Analysis of climate signals in the crop yield record of sub‐Saharan Africa

Food security and agriculture productivity assessments in sub‐Saharan Africa (SSA) require a better understanding of how climate and other drivers influence regional crop yields. In this paper, our objective was to identify the climate signal in the realized yields of maize, sorghum, and groundnut in SSA. We explored the relation between crop yields and scale‐compatible climate data for the 1962–2014 period using Random Forest, a diagnostic machine learning technique. We found that improved agricultural technology and country fixed effects are three times more important than climate variables for explaining changes in crop yields in SSA. We also found that increasing temperatures reduced yields for all three crops in the temperature range observed in SSA, while precipitation increased yields up to a level roughly matching crop evapotranspiration. Crop yields exhibited both linear and nonlinear responses to temperature and precipitation, respectively. For maize, technology steadily increased yields by about 1% (13 kg/ha) per year while increasing temperatures decreased yields by 0.8% (10 kg/ha) per °C. This study demonstrates that although we should expect increases in future crop yields due to improving technology, the potential yields could be progressively reduced due to warmer and drier climates.     

Maize potential yields and yield gaps in the changing climate of northeast China

Northeast China (NEC) is not only one of the major agricultural production areas in China, but it is also the most susceptible to climate variability. This led us to investigate the impact of climate change on maize potential yield and yield gaps in this region, where maize accounts for about 30% of the nation's production. The APSIM‐Maize model was calibrated and validated for maize phenology and yields. The validated model was then used to estimate potential yields, rain‐fed potential yields, and yield gaps for assessing the climate impacts on maize productivity in NEC. During maize growing seasons from 1981 to 2010, the analysis indicates a warming trend all across NEC, whereas the trends in solar radiation and total precipitation tended to decrease. When the same hybrid was specified in APSIM for all years, a simulated increase of maximum temperature resulted in a negative impact on both potential yield and rain‐fed potential yield. A simulated increase in minimum temperature produced no significant changes in potential or rain‐fed potential yield. However, the increase of minimum temperature was shown to result in a positive impact on the on‐farm yield, consistent with our finding that farmers adopted longer season hybrids for which the increase in minimum temperature provided better conditions for germination, emergence, and grain filling during night time. The gap between potential and rain‐fed potential yields was shown to be larger at locations with lower seasonal precipitation (<500 mm). Our results indicate that regions with the largest yield gaps between rain‐fed potential and on‐farm yields were located in the southeast of NEC. Within NEC, on‐farm maize yields were, on average, only 51% of the potential yields, indicating a large exploitable yield gap, which provides an opportunity to significantly increase production by effective irrigation, fertilization, herbicide, and planting density in NEC.     

Effects of precipitation and temperature on crop production variability in northeast Iran

Climate variability adversely impacts crop production and imposes a major constraint on farming planning, mostly under rainfed conditions, across the world. Considering the recent advances in climate science, many studies are trying to provide a reliable basis for climate, and subsequently agricultural production, forecasts. The El Niño-Southern Oscillation phenomenon (ENSO) is one of the principle sources of interannual climatic variability. In Iran, primarily in the northeast, rainfed cereal yield shows a high annual variability. This study investigated the role played by precipitation, temperature and three climate indices [Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and NINO 3.4] in historically observed rainfed crop yields (1983–2005) of both barley and wheat in the northeast of Iran. The results revealed differences in the association between crop yield and climatic factors at different locations. The south of the study area is a very hot location, and the maximum temperature proved to be the limiting and determining factor for crop yields; temperature variability resulted in crop yield variability. For the north of the study area, NINO 3.4 exhibited a clear association trend with crop yields. In central locations, NAO provided a solid basis for the relationship between crop yields and climate factors.     

Contributions of climatic and crop varietal changes to crop production in the North China Plain,since 1980s

The North China Plain (NCP) is the most important agricultural production area in China. Crop production in the NCP is sensitive to changes in both climate and management practices. While previous studies showed a negative impact of climatic change on crop yield since 1980s, the confounding effects of climatic and agronomic factors have not been separately investigated. This paper used 25 years of crop data from three locations (Nanyang, Zhengzhou and Luancheng) across the NCP, together with daily weather data and crop modeling, to analyse the contribution of changes in climatic and agronomic factors to changes in grain yields of wheat and maize. The results showed that the changes in climate were not uniform across the NCP and during different crop growth stages. Warming mainly occurred during the vegetative (preflowering) growth stage of wheat and maize, while there was a cooling trend or no significant change in temperatures during the postflowering stage of wheat (spring) or maize (autumn). If varietal effects were excluded, warming during vegetative stages would lead to a reduction in the length of the growing period for both crops, generally leading to a negative impact on crop production. However, autonomous adoption of new crop varieties in the NCP was able to compensate the negative impact of climatic change. For both wheat and maize, the varietal changes helped stabilize the length of preflowering period against the shortening effect of warming and, together with the slightly reduced temperature in the postflowering period, extend the length of the grain‐filling period. The combined effect led to increased wheat yield at Zhengzhou and Luancheng; increased maize yield at Nanyang and Luancheng; stabilized wheat yield at Nanyang, and a slight reduction in maize yield at Zhengzhou, compared with the yield change caused entirely by climatic change.     

不同气象数据空间内插对区域玉米生长模拟结果的影响

作物模型区域模拟已成为作物模型应用的一个新方向。运用作物模型进行区域研究时,遇到的问题之一就是输入模型的空间数据质量问题,研究不同空间内插法获得的气象数据对作物模型区域模拟结果的影响,可以为区域模拟对输入数据的敏感性研究提供一定的参考。利用区域校准的CERES-Maize模型,将3类内插方法(几何内插、统计内插、动力模型内插)产生的网格化天气数据分别输入到CERES-Maize模型中,模拟了50km×50km网格水平下1961—1990年我国玉米生产状况,并选取1980—1990年模拟的平均产量与同期农调队调查产量进行比较,以了解区域模拟中,不同空间内插方法所得的逐日气象数据对区域模拟结果的影响。结果表明:(1)作物模型区域应用时,所采用的3种内插方法都能满足作物模型区域模拟对网格化天气数据的要求,采用3种天气数据的区域模拟结果都能反映出玉米平均产量的空间变化特征,与网格调查平均产量之间具有极显著的相关关系,但采用不同内插天气数据对模拟结果造成了8%以内的偏差。(2)采用不同内插天气数据,在进行作物区域模拟时,各方法的模拟结果之间呈极显著的相关关系,但这些模拟结果之间,在全国大部分地区是差异显著。     

Crop yield response to climate change varies with cropping intensity

Projections of the response of crop yield to climate change at different spatial scales are known to vary. However, understanding of the causes of systematic differences across scale is limited. Here, we hypothesize that heterogeneous cropping intensity is one source of scale dependency. Analysis of observed global data and regional crop modelling demonstrate that areas of high vs. low cropping intensity can have systematically different yields, in both observations and simulations. Analysis of global crop data suggests that heterogeneity in cropping intensity is a likely source of scale dependency for a number of crops across the globe. Further crop modelling and a meta‐analysis of projected tropical maize yields are used to assess the implications for climate change assessments. The results show that scale dependency is a potential source of systematic bias. We conclude that spatially comprehensive assessments of climate impacts based on yield alone, without accounting for cropping intensity, are prone to systematic overestimation of climate impacts. The findings therefore suggest a need for greater attention to crop suitability and land use change when assessing the impacts of climate change.     

The combined and separate impacts of climate extremes on the current and future US rainfed maize and soybean production under elevated CO2

Heat and drought are two emerging climatic threats to the US maize and soybean production, yet their impacts on yields are collectively determined by the magnitude of climate change and rising atmospheric CO₂ concentrations. This study quantifies the combined and separate impacts of high temperature, heat and drought stresses on the current and future US rainfed maize and soybean production and for the first time characterizes spatial shifts in the relative importance of individual stress. Crop yields are simulated using the Agricultural Production Systems Simulator (APSIM), driven by high‐resolution (12 km) dynamically downscaled climate projections for 1995–2004 and 2085–2094. Results show that maize and soybean yield losses are prominent in the US Midwest by the late 21st century under both Representative Concentration Pathway (RCP) 4.5 and RCP8.5 scenarios, and the magnitude of loss highly depends on the current vulnerability and changes in climate extremes. Elevated atmospheric CO₂ partially but not completely offsets the yield gaps caused by climate extremes, and the effect is greater in soybean than in maize. Our simulations suggest that drought will continue to be the largest threat to US rainfed maize production under RCP4.5 and soybean production under both RCP scenarios, whereas high temperature and heat stress take over the dominant stress of drought on maize under RCP8.5. We also reveal that shifts in the geographic distributions of dominant stresses are characterized by the increase in concurrent stresses, especially for the US Midwest. These findings imply the importance of considering heat and drought stresses simultaneously for future agronomic adaptation and mitigation strategies, particularly for breeding programs and crop management. The modeling framework of partitioning the total effects of climate change into individual stress impacts can be applied to the study of other crops and agriculture systems.     

Can Impacts of Climate Change and Agricultural Adaptation Strategies Be Accurately Quantified if Crop Models Are Annually Re-Initialized?

Estimates of climate change impacts on global food production are generally based on statistical or process-based models. Process-based models can provide robust predictions of agricultural yield responses to changing climate and management. However, applications of these models often suffer from bias due to the common practice of re-initializing soil conditions to the same state for each year of the forecast period. If simulations neglect to include year-to-year changes in initial soil conditions and water content related to agronomic management, adaptation and mitigation strategies designed to maintain stable yields under climate change cannot be properly evaluated. We apply a process-based crop system model that avoids re-initialization bias to demonstrate the importance of simulating both year-to-year and cumulative changes in pre-season soil carbon, nutrient, and water availability. Results are contrasted with simulations using annual re-initialization, and differences are striking. We then demonstrate the potential for the most likely adaptation strategy to offset climate change impacts on yields using continuous simulations through the end of the 21^st century. Simulations that annually re-initialize pre-season soil carbon and water contents introduce an inappropriate yield bias that obscures the potential for agricultural management to ameliorate the deleterious effects of rising temperatures and greater rainfall variability.     

Potential impact of climate change on selected agricultural crops in north-eastern Austria

The vulnerability and adaptation of major agricultural crops to various soils in north‐eastern Austria under a changing climate were investigated. The CERES crop model for winter wheat and the CROPGRO model for soybean were validated for the agrometeorological conditions in the selected region. The simulated winter wheat and soybean yields in most cases agreed with the measured data. Several incremental and transient global circulation model (GCM) climate change scenarios were created and used in the study. In these scenarios, annual temperatures in the selected region are expected to rise between 0.9 and 4.8 °C from the 2020s to the 2080s. The results show that warming will decrease the crop‐growing duration of the selected crops. For winter wheat, a gradual increase in air temperature resulted in a yield decrease. Incremental warming, especially in combination with an increase in precipitation, leads to higher soybean yield. A drier climate will reduce soybean yield, especially on soils with low water storage capacity. All transient GCM climate change scenarios for the 21st century, including the adjustment for only air temperature, precipitation and solar radiation, projected reductions of winter wheat yield. However, when the direct effect of increased levels of CO₂ concentration was assumed, all GCM climate change scenarios projected an increase in winter wheat yield in the region. The increase in simulated soybean yield for the 21st century was primarily because of the positive impact of warming and especially of the beneficial influence of the direct CO₂ effect. Changes in climate variability were found to affect winter wheat and soybean yield in various ways. Results from the adaptation assessments suggest that changes in sowing date, winter wheat and soybean cultivar selection could significantly affect crop production in the 21st century.     

Increasing temperature cuts back crop yields in Hungary over the last 90 years

The transformation of climatic regime has an undeniable impact on plant production, but we rarely have long enough date series to examine the unfolding of such effects. The clarification of the relationship between crop plants and climate has a near‐immediate importance due to the impending human‐made global change. This study investigated the relationship between temperature, precipitation, drought intensity and the yields of four major cereals in Hungary between 1921 and 2010. The analysis of 30‐year segments indicated a monotonously increasing negative impact of temperature on crop yields. A 1°C temperature increase reduced the yield of the four main cereals by 9.6%–14.8% in 1981–2010, which revealed the vulnerability of Eastern European crop farming to recent climate change. Climate accounted for 17%–39% of yield variability over the past 90 years, but this figure reached 33%–67% between 1981 and 2010. Our analysis supports the claim that the mid‐20th century green revolution improved yields “at the mercy of the weather”: during this period, the impact of increasing fertilization and mechanisation coincided with climatic conditions that were more favourable than today. Crop yields in Eastern Europe have been stagnating or decreasing since the mid‐1980s. Although usually attributed to the large socio‐economic changes sweeping the region, our analysis indicates that a warming climate is at least partially responsible for this trend. Such a robust impact of increasing temperatures on crop yields also constitutes an obvious warning for this core grain‐growing region of the world.     

Water stress and insect herbivory interactively reduce crop yield while the insect pollination benefit is conserved

Climate change is predicted to hamper crop production due to precipitation deficits and warmer temperatures inducing both water stress and increasing herbivory due to more abundant insect pests. Consequently, crop yields will be impacted simultaneously by abiotic and biotic stressors. Extensive yield losses due to such climate change stressors might, however, be mitigated by ecosystem services such as insect pollination. We examined the single and combined effects of water stress, insect herbivory and insect pollination on faba bean yield components and above‐ and belowground plant biomass under realistic field conditions. We used rainout shelters to simulate a scenario in line with climate change projections, with adequate water supply at sowing followed by a long period without precipitation. This induced a gradually increasing water stress, culminating around crop flowering and yield formation. We found that gradually increasing water stress combined with insect herbivory by aphids interactively shaped yield in faba beans. Individually, aphid herbivory reduced yield by 79% and water stress reduced yield by 52%. However, the combined effect of water stress and aphid herbivory reduced yield less (84%) than the sum of the individual stressor effects. In contrast, insect pollination increased yield by 68% independently of water availability and insect herbivory. Our results suggest that yield losses can be greatly reduced when both water stress and insect herbivory are reduced simultaneously. In contrast, reducing only one stressor has negligible benefits on yield as long as the crop is suffering from the other stressor. We call for further exploration of interactions among ecosystem services and biotic and abiotic stressors that simulate realistic conditions under climate change.     

Carbon–Temperature–Water change analysis for peanut production under climate change: a prototype for the AgMIP Coordinated Climate‐Crop Modeling Project (C3MP)

Climate change is projected to push the limits of cropping systems and has the potential to disrupt the agricultural sector from local to global scales. This article introduces the Coordinated Climate‐Crop Modeling Project (C3MP), an initiative of the Agricultural Model Intercomparison and Improvement Project (AgMIP) to engage a global network of crop modelers to explore the impacts of climate change via an investigation of crop responses to changes in carbon dioxide concentration ([CO₂]), temperature, and water. As a demonstration of the C3MP protocols and enabled analyses, we apply the Decision Support System for Agrotechnology Transfer (DSSAT) CROPGRO‐Peanut crop model for Henry County, Alabama, to evaluate responses to the range of plausible [CO₂], temperature changes, and precipitation changes projected by climate models out to the end of the 21st century. These sensitivity tests are used to derive crop model emulators that estimate changes in mean yield and the coefficient of variation for seasonal yields across a broad range of climate conditions, reproducing mean yields from sensitivity test simulations with deviations of ca. 2% for rain‐fed conditions. We apply these statistical emulators to investigate how peanuts respond to projections from various global climate models, time periods, and emissions scenarios, finding a robust projection of modest (<10%) median yield losses in the middle of the 21st century accelerating to more severe (>20%) losses and larger uncertainty at the end of the century under the more severe representative concentration pathway (RCP8.5). This projection is not substantially altered by the selection of the AgMERRA global gridded climate dataset rather than the local historical observations, differences between the Third and Fifth Coupled Model Intercomparison Project (CMIP3 and CMIP5), or the use of the delta method of climate impacts analysis rather than the C3MP impacts response surface and emulator approach.     

Crop responses to climatic variation

The yield and quality of food crops is central to the well being of humans and is directly affected by climate and weather. Initial studies of climate change on crops focussed on effects of increased carbon dioxide (CO2) level and/or global mean temperature and/or rainfall and nutrition on crop production. However, crops can respond nonlinearly to changes in their growing conditions, exhibit threshold responses and are subject to combinations of stress factors that affect their growth, development and yield. Thus, climate variability and changes in the frequency of extreme events are important for yield, its stability and quality. In this context, threshold temperatures for crop processes are found not to differ greatly for different crops and are important to define for the major food crops, to assist climate modellers predict the occurrence of crop critical temperatures and their temporal resolution. This paper demonstrates the impacts of climate variability for crop production in a number of crops. Increasing temperature and precipitation variability increases the risks to yield, as shown via computer simulation and experimental studies. The issue of food quality has not been given sufficient importance when assessing the impact of climate change for food and this is addressed. Using simulation models of wheat, the concentration of grain protein is shown to respond to changes in the mean and variability of temperature and precipitation events. The paper concludes with discussion of adaptation possibilities for crops in response to drought and argues that characters that enable better exploration of the soil and slower leaf canopy expansion could lead to crop higher transpiration efficiency.     

中国北方气候暖干化对粮食作物的影响及应对措施

东北、华北和西北50a来的平均气温增幅高于全国平均水平,气候变暖明显,尤其冬季增温最显著。区域增暖的极端最低气温远比极端最高气温的贡献大。东北、华北大部、西北东部降水量明显减少,平均每10a减少20—40mm,尤其春夏季减少最明显。这种趋势一直延续到20世纪90年代以后,干旱化趋势非常突出。在综述我国北方现代气候变化基本特征是暖干化的基础上,重点阐述了喜凉作物冬小麦、春小麦、马铃薯和喜温作物水稻、玉米、谷子、糜子等7种主要粮食作物的生长发育、品种熟性、种植区域与面积、产量与品质等对气候暖干化的响应特征。揭示了气候暖干化使春播作物播期提早,苗期生长发育速度加快,营养生长期提前,生殖生长期和全生育期延长;秋作物发育期推迟,生殖生长期和全生长期延长;越冬作物播期推迟,越冬死亡率降低,种植风险减少,春初提前返青,生殖生长期提早,全生育期缩短。使作物适宜种植区域向高纬度高海拔扩展;品种熟性向偏中晚熟高产品种发展;喜温作物和越冬作物以及冷凉气候区的作物种植面积迅速扩大;在旱作区种植不较耐旱的玉米、春小麦等作物种植面积受到制约。对雨养农业区的作物气候产量影响严重,尤其对不够耐旱的小麦和玉米的气候产量受影响最大;对较耐旱的谷子、糜子、马铃薯等影响较轻。从作物属性而言,对喜温作物水稻、玉米和越冬作物冬小麦有利于气候产量提高;对喜凉作物春小麦和马铃薯的气候产量将产生不利影响。同时,提出了从5个方面应对气候暖干化的技术措施,调整作物种植结构,确保粮食生产安全;根据不同气候年型调整各种作物种植比例;针对不同气候区域发展优势作物和配置作物种植格局;采取不同栽培技术和管理模式应对气候变化;采取综合配套技术提髙抵御灾害能力。为粮食作物安全生产和种植结构调整与布局提供科学依据。     

From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact

General circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. This study analyses the bias introduced to crop simulation when climatic data is aggregated spatially or in time, resulting in loss of relevant variation. A detailed case study was conducted using historical weather data for Senegal, applied to the crop model SARRA-H (version for millet). The study was then extended to a 10 degrees N-17 degrees N climatic gradient and a 31 year climate sequence to evaluate yield sensitivity to the variability of solar radiation and rainfall. Finally, a down-scaling model called LGO (Lebel-Guillot-Onibon), generating local rain patterns from grid cell means, was used to restore the variability lost by aggregation. Results indicate that forcing the crop model with spatially aggregated rainfall causes yield overestimations of 10-50% in dry latitudes, but nearly none in humid zones, due to a biased fraction of rainfall available for crop transpiration. Aggregation of solar radiation data caused significant bias in wetter zones where radiation was limiting yield. Where climatic gradients are steep, these two situations can occur within the same GCM grid cell. Disaggregation of grid cell means into a pattern of virtual synoptic stations having high-resolution rainfall distribution removed much of the bias caused by aggregation and gave realistic simulations of yield. It is concluded that coupling of GCM outputs with plot level crop models can cause large systematic errors due to scale incompatibility. These errors can be avoided by transforming GCM outputs, especially rainfall, to simulate the variability found at plot level.     

Estimation of the carbon dioxide (CO2) fertilization effect using growth rate anomalies of CO2 and crop yields since 1961

The effect of elevated carbon dioxide (CO₂) on crop yields is one of the most uncertain and influential parameters in models used to assess climate change impacts and adaptations. A primary reason for this uncertainty is the limited availability of experimental data on CO₂ responses for crops grown under typical field conditions. However, because of historical variations in CO₂, each year farmers throughout the world perform uncontrolled yield ‘experiments’ under different levels of CO₂. In this study, measurements of atmospheric CO₂ growth rates and crop yields for individual countries since 1961 were compared to empirically determine the average effect of a 1 ppm increase of CO₂ on yields of rice, wheat, and maize. Because the gradual increase in CO₂ is highly correlated with major changes in technology, management, and other yield controlling factors, we focused on first differences of CO₂ and yield time series. Estimates of CO₂ responses obtained from this approach were highly uncertain, reflecting the relatively small importance of year‐to‐year CO₂ changes for yield variability. Combining estimates from the top 20 countries for each crop resulted in estimates with substantially less uncertainty than from any individual country. The results indicate that while current datasets cannot reliably constrain estimates beyond previous experimental studies, an empirical approach supported by large amounts of data may provide a potentially valuable and independent assessment of this critical model parameter. For example, analysis of reliable yield records from hundreds of individual, independent locations (as opposed to national scale yield records with poorly defined errors) may result in empirical estimates with useful levels of uncertainty to complement estimates from experimental studies.     

未来10年黄土高原气候变化对农业和生态环境的影响

利用区域气候模式PRECIS输出的未来A2和B2气候情景及基准气候时段逐日资料,选择生态环境极其脆弱的黄土高原为研究区,分析了未来10a黄土高原气候变化特征及其对主要农作物和生态环境的影响。结果表明,未来10a,黄土高原光热资源增加,降水量减少。增温将对冬小麦和春玉米产量影响较大,对马铃薯产量的影响程度可能较小,但降水量减少对主要农作物的产量都有较大影响。在主要作物品种不发生较大变化的前提下,作物生育期太阳辐射和积温增加可能导致生育期需水量增加10%-15%,冬小麦、春玉米和马铃薯的播期分别延迟或提前1-3d,收获期提前1-2d,生育期缩短3-5d,可能引起冬小麦和春玉米气候产量下降50%-100%。未来10a,降水量减少可能导致草地盖度的增幅下降和人工林地稀疏化,引起黄土高原片状水力侵蚀程度下降。但突发性暴雨洪水和土地利用现状改变可能增强切沟溯源冲蚀能力,增加了黄土高原水土流失和农田及道路被冲毁的风险。     

Negative effects of climate warming on maize yield are reversed by the changing of sowing date and cultivar selection in Northeast China

Northeast China (NEC) accounts for about 30% of the nation's maize production in China. In the past three decades, maize yields in NEC have increased under changes in climate, cultivar selection and crop management. It is important to investigate the contribution of these changing factors to the historical yield increases to improve our understanding of how we can ensure increased yields in the future. In this study, we use phenology observations at six sites from 1981 to 2007 to detect trends in sowing dates and length of maize growing period, and then combine these observations with in situ temperature data to determine the trends of thermal time in the maize growing period, as a measure of changes in maize cultivars. The area in the vicinity of these six sites accounts for 30% of NEC's total maize production. The agricultural production systems simulator, APSIM‐Maize model, was used to separate the impacts of changes in climate, sowing dates and thermal time requirements on maize phenology and yields. In NEC, sowing dates trended earlier in four of six sites and maturity dates trended later by 4–21 days. Therefore, the period from sowing to maturity ranged from 2 to 38 days longer in 2007 than it was in 1981. Our results indicate that climate trends alone would have led to a negative impact on maize. However, results from the adaptation assessments indicate that earlier sowing dates increased yields by up to 4%, and adoption of longer season cultivars caused a substantial increase in yield ranging from 13% to 38% over the past 27 years. Therefore, earlier sowing dates and introduction of cultivars with higher thermal time requirements in NEC have overcome the negative effects of climate change and turned what would have otherwise been a loss into a significant increase in maize yield.     

Effects of Extreme Climate Events on Tea (Camellia sinensis) Functional Quality Validate Indigenous Farmer Knowledge and Sensory Preferences in Tropical China

Climate change is impacting agro-ecosystems, crops, and farmer livelihoods in communities worldwide. While it is well understood that more frequent and intense climate events in many areas are resulting in a decline in crop yields, the impact on crop quality is less acknowledged, yet it is critical for food systems that benefit both farmers and consumers through high-quality products. This study examines tea (Camellia sinensis; Theaceae), the world''s most widely consumed beverage after water, as a study system to measure effects of seasonal precipitation variability on crop functional quality and associated farmer knowledge, preferences, and livelihoods. Sampling was conducted in a major tea producing area of China during an extreme drought through the onset of the East Asian Monsoon in order to capture effects of extreme climate events that are likely to become more frequent with climate change. Compared to the spring drought, tea growth during the monsoon period was up to 50% higher. Concurrently, concentrations of catechin and methylxanthine secondary metabolites, major compounds that determine tea functional quality, were up to 50% lower during the monsoon while total phenolic concentrations and antioxidant activity increased. The inverse relationship between tea growth and concentrations of individual secondary metabolites suggests a dilution effect of precipitation on tea quality. The decrease in concentrations of tea secondary metabolites was accompanied by reduced farmer preference on the basis of sensory characteristics as well as a decline of up to 50% in household income from tea sales. Farmer surveys indicate a high degree of agreement regarding climate patterns and the effects of precipitation on tea yields and quality. Extrapolating findings from this seasonal study to long-term climate scenario projections suggests that farmers and consumers face variable implications with forecasted precipitation scenarios and calls for research on management practices to facilitate climate adaptation for sustainable crop production.     

Impact of derived global weather data on simulated crop yields

Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long‐term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water‐limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well‐maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA‐POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location‐specific observed daily weather databases combined with an appropriate upscaling method.