A field experiment on green pea (Pisum Sativum L.) was conducted under drip irrigation to determine the irrigation schedule based on real-time soil moisture measurements with irrigation treatments (main plots) and fertilizer treatments (sub-plots) in a split-plot design with three replications. Main plots consisted of fourirrigation levels at different matric potential ranges (I₁: −20 kPa; I₂: −30 kPa; I₃: −35 kPa; and I₄: −40 kPa), while the sub-plots consisted of three fertigation levels (F₁: 120%, F₂: 100% and F₃: 80%) of recommended dose of fertilizers (40:60:50 kg/ha of NPK). The tensiometer with digital pressure transducer transferred the soil matric potential data to the irrigation controller, which activated the solenoid valves for irrigation. Observations were collected on plant growth parameters, pod yield, and quality parameters. Descriptive statistics of different plant growth parameters were made. The higher SMP threshold (−20 kPa) and lower SMP threshold (−40 kPa) greatly reduced the yield and water-use efficiency. Considering the results, real-time soil moisture-based irrigation at the soil matric potential threshold level of −30 kPa with 120% of recommended dose of fertilizers through fertigation was recommended for attaining maximum green pea pod yield and water-use efficiency under semi-arid Inceptisols.

A field experiment was conducted on okra (Abelmoschus esculentus L.) for assessing the sustainability of yield with optimum irrigation schedule based on soil moisture depletion. Four irrigation treatments: Irrigation at I₁:20%, I₂:30%, I₃:40% and I₄:50% of soil moisture depletion rate in main plots and three fertilizer treatments: Fertigation at F₁:100%, F₂:80% and F₃:60% of recommended NPK (100:25:40 kg/ha) in subplots were tested. Soil matric potential was recorded continuously using electronic tensiometers. The soil moisture characteristics curve was derived for various soil matric potential value sand the soil water content. The irrigation controller triggered solenoid valves for irrigation when soil moisture depletion reached a prespecified level in each treatment. Soil moisture depletion values were significantly predicted based on a regression model calibrated for each treatment over the crop growing period. The model gave minimum prediction error (PE) for I₁, followed by I₂, I₃ and I₄, respectively. Plant growth and yield parameters were significantly influenced by the soil moisture availability under each treatment. It is recommended that irrigation be scheduled at 20% soil moisture depletion rate together with 100% NPK fertilizer application for attaining sustainable yield of okra (12.3 t/ha), apart from maximum WUE (3.5 kg/m³) and plant growth parameters under semiarid inceptisols.

Climate and climate change are critical factors influencing global ecosystems, agriculture and forestry. Climate refers to the long-term patterns of temperature, precipitation and atmospheric conditions, while climate change refers to significant alterations in these patterns over time. This review examines the impacts of climate change on natural systems, with particular focus on variations in temperature, precipitation and related climatic variables. The review integrates advanced statistical methodologies, including homogeneity tests, trend analysis and change-point detection, to analyze climate data and identify patterns of variability and shifts over time. Homogeneity tests are used to detect sudden changes or inhomogeneities caused by non-climatic variables (from station relocations or instrumental changes). Trend analysis is employed to detect long-term changes in climate variables such as temperature and precipitation, providing information on existing climate changes. Change-point detection tests help identify specific points at which significant shifts in climatic patterns occur, which may influence sudden transitions in ecosystems. Collectively, these tools enhance our ability to detect, interpret and respond to the evolving nature of climate change, thereby supporting timely and informed adaptation strategies in the fields of agriculture and forestry.

Effective monitoring and predicting surface water quality are vital for sustainable water resource control. Traditional in-situ techniques are regularly constrained by their time-consuming nature and restrained spatial coverage. This study seeks to develop a predictive version that combines physio-chemical water quality parameters with remote sensing indices derived from the Sentinel-2A dataset to enhance accuracy and spatial attainment. The research focuses on four urban tanks in Coimbatore namely Krishnampathy, Selvampathy, Kumaraswamy and Ukkadam Periyakulam. The physio-chemical parameters for assessing the water quality which include pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Dissolved oxygen (DO), Calcium (Ca), Magnesium (Mg), Total hardness, Chloride (Cl-), Carbonate (Co3-) and Bicarbonate (HCo3-) have been measured, additionally for the detection of surface water extent using remote sensing indices namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Water Ratio Index, Normalized Difference Chlorophyll Index (NDCI) indices which were extracted from sentinel-2A datasets. The parameters such as EC, TDS, Ca2+, Cl- and Total hardness show a high coefficient of determination (R2). Correlation and regression techniques have been employed to integrate these datasets, resulting in the development of a robust predictive model. By combining these two information sources, the model is constructed using stepwise regression analysis. The model's accuracy turned into established towards ground-truth records, showing large improvements whilst far away Remote sensing indices had been covered.

Water is crucial in agriculture, domestic use and industrial development. In recent years, the demand for groundwater has significantly risen due to industrialization, urbanization, population growth and increased agricultural activities. This study focuses on the groundwater quality spatial distribution and utilizes geostatistical analysis to predict groundwater chemical parameters within the Noyyal sub-basin, employing Geographic Information System (GIS) technology. Data transformation methods were applied to reduce skewness in several chemical parameters to improve the precision of the spatial representation of groundwater chemistry. Comparing the calculated concentrations to the established permissible limits showed that calcium, bicarbonate and sodium absorption ratio concentrations were within acceptable levels. In contrast, parameters such as magnesium, sodium, potassium, chlorine, sulfate, fluoride, pH, total hardness, electrical conductivity and total dissolved solids exceeded the permissible thresholds. The study also identified the most appropriate semi-variogram model for each water quality parameter based on the Root Mean Square Error (RMSE). The Exponential model with log-transformed data was the best fit for Ca, Na, K, HCO3, pH, HAR and EC, providing physically meaningful results. For TDS, Mg, SO4, F and SAR, the Spherical model with log-transformed data yielded the most reliable RMSE values. The Gaussian model produced satisfactory results for Cl and Na %.

Forest fires pose a significant threat to ecosystems, biodiversity and human livelihoods, necessitating the development of advanced predictive models for timely intervention. Machine learning (ML) techniques have emerged as effective tools for predicting forest fires by utilizing various environmental, meteorological and topographical datasets. This review explores recent advancements in ML-based fire prediction, highlighting key methodologies such as Random Forest, Support Vector Machines, Neural Networks and hybrid models. The combination of remote sensing data, real-time observation and cloud computing has notably improved the prediction accuracy. Moreover, the growth of Explainable AI (XAI) has enhanced the interpretability of ML models. Despite advancements, challenges such as data imbalances, model generalization issues and computational constraints persist. Future research must focus on refining ML algorithms to improve regional adaptability, integrating climate change forecasts and establishing real-time early warning systems. By bridging the gap between theoretical advancements and practical applications, ML-driven forest fire prediction models can significantly contribute to mitigating the devastating impact of forest fire and enhancing global fire management strategies.