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1.

001-es BibID:BIBFORM105969
035-os BibID:(WOS)000888719300006 (Scopus)85142617906
Első szerző:Alsafadi, Karam
Cím:Spatial-temporal dynamic impact of changes in rainfall erosivity and vegetation coverage on soil erosion in the Eastern Mediterranean / Karam Alsafadi, Shuoben Bi, Hazem Ghassan Abdo, Mario J. Al Sayah, Tamás Ratonyi, Endre Harsanyi, Safwan Mohammed
Dátum:2022
ISSN:0944-1344 1614-7499
Megjegyzések:In Syria, soil erosion (SoEr) by water is one of the major challenges for sustainability. Thus, the main goals of this research were to evaluate the spatial changes of SoEr between 2000 and 2018 in the whole coastal basin (CB) of Syria and to provide a soil water erosion risk map for the study area. For this purpose, monthly rainfall data, the SoilGrids dataset, satellite image derived NDVI layers, and Digital Elevation Model (DEM) were collected. Through the integration of these layers into the Revised Universal Soil Loss Equation (RUSLE), under a Geographic Information System (GIS), soil loss was assessed. Also, the contribution of land cover changes and R factor on SoEr were evaluated. The outcomes of this assessment illustrated that the R factor ranged from 800 to 2600 MJ mm ha(-)1 h(-1) yr(-1), while the soil erodibility factor (K factor) ranged from 0.048 to 0.035 ton ha MJ(-1) mm(-1). The C factor (vegetation coverage) values ranged between 0.07 and 1 with a spatial average value of 0.44 for the 2000-2009 period and 0.39 for the 2010-2018 interval. The output of RUSLE revealed that average annual SoEr was of 21.35 ton ha(-1) y(-1) (+/- 38) for 2000-2009 and 22.47 ton ha(-1) y(-1)(+/- 41.8) for 2010-2018. Interestingly, the increased SoEr caused by the R factor was dominant (34.65%), followed by changes in both C factor and R factor (13.34%). However, decrease of SoEr rates is due to the increase of the C factor accounting for 36.82% of the CB. The outcome of this research can provide constructive spatial insights for rehabilitation plans for the post-war phase of Syria.
Tárgyszavak:Természettudományok Környezettudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Land degradation
Runoff
erodibility
Modeling
Erosivity
RUSLE
WEPP
vegetation coverage
Megjelenés:Environmental Science And Pollution Research. - [Epub ahead of print] (2022). -
További szerzők:Bi, Shuoben Abdo, Hazem Ghassan Al Sayah, Mario J. Rátonyi Tamás (1967-) (agrármérnök) Harsányi Endre (1976-) (agrármérnök) Mohammed Safwan (1985-) (agrármérnök)
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2.

001-es BibID:BIBFORM111452
035-os BibID:(cikkazonosító)1297 (Scopus)85160411163 (WoS)000994795300001
Első szerző:Harsányi Endre (agrármérnök)
Cím:Data Mining and Machine Learning Algorithms for Optimizing Maize Yield Forecasting in Central Europe / Endre Harsányi, Bashar Bashir, Sana Arshad, Akasairi Ocwa, Attila Vad, Abdullah Alsalman, István Bácskai, Tamás Rátonyi, Omar Hijazi, Adrienn Széles, Safwan Mohammed
Dátum:2023
ISSN:2073-4395
Megjegyzések:Artificial intelligence, specifically machine learning (ML), serves as a valuable tool for decision support in crop management under ongoing climate change. However, ML implementation to predict maize yield is still limited in Central Europe, especially in Hungary. In this context, we assessed the performance of four ML algorithms (Bagging (BG), Decision Table (DT), Random Forest (RF) and Artificial Neural Network-Multi Layer Perceptron (ANN-MLP)) in predicting maize yield based on four different input scenarios. The collected data included both agricultural data (production (PROD) (ton) and maize cropped area (AREA) (ha)) and climate data (annual mean temperature ?C (Tmean), precipitation (PRCP) (mm), rainy days (RD), frosty days (FD) and hot days (HD)). This research adopted four scenarios, as follows: SC1: AREA+ PROD+ Tmean+ PRCP+ RD+ FD+ HD; SC2: AREA+ PROD; SC3: Tmean+ PRCP+ RD+ FD+ HD; and SC4: AREA+ PROD+ Tmean+ PRCP. In the training stage, ANN-MLP-SC1 and ANN-MLP-SC4 outperformed other ML algorithms; the correlation coefficient (r) was 0.99 for both, while the root mean squared errors (RMSEs) were 107.9 (ANN-MLP-SC1) and 110.7 (ANN-MLP-SC4). In the testing phase, the ANN-MLP-SC4 had the highest r value (0.96), followed by ANN-MLP-SC1 (0.94) and RF-SC2 (0.94). The 10-fold cross validation also revealed that the ANN-MLP-SC4 and ANN-MLP-SC1 have the highest performance. We further evaluated the performance of the ANN-MLP-SC4 in predicting maize yield on a regional scale (Budapest). The ANN-MLP-SC4 succeeded in reaching a high-performance standard (r = 0.98, relative absolute error = 21.87%, root relative squared error = 20.4399% and RMSE = 423.23). This research promotes the use of ANN as an efficient tool for predicting maize yield, which could be highly beneficial for planners and decision makers in developing sustainable plans for crop management.
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
maize yield
climate
multilayer perceptron
random forest
optimum model
Megjelenés:Agronomy-Basel. - 13 : 5 (2023), p. 1-22. -
További szerzők:Bashir, Bashar Arshad, Sana Ocwa, Akasairi (1987-) (Crop scientist) Vad Attila (1981-) (agrármérnök) Alsalman, Abdullah Bácskai István (1985-) (Okleveles gépészmérnök) Rátonyi Tamás (1967-) (agrármérnök) Hijazi, Omar Széles Adrienn (1980-) (okleveles agrármérnök) Mohammed Safwan (1985-) (agrármérnök)
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3.

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)
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4.

001-es BibID:BIBFORM097277
035-os BibID:(cikkazonosító)1339
Első szerző:Harsányi Endre (agrármérnök)
Cím:Impact of Agricultural Drought on Sunflower Production across Hungary / Endre Harsányi, Bashar Bashir, Firas Alsilibe, Karam Alsafadi, Abdullah Alsalman, Adrienn Széles, Muhammad Habib ur Rahman, István Bácskai, Csaba Juhász, Tamás Ratonyi, Safwan Mohammed
Dátum:2021
ISSN:2073-4433 2073-4433
Megjegyzések:In the last few decades, agricultural drought (Ag.D) has seriously affected crop production and food security worldwide. In Hungary, little research has been carried out to assess the impacts of climate change, particularly regarding droughts and crop production, and especially on regional scales. Thus, the main aim of this study was to evaluate the impact of agricultural drought on sunflower production across Hungary. Drought data for the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were collected from the CAR BATCLIM database (1961?2010), whereas sunflower production was collected from the Hungarian national statistical center (KSH) on regional and national scales. To address the impact of Ag.D on sunflower production, the sequence of standardized yield residuals (SSYR) and yield losses YlossAD was applied. Additionally, sunflower resilience to Ag.D (SRAg.D) was assessed on a regional scale. The results showed that Ag.D is more severe in the western regions of Hungary, with a significantly positive trend. Interestingly, drought events were more frequent between 1990 and 2010. Moreover, the lowest SSYR values were reported as ?3.20 in the Hajdu-Bihar region (2010). In this sense, during the sunflower growing cycle, the relationship between SSYR and Ag.D revealed that the highest correlations were recorded in the central and western regions of Hungary. However, 75% of the regions showed that the plantation of sunflower is not resilient to drought where SRAg.Dx < 1. To cope with climate change in Hungary, an urgent mitigation plan should be implemented.
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Atmosphere. - 12 : 10 (2021), p. 1-18. -
További szerzők:Bashir, Bashar Alsilibe, Firas Alsafadi, Karam Alsalman, Abdullah Széles Adrienn (1980-) (okleveles agrármérnök) Rahman, Muhammad Habib ur Bacskai István Juhász Csaba (1962-) (környezetgazdálkodási agrármérnök) Rátonyi Tamás (1967-) (agrármérnök) Mohammed Safwan (1985-) (agrármérnök)
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5.

001-es BibID:BIBFORM119276
035-os BibID:(cikkazonosító)130968 (Scopus)85187128031
Első szerző:Mohammed Safwan (agrármérnök)
Cím:Utilizing machine learning and CMIP6 projections for short-term agricultural drought monitoring in central Europe (1900-2100) / Safwan Mohammed, Sana Arshad, Firas Alsilibe, Muhammad Farhan Ul Moazzam, Bashar Bashir, Foyez Ahmed Prodhan, Abdullah Alsalman, Attila Vad, Tamás Ratonyi, Endre Harsányi
Dátum:2024
ISSN:0022-1694
Megjegyzések:Water availability for agricultural practices is dynamically influenced by climatic variables, particularly droughts. Consequently, the assessment of drought events is directly related to the strategic water management in the agricultural sector. The application of machine learning (ML) algorithms in different scenarios of climatic variables is a new approach that needs to be evaluated. In this context, the current research aims to forecast short-term drought i.e., SPI-3 from different climatic predictors under historical (1901-2020) and future (2021-2100) climatic scenarios employing machine learning (bagging (BG), random forest (RF), decision table (DT), and M5P) algorithms in Hungary, Central Europe. Three meteorological stations namely, Budapest (BD) (central Hungary), Szeged (SZ) (east south Hungary), and Szombathely (SzO) (west Hungary) were selected to forecast short-term agriculture drought i.e., Standardized Precipitation Index (SPI-3) in the long run. For this purpose, the ensemble means of three global circulation models GCMs from CMIP6 are being used to get the projected (2021-2100) time series of climatic indicators (i.e., rainfall R, mean temperature T, maximum tem- perature Tmax, and minimum temperature Tmin under two scenarios of socioeconomic pathways (SSP2-4.5 and SSP4-6.0). The results of this study revealed more severe to extreme drought events in past decades, which are projected to increase in the near future (2021-2040). Man-Kendall test (Tau) along with Sen`s slope (SS) also revealed an increasing trend of SPI-3 drought in the historical period with Tau = 0.2, SS = 0.05, and near future with Tau = 0.12, SS = 0.09 in SSP2-4.5 and Tau = 0.1, SS = 0.08 in SSP4-6.0. Implementation of ML algorithms in three scenarios: SC1 (R + T + Tmax + Tmin), SC2 (R), and SC3 (R + T)) at the BD station revealed RF-SC3 with the lowest RMSE RFSC3-TR = 0.33, and the highest NSE RFSC3-TR = 0.89 performed best for forecasting SPI-3 on historical dataset. Hence, the best selected RF-SC3 was implemented on the remaining two stations (SZ and SzO) to forecast SPI-3 from 1901 to 2100 under SSP2-4.5 and SSP4-6.0. Interestingly, RF-SC3 forecasted the SPI-3 under SSP2-4.5, with the lowest RMSE = 0.34 and NSE = 0.88 at SZ and RMSE = 0.34 and NSE = 0.87 at SzO station for SSP2-4.5. Hence, our research findings recommend using SSP2-4.5, to provide more accurate drought predictions from R + T for future projections. This could foster a gradual shift towards sustainability and improve water management resources. However, concrete strategic plans are still needed to mitigate the negative impacts of the projected extreme drought events in 2028, 2030, 2031, and 2034. Finally, the validation of RF for short-term drought prediction on a large historical dataset makes it significant for use in other drought studies and facilitates decision making for future disaster management strategies.
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Standardized precipitation index
Forecasting
CMIP6
Random Forest
Hungary
Megjelenés:Journal Of Hydrology. - 633 (2024), p. 1-21. -
További szerzők:Arshad, Sana Alsilibe, Firas Moazzam, Muhammad Farhan Ul Bashir, Bashar Prodhan, Foyez Ahmed Alsalman, Abdullah Vad Attila (1981-) (agrármérnök) Rátonyi Tamás (1967-) (agrármérnök) Harsányi Endre (1976-) (agrármérnök)
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6.

001-es BibID:BIBFORM119328
Első szerző:Ocwa, Akasairi (Crop scientist)
Cím:Maize Grain Yield and Quality Improvement Through Biostimulant Application: a Systematic Review / Akasairi Ocwa, Safwan Mohammed, Seyed Mohammad Nasir Mousavi, Árpád Illés, Csaba Bojtor, Péter Ragán, Tamás Rátonyi, Endre Harsányi
Dátum:2024
ISSN:0718-9508 0718-9516
Megjegyzések:Increasing the productivity of cereals such as maize while protecting the environment remains a fundamental impetus of healthy food production systems. The use of biostimulants is one of the sustainable strategies to achieve this balance, although the ability of biostimulants to enhance maize productivity varies. Moreover, research on the efcacy of biostimulants is ubiquitous with limited comprehensive global analysis. In this context, this systematic review evaluated the sole and interactive efects of biostimulants on the yield and quality of maize grain from a global perspective. Changes in yield (t ha-1), protein content (%), starch content (%) and oil content (%) of maize grain were assessed. Results revealed that sole and combined application of biostimulants signifcantly improved grain yield. Irrespective of the region, the highest and the lowest grain yields ranged between 16-20 t ha-1 and 1-5 t ha-1, respectively. In sole application, the promising biostimulants were chicken feather (16.5 t ha-1), and endophyte Colletotrichum tofeldiae (14.5 t ha-1). Sewage sludge x NPK (15.4 t ha-1), humic acid x control release urea (12.4 t ha-1), Azospirillum brasilense or Bradyrhizobium japonicum x maize hybrids (11.6 t ha-1), and Rhizophagus intraradices x earthworms (10.0 t ha-1) had higher yield for the interactive efects. The efects of biostimulants on grain quality were minimal, and all attributes improved in the range from 0.1 to 3.7%. Overall, biostimulants had a distinct improvement efect on yield, rather than on the quality of grain. As one way of maximising maize productivity, soil health, and the overall functioning of crop agroecosystems, the integrated application of synergistic microbial and non-microbial biostimulants could provide a viable option. However, the ability to produce consistent yield and quality of grain improvement remains a major concern.
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Biostimulants
Grain
Maize
Oil content
Protein content
Starch content
Yield
Megjelenés:Journal of Soil Science and Plant Nutrition. - [Epub ahead of print] : - (2024), p.1-41. -
További szerzők:Mohammed Safwan (1985-) (agrármérnök) Mousavi, Seyed Mohammad Nasir (1988-) (agrármérnök) Illés Árpád (1994-) (növényorvos) Bojtor Csaba (1993-) (okleveles növényorvos) Ragán Péter (1986-) (környzetgazdálkodási agrármérnök) Rátonyi Tamás (1967-) (agrármérnök) Harsányi Endre (1976-) (agrármérnök)
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7.

001-es BibID:BIBFORM112204
035-os BibID:(cikkazonosító)14 (Scopus)85160908963
Első szerző:Ocwa, Akasairi (Crop scientist)
Cím:A bibliographic review of climate change and fertilization as the main drivers of maize yield: implications for food security / Akasairi Ocwa ; Endre Harsanyi ; Adrienn Széles ; Imre János Holb ; Szilárd Szabó ; Tamás Rátonyi ; Safwan Mohammed
Dátum:2023
ISSN:2048-7010
Megjegyzések:Introduction Crop production contribution to food security faces unprecedented challenge of increasing human population. This is due to the decline in major cereal crop yields including maize resulting from climate change and declining soil infertility. Changes in soil nutrient status and climate have continued to occur and in response, new fertilizer recommendations in terms of formulations and application rates are continuously developed and applied globally. In this sense, this review was conducted to: (i) identify the key areas of concentration of research on fertilizer and climate change effect on maize grain yield, (ii) assess the extent of the effect of climate change on maize grain yield, (iii) evaluate the extent of the effect of fertilization practices on maize grain yield, and (iv) examine the effect of interaction between climate change factors and fertilization practices on maize grain yield at global perspective.MethodologyComprehensive search of global literature was conducted in Web of Science (WoS) database. For objective 1, metadata on co-authorship (country, organisation), and co-occurrence of keywords were exported and analysed using VOSviewer software. For objective 2-4, yield data for each treatment presented in the articles were extracted and yield increment calculated.ResultsThe most significant keywords: soil fertility, nutrient use efficiency, nitrogen use efficiency, integrated nutrient management, sustainability, and climate change adaptation revealed efforts to improve maize production, achieve food security, and protect the environment. A temperature rise of 1-4 °C decreased yield by 5-14% in warm areas and increased by < 5% in cold areas globally. Precipitation reduction decreased yield by 25-32%, while CO2concentration increased and decreased yield by 2.4 to 7.3% and 9 to 14.6%, respectively. A promising fertilizer was a combination of urea +nitrapyrin with an average yield of 5.1 and 14.4 t ha?1 under non-irrigation and irrigation, respectively. Fertilization under climate change was projected to reduce yield in the average range of 10.5-18.3% by 2099.ConclusionThe results signified that sole fertilizer intensification is insufficient to attain sustainable maize yield. Therefore, there is need for integrated agronomic research that combines fertilizers and other technologies for enhancing maize yield, and consequently maize contribution to the attainment of global food security under climate change conditions
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Climate change
Drought
Fertilizers
Heat stress
Maize
Nitrogen
Temperature
Yield
Megjelenés:Agriculture & Food Security. - 12 : 1 (2023), p. 1-18. -
További szerzők:Harsányi Endre (1976-) (agrármérnök) Széles Adrienn (1980-) (okleveles agrármérnök) Holb Imre (1973-) (agrármérnök) Szabó Szilárd (1974-) (geográfus) Rátonyi Tamás (1967-) (agrármérnök) Mohammed Safwan (1985-) (agrármérnök)
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8.

001-es BibID:BIBFORM108703
Első szerző:Ocwa, Akasairi (Crop scientist)
Cím:Mapping evidence of the role of foliar fertilizers in mitigating abiotic stress effects on maize: A review / Akasairi Ocwa, Safwan Mohammed, Attila Vad, Péter Ragán, Tamás Rátonyi, Endre Harsányi
Dátum:2022
ISBN:978-83-966062-1-1
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok előadáskivonat
könyvrészlet
Megjelenés:International Congress on Sustainable development in the Human Environment - Current & Future Challenges. ICSDEV (2022)(Alanya) : Proceedings book / eds. Anna Krakowiak-Bal, Atilgan Atilgan, Roman Rolbiecki, Hakan Aktas. - p. 201. -
További szerzők:Mohammed Safwan (1985-) (agrármérnök) Vad Attila (1981-) (agrármérnök) Ragán Péter (1986-) (környzetgazdálkodási agrármérnök) Rátonyi Tamás (1967-) (agrármérnök) Harsányi Endre (1976-) (agrármérnök)
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