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Potential of water balance and remote sensing-based evapotranspiration models to predict yields of spring barley and winter wheat in the Czech Republic
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SYSNO ASEP 0545755 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Potential of water balance and remote sensing-based evapotranspiration models to predict yields of spring barley and winter wheat in the Czech Republic Author(s) Jurečka, František (UEK-B) SAI, RID
Fischer, Milan (UEK-B) RID, ORCID, SAI
Hlavinka, Petr (UEK-B) RID, ORCID, SAI
Balek, Jan (UEK-B) ORCID, SAI, RID
Semerádová, Daniela (UEK-B) RID, ORCID, SAI
Bláhová, Monika (UEK-B) ORCID, RID, SAI
Anderson, M. C. (US)
Hain, C. (US)
Žalud, Zdeněk (UEK-B) RID, ORCID, SAI
Trnka, Miroslav (UEK-B) RID, ORCID, SAINumber of authors 10 Article number 107064 Source Title Agricultural Water Management. - : Elsevier - ISSN 0378-3774
Roč. 256, OCT (2021)Number of pages 12 s. Language eng - English Country NL - Netherlands Keywords evaporative stress index ; crop yield ; agricultural drought ; central-europe ; united-states ; time-series ; soil ; climate ; system ; parameterization ; Artificial neural network ; Crop yield prediction ; Evapotranspiration ; Evaporative stress index ; Spring barley ; Winter wheat Subject RIV DG - Athmosphere Sciences, Meteorology OECD category Agriculture R&D Projects EF16_019/0000797 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Research Infrastructure CzeCOS III - 90123 - Ústav výzkumu globální změny AV ČR, v. v. i. Method of publishing Limited access Institutional support UEK-B - RVO:86652079 UT WOS 000691191900002 EID SCOPUS 85110307945 DOI 10.1016/j.agwat.2021.107064 Annotation Indicators based on evapotranspiration (ET) provide useful information about surface water status, response of vegetation to drought stress, and potential growth limitations. The capability of ET-based indicators, including actual ET and the evaporative stress index (ESI), to predict crop yields of spring barley and winter wheat was analyzed for 33 districts of the Czech Republic. In this study, the ET-based indicators were computed using two different approaches: (i) a prognostic model, SoilClim, which computes the water balance based on ground weather observations and information about soil and land cover, (ii) the diagnostic Atmosphere-Land Exchange Inverse (ALEXI) model based primarily on remotely sensed land surface temperature data. The capability of both sets of indicators to predict yields of spring barley and winter wheat was tested using artificial neural networks (ANNs) applied to the adjusting number and timeframe of inputs during the growing season. Yield predictions based on ANNs were computed for both crops for all districts together, as well as for individual districts. The mot mean square error (RMSE) and coefficient of determination (R-2) between observed and predicted yields varied with date within the growing season and with the number of ANN inputs used for yield prediction. The period with the highest predictive capability started from early-June to mid-June. This optimal period for yield prediction was identifiable already at the lower number of ANN inputs, nevertheless, the accuracy of the prediction improved as more inputs were included within ANNs.The RMSE values for individual districts varied between 0.4 and 0.7 t ha(-1) while R-2 reached values of 0.5-0.8 during the optimal period. Results of the study demonstrated that ET-based indicators can be used for yield prediction in real time during the growing season and therefore have great potential for decision making at regional and district levels. Workplace Global Change Research Institute Contact Nikola Šviková, svikova.n@czechglobe.cz, Tel.: 511 192 268 Year of Publishing 2022 Electronic address https://www.sciencedirect.com/science/article/pii/S0378377421003292?via%3Dihub
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