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001-es BibID:	BIBFORM103468
035-os BibID:	(cikkazonosító)10653 (WOS)000851188400001 (Scopus)85137562006
Első szerző:	Harsányi Endre (agrármérnök)
Cím:	Predicting Modified Fournier Index by Using Artificial Neural Network in Central Europe / Endre Harsányi, Bashar Bashir, Firas Alsilibe, Muhammad Farhan Ul Moazzam, Tamás Ratonyi, Abdullah Alsalman, Adrienn Széles, Aniko Nyeki, István Takács, Safwan Mohammed
Dátum:	2022
ISSN:	1661-7827 1660-4601
Megjegyzések:	The Modified Fournier Index (MFI) is one of the indices that can assess the erosivity of rainfall. However, the implementation of the artificial neural network (ANN) for the prediction of the MFI is still rare. In this research, climate data (monthly and yearly precipitation (pi, Ptotal) (mm), daily maximum precipitation (Pd-max) (mm), monthly mean temperature (Tavg) ( C), daily maximum mean temperature (Td-max) ( C), and daily minimum mean temperature (Td-min) ( C)) were collected from three stations in Hungary (Budapest, Debrecen, and Pécs) between 1901 and 2020. The MFI was calculated, and then, the performance of two ANNs (multilayer perceptron (MLP) and radial basis function (RBF)) in predicting the MFI was evaluated under four scenarios. The average MFI values were between 66.30 15.40 (low erosivity) in Debrecen and 75.39 15.39 (low erosivity) in Pecs. The prediction of the MFI by using MLP was good (NSEBudapest(SC3) = 0.71, NSEPécs(SC2) = 0.69). Additionally, the performance of RBF was accurate (NSEDebrecen(SC4) = 0.68, NSEPécs(SC3) = 0.73). However, the correlation coefficient between the observed MFI and the predicted one ranged between 0.83 (Budapest (SC2-MLP)) and 0.86 (Pécs (SC3-RBF)). Interestingly, the statistical analyses promoted SC2 (Pd-max + pi + Ptotal) and SC4 (Ptotal + Tavg + Td-max + Td-min) as the best scenarios for predicting MFI by using the ANN?MLP and ANN?RBF, respectively. However, the sensitivity analysis highlighted that Ptotal, pi, and Td-min had the highest relative importance in the prediction process. The output of this research promoted the ANN (MLP and RBF) as an effective tool for predicting rainfall erosivity in Central Europe.
Tárgyszavak:	Társadalomtudományok Szociológiai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk land degradation machine learning climate change Hungary
Megjelenés:	International Journal of Environmental Research and Public Health. - 19 : 17 (2022), p. 1-19. -
További szerzők:	Bashir, Bashar Alsilibe, Firas Moazzam, Muhammad Farhan Ul Rátonyi Tamás (1967-) (agrármérnök) Alsalman, Abdullah Széles Adrienn (1980-) (okleveles agrármérnök) Nyéki Anikó (1989-) (agrármérnök) Takács István (1977-) (terület és településfejlesztési egyetemi szakközgazdász) Mohammed Safwan (1985-) (agrármérnök)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM117901
035-os BibID:	(cikkazonosító)100967 (Scopus)85182387773
Első szerző:	Széles Adrienn (okleveles agrármérnök)
Cím:	Precision agricultural technology for advanced monitoring of maize yield under different fertilization and irrigation regimes: A case study in Eastern Hungary (Debrecen) / Adrienn Széles, László Huzsvai, Safwan Mohammed, Anikó Nyék, Péter Zagyi, Éva Horváth, Károly Simon, Sana Arshad, András Tamás
Dátum:	2024
ISSN:	2666-1543
Megjegyzések:	Precision agricultural (PrA) technology relies on the utilization of special equipment to access real time observations on plant health status, chlorophyll, nitrogen content, and soil moisture content. In this research new PrA technology (i.e., SPAD (Soil Plant Analysis Development), and UAV-based NDVI (Unmanned Aerial Vehicle-based Normalized Difference Vegetation Index) were used to monitor maize yield based on different filed trials in eastern part of Hungary. Our study aimed to examine the utilization of PrA technology specifically SPAD and UAV-based NDVI measurements for monitoring maize GY under irrigated and rainfed experimental setups in Hungary with varied nitrogen treatment for the year 2022. The results showed that the SPAD increased in all treatments (14.7 %; p < 0.05) from V6-V8 in the rainfed treatments, decreased significantly (p < 0.05) by 13.9 % (R1) and 30.6 % (R3). However, implementation of irrigation significantly increased the SPAD values in majority of treatments. Also, results reveal that, under irrigated and rainfed conditions the highest UAV-based NDVI value (0.703, 0.642) was obtained in V12 (A120 treatment) and highest NDVI value (0.728, 0.662) was obtained in Vn (A120 treatment). Remarekedly, irrigation led to significant differences (p < 0.05) of UAV-based NDVI values compared with none irrigated. On the other hand, implementation of 120 kg N ha?1 before sowing led to highest GY, especially under irrigated conditions (8.649 Mg ha?1). The overall mean GY under rainfed treatment was 6.256 Mg ha?1, while under irrigated treatment it increased by 37.2 % and reached 8.581 Mg ha?1 (p < 0.05). In conclusion, PrA technology will support farmers in making informed decisions regarding fertilization strategies and timing, which will in turn maximize yield and minimize risk.
Tárgyszavak:	Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk nitrogen treatment maize drought photosynthetic performance Hungary
Megjelenés:	Journal of Agriculture and Food Research. - 15 (2024), p. 1-16. -
További szerzők:	Huzsvai László (1961-) (talajerőgazdálkodási szakmérnök, agrármérnök) Mohammed Safwan (1985-) (agrármérnök) Nyéki Anikó (1989-) (agrármérnök) Zagyi Péter (1986-) (agrármérnök) Horváth Éva (1993-) (környezetgazdálkodási agrármérnök) Simon Károly (1985-) (okleveles agrármérnök) Arshad, Sana Tamás András (1986-) (gazdasági agrármérnök, növénytermesztés és kertészeti tudományok)
Pályázati támogatás:	TKP2021-NKTA-32 Egyéb Bolyai János Kutatási Ösztöndíj (BO/00068/23/4) Egyéb
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