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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.

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 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°20′–60°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 mo...

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 N 2 O 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.

Accurate estimation of winter wheat frost kill in cold-temperate agricultural regions is limited by lack of data on soil temperature at wheat crown depth, which determines winter survival. We compared the ability of four models of differing complexity to predict observed soil temperature at 2 cm depth during two winter seasons (2013-14 and 2014-15) at Ultuna, Sweden, and at 1 cm depth at Ilseng and As, Norway. Predicted and observed soil temperature at 2 cm depth was then used in FROSTOL model simulations of the frost tolerance of winter wheat at Ultuna. Compared with the observed soil temperature at 2 cm depth, soil temperature was better predicted by detailed models than simpler models for both seasons at Ultuna. The LT50 (temperature at which 50 % of plants die) predictions from FROSTOL model simulations using input from the most detailed soil temperature model agreed better with LT50 FROSTOL outputs from observed soil temperature than what LT50 FROSTOL predictions using temperature from simpler models did. These results highlight the need for simpler temperature prediction tools to be further improved when used to evaluate winter wheat frost kill.

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.

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°C, 8°C, and 18°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 bioma...

peer-reviewed 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. We received financial contributions from the strategic investment funds (IPOP) of Wageningen University & Research, Bill & Melinda Gates Foundation, MACSUR under EU FACCE-JPI which was funded through several national contributions, and TempAg (http://tempag.net/).

Based on soil temperature, snow depth and the grown cultivar's maximum attainable level of frost tolerance (LT50c), the FROSTOL model simulates development of frost tolerance (LT50) and winter damage, thereby enabling risk calculations for winter wheat survival. To explore the accuracy of this model, four winter wheat cultivars were sown in a field experiment in Uppsala, Sweden in 2013 and 2014. The LT50 was determined by tests of frost tolerance in November, and the cultivars’ LT50c was estimated. Further, recorded winter survival from 20 winter wheat field variety trials in Sweden and Norway was collected from two winter seasons with substantial winter damages. FROSTOL simulations were run for selected cultivars at each location. According to percentage of winter damage, the cultivar survival was classified as “survived,” “intermediate” or “killed.” Mean correspondence between recorded and simulated class of winter survival was 75% and 37% for the locations in Sweden and Norway, respectively. Stress factors that were not accounted for in FROSTOL might explain the poorer accuracy at the Norwegian locations. The accuracy was poorest for cultivars with intermediate LT50c levels. When low temperature was the main cause of damage, as at the Swedish locations, the model accuracy was satisfying.

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 cl...

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 (R2) between 0.84 and 0.88. Regressions of the same indices against DO...

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.

A crop model and environmental data were used to simulate genotype-environment interactions for commercial forage maize cultivars. Genotype parameters defined by the MAISPROQ model were calibrated to observed aboveground dry matter (DM) yield and quality (concentrations of DM and starch) data from Swedish field experiments 2009–2011 on four forage maize cultivars with different maturation rates (Avenir, Isberi, Jasmic and Burli). The model calibration predictability (coefficient of determination, R2) ranged from 0.18 to 0.45 for yield and 0.40 to 0.86 for quality. The corresponding values for validation were in a similar range for the growth model but less for the quality model (0.36–0.38 and 0.25–0.54, respectively). Thereafter the model was used to assess the cultivar performance for different locations and future climate conditions. The simulated DM yield averaged for 2003–2009 varied between 5% and 25% among nine locations in Sweden due to differences in weather conditions, depending on cultivar. The ...

Summary Weight loss in overwintering below-ground parts of perennial weeds has been attributed to respiration, but neither its temperature dependence nor its relevance for biomass dynamics under changing climate conditions have been investigated. In two experiments, we quantified weight loss of the perennial weed Sonchus arvensis, by measuring weight changes over time of sprouting roots in dark rooms at temperatures of 4, 8 and 18°C. Dry weight loss rates were 0.47, 0.64 and 1.47% day−1 at 4, 8 and 18°C, respectively, giving a half-life time of 149, 110 and 47 days, respectively. A factor by which weight loss rates increase for every 10° rise in temperature (Q10) was equal to about 2.3. Cumulative weight loss may comprise >40% of the below-ground biomass during overwintering periods. Applying weight loss rates and Q10 to elevated soil temperature projections showed that losses during winter seasons in central Sweden will remain basically constant, the effect of increased weight loss at higher temperatures being balanced by shorter winters. This implies that need for control of S. arvensis in a changing climate will persist, but that shorter winter seasons will provide a longer time window for control of S. arvensis prior to sowing crops.

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"

Europe is, after Asia, the second largest producer of wheat in the world, and provides the largest share of barley. Wheat (and to a similar extent, barley) production in Europe increased by more than 6-fold during the 20th century. During the first half of the 20th century, this was driven by expanding the harvested area. This was followed, from the mid-20th century, by a mas- sive increase in productivity that in many regions has stalled since 2000. However, it remains unclear what role climatic factors have played in these changes. Understanding the net impact of climatic trends over the past century would also aid in our understanding of the potential impact of future climate changes and in assessments of the potential for adaptation across Europe. In this study, we compiled information from several sources on winter wheat and spring barley yields and climatological data from 12 countries/regions covering the period from 1901−2012. The studied area includes the majority of climatic regions in which wheat and barley are grown (from central Italy to Finland). We hypothesized that changes in climatic conditions have led to measurable shifts in climate−yield relationships over the past 112 yr, and that presently grown wheat and bar- ley show a more pronounced response to adverse weather conditions compared to crops from the early 20 th century. The results confirm that climate−yield relationships have changed significantly over the period studied, and that in some regions, different predictors have had a greater effect on yields in recent times (between 1991 and 2012) than in previous decades. It is likely that changes in the climate−yield relationship at the local level might be more pronounced than those across the relatively large regions used in this study, as the latter represents aggregations of yields from various agroclimatic and pedoclimatic conditions that may show opposing trends (1) (PDF) Changing regional weather-crop yield relationships across Europe between 1901 and 2012. Available from: https://www.researchgate.net/publication/307899712_Changing_regional_weather-crop_yield_relationships_across_Europe_between_1901_and_2012 [accessed Nov 10 2018].

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 ...

Perennial sow-thistle (Sonchus arvensis L.) may be a serious weed in organic and conventional farming. To assess the effects of radiation acquisition and resource allocation on competitive ability, S. arvensis was grown together with spring barley (Hordeum distichon L.) in six mixtures in a replacement series with initial above-ground biomass proportions of S. arvensis ranging from 2% to 96%. A one-season experiment was made outdoors in boxes in Uppsala, Sweden, at a low level of nitrogen supply (5 g N m−2). The study tested the predictability of shoot biomass of each species based on two principal assumptions: (i) growth model parameters derived from species in monocultures could be applied in mixtures, and (ii) radiation in the mixed stand was partitioned between species proportional to their leaf area. Calibration of two parameters, for scaling of shoot radiation use efficiency and radiation partitioning respectively, were the base for the evaluation. When the coefficients were close to unity,...

To date, projections of European crop yields under climate change have been based almost entirely on the outputs of crop-growth models. While this strategy can provide good estimates of the effects of climatic factors, soil conditions and management on crop yield, these models usually do not capture all of the important aspects related to crop management, or the relevant environmental factors. Moreover, crop-simulation studies often have severe limitations with respect to the number of crops covered or the spatial extent. The present study, based on agroclimatic indices, provides a general picture of agroclimatic conditions in western and central Europe (study area lays between 8.5°W–27°E and 37–63.5°N), which allows for a more general assessment of climate-change impacts. The results obtained from the analysis of data from 86 different sites were clustered according to an environmental stratification of Europe. The analysis was carried for the baseline (1971–2000) and future climate conditions (time horizons of 2030, 2050 and with a global temperature increase of 5 °C) based on outputs of three global circulation models. For many environmental zones, there were clear signs of deteriorating agroclimatic condition in terms of increased drought stress and shortening of the active growing season, which in some regions become increasingly squeezed between a cold winter and a hot summer. For most zones the projections show a marked need for adaptive measures to either increase soil water availability or drought resistance of crops. This study concludes that rainfed agriculture is likely to face more climate-related risks, although the analyzed agroclimatic indicators will probably remain at a level that should permit rainfed production. However, results suggests that there is a risk of increasing number of extremely unfavorable years in many climate zones, which might result in higher interannual yield variability and constitute a challenge for proper crop management.

The impacts of climate change on ecosystem services are complex in the sense that effective prediction requires consideration of a wide range of factors. Useful analysis of climate-change impacts on crops and native plant systems will often require consideration of the wide array of other biota that interact with plants, including plant diseases, animal herbivores, and weeds. We present a framework for analysis of complexity in climate-change effects mediated by plant disease. This framework can support evaluation of the level of model complexity likely to be required for analysing climate-change impacts mediated by disease. Our analysis incorporates consideration of the following set of questions for a particular host, pathogen, host–pathogen combination, or geographic region. 1. Are multiple biological interactions important? 2. Are there environmental thresholds for population responses? 3. Are there indirect effects of global change factors on disease development? 4. Are spatial components of epidemic processes affected by climate? 5. Are there feedback loops for management? 6. Are networks for intervention technologies slower than epidemic networks? 7. Are there effects of plant disease on multiple ecosystem services? 8. Are there feedback loops from plant disease to climate change? Evaluation of these questions will help in gauging system complexity, as illustrated for fusarium head blight and potato late blight. In practice, it may be necessary to expand models to include more components, identify those components that are the most important, and synthesize such models to include the optimal level of complexity for planning and research prioritization.

Annual variability and trends in winter wheat yields were evaluated for relationships to climate in 6 long-term experiments (in total 1915–2005) and regionally in 3 counties (1965–2008) in southern and central Sweden. The annual yield was predicted as function of a climate index and year. Alternative indices were used based on either monthly temperatures or yield predicted by a simple weather-driven crop growth simulation model. The main results were as follows. (1) The yield predictions were better for regions than for the long-term experiments. (2) The time variable accounted for more of the yield trend in regions than in the experiments, and more in southern than in central Sweden. (3) The models based on a simple temperature index were often better yield predictors than the growth model. (4) The relations to winter temperatures became gradually weaker after ca. 1970. (5) The yield relation to simple temperature indices differed between locations, similarly for experimental and regional yields. (6) The strongest influence of climate was estimated by means of the growth model predictions of regional yields in Gotland County. (7) The fraction of regional yield trends (1965–1996; ranging from 57 to 90 kg ha–1 yr–1) that could be attributed to climate was estimated greatest in the Gotland region (27 to 64%). For climate change assessments of winter wheat yield in central Sweden, the growth model considering temperature, radiation and water conditions during spring and summer would also need to include processes of overwintering.

Shoot radiation-use efficiency (RUE) and nitrogen-uptake efficiency (UPE) of monocultures of perennial sow-thistle (Sonchus arvensis L.) and spring barley (Hordeum distichon L.) were quantified to assess the significance of these traits for the relative performance of the two species. RUE and UPE were derived for shoot growth and N uptake by calibrating a mechanistic model to above-ground biomass and N observations in an outdoor box experiment, conducted during two years at two soil nitrogen levels in Central Sweden. The model, which is driven by climate variables, predicts above-ground biomass and nitrogen increment as a function of intercepted radiation, temperature, and nitrogen availability. Observed values of leaf area and root development are used as input. Shoot RUE in S. arvensis was only 56% of the RUE in spring barley (1.35 and 2.40 g dry weight MJ−1, respectively). On the other hand, shoot UPE in S. arvensis at low N supply during early season was seven times higher than in barley (0.0...

The ability to predict grain protein concentration at harvest (CP) in Swedish malting barley (Hordeum distichum) from observations of soil conditions, sowing day, fertilisation rate, remote sensing at early stem elongation and the temperature sum during grain filling, was tested for two cultivars; Astoria (Secobra, France) and Wikingett (Svalof-Weibulls, Sweden) in 16 fertilisation trials in southern Sweden, encompassing 3 years (2001–2003). Fertilisation was applied either as a single dose at sowing, or as both a starting application and an application at early stem elongation. The highest total application rate was 160 kg N ha−1 y−1. The soil was analysed for phosphorus, potassium, magnesium and calcium in the layer 0–0.3 m, and mineral N down to 0.6 m. Canopy reflectance observations at BBCH 32 was used to calculate a vegetation index (TCARI(32)) to reflect the canopy (leaf and straw) chlorophyll concentration. Harvested grain CP correlated only marginally with the observed soil variables. It was, however, for a specific cultivar, possible to make a prediction of grain CP based on day of sowing and TCARI(32) with R adj 2 = 0.78 . Part of the sowing day effect might be due to thermal stress during grain filling, as the risk for high temperatures during this phase was higher when sowing was late. This might also explain why the introduction of accumulated temperature during grain filling, into a model already including sowing day and TCARI(32) as independent variables, did not improve the predictability of grain CP.

The N concentration over the 2-year life cycle of grass swards was modelled assuming plant total N uptake to be the lowest value of the plant N demand and the availability of soil mineral N. The variability in the N demand and N availability parameters was estimated for seven site conditions in Central and Southern Sweden, at three N addition levels (0, 14 and 20 g N m −2 year −1 , respectively) by calibration to measured above ground N concentration. The N demand was a function of plant growth, plant current N concentration and day length. Plant growth and biomass were taken from growth model predictions calibrated to measured aboveground biomass. The N availability was assumed to be a fraction of the soil mineral N within the root zone, which was simulated with soil water and nitrogen models calibrated against previous soil model applications. The plant N maximum concentration ( n Max ) was successfully modelled as a decreasing function of increasing biomass and decreasing day length in combination. The fraction of available soil mineral N taken up per day under N limiting conditions ( c Uptake ) decreased after cutting. The parameters varied between sites, cuts and N treatment levels. The effects of these variations on predicted seasonal N uptake was most pronounced for c Uptake and sites, and least for n Max and cuts. It is concluded that c Uptake needs to be calibrated for new site conditions in order to accurately predict plant N uptake.

The main objective of this field study was to estimate the total plant uptake of soil mineral N in maize (Zea mays L.) and common bean (Phaseolus vulgaris L.) grown in crop rotations under different N content in Nicaragua. Secondary objectives were to estimate the fraction of the measured soil mineral N content taken up in this way, and to determine how the measured N in plant aboveground parts was related to the total mineral N uptake. A large variation in N content was obtained by using data from fertilisation experiments. Plant total N uptake was estimated as the residual N in a mass balance calculation of soil mineral N. Mineral N content in the top 0–0.3 m soil layer in the field cultivations and in tubes isolated from root uptake, and N content in aboveground plant parts were measured every 30 days. Estimated plant total uptake of soil mineral N varied considerably (2.5–14 g N m−2 30 day−1) over periods and N treatments. The range of variation was similar for maize and bean. The fraction of the soil mineral N that was taken up by the plant daily varied more in maize (about 0.03–0.12 day−1) than in bean (about 0.05–0.08 day−1). Our results suggest that monthly changes in N in aboveground plant parts were linearly related to plant total N uptake during the same period. Aboveground plant N constituted between about 55% and 80% of total uptake of soil mineral N in maize depending on period within season, whereas for bean it was more constant and smaller (about 40%).

Growth and carbon dynamics in mixed grass–red clover leys were simulated using a growth model for pure stands based on radiation use, allocation within plant and loss of biomass. The model and its parameter values were taken from previous applications of the model to pure swards of grass and red clover grown in the same experiment at the same sites and years. The field data used comprised above-ground dry matter production and proportion of clover in a mixture of red clover (Trifolium pratense L.), timothy (Phleum pratense L.) and meadow fescue (Festuca pratensis Huds.) in two seasonal growth cycles during two consecutive years, sampled at six sites in Central and Southern Sweden. On average for all fields, the proportion of clover decreased with increased nitrogen application. It increased from the first to the second cut, but decreased from the first to the second year. The model most often underestimated above-ground biomass when using proportion of clover and estimates of radiation use efficiency (e) and root allocation fraction (br) from values in the pure stands (Method 1). This discrepancy suggested that e might be higher for the mixed stand than the pure stands. Values of e and br for the mixed stands were therefore derived by calibration to the observed above-ground biomass (Method 2). The radiation use efficiency (e) of the mixed stand was on average 6% higher than estimates of e from pure stand values and proportion of clover. Overall, the variation in radiation use efficiency of mixed stands due to cutting and year was similar to that of pure stands of grass and clover, whereas the relationship to N treatment was weak and more similar to that of clover. The explanation that variations in biomass production were due to changes in root allocation seems reasonable. No variations in radiation use efficiency or root allocation fraction could be related to the proportion of clover. The variations due to cutting and year overwhelmed any such effect.

The effects of barley yellow dwarf virus (BYDV) infection on oat plants growing in pots in a greenhouse were incorporated into a plant growth model driven by incident solar radiation and air temperature. Changes in radiation use efficiency (RUE) and leaf area determining parameters were calibrated against observed reductions in leaf area, shoot biomass and grain yield. Plants were infected with BYDV-PAV at the two-leaf stage or left uninfected. Two nitrogen fertilization levels were used. BYDV-infected plants had smaller above-ground biomass and a smaller green plant area than uninfected plants. The decreased biomass could be explained by a decrease in leaf area, causing a decreased radiation interception and growth. Relatively higher RUE in the infected plants during a period of the growing season partially compensated for growth reductions due to the smaller leaf area. Increased RUE of the infected plants was related to higher nitrogen concentration per leaf area. The effects of an infection we...

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.

Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize ) and 3 production situations (potential, waterlimited and nitrogen-water- imited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha−1, whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization. ispartof: Climate Research vol:65 pages:53-69 status: published

Three consecutive crops of malting barley grown during 2002–2004 on clay-loam on a Swedish farm (59°74’ N, 17°00’ E) were monitored for canopy reflectance at growth stages GS32 (second node detectable) and GS69 (anthesis complete), and the crops were sampled for above ground dry matter and nitrogen content. GPS-positioned unfertilised plots were established and used for soil sampling. At harvest, plots of 0.25 m2 were cut in both fertilised and unfertilised plots, and 24 m2 areas were also harvested from fertilised barley. The correlations between nine different vegetation indices (VIs) from each growth stage and yield and grain protein were tested. All indices were significantly correlated (at 5% level) with grain yield (GY), and protein when sampled at GS69 but only four when sampled at GS32. Three variables (the best-correlated vegetation index sampled at GS32; an index for accumulated elevated daily maximum temperatures for the grain filling period, and normalised apparent electrical conductivity (ECa) of the soil) were sufficient input in the final regressions. Using these three variables, it was possible to make either one multivariate (PLS) regression model or two linear multiple regression models for grain yield (GY) and grain protein, with correlation coefficients of 0.90 and 0.73 for yield and protein, respectively.

Nitrogen fixation was simulated for a leafless variety (Delta) of pea (Pisum sativum L.) in central Sweden. It is assumed that N2 fixation is basically proportional to root biomass, but limited by high root N or low substrate carbon concentrations. Input data on root carbon and nitrogen were estimated from observations of above-ground biomass and nitrogen. The simulated N2 fixation was compared with estimated values from observations using the 15N labelling technique. Test data were taken from pea monocultures and pea-oat mixtures with varying pea biomass levels during 1999. Simulated within-season accumulated N2 fixation correlated to the estimated N2 fixation with a correlation coefficient (R 2) of 0.74. For seasonal simulations, the predictability was higher (R 2=0.93). Two alternative non-dynamic models, estimating seasonal N2 fixation as proportional to above-ground biomass and above-ground N, respectively, gave lower predictability (R 2=0.83 and 0.80, respectively). The models were also app...

Process-based models usually are complex and require a large number of input data, which restrain their use for prediction of forest growth. To overcome those difficulties, efforts have been made towards their simplification by integrating both empirical and mechanistic elements at the same hierarchical level. In this study, we investigated the reliability of such a model for projecting productivity in willow coppice systems. Willow forest growth was assumed proportional to the global radiation intercepted by the canopy during the vegetation period. The radiation use efficiency concept was combined with mortality functions based on self-thinning to simulate the accumulated biomass. The model was calibrated against annual stem biomass data from a fertilised and irrigated experimental stand situated near Uppsala (central Sweden) and validated against data from several other fertilised stands located in central and southern Sweden, all representing close to optimal nitrogen conditions. The model was able to explain 81% of the observed variations in standing biomass of 1-year-old shoots, almost none of the variations after the second year, and 52% for 3-year-old shoots. Thus, willow productivity of especially shoots older than 1 year was only partially possible to reproduce based on incident radiation, shoot age and shoot mortality. This might be explained by low performance of the radiation interception and mortality estimations, as well as, the assumption of optimum conditions being maintained throughout the period may not have been met.

The effects of a PAV isolate of barley yellow dwarf virus inoculated to oats at four different growth stages were evaluated in a field trial in Sweden. Oats (cv. Stork) were artificially infested with bird cherry-oat aphids (Rhopalosiphum padi) carrying a local Swedish PAV-Sto isolate. Infection, especially in early growth stages, decreased yield of grain biomass and plant height, but increased the number of tillers and panicles per plant, compared to uninfected plants. There was no effect on 1000-kernel weight and grain volume weight. The results emphasize the importance of barley yellow dwarf virus infection, especially at early growth stages in affecting oat yields.

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.

The impact of adverse weather conditions (AWCs) on crop production is random in both time and space and depends on factors such as severity, previous agrometeorological conditions, and plant vulnerability at a specific crop development stage. Any exclusion or improper treatment of any of these factors can cause crop models to produce significant under- or overestimates of yield. The analysis presented in this paper focuses on a range of agrometeorological indices (AMI) related to AWCs that might affect real yield as well as simulated yield. For this purpose, the analysis addressed four indicators of extreme temperatures and three indicators of dry conditions during the growth period of maize and winter wheat in Austria, Croatia, Serbia, Slovakia, and Sweden. It is shown that increases in the number and intensity of AWCs cannot be unambiguously associated with increased deviations in simulated yields. The identified correlations indicate an increase in modeling uncertainty. This finding represents important information for the crop modeling community. Additionally, it opens a window of opportunity for a statistical (“event scenario”) approach based on correlations between agrometeorological indices of AWCs and crop yield data series. This approach can provide scenarios for certain locations, crop types, and AWC patterns and, therefore, improve yield forecasting in the presence of AWCs.

The possible consequences of climate change on carbon and nitrogen budgets of winter wheat were examined by means of model predictions. Biomass, nitrogen, water and heat dynamics were simulated for long-term climatic conditions in central and southern Sweden for a clay soil and a sandy soil. The effects of elevated atmospheric CO2 and changed climate as predicted for 2050 were simulated daily with two linked process orientated models for soil and plant (SOIL/SOILN). The models had previously been calibrated against several variables at the sites under present conditions, and the long-term predictions at present climate were shown to correspond reasonably well with measured soil C and N trends in long-term experiments. The climate and CO2 conditions for the year 2050 were represented by climatic scenarios from a global climate model, and the elevated atmospheric CO2 concentration was assumed to change plant parameter values in accordance with literature data. For the year 2050, winter wheat production was predicted to increase by 10–20% (depending on soil type) compared with the present value. Plant N concentration decreased although N mineralisation increased by 18%. Drainage was predicted to increase which resulted in increased N leaching by 17%, and the decrease in soil C became larger. The predictions were found to be most sensitive to assumptions concerning changes of radiation use efficiency, stomatal conductance, air temperature and precipitation. Hence, the load of N and C to surrounding ecosystems and the atmosphere, on a ground surface basis, was predicted to increase under climate change. However, because the harvest will increase, these negative effects of climate change on a yield basis will be almost zero, except that N leaching from the sandy soil will still increase.

Nitrogen and carbon dynamics of two Norway spruce stands were compared using a dynamic soil-plant simulation model (SOILN). The objectives were to evaluate differences in properties between the two stands and to explore the consequences on C and N dynamics. The young stand (25 years old) was located in south-west Sweden (Skogaby) growing on a loamy sand soil. The old stand (70 years old) was growing on a sandy soil in western Denmark (Klosterhede). Differences in specific rates of processes between the two sites were estimated in terms of parameter values derived by calibration of the model. Differences in daily flow rates and pathways of N and C were examined and budget components were simulated over a six year period. Simulations showed that the different stands had different specific rates for growth, allocation, litter fall and N uptake resulting in higher N uptake in the younger trees whereas growth was more similar. The soil carbon balance was positive and soil C/N ratio increased for the young stand. For the old stand the soil carbon balance was negative and the C/N ratio decreased. The soil mineral N levels were much lower in the younger stand which was explained by a higher plant uptake of organic N. At both sites the average pools and fluxes over the years were fairly well simulated indicating that N leaching predictions on average might be reliable.

The long-term effects of application of 35 kg n ha −1 year −1 on the status, uptake and leaching of nitrogen were evaluated by application of the dynamic C and N circulation model SOILN, on experimental field data from a N addition experiment in a Norway spruce forest in Denmark. The model describes N uptake by two different pathways, either nitrogen is taken up as inorganic N after mineralisation or nitrogen is taken up directly from the organic pool as amino acids with a C/N ratio 4 assumed to imitate mycorrhizal uptake. In the control plot the organic N uptake supplies 29% of the total plant N uptake, which by N addition was reduced to 14% of the N uptake within the 3 years of the N addition experiment. The long-term effects of chronic N addition were predicted by running the model for a 30 year period until year 2025. The total N load in the N-addition plot exceeded the critical load significantly. The additional N input caused the N-leaching to increase significantly, accounting for 34% of the extra input. The remaining 66% of the additional input accumulated in the ecosystem, 22% went to increased biomass N concentrations in the plants and 45% accumulated in the soil causing C/N in the top soil layers to decrease. The continuous high input of mineral N in the N addition plot reduced the organic uptake to account for c. 4% of total N uptake.

The growth and nitrogen uptake of Italian Rye Grass ( Lolium multiflorum Lam.) used as a catch crop were studied in 1988/1989 and 1990/1991 with the aim of determining the extent to which differences between the two years could be explained by variation in climatic conditions. The experimental field was situated about 50 km south of Halmstad close to the Swedish west coast (56°30′N; 13°00′E, 10 m elevation). The SOILN model, which considers the major flows of nitrogen and carbon in both plant and soil, was used to evaluate the importance of three growth factors, i.e., solar radiation temperature and nitrogen status, under autumn and winter conditions. The model was calibrated against measured total plant biomass ( r 2 = 96%) and nitrogen ( r 2 = 95%) together with soil mineral nitrogen in 1988/1989 and validated on an independent dataset in 1990/1991. Measured increases in biomass and nitrogen were much lower in 1990 than in 1988 (79% and 68% lower, respectively). The simulations were able to explain 90% of the observed between-year variation in relative growth rate. The higher maintenance respiration in 1990 was the main factor responsible for the differences observed. In 1990, plant N was well simulated ( r 2 = 89%), whereas estimates of plant biomass was high ( r 2 = 76%).

Growth, nitrogen uptake and nitrogen allocation between roots, stems, leaves and grains were measured and simulated in winter wheat on a clay soil in three treatments including daily irrigation and fertilization. Special emphasis was placed on biomass and nitrogen allocation within the crop and on the availability of soil nitrogen for crop growth. The model used for the simulations of growth (SOILN-CROP), which was driven by a hydrological model, is based on the light interception concept and empirical allometric functions. Growth is the driving force for nitrogen uptake, which is limited by the availability of mineral N in the soil. The model was calibrated for one treatment. Thereafter, the same parameter set was used to simulate the other two treatments. Frequent irrigation in combination with single-dose fertilization increased crop growth and N leaching compared with the non-irrigated but single-dose fertilized control, whereas irrigation together with daily fertilization increased crop growth and N uptake but not N leaching. Simulated soil mineral N levels agreed well with measurements on a 1-year time scale. Assimilate allocation to roots decreased logarithmically with total crop biomass in all treatments. Allocation to leaves decreased linearly with total above-ground crop mass. The crop availability of mineral N differed considerably between treatments. The model parameter defining the proportion of soil mineral N available for plant uptake had a strong influence on model behaviour. This proportion is indicated to depend on soil water content and the mechanisms of this relation need to be considered in future work to improve our predictions of N uptake.

Daily flows of water, heat, carbon and nitrogen from 1976 until 1991 were simulated with the one-dimensional models soil —soiln for a 105-year-old spruce stand on an acid brown forest soil at Solling (central Germany). Nitrogen deposition in the area exceeded 40 kg N ha −1 yr −1 . Comparisons were made with measured soil water tensions, chloride and mineral nitrogen concentrations in soil water, and tree biomass components. Down to 60-cm depth, the simulated soil water tensions agreed well with measured data when a summer surface resistance of 125 s m −1 was used to calculate potential transpiration. Modelling efficiency (an indicator of agreement between simulations and measurements) for pF-values of soil water tensions ranged from 0.55 to 0.87. At 100-cm depth, the modelling efficiency was only 0.15. The mean value of the chloride concentrations were, at the same time, underestimated. The average deep percolation was 38% of the precipitation. A lower summer surface resistance of 80 s m −1 was necessary to increase the agreement between simulated and measured chloride concentrations, which decreased the deep percolation to 31% of the precipitation. On average, simulation resulted in an accumulation of N in trees (21 kg N ha −1 yr −1 but a decrease of N in soil (7 kg N ha −1 yr −1 ). Simulated leaching was high (27 kg N ha −1 yr −1 ) and probably overestimated due to overestimated soil nitrate concentrations below the root zone. To evaluate the carbon and nitrogen model, too few detailed measurements were available on, especially, plant N uptake and amounts of soil mineral N.