Future energy potential of Miscanthus in Europe

European field experiments have demonstrated Miscanthus can produce some of the highest energy yields per hectare of all potential energy crops. Previous modelling studies using MISCANMOD have calculated the potential energy yield for the EU27 from mean historical climate data (1960–1990). In this paper, we have built on the previous studies by further developing a new Miscanthus crop growth model MISCANFOR in order to analyse (i) interannual variation in yields for past and future climates, (ii) genotype-specific parameters on yield in Europe. Under recent climatic conditions (1960–1990) we show that 10% of arable land could produce 1709 PJ and mitigate 30 Tg of carbon dioxide-carbon (CO₂-C) equivalent greenhouse gasses (GHGs) compared with EU27 primary energy consumption of 65 598 PJ, emitting 1048 Tg of CO₂-C equivalent GHGs in 2005. If we continue to use the clone Miscanthus × giganteus , MISCANFOR shows that, as climate change reduces in-season water availability, energy production and carbon mitigation could fall 80% by 2080 for the Intergovernmental Panel on Climate Change A2 scenario. However, because Miscanthus is found in a huge range of climates in Asia, we propose that new hybrids will incorporate genes conferring superior drought and frost tolerance. Using parameters from characterized germplasm, we calculate energy production could increase from present levels by 88% (to 2360 PJ) and mitigate 42 Tg of CO₂-C equivalent using 10% arable land for the 2080 mid-range A2 scenario. This is equivalent to 3.6% of 2005 EU27 primary energy consumption and 4.0% of total CO₂ equivalent C GHG emissions.     

Bioenergy crop models: descriptions,data requirements,and future challenges

Field studies that address the production of lignocellulosic biomass as a source of renewable energy provide critical data for the development of bioenergy crop models. A literature survey revealed that 14 models have been used for simulating bioenergy crops including herbaceous and woody bioenergy crops, and for crassulacean acid metabolism (CAM) crops. These models simulate field‐scale production of biomass for switchgrass (ALMANAC, EPIC, and Agro‐BGC), miscanthus (MISCANFOR, MISCANMOD, and WIMOVAC), sugarcane (APSIM, AUSCANE, and CANEGRO), and poplar and willow (SECRETS and 3PG). Two models are adaptations of dynamic global vegetation models and simulate biomass yields of miscanthus and sugarcane at regional scales (Agro‐IBIS and LPJmL). Although it lacks the complexity of other bioenergy crop models, the environmental productivity index (EPI) is the only model used to estimate biomass production of CAM (Agave and Opuntia) plants. Except for the EPI model, all models include representations of leaf area dynamics, phenology, radiation interception and utilization, biomass production, and partitioning of biomass to roots and shoots. A few models simulate soil water, nutrient, and carbon cycle dynamics, making them especially useful for assessing the environmental consequences (e.g., erosion and nutrient losses) associated with the large‐scale deployment of bioenergy crops. The rapid increase in use of models for energy crop simulation is encouraging; however, detailed information on the influence of climate, soils, and crop management practices on biomass production is scarce. Thus considerable work remains regarding the parameterization and validation of process‐based models for bioenergy crops; generation and distribution of high‐quality field data for model development and validation; and implementation of an integrated framework for efficient, high‐resolution simulations of biomass production for use in planning sustainable bioenergy systems.     

Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress

Background and Aims

Genetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress.

Methods

Using the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameters were estimated from additive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions.

Key Results

To account for genotypic yield differences, it was necessary to parameterize the model for differences in an additional trait ‘total crop nitrogen uptake’ (N_max) among the ILs. Genetic variation in N_max had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10–36 % more yield than those based on markers for yield per se.

Conclusions

This study outlines a genotype-to-phenotype approach that exploits the potential value of marker-based crop modelling in developing new plant types with high yields. The approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for marker design for improved rice yields and for ideotyping under contrasting conditions.     

Carbon mitigation by the energy crop, Miscanthus

Biomass crops mitigate carbon emissions by both fossil fuel substitution and sequestration of carbon in the soil. We grew Miscanthus x giganteus for 16 years at a site in southern Ireland to (i) compare methods of propagation, (ii) compare response to fertilizer application and quantify nutrient offtakes, (iii) measure long-term annual biomass yields, (iv) estimate carbon sequestration to the soil and (v) quantify the carbon mitigation by the crop. There was no significant difference in the yield between plants established from rhizome cuttings or by micro-propagation. Annual off-takes of N and P were easily met by soil reserves, but soil K reserves were low in unfertilized plots. Potassium deficiency was associated with lower harvestable yield. Yields increased for 5 years following establishment but after 10 years showed some decline which could not be accounted for by the climate driven growth model MISCANMOD. Measured yields were normalized to estimate both autumn (at first frost) and spring harvests (15 March of the subsequent year). Average autumn and spring yields over the 15 harvest years were 13.4±1.1 and 9.0±0.7 t DW ha⁻¹ yr⁻¹ respectively. Below ground biomass in February 2002 was 20.6±4.6 t DW ha⁻¹. Miscanthus derived soil organic carbon sequestration detected by a change in ¹³C signal was 8.9±2.4 t C ha⁻¹ over 15 years. We estimate total carbon mitigation by this crop over 15 years ranged from 5.2 to 7.2 t C ha⁻¹ yr⁻¹ depending on the harvest time.     

A semimechanistic model predicting the growth and production of the bioenergy crop Miscanthus×giganteus: description, parameterization and validation

Biomass based bioenergy is promoted as a major sustainable energy source which can simultaneously decrease net greenhouse gas emissions. Miscanthus × giganteus ( M. × giganteus ), a C₄ perennial grass with high nitrogen, water, and light use efficiencies, is regarded as a promising energy crop for biomass production. Mathematical models which can accurately predict M. × giganteus biomass production potential under different conditions are critical to evaluate the feasibility of its production in different environments. Although previous models based on light-conversion efficiency have been shown to provide good predictions of yield, they cannot easily be used in assessing the value of physiological trait improvement or ecosystem processes. Here, we described in detail the physical and physiological processes of a previously published generic mechanistic eco-physiological model, WIMOVAC, adapted and parameterized for M. × giganteus . Parameterized for one location in England, the model was able to realistically predict daily field diurnal photosynthesis and seasonal biomass at a range of other sites from European studies. The model provides a framework that will allow incorporation of further mechanistic information as it is developed for this new crop.     

Overwintering problems of newly established Miscanthus plantations can be overcome by identifying genotypes with improved rhizome cold tolerance

Miscanthus , a perennial rhizomatous C₄ grass, is a potential biomass crop in Europe, mainly because of its high yield potential and low demand for inputs. However, until recently only a single clone, M. × giganteus , was available for the extensive field trials performed across Europe and this showed poor overwintering in the first year after planting at some locations in Northern Europe. Therefore, field trials with five Miscanthus genotypes, including two acquisitions of Miscanthus × giganteus , one of M. sacchariflorus and two hybrids of M. sinensis were planted in early summer 1997 at four sites, in Sweden, Denmark, England and Germany. The field trials showed that better overwintering of newly established plants at a site was not apparently connected with size or early senescence. An artificial freezing test with rhizomes removed from the field in January 1998 showed that the lethal temperature at which 50% were killed (LT₅₀) for M. × giganteus and M. sacchariflorus genotypes was −3.4 °C. However, LT₅₀ in one of the M. sinensis hybrid genotypes tested was −6.5 °C and this genotype had the highest survival rates in the field in Sweden and Denmark. Although the carbohydrate content of rhizomes, osmotic potential of cell sap and mineral composition were not found to explain differences in frost tolerance adequately, moisture contents correlated with frost hardiness (LT₅₀) in most cases. The results obtained form a basis for identifying suitable Miscanthus genotypes for biomass production in the differing climatic regions of Europe.     

Impact of drought stress on growth and quality of miscanthus for biofuel production

Miscanthus has a high potential as a biomass feedstock for biofuel production. Drought tolerance is an important breeding goal in miscanthus as water deficit is a common abiotic stress and crop irrigation is in most cases uneconomical. Drought may not only severely reduce biomass yields, but also affect biomass quality for biofuel production as cell wall remodeling is a common plant response to abiotic stresses. The quality and plant weight of 50 diverse miscanthus genotypes were evaluated under control and drought conditions (28 days no water) in a glasshouse experiment. Overall, drought treatment decreased plant weight by 45%. Drought tolerance – as defined by maintenance of plant weight – varied extensively among the tested miscanthus genotypes and ranged from 30% to 110%. Biomass composition was drastically altered due to drought stress, with large reductions in cell wall and cellulose content and a substantial increase in hemicellulosic polysaccharides. Stress had only a small effect on lignin content. Cell wall structural rigidity was also affected by drought conditions; substantially higher cellulose conversion rates were observed upon enzymatic saccharification of drought‐treated samples with respect to controls. Both cell wall composition and the extent of cell wall plasticity under drought varied extensively among all genotypes, but only weak correlations were found with the level of drought tolerance, suggesting their independent genetic control. High drought tolerance and biomass quality can thus potentially be advanced simultaneously. The extensive genotypic variation found for most traits in the evaluated miscanthus germplasm provides ample scope for breeding of drought‐tolerant varieties that are able to produce substantial yields of high‐quality biomass under water deficit conditions. The higher degradability of drought‐treated samples makes miscanthus an interesting crop for the production of second‐generation biofuels in marginal soils.     

Evaluation of Agricultural Production Systems Simulator as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments

Simulation models for perennial energy crops such as switchgrass (Panicum virgatum L.) and Miscanthus (Miscanthus x giganteus) can be useful tools to design management strategies for biomass productivity improvement in US environments. The Agricultural Production Systems Simulator (APSIM) is a biophysical model with the potential to simulate the growth of perennial crops. APSIM crop modules do not exist for switchgrass and Miscanthus, however, re‐parameterization of existing APSIM modules could be used to simulate the growth of these perennials. Our aim was to evaluate the ability of APSIM to predict the dry matter (DM) yield of switchgrass and Miscanthus at several US locations. The Lucerne (for switchgrass) and Sugarcane (for Miscanthus) APSIM modules were calibrated using data from four locations in Indiana. A sensitivity analysis informed the relative impact of changes in plant and soil parameters of APSIM Lucerne and APSIM Sugarcane modules. An independent dataset of switchgrass and Miscanthus DM yields from several US environments was used to validate these re‐parameterized APSIM modules. The re‐parameterized modules simulated DM yields of switchgrass [0.95 for CCC (concordance correlation coefficient) and 0 for SB (bias of the simulation from the measurement)] and Miscanthus (0.65 and 0% for CCC and SB, respectively) accurately at most locations with the exception of switchgrass at southern US sites (0.01 for CCC and 2% for SB). Therefore, the APSIM model is a promising tool for simulating DM yields for switchgrass and Miscanthus while accounting for environmental variability. Given our study was strictly based on APSIM calibrations at Indiana locations, additional research using more extensive calibration data may enhance APSIM robustness.     

Climatic impacts on winter wheat in Oklahoma and potential applications to climatic and crop yield prediction

Climatic anomalies can pose severe challenges for farmers and resource managers. This is particularly significant with respect to gradually developing anomalies such as droughts. The impact of the 1995-1996 drought on the Oklahoma wheat crop, and the possibility that predictive information might have reduced some of the losses, is examined through a combined modeling approach using climatological data and a crop growth model that takes into account an extensive range of soil, climatic, and plant variables. The results show potential outcomes and also illustrate the point at which all possible climatic outcomes were predicting a significantly low wheat yield. Based on anecdotal evidence of the 1995-1996 drought, which suggested that farmers who planted at different times experienced different yields, the model was run assuming a variety of different planting dates. Results indicate that there is indeed a noticeable difference in the modeled wheat yields given different planting dates. The information regarding effectiveness of planting date can be used in conjunction with current long-range forecasts to develop improved predictions for the current growing season. This approach produces information regarding the likelihood of extreme precipitation events and the impact on crop yield, which can provide a powerful tool to farmers and others during periods of drought or other climatic extremes.     

基于WOFOST作物生长模型的冬小麦干旱影响评估技术

为了反映作物与干旱的相互关系,人为再现干旱灾害对作物产量的影响程度,选择华北地区冬小麦干旱灾害为研究对象,对作物生长模型WOFOST在区域上进行适应性进行分析、检验的基础上,然后利用区域作物模型实现干旱灾害对作物影响定量分析和动态评估。以减产率和气象条件作为灾害严重程度划分的标准,利用数值模拟试验,确定导致减产的主要气象因子及其量值,对研究区干旱灾害进行影响评估,包括典型灾害年份影响评估和年代际灾害影响评估,并给出了评估结果。     

Water stress resilient cereal crops: Lessons from wild relatives

Cereal crops are significant contributors to global diets. As climate change disrupts weather patterns and wreaks havoc on crops, the need for generating stress-resilient, high-yielding varieties is more urgent than ever. One extremely promising avenue in this regard is to exploit the tremendous genetic diversity expressed by the wild ancestors of current day crop species. These crop wild relatives thrive in a range of environments and accordingly often harbor an array of traits that allow them to do so. The identification and introgression of these traits into our staple cereal crops can lessen yield losses in stressful environments. In the last decades, a surge in extreme drought and flooding events have severely impacted cereal crop production. Climate models predict a persistence of this trend, thus reinforcing the need for research on water stress resilience. Here we review: (i) how water stress (drought and flooding) impacts crop performance; and (ii) how identification of tolerance traits and mechanisms from wild relatives of the main cereal crops, that is, rice, maize, wheat, and barley, can lead to improved survival and sustained yields in these crops under water stress conditions.     

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．     

Geographic origin and past climatic experience influence the response to late spring frost in four common grass species in central Europe

Late spring frost events can affect vegetation. The response of grassland species, however, is generally unknown. We explore the late‐frost sensitivity of four common European grass species and investigate whether these species exhibit local adaptations to late frost on a continental scale and whether past climatic experience influences late frost sensitivity. Ecotypes of Arrhenatherum elatius, Alopecurus pratensis, Festuca pratensis, and Holcus lanatus from Spain, Italy, Bulgaria, Hungary, Sweden and Germany were exposed to late frost after drought and warming manipulations in the preceding growing season in a common garden experiment. Late frost reduced the productivity of the grasses on average by 20%. Ecotypes differed in their late‐frost sensitivity in three of the four species and local adaptations to late frost were identified. Previous exposure to drought and warming caused differences in late‐frost sensitivity in some cases. The impact of late frost events may increase in a warmer world due to an earlier onset of growing and no change in timing of late frost events. The history of climatic exposure can alter the performance of plants, possibly through epigenetic mechanisms. Based on the complex response pattern observed, a maximization of genetic diversity is proposed as a promising adaptation strategy against climate change.     

Sustainable production of crops and pastures under drought in a Mediterranean environment

Mediterranean environments are characterised by cool wet winters and hot dry summers. While native vegetation in Mediterranean-climatic zones usually comprises a mixture of perennial and annual plants, agricultural development in the Mediterranean-climatic region of Australia has led to the clearing of the perennial vegetation and its replacement with annual crops and pastures. In the Mediterranean environments of southern Australia this has led to secondary (dryland) salinisation. In order to slow land degradation, perennial trees and pasture species are being reintroduced to increase the productivity of the saline areas. The annual crops and pastures that form the backbone of dryland farming systems in the Mediterranean-climatic zone of Australia are grown during the cool wet winter months on incoming rainfall and mature during spring and early summer as temperatures and rates of evaporation rise and rainfall decreases. Thus, crop and pasture growth is usually curtailed by terminal drought. Where available, supplementary irrigation in spring can lead to significant increases in yield and water use efficiency. In order to sustain production of annual crops in Mediterranean environments, both agronomic and genetic options have been employed. An analysis of the yield increases of wheat in Mediterranean-climatic regions shows that there has generally been an increase in the yields over the past decades, albeit at a lower rate than in more temperate regions. Approximately half of this increase can be attributed to agronomic improvements and half to genetic improvements. The agronomic improvements that have been utilised to sustain the increased yields include earlier planting to more closely match crop growth to rainfall distribution, use of fertilisers to increase early growth, minimum tillage to enable earlier planting and increase plant transpiration at the expense of soil evaporation, rotations to reduce weed control and disease incidence, and use of herbicides, insecticides and fungicides to reduce losses from weeds, insects and disease. Genetic improvements include changing the phenological development to better match the rainfall, increased early vigour, deeper rooting, osmotic adjustment, increased transpiration efficiency and improved assimilate storage and remobilisation. Mediterranean environments that are subjected annually to terminal drought can be both environmentally and economically sustainable, but to maximise plant water use efficiency while maintaining crop productivity requires an understanding of the interaction between genotypes, environment and management.     

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi‐year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO₂ concentration [CO₂]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model‐based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well‐controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO₂] and temperature.     

Rapid selection and identification of Miscanthus genotypes with enhanced glucan and xylan yields from hydrothermal pretreatment followed by enzymatic hydrolysis

ABSTRACT: BACKGROUND: Because many Miscanthus genotypes can be cultivated with relatively high productivity and carbohydrate content, Miscanthus has great potential as an energy crop that can support large scale biological production of biofuels. RESULTS: In this study, batch hydrothermal pretreatment at 180 °C for 35 min followed by enzymatic hydrolysis was shown to give the highest total sugar yields for Miscanthus x giganteus cv. Illinois planted in Illinois. High throughput pretreatment at 180 °C for 35 min and 17.5 min followed by co-hydrolysis in a multi-well batch reactor identified two varieties out of 80 that had significantly higher sugar yields from pretreatment and enzymatic hydrolysis than others. The differences in performance were then related to compositions of the 80 varieties to provide insights into desirable traits for Miscanthus that enhance sugar yields. CONCLUSIONS: High throughput pretreatment and co-hydrolysis (HTPH) rapidly identified promising genotypes from a wide range of Miscanthus genotypes, including hybrids of Miscanthus sacchariflorus/M. sinensis and Miscanthus lutarioriparius, differentiating the more commercially promising species from the rest. The total glucan plus xylan content in Miscanthus appeared to influence both mass and theoretical yields, while lignin and ash contents did not have a predictable influence on performance.     

European-wide GIS-based modelling system for quantifying the feedstock from Miscanthus and the potential contribution to renewable energy targets

Reliable estimates of feedstock resources are a prerequisite to the establishment of a biomass based-industry for energy and non food products. Field trials in the European Union (EU) show that Miscanthus spp. can produce high yields. Here we use a model (MISCANMOD) coupled with a GIS environment to estimate the contribution that Miscanthus could make to projected national electricity consumption. We describe the integration of different data sets, transformation procedures, and spatial analyses using GIS to produce energy statistics for the EU-25. Overall, Miscanthus grown on the 10% of arable land which is currently in set-aside could generate 282 TWh yr⁻¹ electricity. This would meet 39% of the EU-25 target of 723 TWh yr⁻¹ of electricity from renewable energy sources (RES) by 2010. As RES targets rise, land available for energy crops is also expected to increase. We consider three additional scenarios where Miscanthus could be grown on 10%, 20% and 35% of all agricultural land and we estimate it could generate respectively 345, 691 and 1209 TWh yr⁻¹ of electrical energy. At a national scale France, Poland and Germany have the highest potentials for Miscanthus production based on agricultural land area (respectively 83, 52, 49 TWh yr⁻¹ when 10% agricultural land is used). Finally, we reduced the scale to the EU NUTS2 (Nomenclature of Territorial Units for Statistics) regions to examine regional generation capacities. Key regions have been identified where national RES targets are exceeded. These regions could become net exporters of renewable energy.     

Drought tolerance through biotechnology: improving translation from the laboratory to farmers' fields

Water availability is a significant constraint to crop production, and increasing drought tolerance of crops is one step to gaining greater yield stability. Excellent progress has been made using models to identify pathways and genes that can be manipulated through biotechnology to improve drought tolerance. A current focus is on translation of results from models in controlled environments to crops in the field. Field testing to demonstrate improved yields under water-limiting conditions is challenging and expensive. More extensive phenotyping of transgenic lines in the greenhouse may contribute to improved predictions about field performance. It is possible that multiple mechanisms of drought tolerance may be needed to provide benefit across the diversity of water stress environments relevant to economic yield.     

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.