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Author  |
Zimmermann, A.; Webber, H.; Zhao, G.; Ewert, F.; Kros, J.; Wolf, J.; Britz, W.; de Vries, W. |
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Title |
Climate change impacts on crop yields, land use and environment in response to crop sowing dates and thermal time requirements |
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Journal Article |
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Year |
2017 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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Volume |
157 |
Issue |
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Pages |
81-92 |
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Keywords |
Integrated assessment; Crop management; Climate change; Europe; INTEGRATED ASSESSMENT; EUROPEAN AGRICULTURE; FOOD SECURITY; HEAT-STRESS; ADAPTATION; SYSTEMS; TEMPERATURE; SCENARIOS; WHEAT; PRODUCTIVITY; Vries W., 2011, ENVIRONMENTAL POLLUTION, V159, P3254 |
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Abstract |
Impacts of climate change on European agricultural production, land use and the environment depend on its impact on crop yields. However, many impact studies assume that crop management remains unchanged in future scenarios, while farmers may adapt their sowing dates and cultivar thermal time requirements to minimize yield losses or realize yield gains. The main objective of this study was to investigate the sensitivity of climate change impacts on European crop yields, land use, production and environmental variables to adaptations in crops sowing dates and varieties’ thermal time requirements. A crop, economic and environmental model were coupled in an integrated assessment modelling approach for six important crops, for 27 countries of the European Union (EU27) to assess results of three SRES climate change scenarios to 2050. Crop yields under climate change were simulated considering three different management cases; (i) no change in crop management from baseline conditions (NoAd), (ii) adaptation of sowing date and thermal time requirements to give highest yields to 2050 (Opt) and (iii) a more conservative adaptation of sowing date and thermal time requirements (Act). Averaged across EU27, relative changes in water-limited crop yields due to climate change and increased CO2 varied between -6 and + 21% considering NoAd management, whereas impacts with Opt management varied between + 12 and + 53%, and those under Act management between 2 and + 27%. However, relative yield increases under climate change increased to + 17 and + 51% when technology progress was also considered. Importantly, the sensitivity to crop management assumptions of land use, production and environmental impacts were less pronounced than for crop yields due to the influence of corresponding market, farm resource and land allocation adjustments along the model chain acting via economic optimization of yields. We conclude that assumptions about crop sowing dates and thermal time requirements affect impact variables but to a different extent and generally decreasing for variables affected by economic drivers. |
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2017-11-02 |
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0308-521x |
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CropM, TradeM, ft_macsur |
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MA @ admin @ |
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5178 |
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Author |
Zhen, L.; Deng, X.; Wei, Y.; Jiang, Q.; Lin, Y.; Helming, K.; Wang, C.; König, H.J.; Hu, J. |
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Title |
Future land use and food security scenarios for the Guyuan district of remote western China |
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Journal Article |
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Year |
2014 |
Publication |
iForest |
Abbreviated Journal |
iForest |
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Volume |
7 |
Issue |
6 |
Pages |
372-384 |
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Keywords |
land-use patterns; scenario analysis; dynamics of land systems modeling; food security; guyuan district; north-central china; cultivated land; dynamics; conversion; policy |
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Abstract |
Government policy is a major human factor that causes changes in land use. Decisions on land management and land-use planning, as well as the analysis and quantification of policy consequences, may greatly benefit from the simulation of the dynamics of land-use systems. In the present study, we predicted land-use changes and their potential impacts on food security in the environmentally fragile Guyuan District, Ningxia Hui Autonomous Region (north-central China), under the influence of a program to convert sloping agricultural land to conservation uses. Baseline and conservation policy scenarios (2005 to 2020) were developed based on input from local stakeholders and expert knowledge. For the baseline and conservation policies, we formulated high-, moderate-, and low-growth scenarios, analyzed the driving mechanisms responsible for the land-use dynamics, and then applied a previously developed “dynamics of land systems” model to simulate changes in land uses based on the driving mechanisms. We found that spatially explicit policies can promote the conversion of land to more sustainable uses; however, decreasing the amount of agricultural and urban land and increasing grassland and forest cover will increase the risk of grain shortages, and the effect will be more severe under the conservation and high- growth scenarios than under the baseline and low-growth scenarios. The Guyuan case study suggests that, during the next decade, important trade-offs between environmental conservation and food security will inevitably occur. Future land-use decisions should carefully consider the balance between land resource conservation, agricultural production, and urban expansion. |
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1971-7458 |
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CropM |
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MA @ admin @ |
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4547 |
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Author |
Zhao, G.; Siebert, S.; Enders, A.; Rezaei, E.E.; Yan, C.; Ewert, F. |
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Title |
Demand for multi-scale weather data for regional crop modeling |
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Journal Article |
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Year |
2015 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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200 |
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156-171 |
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Keywords |
multi-scale; spatial heterogeneity; spatial resolution; crop model; climate variability; climate-change scenarios; integrated assessment; large-scale; phenological development; agricultural systems; spatial-resolution; data aggregation; european-union; winter-wheat; input data |
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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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0168-1923 |
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CropM, ft_macsur |
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MA @ admin @ |
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4753 |
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Zhao, G.; Hoffmann, H.; Yeluripati, J.; Xenia, S.; Nendel, C.; Coucheney, E.; Kuhnert, M.; Tao, F.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Kiese, R.; Eckersten, H.; Haas, E.; Cammarano, D.; Kassie, B.; Moriondo, M.; Trombi, G.; Bindi, M.; Biernath, C.; Heinlein, F.; Klein, C.; Priesack, E.; Lewan, E.; Kersebaum, K.-C.; Rötter, R.; Roggero, P.P.; Wallach, D.; Asseng, S.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops |
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Journal Article |
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Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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Volume |
80 |
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Pages |
100-112 |
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Keywords |
Crop model; Stratified random sampling; Simple random sampling; Clustering; Up-scaling; Model comparison; Precision gain; species distribution models; systems simulation; weather data; large-scale; design; soil; optimization; growth; apsim; autocorrelation |
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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. |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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4724 |
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Zhao, G.; Hoffmann, H.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.L.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.G.; Nendel, C.; Kiese, R.; Eckersten, H.; Haas, E.; Vanuytrecht, E.; Wang, E.; Kuhnert, M.; Trombi, G.; Moriondo, M.; Bindi, M.; Lewan, E.; Bach, M.; Kersebaum, K.C.; Rotter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables |
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Journal Article |
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Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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65 |
Issue |
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141-157 |
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Keywords |
crop model; model comparison; spatial resolution; data aggregation; spatial heterogeneity; scaling; climate-change scenarios; sub-saharan africa; winter-wheat; spatial-resolution; yield response; input data; systems simulation; large-scale; soil data; part i |
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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. |
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0936-577x |
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CropM, ft_macsur |
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MA @ admin @ |
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4754 |
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