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Soil-crop models are potentially useful tools to support analyses of the effects of climate and crop and soil management practices on crop production and the environment (e.g. carbon sequestration and greenhouse gas emissions or the leaching of agro-chemicals). However, it is not clear whether current generation models can be used to simulate long-term trends in crop production and the environmental impacts induced by changes in climate or land use because they do not consider the effects of soil structure dynamics at seasonal to decadal (e.g. root growth, activity of macro-fauna) and centennial time scales (e.g. changes in organic matter content) on soil hydraulic functions, hydrological processes, crop growth and carbon cycling.Here, we present a new soil-crop model that accounts for the interactions between soil structure dynamics, carbon cycling, soil physical and hydraulic properties, soil water balance and crop growth. The importance of soil structure dynamics is illustrated by long-term simulations of soil organic matter storage, soil water balance components and crop yields for a field site in central Sweden under climate change and contrasting management practices (organic amendments and crop varieties with an enhanced allocation of carbon to roots).

Crop phenology has a major influence on crop yield and is a major aspect of crop response to global warming. Process-based models of phenology are often used to predict the effect of weather on the development rate of crops through their growth phases, but such models are associated with large uncertainties, as demonstrated by the large variability between simulation results of different models. The purpose of this study is to estimate the relative importance of model structure uncertainty (due to uncertainty in the model equations) and model parameter uncertainty. Previous studies have assumed that the choice of model parameters to be fitted to data is fixed, and have evaluated the effect of uncertainty in those parameters. This underestimates parameter variance, as it ignores uncertainty in many aspects of the calibration approach, in particular the choice of parameters to estimate, as well as uncertainty in the parameters not fitted by calibration. Here, we propose and apply two approaches for estimating parameter variance that take into account uncertainty in all aspects of calibration, and, for one approach, uncertainty in parameters not fitted by calibration. Both approaches are based on previously reported large multi-model studies. The first approach uses components of variance analysis, which is possible because these studies included different modeling groups using the same model structure. The second approach is based on a study where multiple modeling groups each applied two different calibration procedures. The variance calculated from the two calibration approaches gives an estimate of parameter variance. Both methods give an overall result of 69% of total variance coming from parameter variance, for those simulated variables for which there are observed data. We conclude that in order to reduce uncertainty in crop phenology predictions, improving and sharing calibration procedures is as or more important than improving model structure.

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. Here we propose an original, detailed approach, a protocol, for calibration of such models. The protocol covers all the steps in the calibration work-flow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and significantly reduced prediction error.

Projections of global climate models suggest that ongoing human-induced climate change will lead to an increase in the frequency of severe droughts in many important agricultural regions of the world. Eco-hydrological models that integrate current understanding of the interacting processes governing soil water balance and plant growth may be useful tools to predict the impacts of climate change on crop production. However, the validation status of these models for making predictions under climate change is still unclear, since few suitable datasets are available for model testing. One promising approach is to test models using data obtained in “space-for-time” substitution experiments, in which samples are transferred among locations with contrasting current climates in order to mimic future climatic conditions. An important advantage of this approach is that the soil type is the same, so that differences in soil properties are not confounded with the influence of climate on water balance and crop growth. In this study, we evaluate the capability of a relatively simple eco-hydrological model to reproduce 6 years (2013–2018) of measurements of soil water contents, water balance components and grass production made in weighing lysimeters located at two sites within the TERENO-SoilCan network in Germany. Three lysimeters are located at an upland site at Rollesbroich with a cool, wet climate, while three others had been moved from Rollesbroich to a warmer and drier climate on the lower Rhine valley floodplain at Selhausen. Four of the most sensitive parameters in the model were treated as uncertain within the framework of the GLUE (generalized likelihood uncertainty estimation) methodology, while the remaining parameters in the model were set according to site measurements or data in the literature. The model satisfactorily reproduced the measurements at both sites, and some significant differences in the posterior ranges of the four uncertain parameters were found. In particular, the results indicated greater stomatal conductance as well an increase in dry-matter allocation below ground and a significantly larger maximum root depth for the three lysimeters that had been moved to Selhausen. As a consequence, the apparent water use efficiency (above-ground harvest divided by evapotranspiration) was significantly smaller at Selhausen than Rollesbroich. Data on species abundance on the lysimeters provide one possible explanation for the differences in the plant traits at the two sites derived from model calibration. These observations showed that the plant community at Selhausen had changed significantly in response to the drier climate, with a significant decrease in the abundance of herbs and an increase in the proportion of grass species. The differences in root depth and leaf conductance may also be a consequence of plasticity or acclimation at the species level. Regardless of the reason, we may conclude that such adaptations introduce significant additional uncertainties into model predictions of water balance and plant growth in response to climate change.

Projections of global climate models suggest that ongoing human-induced climate change will lead to an increase in the frequency of severe droughts in many important agricultural regions of the world. Eco-hydrological models that integrate current understanding of the interacting processes governing soil water balance and plant growth may be useful tools to predict the impacts of climate change on crop production. However, the validation status of these models for making predictions under climate change is still unclear, since few suitable datasets are available for model testing. One promising approach is to test models using data obtained in “space-for-time” substitution experiments, in which samples are transferred among locations with contrasting current climates in order to mimic future climatic conditions. An important advantage of this approach is that the soil type is the same, so that differences in soil properties are not confounded with the influence of climate on water balance and crop growth. In this study, we evaluate the capability of a relatively simple eco-hydrological model to reproduce 6 years (2013-2018) of measurements of soil water contents, water balance components and grass production made in weighing lysimeters located at two sites within the TERENO-SoilCan network in Germany. Three lysimeters are located at an upland site at Rollesbroich with a cool, wet climate, while three others had been moved from Rollesbroich to a warmer and drier climate on the lower Rhine valley floodplain at Selhausen. Four of the most sensitive parameters in the model were treated as uncertain within the framework of the GLUE (Generalized Likelihood Uncertainty Estimation) methodology, while the remaining parameters in the model were set according to site measurements or data in the literature.The model accurately reproduced the measurements at both sites, and some significant differences in the posterior ranges of the four uncertain parameters were found. In particular, the results indicated greater stomatal conductance as well an increase in dry matter allocation below-ground and a significantly larger maximum root depth for the three lysimeters that had been moved to Selhausen. As a consequence, the apparent water use efficiency (above-ground harvest divided by evapotranspiration) was significantly smaller at Selhausen than Rollesbroich. Data on species abundance on the lysimeters provide one possible explanation for the differences in the plant traits at the two sites derived from model calibration. These observations showed that the plant community at Selhausen had changed significantly in response to the drier climate, with a significant decrease in the abundance of herbs and an increase in the proportion of grass species. The differences in root depth and leaf conductance may also be a consequence of plasticity or acclimation at the species level. Regardless of the reason, we may conclude that such adaptations introduce significant additional uncertainties into model predictions of water balance and plant growth in response to climate change.

International audience; Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.

Funding Information: This work was in part supported by the Collaborative Research Center 1253 CAMPOS (Project 7: Stochastic Modeling Framework), funded by the German Research Foundation ( DFG , Grant Agreement SFB 1253/1 2017 ), the Academy of Finland through projects AICropPro ( 316172 ) and DivCSA ( 316215 ) and Natural Resources Institute Finland (Luke) through a strategic project BoostIA, the BonaRes project ''Soil3'' ( BOMA 03037514 ) of the Federal Ministry of Education and Research ( BMBF ), Germany, the Deutsche Forschungsgemeinschaft ( DFG , German Research Foundation ) under Germany's Excellence Strategy - EXC 2070–390732324 EXC (PhenoRob), the project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B ) funded by the Federal Ministry of Education and Research ( BMBF , Germany), the INRA ACCAF meta-programme, the German Federal Ministry of Education and Research ( BMBF ) in the framework of the funding measure “Soil as a Sustainable Resource for the Bioeconomy – BonaRes”, project “BonaRes (Module B): BonaRes Centre for Soil Research , subproject B” (grant 031B0511B ), the National Key Research and Development Program of China ( 2017YFD0300205 ), the National Science Foundation for Distinguished Young Scholars ( 31725020 ), the Priority Academic Program Development of Jiangsu Higher Education Institutions ( PAPD ), the 111 Project ( B16026 ), and China Scholarship Council , the Agriculture and Agri-Food Canada's Project 1387 under he Canadian Agricultural Partnership, the DFG Research Unit FOR 1695 ‘Agricultural Landscapes under Global Climate Change – Processes and Feedbacks on a Regional Scale, the U.S. Department of Agriculture National Institute of Food and Agriculture (award no. 2015-68007-23133 ) and USDA / NIFA HATCH grant N. MCL02368 , the National Key Research and Development Program of China ( 2016YFD0300105 ), The Broadacre Agriculture Initiative, a research partnership between University of Southern Queensland and the Queensland Department of Agriculture and Fisheries , the Academy of Finland through project AI-CropPro ( 315896 ), the JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies ( D.M. 24064/7303/15 of 6/Nov/2015), the Ministry of Education, Youth and Sports of Czech Republic through SustES - Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (project no. CZ.02.1.01/0.0/0.0/16_019/000797 ). The order in which the donors are listed is arbitrary. Funding Information: This work was in part supported by the Collaborative Research Center 1253 CAMPOS (Project 7: Stochastic Modeling Framework), funded by the German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017), the Academy of Finland through projects AICropPro (316172) and DivCSA (316215) and Natural Resources Institute Finland (Luke) through a strategic project BoostIA, the BonaRes project ''Soil3'' (BOMA 03037514) of the Federal Ministry of Education and Research (BMBF), Germany, the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2070?390732324 EXC (PhenoRob), the project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B) funded by the Federal Ministry of Education and Research (BMBF, Germany), the INRA ACCAF meta-programme, the German Federal Ministry of Education and Research (BMBF) in the framework of the funding measure ?Soil as a Sustainable Resource for the Bioeconomy ? BonaRes?, project ?BonaRes (Module B): BonaRes Centre for Soil Research, subproject B? (grant 031B0511B), the National Key Research and Development Program of China (2017YFD0300205), the National Science Foundation for Distinguished Young Scholars (31725020), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the 111 Project (B16026), and China Scholarship Council, the Agriculture and Agri-Food Canada's Project 1387 under he Canadian Agricultural Partnership, the DFG Research Unit FOR 1695 ?Agricultural Landscapes under Global Climate Change ? Processes and Feedbacks on a Regional Scale, the U.S. Department of Agriculture National Institute of Food and Agriculture (award no. 2015-68007-23133) and USDA/NIFA HATCH grant N. MCL02368, the National Key Research and Development Program of China (2016YFD0300105), The Broadacre Agriculture Initiative, a research partnership between University of Southern Queensland and the Queensland Department of Agriculture and Fisheries, the Academy of Finland through project AI-CropPro (315896), the JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies (D.M. 24064/7303/15 of 6/Nov/2015), the Ministry of Education, Youth and Sports of Czech Republic through SustES - Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (project no. CZ.02.1.01/0.0/0.0/16_019/000797). The order in which the donors are listed is arbitrary. Publisher Copyright: © 2021 Elsevier Ltd Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.

International audience; Due to the more frequent use of crop models at regional and national scale, the effects of spatial data input resolution have gained increased attention. However, little is known about the influence of variability in crop management on model outputs. A constant and uniform crop management is often considered over the simulated area and period. This study determines the influence of crop management adapted to climatic conditions and input data resolution on regional-scale outputs of crop models. For this purpose, winter wheat and maize were simulated over 30 years with spatially and temporally uniform management or adaptive management for North Rhine-Westphalia ((similar to)34 083 km(2)), Germany. Adaptive management to local climatic conditions was used for 1) sowing date, 2) N fertilization dates, 3) N amounts, and 4) crop cycle length. Therefore, the models were applied with four different management sets for each crop. Input data for climate, soil and management were selected at five resolutions, from 1 x 1 km to 100 x 100 km grid size. Overall, 11 crop models were used to predict regional mean crop yield, actual evapotranspiration, and drainage. Adaptive management had little effect (< 10% difference) on the 30-year mean of the three output variables for most models and did not depend on soil, climate, and management resolution. Nevertheless, the effect was substantial for certain models, up to 31% on yield, 27% on evapotranspiration, and 12% on drainage compared to the uniform management reference. In general, effects were stronger on yield than on evapotranspiration and drainage, which had little sensitivity to changes in management. Scaling effects were generally lower than management effects on yield and evapotranspiration as opposed to drainage. Despite this trend, sensitivity to management and scaling varied greatly among the models. At the annual scale, effects were stronger in certain years, particularly the management effect on yield. These results imply that depending on the model, the representation of management should be carefully chosen, particularly when simulating yields and for predictions on annual scale.

Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Model predictions depend not only on model structure but also on the parameter values. This study is thus an evaluation of modeling groups, which choose the structure and fix or estimate the parameters, rather than an evaluation just of model structures. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. For a given modeling group, MAE for the evaluation environments was significantly correlated with MAE for the calibration environments, which suggests that it would be of interest to test ensemble predictors that weight individual modeling groups based on performance for the calibration data. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. Finally, there was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.

Soil degradation is a worsening global phenomenon driven by socio‐economic pressures, poor land management practices and climate change. A deterioration of soil structure at timescales ranging from seconds to centuries is implicated in most forms of soil degradation including the depletion of nutrients and organic matter, erosion and compaction. New soil–crop models that could account for soil structure dynamics at decadal to centennial timescales would provide insights into the relative importance of the various underlying physical (e.g. tillage, traffic compaction, swell/shrink and freeze/thaw) and biological (e.g. plant root growth, soil microbial and faunal activity) mechanisms, their impacts on soil hydrological processes and plant growth, as well as the relevant timescales of soil degradation and recovery. However, the development of such a model remains a challenge due to the enormous complexity of the interactions in the soil–plant system. In this paper, we focus on the impacts of biological processes on soil structure dynamics, especially the growth of plant roots and the activity of soil fauna and microorganisms. We first define what we mean by soil structure and then review current understanding of how these biological agents impact soil structure. We then develop a new framework for modelling soil structure dynamics, which is designed to be compatible with soil–crop models that operate at the soil profile scale and for long temporal scales (i.e. decades, centuries). We illustrate the modelling concept with a case study on the role of root growth and earthworm bioturbation in restoring the structure of a severely compacted soil., This photograph, depicting ant bioturbation, was taken at the compaction recovery experiment at Agroscope, Zurich, Switzerland. Together with other biological processes, faunal bioturbation profoundly influences soil structure and thus soil physical and hydraulic properties, hydrological processes and plant growth. The parsimonious model concept developed in this paper, which is designed to be compatible with profile‐scale soil–crop models, allows simulation of the effects of biological agents (e.g. plant roots and soil‐living organisms) on soil structure dynamics.

Process-oriented agro-ecosystem models are increasingly applied to assess crop management options or impacts of climate change on agricultural production, food security and ecosystem services. Thereby, the aggregation of initial soil and climate information is a widely used approach for performing simulations at larger scales such as regions, nations or even globally. In this context, the ability of models to respond to different site conditions is essential for high quality impact assessment through the use of modelling tools. As part of a model inter-comparison the present study investigated models' yield response on variable site conditions using data sets from two well-documented fields, one located in Germany and one in Italy. The fields were sampled at 60 and 100 grid points, respectively, and soil and crop variables were recorded at varying intensity for the entire simulation period covering three growing seasons. The data was provided successively to the participating modelling groups in three calibration steps (a, b, and c) and the first growing season was considered for calibration. Model validation was based on these steps and each growing season as well as on the entire simulation period considering the soil state variables mineral nitrogen and water content (N, WC) as well as crop yield, biomass, and leaf area index (LAI). The WC was best depicted by the models, resulting in high correlation coefficients (r) up to 0.81 between simulated and observed values. The root mean square error (RMSE) of simulated N ranged from 20 kg ha(-1) to 1072 kg ha(-1) regarding all steps and growing seasons. The annual within-field variability of yields was better simulated by the models when observed subsoil information was provided. However, the RMSE ranged from 0.5 t ha(-1) to 3.5 t ha(-1) at the German field, and from 0.6 t ha(-1) to 5.9 t ha(-1) at the Italian field, respectively. It was found that intensified calibration did not necessarily lead to improved model output. Furthermore, single models showed specific inconsistencies in their algorithms when, for example, underestimated WC was associated with overestimated yields. In total, the sensitivity of models to spatially variable site conditions differed considerably. The importance of quality-assured soil and yield information for model improvement was highlighted.

The effects of aggregating soil data (DAE) by areal majority of soil mapping units was explored for regional simulations with the soil-vegetation model CoupModel for a region in Germany (North Rhine-Westphalia). DAE were analysed for wheat yield, drainage, soil carbon mineralisation and nitrogen leaching below the root zone. DAE were higher for soil C mineralization and N leaching than for yield and drainage and were strongly related to the presence of specific soils within the study region. These soil types were associated to extreme simulated output variables compared to the mean variable in the region. The spatial aggregation of these key functional soils within sub-regions additionally influenced the DAE. A spatial analysis of their spatial pattern (i.e. their presence/absence, coverage and aggregation) can help in defining the appropriate grid resolution that would minimize the error caused by aggregating soil input data in regional simulations.

The winter climate in northwestern Europe is commonly influenced by the North Atlantic Oscillation (NAO). Its intensity, expressed as an index (NAO), has been suggested for use in assessing nutrient leaching from arable land to water and the effects of mitigation measures. We found significant ( < 0.05) positive linear relationships between NAO and an air freezing-thawing index in central and southern Sweden for 2004 to 2016. This period covered winters with both extreme low and high NAO. There were significant negative linear relationships between NAO and a snow depth index. Management and nutrient leaching were studied simultaneously in two agricultural catchments (20.7 ha, code 11M; 788 ha, code M36) in southwestern Sweden. Catchments 11M (silty-clay soil) and M36 (sandy hills with a central, heavy clay plain) are both artificially drained. Total N and total P leaching increased significantly with winter (November-April) NAO in both catchments. In contrast, leaching of dissolved reactive P (DRP) was not related to NAO. The highest DRP concentrations were observed in connection with specific agricultural practices, whereas moderately elevated DRP concentrations were linked to snowmelt events. Concentrations of P in other forms (other P) were even more elevated (1.02 mg L) in 11M in winter 2014-2015, probably due to a large (32% of area) internal buffer (ley-fallow) in a central ravine being plowed down in autumn 2014. No general trend in P or N fertilization was found in catchment M36. Thus NAO may be appropriate for use in trend analyses of nutrient load in the study region.

International audience; Soil-crop models are used to simulate ecological processes from the field to the regional scale. Main inputs are soil and climate data in order to simulate model response variables such as crop yield. We investigate the effect of changing the resolution of input data on simulated crop yields at a regional scale using up to ten dynamic crop models. For these models we compared the effects of spatial input data aggregation for wheat and maize yield of two regions with contrasting climate conditions (1) Tuscany (Italy, Mediterranean climate) and (2) North Rhine Westphalia (NRW, Germany, temperate climate). Soil and climate data of 1 km resolution were aggregated to resolutions of 10, 25, 50, and 100 km by selecting the dominant soil class (and corresponding soil properties) and by arithmetic averaging, respectively. Differences in yield simulated at coarser resolutions from the yields simulated at 1 km resolution were calculated to quantify the effect of the aggregation of the input data (soil and climate data) on simulation results. The mean yield difference (bias) at the regional level was positive due to the upscaling of productive dominant soil(s) to coarser resolution. In both regions and for both crops, aggregation effects (i.e. errors in simulation of crop yields at coarser spatial resolution) due to the combined aggregation of soil and climate input data increased with decreasing resolution, whereby the aggregation error for Tuscany was larger than for North Rhine Westphalia (NRW). The average absolute percentage yield differences between grid cell yields at the coarsest resolution (100 km) compared to the finest resolution (1 km) were by about 20-30% for Tuscany and less than 15 and 20% for NRW for winter wheat and silage maize, respectively. In the Mediterranean area, the prediction errors of the simulated yields could reach up to 60% when looking at individual crop model simulations. Additionally, aggregating soil data caused larger aggregation errors in both regions than aggregating climate data. Those results suggest that a higher spatial resolution of climate and especially of soil input data are necessary in Mediterranean areas than in temperate humid regions of central Europe in order to predict reliable regional yield estimations with crop models. For generalization of these outcomes, further investigations in other sub humid or semi-arid regions will be necessary.

We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known. We compare eight sampling schemes for estimating regional mean crop yield.Precision of eight schemes is compared across fourteen crop models.Compact geographical stratification can always improve the precision.Stratification with soil had very high gains of precision for twelve crop models.Our findings can improve the precision of site-based regional crop modeling.

In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1km and 100km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low. Analysis of soil data aggregation on model errors of up-scaled SOC trends.Determination of factors controlling aggregation effects (AE) on modeled SOC trends.Comparison of variability between 7 biogeochemical models and AE.Development of ex ante methods to approximate AE for SOC simulation studies.

The assessment of climate change impacts on the risk for pesticide leaching needs careful consideration of different sources of uncertainty. We investigated the uncertainty related to climate scenario input and its importance relative to parameter uncertainty of the pesticide leaching model. The pesticide fate model MACRO was calibrated against a comprehensive one-year field data set for a well-structured clay soil in south-west Sweden. We obtained an ensemble of 56 acceptable parameter sets that represented the parameter uncertainty. Nine different climate model projections of the regional climate model RCA3 were available as driven by different combinations of global climate models (GCM), greenhouse gas emission scenarios and initial states of the GCM. The future time series of weather data used to drive the MACRO-model were generated by scaling a reference climate data set (1970–1999) for an important agricultural production area in south-west Sweden based on monthly change factors for 2070–2099. 30 yr simulations were performed for different combinations of pesticide properties and application seasons. Our analysis showed that both the magnitude and the direction of predicted change in pesticide leaching from present to future depended strongly on the particular climate scenario. The effect of parameter uncertainty was of major importance for simulating absolute pesticide losses, whereas the climate uncertainty was relatively more important for predictions of changes of pesticide losses from present to future. The climate uncertainty should be accounted for by applying an ensemble of different climate scenarios. The aggregated ensemble prediction based on both acceptable parameterizations and different climate scenarios could provide robust probabilistic estimates of future pesticide losses and assessments of changes in pesticide leaching risks.

The assessment of climate change impacts on pesticide leaching requires a careful consideration of both parameter uncertainty and model structural error. Our aim was to assess (i) model structure uncertainty, with specific focus on the effects of temperature on pesticide sorption and diffusion, and its relation to parameter uncertainty and (ii) the importance of these sources of uncertainty in long-term predictions of pesticide leaching in the perspective of climate change. We introduced optional functions describing the effects of temperature on sorption and diffusion into the pesticide fate and transport model MACRO5.2, which resulted in four structurally different versions. The generalised likelihood uncertainty estimation (GLUE) method was applied to these four structurally different model versions in order to generate an ensemble of acceptable parameter combinations for long-term predictions of pesticide leaching under climate change. The analysis was performed in a two-step procedure. First, for each model version, 80,000 parameter combinations were tested against a comprehensive field data set of bromide and bentazone concentrations in drainflow and in the soil profile to identify acceptable parameter combinations. In a second step, we performed 30-year predictions based on these acceptable parameter sets for three hypothetical compounds that differed in their sorption strength. The period 1970–1999 was used to represent the current climate for a region in south-west Sweden. The future climate time series was derived for the period 2070–2099 by perturbing the observed time series based on monthly change factors calculated from climate projections of the regional climate model RCA3 with boundary conditions of the global circulation model ECHAM5 forced by the A2-emission scenario. For all three pesticide compounds, total losses were predicted to increase for autumn applications due to projected increases in winter rainfall, but decrease for spring applications. Differences between model versions were found, despite the large uncertainty in parameter estimates. Thus, temperature dependent diffusion was shown to be important for weakly sorbed compounds, while inclusion of temperature dependent sorption significantly affected the predictions for moderately and strongly sorbed compounds. The response to climate change was consistent for the different model versions, but the models that included temperature dependent sorption showed relatively more leaching in a future climate. Our findings stressed the importance of considering temperature dependent processes when modelling future pesticide losses.

Impacts of soil and weather data aggregation in spatial modelling of net primary production of croplands. iCROPM 2016 International Crop Modelling Symposium "Crop Modelling for Agriculture and Food Security under Global Change"

For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km2), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1–100 km grid cells show changes of 0.4–7.8% and 0.0–4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9–4.2 g C m−2 d−1 and 2.7–6.1 g C m−2 d−1 for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution. On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study.

In recent decades European silage maize production has extended northwards, into Scandinavia, and the importance of maize in fodder production has increased substantially. For the northward expansion of maize production it is of interest to evaluate both the role of climate change that has occurred already, and scenarios for possible future climate change. The aim of this study was to assess for Swedish climatic conditions, the annual variation in silage maize yield and quality (dry weight and starch contents) of cultivars currently grown in Germany. The MAISPROQ simulation model currently used in German maize production was applied to evaluate the effects of (i) cultivar differences (four cultivars; four sites; 2003–2009), (ii) intra-regional variation among ten sites representing three regions (two cultivars; 2003–2009), and (iii) climatic variability among two historical periods during 1961–2009 and three future periods during 2011–2100 using A2-emission climate scenarios and the Delta-method ...

An improved understanding of how precipitation patterns control pesticide leaching from structured soils prone to macropore flow could lead to practical mitigation strategies that would help farmers minimize losses by optimizing application timings. A sensitivity analysis of the macropore flow model MACRO was therefore carried out to examine the influence of antecedent soil water content and precipitation patterns on pesticide leaching to drainage systems and groundwater. One thousand model runs were executed (20 four-year weather data series, 50 application dates per season) for both autumn and spring applications of a hypothetical moderately sorbed and quickly degraded herbicide for one of three national scenarios for pesticide risk assessment in Sweden (Nasbygard, a loamy moraine soil in Scania, southern Sweden). Rapid and direct transport of pesticides in macropores to drainage systems and shallow groundwater was predicted to occur rather infrequently in spring (in 4 of the 20 years) and even more rarely in autumn. For autumn applications, the soil water deficit at application (SWDtot) and medium-term precipitation (30�90 days after application) were the two most sensitive variables controlling pesticide leaching. For spring applications, total leaching was most closely linked to rainfall the following winter, while short-term precipitation (5 days after application) and the antecedent soil water deficit were the two most important predictors for maximum pesticide concentrations in drainflow. The potential for reducing leaching by restricting applications to periods of low risk was investigated. The results showed that avoiding applications on wet soil in autumn could potentially reduce total pesticide losses by a factor of two to three. Similarly, the risk of acute toxicological effects in surface waters following pesticide applications in spring could be reduced by a factor of 2�3 by avoiding application when 5-day weather forecasts predict precipitation >10 mm.

Problems related to food security and sustainable development are complex (Ericksenet al., 2009) and require consideration of biophysical, economic, political, and social factors, as well as their interactions, at the level of farms, regions, nations, and globally. While the solution to such societal problems may be largely political, there is a growing recognition of the need for science to provide sound information to decision-makers (Meinke et al., 2009). Achieving this, particularly in light of largely uncertain future climate and socio-economic changes, will necessitate integrated assessment approaches and appropriate integrated assessment modeling (IAM) tools to perform them. Recent (Ewertet al., 2009; van Ittersumet al., 2008) and ongoing (Rosenzweiget al., 2013) studies have tried to advance the integrated use of biophysical and economic models to represent better the complex interactions in agricultural systems that largely determine food supply and sustainable resource use. Nonetheless, the challenges for model integration across disciplines are substantial and range from methodological and technical details to an often still-weak conceptual basis on which to ground model integration (Ewertet al., 2009; Janssenet al., 2011). New generations of integrated assessment models based on well-understood, general relationships that are applicable to different agricultural systems across the world are still to be developed. Initial efforts are underway towards this advancement (Nelsonet al., 2014; Rosenzweiget al., 2013). Together with economic and climate models, crop models constitute an essential model group in IAM for large-area cropping systems climate change impact assessments. However, in addition to challenges associated with model integration, inadequate representation of many crops and crop management systems, as well as a lack of data for model initialization and calibration, limit the integration of crop models with climate and economic models (Ewertet al., 2014). A particular obstacle is the mismatch between the temporal and spatial scale of input/output variables required and delivered by the various models in the IAM model chain. Crop models are typically developed, tested, and calibrated for field-scale application (Booteet al., 2013; see also Part 1, Chapter 4 in this volume) and short time-series limited to one or few seasons. Although crop models are increasingly used for larger areas and longer time-periods (Bondeauet al., 2007; Deryng et al., 2011; Elliottet al., 2014) rigorous evaluation of such applications is pending. Among the different sources of uncertainty related to climate and soil data, model parameters, and structure, the uncertainty from methods used to scale-up crop models has received little attention, though recent evaluations indicate that upscaling of crop models for climate change impact assessment and the resulting errors and uncertainties deserve attention in order to advance crop modeling for climate change assessment (Ewertet al., 2014; R¨ otteret al., 2011). This reality is now reflected in the scientific agendas of new international research projects and programs such as the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweiget al., 2013) and MACSUR (MACSUR, 2014). In this chapter, progress in evaluation of scaling methods with their related uncertainties is reviewed. Specific emphasis is on examining the results of systematic studies recently established in AgMIP and MACSUR. Main features of the respective simulation studies are presented together with preliminary results. Insights from these studies are summarized and conclusions for further work are drawn.

International audience; We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.

The water and heat balances of an arable field and a forest in the boreal zone in Scandinavia were explored using 3 yr of observations and simulations with two different soil–vegetation–atmosphere transfer (SVAT) models over a 30-yr period. Results from a detailed mechanistic model [coupled heat and mass transfer model (COUP)] were compared with those obtained with a large-scale type of SVAT model used in the weather prediction model at the European Centre for Medium-Range Weather Forecasts [ECMWF tiled land surface scheme (TESSEL)]. The COUP model simulations agreed well with the observations from a seasonal perspective. The TESSEL model differed significantly from the measurements when standard operational parameter values were used. The introduction of a seasonal variation in leaf-area index values, tuned canopy resistance for forest, and a reduced roughness length over snow-covered open land reduced the discrepancies. Net radiation was 40% higher in the forest when compared with the arable land, based on 30-yr simulations with both models. Furthermore, the forest was a net source of sensible heat flux, whereas the arable land was a net sink. Because of different treatment of winter interception evaporation, forest latent heat flux based on the COUP model considerably exceeded that from the TESSEL model, and suggested that the total annual evaporation was higher from the forest than from arable land. The representation of interception evaporation in winter, as well as seasonal dynamics in vegetation properties are, thus, of considerable importance for adequate simulation of forest and arable land energy fluxes within the boreal zone.

This study aimed, with the use of simulation models, to quantify the effect of several years incorporation of catch crop material into the soil on soil organic matter storage, N mineralisation capacity and risk for N leaching. C and N dynamics in crop and soil were simulated with the SOILN model (Version 9.2). The simulated results were compared with measurements of crop and soil N, crop biomass and N-leaching from a catch crop field experiment situated in southwestern Sweden. The generality of parameter values determining the long-term turnover of soil organic matter was tested. To reproduce measured soil mineral N and crop N, mineralisation had to be favoured by high C mineralisation. After 6 years of catch crop treatment, simulated soil organic matter content had increased by less than 2%, but the N-mineralisation capacity had increased by 25%, corresponding to 37 kg N ha−1. With a continuous annual use of catch crops only a few per cent of the extra mineralised N was leached. Without a succeeding catch crop, however, 30% of N from the increased mineralisation was leached. The results indicated that the decomposition rate increased immediately after frost events.

Climate change is not only likely to improve conditions for crop production in Sweden, but also to increase weed pressure and the need for herbicides. This study aimed at assessing and contrasting the direct and indirect effects of climate change on herbicide leaching to groundwater in a major crop production region in south-west Sweden with the help of the regional pesticide fate and transport model MACRO-SE. We simulated 37 out of the 41 herbicides that are currently approved for use in Sweden on eight major crop types for the 24 most common soil types in the region. The results were aggregated accounting for the fractional coverage of the crop and the area sprayed with a particular herbicide. For simulations of the future, we used projections of five different climate models as model driving data and assessed three different future scenarios: (A) only changes in climate, (B) changes in climate and land-use (altered crop distribution), and (C) changes in climate, land-use, and an increase in herbicide use. The model successfully distinguished between leachable and non-leachable compounds (88% correctly classified) in a qualitative comparison against regional-scale monitoring data. Leaching was dominated by only a few herbicides and crops under current climate and agronomic conditions. The model simulations suggest that the direct effects of an increase in temperature, which enhances degradation, and precipitation which promotes leaching, cancel each other at a regional scale, resulting in a slight decrease in leachate concentrations in a future climate. However, the area at risk of groundwater contamination doubled when indirect effects of changes in land-use and herbicide use, were considered. We therefore concluded that it is important to consider the indirect effects of climate change alongside the direct effects and that effective mitigation strategies and strict regulation are required to secure future (drinking) water resources.

The results from three studies in central Tunisia are presented, the first and second in November‐December of two consecutive years and the third in April‐May. The spatial variation in reductions of potential evaporation behind the shelterbelts, measured with the Andersson evaporimeter, is reported. The types of shelterbelt systems were a mechanical shelter of porous nylon netting on the open plain, a cactus Opuntia ficus indica shelter with very low porosity, a nursery with an Acacia cyanophylla shelter within a system of rows of tall Eucalyptus camaldulensis situated 40 to >170 m from the shelter, and an orchard of dispersed young fruit trees sheltered by young plantations of Acacia cyanophylla and Casuarina equisetifolia shelterbelts. The mechanical shelter reduced potential evaporation similarly in the three studies. The cactus shelter was inefficient owing to its low porosity. In the nursery the tall eucalyptus trees reduced potential evaporation at distances >20H (H is distance in terms of height of...

Soil water retention curves in eight soil profiles on a sandy soil were determined by field measurements of soil water content and soil water matric potential made during 19 days of continuous drying using time-domain-reflectometry and tensiometers. Evaporation from the different profiles was simulated using the retention curves and estimated unsaturated conductivity functions, meteorological data as driving variables, and measured soil water matric potentials at 40 cm depths as the lower boundary condition. Good agreement was obtained between simulated and measured evaporation from different locations in the field. Areal mean evaporation obtained as the arithmetic mean of individual simulations was 2% smaller than the evaporation obtained using mean physical properties in one simulation. This difference was small compared to the variation in simulated evaporation between individual locations (62–142% of the meanevaporation).

The effects of an undersown catch crop on the dynamics and leaching of nitrogen in cropping systems with spring cereals were investigated in southern Sweden. Field measurements of soil mineral nitrogen and nitrogen concentrations in drainage water were made for 4 years in a sandy soil. The experiment was performed on four tile-drained field plots sown with spring cereals. On two of the plots, Italian rye grass was undersown and ploughed down the following spring during three of the years. The other two plots were treated in a conventional way and served as controls. Soil nitrate levels were substantially reduced in the catch-crop treatment, but increased during the fourth year when no catch crop was grown. The differences between the treatments in soil nitrate were reflected in the nitrate concentrations measured in the drainage water. A mathematical model was used to simulate nitrogen dynamics in corresponding treatments. There was good agreement between measurements and simulations with regard to patterns of change in soil mineral nitrogen and nitrate concentrations in drainage water for each treatment. Simulated leaching of nitrate in the conventional treatment was 1.9–3.9 g N m−2 y−1 during the first three years while calculated leaching based on the measurements was 2.7–4.4 g N m−2 y−1. In the catch-crop treatment leaching of nitrate was reduced by 1.4–2.6 g m−2 y−1 according to the simulations and by 2.2–4.1 g m−2 y−1 according to calculations based on the measurements. Measurements showed that leaching of nitrogen compounds other than nitrate was hardly affected by the catch crop. In the simulations the ploughed-down catch crop resulted in temporary increases of the litter pool, a net increase of the humus pool and a reduced C-N ratio of the litter pool. Simulated net mineralization from the litter pool was substantially higher in the catch-crop treatment compared with the conventional treatment. In the fourth year, the yield of the main crop was 20–25% higher in the catch-crop treatment, and leaching was higher than in the conventional treatment.

Evaporation from cropping systems with and without crop cover during winter (spring cereals with and without undersown Italian rye grass) was studied. Measured daily discharge from field lysimeters and tile-drained plots was used to investigate to what extent discharge measurements could reveal differences in evaporation between the cropping systems. The identified differences were interpreted using a physically based model for simulating water and heat flows in the soil. Measurements were made between 1 April 1988 and 1 April 1991 on a sandy loam in southwestern Sweden. Annual precipitation (1 April–31 March) was 910 mm, 697 mm and 677 mm respectively. Total annual discharge showed a large degree of inconsistent variation between field plots within the same year and treatment, whereas the discharge dynamics showed more consistent differences between treatments. Standard meteorological variables and data on soil properties and crop development were used as input to the model. Calculations of soil evaporation, transpiration and evaporation of intercepted water were based on the Penman-Monteith equation. The model was calibrated against the actual date on which discharge started, which consistently occurred later in the autumn in the cropped soil than in the bare soil. Simulated annual total evaporation amounted to 505 mm, 470 mm and 396 mm from the system with bare soil during winter, and 552 mm, 510 mm and 423 mm from the system with cropped soil during winter. However, during periods with low temperatures and frequent precipitation, total evaporation rates for the bare soil were as high as or even exceeded those for cropped soil. Simulated annual soil evaporation from the system with cropped soil during winter constituted about 38% of the total annual evaporation, whereas it varied between 55 and 65% for the system with bare soil during winter. Discrepancies between simulated and measured discharge could have been due to spatial variability in soil and plant properties in the field, as well as to errors in the estimated unsaturated hydraulic conductivities or root water uptake functions used in the simulations. In simulations based on either modified soil properties or root depths, similar changes in water dynamics were obtained, demonstrating the need for independent determinations of the soil hydraulic properties.

Remote sensing is an interesting tool for monitoring crop production, energy exchanges and mass exchanges between the soil, the biosphere and the atmosphere. The aim of the Alpilles-ReSeDA program was the development of such techniques combining remote sensing data, and soil and vegetation process models. This article focuses on SVAT models (Soil-Vegetation-Atmosphere Transfer models) which may be used for monitoring energy and mass exchanges by using assimilation of remote sensing data procedures. As a first step, we decided to implement a model comparison experiment with the aim of analysing the relationships between the models' complexity, validity and potential for assimilating remote sensing data. This experiment involved the definition of three comparison scenarios with different objectives: (i) test the models' capacity to accurately describe processes using input parameters as measured in the field; (ii) test the portability of the models by using a priori information on input parameters (such as pedotransfer functions) and, (iii) test the robustness of the models by a calibration-validation procedure. These three scenarios took advantage of the experimental network that was implemented during the Alpilles experiment and which combined measurements on different fields that may be used for calibration of models and their validations on independent data sets. The results showed that the models' performances were close whatever their complexity. The simpler models were less sensitive to the specification of input parameters. Significant improvements in the models' results were achieved when calibrating the models in comparison with the first scenario.; Le programme Alpilles-ReSeDA a été mis en place pour développer des méthodes d'utilisation des données des données de télédétection en combinaison avec des modèles de fonctionnement du sol ou de la végétation, dans le but d'estimer ou de suivre la production des cultures ou leurs échanges de masse et d'énergie avec le sol et l'atmosphère. Parmi ces modèles, cet article se focalise sur les modèles de transfert Sol-Végétation-Atmosphère qui peuvent être utilisés pour suivre les échanges d'énergie et de masse au moyen de procédures d'assimilation des données de télédétection. Dans une première étape, nous avons mis en place une expérience de comparaison de plusieurs modèles, avec l'objectif d'analyser les relations entre la complexité des modèles, leur validité et leur utilisation potentielle pour assimiler des données de télédétection. Nous avons défini trois scénarios qui répondent à des objectifs différents: (i) tester la capacité des modèles à décrire les processus en utilisant les paramètres d'entrée des modèles tels qu'ils ont mesurés dans les champs; (ii) tester la portabilité des modèles en utilisant des informations a priori en entrée (comme des fonctions de pédotransfer), et (iii) tester la robustesse des modèles par une procédure de calibration-validation. Ces trois scénarios se basent sur le dispositif expérimental mis en place au cours de l'expérimentation Alpilles, qui combien des mesures sur plusieurs champs et qui peuvent être utilisées pour calibrer et valider les modèles sur des jeux de données indépendants. Les résultats de cette comparaison montrent que les performances des modèles sont proches quelle que soit la complexité des modèles. De plus, les modèles les plus simples apparaissent moins sensibles à une dégradation des paramètres d'entrée. Des améliorations sensibles des résultats ont été obtenues grâce à la calibration des modèles.

The objective of the present study was to assess the performance and recent improvements of the land surface scheme used operationally in the European Centre for Medium-Range Weather Forecasts (ECMWF) in a Scandinavian boreal forest climate/ecosystem. The previous (the 1999 scheme of P. Viterbo and A. K. Betts) and the new (Tiled ECMWF Surface Scheme for Exchange Processes over Land, TESSEL) surface schemes were validated by single-column runs against data from NOPEX (Northern Hemisphere Climate-Processes Land-Surface Experiment). Driving and validation datasets were prepared for a 3-yr period (1994-96). The new surface scheme, with separate surface energy balances for subgrid fractions (tiling), improved predictions of seasonal as well as diurnal variation in surface energy fluxes in comparison with the old scheme. Simulated wintertime evaporation improved significantly as a consequence of the introduced additional aerodynamic resistance for evaporation from snow lying under high vegetation. Simulated springtime evaporation also improved because the limitation of transpiration in frozen soils was now accounted for. However, downward sensible heat flux was still underestimated during winter, especially at nighttime, whereas soil temperatures were underestimated in winter and overestimated in summer. The new scheme also underestimated evaporation during dry periods in summer, whereas soil moisture was overestimated. Sensitivity tests showed that further improvements of simulated surface heat fluxes and soil temperatures could be obtained by calibration of parameters governing the coupling between the surface and the atmosphere and the ground heat flux, and parameters governing the water uptake by the vegetation. Model performance also improved when the seasonal variation in vegetation properties was included.

Soil-crop simulation models are widely used to assess the impacts of soil management and climate change on soil water balance, solute transport and crop production. In this context, it is important that hydrological processes in the soil-crop system are accurately modelled. We suggest here that empirical treatments of soil water flow, water uptake by plant mots and transpiration limit the applicability of crop models and increase prediction errors. We further argue that this empiricism is to a large extent unnecessary, as parsimonious physics-based descriptions of these water flow processes in the soil-crop system are now available. Recent reviews and opinion articles, whilst strongly advocating the need for improvements to crop models, fail to mention the significant role played by accurate treatments of soil hydrology. It seems to us that empirical models of soil water flow have become the elephant in the room.