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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)
Pályázati támogatás:	TKP2021-NKTA-32 Egyéb
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001-es BibID:	BIBFORM093942
Első szerző:	Nagy János (agrármérnök, mérnök-tanár)
Cím:	The effect of soil pH and precipitation variability during the growing season on maize hybrid grain yield in a 17 year long-term experiment / Nagy János
Dátum:	2011
ISSN:	0042-790X
Megjegyzések:	We established a multifactoral long-term field experiment at the Látókép experimental site of the University of Debrecen (Debrecen, Hungary), on mid-heavy calcareous chernozem soil in 1984, using experimental data from 17 years (1990-2008). We examined the extent to which soil fertility affects maize yield under natural conditions (without fertilisation). We analysed the effect of precipitation in the winter period (from the harvest of the previous crop (maize) until sowing (i.e. October-March)) and the growing season (i.e. April-September) on yield and we evaluated yield per FAO group. We examined the joint effect of crop year and hybrid maturity groups on maize yield; then we evaluated how hybrid maturity groups per crop year and wet and dry years per ripening group affected maize yield. It was shown that the pH value of soil significantly affected yield and also that there was a strong positive correlation between pH value and yield (r = 0.81) at a 1% significance level. The correlation between the two variables is described by a linear regression line. The slope shows that a 0.1 soil pH increase results in a 510 kg ha-1 maize yield increase on average. The correlation between the amount of precipitation during the growing season and maize yield is average, positive (r = 0.718) and linear. Based on the parameters of the estimated regression line - within non-fertilised conditions - 1 mm increase of precipitation resulted in a 9 kg ha-1 increase in yield. The analysis of the joint effect of hybrid maturity groups and crop year on yield showed that crop year determines standard deviation six times more than hybrid maturity groups, whereas the effect their interaction was not significant.
Tárgyszavak:	Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Long-term Experiment Maize Grain Yield Hybrid Maturity Groups Growing Season Precipitation Soil pH
Megjelenés:	Journal of Hydrology and Hydromechanics. - 59 : 1 (2011), p. 60-67. -
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