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Recent Submissions
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))
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.
Review of aquifer storage and recovery opportunities and challenges in India
Author(s): Sahoo, Satiprasad; Singha, Chiranjit; Govind, Ajit; Sharma, Prabhakar (Springer (part of Springer Nature), 2025-02-18)
Date: 2025-02-18
Type: Journal Article
Status: Timeless limited access
Managing groundwater is a global challenge as offer rises across agriculture, industry, and energy sectors, while climate change, population explosion, industrialization, and urbanization leads to a decline in surface water resources. Managed aquifer recharge (MAR) is one solution that can enhance long-term water sustainability by increasing the natural replenishment of groundwater supplies through the use of non-traditional water sources. India, as the largest groundwater user, is mitigating over-extraction through MAR initiatives. However, Aquifer Storage and Recovery (ASR) provides a site-specific solution for maintaining a sustainable water supply. This approach targets densely populated regions in the Indian subcontinent, particularly those undergoing agricultural transitions, heavily dependent on groundwater for irrigation and domestic use, and facing water shortages in both ground and surface water supplies. The global land data assimilation systems (GLDAS) of 2003–2023 revealed significant groundwater and total water storage depletion in north-western India, with negative trends between − 27.816 and − 21.186 mm/year. These findings emphasize the urgent need to implement MAR systems in the Western Dry Region, Western Himalayas, and Gangetic Plains to ensure sustainable agricultural planning and management. Thus, the review paper emphasizes the potential of MAR and ASR techniques to meet both current and future demands for high-quality water while addressing the rising need for groundwater. In particular, ASR can tackle issues related to water stress, manage wastewater, alleviate flooding, prevent saltwater intrusion, lessen land subsidence, safeguard crops from damage, avert aquifer depletion, and enhance water quality. The review also discusses the significance of ASR-related groundwater resource projects in India, especially in the context of changing climatic conditions. At last, we explored ASR’s types, challenges, benefits, limitations, and recommendations for sustainable groundwater management. ASR is seen as a viable solution in India to improve water resource policies amid climate change, addressing water rights, public health, and environmental issues. These insights can help identify optimal sites in water-scarce regions of India for the deployment of specific ASR approaches aimed at enhancing water sustainability.
Exploring Nitrogen Use Efficiency in Cereals: Insight into Traits, Metabolism, and Management Strategies Under Climate Change Conditions – A Comprehensive Review
Author(s): Ben Debbane, Fatima Zahra; Baidani, Aziz; Aarbaoui, Maria; Moussadek, Rachid; Mrabet, Rachid; Amamou, Ali (Springer (part of Springer Nature), 2025-03-21)
Date: 2025-03-21
Type: Journal Article
Status: Timeless limited access
Nitrogen (N) is an essential element for cereals growth and development, playing a crucial role in productivity and yield. Consequently, nitrogen fertilizers are extensively used in cereal cultivation. However, the excessive fertilizer application has led to significant environmental challenges, including nitrate leaching, greenhouse gas emissions coupled, and rising production costs due to increasing fertilizer prices. Additionally, has contributed to declining grain quality through reduced Nitrogen Use Efficiency (NUE). Enhancing NUE is crucial to address these issues, requiring comprehension understanding of its components and the physiological mechanisms governing nitrogen uptake, assimilation, and remobilization. This review synthesizes existing literature on NUE components and their influence on NUE variation. It explores nitrogen pathways in plants, interactions with soil properties, and the impact of root architecture and carbon–nitrogen metabolism. The review also highlights practical strategies for enhancing NUE, including agronomic innovations such as precision fertilization and irrigation, remote sensing, and site-specific management, and physiological approaches. Furthermore, emerging high-throughput tools, including remote sensing technologies and precision agriculture, are discussed. Given the challenges posed by climate change, such as heat stress, elevated CO₂ levels, and unpredictable rainfall, developing nitrogen-efficient cereals is essential for insuring sustainability, productivity, and global food security. This review underscores the need for integrated strategies that advanced research, agronomic techniques, and technological innovation. Additionally, limited progress has been made in integrating cutting-edge genetic tools, such as Omics and CRISPR technologies, with a deeper understanding of the complex interaction between genetic and environmental factors to enhance NUE of cereal crops.
Application of Compost as an Organic Amendment for Enhancing Soil Quality and Sweet Basil (Ocimum basilicum L.) Growth: Agronomic and Ecotoxicological Evaluation
Author(s): Lhaj, Majda Oueld; Moussadek, Rachid; Mouhir, Latifa; Sanad, Hatim; Manhou, Khadija; Iben Halima, Oumaima; Yachou, Hasna; Zouahri, Abdelmjid; Mdarhri Alaoui, Meriem (MDPI, 2025-04-26)
Date: 2025-04-26
Type: Journal Article
Status: Open access
This study investigates the effectiveness of organic compost as a sustainable alternative to chemical fertilizers for improving soil health and enhancing crop productivity under greenhouse conditions. The experiment focused on sweet basil (Ocimum basilicum L.), an aromatic herb highly sensitive to soil fertility and structure, cultivated in sandy loam soil—a prevalent substrate in arid and semi-arid regions, often limited by poor water and nutrient retention. Using a randomized complete block design with six compost application rates, this study evaluated the physicochemical, biochemical, and agronomic responses of both soil and plants. The results demonstrated significant improvements across all parameters (p < 0.05), with the 30 t/ha compost treatment yielding the most notable enhancements in soil structure, nutrient content, and plant performance while maintaining acceptable levels of heavy metals. Soil organic matter (SOM) increased to 13.71%, while shoot length (SL), essential oil content (EOC), and the 100-seed weight improved to 42 cm, 0.83%, and 0.32 g, respectively, compared to the control. These finding underscore the benefits of high compost application rates in boosting greenhouse horticultural productivity while promoting sustainable agriculture. Moreover, this study supports the reduction in chemical fertilizer dependency and encourages the adoption of circular economy principles (CEPs) through organic waste recycling.
Advancing flood risk assessment: Multitemporal SAR-based flood inventory generation using transfer learning and hybrid fuzzy-AHP-machine learning for flood susceptibility mapping in the Mahananda River Basin
Author(s): Singha, Chiranjit; Sahoo, Satiprasad; Mahtaj, Alireza Bahrami; Moghimi, Armin; Welzel, Mario; Govind, Ajit (Elsevier (12 months), 2025-04-01)
Date: 2025-03-22
Type: Journal Article
Status: Open access
The Mahananda River basin, located in Eastern India, faces escalating flood risks due to its complex hydrology and geomorphology, threatening socioeconomic and environmental stability. This study presents a novel approach to flood susceptibility (FS) mapping and updates the region's flood inventory. Multitemporal Sentinel-1 (S1) SAR images (2020–2022) were processed using a U-Net transfer learning model to generate a water body frequency map, which was integrated with the Global Flood Dataset (2000–2018) and refined through grid-based classification to create an updated flood inventory. Eleven geospatial layers, including elevation, slope, soil moisture, precipitation, soil type, NDVI, Land Use Land Cover (LULC), geomorphology, wind speed, drainage density, and runoff, were used as flood conditioning factors (FCFs) to develop a hybrid FS mapping approach. This approach integrates the Fuzzy Analytic Hierarchy Process (FuzzyAHP) with six machine learning (ML) algorithms to create hybrid models FuzzyAHP-RF, FuzzyAHP-XGB, FuzzyAHP-GBM, FuzzyAHP-avNNet, FuzzyAHP-AdaBoost, and FuzzyAHP-PLS. Future flood trends (1990–2030) were projected using CMIP6 data under SSP2-4.5 and SSP5-8.5 scenarios with MIROC6 and EC-Earth3 ensembles. The SHAP algorithm identified LULC, NDVI, and soil type as the most influential FCFs, contributing over 60 % to flood susceptibility. Results show that 31.10 % of the basin is highly susceptible to flooding, with the western regions at greatest risk due to low elevation and high drainage density. Future projections indicate that 30.69 % of the area will remain highly vulnerable, with a slight increase under SSP5-8.5. Among the models, FuzzyAHP-XGB achieved the highest accuracy (AUC = 0.970), outperforming FuzzyAHP-GBM (AUC = 0.968) and FuzzyAHP-RF (AUC = 0.965). The experimental results showed that the proposed approach can provide a spatially well-distributed flood inventory derived from freely available remote sensing (RS) datasets and a robust framework for long-term flood risk assessment using hybrid ML techniques. These findings offer critical insights for improving flood risk management and mitigation strategies in the Mahananda River basin.