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Author  |
Schils, R. |
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Title |
Online maps of Yield Gaps of cereals across Europe |
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Report |
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2017 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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10 |
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Xc9.1-D2 |
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The yield gap and water productivity analysis of key cereal crops in Europe is completed and results are available through www.yieldgap.org |
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XC, CropM |
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no |
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MA @ admin @ |
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4961 |
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Author |
Schmid, E. |
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Title |
Integrated land use modelling — a course for doctoral students |
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Report |
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2017 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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10 |
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Pages |
T4.1-4.2-XC4.3-4.4-D |
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TradeM |
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The course on “Integrated land use modelling” took place at BOKU Vienna between 24. – 28. April 2017. It was a five-days course capturing many aspects in quantitative integrated land use modelling using GAMS (see course outline). 10 students have participated the course coming from several countries. Students finishing the course have received 3 ECTS points. The course was offered by BOKU and the Doctoral Certificate Program in Agricultural Economics (https://www.agraroekonomik.de/index.html ). |
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MA @ admin @ |
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5036 |
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Author |
Sharif, B. |
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Title |
Data mining techniques for quantifying and projecting crop yield responses to climate change |
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Book Whole |
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2017 |
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Ph.D. thesis |
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Aarhus University |
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Aarhus |
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PhD |
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CropM |
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MA @ admin @ |
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5165 |
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Sharif, B.; Makowski, D.; Plauborg, F.; Olesen, J.E. |
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Title |
Comparison of regression techniques to predict response of oilseed rape yield to variation in climatic conditions in Denmark |
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Journal Article |
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2017 |
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European Journal of Agronomy |
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Europ. J. Agron. |
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82 |
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11-20 |
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Winter oilseed rape; Statistical models; Yield; Climate; Regression |
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Highlights • Regularization techniques for regression outperformed the classical regression techniques in predicting crop yields. • Different regression techniques with similar prediction accuracy showed different responses of major climatic variables to crop yield. • The regression models showed some responses of crop yield to climatic conditions that is mostly absent in process based crop models. Abstract Statistical regression models represent alternatives to process-based dynamic models for predicting the response of crop yields to variation in climatic conditions. Regression models can be used to quantify the effect of change in temperature and precipitation on yields. However, it is difficult to identify the most relevant input variables that should be included in regression models due to the high number of candidate variables and to their correlations. This paper compares several regression techniques for modeling response of winter oilseed rape yield to a high number of correlated input variables. Several statistical regression methods were fitted to a dataset including 689 observations of winter oilseed rape yield from replicated field experiments conducted in 239 sites in Denmark, covering nearly all regions of the country from 1992 to 2013. Regression methods were compared by cross-validation. The regression methods leading to the most accurate yield predictions were Lasso and Elastic Net, and the least accurate methods were ordinary least squares and stepwise regression. Partial least squares and ridge regression methods gave intermediate results. The estimated relative yield change for a +1°C temperature increase during flowering was estimated to range between 0 and +6 %, depending on choice of regression method. Precipitation was found to have an adverse effect on yield during autumn and winter. It was estimated that an increase in precipitation of +1 mm/day would result in a relative yield change ranging from 0 to −4 %. Soil type was also important for crop yields with lower yields on sandy soils compared to loamy soils. Later sowing was found to result in increased crop yield. The estimated effect of climate on yield was highly sensitive to the chosen regression method. Regression models showing similar performance led in some cases to different conclusions with respect to effect of temperature and precipitation. Hence, it is recommended to apply an ensemble of regression models, in order to account for the sensitivity of the data driven models for projecting crop yield under climate change. |
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1161-0301 |
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CropM, ft_macsur |
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MA @ admin @ |
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4966 |
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Author |
Siebert, S.; Webber, H.; Zhao, G.; Ewert, F.; Siebert, S.; Webber, H.; Zhao, G.; Ewert, F. |
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Title |
Heat stress is overestimated in climate impact studies for irrigated agriculture |
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Journal Article |
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Year |
2017 |
Publication |
Environmental Research Letters |
Abbreviated Journal |
Environ. Res. Lett. |
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Volume |
12 |
Issue |
5 |
Pages |
054023 |
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Keywords |
heat stress; climate change impact assessment; irrigation; canopy temperature; CANOPY TEMPERATURE; WINTER-WHEAT; WATER-STRESS; CROP YIELDS; GROWTH; MAIZE; DROUGHT; UNCERTAINTY; ENVIRONMENT; PHENOLOGY |
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Climate change will increase the number and severity of heat waves, and is expected to negatively affect crop yields. Here we show for wheat and maize across Europe that heat stress is considerably reduced by irrigation due to surface cooling for both current and projected future climate. We demonstrate that crop heat stress impact assessments should be based on canopy temperature because simulations with air temperatures measured at standard weather stations cannot reproduce differences in crop heat stress between irrigated and rainfed conditions. Crop heat stress was overestimated on irrigated land when air temperature was used with errors becoming larger with projected climate change. Corresponding errors in mean crop yield calculated across Europe for baseline climate 1984-2013 of 0.2 Mg yr(-1) (2%) and 0.6 Mg yr(-1) (5%) for irrigated winter wheat and irrigated grain maize, respectively, would increase to up to 1.5 Mg yr (1) (16%) for irrigated winter wheat and 4.1 Mg yr (1) (39%) for irrigated grain maize, depending on the climate change projection/GCM combination considered. We conclude that climate change impact assessments for crop heat stress need to account explicitly for the impact of irrigation. |
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2017-06-22 |
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English |
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1748-9326 |
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CropM, ft_macsur |
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MA @ admin @ |
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5035 |
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