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001-es BibID:BIBFORM100395
035-os BibID:(cikkazonosító)516 (WoS)000764830200001 (Scopus)85125038156
Első szerző:Elbeltagi, Ahmed
Cím:Combination of Limited Meteorological Data for Predicting Reference Crop Evapotranspiration Using Artificial Neural Network Method / Ahmed Elbeltagi, Attila Nagy, Safwan Mohammed, Chaitanya B. Pande, Manish Kumar, Shakeel Ahmad Bhat, József Zsembeli, László Huzsvai, János Tamás, Elza Kovács, Endre Harsányi, Csaba Juhász
Dátum:2022
ISSN:2073-4395
Megjegyzések:Reference crop evapotranspiration (ETo) is an important component of the hydrological cycle that is used for water resource planning, irrigation, and agricultural management, as well as in other hydrological processes. The aim of this study was to estimate the ETo based on limited meteorological data using an artificial neural network (ANN) method. The daily data of minimum temperature (Tmin), maximum temperature (Tmax), mean temperature (Tmean), solar radiation (SR), humidity (H), wind speed (WS), sunshine hours (Ssh), maximum global radiation (gradmax), minimum global radiation (gradmin), day length, and ETo data were obtained over the long-term period from 1969 to 2019. The analysed data were divided into two parts from 1969 to 2007 and from 2008 to 2019 for model training and testing, respectively. The optimal ANN for forecasting ETo included Tmax, Tmin, H, and SR at hidden layers (4, 3); gradmin, SR, and WS at (6, 4); SR, day length, Ssh, and Tmean at (3, 2); all collected parameters at hidden layer (5, 4). The results showed different alternative methods for estimation of ETo in case of a lack of climate data with high performance. Models using ANN can help promote the decision-making for water managers, designers, and development planners.
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Agronomy-Basel. - 12 : 2 (2022), p. 1-18. -
További szerzők:Nagy Attila (1982-) (környezetgazdálkodási agrármérnök) Mohammed Safwan (1985-) (agrármérnök) Pande, Chaitanya Kumar, Manish Bhat, Shakeel Ahmad Zsembeli József (1967-) (agrármérnök) Huzsvai László (1961-) (talajerőgazdálkodási szakmérnök, agrármérnök) Tamás János (1959-) (környezetgazdálkodási agrármérnök) Kovács Elza (1976-) (okleveles vegyész, angol-magyar szakfordító, anyagmérnök MSc) Harsányi Endre (1976-) (agrármérnök) Juhász Csaba (1962-) (környezetgazdálkodási agrármérnök)
Pályázati támogatás:TKP2020-IKA-04
Egyéb
KP2021-NKTA-32
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2.

001-es BibID:BIBFORM092975
035-os BibID:(cikkazonosító)652 (WoS)000642658600001 (Scopus)85106441432
Első szerző:Nagy Attila (környezetgazdálkodási agrármérnök)
Cím:Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics / Attila Nagy, Andrea Szabó, Odunayo David Adeniyi, János Tamás
Dátum:2021
ISSN:2073-4395
Megjegyzések:Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013?2018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVIbased forecasting models were validated based on 2018?2019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41?71) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Agronomy-Basel. - 11 : 4 (2021), p. 1-13. -
További szerzők:Szabó Andrea (1994-) (környezetgazdálkodási agrármérnök) Adeniyi, Odunayo David (1994-) (agrár) Tamás János (1959-) (környezetgazdálkodási agrármérnök)
Pályázati támogatás:H2020-WATERAGRI
Egyéb
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
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