Accurate classification of water quality and precise prediction of the Water Quality Index (WQI) are essential for safeguarding aquatic ecosystems and mitigating disease risks in aquaculture. This study systematically evaluates multiple machine learning models including LightGBM, XGBoost, Random Forest, and Support Vector Machines using grid search optimization to enhance performance. Results show that ensemble models, particularly LightGBM and XGBoost, achieved superior accuracy in water quality classification (up to 99.65 %). For WQI prediction, XGBoost regression delivered the highest performance with an R² of 0.9685. SHAP (SHapley Additive exPlanations) analysis was employed to interpret model predictions and quantify feature contributions. Dissolved oxygen and BOD emerged as the most influential predictors, followed by turbidity, nitrate, and electrical conductivity, aligning with known risk factors for aquatic disease outbreaks. These findings underscore the potential of combining advanced machine learning with explainable AI techniques and real-time water quality data to enable proactive monitoring and early warning systems for sustainable aquatic health management.

Phospholipids (PL) are widely used as aquafeed to enhance aquaculture production, particularly in crustaceans. The most common source of PL for aquaculture is lecithin, which is derived from both animals (e.g., eggs) and plants (e.g., soybeans). Including optimal levels of PL in crustacean diets enhances growth performance, survivability, antioxidant capacity, and lipid metabolism. PL is especially critical for early juveniles, as it supports osmoregulation by elevating Na+/K+‐ATPase (NKA) activity. Furthermore, PL‐enriched diets trigger energy metabolism, enhancing their physiological performance. In reproduction, PL provides energy for lipids mobilization and steroid transformation, improving the process of vitellogenesis in females. However, excessive PL can negatively affect the survival rate (SR), molting frequency (MF), condition factor (CF), and hepatosomatic index (HSI). This comprehensive review explores recent findings on how PL‐enriched diets affect crustacean growth, metabolism, immunity, reproduction, gut microbiota, and osmoregulation. It also addresses gaps in our understanding of specific PL dietary needs for optimal crustacean health and productivity, offering evidence‐based guidelines for effective PL supplementation in aquaculture.

Crop yield is heavily influenced by soil salinity. The objective of the research is to assess the depth wise soil salinity distribution pattern and groundwater quality by measuring physico-chemical parameters in the agricultural land of Nijhum Island, Noakhali, Bangladesh. This study also explicates the spatial variation of soil salinity in the study area. Soil samples were collected from three different depths to estimate the Electrical Conductivity (EC) using a paste of 1:5 soil and distilled water suspension (1:5 weight-to-volume method). Spatial analyst tool of ArcGIS was used and observed that the land of Nijhum Island was affected with severe salinity in almost 85% of sampling plots and 15% of sampling plots were extremely saline. The soil salinity process has been noticed due to seawater intrusion from the Bay of Bengal through the Meghna estuary which threatens crop production and soil fertility, threatening the population's livelihood. This study provides a baseline understanding of soil salinity in Nijhum Island to help decision-makers and smallholder farmers improve their livelihoods. Thus, groundwater depth did not affect soil salinity significantly. The result contradicts most studies that found a strong influence on soil salinity. Because the study area is coastal, most of the salt comes from seawater inundation and capillary rise, which reduces groundwater availability.

This paper presents the data on growth performance of marine Chlorella sp. cultured in different cost-effective media including cow dung, cow urine, poultry litter, compost, NPK (nitrogen, phosphorus, and potassium), and UTR (Urea, TSP, and red potash). Growth curve of Chlorella sp. was determined at 5 mg of cow dung, poultry litter, compost, NPK, UTR and 5 µL of cow urine per 350 ml sea water (25 ppt) to identify the onset of stationary phase. Further four media among these were selected to continue the experiment at 8 mg and 11 mg of concentration. The higher cell densities were 4.21 × 10⁶ and 4.18 × 10⁶ cells/mL for NPK at 8 mg and 11 mg of concentration on 6th and 5th day, respectively. Cow dung with an 11 mg of concentration exhibited 2.67 × 10⁶ cells/mL on the 3rd day, which is around 1.5 times greater than the highest growth in the same concentration of poultry litter. Chlorella sp. had a higher cell density in NPK media than in other media, however it was discarded since it is inorganic and costly. Due to the low cell density in cow urine media and the prolonged stationary phase in poultry litter media, the focus of the subsequent study was then placed on cow dung media. The data will contribute to the selection of locally available and cost-effective culture media by determining the stationary phases for specific microalgal species which will replace the costly and labor-intensive commercial media.