1Regional Horticultural Research and Training Station at Jachh Kangra, Dr. Yashwant Singh Parmar University of Horticulture and Forestry, Solan, Himachal Pradesh, India
2Dr. Yashwant Singh Parmar University of Horticulture and Forestry, Solan, H.P., India
3Krishi Vigyan Kendra, Chamba, Dr. Yashwant Singh Parmar University of Horticulture and Forestry, Solan, H.P., India
*Corresponding Author’s E-mail Address: rnkapoor56@gmail.com
This study evaluated soil fertility at the experimental farm of the Regional Horticultural Research and Training Station, Jachh, Kangra, Himachal Pradesh, India. A total of 78 soil samples, collected during 2023 from 0–30 cm depth, were analyzed to determine soil physico-chemical properties i.e., pH, electrical conductivity (EC), organic carbon (OC), macro-nutrients [i.e., nitrogen (N), phosphorus (P), and potassium (K), micro-nutrients [i.e., iron (Fe), zinc (Zn), copper (Cu), manganese (Mn)]. Further, soil nutrient index was computed and soil fertility distribution map was generated using the inverse distance weighting method in the geographic information system. Soil pH ranged from 6.25 (slightly acidic) to 7.69 (slightly alkaline) with the mean of 7.01. EC was low (0.09 to 0.52) indicating non-saline soil with suitability for all crops. Soil OC ranged from 0.45% (medium) to 1.59% (high), though moderate variability was observed due to differences in land use/land cover and soil management. Available N was deficient (125.44 to 282.40 kg ha-1), whereas P and K were medium (12.38 and 156.02 kg ha-1, respectively) to high (110.27 and 418.18kg ha-1, respectively), suggesting imbalanced nutrient availability. Fe (4.50-18.77mg kg-1) and Cu (0.20-3.87mg kg-1) were in adequate quantities; however, Zn showed medium status (0.66-10.82 mg kg-1) with high variability, and Mn was deficient (0.87-6.35mg kg-1). Soil nutrient index indicated low fertility for N (1.35), high for OC (3.35), P (3.68), and K (3.17), and medium to high (1.86 to 3.27) for the micro-nutrients. Correlation matrix revealed significant relationships among pH, OC, macro-nutrients, and micro-nutrients, indicating that the common soil factors influence the nutrient distribution. Principal component analysis identified Fe, K, Zn, EC, pH and N as the key indicators, explaining 72.3% of the total variance. Overall, the study highlighted a good soil reaction and micro-nutrient status but emphasized on the need for improved N and Mn management to sustain soil fertility and crop productivity.
correlation matrix, geographical information system, inverse distance weighting method, principal component analysis, soil fertility maps