Počet záznamů: 1
Crop yield anomaly forecasting in the Pannonian basin using gradient boosting and its performance in years of severe drought
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SYSNO ASEP 0575101 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Crop yield anomaly forecasting in the Pannonian basin using gradient boosting and its performance in years of severe drought Tvůrce(i) Bueechi, E. (CH)
Fischer, Milan (UEK-B) RID, ORCID, SAI
Crocetti, L. (AT)
Trnka, Miroslav (UEK-B) RID, ORCID, SAI
Grlj, A. (SI)
Zappa, L. (AT)
Dorigo, W. (AT)Číslo článku 109596 Zdroj.dok. Agricultural and Forest Meteorology. - : Elsevier - ISSN 0168-1923
Roč. 340, SEP (2023)Poč.str. 16 s. Jazyk dok. eng - angličtina Země vyd. NL - Nizozemsko Klíč. slova Crop yield forecast ; Remote sensing ; Machine learning ; XGBoost ; Drought Vědní obor RIV DG - Vědy o atmosféře, meteorologie Obor OECD Meteorology and atmospheric sciences CEP EF16_019/0000797 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy Výzkumná infrastruktura CzeCOS IV - 90248 - Ústav výzkumu globální změny AV ČR, v. v. i. Způsob publikování Open access Institucionální podpora UEK-B - RVO:86652079 UT WOS 001044625800001 EID SCOPUS 85166640748 DOI 10.1016/j.agrformet.2023.109596 Anotace 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. Pracoviště Ústav výzkumu globální změny Kontakt Nikola Šviková, svikova.n@czechglobe.cz, Tel.: 511 192 268 Rok sběru 2024 Elektronická adresa https://www.sciencedirect.com/science/article/pii/S0168192323002873?via%3Dihub
Počet záznamů: 1