis. Climate‐smart agriculture (CSA) is a potential comprehensive solution whose adoption can mitigate climate change and its ramifications on the state of food security in the region. CSA combines innovation, the use of technology, extensive research, stakeholder involvement, and data to increase food production, optimize resource allocation and utilization, and enhance the region's ability to feed its population. The adoption of CSA has succeeded in increasing agricultural productivity in several parts of SSA and will help towards the building of resilience food systems across the region amid constant and unpredictable climate change and its far‐reaching impacts in the region. For the sustained and universal adoption of CSA within the region, there is a need for more stakeholder involvement in its development and adaptation to the needs of specific communities. Furthermore, it is necessary to leverage the positive impact of the approach by expanding its scope beyond climate change mitigation to prevention and control, and ultimately help in achieving the United Nations SDG 2 and 13. [ABSTRACT FROM AUTHOR]

Agriculture is directly related to food security as it determines the global food supply. Research in agriculture to predict crop productivity and losses helps avoid high food demand with little supply and price spikes. Here, we review ten crop models and one intercomparison project used for simulating crop growth and productivity under various impacts from soil–crop–atmosphere interactions. The review outlines food security and production assessments using numerical models for maize, wheat, and rice production. A summary of reviewed studies shows the following: (1) model ensembles provide smaller modeling errors compared to single models, (2) single models show better results when coupled with other types of models, (3) the ten reviewed crop models had improvements over the years and can accurately predict crop growth and yield for most of the locations, management conditions, and genotypes tested, (4) APSIM and DSSAT are fast and reliable in assessing broader output variables, (5) AquaCrop is indicated to investigate water footprint, quality and use efficiency in rainfed and irrigated systems, (6) all models assess nitrogen dynamics and use efficiency efficiently, excluding AquaCrop and WOFOST, (7) JULES specifies in evaluating food security vulnerability, (8) ORYZA is the main crop model used to evaluate paddy rice production, (9) grain filling is usually assessed with APSIM, DAISY, and DSSAT, and (10) the ten crop models can be used as tools to evaluate food production, availability, and security. [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]

In this paper, we examine how the complexities introduced by trends in agricultural productivity and climate change affect the future of global food security. We use a partial equilibrium model of global agriculture incorporating a food security module that links changes in the average dietary energy intake to shifts in the full caloric distribution, allowing us to compute changes in the incidence, headcount and average depth of malnutrition. After validating the model against an historical period, we implement a series of future scenarios to understand the impacts of key exogenous drivers on selected food security outcomes. Our results show improvements in global food security for the period 2006-2050. Despite growing population and increased biofuel demand, baseline income growth, coupled with projected increases in agricultural productivity lead to a 24 per cent rise in global average dietary energy intake. Consequently, the incidence of malnutrition falls by 84 per cent, lifting more than half a billion people out of extreme hunger. However, these results hinge heavily on agricultural productivity growth. Without such growth, there could be a substantial setback on food security improvements. Climate change adds uncertainty to these projections, depending critically on the crop yield impacts of increasing CO2 concentrations in the atmosphere. [ABSTRACT FROM AUTHOR]

Environmental stresses including salinity, drought, cold, warmer temperatures, alterations in precipitation patterns, fluctuations of weather events, and increasing insect and disease infestations negatively affect crop production and nutritional values. This situation becomes further complicated due to the changing climatic conditions, thus raising concern about food security worldwide. Some worst‐case projections indicated that by 2100, CO2 concentrations will reach 950 parts per million, temperatures will climb by 3.5 to more than 8°C, sea level will rise by more than 2.4 meters, and the average farmland drought risk index will increase from 52.45 to 129. In addition, average precipitation will increase by 1%–3% in some areas and atmospheric water vapor will increase by 6%–7% for every degree of temperature rise. Rice (Oryza sativa L.) is a staple crop in many parts of the world. The main objective of this review is to highlight the prospects of rice for future climatic conditions. The present review depicts the advantages and prospects of rice and addresses why rice is a better option as a cereal crop for the future situations for food and nutritional sustainability. The impact of climate change on food and nutritional security can be mitigated by developing biotic and abiotic stress‐tolerant and biofortified rice varieties. These rice cultivars can withstand the negative effects of climate change while also meeting the nutritional needs of future generations. Furthermore, this review underlines the forthcoming issues and measures that should be addressed to assure a sustainable food and nutritional supply in the era of global climate change. [ABSTRACT FROM AUTHOR]

The world needs more than double its current agricultural productivity by 2050 to produce enough food and feed, as well as to provide feedstock for the bioeconomy. These future increases will not only need to be sustainable but without comprommising the nutritional quality, and ideally also need to decrease greenhouse gas emissions and increase carbon sequestration to help mitigate the consequences of global climate change. These challenges could be tackled by developing and integrating new future‐proof crops into our food system. The H2020 CropBooster‐P project sets out plant‐centered breeding approaches guided by a broad socio‐economic and societal support. First, the potential approaches for breeding crops with sustainably increased yields adapted to the future climate of Europe are identified. These crop‐breeding options are subsequently prioritized and their adoption considered by experts across the agri‐food system and the wider public, taking into account environmental, economic and other technical criteria. In this way, a specific research agenda to future‐proof our crops was developed, supported by an eventual implementation plan. [ABSTRACT FROM AUTHOR]

The European Commission plans to reduce pesticides and fertilizer use in agriculture substantially under its Green Deal's Farm to Fork strategy. Policy alternatives are needed to overcome these input reductions, for instance by allocating R&D towards commodities most affected by climage change and improving the precision of input use. Since climate change is likely to impact agriculture negatively, productivity measures that account for all inputs and climate change impacts can help to weigh such policy responses. We adopt U.S. farm‐level data and a multi‐output and multi‐input production technology to estimate relative farm productivity performances with climatic effects. Exploiting long‐term weather patterns, we find comparative advantages among some crop farms and ample productivity improvement potential across all of them. [ABSTRACT FROM AUTHOR]

The response of bees to changing environmental temperatures has implications for pollination in natural and agricultural systems, with rising average temperatures and increased environmental stochasticity predicted to cause pollinator population declines. A growing body of evidence for the role of native bees in crop pollination suggests that understanding the temperatures at which bees are active is important for maintaining agricultural productivity under climate change. This study used two methods to sample bees at strawberry farms in south‐eastern Australia, matching activity observations with microclimate temperature to understand how temperature impacts bee activity. Apart from Apis mellifera (introduced), two native bees were identified, Lasioglossum spp. and Exoneura robusta. Apis mellifera was the most abundant species across all environmental temperatures, and E. robusta the least. Visual and sweep‐netting survey results found activity temperature range was broader for A. mellifera (16.21–41.05°C) than Lasioglossum (16.49–38.91°C) and E. robusta (26–38.82°C). The results suggest that activity temperature varies among bee species, with potential implications for community composition and plant pollination under climate change. [ABSTRACT FROM AUTHOR]

BACKGROUND The agricultural production system is facing increasing demand pressure and environmental pressure. Green and efficient production methods are urgently needed in order to further enhance the yield of winter wheat and reduce the negative impact on the environment. Here, we analyzed the potential yield and yield gap of winter wheat in Shandong Province of China from 1981 to 2009. Meanwhile, we specified the effects of sowing time, irrigation and fertilization scheme, and variety characteristics on winter wheat. RESULTS: In the past 29 years, the yield gap in most areas of Shandong has become smaller, because the actual yield has increased and the potential yield has changed little under the background of climate change. In addition, it is found that delaying sowing date is beneficial to increase yield by helping winter wheat avoid adverse climate conditions. Also, an irrigation amount of 240 mm and nitrogen application amount of 180–210 kg ha−1 are best to maintain high yield, high resource utilization rate and low environmental pollution in this area. These suggested levels are lower than those currently used by many local farmers. Wheat varieties with longer grain‐filling period and photoperiod response, higher grain‐filling rate and grain weight were more adaptable to climate change. CONCLUSION: Improving agronomic management measures can significantly increase the yield of winter wheat and narrow the yield gap. This study can provide valuable information for improving the production potential of winter wheat, and for reducing the damage of agricultural activities to the environment. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Today's climate‐turbulent world is rife with food insecurity—threatening economic development trajectories by stunting growth, subsequently reducing employability and labor productivity, especially for developing countries. The pandemic has exacerbated these adverse effects and reveals the critical need to put in place policies that can safeguard and sustain trade flows in agricultural products. For the vast majority of countries, food security cannot be guaranteed by domestic production alone. If certain crops are domestically untenable or yields are temporarily poor, imports are essential. Additionally, exports of agricultural products provide a major source of income for some of the poorest people around the world. This article explores the interplay between climate change, the production and the trade of agricultural products, and the resulting food security implications. It then examines the existing trade policies governing agricultural trade and provides recommendations for action at the global, regional, and country levels. [ABSTRACT FROM AUTHOR]

Summary: Climate change is increasing global temperatures and the frequency and severity of droughts in many regions. These anthropogenic stresses pose a significant threat to plant performance and crop production. The plant‐associated microbiome modulates the impacts of biotic and abiotic stresses on plant fitness. However, climate change‐induced alteration in composition and activities of plant microbiomes can affect host functions. Here, we highlight recent advancements in our understanding of the impact of climate change (warming and drought) on plant–microbiome interactions and on their ecological functions from genome to ecosystem scales. We identify knowledge gaps, propose new concepts and make recommendations for future research directions. It is proposed that in the short term (years to decades), the adaptation of plants to climate change is mainly driven by the plant microbiome, whereas in the long term (century to millennia), the adaptation of plants will be driven equally by eco‐evolutionary interactions between the plant microbiome and its host. A better understanding of the response of the plant and its microbiome interactions to climate change and the ways in which microbiomes can mitigate the negative impacts will better inform predictions of climate change impacts on primary productivity and aid in developing management and policy tools to improve the resilience of plant systems. [ABSTRACT FROM AUTHOR]

Aims: Terrestrial biodiversity is threatened by land use change. Modelling suggests that the remaining, potentially arable areas of natural intact vegetation (rNIV) of 9 of 35 global biodiversity hotspots may be converted to agriculture by 2050, committing their endemic species to extinction. Studies have shown that if the global population adopted a healthy, mostly plant based diet, agricultural area expansion can be reduced. We want to examine to what degree this applies to the regions covered by biodiversity hotspots. Location: Global. Time period: 2020–2050. Major taxa studied: No particular taxa. Methods: For every biodiversity hotspot, we simulate climate change impacts on agricultural productivity, and estimate food demand shifts from 2010 to 2050 by processing population and income growth projections. We quantify the net change in rNIV by 2050 in all hotspots by calculating the agricultural area necessary to meet the food demand under a business as usual, a healthy diet, a healthy diet plus agricultural intensification, and a healthy diet plus agricultural intensification plus crop change scenario. Results: In the healthy diet scenario, the rNIV of 16–21 hotspots can be preserved entirely, but 5–6 hotspots in less developed regions may lose all rNIV to agricultural area expansion. In these regions, a healthy diet implies an increase in consumed calories and no change in the already high share of plant‐based calories. When combined with agricultural intensification, these hotspots will still lose 7%–92% of rNIV. Only an additional change in crop mix patterns may preserve all. Main conclusions: While a change in dietary habits may be sufficient for preservation of rNIV in many hotspot regions, a healthy diet actually puts more pressure on rNIV in other hotspot regions. Intensifying agriculture and optimizing crop selection should be priorities in those regions to mitigate the expected loss of rNIV to agricultural expansion. [ABSTRACT FROM AUTHOR]

Water has always been a driver of human mobility, migration, and displacement. But water is increasingly central to explaining environmental migration in the context of climate change. Most studies of the relationship between water and environmental migration are framed around punctuated, extreme weather events and disasters that either limit agricultural or livestock productivity or make a community physically unlivable. The chronic experiences of household water insecurity and poor water governance also shape migration decision‐making through a variety of social, political, and economic factors, but these relationships have received considerably less attention. This article provides an overview of punctuated and chronic water‐related triggers of environmental migration at the household level. We also offer a conceptual framework based on multiphasic response theory that highlights water's multiple roles in migration decision‐making. We close by reflecting on key gaps in the climate‐water‐migration literature, identifying research questions that might help us better understand these relationships, and considering the implications for sustainable development policies that could potentially ease pressures on water‐related displacement. [ABSTRACT FROM AUTHOR]

Agriculture accounts for 70% of the global use of available freshwater. Projections show that demand for water will increase significantly due to climate change, population growth and development of agricultural enterprises globally. There is a need to develop water‐use efficient crop cultivars for sustainable agricultural production. Sorghum [Sorghum bicolor (L.) Moench.] is a powerhouse crop in drier regions supporting more than 500 million people. It is a relatively drought‐tolerant crop adapted to grow and yield in marginal environments where other dominant crops such as maize and wheat fail to survive. However, the mean yield of sorghum in the semi‐arid regions has stagnated around 1.0 ton/ha compared with the global average of 2.5 ton/ha, mainly due to recurrent droughts and heat stress. Breeding for drought‐tolerant cultivars is an economic and sustainable mitigation strategy against the current and projected drought stress. Therefore, the objectives of this review were to document the impact of drought stress and the key mitigation strategies under drought‐prone semi‐arid sorghum production systems. The first section of the review highlighted the impact of drought and its mitigation strategies emphasizing on the use of drought‐tolerant cultivars as the best strategy. This is followed by perspectives on aspects of drought‐response mechanisms, breeding methods and complementary technologies for drought tolerance. Integration of the conventional and molecular breeding technologies with rapid generation advancement methods could reduce the breeding cycle and increase the efficiency of deploying new varieties. Information presented in this review will guide agronomists and breeders to develop and deploy drought‐tolerant sorghum cultivars that are adapted to the changing production environments in the semi‐arid regions. [ABSTRACT FROM AUTHOR]

BACKGROUND A decrease in rainfall is one of the main constraints on wheat production, although the association of wheat with arbuscular mycorrhizal fungi (AMF) may be an alternative for crop production under drought conditions. In the present study, we used three wheat cultivars (Purple, Ilustre and Maxi Baer) inoculated with two AMF strains, one obtained from the hyperarid Atacama Desert (northern Chile; Fm) and the other obtained from southern Chile (Cc). Plants were maintained under two irrigation conditions (normal irrigation and drought) and the physiological behaviour and enzymatic and nonenzymatic antioxidant activities in the shoots were determined. In addition, the phenolic compounds were identified by high‐performance liquid chromatography‐dioide array detection‐electrospray ionization‐tandem mass spectrometry and quantified. RESULTS: AMF colonization produced higher levels of efficiency of photosystem II and photosynthetic pigments. High values of catalase in Purple‐Cc, ascorbate peroxidase in Purple‐Cc, glutathione reductase in Maxi‐Cc and superoxide dismutase in Purple‐Cc, all under stress, were registered. Of the inoculated cultivars, Purple‐Cc showed the highest flavonoid levels, whereas hydroxycinnamic acids were higher in Maxi‐Fm without drought, with apigenin and luteolin being the most abundant. High levels of phenols were present in the Ilustre‐Fm plants without drought. Under normal irrigation, high levels of antioxidant activity were registered in the AMF treatments, whereas, under stress conditions, in general, high values were observed under the Fm inoculation. CONCLUSION: Our results showed that the greatest antioxidant activity and phenolic content occurred in wheat plants inoculated with AMF, indicating their influence on coping with water stress, which is of importance in vast areas where global climate change is resulting in diminished rainfall. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

With the objective of trying to understand the adaptability of agriculture across the Canadian Prairies under climate change, simple‐to‐use agroclimatic indices were calculated for the base climate period of 1981 to 2010 and for both the medium (RCP4.5) and high (RCP8.5) emission projections extending to the distant future (2071–2100). The agroclimatic indices included the Effective Growing Degree Days (EGDDs), Growing Season Length (GSL), the Climate Moisture Index (CMI), and the Temperature Humidity Index (THI). For climate change in 30‐year periods, these indices were calculated as multi‐model ensembles of six Global Climate Models recommended under the Coupled Model Intercomparison Project Phase 5 (CMIP5) for the study area. We found that the GSL, EGDDs, CMI, and THI were amplified above the values of the base climate period in the order of 40–50 days, 600–1200 heat units, −100 to −120 mm and 3–4 THI units by the close of the distant future (2071–2100) under the RCP4.5 and RCP8.5, respectively. This amplification has implications on where crop and livestock production could become more suitable or riskier in future. Opportunities include expanding crop and livestock production to more northerly regions which currently have insufficient heat units, a short growing season and unfavourable temperature humidity thresholds for livestock production. Moisture deficits will continue to be the greatest risk during the growing season under climate change scenarios but options exist to implement adaptive measures such as staggering seeding times to take advantage of moisture availability in the spring and autumn seasons and crop substitution. This study has relevance for policy and program formulation and implementation in Canada's agricultural regions and potentially, other areas of the world with similar climate change outcomes. [ABSTRACT FROM AUTHOR]

Understanding concurrent drought events in global main croplands is crucially important for food security, effective adaptation to climate change and human well‐being. Yet, there is a lack of comprehensive studies on concurrent drought probability changes among main crop production countries on a global scale, especially for the future. Here, we studied concurrent drought among 26 main crop production countries for the time period 1861–2099. During the historical period, probability of concurrent moderate and severe drought among the countries was relatively low, and the maximum concurrent moderate and severe drought probability will double and triple under RCP8.5 extreme climate, respectively. Concurrent probability of moderate and severe drought between China and United States of America, Brazil and Russia will be at least 6% and 5% under RCP8.5, respectively, compared with zero in the historical period. Limiting RCPs to RCP2.6 can decrease the concurrent probability of severe drought at least 2% (at least 3% for RCP8.5). Plain Language Summary: In the past over 100 years, the world had rarely experienced extreme drought events that occurred at the same time among major food producing countries. Yet, there is a lack of comprehensive studies on the changes of the concurrent drought probability among global main crop production countries, especially for the future. We studied the concurrent drought among 26 global main crop production countries for the time period 1861–2099. We find that the maximum concurrent probability of moderate and severe drought will double and triple, respectively, under the business as usual climate scenario. Concurrent probability of moderate and severe drought between China and United States of America, Brazil and Russia will be at least 6% and 5% under future business as usual climate, compared with zero in the historical period. Key Points: Maximum concurrent probability of moderate and severe drought will double and triple under RCP8.5, respectivelyConcurrent probability of moderate and severe drought between China and United States of America, Brazil and Russia will be at least 6% and 5% under RCP8.5Limiting RCPs to RCP2.6 can decrease the concurrent probability of severe drought at least 2% (at least 3% for RCP8.5) [ABSTRACT FROM AUTHOR]

Climate projections indicate that temperatures in Latin America and the Caribbean (LAC) will rise by between 1.6 °C and 4 °C by the end of the century while changes in precipitation levels are expected to vary significantly across the region. This article estimates the impact of climate change on total factor productivity (TFP) and production. It combines data from the University of East Anglia's Climatic Research Unit (CRU), the Intergovernmental Panel on Climate Change (IPCC), and the Food and Agriculture Organization (FAO) on 28 LAC countries for a 54‐year period (1961–2014) in order to inform policy makers of the cost of output losses in the absence of viable climate adaptation strategies. We use estimates of a random‐parameter stochastic production frontier (SPF) model specification to capture heterogeneity in technology and partial output elasticities across countries and unobserved environmental characteristics. Results show that change in output is mainly driven by an average annual TFP growth rate of.95%. Climate change induces significant reductions (9.03–12.7%) in productivity over the 2015–2050 period. In terms of output, these losses range from USD $14.7 to $31.4 billion dollars in the LAC region, depending on the scenario and the discount rate used. [ABSTRACT FROM AUTHOR]

Water‐limited environments account for half of the earth's land surface, and dryland agriculture acreage is projected to expand due to climate changes. Examining typical dryland yield potentials and yield improvement measures is crucial for developing future dryland crop production systems. This case study used crop modeling to analyze dryland maize yield potential (YPd), farmers' yield potential (YPf), and actual farm yields (Ya) from 1990 to 2015 in three counties in western Kansas (i.e., Thomas, Greeley, and Finney in the U.S. Great Plains region). The calibrated APSIM‐Maize model along with actual yields was used to estimate yield gaps attributed to: (a) agronomic factors (YG1 = YPd − YPf) and (b) socioeconomic constraints (YG2 = YPf − Ya). Observed climate conditions during maize‐growing seasons showed warming, brightening, and drying trends for all three counties in western Kansas from 1990 to 2015. Our results showed that the current actual farm yields (Ya) in western Kansas represented only 34%, 32%, and 28% of YPd in Thomas, Greeley, and Finney counties, respectively, indicating significant exploitable yield gaps. Agronomic factors (YG1) contributed the greatest to the yield gap in Greeley and Finney counties, whereas socioeconomic constraints (YG2) offered the greatest opportunity for improvement in Thomas county. Our analysis suggested that improving agronomic management could be a greater priority for further yield improvement in Greeley county, but selecting an appropriate hybrid was a greater priority in Finney county. [ABSTRACT FROM AUTHOR]

In this article, we use spatially granular climate data merged with four waves of household survey data in Uganda to examine empirically the relationships among high temperatures, total value of crop production, and the adoption and adoption duration of two sustainable agricultural practices (organic fertilizer adoption and maize–legume intercropping). We do this using a fixed‐effect model with instrumental variables to address potential endogeneity issues. Our findings indicate that the adoption of these practices has a positive effect on the total value of crop production, and such effect increases monotonically as temperatures increase from long‐term averages. Moreover, the number of years a farmer uses the practice is associated with higher total value of crop production, and this relationship holds across the full distribution of high temperature deviations. Taken together, the results suggest that promoting the adoption, and particularly the sustained adoption, of these practices can help to address the risks posed by rising temperatures to Ugandan agriculture and the livelihoods of farmers who depend on it. [ABSTRACT FROM AUTHOR]

Climate‐smart practices that have added benefits of improving agricultural productivity are an imperative for improving smallholder farming. However, there are few insights into how smallholder climate‐smart adaptation practices influence their technical (in)efficiency. Thus, the objective of this study was to evaluate the impact of smallholders' climate‐smart adaptation practices on their crop production technical efficiency (TE) in the Lemo district of southern Ethiopia. We used focus group discussion, experts' consultations and household survey to collect data from 600 smallholder crop producers across six rural kebeles. We computed smallholder farmers' climate change adaptation indices based on experts' consultations and estimated the TE of the smallholder farmers using Stochastic Frontier Analysis. The smallholder farmers in the Lemo district have adopted climate‐smart agricultural practices such as terracing, crop diversification, improved soil amendment practices, varying planting or harvesting schedules, and crop rotation. The smallholder farm households practicing more adaptation strategies on a larger scale were more technically efficient than their counterparts with their TE averaging 11.31, 8.62, and 6.71% for major crops, wheat (Triticum aestivum L.), and teff [Eragrostis tef) (Zucc.) Trotter] production, respectively. Our model also revealed that adaptation to climate change has a positive and significant contribution to the technical efficiency of major crops, wheat, and teff production. Other key determinants of TE are farming experience, education, access to extension services, livestock holdings, and farm household income. Overall, our study suggests a policy shift to promote smallholder farmers adaptation to climate change using climate‐smart practices for an effective response to climate change impact while enhancing TE. Core Ideas: An activity‐based adaptation index for climate change was measured.There was positive correlation between climate change adaptation practices and crop production efficiency.Smallholder climate‐smart adaptation practices have significant effect on technical efficiency.Farmers using climate‐smart practices at a larger scale are more technically efficient. [ABSTRACT FROM AUTHOR]

Climate change will lead to more frequent and severe drought periods which massively reduce crop production worldwide. Besides drought, nitrogen (N)‐deficiency is another critical threat to crop yield production. Drought and N‐deficiency both decrease photosynthesis and induce similar adaptive strategies such as longer roots, reduction of biomass, induction of reactive oxygen species (ROS), and antioxidative enzymes. Due to the overlapping response to N‐deficiency and drought, understanding the physiological and molecular mechanisms involved in cross‐stresses tolerance is crucial for breeding strategies and achieving multiple stress resistance and eventually more sustainable agriculture. The objective of this study was to investigate the effect of a mild N‐deficiency on drought stress tolerance of tomato plants (Solanum lycopersicum L., cv. Moneymaker). Various morphological and physiological parameters such as dry biomass, root length, water potential, SPAD values, stomatal conductance, and compatible solutes accumulation (proline and sugar) were analyzed. Moreover, the expression of ROS scavenging marker genes, cytosolic ASCORBATE PEROXIDASES (cAPX1, cAPX2, and cAPX3), were investigated. Our results showed that a former mild N‐deficiency (2 mM NO3−) enhances plant adaptive response to drought stress (4 days) when compared to the plants treated with adequate N (5 mM NO3−). The improved adaptive response was reflected in higher aboveground biomass, longer root, increased specific leaf weight, enhanced stomatal conductance (without reducing water content), and higher leaf sugar content. Moreover, the APX1 gene showed a higher expression level compared to control under N‐deficiency and in combination with drought in the leaf, after a one‐week recovery period. Our finding highlights a potentially positive link between a former mild N‐deficiency and subsequent drought stress response in tomato. Combining the morphological and physiological response with underlying gene regulatory networks under consecutive stress, provide a powerful tool for improving multiple stress resistance in tomato which can be further transferred to other economically important crops. [ABSTRACT FROM AUTHOR]

Climate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5‐8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1‐2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro‐ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5‐8.5. The results highlight that region‐specific breeding efforts are required to allow for a successful adaptation to climate change. [ABSTRACT FROM AUTHOR]

The arid and semi‐arid regions are highly vulnerable to climate change and variability. Agricultural production in these regions is particularly vulnerable because of its heavy dependence on climate conditions. Therefore, it is important to improve the projections of future agro‐climatic conditions. This study investigates the changes in agroclimatic conditions for growing Chinese Medicinal Yam (CMY) in the semi‐arid North China during 2031–2050 under the Representative Concentration Pathway (RCP) 4.5 and 8.5 emission scenarios. It employs high‐resolution climate data provided by the regional climate model (RCM) of Providing Regional Climates for Impacts Studies (PRECIS‐2.0). The changes in CMY yield and producing area under future climate change are simulated by the Agro‐Ecological Zone model. The simulations are based on newly calibrated cultivar parameters. The results show that the high‐resolution RCM simulation corresponds better with the observations of precipitation and temperature than the general circulation model (GCM). Because of the increased temperature and precipitation, the agroclimatic conditions for CMY are going to improve. While the traditional yam production area will maintain suitable for CMY production, the CMY production areas will further expand northward in the future. The average yield will increase by 1,196 and 1,336 kg DM/ha under RCP 4.5 and 8.5 emission scenarios, respectively. Key Points: Improving the projections of future agro‐climatic conditions in the semi‐arid North ChinaThe high‐resolution Providing Regional Climates for Impacts Studies projections correspond better with the observationsThe Chinese Medicinal Yam production areas will expand northward in the future, the average yield will increase by 1196–1336 kg DM/ha in the future [ABSTRACT FROM AUTHOR]

In this study, the Land Use Dynamic Simulator model was applied to investigate the impact of farm credit as an adaptation strategy to cope with effects of climate variability on agricultural land‐use change and crop production in the Vea watershed in Ghana. The authors identified the determinants of crop choices within the landscape (e.g., farm household and biophysical characteristics of farm plot). The crop choice sub‐model was then linked to the crop yield sub‐model to determine the yields of selected crops. In adapting to the impacts of climate variability, the maize credit adoption sub‐model under the maize cultivation credit scenario was integrated into decision‐making. This was simulated for a 20‐year period, and compared with the business‐as‐usual scenario. Under the simulated maize credit scenario, maize adopters increased from about 20 per cent to about 50 per cent and the area allocated for maize cultivation significantly increased by about 266 per cent. Consequently, the average annual aggregated household crop yield increased by 6.3 per cent higher than in the business‐as‐usual scenario. This simulation study shows that access to maize credit can significantly influence agricultural land‐use change and food availability in the study area. However, although access to farm credit may translate into food availability, the sustainability of this strategy is questionable. [ABSTRACT FROM AUTHOR]

Models are key tools in our quest to better understand the impacts of soil waterlogging on plant growth and crop production. Here, we reviewed the state of the art of modeling approaches and compared the conceptual design of these models with recent experimental findings. We show that many models adopt an aeration stress (AS) principle where surplus water reduces air‐filled porosity, with implications for root growth. However, subsequent effects of AS within each model vary considerably. In some cases, AS inhibits biomass accumulation (e.g. AquaCrop), altering processes prior to biomass accumulation such as light interception (e.g. APSIM), or photosynthesis and carbohydrate accumulation (e.g. SWAGMAN Destiny). While many models account for stage‐dependent waterlogging effects, few models account for experimentally observed delays in phenology caused by waterlogging. A model intercomparison specifically designed for long‐term waterlogged conditions (APSIM‐Oryza) with models developed for dryland conditions with transient waterlogging would advance our understanding of the current fitness for purpose of exsiting frameworks for simulating transient waterlogging in dryland cropping systems. Of the point‐based dynamic models examined here, APSIM‐Soybean and APSIM‐Oryza simulations most closely matched with the observed data, while GLAM‐WOFOST achieved the highest performance of the spatial‐regional models examined. We conclude that future models should incorporate waterlogging effects on genetic tolerance parameters such as (1) phenology of stress onset, (2) aerenchyma, (3) root hydraulic conductance, (4) nutrient‐use efficiency, and (5) plant ion (e.g. Fe/Mn) tolerance. Incorporating these traits/effects into models, together with a more systematic model intercomparison using consistent initialization data, will significantly improve our understanding of the relative importance of such factors in a systems context, including feedbacks between biological factors, emergent properties, and sensitive variables responsible for yield losses under waterlogging. Key Points: We review the state of the art of approaches used to modeling the impacts of waterlogging on plantsMost models use an aeration stress principle that effects root growth, few models account for phenology delays caused by waterloggingNext generation models should consider new genotypic traits and tolerance (e.g., aerenchyma, ion tolerance, and oxidative stress) [ABSTRACT FROM AUTHOR]

Climate change and land use are altering the landscape of the U.S. Great Plains, producing increases in windblown dust. These increases are investigated by combining coarse mode aerosol observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor in addition to the Aerosol Robotic Network (AERONET) and Interagency Monitoring of Protected Visual Environments (IMPROVE) aerosol monitoring networks. Increasing trends of up to 5%/year in MODIS aerosol optical depth for dust observations are observed throughout the Great Plains (2000–2018). Cropland coverage has increased 5–10% over the majority of the Great Plains (2008–2018), and positive monthly trends in IMPROVE (1988–2018) and AERONET (1995–2018) coarse mode 90th percentile observations coincide with planting and harvesting seasons of predominant crops. Presently, results suggest increased dust due to agricultural expansion is negatively influencing human health and visibility in the Great Plains. Furthermore, results foreshadow a future where desertification becomes an increasing risk in the Great Plains. Plain Language Summary: Throughout the U.S. Great Plains, satellite data combined with surface networks have shown a significant increase in airborne dust over the last two decades. This airborne dust is negatively influencing human health and visibility and coincides with increases in agricultural production. Key Points: Dust loading throughout the U.S. Great Plains has increased over the last two decadesPositive correlations between cropland expansion and dust trends downwind suggest that agriculture contributed to these increasesIn the event of enhanced drought due to climate change, results support an increased risk of desertification from agricultural development [ABSTRACT FROM AUTHOR]

Increasing demand for land to ensure human food security in the future has already impelled agricultural production into marginal areas. The environmental conditions found there have a more pronounced impact on agricultural productivity than in the systems used so far under favourable conditions. In addition to this challenge, climate change is expected to increase the unreliability of weather conditions (through increased variability and occurrence of extremes) for farmers considerably. This unreliability is even more serious in developing countries' farming system where food security is vulnerable. Current efforts in digitalization offer great possibilities to improve farmers' decision‐making processes. A wide range of online tools and smartphone applications is available to support both agricultural extension services and smallholder farmers alike. These apps are often parameterized and validated to certain environments and are troubled when applied to new geographical locations and different environmental conditions. We have conducted field trials to demonstrate potential methods to close knowledge gaps in the data background for one of these apps, RiceAdvice, concerning three key aspects: shifting of cropping calendar, adjustment of fertilizer management and genotype selection. Sites in Ethiopia, Madagascar and Rwanda were selected to represent altitudinal gradients, with overlapping elevations reflecting differences in temperature to enable cross‐country comparisons. Planting dates were distributed throughout three calendar years, with continuous iterative planting dates taking place in Madagascar, in‐ and off‐season planting dates in Rwanda with different fertilizer applications, and one planting date during each rainy season in Ethiopia with different management options. With these trials, we have been able to identify key data sets needed for the adaptation of agricultural decision support tools to new environments. These include the assessment of climatic constraints on innovations to cropping calendars (e.g. double cropping), informed selection of alternative varieties able to complete crucial parts of their phenological development to avoid temperature‐related stress inducing, for example spikelet sterility in rice in late development stages and the effectivity of potential innovations in fertilizer management strategies. [ABSTRACT FROM AUTHOR]

Pig production systems provide multiple benefits to humans. However, the global increase in meat consumption has profound consequences for our earth. This perspective describes two alternative scenarios for improving the sustainability of future pig production systems. The first scenario is a high input–high output system based on sustainable intensification, maximizing animal protein production efficiency on a limited land surface at the same time as minimizing environmental impacts. The second scenario is a reduced input–reduced output system based on selecting animals that are more robust to climate change and are better adapted to transform low quality feed (local feeds, feedstuff co‐products, food waste) into meat. However, in contrast to the first scenario, the latter scenario results in reduced predicted yields, reduced production efficiency and possibly increased costs to the consumer. National evaluation of the availability of local feed and feedstuff co‐product alternatives, determination of limits to feed sourced from international markets, available land for crop and livestock production, desired production levels, and a willingness to politically enforce policies through subsidies and/or penalties are some of the considerations to combine these two scenarios. Given future novel sustainable alternatives to livestock animal protein, it may become reasonable to move towards an added general premium price on 'protein from livestock animals' to the benefit of promoting higher incomes to farmers at the same time as covering the extra costs of, politically enforced, welfare of livestock animals in sustainable production systems. © 2020 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [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]

Crop yield modeling is critical in the design of national strategies for agricultural production, particularly in the context of a changing climate. Forecasting yields of bioenergy crops at fine spatial resolutions can help to evaluate near‐term and long‐term pathways for scaling up bio‐based fuel and chemical production, and for understanding the impacts of abiotic stressors such as severe droughts and temperature extremes on potential biomass supply. We used a large dataset of 28,364 Sorghum bicolor yield samples (uniquely identified by county and year of observation), environmental variables, and multiple approaches to analyze historical trends in sorghum productivity across the USA. We selected the most accurate machine learning approach (a variation of the random forest approach) to predict future trends in sorghum yields under four greenhouse gas (GHG) emission scenarios and two irrigation regimes. We identified irrigation practices, vapor pressure deficit, and time (a proxy for technological improvement) as the most important predictors of sorghum productivity. Our results showed a decreasing trend of sorghum yields over future years (on average 2.7% from 2018 to 2099), with greater decline under a high GHG emissions scenario (3.8%) and in the absence of irrigation (4.6%). Geographically, we observed the steepest predicted declines in the Great Lakes (8.2%), Upper Midwest (7.5%), and Heartland (6.7%) regions. Our study demonstrates the use of machine learning to identify environmental controllers of sorghum biomass yield and predict yields with reasonable accuracy. These results can inform the development of more realistic biomass supply projections for bioenergy if sorghum production is scaled up. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd [ABSTRACT FROM AUTHOR]

This study assesses the impacts of climate variation on land productivity for major Indian food and non‐food grain crops. We collected data for 50 years from (1967–2016) with 15 crops across India. To estimate the variation of agriculture production for each crop by different variables, for instance, rainfall and temperature estimation and future prediction for 20 years, that is, until 2036. Our results specify that land productivity drops with a rise in annual mean temperature in most of the crops. The adverse impact of climate variation on agricultural production recommends food security risk to minor and marginal agricultural families, badly affected by climatic variations. Results show that a rise in temperature would reduce agricultural productivity and assessed sensitivity of Indian agriculture to climate change. We did forecasting using the autoregressive integrated moving average model for 20 years. It shows that as temperature and rainfall upturns in the future, production of some crops, such as gram, sesamum (til), jowar, groundnut, sugarcane, and bajra, will also increase. Some crops are climate sensitive, such as arhar, wheat, rice, cotton, and tea. As temperature increases, the production of these crops slightly increase or decrease. [ABSTRACT FROM AUTHOR]

BACKGROUND Global warming and extreme or adverse events induced by climatic fluctuations are an important threat for plants growth and agricultural production. Adaptability to environmental changes prevalently derives from a large set of genetic traits affecting physiological and agronomic parameters. Therefore, the identification of genotypes that are good yield performer at high temperatures is becoming increasingly necessary for future breeding programs. Here, we analyzed the performances of different tomato landraces grown under elevated temperatures in terms of yield and nutritional quality of the fruit. Finally, we evaluated the antioxidant and anti‐inflammatory activities of fruit extracts from the tomato landraces selected. RESULTS: The tomato landraces analyzed here in a hot climate differed in terms of yield performance, physicochemical parameters of fruit (pH, titratable acidity, degrees Brix, firmness), bioactive compounds (ascorbic acid, carotenoids, and polyphenols), and anti‐inflammatory potential. Three of these landraces (named E30, E94, and PDVIT) showed higher fruit quality and nutritional value. An estimated evaluation index allowed identification of PDVIT as the best performer in terms of yield and fruit quality under high temperatures. CONCLUSION: The analyses performed here highlight the possibility to identify new landraces that can combine good yield performances and fruit nutritional quality at high temperatures, information that is useful for future breeding programs. © 2020 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Crop improvement efforts aiming at increasing crop production (quantity, quality) and adapting to climate change have been subject of active research over the past years. But, the question remains 'to what extent can breeding gains be achieved under a changing climate, at a pace sufficient to usefully contribute to climate adaptation, mitigation and food security?'. Here, we address this question by critically reviewing how model‐based approaches can be used to assist breeding activities, with particular focus on all CGIAR (formerly the Consultative Group on International Agricultural Research but now known simply as CGIAR) breeding programs. Crop modeling can underpin breeding efforts in many different ways, including assessing genotypic adaptability and stability, characterizing and identifying target breeding environments, identifying tradeoffs among traits for such environments, and making predictions of the likely breeding value of the genotypes. Crop modeling science within the CGIAR has contributed to all of these. However, much progress remains to be done if modeling is to effectively contribute to more targeted and impactful breeding programs under changing climates. In a period in which CGIAR breeding programs are undergoing a major modernization process, crop modelers will need to be part of crop improvement teams, with a common understanding of breeding pipelines and model capabilities and limitations, and common data standards and protocols, to ensure they follow and deliver according to clearly defined breeding products. This will, in turn, enable more rapid and better‐targeted crop modeling activities, thus directly contributing to accelerated and more impactful breeding efforts. [ABSTRACT FROM AUTHOR]

Relative agricultural productivity shocks emerging from climate change will alter regional cropland use. Land allocations are sensitive to crop profits that in turn depend on yield effects induced by changes in climate and technology. We develop and apply an integrated framework to assess the impact of climate change on agricultural productivity and land use for the U.S. Northern Great Plains. Crop‐specific yield–weather models reveal crop comparative advantage due to differential yield impacts of weather across the region's major crops, that is, alfalfa, wheat, soybeans, and maize. We define crop profits as a function of the weather‐driven yields, which are then used to model land use allocation decisions. This ultimately allows us to simulate the impact of climate change under the RCP4.5 emissions scenario on land allocated to the region's major crops as well as to grass/pasture. Upon removing the trends effects in yields, climate change is projected to lower yields by 33–64% over 2031–2055 relative to 1981–2005, with soybean being the least and alfalfa the most affected crops. Yield projections applied to the land use model at present‐day input costs and output prices reveals that Dakotas' grass acreage will increase by up to 23%, displacing croplands. Wheat acreage is expected to increase by up to 54% in select southeastern counties of North Dakota and South Dakota, where maize/soy acreage had increased by up to 58% during 1995–2016. [ABSTRACT FROM AUTHOR]

This study assessed the climate change impacts on land productivity for major food and nonfood grain crops in India. We compiled data for 50 years (1967–2016) using 15 crops across India to estimate the variation of agriculture production for each crop by different variables such as temperature and rainfall estimation. Our results indicate that land productivity decreases with an increase in annual average temperature in most of the crops. The adverse impact of climate change on agricultural production indicates food security threat to small and marginal farming households and adversely affected due to climatic fluctuations. Results show that a rise in temperature would reduce agricultural productivity and assessed sensitivity of Indian agriculture to climate change. [ABSTRACT FROM AUTHOR]