TY - JOUR AU - Ocwa, Akasairi AU - Mohammed, Safwan AU - Mousavi, Seyed Mohammad Nasir AU - Illés, Árpád AU - Bojtor, Csaba AU - Ragán, Péter AU - Rátonyi, Tamás AU - Harsányi, Endre TI - Maize Grain Yield and Quality Improvement Through Biostimulant Application: a Systematic Review JF - JOURNAL OF SOIL SCIENCE AND PLANT NUTRITION J2 - J SOIL SCI PLANT NUT PY - 2024 SN - 0718-9508 DO - 10.1007/s42729-024-01687-z UR - https://m2.mtmt.hu/api/publication/34731789 ID - 34731789 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Mohammed, Safwan AU - Arshad, Sana AU - Alsilibe, Firas AU - Moazzam, Muhammad Farhan Ul AU - Bashir, Bashar AU - Prodhan, Foyez Ahmed AU - Alsalman, Abdullah AU - Vad, Attila Miklós AU - Rátonyi, Tamás AU - Harsányi, Endre TI - Utilizing machine learning and CMIP6 projections for short-term agricultural drought monitoring in central Europe (1900–2100) JF - JOURNAL OF HYDROLOGY J2 - J HYDROL VL - 633 PY - 2024 SP - 1 EP - 21 PG - 21 SN - 0022-1694 DO - 10.1016/j.jhydrol.2024.130968 UR - https://m2.mtmt.hu/api/publication/34694318 ID - 34694318 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Faheem, Zulqadar AU - Kazmi, Jamil Hasan AU - Shaikh, Saima AU - Arshad, Sana AU - Mohammed, Safwan TI - Random forest-based analysis of land cover/land use LCLU dynamics associated with meteorological droughts in the desert ecosystem of Pakistan JF - ECOLOGICAL INDICATORS J2 - ECOL INDIC VL - 159 PY - 2024 PG - 20 SN - 1470-160X DO - 10.1016/j.ecolind.2024.111670 UR - https://m2.mtmt.hu/api/publication/34559057 ID - 34559057 AB - 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 LA - English DB - MTMT ER - TY - JOUR AU - Mohammed, Safwan AU - Arshad, Sana AU - Bashir, Bashar AU - Vad, Attila Miklós AU - Alsalman, Abdullah AU - Harsányi, Endre TI - Machine learning driven forecasts of agricultural water quality from rainfall ionic characteristics in Central Europe JF - AGRICULTURAL WATER MANAGEMENT J2 - AGR WATER MANAGE VL - 293 PY - 2024 PG - 15 SN - 0378-3774 DO - 10.1016/j.agwat.2024.108690 UR - https://m2.mtmt.hu/api/publication/34524718 ID - 34524718 LA - English DB - MTMT ER - TY - JOUR AU - Széles, Adrienn [Ványiné] AU - Huzsvai, László AU - Mohammed, Safwan AU - Nyéki, Anikó Éva AU - Zagyi , Péter AU - Horváth, Éva AU - Simon, Károly AU - Arshad, Sana AU - Tamás, András TI - Precision agricultural technology for advanced monitoring of maize yield under different fertilization and irrigation regimes: A case study in Eastern Hungary (Debrecen) JF - JOURNAL OF AGRICULTURE AND FOOD RESEARCH J2 - J AGRICULT FOOD RES VL - 15 PY - 2024 PG - 16 SN - 2666-1543 DO - 10.1016/j.jafr.2024.100967 UR - https://m2.mtmt.hu/api/publication/34501873 ID - 34501873 LA - English DB - MTMT ER - TY - JOUR AU - Altouma, Ahmed AU - Bashir, Bashar AU - Ata, Behnam AU - Ocwa, Akasairi AU - Alsalman, Abdullah AU - Harsányi, Endre AU - Mohammed, Safwan TI - An environmental impact assessment of Saudi Arabia's vision 2030 for sustainable urban development: A policy perspective on greenhouse gas emissions JF - ENVIRONMENTAL AND SUSTAINABILITY INDICATORS J2 - ENVIRON SUSTAIN INDIC VL - 21 PY - 2024 PG - 13 SN - 2665-9727 DO - 10.1016/j.indic.2023.100323 UR - https://m2.mtmt.hu/api/publication/34444153 ID - 34444153 LA - English DB - MTMT ER - TY - JOUR AU - Mohammed, Safwan AU - Gill, Abid Rashid AU - Ghosal, Kaushik AU - Al-Dalahmeh, Main AU - Alsafadi, Karam AU - Szabó, Szilárd AU - Oláh, Judit AU - Alkerdi, Ali AU - Ocwa, Akasairi AU - Harsányi, Endre TI - Assessment of the environmental kuznets curve within EU-27: Steps toward environmental sustainability (1990–2019) JF - Environmental Science and Ecotechnology J2 - Environmental Science and Ecotechnology VL - 18 PY - 2024 PG - 36 SN - 2666-4984 DO - 10.1016/j.ese.2023.100312 UR - https://m2.mtmt.hu/api/publication/34148822 ID - 34148822 LA - English DB - MTMT ER - TY - JOUR AU - Rodrigo-Comino, Jesús AU - Muñoz-Gómez, Casandra AU - Rahdari, Mohammad Reza AU - Mohammed, Safwan AU - Salvati, Luca TI - Land degradation risks JF - CUADERNOS DE INVESTIGACION GEOGRAFICA J2 - CUAD INVEST GEOG VL - 49 PY - 2023 IS - 2 SP - 3 EP - 4 PG - 2 SN - 0211-6820 DO - 10.18172/cig.5869 UR - https://m2.mtmt.hu/api/publication/34500637 ID - 34500637 LA - English DB - MTMT ER - TY - JOUR AU - Arshad, Sana AU - Kazmi, Jamil Hasan AU - Prodhan, Foyez Ahmed AU - Mohammed, Safwan TI - Exploring dynamic response of agrometeorological droughts towards winter wheat yield loss risk using machine learning approach at a regional scale in Pakistan JF - FIELD CROPS RESEARCH J2 - FIELD CROP RES VL - 302 PY - 2023 PG - 20 SN - 0378-4290 DO - 10.1016/j.fcr.2023.109057 UR - https://m2.mtmt.hu/api/publication/34083638 ID - 34083638 AB - 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. LA - English DB - MTMT ER - TY - CHAP AU - Alsafadi, Karam AU - Bi, Shuoben AU - Mohammed, Safwan AU - Mokhtar, Ali AU - Abdo, Hazem Ghassan AU - He, Hongming TI - Evaluation of an Evapotranspiration Deficit-Based Drought Index and Its Impacts on Carbon Productivity in the Levant and Iraq T2 - Integrated Drought Management, Volume 1 PB - CRC Press CY - Boca Raton, Florida SN - 9781003276555 PY - 2023 SP - 249 EP - 278 PG - 30 DO - 10.1201/9781003276555-12 UR - https://m2.mtmt.hu/api/publication/34080514 ID - 34080514 LA - English DB - MTMT ER -