SALTMED model and its application on field crops, different water and field management and under current and future climate change

Models can be very useful tools in agriculture water management. They could help in irrigation scheduling and crop water requirement estimation and to predict yields and soil salinization. SALTMED model is a generic model that can be used for a variety of irrigation systems, soil types, crops and trees, water application strategies and different water qualities. The early version was successfully tested against field experimental data. The current version, SALTMED 2015, includes additional sub-models, crop growth according to heat units/degree days, crop rotations, nitrogen dynamics, soil temperature, dry matter and yield, subsurface irrigation, deficit irrigation including the Partial Root Drying, PRD, drainage flow to tile or open drains systems, presence of shallow groundwater, evapotranspiration (ET) using Penman–Monteith equation, with different options to obtain the canopy conductance. The current version allows up to 20 fields or treatments to run simultaneously. The model was applied on field experiments in Agadir in the Souss-Massa river basin. These experiments included several crops, such as quinoa, sweetcorn and chickpea; different water qualities, such as saline water, treated waste water and fresh water; different irrigation strategies, such as deficit irrigation (applying less water than the total crop water requirement) and applied water stress during certain growth stages. The model was successful in predicting the soil moisture, yield and dry matter for all the crops under different water qualities and all the water application strategies. The results showed that quinoa is the most drought and salt tolerant cereal crop. The results also showed the possibility of significant fresh water saving when using treated waste water and applying moderate water deficit/stress especially during the non-sensitive growth stages. The SALTMED model, for three growing seasons, supplied “baseline data” for sweetcorn. The SALTMED model was run in forecasting mode to obtain future projections of crop ET and crop productivity under changing climate. The results suggested that, with increasing temperature, the crop ET is expected to increase by 15% while crop water requirement is expected to decrease by 13%, due to the shortening growth season of corn. The results also show that the crop harvest is expected to be 20 days earlier. Crop productivity in terms of dry matter and yield could exhibit a reduction of 2.5% towards the end of the twenty-first century. This study was applied on corn but it is likely that a similar trend could be found for other crops grown in the Souss region. These results indicate that climate change could have a negative impact on water availability in this water poor region and subsequently may pose a serious threat to the region’s food security

Changing regional weather-crop yield relationships across Europe between 1901 and 2012

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 massive 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 barley show a more pronounced response to adverse weather conditions compared to crops from the early 20th 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.201

Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

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.status: publishe

SOIL DATA AGGREGATION EFFECTS IN REGIONAL YIELD SIMULATIONS

Large-scale yield simulations often use data of coarse spatial resolution as input for process-based models. However, using aggregated data as input for process-based models entails the risks of introducing errors due to aggregation (AE). Such AE depend on the aggregation method, on the type of aggregated data as well as on its spatial heterogeneity. However, previous studies indicated that AE in Central Europe might be largely driven by aggregating soil data. AE in yield could therefore be assessed prior to simulation for those regions with a distinct relationship between spatial yield variability and soil heterogeneity. The present study investigates the AE for soil data and its contribution to the total AE for soil and climate data for a range of different crop models. Soil data is aggregated by area majority in order to maintain physical consistency among soil variables. AE are assessed for climate and soil data in North Rhine-Westphalia, German, upscaling from 1 to 100 km resolution. We present a model comparison on AE for a range of environmental conditions differing in climate and soil for two crops grown under water-limited conditions. Winter wheat and silage maize yields of 1982-2011 were simulated with crop models after calibration to average regional sowing date, harvest date and crop yield. Results point to the importance of estimating AE for soil data. Ways to generalize from these results to other regions are discussed

Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops

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Data Aggregation Effects in Regional Yield Simulations

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