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Author |
van Bussel, L.G.J.; Ewert, F.; Zhao, G.; Hoffmann, H.; Enders, A.; Wallach, D.; Asseng, S.; Baigorria, G.A.; Basso, B.; Biernath, C.; Cammarano, D.; Chryssanthacopoulos, J.; Constantin, J.; Elliott, J.; Glotter, M.; Heinlein, F.; Kersebaum, K.-C.; Klein, C.; Nendel, C.; Priesack, E.; Raynal, H.; Romero, C.C.; Rötter, R.P.; Specka, X.; Tao, F. |
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Title |
Spatial sampling of weather data for regional crop yield simulations |
Type |
Journal Article |
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Year |
2016 |
Publication  |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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Volume |
220 |
Issue |
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Pages |
101-115 |
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Keywords |
Regional crop simulations; Winter wheat; Upscaling; Stratified sampling; Yield estimates; climate-change scenarios; water availability; growth simulation; potential impact; food-production; winter-wheat; model; resolution; systems; soil |
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Abstract |
Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982-2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50,100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated. The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management data for regional simulations of crop yields is still needed. |
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English |
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0168-1923 |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4673 |
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Author |
Rosenzweig, C.; Jones, J.W.; Hatfield, J.L.; Ruane, A.C.; Boote, K.J.; Thorburn, P.; Antle, J.M.; Nelson, G.C.; Porter, C.; Janssen, S.; Asseng, S.; Basso, B.; Ewert, F.; Wallach, D.; Baigorria, G.; Winter, J.M. |
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Title |
The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies |
Type |
Journal Article |
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Year |
2013 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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Volume |
170 |
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Pages |
166-182 |
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0168-1923 |
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Notes |
CropM, ftnotmacsur, IPCC-AR5 |
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Call Number |
MA @ admin @ |
Serial |
4927 |
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Author |
Hoffmann, M.P.; Haakana, M.; Asseng, S.; Höhn, J.G.; Palosuo, T.; Ruiz-Ramos, M.; Fronzek, S.; Ewert, F.; Gaiser, T.; Kassie, B.T.; Paff, K.; Rezaei, E.E.; Rodríguez, A.; Semenov, M.; Srivastava, A.K.; Stratonovitch, P.; Tao, F.; Chen, Y.; Rötter, R.P. |
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Title |
How does inter-annual variability of attainable yield affect the magnitude of yield gaps for wheat and maize? An analysis at ten sites |
Type |
Journal Article |
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Year |
2017 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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Pages |
in press |
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Abstract |
Highlights • The larger simulated attainable yield for a specific crop season, the larger the yield gap. • Average size of the yield gap is not affected by the inter-annual variability of attainable yield. • Technology levels (resource input and accessibility) determine average yield gap. • To reduce yield gaps in rainfed environments, farmers need to improve season-specific crop management. Abstract Provision of food security in the face of increasing global food demand requires narrowing of the gap between actual farmer’s yield and maximum attainable yield. So far, assessments of yield gaps have focused on average yield over 5–10 years, but yield gaps can vary substantially between crop seasons. In this study we hypothesized that climate-induced inter-annual yield variability and associated risk is a major barrier for farmers to invest, i.e. increase inputs to narrow the yield gap. We evaluated the importance of inter-annual attainable yield variability for the magnitude of the yield gap by utilizing data for wheat and maize at ten sites representing some major food production systems and a large range of climate and soil conditions across the world. Yield gaps were derived from the difference of simulated attainable yields and regional recorded farmer yields for 1981 to 2010. The size of the yield gap did not correlate with the amplitude of attainable yield variability at a site, but was rather associated with the level of available resources such as labor, fertilizer and plant protection inputs. For the sites in Africa, recorded yield reached only 20% of the attainable yield, while for European, Asian and North American sites it was 56–84%. Most sites showed that the higher the attainable yield of a specific season the larger was the yield gap. This significant relationship indicated that farmers were not able to take advantage of favorable seasonal weather conditions. To reduce yield gaps in the different environments, reliable seasonal weather forecasts would be required to allow farmers to manage each seasonal potential, i.e. overcoming season-specific yield limitations. |
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0308521x |
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CropM |
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Notes |
CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4985 |
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Challinor, A.J.; Müller, C.; Asseng, S.; Deva, C.; Nicklin, K.J.; Wallach, D.; Vanuytrecht, E.; Whitfield, S.; Ramirez-Villegas, J.; Koehler, A.-K. |
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Title |
Improving the use of crop models for risk assessment and climate change adaptation |
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Journal Article |
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Year |
2017 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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Volume |
159 |
Issue |
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Pages |
296-306 |
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Keywords |
Crop model; Risk assessment; Climate change impacts; Adaptation; Climate models; Uncertainty |
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Highlights
• 14 criteria for use of crop models in assessments of impacts, adaptation and risk • Working with stakeholders to identify timing of risks is key to risk assessments. • Multiple methods needed to critically assess the use of climate model output • Increasing transparency and inter-comparability needed in risk assessments
Abstract
Crop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1. Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk? 2. Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output. 3. Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper. |
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phase 2+ |
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0308521x |
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CropM |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
5175 |
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Author |
Hoffmann, M.P.; Haakana, M.; Asseng, S.; Höhn, J.G.; Palosuo, T.; Ruiz-Ramos, M.; Fronzek, S.; Ewert, F.; Gaiser, T.; Kassie, B.T.; Paff, K.; Rezaei, E.E.; Rodríguez, A.; Semenov, M.; Srivastava, A.K.; Stratonovitch, P.; Tao, F.; Chen, Y.; Rötter, R.P. |
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Title |
How does inter-annual variability of attainable yield affect the magnitude of yield gaps for wheat and maize? An analysis at ten sites |
Type |
Journal Article |
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Year |
2017 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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Volume |
159 |
Issue |
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Pages |
199-208 |
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Keywords |
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Abstract |
Highlights • The larger simulated attainable yield for a specific crop season, the larger the yield gap. • Average size of the yield gap is not affected by the inter-annual variability of attainable yield. • Technology levels (resource input and accessibility) determine average yield gap. • To reduce yield gaps in rainfed environments, farmers need to improve season-specific crop management. Abstract Provision of food security in the face of increasing global food demand requires narrowing of the gap between actual farmer’s yield and maximum attainable yield. So far, assessments of yield gaps have focused on average yield over 5–10 years, but yield gaps can vary substantially between crop seasons. In this study we hypothesized that climate-induced inter-annual yield variability and associated risk is a major barrier for farmers to invest, i.e. increase inputs to narrow the yield gap. We evaluated the importance of inter-annual attainable yield variability for the magnitude of the yield gap by utilizing data for wheat and maize at ten sites representing some major food production systems and a large range of climate and soil conditions across the world. Yield gaps were derived from the difference of simulated attainable yields and regional recorded farmer yields for 1981 to 2010. The size of the yield gap did not correlate with the amplitude of attainable yield variability at a site, but was rather associated with the level of available resources such as labor, fertilizer and plant protection inputs. For the sites in Africa, recorded yield reached only 20% of the attainable yield, while for European, Asian and North American sites it was 56–84%. Most sites showed that the higher the attainable yield of a specific season the larger was the yield gap. This significant relationship indicated that farmers were not able to take advantage of favorable seasonal weather conditions. To reduce yield gaps in the different environments, reliable seasonal weather forecasts would be required to allow farmers to manage each seasonal potential, i.e. overcoming season-specific yield limitations. |
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phase 2+ |
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0308521x |
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Notes |
CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
5185 |
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