Mass balance of Nehnar glacier from 2000 to 2020, using temperature indexed-IAAR approach.

Glacier mass balance is inextricably linked to annual meteorological conditions and is a key indicator for assessing the ice reserves of a glacier. As a result, a number of studies have estimated glacier mass balance using different methods. Here, we have used the improved accumulation area-ratio (IAAR) method to study the mass balance of the Nehnar glacier from 2000 to 2020. This study also aims to study the spatiotemporal behavior and other dynamics of the glacier. Results have shown that the glacier has continuously lost its ice reserves throughout the studied period though at a lower rate since 2010. Its annual specific mass balance has changed from - 50.10 ± 3 cm w.e in 2000 to - 59.46 ± 3 cm w.e. in 2020. The equilibrium line altitude (ELA) of the glacier rose by 90 m and has shifted from 4260 masl in 2000 to 4350 masl in 2020. The glacier has shrunk from an area of 1.64 km2 in 2000 to 1.38 km2 in 2020 losing nearly 16% of its area. The study highlights the need for continued monitoring of glacier mass balance to better understand and predict the effects of climate change. These findings have important implications for the future of glacier retreat and water reserves of the Jhelum basin.

Spatial variability and delineation of management zones based on soil micronutrient status in apple orchard soils of Kashmir valley, India.

Knowledge and up-scaling of status, availability, and distribution of soil micronutrients are hugely significant for enhancing crop productivity and profitability. However, regional database entailing spatial variability of key micronutrients for a major apple-producing region like the Kashmir Himalayan Region (KHR) is missing. We investigated 588 topsoil samples between 2016 and 2017 (to 30 cm depth) to estimate spatial variability of extractable Zn, Cu, Mn, and Fe; develop spatial maps; and delineate potential management zones (MZs) in apple orchards using geo-statistical methods. Soil sampling was performed post-harvest season. Principal component analysis and fuzzy c-mean clustering were performed to develop MZs. Results exhibited wide variation, with high coefficient of variation (%) for Mn and Cu (123.9 and 114.4, respectively) and low (71.6) and medium (104) variability for Fe and Zn. Mean concentrations followed the order: Fe > Mn > Cu > Zn, with deficiencies evident towards central and northern regions of the study area. The best-fitted model was exponential. The nugget/sill ratio values were 0.41, 0.08, 0.37, and 0.38 for Zn, Cu, Mn, and Fe, respectively, indicating strong spatial dependence for Cu and moderate dependence for Fe, Zn, and Mn. Correlations between elevation, slope, and soil pH with micronutrients were negative, whereas with soil organic carbon and electrical conductivity positive relationships were identified. Three principal components accounted for 67·18% data variance. Based on the fuzzy performance index and modified partition entropy, five MZs were delineated, which exhibited variations from each other. These MZs highlighted the need for specific supplemental provisions in increasing soil fertility and apple productivity.

Management zone delineation and spatial distribution of micronutrients in cold-arid region of India.

The plant health is governed by many factors: soil playing a central role which exhibits huge variability in its characteristics. Micronutrients even though needed in small quantities by plants play an indispensable role in affecting the crop growth and development. The assessment of spatial variability of different soil parameters is incumbent for tackling the loss of crop productivity on account of non-receipt of desirable inputs. The present investigation centered on the spatial distribution of soil parameters and micronutrients was conducted to delineate management zones (MZs) in cold arid region (Kargil) of India for proficient micronutrient management. Overall 454 georeferenced representative soil samples at the depths of 0-15 cm were garnered. The soil samples were processed and analyzed for different soil parameters encompassing pH, EC (electrical conductivity), SOC (soil organic carbon), and available micro-nutrient (iron, manganese, zinc, copper and nickel) concentrations. The distinct variation in the soil properties including micronutrients was identified with coefficient of variation ranging as low as 5.62% to moderate (21.16 to 42.49%) and as high as 159.63%. Semivariogram analysis and ordinary kriging of soil variables under study revealed diverse spatial distribution exhibiting medium to high spatial dependence in the region. PCA (principal component analysis) and K-means clustering were expended for the delineation of MZs. Four principal components (PCs) having eigen values > 1 and accounting for 70% of the total variation were subjected to further analysis. The five potential MZs were demarcated on the basis of K-means cluster performance index, and heterogeneity in parameters was discerned. The results of study corroborate that the spatial variability analysis of different soil parameters for delineation/identification of MZs might be effectually employed for site-specific micronutrient management.