@article{MTMT:34731789,
	title = {Maize Grain Yield and Quality Improvement Through Biostimulant Application: a Systematic Review},
	url = {https://m2.mtmt.hu/api/publication/34731789},
	author = {Ocwa, Akasairi and Mohammed, Safwan and Mousavi, Seyed Mohammad Nasir and Illés, Árpád and Bojtor, Csaba and Ragán, Péter and Rátonyi, Tamás and Harsányi, Endre},
	doi = {10.1007/s42729-024-01687-z},
	journal-iso = {J SOIL SCI PLANT NUT},
	journal = {JOURNAL OF SOIL SCIENCE AND PLANT NUTRITION},
	unique-id = {34731789},
	issn = {0718-9508},
	abstract = {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 efficacy of biostimulants is ubiquitous with limited comprehensive global analysis. In this context, this systematic review evaluated the sole and interactive effects 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 significantly 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 tofieldiae (14.5 t ha -1 ). Sewage sludge × NPK (15.4 t ha -1 ), humic acid × control release urea (12.4 t ha -1 ), Azospirillum brasilense or Bradyrhizobium japonicum × maize hybrids (11.6 t ha -1 ), and Rhizophagus intraradices × earthworms (10.0 t ha -1 ) had higher yield for the interactive effects. The effects 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 effect 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.},
	keywords = {YIELD; MAIZE; Grain; protein content; Oil content; starch content; Biostimulants},
	year = {2024},
	eissn = {0718-9516},
	orcid-numbers = {Bojtor, Csaba/0000-0001-5870-9977; Ragán, Péter/0000-0002-2580-9346}
}

@article{MTMT:34694318,
	title = {Utilizing machine learning and CMIP6 projections for short-term agricultural drought monitoring in central Europe (1900–2100)},
	url = {https://m2.mtmt.hu/api/publication/34694318},
	author = {Mohammed, Safwan and Arshad, Sana and Alsilibe, Firas and Moazzam, Muhammad Farhan Ul and Bashir, Bashar and Prodhan, Foyez Ahmed and Alsalman, Abdullah and Vad, Attila Miklós and Rátonyi, Tamás and Harsányi, Endre},
	doi = {10.1016/j.jhydrol.2024.130968},
	journal-iso = {J HYDROL},
	journal = {JOURNAL OF HYDROLOGY},
	volume = {633},
	unique-id = {34694318},
	issn = {0022-1694},
	abstract = {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.},
	year = {2024},
	eissn = {1879-2707},
	pages = {1-21},
	orcid-numbers = {Arshad, Sana/0000-0003-4809-5742}
}

@article{MTMT:34559057,
	title = {Random forest-based analysis of land cover/land use LCLU dynamics associated with meteorological droughts in the desert ecosystem of Pakistan},
	url = {https://m2.mtmt.hu/api/publication/34559057},
	author = {Faheem, Zulqadar and Kazmi, Jamil Hasan and Shaikh, Saima and Arshad, Sana and Mohammed, Safwan},
	doi = {10.1016/j.ecolind.2024.111670},
	journal-iso = {ECOL INDIC},
	journal = {ECOLOGICAL INDICATORS},
	volume = {159},
	unique-id = {34559057},
	issn = {1470-160X},
	abstract = {Dry land ecosystems extend over 40 % of the Earth, supporting an estimated 3 billion human population. Thus,
		quantifying LCLU changes in such ecosystems is essential for achieving sustainable development goals. In this
		context, this research aimed to examine the LCLU changes in the past three decades (1990 – 2020) in an arid
		ecosystem of Pakistan, i.e., the Cholisatn desert. Three remote sensing indices, the normalized difference
		vegetation index (NDVI), normalized difference barren index (NDBaI), and top grain soil index (TGSI) are taken
		as LCLU representatives to examine their temporal relationship associated with meteorological drought, e.g. the
		standardized precipitation index (SPI). Moreover, machine learning-based random forest (RF) classification
		followed by change detection techniques was implemented. Results from RF classifier revealed the applicability
		of RF in accurately predicting LULC with validation overall accuracy of 0.99. Output of the research revealed an
		interesting finding where the desert experienced significant LCLU change over the last three decades. The highest
		vegetation expansion (4.4 %) took place from 2014 to 2020 at the expense of the highest reduction of barren land
		(-6.3 %). Mann-Kendall trend (MK) and Sen’s slope (SS) analysis showed a significant (P < 0.001) increasing
		trend of NDVI (SS = 0.004), SPI (SS = 0.01 and 0.04) and decreasing trend of NDBaI and TGSI (SS = -0.001,
		− 0.005). Interestingly, the significant positive Pearson correlation range (r = 0.6–0.8) of NDVI with SPI-1 to 6,
		and negative correlation range (r = 0.5–0.7) of NDBaI with SPI indices reveals a strong linear relationship between LCLU and meteorological drought. The research provides substantial implications for policy makers and
		stakeholders emphasizing the need for proactive strategies such as drought resistant vegetation to improve and
		maintain the ecological health of desert and combating the negative impacts of climatic change},
	year = {2024},
	eissn = {1872-7034}
}

@article{MTMT:34524718,
	title = {Machine learning driven forecasts of agricultural water quality from rainfall ionic characteristics in Central Europe},
	url = {https://m2.mtmt.hu/api/publication/34524718},
	author = {Mohammed, Safwan and Arshad, Sana and Bashir, Bashar and Vad, Attila Miklós and Alsalman, Abdullah and Harsányi, Endre},
	doi = {10.1016/j.agwat.2024.108690},
	journal-iso = {AGR WATER MANAGE},
	journal = {AGRICULTURAL WATER MANAGEMENT},
	volume = {293},
	unique-id = {34524718},
	issn = {0378-3774},
	keywords = {Hungary; Rainwater chemistry; Multilayer perceptron; Sodium adsorption ratio; agriculture water optimization},
	year = {2024},
	eissn = {1873-2283}
}

@article{MTMT:34501873,
	title = {Precision agricultural technology for advanced monitoring of maize yield under different fertilization and irrigation regimes: A case study in Eastern Hungary (Debrecen)},
	url = {https://m2.mtmt.hu/api/publication/34501873},
	author = {Széles, Adrienn [Ványiné] and Huzsvai, László and Mohammed, Safwan and Nyéki, Anikó Éva and Zagyi , Péter and Horváth, Éva and Simon, Károly and Arshad, Sana and Tamás, András},
	doi = {10.1016/j.jafr.2024.100967},
	journal-iso = {J AGRICULT FOOD RES},
	journal = {JOURNAL OF AGRICULTURE AND FOOD RESEARCH},
	volume = {15},
	unique-id = {34501873},
	issn = {2666-1543},
	year = {2024},
	orcid-numbers = {Arshad, Sana/0000-0003-4809-5742}
}

@article{MTMT:34444153,
	title = {An environmental impact assessment of Saudi Arabia's vision 2030 for sustainable urban development: A policy perspective on greenhouse gas emissions},
	url = {https://m2.mtmt.hu/api/publication/34444153},
	author = {Altouma, Ahmed and Bashir, Bashar and Ata, Behnam and Ocwa, Akasairi and Alsalman, Abdullah and Harsányi, Endre and Mohammed, Safwan},
	doi = {10.1016/j.indic.2023.100323},
	journal-iso = {ENVIRON SUSTAIN INDIC},
	journal = {ENVIRONMENTAL AND SUSTAINABILITY INDICATORS},
	volume = {21},
	unique-id = {34444153},
	issn = {2665-9727},
	year = {2024}
}

@article{MTMT:34148822,
	title = {Assessment of the environmental kuznets curve within EU-27: Steps toward environmental sustainability (1990–2019)},
	url = {https://m2.mtmt.hu/api/publication/34148822},
	author = {Mohammed, Safwan and Gill, Abid Rashid and Ghosal, Kaushik and Al-Dalahmeh, Main and Alsafadi, Karam and Szabó, Szilárd and Oláh, Judit and Alkerdi, Ali and Ocwa, Akasairi and Harsányi, Endre},
	doi = {10.1016/j.ese.2023.100312},
	journal-iso = {Environmental Science and Ecotechnology},
	journal = {Environmental Science and Ecotechnology},
	volume = {18},
	unique-id = {34148822},
	issn = {2666-4984},
	year = {2024},
	eissn = {2666-4984},
	orcid-numbers = {Gill, Abid Rashid/0000-0003-1961-5139; Ghosal, Kaushik/0000-0003-3235-0278; Alsafadi, Karam/0000-0001-8925-7918; Szabó, Szilárd/0000-0002-2670-7384; Oláh, Judit/0000-0003-2247-1711}
}

@article{MTMT:34500637,
	title = {Land degradation risks},
	url = {https://m2.mtmt.hu/api/publication/34500637},
	author = {Rodrigo-Comino, Jesús and Muñoz-Gómez, Casandra and Rahdari, Mohammad Reza and Mohammed, Safwan and Salvati, Luca},
	doi = {10.18172/cig.5869},
	journal-iso = {CUAD INVEST GEOG},
	journal = {CUADERNOS DE INVESTIGACION GEOGRAFICA},
	volume = {49},
	unique-id = {34500637},
	issn = {0211-6820},
	year = {2023},
	eissn = {1697-9540},
	pages = {3-4},
	orcid-numbers = {Rodrigo-Comino, Jesús/0000-0002-4823-0871; Rahdari, Mohammad Reza/0000-0003-3493-6600}
}

@article{MTMT:34083638,
	title = {Exploring dynamic response of agrometeorological droughts towards winter wheat yield loss risk using machine learning approach at a regional scale in Pakistan},
	url = {https://m2.mtmt.hu/api/publication/34083638},
	author = {Arshad, Sana and Kazmi, Jamil Hasan and Prodhan, Foyez Ahmed and Mohammed, Safwan},
	doi = {10.1016/j.fcr.2023.109057},
	journal-iso = {FIELD CROP RES},
	journal = {FIELD CROPS RESEARCH},
	volume = {302},
	unique-id = {34083638},
	issn = {0378-4290},
	abstract = {Context: Crop yield is a major agriculture sector affected by climate change; especially agrometeorological
		droughts experienced by south Asian countries in past decades.
		Research objective: The main goals of this research were to explore the spatiotemporal characteristics of seven
		agrometeorological drought indices at a regional scale in Pakistan. Secondly, to forecast the wheat yield loss risk
		(YLR) due to droughts under current and future climate scenarios by employing three machine learning (ML)
		methods; random forest (RF), gradient boosting machine (GBM), and generalized additive model (GAM).
		Method: The relationship between detrended wheat yield and a combination of five remote sensing indices
		Normalized Difference Water Index (NDWI), Vegetation Condition Index (VCI), Temperature Condition Index
		(TCI), Vegetation Health Index (VHI), and Drought Severity Index (DSI)), and two meteorological drought
		indices (Palmer’s Drought Severity Index (PDSI), and Standardized Precipitation Evapotranspiration Index
		(SPEI)) was analyzed. Mann-Kendall trend (MK), Sens’s slope, and Sequential Mann-Kendall (SQMK) tests are
		applied to explore the trend and trend-changing years for all indices over the historical time of 20 years.
		The YLR and all indices were projected (2021–2050) from the baseline period (2001–2020) using PROPHET time
		series forecasting and CMIP6 climatic models. YLR was forecasted on present and future projected time series by
		employing three non-linear ML regression models.
		Results: The output of the drought analysis revealed that the study area was hit by high to severe drought events
		in 2001–2004, 2006, 2008, 2010, 2012, and 2017. Trend analysis revealed intersection years breaking the rising
		trend of drought indices. All drought indices are significantly correlated with meteorological wheat yield with a
		sequence of NDWI>DSI>VCI>VHI>PDSI>SPEI>TCI. Future projections under high emission scenarios revealed
		a rise in YLR associated with frequent projected droughts from VHI, DSI, SPEI, and PDSI. YLR forecasting from
		agrometeorological indices is best predicted by random forest with the lowest RMSE = 0.005314. NDWI (26%)
		and VCI (19%) are found to be significant relative predictors associated with 51% high YLR in the baseline period
		and SPEI (20%) and NDWI (17%) as the most important relative predictors associated with 39% high YLR in
		future.
		Conclusion: The region is vulnerable to agrometeorological droughts with more susceptibility to less rain and high
		temperature affecting crop health and a high risk of yield loss in the future.
		Implication: The study provides a direction to stakeholders and policymakers to develop and adapt better strategies to mitigate and prevent drought-related yield loss risk in the future.},
	year = {2023},
	eissn = {1872-6852},
	orcid-numbers = {Arshad, Sana/0000-0003-4809-5742}
}

@{MTMT:34080514,
	title = {Evaluation of an Evapotranspiration Deficit-Based Drought Index and Its Impacts on Carbon Productivity in the Levant and Iraq},
	url = {https://m2.mtmt.hu/api/publication/34080514},
	author = {Alsafadi, Karam and Bi, Shuoben and Mohammed, Safwan and Mokhtar, Ali and Abdo, Hazem Ghassan and He, Hongming},
	booktitle = {Integrated Drought Management, Volume 1},
	doi = {10.1201/9781003276555-12},
	unique-id = {34080514},
	keywords = {Droughts; Climate changes; agrometeorology},
	year = {2023},
	pages = {249-278}
}