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To meet the growing international demand for aromatic rice, this study, conducted at Uttar Banga Krishi Viswavidyalaya in Cooch Behar, West Bengal, aimed to enhance the yield and quality of the ‘Tulaipanji’ rice cultivar through advanced establishment methods and the use of organic nutrients over two years. The research tested three planting techniques: mechanical transplanting, wet direct seeding (using a drum seeder), and traditional methods, alongside four nutrient management strategies: vermicompost, farmyard manure, a mix of both, and conventional fertilizers. Findings revealed that mechanical transplanting significantly increased yield by over 31.98% and 71.05% compared to traditional methods and wet direct seeding, respectively. Using vermicompost alone as a nutrient source not only boosted yields by 21.31% over conventional fertilizers but also enhanced the rice's nutritional value and cooking quality. Moreover, soils treated with vermicompost showed higher dehydrogenase activity, indicating better soil health. Economically, mechanical transplanting with vermicompost was the most beneficial, yielding the highest net returns and benefit–cost ratios in both years studied. This approach presents a viable model for improving the sustainability of aromatic rice production globally, emphasizing the economic and environmental advantages of adopting mechanical planting techniques and organic fertilization methods.

This study aimed to assess the genetic basis and combining ability of 10 morphological traits in Indian mustard. The experiment involved eight parent lines and 28 crosses derived from a half-diallel mating design. Combining ability analysis is vital for identifying parents and hybrids with favorable genetic effects to enhance breeding efficiency. The study found significant variations across treatments, parents and parent vs. cross for all attributes related to seed yield. Some traits exhibited notable disparities between parents and crosses, underscoring the intricate genetic dynamics at play. The estimation of genetic components of variance underscored a predominant influence of non-additive gene action, especially in traits linked to yield. Specific combining ability (SCA) consistently surpassed general combining ability (GCA), underscoring the substantial role of non-additive genetic effects. Parental genotypes NPJ-194, DRMR-15-16, Kranti and NPJ-194 were identified as consistent and potent general combiners, indicating their potential to pass on favorable alleles to their offspring. Hybrid combinations such as SKJM-05 × Kranti, RW-85-59 × SKJM-05, and NPJ-194 × SKJM-05 exhibited notable GCA effects of parents, per se performance and SCA effects of hybrids for seed yield plant −1 . Heterosis breeding proved to be a viable strategy, with crosses such as RW-85-59 × SKJM-05, RW-85-59 × Giriraj, RW-85-59 × PHR-2, DRMR-15-16 × Giriraj, and SKJM-05 × PHR-2 exhibiting significant positive heterosis for OC over both mid-parent and better-parent values. Overall, this research provides valuable insights into the genetic basis of morphological traits in Indian mustard, offering strategic directions for focused breeding efforts and trait refinement.

Jute crop suffers a substantial amount of physical and economic loss every year due to the infestation of several insect pests, such as yellow mite ( Polyphagotarsonemus latus Banks) and jute semilooper ( Anomis sabulifera Guen), at different stages of crop growth. This study utilizes data on the mean incidence of yellow mite and jute semilooper at different days after sowing (DAS) from 2013 to 2023, along with weather variables, collected at the AINP-JAF, UBKV Centre, Cooch Behar, West Bengal. The results indicate that the incidence of jute semilooper follows a seasonal pattern, with most peaks occurring at approximately 45 DAS. Additionally, the mean incidence of yellow mite is found to be significantly positively correlated with maximum temperature and negatively correlated with minimum and maximum relative humidity at a 2-week lag. This suggests that dry weather with high temperatures 2 weeks prior contributes to higher yellow mite infestations at the current time. A similar correlation is observed for jute semilooper infestation. Various time series and machine learning models, including Autoregressive Integrated Moving Average (ARIMA), ARIMA-T, Seasonal ARIMA (SARIMA), SARIMA-T, ARIMA with exogenous variables (ARIMAX), SARIMA with exogenous variables (SARIMAX)-T, Random Forest, Support Vector Regression (SVR), and TDNNX, are applied to the training dataset from 2013 to 2022. The models are validated using the test data for the year 2023, based on root mean square error (RMSE) and root median square error (RMdSE) values. For yellow mite, TDNNX is found to be the best fitted model followed by SVR and SARIMAX-T in terms of RMSE and RMdSE values. Similarly, for jute semilooper, TDNNX is found to be the best fitted model followed by Random Forest and SARIMA. Finally, pest incidence forecasts for yellow mite and jute semilooper are obtained for 2024 using the forecasted and average weather data, applying the TDNNX model.

Late blight is one of the most devastating diseases on potato the world over, including West Bengal, India. The economic and yield losses from outbreaks of potato late blight can be huge. In this article, application of statistical models such as autoregressive integrated moving average (ARIMA), autoregressive integrated moving average with exogenous variables (ARIMAX) in combination with machine learning models such as, neural network auto regression (NNAR), support vector regression (SVR) and classification and regression tree (CART) have been explored to predict the percentage disease index (PDI) of potato late blight in the northern part of West Bengal. Models were developed to predict PDI at 3- and 7-days interval using the weather variables viz., rainfall, maximum and minimum temperature, maximum and minimum relative humidity, and dew point temperature. Among the developed models, CART to predict PDI at 7 days interval was found to be the best fitted model on the basis of least RMSE, MAE and MAPE. The results of decision tree (CART) model showed that dew point temperature had a significant effect on PDI at 7 days interval and the incidence of potato late blight was high when dew point temperature was greater than 12 0C in the preceding week.

Rapeseed-mustard (Brassica spp.) is one of the important edible oilseeds crops in India. The same level of weather condition impacts the growth and establishment of rapeseed-mustard plant differently in different stages of crop which lead to large intra-seasonal yield variations. Hence it is essential to give weightage to weekly weather conditions while fitting predictive model. In this present study, path-coefficient based weighted index was proposed along with existing unweighted and correlation based weighted index. The performance of penalized regression models viz. Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (ENET) were compared with Multiple Linear Regression (MLR) for predicting rapeseed-mustard yield using weather-indices. The results revealed that the path-coefficient based weighting of weather parameters to the yield were stable than correlation based weighted-indices. Path-coefficient based weighted indices of maximum temperature, minimum temperature and windspeed were important variables in projection of yield. The performance of MLR was poor during validation of model due to overfitting issue. The performance of penalized models was stable in both calibration and validation of the model. The LASSO and ENET models that accompanied with coefficient shrinkage and variable selection were found to be the best fitted models for predicting Rapeseed-Mustard yield.

Crop yield prediction gains growing importance for all stakeholders in agriculture. Since the growth and development of crops are fully connected with many weather factors, it is inevitable to incorporate meteorological information into yield prediction mechanism. The changes in climate-yield relationship are more pronounced at a local level than across relatively large regions. Hence, district or sub-region-level modeling may be an appropriate approach. To obtain a location- and crop-specific model, different models with different functional forms have to be explored. This systematic review aims to discuss research papers related to statistical and machine-learning models commonly used to predict crop yield using weather factors. It was found that Artificial Neural Network (ANN) and Multiple Linear Regression were the most applied models. Support Vector Regression (SVR) model has a high success ratio as it performed well in most of the cases. The optimization options in ANN and SVR models allow us to tune models to specific patterns of association between weather conditions of a location and crop yield. ANN model can be trained using different activation functions with optimized learning rate and number of hidden layer neurons. Similarly, the SVR model can be trained with different kernel functions and various combinations of hyperparameters. Penalized regression models namely, LASSO and Elastic Net are better alternatives to simple linear regression. The nonlinear machine learning models namely, SVR and ANN were found to perform better in most of the cases which indicates there exists a nonlinear complex association between crop yield and weather factors.

The upsurge of stemphylium blight disease noticed during recent cropping years is the prime global threat for lentil (Lens culinaris Medik.) production. Identification of factors that influence lentil yield with the help of an advanced regression model will speed up the progress of lentil crop improvement for biotic stress tolerance. In this context, an experiment was undertaken to identify the key control factors of lentil yield under stemphylium blight stress. The field experiment was laid out under alpha lattice design using fifty lentil genotypes with two replications. An advanced dimension reduction cum regression approach Partial Least Square Regression (PLSR) was employed to mitigate the effect of multi-collinearity among 23 yield-influencing traits along with traditional Stepwise Multiple Linear Regression (SMLR). The results of SMLR analysis indicated that pods per plant, number of seeds per pod, hundred seed weight, superoxide dismutase and pod yield per plant had considerable effects on seed yield per plant with the R-squared value of 0.940. The first four PLSR components were considered to be optimum which were cumulatively explained 93.10% of the total variance towards lentil seed yield. The trait pods per plant was recorded with the highest PLSR regression coefficient devoid of multi-collinearity effects among the independent yield attributing variables under stemphylium blight environment and hence concluded to be the most influencing trait towards lentil seed yield followed by seeds per pod, hundred seed weight, pod yield per plant and superoxide dismutase.

This research aims to study the variation in phytosociology and plant diversity of large cardamom-based traditional agroforestry systems along an altitudinal gradient (700–2000 m) in the Darjeeling Himalayas. We analyzed the changes in phytosociology and plant diversity by adopting stratified random nested quadrate sampling method. The agroforestry managers were interviewed for their perception of ecosystem service following Millennium Ecosystem Assessment guidelines. The present study showed altitudinal location significantly influences plant diversity. Overall, 130 plant species were documented, of which 37 were trees, 25 shrubs, 46 herbs, 8 ferns, 11 climbers and 3 orchids. The low-, mid- and high-altitude classes were documented with 76, 60 and 52 plant species, respectively. Overall, the study system was highly heterogeneous and diverse with a higher Shannon and Wiener diversity index of 4.09 which decreased progressively with increasing altitude as evidenced from significant negative relationship of altitude with diversity (r =− 0.582**), species richness (r =− 0.648**) and plant population (r =− 0.587**). Of the total listed plant species, about 68% were data deficit, 29% were least concerned; two species (Cryptomeria japonica and Cupressus cashmeriana) were near threatened, and one species (Brugmansia suaveolens) was extinct in the wild. This indicates that the study system plays a vital role in harbouring and conserving regional plant diversity. The plant species documented were also classified based on their ecosystem services with 120, 47, 34 and 33 species providing provisional, cultural, regulatory and supporting services, respectively.

The series-based information on boron (B) is not comprehensively available in the Fluvisols of north-eastern Terai region of India. This region is frequently reported to be deficient in available B (av-B) due to intense leaching and low solubility of primary B minerals. The present experiment was conducted in the Cooch Behar district with the aim to assess the surface soil (0–15 cm) status of av-B in four dominant soil series (Lotafela, Matiarkuthi, Rajpur and Balarampur) in post-monsoon months of dry winter. Hot water (HW) and 0.01 M hot calcium chloride (HCC) (0.01 M CaCl 2 ) were used to extract av-B, where HW extracted higher amount of B than HCC in all soil series. The mean HW-B concentration was highest in the Rajpur series (1.71 mg kg –1 ) followed by Balarampur (1.64 mg kg –1 ), Matiarkuthi (1.58 mg kg –1 ) and Lotafela (1.57 mg kg –1 ). Similar result was also notice in HCC-B. The four principal components explained 79.58% of the total variance, while pH and SOC contributed maximum variability among all the soil factors under study. Spatial interpolated (Inverse Distance Weighted) maps and nutrient index value (NIV) based fertility rating showed the soils in the study area were not deficient in av-B, with a majority of portions exceeding the B critical limit of toxicity for sensitive crops. Boron availability also got increased in dry periods with assured irrigation supply to winter crops along with the high depth of water table of the Terai region. Accordingly, the local farmers need to check excess B fertilizer (borax) application in dry post-monsoon periods considering long-term effects of B fertilizers on soil, cropping system and production economics.

The present investigation was conducted to evaluate the genetic variability based on flower yield and different morpho-physiological traits of sixteen genotypes of China aster. The estimates of heritability (broad sense) varied from 73.24 to 99.01% for different traits under the study. High heritability coupled with high genetic advance as per cent mean were recorded for the all characters except duration of flowering. The highest values of the genotypic correlations were observed as compared to phenotypic correlations for the studied characters. A significant genetic diversity was observed among the genotypes which were found to be distributed in 5 clusters. The genotypes belong to Cluster I and Cluster IV were the best performers for flower and seed yield per plant, respectively. Thus, heterosis breeding between these widely distant genotypes would be recommended towards higher flower and seed yield. Also the genotypes with superior traits can be utilized in hybridization to transfer desirable traits into a single genotype.