GIS Interpolation and Mapping of Soil Physicochemical Properties in Deep Medium Black Soils of Established Citrus Orchards

Seema Bhardwaj *

ICAR-Indian Institute of Soil Science, Bhopal, 462038. India and Rajmata Vijayaraje Scindia Krishi Vishwavidyalaya, Gwalior, 470042, India.

Sanjib Kumar Behera

ICAR-Indian Institute of Soil Science, Bhopal, 462038. India.

S. K. Sharma

Rajmata Vijayaraje Scindia Krishi Vishwavidyalaya, Gwalior, 470042, India.

S. K. Trivedi

Rajmata Vijayaraje Scindia Krishi Vishwavidyalaya, Gwalior, 470042, India.

Rahul Mishra

ICAR-Indian Institute of Soil Science, Bhopal, 462038. India.

Vimal Shukla

ICAR-Indian Institute of Soil Science, Bhopal, 462038. India.

Yogesh Sikaniya

ICAR-Indian Institute of Soil Science, Bhopal, 462038. India.

Akanksha Sikarwar

ICAR-Indian Institute of Soil Science, Bhopal, 462038. India.

Sashi S Yadav

Rajmata Vijayaraje Scindia Krishi Vishwavidyalaya, Gwalior, 470042, India.

*Author to whom correspondence should be addressed.


Soil properties are an important factor for orchard establishment, precise nutrient management and sustainable production of fruit crops. Therefore, it is important to assess the spatial distribution of fundamental soil properties in well-established orchards. Hence an attempt has been made to assess the extent of soil properties and its spatial distribution in citrus orchards in medium black soils of Madhya Pradesh. The present study was conducted for the assessment of the spatial distribution of physicochemical properties viz. pH, electrical conductivity (EC) and soil organic carbon (SOC) of citrus orchards in medium-deep black soils of India. Results revealed that soil pH ranged from 6.83-8.84 (mean 7.80), soil EC varied from 0.07-0.34dS m-1 (mean 0.18 dS m-1) and soil organic carbon ranged from 0.13-0.89% (mean 0.47%) in 0-20 cm of surface soil layer. Geostatistical analysis showed that the slope of the prediction function of best-fit model (exponential) for soil pH, EC and SOC was 0.31, 0.22 and 0.77, respectively. The corresponding values of root mean square error (RMSE) were 0.35, 0.03, and 0.14. Interpolation of soil properties indicated that 89.2 % area had soil pH between 7.20 to 8.00, 83.4 % area had soil EC between 0.10 to 0.20 dS m-1, while>90 % area had SOC content ranged from 0.25 to 0.75%. Geo-statistical analysis revealed that spatial dependency was moderate for pH and strong spatial dependency was estimated for EC and SOC content. Based on RMSE and slope of prediction function, an exponential model was best-fit model in ordinary kriging for interpolation of measured soil properties.

Keywords: Geo-statistics, Madhya Pradesh region, soil pH, soil EC, soil organic carbon, Spatial dependency

How to Cite

Bhardwaj, S., Behera, S. K., Sharma , S. K., Trivedi , S. K., Mishra , R., Shukla, V., Sikaniya , Y., Sikarwar , A., & Yadav, S. S. (2024). GIS Interpolation and Mapping of Soil Physicochemical Properties in Deep Medium Black Soils of Established Citrus Orchards. Journal of Scientific Research and Reports, 30(3), 150–163.


Download data is not yet available.


FAO Statistics. FAO Stat Website; 2017. Available:

Dhaliwal SS, Naresh RK, Mandal A, Singh R, Dhaliwal MK. Dynamics and transformations of micronutrients in agricultural soils as influenced by organic matter build-up: A review. Environmental and Sustainability Indicators. 2019;1: 100007.

Corwin DL, Lesch SM. Characterizing soil spatial variability with apparent soil electrical conductivity: I. Survey protocols. Computers and Electronics in Agriculture. 2005;46(1-3):103-133.

Tisdale SL., Nelson WL, Beaton JD. Soil fertility and fertilizers. Collier Macmillan Publishers; 1985

Zhang W, Wang K, Chen H, He X, Zhang J. Ancillary information improves kriging on soil organic carbon data for a typical karst peak cluster depression landscape. Journal of the Science of Food and Agriculture. 2012;92(5):1094-1102.

Liu L, Wang H, Dai W, Lei X, Yang,X., Li X. Spatial variability of soil organic carbon in the forestlands of northeast China. Journal of Forestry Research. 2014;25(4):867-876.

Sharma, R., & Sood K. Characterization of spatial variability of soil parameters in apple orchards of Himalayan region using geostatistical analysis. Communications in Soil Science and Plant Analysis. 2020; 51(8):1065-1077.

Kværnø SH, Haugen LE, Børresen T. Variability in topsoil texture and carbon content within soil map units and its implications in predicting soil water content for optimum workability. Soil and Tillage Research. 2007;95(1-2):332-347.

Patzold S, Mertens FM, Bornemann L, Koleczek B, Franke J, Feilhauer H, Welp G. Soil heterogeneity at the field scale: A challenge for precision crop protection. Precision Agriculture. 2008;9: 367-390.

Mabit L, Bernard C, Makhlouf M, Laverdière MR. Spatial variability of erosion and soil organic matter content estimated from 137Cs measurements and geostatistics. Geoderma. 2008;145(3-4): 245-251.

Tesfahunegn GB, Tamene L, Vlek PL. Catchment-scale spatial variability of soil properties and implications on site-specific soil management in northern Ethiopia. Soil and Tillage Research. 2011;117, 124-139.

Adornado HA, Yoshida M. Crop suitability and soil fertility mapping using geographic information system (GIS). Agricultural Information Research. 2008;17(2):60-68.

Cambule AH, Rossiter DG, Stoorvogel JJ, Smaling EMA. Soil organic carbon stocks in the Limpopo National Park, Mozambique: Amount, spatial distribution and uncertainty. Geoderma. 2014;213:46-56.

Mani JK, Varghese AO, Sreenivasan G, Jha CS. Management of Citrus orchards in Central India using Geospatial Technology. In Geospatial Technologies for Resources Planning and Management Cham: Springer International Publishing. 2022; 297-314

Mishra R, Datta SP, Meena MC, Golui D, Bandyopadhyay KK, Bhatia A, Chaudhary A. Geostatistical analysis of arsenic contamination in soil and comparison of interpolation techniques in Nadia district of Bengal, India; 2023.

Iftikar W, Chattopadhyay GN, Majumdar K, Sulewski G. Use of village level soil fertility maps as a fertilizer decision support tool in the red and lateritic soil zone of India; 2009.

Habibie MI, Noguchi R, Shusuke M, Ahamed T. Land suitability analysis for maize production in Indonesia using satellite remote sensing and GIS-based multicriteria decision support system. Geo Journal, 2021;86:777-807.

Avanidou K, Alexandridis T, Kavroudakis D, Kizos T. Development of a multi scale interactive web-GIS system to monitor farming practices: a case study in Lemnos Island, Greece. Smart Agricultural Technology. 2023;100313.

Raihan A, Tuspekova A. Towards sustainability: Dynamic nexus between carbon emission and its determining factors in Mexico. Energy Nexus. 2022;8: 100148.

Rehman H, Akhtar S, Mishra A. Evaluating spatial soil parameters of an apple orchard in ‘r’ software: A study from Kashmir Valley. International Journal of Multidisciplinary Research. 2023;5(6):1-12.

Mali SS, Naik SK, Bhatt BP. Spatial variability in soil properties of mango orchards in eastern plateau and hill region of India. Vegetos. 2016;29(3):74-79.

Behera SK, Rao BN, Suresh K, Manorama K, Ramachandrudu K, Manoja K. Distribution variability of soil properties of oil palm (Elaeisguineensis) plantations in southern plateau of India. Indian J. Agric. Sci. 2015;85:1170-1174.

SIN MS. Soil Quality and Spatial Variability of Physico-Chemical Properties of a Fruit Growing Area in Kluang, Malaysia. Mater dissertation, Universiti Putra Malaysia; 2011.

Khan FS, Zaman QU, Farooque AA, Saleem SR, Schumann AW, Madani A, Percival DC. Relationship of soil properties to apparent ground conductivity in wild blueberry fields. Truro, Nova Scotia, Canada; 2012.

Jackson ML. Soil chemical analysis, pentice hall of India Pvt. Ltd., New Delhi, India. 1973;498:151-154.

Walkley A, Black IA. An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science. 1934;37(1): 29-38.

Goovaerts P. Geostatistical tools for characterizing the spatial variability of microbiological and physico-chemical soil properties. Biology and Fertility of Soils. 1998;27:315-334.

Dahule DD. Characteristics and properties of mandarin growing soils of Katol tahsil in Nagpur. Journal of Pharmacognosy and Phytochemistry. 2020;9(6S):360-364.

Surwase SA, Kadu PR, Patil DS. Soil micronutrient status and fruit quality of orange orchards in Kalmeshwar Tehsil, district Nagpur (MS). Journal of Global Biosciences. 2016;5(1):3523-3533.

Nielsen DR. Soil spatial variability. In Soil Spatial Variability. Proc. Workshop. 1985;1-2). ISSS and SSSA.

Bogunovic I, Pereira P, Brevik EC. Spatial distribution of soil chemical properties in an organic farm in Croatia. Science of the total environment. 2017;584:535-545.

Behera SK, Shukla AK. Spatial distribution of surface soil acidity, electrical conductivity, soil organic carbon content and exchangeable potassium, calcium and magnesium in some cropped acid soils of India. Land Degradation & Development. 2015;26(1):71-79.

Ferreira V, Panagopoulos T, Andrade R, Guerrero C, Loures L. Spatial variability of soil properties and soil erodibility in the Alqueva reservoir watershed. Solid Earth. 2015;6(2):383-392.

Houlong J, Hongfeng W, Najia L, Anding X, Chao Y, Yiyin C, Guo‐ Shun L. Evaluation of spatial variability of soil properties in a long‐term experimental tobacco station in southwest China. J of Agric Sci Tech. 2014;4:723-735.

Mulla DJ, McBratney AB. Soil spatial variability Soil physics companion. Boca Raton: CRC Press. 2001;343-377.

Cambardella CA, Karlen DL. Spatial analysis of soil fertility parameters. Precision Agriculture. 1999;1(1):5-14.

Mallarino AP, Oyarzabal ES, Hinz PN. Interpreting within-field relationships between crop yields and soil and plant variables using factor analysis. Precision Agriculture.1999;1:15-25.

Webster R. Statistics to support soil research and their presentation. European journal of soil science. 2001;52(2):331-340.

Miransari M, Mackenzie AF. Wheat grain nitrogen uptake, as affected by soil total and mineral nitrogen, for the determination of optimum nitrogen fertilizer rates for wheat production. Communications in Soil Science and Plant Analysis. 2010;41(13): 1644-1653.

Liu D, Wang Z, Zhang B, Song K, Li X, Li J, Duan H. Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, Northeast China. Agriculture, Ecosystems & Environment. 2006;113(1-4):73-81.

Cambardella CA, Moorman TB, Novak JM, Parkin TB, Karlen DL, Turco RF, Konopka AE. Field‐scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal. 1994;58(5):1501-1511.

Bhunia GS, Shit PK, Maiti R. Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC). Journal of the Saudi Society of Agricultural Sciences,. 2018;17(2):114-126.

Behera SK, Suresh K, Rao BN, Mathur RK, Shukla AK, Manorama K., Ramachandrudu, K., Harinarayana, P. and Prakash, C., 2016. Spatial variability of some soil properties varies in oil palm (Elaeisguineensis Jacq.) plantations of west coastal area of India. Solid Earth. 2016;7(3): 979-993.

Kumar P, Kumar P, Sharma M, Shukla AK, Butail NP. Spatial variability of soil nutrients in apple orchards and agricultural areas in Kinnaur region of cold desert, Trans-Himalaya, India. Environmental Monitoring and Assessment. 2022; 194(4):290.

Fischer A, Lee MK, Ojeda AS, Rogers SR. GIS interpolation is key in assessing spatial and temporal bioremediation of groundwater arsenic contamination. Journal of Environmental Management. 2021;280:111683.

Lange J, Krause E. Spatial interpolation with ArcGIS Pro Esri Training Seminar; 2019 Available: arcgis-pro/.

Bangroo SA, Sofi JA, Bhat MI, Mir SA, Mubarak T, Bashir O. Quantifying spatial variability of soil properties in apple orchards of Kashmir, India, using geospatial techniques. Arabian Journal of Geosciences. 2021;14:1-10.

Krause E. Model Water Quality Using Interpolation; 2019.

Foroughifar H, Jafarzadeh AA, Torabi H, Pakpour A, Miransari M. Using geostatistics and geographic information system techniques to characterize spatial variability of soil properties, including micronutrients. Communications in Soil Science and Plant Analysis. 2013;44(8): 1273-1281.

Liu X, Zhang W, Zhang M, Ficklin DL, Wang F. Spatio-temporal variations of soil nutrients influenced by an altered land tenure system in China. Geoderma. 2009;152(1-2):23-34.