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.

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.

ABSTRACT To what extent might a crop with increased plant N uptake efficiency and/or N demand increase plant biomass and soil carbon storage, decrease N leaching, and reduce the need for N fertilisation? This was assessed for a fertilised sandy loam site in central Sweden cultivated with spring barley for a four year period using a process based crop and soil simulation model (SOILN) calibrated to fit observations of field experiments with non-modified crops. Crop properties were changed in accordance with previous model applications to other crops with higher N uptake and utilisation efficiencies, to resemble potential effects of breeding. For the modified crops a doubling of daily uptake efficiency of soil mineral N and/or increase of radiation use efficiency by 30%, increased plant biomass by 3%–30%, decreased N leaching by 1%–30% and increased soil organic carbon (SOC) content by 1–12 g C m−2 year−1. The larger changes were mainly due to increased uptake efficiency. Fertilisation of the modified spring barley crop could be reduced while still producing the same plant biomass as the non-modified crop. The plant biomass to N leaching ratio of the modified crops increased. The simulated changes in plant biomass and SOC were sensitive to weather conditions suggesting that in situ experiments would need to cover a large range of weather conditions to evaluate the performance of new crop traits under climatic variability. The study suggests a strong need that field experiments are accompanied with model applications, when exploring the potential of the modified crops under variable conditions.

Europe accounts for around 20% of the global cereal production and is a net exporter of ca. 15% of that production. Increasing global demand for cereals justifies questions as to where and by how much Europe's production can be increased to meet future global market demands, and how much additional nitrogen (N) crops would require. The latter is important as environmental concern and legislation are equally important as production aims in Europe. Here, we used a country-by-country, bottom-up approach to establish statistical estimates of actual grain yield, and compare these to modelled estimates of potential yields for either irrigated or rainfed conditions. In this way, we identified the yield gaps and the opportunities for increased cereal production for wheat, barley and maize, which represent 90% of the cereals grown in Europe. The combined mean annual yield gap of wheat, barley, maize was 239 Mt, or 42% of the yield potential. The national yield gaps ranged between 10 and 70%, with small gaps in many north-western European countries, and large gaps in eastern and south-western Europe. Yield gaps for rainfed and irrigated maize were consistently lower than those of wheat and barley. If the yield gaps of maize, wheat and barley would be reduced from 42% to 20% of potential yields, this would increase annual cereal production by 128 Mt (39%). Potential for higher cereal production exists predominantly in Eastern Europe, and half of Europe's potential increase is located in Ukraine, Romania and Poland. Unlocking the identified potential for production growth requires a substantial increase of the crop N uptake of 4.8 Mt. Across Europe, the average N uptake gaps, to achieve 80% of the yield potential, were 87, 77 and 43 kg N ha(-1) for wheat, barley and maize, respectively. Emphasis on increasing the N use efficiency is necessary to minimize the need for additional N inputs. Whether yield gap reduction is desirable and feasible is a matter of balancing Europe's role in global food security, farm economic objectives and environmental targets.

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 km(2)), 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 gC m(-2) d(-1) and 2.7-6.1 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. (C) 2016 Elsevier B.V. All rights reserved.

Deoxynivalenol (DON) in cereals, produced by Fusarium fungi, cause poisoning in humans and animals. Fusarium infections in cereals are favoured by humid conditions. Host species are susceptible mainly during the anthesis stage. Infections are also positively correlated with a regional history of Fusarium infections, frequent cereal production and non-tillage field management practices. Here, previously developed process-based models based on relative air humidity, rain and temperature conditions, Fusarium sporulation, host phenology and mycelium growth in host tissue were adapted and tested on oats. Model outputs were used to calculate risk indices. Statistical multivariate models, where independent variables were constructed from weather data, were also developed. Regressions of the risk indices obtained against DON concentrations in field experiments on oats in Sweden and Norway 2012-14 had coefficient of determination values (R-2) between 0.84 and 0.88. Regressions of the same indices against DON concentrations in oat samples averaged for 11x11km grids in farmers' fields in Sweden 2012-14 resulted in R-2 values between 0.27 and 0.41 for randomly selected grids and between 0.31 and 0.62 for grids with average DON concentration above 1000gkg(-1) grain in the previous year. When data from all three years were evaluated together, a cross-validated statistical partial least squares model resulted in R-2=0.70 and a standard error of cross-validation (SECV)=522gkg(-1) grain for the period 1 April-28 August in the construction of independent variables and R-2=0.54 and SECV=647gkg(-1) grain for 1 April-23 June. Factors that were not accounted for in this study probably explain large parts of the variation in DON among samples and make further model development necessary before these models can be used practically. DON prediction in oats could potentially be improved by combining weather-based risk index outputs with agronomic factors.

The potential of modified spring barley crops with improved nitrogen (N) use efficiency to reduce nitrogen (N) leaching and to increase soil organic carbon (SOC) storage was assessed at the regional scale. This was done using simulation model applications designed for reporting according to the Helcom (Helsinki Commission) and Kyoto protocols. Using model simulations based on modified crops N dynamics and SOC were assessed for three agro-ecological regions (latitudes ranging 55 degrees 20-60 degrees 40 N) in Sweden over a 20-year period. The modified N use properties of spring barley were implemented in the SOILNDB model (simulating soil C, N, water and heat, and plant N dynamics) by changing the parameters for root N uptake efficiency and plant N demand within a range given by previous model applications to different crops. A doubling of the daily N uptake efficiency and increased N demand (by ca 30%) reduced N leaching by 24%-31%, increased plant N content by 9%-12%, depending on region. The effects of the modified crop on SOC was simulated with the ICBM model, resulting in an increased SOC content (0-25 cm depth) by 57-79 kgCha(-1)y(-1). The results suggest that a modified crop might reduce N leaching from spring barley area, in a range similar to the targets of relevant environmental protection directives, a result which held more in the northern than southern regions. The simulated SOC increase on a hectare basis was highest for the central region and least for the most northern region. For the total agricultural area the share of spring barley area was small and more crops would need to be modified to reach the emission reduction targets.

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 1 km and 100 km 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. (C)2017 Elsevier Ltd. All rights reserved.

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

Below-ground (bg) shoot emergence rates of Sonchus arvensis are dependent on temperature and root weight. However, it is unknown to what extent this is due to a root depletion rate that depends on initial root weight, or due to differences in resource allocation to fine root and bg shoot growth. To resolve this, we retrieved data from an experiment in which plants were grown in the dark at constant temperature (4 degrees C, 8 degrees C, and 18 degrees C) and harvested prior to or at shoot emergence. A dynamic mass-balance model, in which biomass of the initial root was allocated to bg shoot and fine root daily growth, and where respiration took place from all tissues, was used. The relative depletion rate of root biomass (RDR; d(-1)) and fraction of the depleted biomass allocated to bg shoots (SFRR) were estimated and calibrated to observed biomass. The RDR increased with initial root weight and temperature and SFFR was highest for light roots and lowest for heaviest roots, whereas the rest was allocated to fine root biomass. The length-to-biomass ratio of bg shoots decreased with initial root weight. Under betweenyear weather variations (2004-2010), the reduction in root biomass during the coldest AprilMay was simulated to be over 12 days delayed compared with the warmest spring. The influence of biomass allocation on bg shoot elongation of heavier roots was thus stimulated by a larger fraction of root biomass being depleted, but counteracted by a smaller fraction of it allocated into bg shoot elongation, compared with lighter roots. The complexity of shoot emergence based on root depletion estimates may be a reason why predictions based on only an accumulated root weight-specific temperature sum, as proposed by a previous study, are expected to be less uncertain than those based on root depletion estimates.

Plant breeding is a powerful tool for improving nitrogen (N) uptake efficiency and thus reducing the environmental impact relating to crop production. This study evaluated the environmental impact of current barley production systems in two Swedish agricultural areas (South and East) compared with scenarios with improved N uptake efficiency at two levels, in which the fraction of mineral N available for daily crop uptake was increased by 50 and 100%. Life cycle assessment (LCA) methodology was used to quantify energy use, global warming potential (GWP) and acidification and eutrophication potentials along the production chain for spring barley with differing N uptake efficiency, but similar N application rate. The functional unit, to which all energy use and emissions were related, was 1 Mg barley grain. Energy use, GWP and acidification proved to be higher for the East production system, mainly due to lower yield, while eutrophication was higher for South. The two impacts most affected by improved N uptake efficiency were eutrophication and GWP, with GWP decreasing due to a combination of higher yield, soil carbon sequestration and lower indirect emissions of N2O due to lower N leaching. Accounting for land savings due to increased yield, reducing the pressure to transform land elsewhere, would further lower the carbon footprint. Potential eutrophication per Mg grain was reduced by 15% in the production system with the highest N uptake efficiency in southern Sweden. Crops with improved N uptake efficiency can thus be an important complementary measure for reducing N losses to water, provided that the N application rate does not increase. However, incentives for farmers to maintain or even lower the N application rate might be required. Using simulation modelling is a promising approach for assessment of expected effects of improved crop varieties when no long-term experimental data are available. However, advanced crop models are required to better reflect the effect of plant breeding on e.g. expected yield. Future model development should involve expertise in plant breeding, plant physiology and dynamic crop and soil modelling. (C) 2016 Elsevier B.V. All rights reserved.

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.

Regional yield simulations with process-based models often rely on input data of coarse spatial resolution (Ewert et al., 2015; Zhao et al., 2015). Using aggregated data as input for process-based models entails the risks of introducing so-called aggregation errors (AE). Such AE depend on the model structure in combination with the aggregation method, the type of aggregated data as well as its spatial heterogeneity. While the regional crop yield bias is usually <5 % on average over all years, it may increase in single years (Hoffmann et al. 2015), depending on the model. Here we present a model intercomparison on AE for a range of environmental conditions with varying combinations of aggregated climate, soil and crop management data for two crops grown under varying production situations. Multi-model ensemble runs were conducted with soil, climate and crop management input data at resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Climate data was spatially averaged. Soil data was aggregated by area majority. Aggregated crop management data was obtained by applying management rules on aggregated climate data. Winter wheat and silage maize yields of 1982-2011 were simulated with 11 models for potential, water-limited and water-nitrogen-limited production after calibration to average regional sowing date, harvest date and crop yield. Regional yields were reproduced by the models on average, regardless of input data type and resolution. However, large AE were observed in dry years as well as due to soil aggregation. AE due to aggregated management data were comparatively lower. Finally, models differed considerably in AE. The results highlight the interactions between model, data and aggregation method with AE, emphasizing the importance of models intercomparison analyses.

We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions.Using 13 processbased 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 (Δ), 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 Δ, 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.

Weather is one of the key factors controlling crop growth and development. To support decision making, it is essential to know how often extreme weather events have affected crop production and the ...

To assess emergence time of shoots from roots of the perennial weed Sonchus arvensis as a function of root weight and soil temperature, we performed an experiment to which linear models were fitted. Root parts of three distinct initial weight classes were grown in pots in the dark at constant temperatures of 4, 8 and 18°C, respectively. During five harvest occasions, prior to or at shoot emergence, below-ground shoot length was measured. Root planting depth (3, 10 and 17 cm) did not influence shoot elongation rate. The below-ground shoot elongation rate for a given initial root-weight class was estimated from the observations to be constant with time, but to increase with temperature and initial root weight. By expressing shoot length for a given day as a linear function of the number of days from planting date, and elongation rate as a linear function of temperature, we calculated (1) the accumulated temperature-sum requirement for emergence, (2) emergence time for variable temperature conditions in a clay soil using soil temperature recordings at 5-cm depth for seven seasons in central Sweden and (3) the emergence time at three elevated temperature levels and initial root-weight classes. The accumulated temperature-sum requirements for below-ground shoots of S. arvensis to reach soil surface are independent of temperature regime for roots of a given initial weight but lower for heavier than lighter roots. The temperature limit for below-ground shoot elongation to occur is about 2.0–2.5°C. Between-year variations in temperature under field conditions cause larger variation to emergence time than initial root-weight differences. An average temperature increase of 3°C would cause an earlier emergence time, in the same range (about 2 weeks) as the difference between the earliest and latest year in the current weather conditions.