Dhananjay Barman

Climate variability increasingly threatens agriculture in monsoon-dominated regions, where crop performance depends on both rainfall amount and temporal distribution. This study assessed 26 years (2000–2025) rainfall variability, trends, change points and rainfall state persistence across six agro-climatic zones of West Bengal, India, considering three agricultural seasons: pre-kharif (March–June), kharif (July–October), and rabi (November–February). Seasonal and annual rainfall were analysed using Mann–Kendall and Hamed Rao Kendall tests, Sen’s slope, Pettitt change-point analysis, Innovative Trend Analysis (ITA), rainfall anomalies and LOESS, while rainfall-state dynamics were evaluated using a four-state Markov-chain framework. The Old Alluvial Zone showed the strongest drying signal, with significant declines in pre-kharif, kharif and annual rainfall. ITA confirmed significant declines in kharif and annual rainfall. The Hill Zone exhibited contrasting seasonal trajectories, with increasing kharif rainfall (τ = 0.300; 17.72 mm yr⁻¹; p = 0.038) but declining rabi rainfall (τ = −0.327; −2.17 mm yr⁻¹; p = 0.024), highlighting pronounced seasonal divergence. Markov analysis showed dominant no-rain conditions during rabi, with steady-state probabilities of 0.84–0.92, while kharif heavy-rain probabilities reached 0.33 and 0.30 in the Hill and Terai (Terai-Teesta Alluvial Zone) zones, respectively. The integrated assessment helps identify zones where declining seasonal rainfall, persistent dry conditions or greater monsoon rainfall may have increasing relevance for crop and water management. These rainfall-based insights can support location-specific consideration of supplemental irrigation, moisture conservation, rainwater harvesting, drainage and contingency crop planning.

The Sundarbans is the world’s largest mangrove forest, spanning across India and Bangladesh. Several studies have been conducted on various aspects of the ecosystem, primarily at a local scale, with most broad-scale studies confined to either the Indian or Bangladeshi part. To enhance the conservation and management of this extensive mangrove ecosystem, comprehensive studies encompassing the entire Sundarbans are essential. The available zonation maps are coarse and inconsistent across the two countries. Considering this, we aimed for a hierarchical zonation of all the Sundarbans. We performed geospatial analysis of high-resolution remote sensing images together with existing zonation information and geospatial characteristics. Using near-infrared and red bands, we derived NDVI and applied reclassification to obtain terrestrial areas of the Sundarbans. We propose five hierarchical levels with over 200 geographic units at the lowest hierarchical level. Additionally, the data set is inclusive and existing zones/geographic units used in past studies are included. Geospatial analysis was employed to delineate territorial boundaries for each zone, incorporating canals, rivers, and adjacent oceanic areas. This approach accounts for the dynamic nature of mangrove islands, particularly those situated along the southern margin of the ecosystem. This was done to consider the dynamic nature of mangrove islands, especially the ones on the southern side of the ecosystem. Such a geospatial data set enables a temporal comparison of geographic units for parameters such as vegetation and land area changes, especially after major cyclones. This study also demonstrates the use of this dataset in identifying vulnerable areas of the ecosystem using geospatial information on elevation, biomass, anthropogenic stress, and vegetation changes.

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.

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.

Present study is a maiden attempt to assess net ecosystem exchange (NEE) of carbon dioxide (CO₂) flux from jute crop (Corchorus olitorius L.) in the Indo-Gangetic plain by using open-path eddy covariance (EC) technique. Diurnal variations of NEE were strongly influenced by growth stages of jute crop. Daytime peak NEE varied from − 5 µmol m⁻² s⁻¹ (in germination stage) to − 23 µmol m⁻² s⁻¹ (in fibre development stage). The ecosystem was net CO₂ source during nighttime with an average NEE value of 5–8 μmol m⁻² s⁻¹. Combining both daytime and nighttime CO₂ fluxes, jute ecosystem was found to be a net CO₂ sink on a daily basis except the initial 9 days from date of sowing. Seasonal and growth stage-wise NEEs were computed, and the seasonal total NEE over the jute season was found to be − 268.5 gC m⁻² (i.e. 10.3 t CO₂ ha^-1). In different jute growth stages, diurnal variations of NEE were strongly correlated (R² > 0.9) with photosynthetic photon flux density (PPFD). Ecosystem level photosynthetic efficiency parameters were estimated at each growth stage of jute crop using the Michaelis–Menten equation. The maximum values of photosynthetic capacity (P_max, 63.3 ± 1.15 µmol CO₂ m⁻² s⁻¹) and apparent quantum yield (α, 0.072 ± 0.0045 µmol CO₂ µmol photon⁻¹) were observed during the active vegetative stage, and the fibre development stage, respectively. Results of the present study would significantly contribute to understanding of the carbon flux from the Indian agro-ecosystems, which otherwise are very sparse.

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.

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.