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Daccache, A., Ciurana, J. S., Diaz, J. A. R., & Knox, J. W. (2014). Water and energy footprint of irrigated agriculture in the Mediterranean region. Environ. Res. Lett., 9(12), 124014.
Abstract: Irrigated agriculture constitutes the largest consumer of freshwater in the Mediterranean region and provides a major source of income and employment for rural livelihoods. However, increasing droughts and water scarcity have highlighted concerns regarding the environmental sustainability of agriculture in the region. An integrated assessment combining a gridded water balance model with a geodatabase and GIS has been developed and used to assess the water demand and energy footprint of irrigated production in the region. Modelled outputs were linked with crop yield and water resources data to estimate water (m(3) kg(-1)) and energy (CO2 kg(-1)) productivity and identify vulnerable areas or `hotspots’. For a selected key crops in the region, irrigation accounts for 61 km(3) yr(-1) of water abstraction and 1.78 Gt CO2 emissions yr-1, with most emissions from sunflower (73 kg CO2/t) and cotton (60 kg CO2/t) production. Wheat is a major strategic crop in the region and was estimated to have a water productivity of 1000 tMm(-3) and emissions of 31 kg CO2/t. Irrigation modernization would save around 8 km(3) of water but would correspondingly increase CO2 emissions by around +135\%. Shifting from rain-fed to irrigated production would increase irrigation demand to 166 km(3) yr(-1) (+137\%) whilst CO2 emissions would rise by +270\%. The study has major policy implications for understanding the water-energy-food nexus in the region and the trade-offs between strategies to save water, reduce CO2 emissions and/or intensify food production.
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Zhao, G., Siebert, S., Enders, A., Rezaei, E. E., Yan, C., & Ewert, F. (2015). Demand for multi-scale weather data for regional crop modeling. Agricultural and Forest Meteorology, 200, 156–171.
Abstract: A spatial resolution needs to be determined prior to using models to simulate crop yields at a regional scale, but a dilemma exists in compromising between different demands. A fine spatial resolution demands extensive computation load for input data assembly, model runs, and output analysis. A coarse spatial resolution could result in loss of spatial detail in variability. This paper studied the impact of spatial resolution, data aggregation and spatial heterogeneity of weather data on simulations of crop yields, thus providing guidelines for choosing a proper spatial resolution for simulations of crop yields at regional scale. Using a process-based crop model SIMPLACE (LINTUL2) and daily weather data at 1 km resolution we simulated a continuous rainfed winter wheat cropping system at the national scale of Germany. Then we aggregated the weather data to four resolutions from 10 to 100 km, repeated the simulation, compared them with the 1 km results, and correlated the difference with the intra-pixel heterogeneity quantified by an ensemble of four semivariogram models. Aggregation of weather data had small effects over regions with a flat terrain located in northern Germany, but large effects over southern regions with a complex topography. The spatial distribution of yield bias at different spatial resolutions was consistent with the intra-pixel spatial heterogeneity of the terrain and a log-log linear relationship between them was established. By using this relationship we demonstrated the way to optimize the model resolution to minimize both the number of simulation runs and the expected loss of spatial detail in variability due to aggregation effects. We concluded that a high spatial resolution is desired for regions with high spatial environmental heterogeneity, and vice versa. This calls for the development of multi-scale approaches in regional and global crop modeling. The obtained results require substantiation for other production situations, crops, output variables and for different crop models. (C) 2014 Elsevier B.V. All rights reserved.
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Eyshi Rezaei, E., Siebert, S., & Ewert, F. (2015). Impact of data resolution on heat and drought stress simulated for winter wheat in Germany. European Journal of Agronomy, 65, 69–82.
Abstract: Heat and drought stress can reduce crop yields considerably which is increasingly assessed with crop models for larger areas. Applying these models originally developed for the field scale at large spatial extent typically implies the use of input data with coarse resolution. Little is known about the effect of data resolution on the simulated impact of extreme events like heat and drought on crops. Hence, in this study the effect of input and output data aggregation on simulated heat and drought stress and their impact on yield of winter wheat is systematically analyzed. The crop model SIMPLACE was applied for the period 1980-2011 across Germany at a resolution of 1 km x 1 km. Weather and soil input data and model output data were then aggregated to 10 km x 10 km, 25 km x 25 km, 50 km x 50 km and 100 km x 100 km resolution to analyze the aggregation effect on heat and drought stress and crop yield. We found that aggregation of model input and output data barely influenced the mean and median of heat and drought stress reduction factors and crop yields simulated across Germany. However, data aggregation resulted in less spatial variability of model results and a reduced severity of simulated stress events, particularly for regions with high heterogeneity in weather and soil conditions. Comparisons of simulations at coarse resolution with those at high resolution showed distinct patterns of positive and negative deviations which compensated each other so that aggregation effects for large regions were small for mean or median yields. Therefore, modelling at a resolution of 100 km x 100 km was sufficient to determine mean wheat yield as affected by heat and drought stress for Germany. Further research is required to clarify whether the results can be generalized across crop models differing in structure and detail. Attention should also be given to better understand the effect of data resolution on interactions between heat and drought impacts. (C) 2015 Elsevier B.V. All rights reserved.
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Ewert, F., Boote, K. J., Rötter, R. P., Thorburn, P., & Nendel, C. (Eds.). (2016). Crop modelling for agriculture and food security under global change. Abstracts. International Crop Modelling Symposium iCROPM2016, 15-17 March 2016, Berlin, Germany. Berlin.
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Niemi, J. (2016). Framework of stochastic gross margin volatility modeling of crop rotation with farm management practices (Vol. 9 C6 -).
Abstract: DP models with risk aversion through meanvariancespecification is already implemented inLuke and applied in North Savo regionHOWEVER climate change, e.g. changes in mean andvariance of crop yiels, still not yet taken into account– Recently, such crop modelling results have becomeavailble for wheat as well, not only for barley– Still CC impact available for 2 cereals crops only, whilemost farms cultivate more than 2 crops Some early conclusions• The suggested approach is consistent in terms of DPprinciples and mean-variance approach and can provideconsistent results for farm scale risk analysis• It is however hard to utilise the approach except assuming afarm with only few crops (those with crop modelling / otherresults of climate change effects on mean and (co-variance)© Natural Resources Institute Finland• Assuming no change in price (co)variability is a majorsimplification results show farm level (or local) effects ofchanges in mean yields and yield (co)variability only
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