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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)
Pályázati támogatás:	TKP2021-NKTA-32 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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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)
Pályázati támogatás:	TKP2021-NKTA-32 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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