Potential of water balance and remote sensing-based evapotranspiration models to predict yields of spring barley and winter wheat in the Czech Republic

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F21%3A00545755" target="_blank" >RIV/86652079:_____/21:00545755 - isvavai.cz</a>

Alternative codes found

RIV/62156489:43210/21:43920090

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0378377421003292?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0378377421003292?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.agwat.2021.107064" target="_blank" >10.1016/j.agwat.2021.107064</a>

Result language

angličtina

Original language name

Potential of water balance and remote sensing-based evapotranspiration models to predict yields of spring barley and winter wheat in the Czech Republic

Original language description

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.

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

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OECD FORD branch

40101 - Agriculture

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

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

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

Name of the periodical

Agricultural Water Management

ISSN

0378-3774

e-ISSN

1873-2283

Volume of the periodical

256

Issue of the periodical within the volume

OCT

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

12

Pages from-to

107064

UT code for WoS article

000691191900002

EID of the result in the Scopus database

2-s2.0-85110307945