Quantifying spatial variability of soil properties in apple orchards of Kashmir, India, using geospatial techniques

被引：1
作者：
Bangroo S.A. [1 ]
Sofi J.A. [1 ]
Bhat M.I. [1 ]
Mir S.A. [2 ]
Mubarak T. [2 ]
Bashir O. [1 ]
机构：
[1] Division of Soil Science, Faculty of Horticulture, Sher-e-Kashmir University of Agricultural Sciences & Technology of Kashmir, Shalimar, Srinagar, 190 025, Jammu and Kashmir
[2] Krishi Vigyan Kendra (KVK), Kulgam, Sher-e-Kashmir University of Agricultural Sciences & Technology of Kashmir, Shalimar, Srinagar, 190 025, Jammu and Kashmir
来源：
Arabian Journal of Geosciences | 2021年 / 14卷 / 19期
关键词：
Apple orchards; Kashmir Himalayas; Ordinary kriging; Soil properties; Spatial variability;
D O I：
10.1007/s12517-021-08457-6
中图分类号：
学科分类号：
摘要：
Knowledge about quantifiable spatial variability and spread of the soil physico-chemical parameters is critical for elucidating the ecosystem functioning and designing the sustainable soil-plant-environment management practices. The spatial variability of soil parameters of apple orchards of Kashmir have not been reported so far. Therefore, the study examined the soil spatial distribution of selected soil properties through classical and ordinary kriging technique of geostatistical approach to acquire information for soil-crop specific nutrient management in the apple orchards of Kashmir. Soil samples based on topography, and land management zones identified through field observation and by the indigenous local farming knowledge were collected and analyzed for the various soil properties viz., pH, electrical conductivity (EC), organic carbon (OC), and available N, P, K, Ca, and Mg. The soil properties varied with a coefficient of variation (CV) ranging from 9.0% (pH) to 30.0% (OC). The average soil organic carbon (OC), nitrogen (N), available phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) were 1.17%, 251.7 kg ha−1, 17.58 kg ha−1, 193.9 kg ha−1, 501.1 mg kg−1, and 269.4 mg kg−1 respectively. The parameters of the semi-variogram (nugget/sill ratio, range, and slope) signified that the spatial variation of soil properties was mutually exclusive. The spatial distribution of soil parameters was plotted by ordinary kriging (OK) based on mean square error (MSE) values of spherical (pH, N, P, K, and Ca), exponential (EC and OC), and Gaussian (Mg) models. The results of degree of spatial dependence from the semi-variogram analyses indicated a strong (17.6%) to moderate (74.2%) dependence. This study signified a broad range of spatial soil variability as the interpolated maps exhibited clear gradient in pH (5.7–6.6), EC (0.57–0.64 dSm−1), OC (0.9–1.4%), N (200–320 kg ha−1), P (16–21 kg ha−1), K (120–280 kg ha−1), Ca (660–1690 kg kg−1), and Mg (370–890 kg kg−1) at regional-scale. Adoption of appropriate management practices like minimum tillage, variable fertilizer application, horti-forestry measures, and site-specific practices based on the generated interpolated soil maps is critical for sustainable management of orchard soils. The spatial distribution maps of soil properties produced by this study can be used as a baseline information and an efficient tool for farm planners and managers in orchard nutrient management. © 2021, Saudi Society for Geosciences.
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  • [21] Fu W.J., Tunney H., Zhang C.S., Spatial variation of soil nutrients in a dairy farm and its implications for site-specific fertilizer application, Soil Tillage Res, 106, pp. 185-193, (2010)
  • [22] Goovaerts P., Geostatistical tools for characterizing the spatial variability of microbiological and physico-chemical soil properties, Biol Fertil Soil, 27, pp. 315-334, (1998)
  • [23] Hengl T., Leenaars J.G.B., Shepherd K.D., Walsh M.G., Heuvelink G.B.M., Mamo T., Tilahun H., Berkhout E., Cooper M., Fegraus E., Wheeler I., Kwabena N.A., Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning, Nutr Cycl Agroecosyst, 109, pp. 77-102, (2017)
  • [24] Isaaks E.H., Srivastava R.M., ) An introduction to applied geostatistics., (1989)
  • [25] Ivanov V.F., Main principles of fruit crop salt resistance determination, Pochvovedenie, 4, pp. 78-85, (1970)
  • [26] Jackson M.L., Soil chemical analysis, (1973)
  • [27] Juan P., Mateu J., Jordan M.M., Solera J.M., Pastor I.M., Pedreno J.N., Geostatistical methods to identify and map spatial variations of soil salinity, J Geochem Explor, 108, 1, pp. 62-72, (2011)
  • [28] Kirmani N.A., Sofi J.A., Bhat M.A., Bangroo S.A., Bhat S.A., Soil micronutrient status of District Budgam, Res J Agric Sci, 2, 1, pp. 30-32, (2011)
  • [29] Kumar K., Arora M.K., Hariprasad K.S., Geostatistical analysis of soil moisture distribution in a part of Solani River catchment, Appl Water Sci, 6, pp. 25-34, (2016)
  • [30] Lin Y., Multivariate geostatistical methods to identify and map spatial variations of soil heavy metals, Env Geol, 42, pp. 1-10, (2002)
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