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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 OTKA
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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 Egyéb
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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 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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