Climate Change and Rice Yields in Diverse Agro-Environments of India. II. Effect of Uncertainties in Scenarios and Crop Models on Impact Assessment

Estimates of impact of climate change on crop production could be biased depending upon the uncertainties in climate change scenarios, region of study, crop models used for impact assessment and the level of management. This study reports the results of a study where the impact of various climate change scenarios has been assessed on grain yields of irrigated rice with two popular crop simulation models- Ceres-Rice and ORYZA1N at different levels of N management. The results showed that the direct effect of climate change on rice crops in different agroclimatic regions in India would always be positive irrespective of the various uncertainties. Rice yields increased between 1.0 and 16.8% in pessimistic scenarios of climate change depending upon the level of management and model used. These increases were between 3.5 and 33.8% in optimistic scenarios. At current as well as improved level of management, southern and western parts of India which currently have relatively lower temperatures compared to northern and eastern regions, are likely to show greater sensitivity in rice yields under climate change. The response to climate change is small at low N management compared to optimal management. The magnitude of this impact can be biased upto 32% depending on the uncertainty in climate change scenario, level of management and crop model used. These conclusions are highly dependent on the specific thresholds of phenology and photosynthesis to change in temperature used in the models. Caution is needed in using the impact assessment results made with the average simulated grain yields and mean changes in climatic parameters.     

用经济-气候模型模拟粮食单产的方法探讨

综合考虑作物生育期内逐旬光、温、水气候条件的影响,通过SVD和EOF方法构建一个综合气候因子,结合经济资料建立吉林省经济-气候模型,并应用于吉林省粮食单产的模拟和年景评估。结果表明:综合气候因子对吉林省粮食单产的影响主要为正效应,经济-气候模型能对吉林省粮食单产和年景进行较好的评估。     

An essay on the impact of climate change on US agriculture: weather fluctuations, climatic shifts, and adaptation strategies

The impact of climate change on US agriculture has been debated for more than two decades, but the estimates ranged from no damage at the lower end to 80 % losses of grain yields at the higher end. This essay aims to help understand such divergent predictions by clarifying the concepts of weather and climate. First, the widely-read panel fixed effects models capture only the impacts of weather fluctuations but not of climate normals. Random weather fluctuations and climatic shifts are two different meteorological events and they have distinct implications on farming decisions. The former is perceived as random while the latter is perceived as non-random by the farmers. Using the historical corn yield data in the US, I explain the differences between the impact of random weather and that of climate change. Second, adaptation strategies to climatic changes and increased climate risks cannot be accounted for by the panel fixed effects models. Using the farm household data collected in sub-Saharan Africa and Latin America, I discuss quantitative significance of modeling adaptation strategies in the estimates of climate damage. Distinction between random weather fluctuations and climatic shifts is critical in modeling farming decisions, as they are fundamental to climate science, but is poorly understood by the impact researchers.     

Attribution of Maize Yield Increase in China to Climate Change and Technological Advancement Between 1980 and 2010

Crop yields are affected by climate change and technological advancement. Objectively and quantitatively evaluating the attribution of crop yield change to climate change and technological advancement will ensure sustainable development of agriculture under climate change. In this study, daily climate variables obtained from 553 meteorological stations in China for the period 1961-2010, detailed observations of maize from 653 agricultural meteorological stations for the period 1981-2010, and results using an Agro-Ecological Zones (AEZ) model, are used to explore the attribution of maize (Zea mays L.) yield change to climate change and technological advancement. In the AEZ model, the climatic potential productivity is examined through three step-by-step levels: photosynthetic potential productivity, photosynthetic thermal potential productivity, and climatic potential productivity. The relative impacts of different climate variables on climatic potential productivity of maize from 1961 to 2010 in China are then evaluated. Combined with the observations of maize, the contributions of climate change and technological advancement to maize yield from 1981 to 2010 in China are separated. The results show that, from 1961 to 2010, climate change had a significant adverse impact on the climatic potential productivity of maize in China. Decreased radiation and increased temperature were the main factors leading to the decrease of climatic potential productivity. However, changes in precipitation had only a small effect. The maize yields of the 14 main planting provinces in China increased obviously over the past 30 years, which was opposite to the decreasing trends of climatic potential productivity. This suggests that technological advancement has offset the negative effects of climate change on maize yield. Technological advancement contributed to maize yield increases by 99.6%-141.6%, while climate change contribution was from-41.4% to 0.4%. In particular, the actual maize yields in Shandong, Henan, Jilin, and Inner Mongolia increased by 98.4, 90.4, 98.7, and 121.5 kg hm^-2 yr^-1 over the past 30 years, respectively. Correspondingly, the maize yields affected by technological advancement increased by 113.7, 97.9, 111.5, and 124.8 kg hm^-2 yr^-1, respectively. On the contrary, maize yields reduced markedly under climate change, with an average reduction of-9.0 kg hm^-2 yr^-1. Our findings highlight that agronomic technological advancement has contributed dominantly to maize yield increases in China in the past three decades.     

Application of an Economy--Climate Model to Assess the Impact of Climate Change

An interdisciplinary investigation was conducted to assess the impact of climate change on grain yields using an economy--climate model (C-D-C). The model was formulated by incorporating climate factors into the classic Cobb-Douglas (C-D) economic production function model. The economic meanings of the model output elasticities are described and elucidated. The C-D-C model was applied to the assessment of the impact of climate change on grain yields in China during the past 20 years, from 1983 through 2002. In the study, the land of China was divided into eight regions, and both the C-D-C and C-D models were applied to each individual region. The results suggest that the C-D-C model is superior to the classic C-D model, indicating the importance of climate factors. Prospective applications of the C-D-C model are discussed.     

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.     

Climatic change and grain corn yields in the North American Great Plains

A basic parametric crop yield model (YIELD) that uses climatic and environmental data to calculate yield and associated parameters for grain corn (maize) was applied to a transect through the North American Great Plains. This paper continues our examination of the impact of probable climatic change scenarios on crop evapotranspiration and irrigation requirements (Terjung et al., 1984), This study of grain corn yields showed highest yield for the first (or primary) harvest under full irrigation occurring under a sunny and cold scenario in Austin, TX, sunny and cool in Kansas City, KS, and sunny and warm in Bismarck, ND. Lowest irrigated yield was found with cloudy and hot and very dry climate change scenarios. Under rainfed-only conditions, minima were obtained under the sunny-hot and -warm scenarios and very dry conditions.J. T. Hayes is a professor at the Department of Geography and Regional Planning, State University of New York, Albany, NY, U.S.A.L. O. Mearns is also currently associated with the National Center for Atmospheric Research, funded by the National Science Foundation.Dr. Liverman is a professor at the Department of Geography, University of Wisconsin, Madison, Wis., U.S.A.     

Estimating the consequences of CO2-induced climatic change on north american grain agriculture using general circulation model information

A procedure to estimate the potential climatic effects of a doubling of atmospheric carbon dioxide concentration on agricultural production is illustrated. The method combines use of atmospheric general circulation models (GCMs) and process-oriented crop models. Wheat and corn (maize) yields in three important North American grain cropping regions are treated. Combined use of these two types of models can provide insights into the impacts of climate changes at the level of plant physiology, and potential means by which agricultural production practices may adapt to these changes.Specific agronomic predictions are found to depend critically on the details of the projected climate change. Uncertainties in the specification of the doubled-CO₂ climate by the GCM, particularly with respect to precipitation, dictate that agricultural predictions derived from them at this time must be regarded only as illustrative of the impact assessment method.     

Projections of uncertainties in climate change scenarios into expected winter wheat yields

Summary The crop model CERES-Wheat in combination with the stochastic weather generator were used to quantify the effect of uncertainties in selected climate change scenarios on the yields of winter wheat, which is the most important European cereal crop. Seven experimental sites with the high quality experimental data were selected in order to evaluate the crop model and to carry out the climate change impact analysis. The analysis was based on the multi-year crop model simulations run with the daily weather series prepared by the stochastic weather generator. Seven global circulation models (GCMs) were used to derive the climate change scenarios. In addition, seven GCM-based scenarios were averaged in order to derive the average scenario (AVG). The scenarios were constructed for three time periods (2025, 2050 and 2100) and two SRES emission scenarios (A2 and B1). The simulated results showed that: (1) Wheat yields tend to increase (40 out of 42 applied scenarios) in most locations in the range of 7.5–25.3% in all three time periods. In case of the CCSR scenario that predicts the most severe increase of air temperature, the yields would be reduced by 9.6% in 2050 and by 25.8% in 2100 if the A2 emission scenario would become reality. Differences between individual scenarios are large and statistically significant. Particularly for the time periods 2050 and 2100 there are doubts about the trend of the yield shifts. (2) The site effect was caused by the site-specific soil and climatic conditions. Importance of the site influence increases with increasing severity of imposed climatic changes and culminates for the emission scenario A2 and the time period 2100. The sustained tendency benefiting two warmest sites has been found as well as more positive response to the changed climatic conditions of the sites with deeper soil profiles. (3) Temperature variability proved to be an important factor and influenced both mean and standard deviation of the yields. Change of temperature variability by more than 25% leads to statistically significant changes in yield distribution. The effect of temperature variability decreases with increased values of mean temperature. (4) The study proved that the application of the AVG scenarios – despite possible objections of physical inconsistency – might be justifiable and convenient in some cases. It might bring results comparable to those derived from averaging outputs based on number of scenarios and provide more robust estimate than the application of only one selected GCM scenario.     

Irrigation as adaptation strategy to climate change��a biophysical and economic appraisal for Swiss maize production

The impact of climate change on Swiss maize production is assessed using an approach that integrates a biophysical and an economic model. Simple adaptation options such as shifts in sowing dates and adjustments of production intensity are considered. In addition, irrigation is evaluated as an adaptation strategy. It shows that the impact of climate change on yield levels is small but yield variability increases in rainfed production. Even though the adoption of irrigation leads to higher and less variable maize yields in the future, economic benefits of this adoption decision are expected to be rather small. Thus, no shift from the currently used rainfed system to irrigated production is expected in the future. Moreover, we find that changes in institutional and market conditions rather than changes in climatic conditions will influence the development of the Swiss maize production and the adoption of irrigation in the future.     

吉林省气候变化及其对粮食生产的影响

The climate observation data,reanalysis data,and grain/soybean yields per unit area were used to analyze and interpret the impact of climate change on grain production.The results show that Jilin Province was located in a remarkable increase area of temperature during the growing season(May-September)from 1948 in the middle latitudes of the Northern Hemisphere.The mid-west and south of Jilin Province and Liaoning Province were located in a clear linear decrease tendency area of annual precipitation,wherein a warm/dry tendency of climate change dominated,while the east of Jilin Province lay in a clear linear decrease tendency area of annual precipitation.The climate warming played an important role in continuous increase in the grain yield per unit area since the 1980's in the main grain production areas of Jilin Province,however,from the end of the 20th century to the beginning of the 21st century,the beneficial effect seemed to be not obvious any longer,the grain yield per unit area fluctuated with annual precipitation.     

中国七大地区“气候变化—作物产量—经济影响”综合评价

农业作为响应气候变化最敏感的领域之一,未来作物产量可能受到深刻影响。量化气候变化冲击作物产量导致的最终经济影响,需要综合“气候变化—作物产量—经济影响”开展链式研究。文中采用系统回顾和Meta回归分析方法整合了55篇文献的667项研究结果,推导出我国七大地区主要作物（水稻、玉米、小麦）产量与地区内未来温度和降水变化的定量关系,并将其作为农业部门的损失量代入改进的多区域投入产出模型,量化七大地区内与地区间遭受的经济波及影响（ERE)。结果显示：(1)气候变化对我国作物产量的影响主要体现在温度升高上,每升温1℃减产2.6%~12.7%,东北和西北地区作物受升温影响最显著;(2) 气候变化导致的作物减产将对经济产生更严重的波及影响,GDP因作物减产每下降1%将额外产生17.8%的波及影响;(3) 21世纪末,若不考虑CO₂肥效作用,作物减产导致的ERE将占GDP的-0.1%~13.6%（负值表示收益),最悲观情况下ERE与当前我国农业总产值相当（2012年为基准年);(4)不同地区受ERE影响程度的差异较大,因各区之间产业结构、贸易联系及经济发展程度存在差异,西南地区遭受本区及来自其他地区的ERE比华东地区高2.8~8.5倍。     

Climate change impact assessment: the role of climate extremes in crop yield simulation

This work was aimed at assessing the role of climate extremes in climate change impact assessment of typical winter and summer Mediterranean crops by using Regional Circulation Model (RCM) outputs as drivers of a modified version of the CropSyst model. More specifically, climate change effects were investigated on sunflower (Helianthus annuus L.) and winter wheat (Triticum aestivum L.) development and yield under the A2 and B2 scenarios of the IPCC Special Report on Emissions Scenarios (SRES). The direct impact of extreme climate events (i.e. heat stress at anthesis stage) was also included. The increase in both mean temperatures and temperature extremes under A2 and B2 scenarios (2071?C2100) resulted in: a general advancement of the main phenological stages, shortening of the growing season and an increase in the frequency of heat stress during anthesis with respect to the baseline (1961?C1990). The potential impact of these changes on crop yields was evaluated. It was found that winter and summer crops may possess a different fitting capacity to climate change. Sunflower, cultivated in the southern regions of the Mediterranean countries, was more prone to the direct effect of heat stress at anthesis and drought during its growing cycle. These factors resulted in severe yield reduction. In contrast, the lower frequency of heat stress and drought allowed the winter wheat crop to attain increased yields with respect to the baseline period. It can be concluded that the impact of extreme events should be included in crop-modelling approaches, otherwise there is the risk of underestimating crop yield losses, which in turn would result in the application of incorrect policies for coping with climate change.     

On the added value of regional climate modeling in climate change assessment

Regional climate models represent a promising tool to assess the regional dimension of future climate change and are widely used in climate impact research. While the added value of regional climate models has been highlighted with respect to a better representation of land-surface interactions and atmospheric processes, it is still unclear whether radiative heating implies predictability down to the typical scale of a regional climate model. As a quantitative assessment, we apply an optimal statistical filter to compare the coherence between observed and simulated patterns of Mediterranean climate change from a global and a regional climate model. It is found that the regional climate model has indeed an added value in the detection of regional climate change, contrary to former assumptions. The optimal filter may also serve as a weighting factor in multi-model averaging.     

西北干旱区棉花对气候变化响应的评价指标体系

近年来,在全球变暖的大背景下,西北也经历着同样的变暖趋势,尤其在西北干旱区新疆、甘肃河西走廊等地的棉花种植区,气候变暖对棉花生长发育的影响研究日益增多,但如何正确、合理地评价棉花对气候变暖的响应,已成为目前十分突出的科学和实际问题。在评价的过程中,评价指标的选择和确定是重中之重。鉴于此,本文筛选了几类重要的评价指标,主要有棉花生长发育、产量品质、种植结构、地理分布、农业气象灾害、农业病虫害气象指标等,这些指标的选择为科学评价棉花对气候变化的响应奠定了一定的基础。     

Climate Change and Its Impacts on Grain Production in Jilin Province

 The climate observation data, reanalysis data, and grain/soybean yields per unit area were used to analyze and interpret the impact of climate change on grain production. The results show that Jilin Province was located in a remarkable increase area of temperature during the growing season (May-September) from 1948 in the middle latitudes of the Northern Hemisphere. The mid-west and south of Jilin Province and Liaoning Province were located in a clear linear decrease tendency area of annual precipitation, wherein a warm/dry tendency of climate change dominated, while the east of Jilin Province lay in a clear linear decrease tendency area of annual precipitation. The climate warming played an important role in continuous increase in the grain yield per unit area since the1980's in the main grain production areas of Jilin Province, however, from the end of the 20th century to the beginning of the 21st century, the beneficial effect seemed to be not obvious any longer, the grain yield per unit area fluctuated with annual precipitation.     

A comparison of downscaling techniques in the projection of local climate change and wheat yields

This study aims to evaluate the performance of two mainstream downscaling techniques: statistical and dynamical downscaling and to compare the differences in their projection of future climate change and the resultant impact on wheat crop yields for three locations across New South Wales, Australia. Bureau of Meteorology statistically- and CSIRO dynamically-downscaled climate, derived or driven by the CSIRO Mk 3.5 coupled general circulation model, were firstly evaluated against observed climate data for the period 1980–1999. Future climate projections derived from the two downscaling approaches for the period centred on 2055 were then compared. A stochastic weather generator, LARS-WG, was used in this study to derive monthly climate changes and to construct climate change scenarios. The Agricultural Production System sIMulator-Wheat model was then combined with the constructed climate change scenarios to quantify the impact of climate change on wheat grain yield. Statistical results show that (1) in terms of reproducing the past climate, statistical downscaling performed better over dynamical downscaling in most of the cases including climate variables, their mean, variance and distribution, and study locations, (2) there is significant difference between the two downscaling techniques in projected future climate change except the mean value of rainfall across the three locations for most of the months; and (3) there is significant difference in projected wheat grain yields between the two downscaling techniques at two of the three locations.     

吉林省气候变化及其对粮食生产的影响

应用气候观测、再分析资料和吉林省粮豆单产资料,研究了气候变化对粮食生产的影响。结果表明：近40多年来在北半球中纬度地区,吉林省是夏季农业生长季（5-9月）的平均温度上升趋势最显著的地区,该省中西部、南部和辽宁省为东北地区年降水量线性减少趋势较显著的地区,气候变化以暖干倾向为主;吉林省东部为年降水量线性增加趋势的显著地区。吉林省气候变暖对自20世纪80年代以来粮豆单产的持续增长起着重要的作用,但在20世纪末期至21世纪初,这种有利作用已不明显,呈现出粮豆单产年际变化随降水量的多寡而振动的特点。     

The Effect of Spatial Scale of Climatic Change Scenarios on Simulated Maize, Winter Wheat, and Rice Production in the Southeastern United States

We use the CERES family of crop models to assess the effect of different spatial scales of climate change scenarios on the simulated yield changes of maize (Zea mays L.), winter wheat (Triticum aestivum L.),and rice (Oryza sativa L.) in the Southeastern United States. The climate change scenarios were produced with the control and doubled CO₂ runs of a high resolution regional climate model anda coarse resolution general circulation model, which provided the initial and lateral boundary conditions for the regional model. Three different cases were considered for each scenario: climate change alone, climate change plus elevated CO₂, and the latter with adaptations. On the state level,for most cases, significant differences in the climate changed yields for corn were found, the coarse scale scenario usually producing larger modeled yield decreases or smaller increases. For wheat, however, which suffered large decreases in yields for all cases, very little contrast in yield based on scale of scenario was found. Scenario scale resulted in significantly different rice yields, but mainly because of low variability in yields. For maize the primary climate variable that explained the contrast in the yields calculated from the two scenarios is the precipitation during grain fill leading to different water stress levels. Temperature during vernalization explains some contrasts in winter wheat yields. With adaptation, the contrasts in the yields of all crops produced by the scenarios were reduced but not entirely removed. Our results indicate that spatial resolution of climate change scenarios can be an important uncertainty in climate change impact assessments, depending on the crop and management conditions.