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Composting is the most adaptable and fruitful method for managing biodegradable solid wastes; it is a crucial agricultural practice that contributes to recycling farm and agricultural wastes. Composting is profitable for various plant, animal, and synthetic wastes, from residential bins to large corporations. Composting and agricultural waste management (AWM) practices flourish in developing countries, especially Pakistan. Composting has advantages over other AWM practices, such as landfilling agricultural waste, which increases the potential for pollution of groundwater by leachate, while composting reduces water contamination. Furthermore, waste is burned, open-dumped on land surfaces, and disposed of into bodies of water, leading to environmental and global warming concerns. Among AWM practices, composting is an environment-friendly and cost-effective practice for agricultural waste disposal. This review investigates improved AWM via various conventional and emerging composting processes and stages: composting, underlying mechanisms, and factors that influence composting of discrete crop residue, municipal solid waste (MSW), and biomedical waste (BMW). Additionally, this review describes and compares conventional and emerging composting. In the conclusion, current trends and future composting possibilities are summarized and reviewed. Recent developments in composting for AWM are highlighted in this critical review; various recommendations are developed to aid its technological growth, recognize its advantages, and increase research interest in composting processes.

Climate change has caused droughts to increase in frequency and severity worldwide, which has attracted scientists to create drought prediction models to mitigate the impacts of droughts. One of the most important challenges in addressing droughts is developing accurate models to predict their discrete characteristics, i.e., occurrence, duration, and severity. The current research examined the performance of several different machine learning models, including Artificial Neural Network (ANN) and M5P Tree in forecasting the most widely used drought measure, the Standardized Precipitation Index (SPI), at both discrete time scales (SPI 3, SPI 6). The drought model was developed utilizing rainfall data from two stations in India (i.e., Angangaon and Dahalewadi) for 2000–2019, wherein the first 14 years are employed for model training, while the remaining six years are employed for model validation. The subset regression analysis was performed on 12 different input combinations to choose the best input combination for SPI 3 and SPI 6. The sensitivity analysis was carried out on the given best input combination to find the most effective parameter for forecasting. The performance of all the developed models for ANN (4, 5), ANN (5, 6), ANN (6, 7), and M5P models was assessed through the different statistical indicators, namely, MAE, RMSE, RAE, RRSE, and r. The results revealed that SPI (t-1) is the most sensitive parameters with highest values of β = 0.916, 1.017, respectively, for SPI-3 and SPI-6 prediction at both stations on the best input combinations i.e., combination 7 (SPI-1/SPI-3/SPI-4/SPI-5/SPI-8/SPI-9/SPI-11) and combination 4 (SPI-1/SPI-2/SPI-6/SPI-7) based on the higher values of R2 and Adjusted R2 while the lowest values of MSE values. It is clear from the performance of models that the M5P model has higher r values and lesser RMSE values as compared to ANN (4, 5), ANN (5, 6), and ANN (6, 7) models. Therefore, the M5P model was superior to other developed models at both stations.

Co-digestion of organic biomass mixed with inorganic amendments could have an impact on composting dynamics. Various studies highlighted fertilizers’ role as an additive to lesser the nitrogen loss, while some studies focused on the addition of fertilizers to enhance the efficiency. The changes in carbon, nitrogen components, and humic substances during the organic-inorganic co-compost process were seldom studied. Clarifying these changes might help improve the production process and compost nutrients contents. Thus, this study’s purpose is to investigate the effects of inorganic amendments on compost characteristics, compost temperature, biochemical methane production (BMP), and nutritional contents. The inorganic phosphorous (P), sulfur (S), and sulfur solubilizing agent (SSA) were added to Farmyard manure (FYM) mixed with biodegradable waste (BW), including wheat straw, corn stalks, and green lawn waste. The P and S amended treatments were carried out into two sets, with and without SSA. The mixed feedstocks were added in the insulated RBC composting pit (15 x 15 x 10 feet). The compost material’s moisture content was maintained 50–65% during the entire composting process for optimum waste digestion i.e., the moisture content (MC) of FYM was 82.7% and for BW ranged 8.8–10.2%, while the C/N ratio was found 10.5 for FYM, 74.5 for wheat straw, 83.5 for corn stalks, and 84.8 for lawn waste. At the condition of compost maturity, the inorganic amendments have no significant effect on composted material’s moisture content. The maximum organic matter of 69.7% and C/N ratio of 44.6 was measured in T1. On the 6th day of composting, the temperature reached to thermophilic range (>45 oC) in all the treatments due to aeration of compost increased microbial activities and waste decomposition rate and decreased gradually to mesophilic range (35–45 oC) because the supply of high-energy compounds becomes exhausted. The highest temperature was reached in T4 (58 oC) and lowest in CT (47 oC). The significantly maximum methane of 8.95 m3 and biogas burning was 818 minutes in CT, followed by T1 and T4. The results of this study revealed that P enriched compost is a feasible and sustainable way to overcome P deficiency in the soil as well as in plants and best way to use low-grade P and organic waste material.

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