Adaptation to climate change through the choice of cropping system and sowing date in sub-Saharan Africa

Multiple cropping systems provide more harvest security for farmers, allow for crop intensification and furthermore influence ground cover, soil erosion, albedo, soil chemical properties, pest infestation and the carbon sequestration potential. We identify the traditional sequential cropping systems in ten sub-Saharan African countries from a survey dataset of more than 8600 households. We find that at least one sequential cropping system is traditionally used in 35% of all administrative units in the dataset, mainly including maize or groundnuts. We compare six different management scenarios and test their susceptibility as adaptation measure to climate change using the dynamic global vegetation model for managed land LPJmL. Aggregated mean crop yields in sub-Saharan Africa decrease by 6–24% due to climate change depending on the climate scenario and the management strategy. As an exception, some traditional sequential cropping systems in Kenya and South Africa gain by at least 25%. The crop yield decrease is typically weakest in sequential cropping systems and if farmers adapt the sowing date to changing climatic conditions. Crop calorific yields in single cropping systems only reach 40–55% of crop calorific yields obtained in sequential cropping systems at the end of the 21st century. The farmers’ choice of adequate crops, cropping systems and sowing dates can be an important adaptation strategy to climate change and these management options should be considered in climate change impact studies on agriculture.     

Temporal statistical downscaling of precipitation and temperature forecasts using a stochastic weather generator

Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society(IRI). In conjunction with the GLM(generalized linear modeling) weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts(the IRI format only specifies probabilities for three categories: below normal, near normal, and above normal) into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.     

Assessing Winter Wheat Responses to Climate Change Scenarios: A Simulation Study in the U.S. Great Plains

Hard red winter wheat (Triticum aestivum L.) is a major crop in the Great Plains region of the U.S. The goal of this assessment effort was to investigate the influence of two contrasting global climate change projections (U.K. Hadley Center for Climate Prediction and Research and Canadian Centre for Climate Modelling and Analysis) on the yield and percent kernel nitrogen content of winter wheat at three locations in Nebraska. These three locations represent sub-humid and semi arid areas and the transition between these areas and are also representative of major portions of the winter wheat growing areas of the central Great Plains. Climate scenarios based on each of the projections for each location were developed using the LARS-WG weather generator along with data from automated weather stations. CERES-Wheat was used to simulate the responses for two contrasting cultivars of wheat using two sowing dates. The first sowing date represented current sowing dates appropriate for each location. The second sowing date was later and represents the approximate date when the mean air temperature from the climate scenarios is the same as the mean air temperature from the actual climate data at the current sowing dates. The yield and percent kernel nitrogen content using the two climate scenarios generally decrease going from the sub-humid eastern to the semi arid western parts of Nebraska. Results from these simulations indicate that yield and percent kernel nitrogen content using the two climate scenarios could not both be maintained at levels currently simulated. Protein content (directly related to kernel nitrogen content) and end-use quality are the primary determinants for the use of hard red winter wheat in baked goods. Nitrogen management and new cultivars, which can enhance the uptake and translocation of nitrogen, will be proactive steps to meet the challenges of global climate change as represented by these climate scenarios.     

Seasonal Climate Forecasts – Potential Agricultural-Risk Management Tools?

The importance of anchoring seasonal climate forecasts to user needs is examined in this paper. Although it is generally accepted that seasonal climate forecasts have potential value, many constraints preclude the optimal use of these forecasts, including the way forecasts are produced, interpreted and applied in a variety of decision-making processes. In South Africa, a variety of agricultural users exists, ranging from the small-scale farmer to larger commercial farming entities. Useful seasonal are those produced and disseminated with the end user in mind. A retroactive test period during the 1990s, evaluates the perceived impact of incorporating seasonal rainfall forecasts into decisions made by commercial crop farmers in the central parts of South Africa. Although a small sample of commercial farmers was interviewed, the results show some benefits to commercial agriculture if seasonal climate forecast information is continuously and effectively applied over the long-term.     

Seasonal forecasts in the Sahel region: the use of rainfall-based predictive variables

In the Sahel region, seasonal predictions are crucial to alleviate the impacts of climate variability on populations' livelihoods. Agricultural planning (e.g., decisions about sowing date, fertilizer application date, and choice of crop or cultivar) is based on empirical predictive indices whose accuracy to date has not been scientifically proven. This paper attempts to statistically test whether the pattern of rainfall distribution over the May–July period contributes to predicting the real onset date and the nature (wet or dry) of the rainy season, as farmers believe. To that end, we considered historical records of daily rainfall from 51 stations spanning the period 1920–2008 and the different agro-climatic zones in Burkina Faso. We performed (1) principal component analysis to identify climatic zones, based on the patterns of intra-seasonal rainfall, (2) and linear discriminant analysis to find the best rainfall-based variables to distinguish between real and false onset dates of the rainy season, and between wet and dry seasons in each climatic zone. A total of nine climatic zones were identified in each of which, based on rainfall records from May to July, we derived linear discriminant functions to correctly predict the nature of a potential onset date of the rainy season (real or false) and that of the rainy season (dry or wet) in at least three cases out of five. These functions should contribute to alleviating the negative impacts of climate variability in the different climatic zones of Burkina Faso.     

WOFOST模型蒸散过程改进对玉米干旱模拟影响

分别利用优化蒸散计算（PM方案）、作物系数（CC方案）和二者同时优化（PMCC方案）改进WOFOST模型,基于降水适宜年（2012年）和干旱年（2015年和2018年）在辽宁省锦州开展的玉米分期（4月20日、4月30日和5月10日）播种试验资料评价模型改进效果。结果表明:2012年,PM方案可增大潜在蒸散,CC方案在作物系数小（大）于1时使潜在蒸散减小（增大）;3个方案对叶面积指数、地上生物量和土壤湿度模拟几乎不产生影响。2015年,PM方案的拔节后叶面积指数、地上生物量、产量和土壤湿度较原模型明显减小,蒸腾速率在喇叭口期之前增大,之后减小;CC方案在喇叭口期之前蒸腾速率小于原模型,之后大于原模型,其他4个变量略大于原模型。PMCC方案的各变量模拟值介于PM方案和CC方案之间,3个播期模拟精度叶面积指数分别提高6%,21%和3%,地上生物量分别提高8%,8%和14%,产量分别提高66%,63%和66%。2018年,PMCC方案前两播期地上生物量模拟精度分别提高5%和1%,产量模拟精度分别提高32%和5%。PMCC方案可改善模型在干旱条件下的模型性能。     

基于气象要素的逐日玉米产量气象影响指数

利用1981—2020年5—9月气象数据与玉米产量数据,通过改进逐日降水适宜度并构建逐日气候适宜度模型,建立基于相似年逐日气象要素的作物生育期气候适宜度序列,利用气象产量与气候适宜指数建立模型,设计逐日作物产量气象影响指数以表征气象条件对作物的影响程度,基于该指数构建东北地区玉米逐日产量预报模型并分析其逐日预报准确率,用以表明该指数的准确性。结果表明:利用3个相似年预报结果加权集成综合相似年逐日作物产量气象影响指数可提高逐日预报准确率,黑龙江年尺度逐日预报准确率年际间波动小于东北其他地区。综合相似年月尺度下,随着玉米发育期的推进和实时气象数据的引入,月尺度平均预报准确率逐渐提高。东北地区玉米产量8月31日的日尺度预报准确率普遍高于7月31日;辽宁日尺度预报差异较大,但随着玉米发育期推进逐日预报产量和实际产量接近,准确率也提高。基于气象要素构建的逐日作物产量影响指数和同期气象影响指数可以定量评估不同时段气象条件对作物产量的影响程度,在一定程度上可提高农业气象业务定量化评价水平。     

A decision‐analytic study of the joint value of seasonal precipitation and temperature forecasts in a choice‐of‐crop problem

Abstract

A static decision‐analytic method is used to investigate the economic value of bivariate ‐ precipitation and temperature ‐ seasonal forecasts of the form currently issued by the U.S. National Weather Service. This method is applied to a corn versus spring wheat choice‐of‐crop decision‐making problem by considering a transect of four counties across the northwestern margin of the North American corn belt. Numerical results indicate that seasonal forecasts of current quality can be of appreciable value (≥$1/ha) for some locations when the optimal action chosen on the basis of climatological information is only marginally preferred to another action. Increases in forecast value follow from hypothetical increases in the quality of both the precipitation and temperature components of the forecasts in the spring wheat region, whereas forecast value increases primarily as a function of the quality of the precipitation forecasts alone in the corn belt region. The results are very sensitive to absolute and relative crop prices.     

最优气候值方法在中国的应用

该文用最优气候值方法研究了 1 951～ 1 998年中国 1 60个站点季、月的气温和降水 .结果表明 ,在中国最优气候值方法对气温具有相当的可用性 ,尤其是对冬季气温有很好的预报效果 ;对于降水 ,只对个别时间段的个别地区有一定的可用性 .对季的预报好于对月的预报 .无论是预报值与观测值的相关系数还是最优气候值的平均年份数在每一固定时期内都呈现出有规则的连续性空间分布 ,但是空间分布随时间变化而变化     

Combined Impact of Climate Change,Cultivar Shift,and Sowing Date on Spring Wheat Phenology in Northern China

Distinct climate changes since the end of the 1980 s have led to clear responses in crop phenology in many parts of the world. This study investigated the trends in the dates of spring wheat phenology in relation to mean temperature for different growth stages. It also analyzed the impacts of climate change, cultivar shift, and sowing date adjustments on phenological events/phases of spring wheat in northern China(NC).The results showed that significant changes have occurred in spring wheat phenology in NC due to climate warming in the past 30 years. Specifically, the dates of anthesis and maturity of spring wheat advanced on average by 1.8 and 1.7 day(10 yr)~(-1). Moreover, while the vegetative growth period(VGP) shortened at most stations, the reproductive growth period(RGP) prolonged slightly at half of the investigated stations. As a result, the whole growth period(WGP) of spring wheat shortened at most stations. The findings from the Agricultural Production Systems Simulator(APSIM)-Wheat model simulated results for six representative stations further suggested that temperature rise generally shortened the spring wheat growth period in NC.Although the warming trend shortened the lengths of VGP, RGP, and WGP, the shift of new cultivars with high accumulated temperature requirements, to some extent, mitigated and adapted to the ongoing climate change. Furthermore, shifts in sowing date exerted significant impacts on the phenology of spring wheat.Generally, an advanced sowing date was able to lower the rise in mean temperature during the different growth stages(i.e., VGP, RGP, and WGP) of spring wheat. As a result, the lengths of the growth stages should be prolonged. Both measures(cultivar shift and sowing date adjustments) could be vital adaptation strategies of spring wheat to a warming climate, with potentially beneficial effects in terms of productivity.     

Combined impact of climate change,cultivar shift,and sowing date on spring wheat phenology in Northern China

Distinct climate changes since the end of the 1980s have led to clear responses in crop phenology in many parts of the world. This study investigated the trends in the dates of spring wheat phenology in relation to mean temperature for different growth stages. It also analyzed the impacts of climate change, cultivar shift, and sowing date adjustments on phenological events/phases of spring wheat in northern China (NC). The results showed that significant changes have occurred in spring wheat phenology in NC due to climate warming in the past 30 years. Specifically, the dates of anthesis and maturity of spring wheat advanced on average by 1.8 and 1.7 day (10 yr)^?1. Moreover, while the vegetative growth period (VGP) shortened at most stations, the reproductive growth period (RGP) prolonged slightly at half of the investigated stations. As a result, the whole growth period (WGP) of spring wheat shortened at most stations. The findings from the Agricultural Production Systems Simulator (APSIM)-Wheat model simulated results for six representative stations further suggested that temperature rise generally shortened the spring wheat growth period in NC. Although the warming trend shortened the lengths of VGP, RGP, and WGP, the shift of new cultivars with high accumulated temperature requirements, to some extent, mitigated and adapted to the ongoing climate change. Furthermore, shifts in sowing date exerted significant impacts on the phenology of spring wheat. Generally, an advanced sowing date was able to lower the rise in mean temperature during the different growth stages (i.e., VGP, RGP, and WGP) of spring wheat. As a result, the lengths of the growth stages should be prolonged. Both measures (cultivar shift and sowing date adjustments) could be vital adaptation strategies of spring wheat to a warming climate, with potentially beneficial effects in terms of productivity.     

Using climate information for supporting climate change adaptation in water resource management in South Africa

Water resources, and in particular run-off, are significantly affected by climate variability. At present, there are few examples of how the water management sector integrates information about changing intra-annual climate conditions in a systematic manner in developing countries. This paper, using the case study of Cape Town in the Western Cape, South Africa, identifies processes and products to facilitate increased uptake of seasonal climate forecasts among water resource managers. Results suggest that existing seasonal forecasts do not focus enough on specific users’ needs. In order to increase uptake, forecasts need to include information on the likely impact of precipitation variability on runoff and water availability. More opportunities are also needed for those with climate knowledge to interact with water resource managers, particularly in the developing country context where municipal managers’ capacity is strained. Although there are challenges that need to be overcome in using probabilistic climate information, seasonal forecast information tailored to the needs of water resource planners has the potential to support annual planning and is therefore a means of adapting to climate change.     

Changes in Climate and Crop Production During the 20th Century in Argentina

This work was focused on the assessment of changes occurring in crop production and climate during the 20th century in Argentina. The study was carried out for nine sites located in the Pampas region that are representative of contrasting environments. We have considered the four main crops cultivated in this area (wheat, maize, sunflower and soybean). Historical climatic data and crop production related variables (yield, planted area, harvested area) were analyzed and, by means of crop simulation models, we quantified the impact of climate on crop yields. Changes occurring in climate during the three last decades of the 20th century were characterized by important increases in precipitation especially between October and March, decreases in maximum temperature and solar radiation in particular during spring and summer and increases in minimum temperature during almost all of the year. These changes contributed to increases in yields, especially in summer crops and in the semiarid zone, mostly due to increases in precipitation, although changes in temperature and radiation also affected crop yields but to a lesser extent. Comparing the period 1950–1970 with 1971–1999, yields increases attributable to changes in climate were 38% in soybean, 18% in maize, 13% in wheat, and 12% in sunflower while mean observed yield increases were 110% for maize, 56% for wheat and 102% for sunflower.     

Impact of ENSO-Related Climate Anomalies on Crop Yields in the U.S.

Historical daily thermal and precipitation data from selected stations across the United States are composited into climate scenarios for the three phases of ENSO: Warm Events (El Niño), Cold Events (El Viejo or La Niña), and Neutral. Using these scenarios, yields of 7 field crops were simulated using the EPIC biophysical model during the one-year period coincident with maximum SST anomalies in the equatorial Pacific. The response of simulated agricultural productivity to the ENSO-related climate-variability parameters, is presented. A sensitivity calculation confirms the relevance of precipitation totals/medians and suggests ENSO-related yields are sensitive to changes in statistical properties characterizing precipitation distribution and occurrence. Results are spatially dependent, with the southwest and northern plains regions indicating the highest sensitivity to the inclusion of additional precipitation characteristics. The southeast yields are not as sensitive. The yield deviations (expressed as normalized differences to neutral yields) associated with the two extreme ENSO phases (Warm Events and Cold Events) are spatially and crop dependent with ranges up to ±120%. The largest yield deviations are in the south, southwest, and northern plains. Overall, Cold Events demonstrate larger impacts in the southern regions and Warm Events have a larger impact in the north. Additionally, the notion that climate anomalies associated with Cold and Warm Events and subsequent impacts on yields should be of opposite sign (i.e., linear) is not valid in many regions. For the eastern half of the U.S., modeled yield deviations under Warm Event conditions are nearly all less than neutral. Conversely, in the western half, results are more mixed. Under Cold Event conditions, yields in the east are enhanced in the south, but worsened in the north; while in the western half, yields have decreased in general. The results highlight the critical role of climate and production-related data on station or county levels in quantifying the impact of ENSO climate anomalies on yields. Both the diverse nature of the ENSO-related yield deviations as well as their sensitivity to monthly frequency distribution and occurrence characteristics imply that ENSO-related seasonal precipitation forecasts might be beneficial for agricultural application only if details were provided regarding not only totals, but also predicted changes in temporal and spatial variability of a more comprehensive suite of characteristics.     

Interrelation of the intraseasonal precipitation variability over Eurasia to the Eliassen-Palm flux characteristics over the Northern Hemisphere

Conditional averages of principal components of the Eliassen-Palm flux divergence variability are projected onto the daily precipitation amounts chart. The conditions of calculating the average values are determined by the dates classified in three equiprobable precipitation categories. The classification and calculation of characteristics are performed for the summer and winter seasons. Using the rotated principal component analysis, several regions are revealed of statistically significant interrelation between extreme precipitation and the first EP flux divergence variability modes, a simplified exploratory interpretation of interrelations is given and several recommendations are formulated for correcting seasonal forecasts of meteorological conditions with the use of results obtained.     

Effects of climate change on grain maize yield potential in the european community

Grain maize yield in the main arable areas of the European Community (E.C.) was calculated with a simulation model, WOFOST, using historical weather data and average soil characteristics. The sensitivity of the model to individual weather variables was determined. Subsequent analyses were made using climate change scenarios with and without the direct effects of increased atmospheric CO₂. The impact of crop management (sowing date, irrigation and cultivar type) in a changed climate was also assessed. Scenario climate change generally results in larger grain yields for the northern E.C., similar or slightly smaller yields for the central E.C. and considerably smaller yields for the southern E.C. The various climate change scenarios used appear to give considerably different changes in grain yield, both for each location and for the E.C. as a whole. Management analyses show that for both current and scenario climates the largest grain yield will be attained by varieties with an early start of grain filling, that average irrigation requirements to attain potential grain yield in the E.C. will increase with climate change but will decrease with both increased CO₂ and climate change, and that sowing at both current and scenarios climate should occur as early as possible.The U.S. Government right to retain a nonexclusive, royalty-free licence in and to any copyright is acknowledged.     

Crop yield response to economic, site and climatic variables

This paper examines the effects of climatic and non-climatic factors on the mean and variance of corn, soybean and winter wheat yield in southwestern Ontario, Canada over a period of 26 years. Average crop yields increase at a decreasing rate with the quantity of inputs used, and decrease with the area planted to the crop. Climate variables have a major impact on mean yield with the length of the growing season being the primary determinant across all three crops. Increases in the variability of temperature and precipitation decrease mean yield and increase its variance. Yield variance is poorly explained by both seasonal and monthly climate variable models. Projections of future climate change suggest that average crop yield will increase with warmer temperatures and a longer growing season which is only partially offset by forecast increases in the variability of temperature and rainfall. The projections would also depend on future technological developments, which have generated significant increases in yield over time despite changing annual weather conditions.     

Rain-based factors of high agricultural impacts over Senegal. Part I: integration of local to sub-regional trends and variability

The evolution of seasonal cycle and interannual rainfall, the number of rainy days and daily rainfall types, dry spells frequency of occurrence, onset/cessation/length of rainy season, sowing dates, and the duration of the cropping period, are investigated at local (individual sites) and sub-regional scales (four different rainfall zones) using daily records of station data (83 sites) over Senegal. In the limits of a case study, these analyses complement and update previous studies conducted in the extreme Western Sahel (11?C16° N and 20° W?C10° E). The results unveil noticeable evolution of some of these rain-based factors in the recent periods as compared to the previous dry years. In the regions recording less than 800?mm/year (Sudan and Sahel sub-regions), the positive and statistically significant trends of rainfall amount are associated with new features of increasing frequency of short dry spell category, increasing number of some classes of extreme daily rainfall amounts and shifts in the peak number of rainy days. At sub-regional scales, the starting years (or change points) the magnitude and the signs of the new trends are unevenly distributed in the period post-1990. Earlier and higher amplitude changes are found at local scales and not less than one third of the sites in each sub-regional network are significantly affected. The extreme Southern sub-region exhibits no significant changes. Statistically significant trends are not observed on daily rain records ??10?mm, onset/cessation dates, successful sowing dates, rainy season length, cropping period, medium and extreme dry spell categories. Rather, some of these factors such as the successful sowing date and the cropping season length exhibit significant variability. The onset (cessation) dates of the rainy season are followed (preceded) by extreme dry spell episodes. In the perspectives of climate impact assessments on the local agriculture a sub-regional periodic synopsis of the major rain-based factors of interest to agricultural applications are provided at the end the paper. They document some important internal variability patterns to reckon with in a multi-decadal work over the 1950?C2008 period for this region.     

气候变暖对广西双季稻安全生产期的影响

利用1960—2019年广西88个国家气象观测站逐日气温资料，将研究时段分成前30年（P1：1960—1989年）和后30年（P2：1990—2019年）2个阶段，分析早稻安全播种期和安全移栽期、晚稻安全齐穗期和安全成熟期的年际变化规律，并通过比较P1和P2阶段不同保证率下各安全生产期的差异，分析气候变暖背景下广西双季稻安全生产期的变化特征。结果表明：①早稻安全播种期和安全移栽期主要表现为提前变化趋势,晚稻安全齐穗期和安全成熟期主要表现为推迟变化趋势，全区80%和90%保证率安全播种期和安全移栽期平均提前2 d，80%和90%保证率安全齐穗期平均推迟2 d，80%和90%保证率安全成熟期平均推迟5 d和6 d。②安全生产期的变化特征空间差异性较大，桂东北安全播种期和安全移栽期提前天数最多,达6 d以上，百色那坡、靖西地区安全齐穗期推迟天数最多，达6 d以上，全区大部分地区安全成熟期推迟天数在6～10 d，空间差异相对较小。     

MEAN AND VARIANCE CHANGE IN CLIMATE SCENARIOS: METHODS, AGRICULTURAL APPLICATIONS, AND MEASURES OF UNCERTAINTY

Our central goal is to determine the importance of including both mean and variability changes in climate change scenarios in an agricultural context. By adapting and applying a stochastic weather generator, we first tested the sensitivity of the CERES-Wheat model to combinations of mean and variability changes of temperature and precipitation for two locations in Kansas. With a 2°C increase in temperature with daily (and interannual) variance doubled, yields were further reduced compared to the mean only change. In contrast, the negative effects of the mean temperature increase were greatly ameliorated by variance decreased by one-half. Changes for precipitation are more complex, since change in variability naturally attends change in mean, and constraining the stochastic generator to mean change only is highly artificial. The crop model is sensitive to precipitation variance increases with increased mean and variance decreases with decreased mean. With increased mean precipitation and a further increase in variability Topeka (where wheat cropping is not very moisture limited) experiences decrease in yield after an initial increase from the 'mean change only case. At Goodland Kansas, a moisture-limited site where summer fallowing is practiced, yields are decreased with decreased precipitation, but are further decreased when variability is further reduced. The range of mean and variability changes to which the crop model is sensitive are within the range of changes found in regional climate modeling (RegCM) experiments for a CO₂ doubling (compared to a control run experiment). We then formed two types of climate change scenarios based on the changes in climate found in the control and doubled CO₂ experiments over the conterminous U. S. of RegCM: (1) one using only mean monthly changes in temperature, precipitation, and solar radiation; and (2) another that included these mean changes plus changes in daily (and interannual) variability. The scenarios were then applied to the CERES-Wheat model at four locations (Goodland, Topeka, Des Moines, Spokane) in the United States. Contrasting model responses to the two scenarios were found at three of the four sites. At Goodland, and Des Moines mean climate change increased mean yields and decreased yield variability, but the mean plus variance climate change reduced yields to levels closer to their base (unchanged) condition. At Spokane mean climate change increased yields, which were somewhat further increased with climate variability change. Three key aspects that contribute to crop response are identified: the marginality of the current climate for crop growth, the relative size of the mean and variance changes, and timing of these changes. Indices for quantifying uncertainty in the impact assessment were developed based on the nature of the climate scenario formed, and the magnitude of difference between model and observed values of relevant climate variables.