Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.     

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.     

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.     

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.     

站点CERES-Rice模型区域应用效果和误差来源

作物区域模拟是利用有限的空间数据,最大限度地反映出生育期、产量等作物性状的时空变化规律.由于目前的作物模型大多是田间尺度的站点模型,把它运用到区域水平的效果如何研究甚少.文章利用CERES-Rice模型,对作物模型在我国的区域应用效果进行了分析.首先利用田间观测数据在各实验点上对模型进行了详细的站点校准,以验证模型在我国的模拟能力;然后以我国水稻生态区(精确到亚区)为单位,运用均方根差(RMSE)法进行了区域校准和验证;最后利用区域校准后的CERES-Rice模型,模拟了1980～2000年的网格(50km×50km)水稻产量,并与同期农调队调查产量进行统计比较,以验证区域应用的效果,为区域模拟的推广和应用提供参考.结果表明:经过空间校准后的CERES-Rice模型,在水稻的主产区1～4区(占种植面积的95%)模拟的平均产量与调查产量相对均方根差在22%以内,两者的符合度也较好,个别区域(5、6) RMSE%在24%～30%之间;1980～2000年水稻各产区模拟的平均产量与调查产量随时间变化趋势也具有一定的一致性;全国1896个网格中,大部分网格(71.01%)模拟的21年水稻年产量与调查产量的RMSE%在30%之内,且大部分分布在水稻主产区,考虑到水稻种植面积的权重后,认为利用区域校准和验证后的CERES-Rice模型进行水稻区域模拟,可以反映出产量的时空分布特征,能够为宏观决策提供相应的信息.但目前区域模拟中还存在着一定的误差,有待今后进一步研究.     

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.     

Development and assessment of a coupled crop–climate model

It is well established that crop production is inherently vulnerable to variations in the weather and climate. More recently the influence of vegetation on the state of the atmosphere has been recognized. The seasonal growth of crops can influence the atmosphere and have local impacts on the weather, which in turn affects the rate of seasonal crop growth and development. Considering the coupled nature of the crop–climate system, and the fact that a significant proportion of land is devoted to the cultivation of crops, important interactions may be missed when studying crops and the climate system in isolation, particularly in the context of land use and climate change.
To represent the two-way interactions between seasonal crop growth and atmospheric variability, we integrate a crop model developed specifically to operate at large spatial scales (General Large Area Model for annual crops) into the land surface component of a global climate model (GCM; HadAM3). In the new coupled crop–climate model, the simulated environment (atmosphere and soil states) influences growth and development of the crop, while simultaneously the temporal variations in crop leaf area and height across its growing season alter the characteristics of the land surface that are important determinants of surface fluxes of heat and moisture, as well as other aspects of the land-surface hydrological cycle. The coupled model realistically simulates the seasonal growth of a summer annual crop in response to the GCM's simulated weather and climate. The model also reproduces the observed relationship between seasonal rainfall and crop yield. The integration of a large-scale single crop model into a GCM, as described here, represents a first step towards the development of fully coupled crop and climate models. Future development priorities and challenges related to coupling crop and climate models are discussed.     

Back to the future: using historical climate variation to project near‐term shifts in habitat suitable for coast redwood

Studies that model the effect of climate change on terrestrial ecosystems often use climate projections from downscaled global climate models (GCMs). These simulations are generally too coarse to capture patterns of fine‐scale climate variation, such as the sharp coastal energy and moisture gradients associated with wind‐driven upwelling of cold water. Coastal upwelling may limit future increases in coastal temperatures, compromising GCMs’ ability to provide realistic scenarios of future climate in these coastal ecosystems. Taking advantage of naturally occurring variability in the high‐resolution historic climatic record, we developed multiple fine‐scale scenarios of California climate that maintain coherent relationships between regional climate and coastal upwelling. We compared these scenarios against coarse resolution GCM projections at a regional scale to evaluate their temporal equivalency. We used these historically based scenarios to estimate potential suitable habitat for coast redwood (Sequoia sempervirens D. Don) under ‘normal’ combinations of temperature and precipitation, and under anomalous combinations representative of potential future climates. We found that a scenario of warmer temperature with historically normal precipitation is equivalent to climate projected by GCMs for California by 2020–2030 and that under these conditions, climatically suitable habitat for coast redwood significantly contracts at the southern end of its current range. Our results suggest that historical climate data provide a high‐resolution alternative to downscaled GCM outputs for near‐term ecological forecasts. This method may be particularly useful in other regions where local climate is strongly influenced by ocean–atmosphere dynamics that are not represented by coarse‐scale GCMs.     

Designing ecological climate change impact assessments to reflect key climatic drivers

Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive – such as means or extremes – can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the ‘model space’ approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling.     

Analysing environments for plant breeding purposes as exemplified by multivariate analyses of long term wheat yields

Summary A previous attempt to relate long term yields from the Western Australian wheat belt to climatic factors proved only partially successful. Here, principal component analysis has been used to examine the patterns of variability created by those socio-economic factors which may have obscured any underlying relationship which existed between yield and climate. In fact, these analyses revealed the existence of variation peculiar to particular groups of years, a result which could explain why many attempts to relate crop yields directly to climatic factors have proved unsuccessful. The plant breeding implications of these genotype x environment interactions are considered.     

Tree growth variation in the tropical forest: understanding effects of temperature,rainfall and CO2

Tropical forest responses to climatic variability have important consequences for global carbon cycling, but are poorly understood. As empirical, correlative studies cannot disentangle the interactive effects of climatic variables on tree growth, we used a tree growth model (IBTREE) to unravel the climate effects on different physiological pathways and in turn on stem growth variation. We parameterized the model for canopy trees of Toona ciliata (Meliaceae) from a Thai monsoon forest and compared predicted and measured variation from a tree‐ring study over a 30‐year period. We used historical climatic variation of minimum and maximum day temperature, precipitation and carbon dioxide (CO₂) in different combinations to estimate the contribution of each climate factor in explaining the inter‐annual variation in stem growth. Running the model with only variation in maximum temperature and rainfall yielded stem growth patterns that explained almost 70% of the observed inter‐annual variation in stem growth. Our results show that maximum temperature had a strong negative effect on the stem growth by increasing respiration, reducing stomatal conductance and thus mitigating a higher transpiration demand, and – to a lesser extent – by directly reducing photosynthesis. Although stem growth was rather weakly sensitive to rain, stem growth variation responded strongly and positively to rainfall variation owing to the strong inter‐annual fluctuations in rainfall. Minimum temperature and atmospheric CO₂ concentration did not significantly contribute to explaining the inter‐annual variation in stem growth. Our innovative approach – combining a simulation model with historical data on tree‐ring growth and climate – allowed disentangling the effects of strongly correlated climate variables on growth through different physiological pathways. Similar studies on different species and in different forest types are needed to further improve our understanding of the sensitivity of tropical tree growth to climatic variability and change.     

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.     

Application of a progressive-difference method to identify climatic factors causing variation in the rice yield in the Yangtze Delta, China

Time series of rice yields consist of a technology-driven trend and variations caused by climate fluctuations. To explore the relationship between yields and climate, the trend and temporal variation often have to be separated. In this study, a progressive-difference method was applied to eliminate the trend in time series. By differentiating yields and climatic factors in 2 successive years, the relationship between variations in yield and climatic factors was determined with multiple- regression analysis. The number of hours of sunshine, the temperature and the precipitation were each defined for different intervals during the growing season and used as different regression variables. Rice yields and climate data for the Yangtze Delta of China from 1961 to 1990 were used as a case study. The number of hours of sunshine during the tillering stage and the heading to milk stage particularly affected the yield. In both periods radiation was low. In the first period, the vegetative organs of the rice crop were formed while in the second period solar radiation was important for grain filling. The average temperature during the tillering to jointing stage reached its maximum, which affected rice yields negatively. Precipitation was generally low during the jointing and booting stages, which had a positive correlation with yield, while high precipitation had a negative effect during the milk stage. The results indicate that the climatic factors should be expressed as 20- to 30-day averages in the Yangtze Delta; a shorter or longer period, e.g. 10 or 40 days, is less appropriate. Received: 30 May 2000 / Revised: 27 October 2000 / Accepted: 30 October 2000     

A simplified GIS approach to modeling global leaf water isoscapes

The stable hydrogen (delta(2)H) and oxygen (delta(18)O) isotope ratios of organic and inorganic materials record biological and physical processes through the effects of substrate isotopic composition and fractionations that occur as reactions proceed. At large scales, these processes can exhibit spatial predictability because of the effects of coherent climatic patterns over the Earth's surface. Attempts to model spatial variation in the stable isotope ratios of water have been made for decades. Leaf water has a particular importance for some applications, including plant organic materials that record spatial and temporal climate variability and that may be a source of food for migrating animals. It is also an important source of the variability in the isotopic composition of atmospheric gases. Although efforts to model global-scale leaf water isotope ratio spatial variation have been made (especially of delta(18)O), significant uncertainty remains in models and their execution across spatial domains. We introduce here a Geographic Information System (GIS) approach to the generation of global, spatially-explicit isotope landscapes (= isoscapes) of "climate normal" leaf water isotope ratios. We evaluate the approach and the resulting products by comparison with simulation model outputs and point measurements, where obtainable, over the Earth's surface. The isoscapes were generated using biophysical models of isotope fractionation and spatially continuous precipitation isotope and climate layers as input model drivers. Leaf water delta(18)O isoscapes produced here generally agreed with latitudinal averages from GCM/biophysical model products, as well as mean values from point measurements. These results show global-scale spatial coherence in leaf water isotope ratios, similar to that observed for precipitation and validate the GIS approach to modeling leaf water isotopes. These results demonstrate that relatively simple models of leaf water enrichment combined with spatially continuous precipitation isotope ratio and climate data layers yield accurate global leaf water estimates applicable to important questions in ecology and atmospheric science.     

宁夏沙湖几种主要荒漠植物成丛性分析

该文对宁夏沙湖地区几种主要荒漠植物成丛或聚集分布的空间特征进行了研究,提出应用成丛性表征植物丛聚水平,并以丛生植物的丛径或散生植物的聚集分布尺度范围衡量植物成丛性的发育程度。其中散生植物的聚集尺度以Riplay's K点格局法进行计算。结果表明,植物的丛聚水平与生境条件密切相关,强日照、干热风、空旷的立地、较高土壤含水率、强透水蓄水能力等生境条件会促进植物成丛性的发育,而蒸腾胁迫低、土壤供水能力较差的生境条件下植物成丛性发育较弱。荒漠植物通过成丛或聚集分布可以形成局部微生境,减少地上部分的水分胁迫,是植物在群落水平适应环境的重要途径。     

Scale effects in species distribution models: implications for conservation planning under climate change

Predictions of future species' ranges under climate change are needed for conservation planning, for which species distribution models (SDMs) are widely used. However, global climate model-based (GCM) output grids can bias the area identified as suitable when these are used as SDM predictor variables, because GCM outputs, typically at least 50x50 km, are biologically coarse. We tested the assumption that species ranges can be equally well portrayed in SDMs operating on base data of different grid sizes by comparing SDM performance statistics and area selected by four SDMs run at seven grid sizes, for nine species of contrasting range size. Area selected was disproportionately larger for SDMs run on larger grid sizes, indicating a cut-off point above which model results were less reliable. Up to 2.89 times more species range area was selected by SDMs operating on grids above 50x50 km, compared to SDMs operating at 1 km2. Spatial congruence between areas selected as range also diverged as grid size increased, particularly for species with ranges between 20000 and 90000 km2. These results indicate the need for caution when using such data to plan future protected areas, because an overly large predicted range could lead to inappropriate reserve location selection.     

不同空间尺度下的ALMANAC模型验证

ALMANAC模型最早作为EPIC模型的一部分,用于模拟土壤侵蚀导致的土地生产力的下降．它将试验数据的统计过程和作物生长的机理过程结合起来,是一种典型的基于过程模拟的应用型作物生长模型．如能在不同的空间尺度上验证模型的适用性,无疑会大大扩展模型的应用范围．从这一目的出发,利用美国得克萨斯州19个试验田和9个县的玉米和高粱产量资料及其相关的作物、土壤、田问管理等数据,模拟了1998年田间尺度,1989～1998年县级尺度的平均作物产量．模拟结果表明,ALMANAC模型能够很好地模拟两种不同空间尺度的作物产量,其相对误差在田问尺度上分别为8．9％(高粱)和9．4％(玉米),在县级尺度上分别达到2．6％(玉米)和—0．6％(高粱)．该模型在进行产量预测、掌握作物生长动态,指导农业生产管理和土地利用等方面具有很好的应用前景．     

Increasing influence of heat stress on French maize yields from the 1960s to the 2030s

Improved crop yield forecasts could enable more effective adaptation to climate variability and change. Here, we explore how to combine historical observations of crop yields and weather with climate model simulations to produce crop yield projections for decision relevant timescales. Firstly, the effects on historical crop yields of improved technology, precipitation and daily maximum temperatures are modelled empirically, accounting for a nonlinear technology trend and interactions between temperature and precipitation, and applied specifically for a case study of maize in France. The relative importance of precipitation variability for maize yields in France has decreased significantly since the 1960s, likely due to increased irrigation. In addition, heat stress is found to be as important for yield as precipitation since around 2000. A significant reduction in maize yield is found for each day with a maximum temperature above 32 °C, in broad agreement with previous estimates. The recent increase in such hot days has likely contributed to the observed yield stagnation. Furthermore, a general method for producing near‐term crop yield projections, based on climate model simulations, is developed and utilized. We use projections of future daily maximum temperatures to assess the likely change in yields due to variations in climate. Importantly, we calibrate the climate model projections using observed data to ensure both reliable temperature mean and daily variability characteristics, and demonstrate that these methods work using retrospective predictions. We conclude that, to offset the projected increased daily maximum temperatures over France, improved technology will need to increase base level yields by 12% to be confident about maintaining current levels of yield for the period 2016–2035; the current rate of yield technology increase is not sufficient to meet this target.     

基于GIS的黄土丘陵沟壑区作物生产潜力模拟研究

从YIELD模型的来源、输入文件及基本参数,模型中作物生产力计算各个子模型以及计算流程4个方面作了简单的叙述,以黄土丘陵沟壑区典型小流域晋西狼窝沟为例,在地理信息系统（GIS）技术十,应用YILD模型对该流域的作物生产潜力进行了模拟,并从作物类型,地类,耕作措施及气候条件4个方面对影响该流域作物产量的因素进行了分析。结果表明,该模型对不同作物的模拟产量在总体上与实体产量基本相符合,表明模型可以应用于黄土丘陵沟壑区的作物产量模拟之中,对于不同地类来说,坝地的土壤水分和以力条件明显高于梯田和坡耕地,因而坝地的模拟产量地高于梯田和坡地,但三者之间的差距没有实测产量显著,耕作措施是提高作物生产力的有效途径,对地膜覆盖,梯田以及施肥等耕作措施的模拟产量表明,这3种耕作措施均能有效的物生产力;其产量提高率均平均在85％以上,其中以施肥对作物的增产作用最大,增产率高达95％,,这与实测产量资料基本一致;气候条件是影响作物生产的直接因素,模拟结果表明模型对降水量和温度等气候条件十分敏感,不同年份降水量和温度的差异将直接导致作物生产力的显著不同。对YIELD模型的模拟结果分析表明,该模型可以有效地应用于黄土丘陵沟壑区的作物生产潜力研究。     

Modelling the predicted geographic and economic response of UK cropping systems to climate change scenarios: the case of potatoes

Geographical changes in suitability in England and Wales for the cultivation of potatoes under a climate change scenario were predicted for the years 2023 and 2065 by integrating a climate database (1951-80) with climate-driven crop growth models. Initially, model outputs were produced as point values (meteorological site locations) of predicted potential yields for current crop production. The model outputs were validated statistically using actual crop yield figures collated from bibliographic analysis. The most suitable model was run again incorporating projected temperature and precipitation changes for 2023 and 2065. These outputs were then used to predict possible economic changes to farm profitability and general market trends. Results indicated that, although yields may rise, gross margins for maincrop and especially early potatoes may also rise due to shifts in production, to a fall in overall potato output and to price increases.