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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10509 - Meteorology and atmospheric sciences

Project

<a href="/en/project/EF16_019%2F0000797" target="_blank" >EF16_019/0000797: SustES - Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

Agricultural and Forest Meteorology

ISSN

0168-1923

e-ISSN

1873-2240

Volume of the periodical

340

Issue of the periodical within the volume

SEP

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

16

Pages from-to

109596

UT code for WoS article

001044625800001

EID of the result in the Scopus database

2-s2.0-85166640748