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Assessing the information in crop model and meteorological indicators to forecast crop yield over Europe

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  • Lecerf, Rémi
  • Ceglar, Andrej
  • López-Lozano, Raúl
  • Van Der Velde, Marijn
  • Baruth, Bettina

Abstract

The MARS-Crop Yield Forecasting System (M-CYFS) is used since 1993 to forecast the yields of all major crops in the European Union (EU) based on gridded runs of the WOFOST crop model. Using 28 years of observation, from 1988 to 2015, we quantified the variability in crop yield reported by all 28 EU Member States (MS) that can be explained by each individual WOFOST crop model based predictors and a few simple meteorological variables. A linear regression is used as a screening tool to quantify the relationship between each predictor and the yield residuals from the trend throughout the crop cycle for 168 country/crop combinations, assuming the yield residuals from the trend depend on the inter-annual climate variability. The results are plotted and analyzed at different level: every 10 days for each country crop/combination and each predictor; synthetized every 10 days for each country/crop combination keeping the predictor showing the best relationship with the yield residuals; finally, the best predictor found for each MS during the entire growing season is used to evaluate the ability of the model to estimate yield variability of each crop at European scale.

Suggested Citation

  • Lecerf, Rémi & Ceglar, Andrej & López-Lozano, Raúl & Van Der Velde, Marijn & Baruth, Bettina, 2019. "Assessing the information in crop model and meteorological indicators to forecast crop yield over Europe," Agricultural Systems, Elsevier, vol. 168(C), pages 191-202.
    • Handle: RePEc:eee:agisys:v:168:y:2019:i:c:p:191-202
    DOI: 10.1016/j.agsy.2018.03.002
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    2. Gaona, Jaime & Benito-Verdugo, Pilar & Martínez-Fernández, José & González-Zamora, Ángel & Almendra-Martín, Laura & Herrero-Jiménez, Carlos Miguel, 2023. "Predictive value of soil moisture and concurrent variables in the multivariate modelling of cereal yields in water-limited environments," Agricultural Water Management, Elsevier, vol. 282(C).
    3. Potopová, Vera & Trnka, Miroslav & Hamouz, Pavel & Soukup, Josef & Castraveț, Tudor, 2020. "Statistical modelling of drought-related yield losses using soil moisture-vegetation remote sensing and multiscalar indices in the south-eastern Europe," Agricultural Water Management, Elsevier, vol. 236(C).
    4. Wu, Bingfang & Ma, Zonghan & Boken, Vijendra K. & Zeng, Hongwei & Shang, Jiali & Igor, Savin & Wang, Jinxia & Yan, Nana, 2022. "Regional differences in the performance of drought mitigation measures in 12 major wheat-growing regions of the world," Agricultural Water Management, Elsevier, vol. 273(C).
    5. Yan, Ling & Jin, Jiming & Wu, Pute, 2020. "Impact of parameter uncertainty and water stress parameterization on wheat growth simulations using CERES-Wheat with GLUE," Agricultural Systems, Elsevier, vol. 181(C).

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