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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. [ABSTRACT FROM AUTHOR]

Present study examined the overall performance of 12 CMIP6 GCMs for rainfall, maximum and minimum temperatures for rice crop-growing seasons i.e., Boro (January to May) and Kharif (June to October) over the new alluvial zone of West Bengal. A wide range of indices i.e., index of agreement, error indices and bias estimators were utilized to put more confidence on the results. Results indicated that CMIP6 models were able to reproduce observed mean climatology and inter-annual variability of maximum and minimum temperature adequately for both seasons while a smaller number of models (3-4 models) out of a total of 12 GCM-CMIP6 models showed satisfactory performance for rainfall. The ranks assigned to the models revealed that CNRM–ESM2–1 was the best-performing model for Kharif and MRI-ESM2-0 showed the highest skill for Boro. ACCESS-CM2 and MPI-ESM1-2-LR performed worst for Kharif and Boro seasons respectively. Further, CNRM–ESM2–1 and MRIESM2-0 were used to project the future climate for Kharif and Boro seasons respectively under both moderate (SSP2-4.5) and extreme scenarios (SSP5-8.5). Higher warming was projected during Boro season than Kharif. Projections revealed increasing rainfall during Kharif season but decreasing rainfall in Boro season in both the moderate and extreme future scenarios. [ABSTRACT FROM AUTHOR]

The present research focuses on the variation of field pea production under different prevailing weather parameters, aiming to develop a reliable forecasting model. For that a field experiment was conducted in New Alluvial Zone of West Bengal during 2018-19 and 2019-20 with three different varieties (VL42, Indira Matar, Rachana) of this region. Biomass predicting equation based on maximum temperature, minimum temperature and solar radiation was developed to estimate field pea yield for 2040-2099 period under SSP 2-4.5 and SSP 5-8.5 scenarios. It reveals that solar radiation positively influences crop biomass, while high maximum and minimum temperatures have adverse effects on yield. The developed forecasting equation demonstrated its accuracy (nRMSE=17.37%) by aligning closely with historical data, showcasing its potential for reliable predictions. Furthermore, the study delves into future climate scenarios, showing that increasing temperatures are likely to impact field pea yield negatively. Both biomass and yield showed decreasing trend for the years from 2040 to 2099. SSP 5-8.5 scenario, which is more pessimistic one, foresees a substantial reduction in crop productivity. This weather parameter-based biomass prediction equation can be effectively utilized as a method to assess the impact of climate change on agriculture. [ABSTRACT FROM AUTHOR]

An experiment was conducted in the Lower Gangetic Plains of West Bengal during 2017 and 2018 with three popular green gram varieties of the region (viz. Samrat, PM05 and Meha). Along with studying the variation of PAR components, a radiation use efficiency (RUE) based equation irrespective of varieties was developed and used to estimate the green gram yield for 2040-2090 period under RCP 4.5 and 8.5 scenarios. Field experimental results showed that almost 33.33 to 52.12% higher yield was recorded in 2017 in comparison to 2018. As observed through pooled experimental data of two years, PM05 produced 3 to 4% higher pod and 4 to 15% more biomass than Samrat and Meha with the highest radiation use efficiency (1.786 g MJ-1). Results also depicted that enhanced thermal condition would cause 9 to 15 days of advancement in maturity. Biomass and yield would also decrease gradually from 2040 to 2090 with an average rate of 7.60-11.70% and 10.19-14.17% respectively. The supporting literature confirms that future yield prediction under projected climate based on "radiation to biomass" conversion efficiency can be used successfully as a method to evaluate climate change impact in agriculture. [ABSTRACT FROM AUTHOR]

The article offers information on the effect of weather parameters on wheat yield of Nadia district of West Bengal, India. It mentions that growing period of wheat differs in different regions due to climatic variations that directly impact the vegetative and reproductive stages leading to yield variation.