Crop yield anomaly forecasting in the Pannonian basin using gradient boosting and its performance in years of severe drought

0575101 - ÚVGZ 2024 RIV NL eng J - Článek v odborném periodiku
Bueechi, E. - Fischer, Milan - Crocetti, L. - Trnka, Miroslav - Grlj, A. - Zappa, L. - Dorigo, W.
Crop yield anomaly forecasting in the Pannonian basin using gradient boosting and its performance in years of severe drought.
Agricultural and Forest Meteorology. Roč. 340, SEP (2023), č. článku 109596. ISSN 0168-1923. E-ISSN 1873-2240
Grant CEP: GA MŠMT(CZ) EF16_019/0000797
Výzkumná infrastruktura: CzeCOS IV - 90248
Institucionální podpora: RVO:86652079
Klíčová slova: Crop yield forecast * Remote sensing * Machine learning * XGBoost * Drought
Obor OECD: Meteorology and atmospheric sciences
Impakt faktor: 6.2, rok: 2022
Způsob publikování: Open access
https://www.sciencedirect.com/science/article/pii/S0168192323002873?via%3Dihub

The increasing frequency and intensity of severe droughts over recent decades have led to substantial crop yield losses in the Pannonian Basin in southeastern Europe. Their socioeconomic consequences can be minimized by accurate crop yield forecasts, but such forecasts often underestimate the impact of severe droughts on crop yields. We developed a gradient-boosting-based crop yield anomaly forecasting system for the Pannonian Basin and examined its performance, with a focus on drought years. Winter wheat and maize yield anomalies are forecasted for 42 regions in the Pannonian Basin using predictor datasets from Earth observation and reanalysis describing vegetation state, weather, and soil moisture conditions. Our results show that crop yield anomaly estimates in the two months preceding harvest have better performance (maize errors 14-17%, wheat 13-14%) than earlier in the year (maize errors 21%, wheat 17%). The forecast models can satisfactorily capture the interannual yield anomalies, but spatial yield variability is only partially reproduced. In years of severe drought, the wheat model performs better than under average conditions with errors below 12%. The errors of the maize forecasts in drought years are larger than average forecast skill: 31% two months ahead and 20% one month ahead. However, for both crops the yield losses remain underestimated by the forecasts in severe drought years. The feature importance analysis shows that during the last two months before harvest, wheat yield anomalies are controlled by temperature and evaporation and maize by the combined effects of temperature and water availability as expressed by several drought indices. In severe drought years, during the two months before harvest the seasonal temperature forecast becomes the most important predictor for the wheat forecasts and soil moisture for the maize model. Overall, this study provides indepth insights into the impact of droughts on crop yield forecasts in the Pannonian Basin.
Trvalý link: https://hdl.handle.net/11104/0344960