Himalayan forest has been threatened by rapid anthropogenic activities, resulting in the loss of forest diversity and climate change. The present study was carried out on four aspects (northern, southern, western and eastern), at three different altitudinal ranges, namely, 1000–1300 m above sea level (m a.s.l.), 1300–1600 m a.s.l. and 1600–1900 m a.s.l., and at three diverse mountain ranges (Kalaghat, Barog and Nangali) of sub-temperate forest ecosystems of the mid Himalayan ranges, to elucidate their influence on vegetation, tree characteristics and ecosystem carbon density. The results revealed that Pinus roxburghii is the most dominant forest community of the mid Himalaya’s forest, irrespective of altitudinal gradient and slope. The south-facing slopes are occupied by the xerophytic tree species frequently found in the lower Shiwalik P. roxburghii forest, whereas the north-facing ones are dominated by mesophyllic species, such as Cedrus deodara and Quercus leucotrichophora, which commonly grows in the northwestern Himalayan temperate forest ecosystem. The maximum stem density (211.00 Nha⁻¹) was found at 1000–1300 m a.s.l., and on the northern aspect (211.00 Nha⁻¹). The maximum stem volume (236.50 m³ ha⁻¹) was observed on the northern aspect at 1000–1300 m a.s.l., whereas the minimum (32.167 m³ ha⁻¹) in the southern aspect at 1300–1600 m a.s.l. The maximum carbon density (149.90 Mg ha⁻¹) was found on the northern aspect and declined with increasing elevation from 123.20 to 74.78 Mg ha⁻¹. Overall, the study establishes that the southern and western aspects are very low in carbon density, whereas the northern aspect represents higher biodiversity as well as carbon and nutrient stocks. Therefore, aspect and altitude should be given due importance for efficient managing of biodiversity and mitigating climate change.

The yield of the crop is a complex function of a number of dependent traits, which makes yield prediction a statistically difficult task. A number of work on yield prediction using morphological characters already exists in the literature. Most of the work used statistical techniques such as linear regression and crop yield models, which assume a linear relationship between yield and the morphological traits; in actual practice, such a linear relationship is seldom achieved. With the advancement in the field of machine learning techniques, these methods can provide a viable alternative for dealing with nonlinear relationships for yield prediction. Globally, apples are the most consumed fruit. In this paper, attempts have been made to predict the yield of the apple crop using morphological traits. PCA was used for selection of the significant variables. These variables were later used as input variables in the ANN model with different hidden layers for predicting crop yield. The predictive performance of the model was evaluated using standard statistical tests. Sensitivity analysis was performed to find out the individual effects of each character on the apple yield. The study contributes to a better understanding of the complex relationships between crop yield and morphological traits.

Millets, recognized for their nutritional richness and adaptability to diverse environmental conditions, have regained global attention due to their sustainable agricultural practices and nutritional benefits. This paper explores the historical significance, global and Indian production trends, cost dynamics, export patterns, and governmental initiatives driving millet cultivation in India. By analyzing statistical data and research findings, the study underscores millets' potential to enhance food security, mitigate environmental impact, and foster socio-economic development.

Juniperus macropoda is the only tree species of a cold desert ecosystem that is experiencing high anthropogenic pressure and has a poor regeneration status due to harsh environmental conditions. Due to the limited distribution of Juniperus macropoda in this region, the species have remained largely unexplored in terms of understanding the distribution pattern along the elevation and soil fertility gradients. Therefore, the current research was carried out along the elevational gradient, starting from the base line at 3000 m above sea level (m asl) asl with an elevational plot distance of 180 m. The study revealed that the average density of J. macropoda declined gradually from the first elevation range, i.e., 3000–3180 m asl onward, and extended up to the elevation range of 3900–4080 m asl. However, the average seedling and sapling densities were highest at mid-elevation and extended up to an elevation range of 4080–4260 m asl. The J. macropoda population formed a reverse J-shaped structure only up to 3540–3720 m asl. The maximum total biomass and carbon density were recorded in the lowest elevational range, and decreased subsequently. The primary soil nutrients under study decreased sharply along the elevational gradient. Seedling, sapling and tree distributions had a significantly positive relationship (p < 0.05) with available N, P, K, SOC, silt and clay contents and were negatively correlated (p < 0.05) with sand contents. The outcome of the study will form the basis for devising a plan for the management and conservation of J. macropoda forests.