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Van der Linden, A. |
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Exploring grass-based beef production under climate change by integration of grass and cattle growth models |
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2016 |
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FACCE MACSUR Reports |
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8 C6 - |
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Sp8-18 |
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MA @ admin @ |
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4852 |
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van der Linden, A.; van de Ven, G.W.J.; Oosting, S.J.; van Ittersum, M.K.; de Boer, I.J.M. |
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Title |
Exploring grass-based beef production under climate change by integration of grass and cattle growth models |
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Journal Article |
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Year |
2016 |
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Advances in Animal Biosciences |
Abbreviated Journal |
Advances in Animal Biosciences |
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7 |
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03 |
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224-226 |
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2040-4700 |
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LiveM, ft_macsur |
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MA @ admin @ |
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4877 |
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Conradt, T.; Gornott, C.; Wechsung, F. |
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Title |
Extending and improving regionalized winter wheat and silage maize yield regression models for Germany: Enhancing the predictive skill by panel definition through cluster analysis |
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Journal Article |
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2016 |
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Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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216 |
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68-81 |
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cluster analysis; crop yield estimation; germany; multivariate regression; silage maize; winter wheat; climate-change; canadian prairies; crop yield; temperature; responses; environments; variability; cultivar; china |
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Regional agricultural yield assessments allowing for weather effect quantifications are a valuable basis for deriving scenarios of climate change effects and developing adaptation strategies. Assessing weather effects by statistical methods is a classical approach, but for obtaining robust results many details deserve attention and require individual decisions as is demonstrated in this paper. We evaluated regression models for annual yield changes of winter wheat and silage maize in more than 300 German counties and revised them to increase their predictive power. A major effort of this study was, however, aggregating separately estimated time series models (STSM) into panel data models (PDM) based on cluster analyses. The cluster analyses were based on the per-county estimates of STSM parameters. The original STSM formulations (adopted from a parallel study) contained also the non-meteorological input variables acreage and fertilizer price. The models were revised to use only weather variables as estimation basis. These consisted of time aggregates of radiation, precipitation, temperature, and potential evapotranspiration. Altering the input variables generally increased the predictive power of the models as did their clustering into PDM. For each crop, five alternative clusterings were produced by three different methods, and similarities between their spatial structures seem to confirm the existence of objective clusters about common model parameters. Observed smooth transitions of STSM parameter values in space suggest, however, spatial autocorrelation effects that could also be modeled explicitly. Both clustering and autocorrelation approaches can effectively reduce the noise in parameter estimation through targeted aggregation of input data. (C) 2015 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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4709 |
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Author |
Eory, V.; Hutchings, N. |
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Title |
Farm management and sustainability indicators: What and how to include in farm scale models |
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2016 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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8 |
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Sp8-7 |
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LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change |
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MA @ admin @ |
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4851 |
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Deppermann et al. |
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Title |
Food and nutrition security in Europe – a quantification of multi-stakeholder scenarios |
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2016 |
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FACCE MACSUR Reports |
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8 |
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SP8-5 |
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LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change |
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MA @ admin @ |
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4850 |
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