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Recent Submissions
Assessing the effectiveness of low-enthalpy geothermal energy for greenhouse temperature regulation towards enhancing desert agriculture
Author(s): Hegazy, Anwar; Govind, Ajit (Nature)
Date: 2025-11-07
Type: Journal Article
Status: Open access
This study investigates the viability and potential of the Earth-Air Heat Exchanger (EAHE) lowenthalpy geothermal system for greenhouse climate control in arid regions, specifically addressing the prevalent challenge of limited meteorological data. Our approach integrates ERA5-Land data with a subsurface soil temperature model, enabling accurate EAHE design and performance prediction in data-scarce environments like Bahariya Oasis, Egypt. The research confirmed the significant thermal stability of the subsurface soil, establishing its potential as a consistent heat source/sink. Initial simulations highlighted effective winter heating but revealed a need for enhanced summer cooling. We demonstrated that optimizing the EAHE system by increasing airflow successfully maintained greenhouse temperatures within near-optimal ranges (below 35 ◦C in summer, above 20 ◦C in winter) throughout the year. This achievement validates EAHE’s effectiveness for dual heating and cooling in extreme climates. This work provides a robust, data-driven methodology for designing and implementing sustainable, climate-controlled greenhouses in challenging arid zones.
Monitoring, Reporting, and Verification (MRV) Protocols Used in Carbon Trading Applied to Dryland Nations in the Global South for Climate Change Mitigation
Author(s): Thornbush, Mary; Govind, Ajit (MDPI)
Date: 2025-12-08
Type: Journal Article
Status: Open access
Climate change mitigation involves carbon sequestration that can be supported by Voluntary
Carbon Markets (VCMs) and counted as Nationally Determined Contributions (NDCs)
in national climate change strategies. Integrating these allows for the determination of
greenhouse gas (GHG) emissions and carbon sequestration at the national level. The case
for Egypt and other nontropical dryland nations is made in this systematic review article
through consideration of monitoring, reporting, and verification (MRV) protocol challenges
and initiatives. Improvements are indicated based on the literature, encompassing the
academic literature as well as organizational reports and governmental policy documents.
Agricultural MRV protocols depending on soil organic carbon (SOC) measurements are
specifically considered, delineating the challenges and barriers for SOC MRV methods.
Considering the impacts of climate zones affecting soils and providing as much standardization
as possible for MRV protocols will improve the accuracy and generalizability
of data. Measurements in carbon sequestration monitoring based on SOC MRV protocols
need to be informed by soil experts alongside climatologists and policymakers in a
multidisciplinary approach.
Enhanced soil salinity index prediction using hybrid stacking ensemble machine learning with explainable artificial intelligence (XAI) technique: a case study of the Nile Delta, Egypt
Author(s): Sahoo, Satiprasad; Singha, Chiranjit; Govind, Ajit (Springer (part of Springer Nature), 2025-11-25)
Date: 2025-11-25
Type: Journal Article
Status: Open access
Soil salinity represents the leading form of land degradation in arid and semi-arid regions. This study employed five hybrid stacking ensemble (SE) machine learning models (SE-GBM, SE-RF, SE-SVM, SE-XGB, and SE-MARS) to map salinity distribution across Egypt’s Nile Delta for 2023 and projected conditions for 2030, using EC-Earth3 and MIROC6 CMIP6 climate scenarios under SSP2-4.5 and SSP5-8.5. Results reveal substantial differences between scenarios, with SSP5-8.5 indicating up to a 15% higher salinity increase in the eastern Delta compared to SSP2-4.5. This highlights its reliability for assessing future salinity dynamics across the Nile Delta. Model validation confirmed that the SE-GBM model achieved the highest accuracy in predicting soil salinity, with an R² of 0.396 and RMSE of 0.061. Except for MARS model due to their low accuracy, all models indicated that the north-eastern, eastern, and south-eastern Nile Delta had the highest soil salinity in 2023. Salinization in these zones is driven by climate change, seawater intrusion, poor irrigation, and human pressures. Boruta analysis highlighted pH as the most influential predictor, while bulk density was least significant. SHAP (SHapley Additive exPlanations) results further showed precipitation and clay content as key drivers of salinity variability. These findings underline the robustness of Machine learning models in capturing complex soil–climate interactions. Future work should expand applications in globally, especially in resource-constrained regions.
A field-based data-driven modeling approach for livelihood vulnerability examination of rice farmers' considering climate risk in parts of West Bengal, Eastern India
Author(s): Maity, Subrata; Sahoo, Satiprasad; Govind, Ajit (Springer (part of Springer Nature))
Date: 2025-08-25
Type: Journal Article
Status: Open access
Agriculture is crucial for the rural economy in India, making it essential to assess how environmental and climatic changes impact rice farmers’ livelihoods. This study employed the Evaluation based on Distance from Average Solution (EDAS) model for a multi-criteria decision analysis method, to compute a Livelihood Vulnerability Index (LVI) and an IPCC-based LVI, reflecting rice farmers’ adaptive capacity, exposure, and sensitivity. Data from 1814 rice farmers across eight districts in West Bengal were collected in 2023. In 2023, field surveys of 1814 rice farmers across eight West Bengal districts were conducted. The LVI values ranged from 0.17 to 0.93, with an ROC-AUC accuracy of a model classification accuracy (ROC-AUC) of 0.89. LVI-AC ranged from 0.02 to 0.97, LVI-E and LVI-S from 0.00 to 1.00, and LVI-IPCC values from − 0.48 to 0.54, with an ROC-AUC accuracy of 0.86. Component indices varied widely: adaptive capacity (LVI-AC) ranged from 0.02 to 0.97, and exposure/sensitivity (LVI-E/S) from 0.00 to 1.00. The composite LVI-IPCC ranged from − 0.48 to 0.54 (ROC-AUC = 0.86). District-level analysis showed that Birbhum and Murshidabad were the most vulnerable districts, whereas Purba and Paschim Bardhaman were relatively less vulnerable (e.g. nearly half of Murshidabad’s area was highly vulnerable vs. less than 10% in Purba Bardhaman). Adaptive capacity was lowest in Jhargram and Paschim Bardhaman, reflecting limited adaptation resources, whereas Purba Bardhaman was the most exposed to climate risk. In contrast, exposure vulnerability was minimal in Bankura. Sensitivity to climate hazards was highest in Bankura and Jhargram. The LVI-IPCC analysis identified Birbhum as highly vulnerable to climate change. Notably, the combined LVI-IPCC measure singled out Birbhum as particularly vulnerable to climate change impacts. By highlighting livelihood vulnerabilities, this study informs interventions that support poverty reduction and food security (SDGs 1–3) while promoting sustainable economic growth (SDG 8) and climate resilience (SDG 13). The approach provides a practical tool for policymakers to target adaptation strategies and enhance climate-adaptive farming practices among vulnerable communities.
Leveraging ML to predict climate change impact on rice crop disease in Eastern India
Author(s): Sahoo, Satiprasad; Singha, Chiranjit; Govind, Ajit; Mamta, Sharma (Springer (part of Springer Nature) (Springer Open Choice Hybrid Journals), 2025-03-08)
Date: 2025-03-08
Type: Journal Article
Status: Timeless limited access
Rice crop disease is critical in precision agriculture due to various influencing components and unstable environments. The current study uses machine learning (ML) models to predict rice crop disease in Eastern India based on biophysical factors for current and future scenarios. The nine biophysical parameters are precipitation (Pr), maximum temperature (Tmax), minimum temperature (Tmin), soil texture (ST), available water capacity (AWC), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference chlorophyll index (NDCI), and normalized difference moisture index (NDMI) by Random forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), Artificial Neural Net (ANN), and Support vector Machine (SVM). The multicollinearity test Boruta feature selection techniques that assessed interdependency and prioritized the factors impacting crop disease. However, climatic change scenarios were created using the most recent Climate Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP5-8.5 datasets. The rice crop disease validation was accomplished using 1105 field-based farmer observation recordings. According to the current findings, Purba Bardhaman district experienced a 96.72% spread of rice brown spot disease due to weather conditions. In contrast, rice blast diseases are prevalent in the north-western region of Birbhum district, affecting 72.38% of rice plants due to high temperatures, water deficits, and low soil moisture. Rice tungro disease affects 63.45% of the rice plants in Bankura district due to nitrogen and zinc deficiencies. It was discovered that the link between NDMI and NDVI is robust and positive, with values ranging from 0.8 to 1. According to SHAP analysis, Pr, Tmin, and Tmax are the top three climatic variables impacting all types of disease cases. The study’s findings could have a substantial impact on precision crop protection and meeting the United Nations Sustainable Development Goals.