Estimating Drought-Induced Crop Yield Losses at the Cadastral Area Level in the Czech Republic

0574339 - ÚVGZ 2024 RIV CH eng J - Článek v odborném periodiku
Meitner, Jan - Balek, Jan - Bláhová, Monika - Semerádová, Daniela - Hlavinka, Petr - Lukas, V. - Jurečka, František - Žalud, Zdeněk - Klem, Karel - Anderson, M. C. - Wouter, D. - Fischer, Milan - Trnka, Miroslav
Estimating Drought-Induced Crop Yield Losses at the Cadastral Area Level in the Czech Republic.
Agronomy. Roč. 13, č. 7 (2023), č. článku 1669. E-ISSN 2073-4395
Grant CEP: GA MŠMT(CZ) EF16_019/0000797
Výzkumná infrastruktura: CzeCOS IV - 90248
Institucionální podpora: RVO:86652079
Klíčová slova: crop yield loss * drought * remote sensing * artificial neural network
Obor OECD: Agriculture
Impakt faktor: 3.7, rok: 2022
Způsob publikování: Open access
https://www.mdpi.com/2073-4395/13/7/1669

In the Czech Republic, soil moisture content during the growing season has been decreasing over the past six decades, and drought events have become significantly more frequent. In 2003, 2015, 2018 and 2019, drought affected almost the entire country, with droughts in 2000, 2004, 2007, 2012, 2014 and 2017 having smaller extents but still severe intensities in some regions. The current methods of visiting cadastral areas (approximately 13,000) to allocate compensation funds for the crop yield losses caused by drought or aggregating the losses to district areas (approximately 1000 km2
) based on proxy data are both inappropriate. The former due to the required time and resources, the later due to low resolution, which leads to many falsely negative and falsely positive results. Therefore, the study presents a new method to combine ground survey, remotely sensed and model data for determining crop yield losses. The study shows that it is possible to estimate them at the cadastral area level in the Czech Republic and attribute those losses to drought. This can be done with remotely sensed vegetation, water stress and soil moisture conditions with modeled soil moisture anomalies coupled with near-real-time feedback from reporters and with crop status surveys. The newly developed approach allowed the achievement of a proportion of falsely positive errors of less than 10% (e.g., oat 2%, 8%, spring barley 4%, 3%, sugar beets 2%, 21% and winter wheat 2%, 6% in years 2017, resp. 2018) and allowed for cutting the loss assessment time from eight months in 2017 to eight weeks in 2018.
Trvalý link: https://hdl.handle.net/11104/0344681