Scientific literature highlights that rising mean temperatures, combined with increasing frequency and duration of extreme heat events, pose risks to food security, particularly in climate sensitive regions of South Asia. These impacts are especially critical in West Bengal, India, where diverse agroecological condition and climate-sensitive farming systems create uneven socio-climatic stress. Despite growing concern, district-level heat wave risk assessment for agricultural communities in West Bengal remains limited, particularly in frameworks that integrate climatic, agricultural, and socio-economic dimensions with predictive modelling. In this milieu, the present research makes a significant contribution to climate resilience building by advancing a composite, spatially explicit heat wave risk assessment grounded in the IPCC Sixth Assessment Report (AR6) risk framework. By integrating climatic hazards with exposure and socio-economic vulnerability and using Principal Component Analysis (PCA) Composite heat wave risk index (CHWRI) were derived. Further to capture nonlinear spatio-temporal dynamics, Long Short-Term Memory (LSTM) model optimized using the Hippopotamus Optimization Algorithm (HOA) was employed to predict district-level heat wave risk classes. The results revealed pronounced spatial heterogeneity, with the red and lateritic, coastal saline, and old alluvial zones exhibiting compounded risk. The observed and predicted district-level CHWRI classes showed strong agreement, with 16 of 22 districts correctly classified (72.73%). Spatial statistics confirmed significant clustering (Global Moran's I = 0.47), while Purulia emerged as a major hotspot in Getis-Ord Gi∗ analysis. The proposed PCA-LSTM-HOA framework offers a novel, interpretable, and policy relevant tool for hotspot prioritization, early warning, and climate resilient agricultural planning -an urgent priority in the face of accelerating global warming.

We assessed long-term yield trajectories in rice-rice (RR) and rice-wheat (RW) in South Asia, using multi-decadal records from 11 RR and 28 RW experiments under Control, NPK, and NPK+farmyard manure (FYM). Trends were classified as positive or declining using site-level regressions (slope ≠ 0, P ≤ 0.05). In RR, positive system productivity trends occurred at 10% (Control) vs. 46% (NPK) and 60% (NPK+FYM), while declines fell from 40% to 9-10%. In RW, corresponding shares of positive trends were 10%, 18%, and 44% for Control, NPK, and NPK+FYM, respectively, with declines dropping from 30% to 11% and 9%. Meta-analysis of crop yields showed consistent patterns: under NPK+FYM, pooled correlations were positive for RR (kharif rice: r = 0.41, P = 0.001; rabi rice= 0.28, P = 0.004), and RW (rice: 0.27, P = 0.011; wheat r = 0.18; P = 0.002), whereas NPK effects were weaker – positive for RW-wheat (0.15, p = 0.048) and RR-rice (rabi; r = 0.29, P = 0.007) but near zero for RR-rice (kharif) and RW-rice. Residual coefficient of variation (residual CV) fell from about 20-30% (Control) to 15-18% (fertilized). Agronomic efficiency of fertilizer-N (AEN) and the sustainability yield index (SYI) were higher under NPK and NPK+FYM than under control, and NPK+FYM kept AEN similar to NPK while reducing the number of sites with low AEN and low SYI. Practical priorities include combining organic resources with balanced NPK; retaining crop residues where possible, reducing puddling where it harms wheat, and routinely tracking AEN, SYI, and trend/stability indicators alongside mean yield.

Future predictions of potential evapotranspiration (PET) and rainfall are important factors that play pivotal role inAeffective crop planning and management. In this study, ensembled results of three Global Circulation Models (GCMs) were used to evaluate the changes in future PET and effective rainfall (ER) of the Lower Gangetic Plain (LGP) in India for two time slices: mid-century (2030-2040) and late-century (2070-2090) using two Representative Concentration Pathway (RCP) scenarios (RCP 4.5 and RCP 8.5). AThe MarkSim DSSAT Weather File Generator was used to downscale the climate projections. Temperatures, solar radiation, and rainfall simulated by the model majorly increased over the century, with slight decadal variations. The ensemble total PET for all stations combined has been projected to increase at the rate of 2.02 mm per year for 2030-2050 and 0.88 mm per year for 2070-2090 under RCP 4.5. Under RCP 8.5, the same is as high as 2.29 mm per year for 2030-2050 and 3.02 mm per year for 2070-2090. The highest monthly PET is recorded in May. Despite large variation in rainfall within decades, RCP 4.5 showed an overall increasing trend (approximately 5.5%), whereas RCP 8.5 showed a decreasing trend. Kalyani (New alluvial zone) demonstrated maximum decline in PET (22.38%) by late century (RCP 8.5) compared to other stations. Over the projected timeframe, "ER-PET" value will decrease, indicating a high demand for irrigation water. The results provided valuable insights into the economic planning of crops to support optimum production.

Rice self-sufficiency has become a necessity amid global geopolitical uncertainty and climate change. Over the past decade, rice-importing countries such as Indonesia have pursued research-based agricultural transformations to strengthen domestic production. This review analyses the trends in rice agricultural research in Indonesia. Particular attention is given to emerging regenerative and climate-resilient technologies. We conducted a bibliometric analysis in RStudio and VOSviewer. Our method followed PRISMA guidelines and drew on the literature from the Scopus and Web of Science databases. During the identification phase, 4,713 documents were found, and 296 were selected for further analysis. The bibliometrix package in RStudio and VOSviewer were used to examine trends, sources, authors, affiliations, documents, collaboration networks, and the thematic structure. Rice research publications in Indonesia have increased from 2015 to 2025. Hasanuddin University is the most productive affiliation, and Muhammad Riadi is the most relevant researcher. Rice research in Indonesia focuses on rice cultivation, the development of climate-resistant rice varieties, the use of biofertilizers, soil engineering for suboptimal soils, and precision farming. Precision agriculture, remote sensing, and data-driven decision-making are new topics that are important for increasing agricultural productivity and sustainability. This review also identifies research gaps in the implementation of large-scale regenerative practices and in the integration of digital agriculture. These findings offer insights that help researchers, policymakers, and stakeholders identify priorities and strengthen research collaborations on sustainable rice farming practices, thereby supporting the achievement of Sustainable Development Goals.

Context Under a changing climate scenario, crop simulation models like CERES-Maize model become an essential research tool for predicting future yield potential at different levels of input management helping in decision-making. Objective To observe winter maize performance and validate the field results and predict future yields through DSSAT CERES-Maize Model (v. 4.7). Method A two-year research work was executed in Gangetic plains of West Bengal with three irrigation regimes (I1: 30 % depletion of available soil moisture (DASM), I2: 50 % DASM and I3: 70 % DASM) and four nutrient levels (F0: 0:0:0, F1: 100:50:50, F2: 150:75:75 and F3: 200:100:100 kg N: P2O5: K2O ha-1). Results and conclusions The CERES-Maize model effectively simulated grain yield, biological yield, harvest index, and total nitrogen uptake of winter maize. However, it failed to accurately simulate maximum leaf area index. In a changing climate scenario, the model predicted a decrease in winter maize grain yields in 2050–51 and 2075–76 from baseline period of 2018–19. Significance In order to maximize winter maize productivity, farmers may find the CERES-Maize model to be a helpful tool for making decisions on the best irrigation and nutrient management from regional to global levels.

Abstract. Hindersah R, Asyiah IN, Amaria W, Fitriatin BN, Mudakir I, Banerjee S. 2025. Enhanced mycorrhiza helper bacterial inoculant for improving the health of Arabica coffee seedlings grown in nematode-infected soil. Biodiversitas 26: 127-124. The Arbuscular Mycorrhizal Fungi (AMF) and Mycorrhiza Helper Bacteria (MHB) combine to combat the Pratylenchus coffeae nematode infection on coffee plantations sustainably and synergistically. Additionally, AMF facilitates the availability of phosphorus in plants. The objectives of present study are to formulate an enhanced MHB liquid inoculant containing Bacillus subtilis and Pseudomonas diminuta, and to test its efficacy in controlling P. coffeae in roots, improving P status in soil and plants, and promoting the growth of Arabica coffee seedlings infested with the nematodes. MHB liquid inoculant was enhanced by optimizing molasses, nitrogen, phosphorus, and MHB concentrations. The five treatments were used, and five replications were in a randomized block-design greenhouse experiment to investigate the AMF Glomus agregatum and MHB inoculant. The improved substrate for MHB liquid inoculant comprised 2% molasses, 0.05% NH4Cl, and 0.1% KH2PO4, with a 2:3 initial volume ratio of B. subtilis and P. diminuta. Scaling up the MHB inoculant in the 2 L reactor boosted the bacterial population to 1010 CFU/mL and the P content to 100 mg/kg. Applying 200 AMF spores and 109 CFU/mL MHB increased leaf number, plant P uptake, and soil P while decreasing root damage and nematode population in soil and roots. Combined AMF and MHB reduced P. coffeae infestation in roots by 70.79% and increased P content in soil and plants by 57.2% and 61.9%, respectively.

ABSTRACTThe 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.

The planting date, plant population, and variety are crucial factors that interact with the physical environment and hence, meticulously influence crop output and performance. To determine the impacts of planting time, spacing, and varietal effects on field pea crop phenology, growth and yield at the Indo-Gangetic plains, the present experiment was designed for three rabi seasons. The treatments included four sowing dates from the first week of November to mid-December, with 10-day intervals, as the main plots; two varieties, that is, VL-42 and Rachana,and two spacing treatments (S1: 30 cm x 10 cm and S2: 45 cm x 10 cm), were included as subsubplots. A gradual delay in field pea sowing from early November to mid-December resulted in a 27% reduction in grain yield and VL-42 (1256.2 kg ha-1) prominently outperformed Rachana by 26% in terms of grain yield (920.5 kg ha-1). Narrow-spaced field pea crops presented significantly greater grain yields (1136.5 kg ha-1) than did wider crops (1040.2 kg ha-1). Microclimatic interactions of the treatments led to differences in the performance of the treatments. The field pea variety, VL-42 accumulated relatively higher growing degree day (GDD) of 1883.4 degrees C days and heliothermal unit (HTU) of 11,949.2 degrees C day-h compared to Rachana variety's 1694.2 degrees C days and 10,475.8 degrees C day-h, respectively. Variation in crop phenophase duration was also observed, as a delay in sowing leads to exposure to a relatively high thermal regime, significantly shortening the length of the growing period by nearly 10 days. Agroecological indices (i.e., GDD and HTU) are also affected by phenology resulting higher values in within mid-November sown field pea crop compared to crops sown in mid December. Closely spaced (30 cm x 10 cm) field pea plants performed better in terms of grain yield, typically because of the greater plant population. Micrometeorological studies have indicated that in mid-November, field pea plants that are exposed to approximately 20 to 25 degrees C mean air temperatures during their growth stages perform best in this region. VL-42 was found to be more climate resilient than Rachana and hence, can be adapted in the lower Gangetic plains of India for better yield potential.

Rice farming is a double-edged sword essential to humans as a staple food, yet it is also a source of greenhouse gases that contribute to climate change, a threat to human life. Adopting innovative technologies is one of the sustainable ways to maintain rice production and mitigate climate change. This review aims to comprehensively explore and analyze innovative climate-resilient technologies on productivity, environment, and economic sustainability to implement net-zero agriculture. We conducted a bibliometric analysis based on Scopus data using RStudio and VOSViewer and a systematic literature review using PRISMA guidelines with keywords such as rice, agriculture, technology, land, sustainable, economy, profitability, environment, and ecology. A total of 703 articles were obtained in the initial stage, and 27 articles were deemed eligible for further analysis. We found that precision agriculture, biofertilizers, climate-resilient rice varieties, irrigation technologies, carbon and methane mitigation strategies, and mechanization technologies can increase productivity and mitigate climate change. Adopting these innovative technologies also has a positive impact on environmental and economic sustainability, as well as farmers’ livelihoods. This review emphasizes the importance of collaboration among scientists, the private sector, farmers, and policymakers to achieve food security amidst climate change.

Micrometeorological variations within the tea canopy influence the tea yield to a considerable extent. An experiment was conducted at the Experimental Garden of Assam Agricultural University Jorhat, Assam during 2022 - 2024 to examine the effects of five shade tree species viz., Sao koroi (Albiziachinensis), Xil koroi (Albizia odoratissima), Neem (Azadirachta indica), Amla (Phyllanthus emblica), and Areca nut (Areca catechu) on micrometeorological parameters such as air temperature (AT), canopy temperature (CT), relative humidity (RH), photosynthetically active radiation (PAR), soil moisture (SM), soil temperature (ST), and rainfall (RF) affecting leaf growth and yield. The highest green leaf growth rate (GLGR) occurred during the monsoon season (41.7 ± 12.1 kgha-1day-1), with the highest GLGR (41.8 ± 13.1 kgha-1day-1) achieved under Neem shade. GLGR has a significant positive correlation with most of the parameters except rainfall which showed no significant influence on GLGR. Regression analysis revealed that rainfall negatively impacted GLGR. This study highlights the role of shade trees in mitigating stress and optimizing growth conditions, providing insights into sustainable tea cultivation practices.

West Bengal is a key producer of raw jute fiber in the country. Identifying and managing dry spells during the jute growing period is crucial, necessitating contingency crop planning for enhanced productivity. Keeping this view in mind, standardized precipitation index (SPI) was calculated over five locations, representing five different districts of southern West Bengal. These locations are Barrackpore (North 24 Parganas District), Panagarh (Burdwan District), Bagati (Hooghly District), Krishnanagar (Nadia District) and Uluberia (Howrah District). This rainfall dependent dryness index (SPI) was calculated in 1 month and 3 months interval to identify short term dryness as well as mid-term dryness, applicable for seasonal crops. The trend analysis of the SPI values indicated that North 24 Parganas and Nadia experienced increased dryness during vegetative phase of Jute. Nadia district showed a significant increase in both short term and long-term dryness. The yield reduction index is well correlated with SPI values in all the study locations except Howrah. Arrangement of irrigation during the early stages of Jute can help the crop to cope up with the break of monsoon in this region

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, Indrira 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. Keywords: Field pea, weather parameters, crop yield prediction, New Alluvial Zone, nRMSE

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.

A field experiment was conducted during the rainy (kharif) season of 2021 at the Uttar Banga Krishi Viswavidyalaya, Kalimpong, West Bengal, to study the response of 2 aromatic rice cultivars (‘Kalture’ and ‘Kalonunia’) under 4 organic nutrient management (cowdung manure @ 5 t/ha, vermicompost @ 1.5 t/ha, mustardcake @ 0.5 t/ha, and leaf mould @ 1 t/ha). ‘Kalonunia’ exhibited greater tiller production (435/m2 ), leaf-area index (3.08) and dry-matter accumulation (452 g/m2 ) at 63 days after planting (DAT), and ‘Kalture’ showed taller plants (137.1 cm) and lodging susceptibility (score 4.0) at maturity. ‘Kalonunia’ performed significantly better in terms of grain yield (3.32 t/ha), non-lodging habit, protein content (7.25%) and net income (`57,043/ha) than ‘Kalture’ cultivar. Although the application of vermicompost @ 1.5 t/ha resulted in the maximum grain yield (3.22 t/ha) and nutrient uptake (44.6 kg N, 16.4 kg P and 39.0 kg K/ha), mustard-cake @ 0.5 t/ha could be an alternative option owing to near-maximum grain yield (3.11 t/ha) with high protein content (7.1%), medium aroma (score 1.7), maximum net income (`51,040/ha) and benefit: cost ratio (2.01) in hill zone of West Bengal.

Replacement of water-intensive winter rice with strawberry (Fragaria × ananassa Duch.) may restrict groundwater extraction and improve water productivity and sustainability of agricultural production in the arsenic-contaminated Bengal basin. The potential of strawberry cultivation in terms of yield obtained and water use efficiency need to be evaluated under predominant soil types with mulch applications. Water-driven model AquaCrop was used to predict the canopy cover, soil water storage and above-ground biomass of strawberry in an arsenic-contaminated area in the Bengal basin. After successful calibration and validation over three seasons, AquaCrop was used over a range of management scenarios (nine drip-irrigation × three soil types × four mulch materials) to identify the best irrigation options for a drip-irrigated strawberry crop. The most appropriate irrigation of 176 mm for clay loam soil in lowland and 189 mm for sandy clay loam in medium land rice areas and the use of organic mulch from locally available jute agrotextile improved 1.4 times higher yield and 1.7 times higher water productivity than that of without mulch. Strawberry can be introduced as an alternative crop replacing rice in non-traditional upland and medium land areas of the arsenic-contaminated Bengal basin with 88% lower groundwater extraction load and better economic return to farmers.