Soil temperature is an important factor in biogeochemical processes. On-site monitoring of soil temperature is limited in spatiotemporal scale as compared to air temperature data inventories due to various management difficulties. Therefore, empirical models were developed by taking 30-year long-term (1985-2014) air and soil temperature data for prediction of soil temperatures at three depths (5, 15, 30 cm) in morning (0636 Indian standard time) and afternoon (1336 Indian standard time) for alluvial soils in lower Indo-Gangetic plain. At 5 cm depth, power and exponential regression models were best fitted for daily data in morning and afternoon, respectively, but it was reverse at 15 cm. However, at 30 cm, exponential models were best fitted for both the times. Regression analysis revealed that in morning for all three depths and in afternoon for 30 cm depth, soil temperatures (daily, weekly, and monthly) could be predicted more efficiently with the help of corresponding mean air temperature than that of maximum and minimum. However, in afternoon, prediction of soil temperature at 5 and 15 cm depths were more precised for all the time intervals when maximum air temperature was used, except for weekly soil temperature at 15 cm, where the use of mean air temperature gave better prediction.

Mangrove forests, apart from their carbon sequestration and coastal protection benefits, provide a wide range of ecosystem services to people in tropical developing countries. Local people living in and around forests in the developing tropics also depend heavily on these mangrove ecosystem services for their livelihoods. This study examines the impact of mangrove ecosystem services on the livelihoods of people in Indian part of the Sundarbans—the largest contagious mangrove forest on earth. To achieve this objective, a household survey was undertaken to gather data on the diverse range of provisioning and regulating ES local people derived from mangrove forests living near the Indian Sundarbans. Surveys were carried out in nine villages across the Kultali, Basanti, and Gosaba blocks, involving over one hundred respondents. Our study reveals the active participation of locals in gathering various ecosystem services, with fishing and crab collection being the most common in the area. Due to numerous challenges in the agricultural sector, such as soil salinity and frequent extreme weather events, people increasingly depend on non-farming incomes, particularly fishing. A questionnaire was used to assess the dependence of local people on different ecosystem services. Some villages, such as Amlamethi, Satyanarayanpur, Mathurakhand, Vivekananda Palli, and Second Scheme, demonstrated a higher reliance on forest ecosystem services compared to other villages. The study indicates that the contribution of ecosystem services sometimes surpasses traditional activities like farming and daily contractual work. River transportation emerged as the most crucial service, followed by freshwater, food, and fiber. While certain resources like fuel, natural medicine, and genetic resources may not be prioritized, they still hold significance within the community, contrasting with ornamental resources, which are considered the least important. Our findings underscore the importance of preserving natural services in the Sundarbans forest, highlighting the need to conserve the mangrove ecosystem services to ensure the long-term well-being of local communities.

Soil stability, yield decline and higher carbon decomposition are the key issues in intensive agriculture. Long-term integrated nutrient management (INM) influences carbon (C) dynamics and maintains the soil aggregate stability, which driven by the type of cropping system and climatic zone. Therefore, the specific effects of rice-wheat-jute cropping system on soil aggregate distribution, C-stability, and trade-off between C-storage and decomposition rate were studied of a long-term INM trial in the sub-tropical zone. We investigated the effect of four long-term nutrient management practices that include, control, N (nitrogen (N) and no phosphorus (P), and potassium (K)), NPK (N, P and K) and INM (N, P, K + farmyard manure (FYM)). Results showed that the addition of FYM with NPK improved macro-aggregates by 71%, and associated C-pools by 30%. The INM enhanced C-storage (20%), however, the decomposition rate was also higher (28%) over control at 35 °C at field capacity. A trade-off exists between the soil-aggregate stabilization and C-decomposition rate. The proportion of C-storage was higher than decomposition in INM. Further, the C-sequestration varied (0.17 to 0.26 t ha⁻¹ y⁻¹) under different treatments and were found highest in INM, indicating long-term soil stability and yield sustainability in the rice-wheat-jute system.

Knowledge about the rainfall distribution, frequency of dry and wet spells, and the probable weeks of the onset and termination of monsoon season are the prerequisite for planning agricultural operations particularly for rainfed crops such as jute in eastern India. In this paper, therefore, the marginal, conditional, and consecutive dry and wet week probabilities were analysed using Markov chain model, and the forward and backward accumulation method was used for computing probabilities of onset and withdrawal of monsoon season for planning jute-based cropping systems in eastern India. Both the methods were applied for weekly rainfall data of the four stations situated in jute growing areas of eastern India such as Nagaon (period: 2011-2020), Pundibari (period: 1998-2016), Barrackpore (period: 1983-2020) and Bhubaneswar (period: 2008-2020). In jute season (13-29 weeks), the probability of dry weeks was maximum (3-81%) in Barrackpore and minimum (0-19%) in Nagaon. The probability of dry week preceded by another dry week was maximum in Bhubaneswar and minimum in Nagaon. The probability of two and three consecutive dry weeks was maximum (0-65% and 0-52%, respectively) in Barrackpore and minimum (0-9% and 0-6%, respectively) in Nagaon. The onset and withdrawal of monsoon season were also computed for all the four stations data. This analysis will be helpful for water management and determining or adjusting proper sowing time of jute or any other crops especially rainfed crops so that the critical water-sensitive phenological phases might not coincide any water stress situation and thus ensuring to reach potential yield of that particular crop.