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001-es BibID:BIBFORM113752
035-os BibID:(cikkazonosító)109057 (Scopus)85165617432
Első szerző:Arshad, Sana
Cím:Exploring dynamic response of agrometeorological droughts towards winter wheat yield loss risk using machine learning approach at a regional scale in Pakistan / Sana Arshad, Jamil Hasan Kazmi, Foyez Ahmed Prodhan, Safwan Mohammed
Dátum:2023
ISSN:0378-4290
Megjegyzések: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
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény hazai lapban
folyóiratcikk
Drought
Projections
Wheat
Remote sensing
Random forest
CMIP6
Megjelenés:Field Crops Research. - 302 (2023), p. 1-20. -
További szerzők:Kazmi, Jamil Hasan Prodhan, Foyez Ahmed Mohammed Safwan (1985-) (agrármérnök)
Pályázati támogatás:TKP2021-NKTA-32
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2.

001-es BibID:BIBFORM110882
035-os BibID:(cikkazonosító)126837 (WoS)000990574800001 (Scopus)85152737381
Első szerző:Arshad, Sana
Cím:Applicability of machine learning techniques in predicting wheat yield based on remote sensing and climate data in Pakistan, South Asia / Sana Arshad, Jamil Hasan Kazmi, Muhammad Gohar Javed, Safwan Mohammed
Dátum:2023
ISSN:1161-0301 1873-7331
Megjegyzések:Machine learning (ML) algorithms perform better than classical statistical approaches to explore hidden nonlinear relationships. In this context, the goal of this research is to predict wheat yield utilizing remote sensing and climatic data in southern part of Pakistan. Four remote sensing indices, viz.., Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI) are integrated with five climatic variables, i.e., Maximum Temperature (Tmax), Minimum Temperature (Tmin), Rainfall (R), Relative humidity (RH) and windspeed (WS) and one drought index, i.e., Standardized Precipitation Evapotranspiration Index (SPEI). Eight model combinations are built within two scenarios of wheat season, i.e., Whole Seasonal mean (WSM) (SC1), and Peak of Seasonal Mean (POSM) (SC2). Two nonlinear ML algorithms, i.e., Random Forest (RF), and Support Vector Machines (SVM), and one linear model, i.e., LASSO is being employed for wheat yield prediction to find the best combination and ML algorithm in two scenarios. Results revealed that in SC1, RF regression for the model combination (GNDVI +Tmax+ Tmin + R + RH + WS) outperformed other models (R2 = 0.71, RMSE = 2.365). Similarly, in SC2 RF regression outperformed SVM with model combination (GNDVI + Tmax+ Tmin + R + RH + WS) performed highest with R2 = 0.78, and lowest RMSE = 2.07, followed by (GNDVI + SPEI + RH + WS; R2 = 0.75). Interestingly, linear LASSSO also performed equally with RF with R2 = 0.77-0.73 in both scenarios. However, the output of this research recommends using SC2 for yield prediction in ML models. Overall, this research reveals the significance and potential of ML techniques for timely prediction of crop yield in different stages of crop growth that provide a solid foundation for food security in the region.
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Drought
Random Forest
LASOO
Early prediction
Food security
Megjelenés:European Journal of Agronomy. - 147 (2023), p. 1-19. -
További szerzők:Kazmi, Jamil Hasan Javed, Muhammad Gohar Mohammed Safwan (1985-) (agrármérnök)
Pályázati támogatás:TKP2021-NKTA-32
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3.

001-es BibID:BIBFORM118417
035-os BibID:(cikkazonosító)111670 (Scopus)85184025744
Első szerző:Faheem, Zulqadar
Cím:Random forest-based analysis of land cover/land use LCLU dynamics associated with meteorological droughts in the desert ecosystem of Pakistan / Zulqadar Faheem, Jamil Hasan Kazmi, Saima Shaikh, Sana Arshad Noreena, Safwan Mohammed
Dátum:2024
ISSN:1470-160X
Tárgyszavak:Növénytermesztési és kertészeti tudományok Agrártudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Change Detection
Climate Change
SPI
Development
Arid ecosystem
Megjelenés:Ecological Indicators. - 159 (2024), p.1-20. -
További szerzők:Kazmi, Jamil Hasan Shaikh, Saima Arshad, Sana Mohammed Safwan (1985-) (agrármérnök)
Pályázati támogatás:TKP2021-NKTA-32
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4.

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
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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)
Pályázati támogatás:TKP2021-NKTA-32
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6.

001-es BibID:BIBFORM118218
035-os BibID:(cikkazonosító)108690 (WoS)001167671000001 (Scopus)85184840839
Első szerző:Mohammed Safwan (agrármérnök)
Cím:Machine learning driven forecasts of agricultural water quality from rainfall ionic characteristics in Central Europe / Safwan Mohammed, Sana Arshad, Bashar Bashir, Attila Vad, Abdullah Alsalman, Endre Harsányi
Dátum:2024
ISSN:0378-3774
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
rainwater chemistry
sodium adsorption ratio
multilayer perceptron
agriculture water optimization
Hungary
Megjelenés:Agricultural Water Management. - 293 (2024), p. 1-15. -
További szerzők:Arshad, Sana Bashir, Bashar Vad Attila (1981-) (agrármérnök) Alsalman, Abdullah Harsányi Endre (1976-) (agrármérnök)
Pályázati támogatás:TKP2021-NKTA-32
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7.

001-es BibID:BIBFORM105967
035-os BibID:(cikkazonosító)16469 (WOS)000902497100001 (Scopus)85144544776
Első szerző:Nxumalo, Gift
Cím:Meteorological Drought Variability and Its Impact on Wheat Yields across South Africa / Gift Nxumalo, Bashar Bashir, Karam Alsafadi, Hussein Bachir, Endre Harsányi, Sana Arshad, Safwan Mohammed
Dátum:2022
ISSN:1661-7827 1660-4601
Megjegyzések:Drought is one of the natural hazards that have negatively affected the agricultural sector worldwide. The aims of this study were to track drought characteristics (duration (DD), severity (DS), and frequency (DF)) in South Africa between 2002 and 2021 and to evaluate its impact on wheat production. Climate data were collected from the South African Weather Service (SAWS) along with wheat yield data from the Department of Agriculture, Forestry and Fisheries (2002-2021). The standard precipitation index (SPI) was calculated on 3-, 6-, 9-, and 12-month time scales, and the trend was then tracked using the Mann-Kendall (MK) test. To signify the climatic effects on crop yield, the standardized yield residual series (SYRS) was computed along with the crop-drought resilience factor (CR) on a provincial scale (2002-2021). The output of the SPI analysis for 32 stations covering all of South Africa indicates a drought tendency across the country. On a regional scale, western coastal provinces (WES-C and NR-C) have been more vulnerable to meteorological droughts over the past 20 years. Positive correlation results between SYRS and wheat yield indicate that the WES-C province was highly influenced by drought during all stages of wheat growth (Apr-Nov). Historical drought spells in 2003, 2009, and 2010 with low CR = 0.64 caused the province to be highly impacted by the negative impacts of droughts on yield loss. Overall, drought events have historically impacted the western part of the country and dominated in the coastal area. Thus, mitigation plans should be commenced, and priority should be given to this region. These findings can assist policymakers in budgeting for irrigation demand in rainfed agricultural regions.
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
water
meteorological drought
crop yield
food security
land
climate change
South Africa
Megjelenés:International Journal of Environmental Research and Public Health. - 19 : 24 (2022), p. 1-22. -
További szerzők:Bashir, Bashar Alsafadi, Karam Bachir, Hussein Harsányi Endre (1976-) (agrármérnök) Arshad, Sana Mohammed Safwan (1985-) (agrármérnök)
Pályázati támogatás:NKFIA-TKP2021-NKTA-32
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8.

001-es BibID:BIBFORM117901
035-os BibID:(cikkazonosító)100967 (Scopus)85182387773
Első szerző:Széles Adrienn (okleveles agrármérnök)
Cím:Precision agricultural technology for advanced monitoring of maize yield under different fertilization and irrigation regimes: A case study in Eastern Hungary (Debrecen) / Adrienn Széles, László Huzsvai, Safwan Mohammed, Anikó Nyék, Péter Zagyi, Éva Horváth, Károly Simon, Sana Arshad, András Tamás
Dátum:2024
ISSN:2666-1543
Megjegyzések:Precision agricultural (PrA) technology relies on the utilization of special equipment to access real time observations on plant health status, chlorophyll, nitrogen content, and soil moisture content. In this research new PrA technology (i.e., SPAD (Soil Plant Analysis Development), and UAV-based NDVI (Unmanned Aerial Vehicle-based Normalized Difference Vegetation Index) were used to monitor maize yield based on different filed trials in eastern part of Hungary. Our study aimed to examine the utilization of PrA technology specifically SPAD and UAV-based NDVI measurements for monitoring maize GY under irrigated and rainfed experimental setups in Hungary with varied nitrogen treatment for the year 2022. The results showed that the SPAD increased in all treatments (14.7 %; p < 0.05) from V6-V8 in the rainfed treatments, decreased significantly (p < 0.05) by 13.9 % (R1) and 30.6 % (R3). However, implementation of irrigation significantly increased the SPAD values in majority of treatments. Also, results reveal that, under irrigated and rainfed conditions the highest UAV-based NDVI value (0.703, 0.642) was obtained in V12 (A120 treatment) and highest NDVI value (0.728, 0.662) was obtained in Vn (A120 treatment). Remarekedly, irrigation led to significant differences (p < 0.05) of UAV-based NDVI values compared with none irrigated. On the other hand, implementation of 120 kg N ha?1 before sowing led to highest GY, especially under irrigated conditions (8.649 Mg ha?1). The overall mean GY under rainfed treatment was 6.256 Mg ha?1, while under irrigated treatment it increased by 37.2 % and reached 8.581 Mg ha?1 (p < 0.05). In conclusion, PrA technology will support farmers in making informed decisions regarding fertilization strategies and timing, which will in turn maximize yield and minimize risk.
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
nitrogen treatment
maize
drought
photosynthetic performance
Hungary
Megjelenés:Journal of Agriculture and Food Research. - 15 (2024), p. 1-16. -
További szerzők:Huzsvai László (1961-) (talajerőgazdálkodási szakmérnök, agrármérnök) Mohammed Safwan (1985-) (agrármérnök) Nyéki Anikó (1989-) (agrármérnök) Zagyi Péter (1986-) (agrármérnök) Horváth Éva (1993-) (környezetgazdálkodási agrármérnök) Simon Károly (1985-) (okleveles agrármérnök) Arshad, Sana Tamás András (1986-) (gazdasági agrármérnök, növénytermesztés és kertészeti tudományok)
Pályázati támogatás:TKP2021-NKTA-32
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Bolyai János Kutatási Ösztöndíj (BO/00068/23/4)
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