Geostatistical interpolation and aggregation of crop growth model outputs
| Autor: | Luc Steinbuch, Gerard B. M. Heuvelink, Lenny G.J. van Bussel, Dick J. Brus |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
010504 meteorology & atmospheric sciences
Soil Science Plant Science Geostatistics Wiskundige en Statistische Methoden - Biometris 01 natural sciences Multivariate interpolation Kriging Linear regression Statistics Yield potential DIR Directiestaf Spatial dependence Mathematical and Statistical Methods - Biometris 0105 earth and related environmental sciences Mathematics Crop yield Yield gap Uncertainty 04 agricultural and veterinary sciences PE&RC Spatial aggregation Bodemgeografie en Landschap Spatial prediction Environmental Systems Analysis Milieusysteemanalyse Soil Geography and Landscape 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Spatial variability Agronomy and Crop Science ISRIC - World Soil Information |
| Zdroj: | European Journal of Agronomy 77 (2016) European Journal of Agronomy, 77, 111-121 |
| ISSN: | 1161-0301 |
| Popis: | Many crop growth models require daily meteorological data. Consequently, model simulations can be obtained only at a limited number of locations, i.e. at weather stations with long-term records of daily data. To estimate the potential crop production at country level, we present in this study a geostatistical approach for spatial interpolation and aggregation of crop growth model outputs. As case study, we interpolated, simulated and aggregated crop growth model outputs of sorghum and millet in West-Africa. We used crop growth model outputs to calibrate a linear regression model using environmental covariates as predictors. The spatial regression residuals were investigated for spatial correlation. The linear regression model and the spatial correlation of residuals together were used to predict theoretical crop yield at all locations using kriging with external drift. A spatial standard deviation comes along with this prediction, indicating the uncertainty of the prediction. In combination with land use data and country borders, we summed the crop yield predictions to determine an area total. With spatial stochastic simulation, we estimated the uncertainty of that total production potential as well as the spatial cumulative distribution function. We compared our results with the prevailing agro-ecological Climate Zones approach used for spatial aggregation. Linear regression could explain up to 70% of the spatial variation of the yield. In three out of four cases the regression residuals showed spatial correlation. The potential crop production per country according to the Climate Zones approach was in all countries and cases except one within the 95% prediction interval as obtained after yield aggregation. We concluded that the geostatistical approach can estimate a country’s crop production, including a quantification of uncertainty. In addition, we stress the importance of the use of geostatistics to create tools for crop modelling scientists to explore relationships between yields and spatial environmental variables and to assist policy makers with tangible results on yield gaps at multiple levels of spatial aggregation. |
| Databáze: | OpenAIRE |
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