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Statistical modelling of drought-related yield losses using soil moisture-vegetation remote sensing and multiscalar indices in the south-eastern Europe

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  • Potopová, Vera
  • Trnka, Miroslav
  • Hamouz, Pavel
  • Soukup, Josef
  • Castraveț, Tudor

Abstract

Meteorological and agricultural information coupled with remote sensing observations has been used to assess the effectiveness of satellite-derived indices in yield estimations. The estimate yield models generated by both the regression (MLR) and Bayesian network (BBN) algorithms and their levels of predictive skill were assessed. The enhanced vegetation index (EVI2), soil water index (SWI), standardized precipitation evaporation index (SPEI) have been considered predictors for three rainfed crops (maize, sunflower and grapevine) grown in 37 districts in the Republic of Moldova (RM). We used the weekly EVI2, which was collected by MODIS instruments aboard the Terra satellite with a 250m × 250m spatial resolution and aggregated for each district during the 2000–2018 period. We also used the weekly SWI, which was collected from the ASCAT instruments with a 12 km x 12 km spatial resolution and aggregated for each district at the topsoil (0–40 cm; SWI-12) and the root-zone layer (0–100 cm; SWI-14) during 2000–2018. The multiscalar SPEI during 1951–2018 farming years proved to be a significant addition to the remote sensing indices and led to the development of a model that improved the yield assessment. The study also summarized (i) the optimal time window of satellite-derived SWIi and EVI2i for yield estimation, and (ii) the capability of remotely sensed indices for representing the spatio–temporal variations of agricultural droughts. We developed statistical soil-vegetation-atmosphere models to explore drought-related yield losses. The skill scores of the sunflower MLR and BBN models were higher than those for the maize and grape models and were able to estimate yields with reasonable accuracy and predictive power. The accurate estimation of maize, sunflower and grapevine yields was observed two months before the harvest (RMSE of ∼1.2 tha-1). Despite the fact that summer crops (maize, sunflower) are able to develop a root system that uses the entire root zone depth, however, the SWI-12 had the stronger correlation with crop yield, then SWI-14. This explains much better the fit between yields of the crops and SWI-12, which represents soil moisture anomaly in the key rooting layer of soil. In any case, all summer crops showed negative correlations with each of the remote sensing soil moisture indices in the early and middle of the growing season, with SWI-12 performing better than SWI-14. Based on the crop-specific soil moisture model, we found that topsoil moisture declines in the most drought-susceptible crop growth stages, which indicates that RM is a good candidate for studying drought persists as main driver of rainfed yield losses in the south-eastern Europe.

Suggested Citation

  • Potopová, Vera & Trnka, Miroslav & Hamouz, Pavel & Soukup, Josef & Castraveț, Tudor, 2020. "Statistical modelling of drought-related yield losses using soil moisture-vegetation remote sensing and multiscalar indices in the south-eastern Europe," Agricultural Water Management, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:agiwat:v:236:y:2020:i:c:s0378377420303656
    DOI: 10.1016/j.agwat.2020.106168
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    5. Abad, Francisco Javier & Marín, Diana & Loidi, Maite & Miranda, Carlos & Royo, José Bernardo & Urrestarazu, Jorge & Santesteban, Luis Gonzaga, 2019. "Evaluation of the incidence of severe trimming on grapevine (Vitis vinifera L.) water consumption," Agricultural Water Management, Elsevier, vol. 213(C), pages 646-653.
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    1. Vera Potopová & Marie Musiolková & Juliana Arbelaez Gaviria & Miroslav Trnka & Petr Havlík & Esther Boere & Tudor Trifan & Nina Muntean & Md Rafique Ahasan Chawdhery, 2023. "Water Consumption by Livestock Systems from 2002–2020 and Predictions for 2030–2050 under Climate Changes in the Czech Republic," Agriculture, MDPI, vol. 13(7), pages 1-29, June.
    2. Manman Zhang & Dang Luo & Yongqiang Su, 2022. "Drought monitoring and agricultural drought loss risk assessment based on multisource information fusion," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(1), pages 775-801, March.
    3. Potopová, V. & Trifan, T. & Trnka, M. & De Michele, C. & Semerádová, D. & Fischer, M. & Meitner, J. & Musiolková, M. & Muntean, N. & Clothier, B., 2023. "Copulas modelling of maize yield losses – drought compound events using the multiple remote sensing indices over the Danube River Basin," Agricultural Water Management, Elsevier, vol. 280(C).
    4. Meng, Huayue & Qian, Long, 2024. "Performances of different yield-detrending methods in assessing the impacts of agricultural drought and flooding: A case study in the middle-and-lower reach of the Yangtze River, China," Agricultural Water Management, Elsevier, vol. 296(C).
    5. Zhang, Yu & Hao, Zengchao & Feng, Sifang & Zhang, Xuan & Xu, Yang & Hao, Fanghua, 2021. "Agricultural drought prediction in China based on drought propagation and large-scale drivers," Agricultural Water Management, Elsevier, vol. 255(C).
    6. Araneda-Cabrera, Ronnie J. & Bermúdez, María & Puertas, Jerónimo, 2021. "Assessment of the performance of drought indices for explaining crop yield variability at the national scale: Methodological framework and application to Mozambique," Agricultural Water Management, Elsevier, vol. 246(C).
    7. Potopová, V. & Trnka, M. & Vizina, A. & Semerádová, D. & Balek, J. & Chawdhery, M.R.A. & Musiolková, M. & Pavlík, P. & Možný, M. & Štěpánek, P. & Clothier, B., 2022. "Projection of 21st century irrigation water requirements for sensitive agricultural crop commodities across the Czech Republic," Agricultural Water Management, Elsevier, vol. 262(C).
    8. Soumyashree Dixit & V. Neethin & K. V. Jayakumar, 2023. "Assessment of Crop-Drought Relationship: A Climate Change Perspective," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 4075-4095, August.
    9. Zhang, Yu & Hao, Zengchao & Feng, Sifang & Zhang, Xuan & Hao, Fanghua, 2022. "Changes and driving factors of compound agricultural droughts and hot events in eastern China," Agricultural Water Management, Elsevier, vol. 263(C).
    10. Rigas Giovos & Dimitrios Tassopoulos & Dionissios Kalivas & Nestor Lougkos & Anastasia Priovolou, 2021. "Remote Sensing Vegetation Indices in Viticulture: A Critical Review," Agriculture, MDPI, vol. 11(5), pages 1-20, May.

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