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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F23%3A00575101" target="_blank" >RIV/86652079:_____/23:00575101 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0168192323002873?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0168192323002873?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.agrformet.2023.109596" target="_blank" >10.1016/j.agrformet.2023.109596</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10509 - Meteorology and atmospheric sciences

Projekt

<a href="/cs/project/EF16_019%2F0000797" target="_blank" >EF16_019/0000797: SustES - Adaptační strategie pro udržitelnost ekosystémových služeb a potravinové bezpečnosti v nepříznivých přírodních podmínkách</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Agricultural and Forest Meteorology

ISSN

0168-1923

e-ISSN

1873-2240

Svazek periodika

340

Číslo periodika v rámci svazku

SEP

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

109596

Kód UT WoS článku

001044625800001

EID výsledku v databázi Scopus

2-s2.0-85166640748