Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F18%3A78946" target="_blank" >RIV/60460709:41320/18:78946 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.agrformet.2018.06.009" target="_blank" >http://dx.doi.org/10.1016/j.agrformet.2018.06.009</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.agrformet.2018.06.009" target="_blank" >10.1016/j.agrformet.2018.06.009</a>

Result language
angličtina
Original language name
Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices
Original language description
In the present study, multiple linear regression models were constructed to simulate the yield of winter wheat, rapeseed, maize and sunflower in Hungary for the 2000-2016 time period. We used meteorological data and soil water content from meteorological reanalysis as predictors of the models in monthly resolution. We included annual fertilizer amount in the analysis to remove trend from the census data. We also used remote sensing based vegetation index to extend the approach for early crop yield forecast purposes and to study the added value of proxy data on the predictive power of the statistical models. Using a stepwise linear regression-like method the most appropriate models were selected based on the statistical evaluation of the model fitting. We provided simple equations with well interpretable coefficients that can estimate crop yield with high accuracy. Cross-validated explained variance were 67% for winter wheat, 76% for rapeseed, 81% for maize and 68,5% for sunflower. The modelling exerc
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
40102 - Forestry

Project
<a href="/en/project/EF16_019%2F0000803" target="_blank" >EF16_019/0000803: Advanced research supporting the forestry and wood-processing sector´s adaptation to global change and the 4th industrial revolution</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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