Abstract
Digital maps of soil parameters are traditionally generated by dragging and drop** the desired tool of Geographical Information System (GIS) under the project of the Soil Health Card (SHC), India, and require knowledge in GIS, time, cost, and human resources to complete at the country level. Thus, there is need for a model, to generate soil parameter-wise maps quickly and efficiently. A model was proposed based on the Model Builder in ArcMap of ArcGIS for the automatic creation of files: interpolation, reclassification, color, raster to polygon, union, projection, and legend information for the generation of soil fertility maps. The model was validated using data (n = 161) from the SHC, Chitrasari (village), Bihar (state), India. Model results show that a map of one soil parameter can be produced in less time, at a lower cost, and with minimum human intervention as compared to traditional manner. The coefficient of variation (CV) of soil parameters varied from 6 to 112.9%. Micronutrients are positively correlated. The soil pH had a significantly negative correlation with Fe, Cu, and Mn. Soil B, Fe, K, Mn, N, OC, P, S, and Zn deficiencies were found in 16.59, 15.73, 7.64, 17.21, 1.0, 1.46, 0.61, 26.03, and 74.41% of the study area, respectively. The proposed model was implemented to create maps for 500 SHC model villages. Therefore, this model could be an effective tool for planners and decision-makers to identify issues at the village level and take timely action to save earth, soil health, environment, and site-specific fertilizer management.
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References
Abdella Y, Alfredsen K (2010) A GIS toolset for automated processing and analysis of radar precipitation data. Comput Geosci 36(4):422–429. https://doi.org/10.1016/j.cageo.2009.08.008
AbdelRahman MAE, Tahoun S (2019) GIS model-builder based on comprehensive geostatistical approach to assess soil quality. Remote Sens Appl: Soc Environ 13:204–214. https://doi.org/10.1016/j.rsase.2018.10.012
AbdelRahman MAE, Natarajan A, Hegde R, Prakash SS (2019) Assessment of land degradation using comprehensive geostatistical approach and remote sensing data in GIS-model builder. Egypt J Remote Sens Space Sci 22(3):323–334. https://doi.org/10.1016/j.ejrs.2018.03.002
Addinsoft (2022) XLSTAT statistical and data analysis solution. Long Island, NY, USA. Available Via https://www.xlstat.com Accessed 24 Aug 2022
Akinola AK, Olorunfemi IE, Oloruntoba C, Akinluyi FO (2021) Spatial prediction of soil nutrients from soil, topography and environmental attributes in the northern part of Ekiti State, Nigeria. Remote Sens Appl: Soc Environ 21:100450. https://doi.org/10.1016/j.rsase.2020.100450
Allen DW (2011) Getting to know ArcGIS modelBuilder. ESRI Press, Redlands, p 362
ArcGIS (2018) What is ModelBuilder?: ArcGIS Pro [Online]. Available:(2018) Available Via http://pro.arcgis.com/en/pro-app/help/analysis/geoprocessing/modelbuilder/what-is-modelbuilder-.htm Accessed 14 November 2020
Arunachalam P, Kannan P, Prabukumar G et al (2013) Zinc deficiency in Indian soils with special focus to enrich zinc in peanut. Afr J Agric Res 8(50):6681–6688
Awang Z (2012) Research Methodology and Data Analysis. UiTM Press, UiTM, Malaysia
Bagherzadeh A, Gholizadeh A (2018) Assessment of soil fertility for sugar beet production using fuzzy AHP approach and GIS in the Northeastern region of Iran. Agric Res 7(1):61–71. https://doi.org/10.1007/s40003-018-0295-9
Bagoria N, Vimal BK, Singh YK, Kumar R, Kumari R, Kumar RK, Kumar B, Kumar S, Bairwa R (2020) Spatial Variability of Soil pH, EC and organic carbon in different Panchayats of Sabour Block of Bhagalpur District, Bihar, India. Int J Curr Microbiol Appl Sci 9(09):756–763. https://doi.org/10.20546/ijcmas.2020.909.095
Behera SK, Shukla AK (2013) Depth-wise distribution of zinc, copper, manganese and iron in acid soils of India and their relationship with some soil properties. J Indian Soc Soil Sci 61(3):244–252
Behera SK, Shukla AK (2015) 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 Degrad Dev 26(1):71–79. https://doi.org/10.1002/ldr.2306
Behera SK, Shukla AK, Prakash C, Tripathi A, Kumar A, Trivedi V (2020) Establishing management zones of soil sulfur and micronutrients for sustainable crop production. Land Degrad Dev 32(13):3614–3625. https://doi.org/10.1002/ldr.3698
Benbi DK, Brar SPS (1992) Dependence of DTPA-extractable Zn, Fe, Mn, and Cu availability on organic carbon presence in arid and semiarid soils of Punjab. Arid Soil Res Rehabil 6(3):207–216. https://doi.org/10.1080/15324989209381315
Bhunia GS, Shit PK, Maiti R (2018) Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC). J Saudi Soc Agric Sci 17(2):114–126. https://doi.org/10.1016/j.jssas.2016.02.001
Bhuyan N, Barur NG, Borah DK, Bhattacharyya D, Basumatari A (2014) Georeferenced micro-nutrient status in soils of Lakhimpur district of Assam. J Indian Soc Soil Sci 62(2):102–107
Bi X, Li B, Nan B, Fan Y, Fu Q, Zhang X (2018) Characteristics of soil organic carbon and total nitrogen under various grassland types along a transect in a mountain-basin system in **njiang, China. J Arid Land 10(4):612–627. https://doi.org/10.1007/s40333-018-0006-1
Brady NC, Weil RR (2008) The nature and properties of soils, 14th edn. Pearson Education, Inc., Upper Saddle River, NJ 07458, United States
Brdar-Jokanović M (2020) Boron Toxicity and Deficiency in Agricultural Plants. Int J Mol Sci 21(4):1424. https://doi.org/10.3390/ijms21041424
Brown JL (2014) SDM toolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol Evol 5:694–700. https://doi.org/10.1111/2041-210X.12200
Budak M (2018) Importance of spatial soil variability for land use planning of a farmland in a semiarid region. Fresenius Environ Bull 27(7):5053–5065
Cambardella CA, Karlen DL (1999) Spatial analysis of soil fertility parameters. Precis Agric 1(1):5–14. https://doi.org/10.1023/A:1009925919134
Cambardella CA, Moorman TB, Novak JM, Parkin TB, Karlen DL, Turco RF, Konopka AE (1994) Field-scale variability of soil properties in central Iowa soils. Soil Sci Soc Am J 58(5):1501–1511. https://doi.org/10.2136/sssaj1994.03615995005800050033x
Cassman KG, Dobermann A, Walters DT et al (2003) Meeting cereal demand while protecting natural resources and improving environmental quality. Annu Rev Environ Resour 28:315–358. https://doi.org/10.1146/annurev.energy.28.040202.122858
Chatterjee S, Santra P, Majumdar K, Ghosh D, Das I, Sanyal SK (2015) Geostatistical approach for management of soil nutrients with special emphasis on different forms of potassium considering their spatial variation in intensive crop** system of West Bengal, India. Environ Monit Assess 187:183. https://doi.org/10.1007/s10661-015-4414-9
Clay DE, Kitchen N, Carlson CG, Kleinjan JL, Tjentland WA (2002) Collecting representative soil samples for N and P fertilizer recommendations. Crop Manag 1(1):1–5. https://doi.org/10.1094/CM-2002-1216-01-MA
Corwin DL, Lesch SM (2005) Apparent soil electrical conductivity measurements in agriculture. Comput Electron Agric 46:11–43. https://doi.org/10.1016/j.compag.2004.10.005
Dafonte JD, Montserrat MU, Pazferreiro J, Siqueira GM, Vázquez EV (2010) Map** of soil micronutrients in an European Atlantic agricultural landscape using ordinary kriging and indicator approach. Bragantia 69:175–186. https://doi.org/10.1590/S0006-87052010000500018
Das BS, Wani SP, Benbi DK, Muddu S et al (2022) Soil health and its relationship with food security and human health to meet the sustainable development goals in India. Soil Secur 8:100071. https://doi.org/10.1016/j.soisec.2022.100071
Desavathu RN, Nadipena AR, Peddada JR (2018) Assessment of soil fertility status in Paderu Mandal, Visakhapatnam district of Andhra Pradesh through Geospatial techniques. Egypt J Remote Sens Space Sci 21(1):73–81. https://doi.org/10.1016/j.ejrs.2017.01.006
El-Zeiny AM, Elbeih SF (2019) GIS-based evaluation of groundwater quality and suitability in Dakhla Oases. Earth Syst Environ 3:507–523. https://doi.org/10.1007/s41748-019-00112-1
Favre C, Fahland D, Völzer H (2015) The Relationship between Workflow Graphs and FreeChoice Workflow Nets. Inf Syst 47:197–219. https://doi.org/10.1016/j.is.2013.12.004
Field A (2013) Discovering Statistics Using IBM SPSS Statistics. Sage, Newcastle upon Tyne, UK
Foroughifar H, Jafarzadeh AA, Torabi H, Pakpour A, Miransari M (2013) Using Geostatistics and Geographic Information System Techniques to Characterize Spatial Variability of Soil Properties, Including Micronutrients. Commun Soil Sci Plant Anal 44(8):1273–1281. https://doi.org/10.1080/00103624.2012.758279
Goovaerts P (1998) Geostatistical tools for characterizing the spatial variability of microbiological and physico-chemical soil properties. Biol Fertil Soils 27:315–334. https://doi.org/10.1007/s003740050439
Gruhn P, Goletti F, Yudelman M (2000) Integrated Nutrient Management, Soil Fertility, and Sustainable Agriculture: Current Issues and Future Challenges. International Food Policy Research Institute, USA, pp 1–26
Guan F, **a M, Tang X, Fan S (2017) Spatial variability of soil nitrogen, phosphorus and potassium contents in Moso bamboo forests in Yong’an City, China. CATENA 150:161–172. https://doi.org/10.1016/j.catena.2016.11.017
Havlin JL, Beaton JD, Tisdale SL et al (2014) Soil fertility and fertilizers: an introduction to nutrient management, 8th edn. Pearson Education, Inc., Upper Saddle River, NJ 07458, United States
Hegde R, Bardhan G, Niranjana KV, Bhaskar BP, Singh SK (2019) Spatial variability and map** of selected soil properties in Kaligaudanahalli Microwatershed, Gundlupet Taluk, Chamarajanagar District, under hot semi-arid agrosubregion of Central Karnataka Plateau, India. N Appl Sci 1:518. https://doi.org/10.1007/s42452-019-0486-4
Hengl T, Heuvelink GBM, Rossiter DG (2007) About Regression-Kriging: From Equations to Case Studies. Comput Geosci 33(10):1301–1315. https://doi.org/10.1016/j.cageo.2007.05.001
Hoque ASMM, Awang Z (2016) Exploratory factor analysis of entrepreneurial marketing: scale development and validation in the SME context of Bangladesh. In: Proceedings of the international social sciences and tourism research conference, Terengganu, UniSZA, Malaysia, pp 20–22
Hysa A, Baskaya FAT (2018) A GIS-based method for revealing the transversal continuum of natural landscapes in the coastal zone. MethodsX 5:514–523. https://doi.org/10.1016/j.mex.2018.05.012
IBM (2022) SPSS, Available Via https://www.ibm.com/in-en/spss Accessed 04 Sept 2022
Jamovi project (2022) Jamovi (Version 2.3). Available Via https://cloud.jamovi.org/ Accessed 18 Oct 2022
Jolma A, Ames DP, Horning N, Mitasova H, Neteler M, Racicot A, Sutton T (2008) Chapter ten free and open source geospatial tools for environmental modelling and management. Dev Integr Environ Assess 3:163–180. https://doi.org/10.1016/S1574-101X(08)00610-8
Kaiser HF (1974) An index of factorial simplicity. Psychometrika 39:31–36. https://doi.org/10.1007/BF02291575
Katyal JC, Sharma BD (1991) DTPA-extractable and total Zn, Cu, Mn, and Fe in Indian soils and their association with some soil properties. Geoderma 49(1–2):165–179. https://doi.org/10.1016/0016-7061(91)90099-F
Katyal JC, Vlek PLG (1985) Micronutrient problems in tropical Asia. In: Vlek PLG (ed) Micronutrients in Tropical Food Crop Production. Developments in Plant and Soil Sciences, vol 14. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-5055-9_3
Khan ST, Malik A, Alwarthan A, Shaik MR (2022) The enormity of the zinc deficiency problem and available solutions; an overview. Arab J Chem 15(3):103668. https://doi.org/10.1016/j.arabjc.2021.103668
Kitundu KMB, Mrema JP (2006) The status of Zn, Cu, Mil, and Fe in the soils and tea-leaves of-Kibena-tea. Estates-Njombe, Tanzania. Tanzan J Agric Sci 7(1):34–41
Kumar U, Nayak AK, Shahid M, Gupta VV, Panneerselvam P, Mohantry S, Kaviraj M, Kumar A, Chatterjee D, Lal B, Gautam P, Tripathi R, Panda BB (2018) Continuous application of inorganic and organic fertilizer over 47 years in paddy soil alters the bacterial community structure and its influence on rice production. Agric Ecosyst Environ 262:65–75. https://doi.org/10.1016/j.agee.2018.04.016
Lee CL, Huang SL, Chan SL (2008) Biophysical and system approaches for simulating land-use change. Landsc Urban Plan 86(2):187–203. https://doi.org/10.1016/j.landurbplan.2008.02.006
Leena HU, Premasudha BG, Basavaraja PK, Saqeebulla HM, Gangamrutha GV (2019) Assessment of geostatistical models for the major soil nutrients for Tumakur District of Karnataka. Int J Recent Technol Eng 8(4):9382–9387. https://doi.org/10.35940/ijrte.D9606.118419
Leena HU, Premasudha BG, Basavaraja PK (2020) Geospatial analytical framework for soil fertility management. Int J Geoinformatics 16(2):41–51
Leena HU, Premasudha BG, Panneerselvam S, Basavaraja PK (2021) Pedometric map** for soil fertility management – A case study. J Saudi Soc Agric Sci 20(2):128–135. https://doi.org/10.1016/j.jssas.2020.12.008
Li J, Heap AD (2011) A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecol Inform 6(3–4):228–241. https://doi.org/10.1016/j.ecoinf.2010.12.003
Li J, Heap AD (2014) Spatial interpolation methods applied in the environmental sciences: A review. Environ Model Softw 53:173–189. https://doi.org/10.1016/j.envsoft.2013.12.008
Li J, Heap AD, Potter A, Daniell JJ (2011) Application of machine learning methods to spatial interpolation of environmental variables. Environ Model Softw 26(12):1647–1659. https://doi.org/10.1016/j.envsoft.2011.07.004
Lü G (2011) Geographic analysis-oriented virtual geographic environment: framework, structure and functions. Sci China Earth Sci 54(5):733–743. https://doi.org/10.1007/s11430-011-4193-2
Mahapatro D, Panigrahy RC, Naik S, Pati SK (2011) Macrobenthos of shelf zone off Dhamara estuary, Bay of Bengal. J Oceanog Mar Sci 2(2):32–44
Marschner H (2008) Mineral Nutrition of Higher Plants. Academic Press, London-UK, p 889
Matcham EG, Subburayalu SK, Culman SW, Lindsey LE (2021) Implications of choosing different interpolation methods: A case study for soil test phosphorus. Crop, Forage Turfgrass Manag 7(2):e20126. https://doi.org/10.1002/cft2.20126
Mericskay B (2018) Automation of Workflows for the Installation of a Wind Farm. In: Baghdadi N, Mallet C, Zribi M (Eds.), QGIS and Applications in Territorial Planning 3:125–168https://doi.org/10.1002/9781119457121.ch5
MoAFW (2015) Ministry of Agriculture & Farmers Welfare Annual Report, Government of India. Available via https://agricoop.nic.in/Documents/Annual%20Report-2015-16.pdf Accessed 12 Jan 2022
Mohamed ES, Saleh AM, Belal AB, Gad A (2018) Application of near-infrared reflectance for quantitative assessment of soil properties. Egypt J Remote Sens Space Sci 21(1):1–14. https://doi.org/10.1016/j.ejrs.2017.02.001
Morari F, Vellidis G, Gay P (2011) Fertilizers. In: Nriagu JO (Eds.), Encyclopedia of Environmental Health, Elsevier, 727–737, ISBN 9780444522726, https://doi.org/10.1016/B978-0-444-52272-6.00464-5
Neteler M, Bowman MH, Landa M, Metz M (2012) GRASS GIS: A multi-purpose open source GIS. Environ Model Softw 31:124–130. https://doi.org/10.1016/j.envsoft.2011.11.014
Nowak, MM, Pędziwiatr K (2018) Dataset and GIS toolbox for modeling potential tree belt functions. Data in Brief 20: 326–332, ISSN 2352–3409, https://doi.org/10.1016/j.dib.2018.08.005
Omran A, Dietrich S, Abouelmagd A, Michael M (2016) New ArcGIS tools developed for stream network extraction and basin delineations using Python and java script. Comput Geosci 94:140–149. https://doi.org/10.1016/j.cageo.2016.06.012
Onwuka B, Mang B (2018) Effects of soil temperature on some soil properties and plant 558 growth. Adv Plants Agric Res 8(1):34–37. https://doi.org/10.15406/apar.2018.08.00288
Pallant J (2013) SPSS survival manual. A step by step guide to data analysis using SPSS, 4th edn. Allen & Unwin, Berkshire, p 345
Panhalakr SS, Jarag AP (2015) Assessment of spatial interpolation techniques for river bathymetry generation of Panchganga River basin using geoinformatic techniques. Asian J Geoinformatics 15(3):9–15
Pati R, Mukhopadhya D (2011) Distribution of cationic micronutrients in some acid soils West Bengal. J Indian Soc Soil Sci 59:125–133
Paul R, Brindha K, Gowrisankar G, Tan ML, Singh MK (2019) Identification of hydrogeochemical processes controlling groundwater quality in Tripura, Northeast India using evaluation indices, GIS, and multivariate statistical methods. Environ Earth Sci 78:470. https://doi.org/10.1007/s12665-019-8479-6
Pfaff RM, Glennon JA (2004) Building a groundwater protection model. ArcUser July-September 2004:54–59
PIB (2022) https://pib.gov.in/FactsheetDetails.aspx?Id=148602 (accessed on 08/09/2022)
Prasad PNS, Subbarayappa CT, Ramamurthy V, Sathish A (2020) Quantifying and map** of major, secondary and micronutrient status of tomato growing soils in Kolar District, Karnataka using GIS and GPS approach. Int J Plant Soil Sci 32(14):14–27. https://doi.org/10.9734/ijpss/2020/v32i1430363
Pratibha TD, Saikia B, Lakiang T, Jha DK, Longmailai P, Raju PLN (2020) Soil Fertility Map** Using Soil Health Card data and Geospatial Technology in Ri Bhoi District of Meghalaya. East Afr Sch J Agric Life Sci11(3):ISSN 2617–7277 (Online). https://doi.org/10.36349/easjals.2020.v03i11.004
Qu L, **ao H, Zheng N, Zhang Z, Xu Y (2017) Comparison of four methods for spatial interpolation of estimated atmospheric nitrogen deposition in South China. Environ Sci Pollut Res 24:2578–2588. https://doi.org/10.1007/s11356-016-7995-0
Rahmati O, Kornejady A, Samadi M, Nobre AD, Melesse AM (2018) Development of an automated GIS tool for reproducing the HAND terrain model. Environ Model Softw 102:1–12. https://doi.org/10.1016/j.envsoft.2018.01.004
Ramana SYV, Jat LK, Meena SK, Singh L, Jatav HS, Paul A (2015) Available macro nutrient status and their relationship with soil physico- chemical properties of Sri Ganganagar district of Rajasthan, India. J Pure Appl Microbiol 9(4):2887–2894
Ramírez-Cuesta JM, Mirás-Avalos JM, Rubio-Asensio JS, Intrigliolo DS (2019) A Novel ArcGIS Toolbox for Estimating Crop Water Demands by Integrating the Dual Crop Coefficient Approach with Multi-Satellite Imagery. Water 11(1):38. https://doi.org/10.3390/w11010038
Rego TJ, Sahrawat KL, Wani SP, Pardhasaradhi G (2007) Widespread deficiencies of sulphur, boron and zinc in dryland soils of the Indian semi-arid tropical soils: on farm crop responses. J Plant Nutr 30(10):1569–1583. https://doi.org/10.1080/01904160701615475
Reza SK, Baruah U, Sarkar D, Dutta DP (2011) Influence of slope positions on soil fertility index, soil evaluation factor and microbial indices in acid soil of Humid Subtropical India. J Soil Water Conserv 39(1):44–49
Reza SK, Nayak DC, Chattopadhyay T, Mukhopadhyay S, Singh SK, Srinivasan R (2015) Spatial distribution of soil physical properties of alluvial soils: A geostatistical approach. Arch Agron Soil Sci 62(7):972–981. https://doi.org/10.1080/03650340.2015.1107678
Reza SK, Baruah U, Sarkar D, Singh SK (2016) Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India. Arab J Geosci 9:446. https://doi.org/10.1007/s12517-016-2474-y
Reza SK, Nayak DC, Mukhopadhyay S, Chattopadhyay T, Singh SK (2017) Characterizing spatial variability of soil properties in alluvial soils of India using geostatistics and geographical information system. Arch Agron Soil Sci 63(11):1489–1498. https://doi.org/10.1080/03650340.2017.1296134
Rezaei SA, Gilkes RJ (2005) The effects of landscape attributes and plant community on soil chemical properties in rangelands. Geoderma 125(1–2):167–176. https://doi.org/10.1016/j.geoderma.2004.07.010
Robinson TP, Metternicht G (2006) Testing the performance of spatial interpolation techniques for map** soil properties. Comput Electron Agric 50(2):97–108. https://doi.org/10.1016/j.compag.2005.07.003
Sathish A, Ramachandrappa BK, Devaraja K, Savitha MS, ThimmeGowda MN, Prashanth KM (2017) Assessment of spatial variability in fertility status and nutrient recommendation in Alanatha cluster villages, Kanakapura taluk, Ramanagara district, Karnataka Using GIS Techniques. Int J Curr Microbiol Appl Sci 6(5):211–224. https://doi.org/10.20546/ijcmas.2017.605.025
Sen P, Majumdar K, Sulewski G (2008) Importance of spatial nutrient variability map** to facilitate SSNM in small land holding systems. Indian J Fert 4(11):43–50
Sen P, Majumdar K (2006) Spatial variability in soil physico-chemical properties and nutrient status in an intensively cultivated village of West Bengal. Proceedings of the fifth international conference of the Asian federation for Information Technology in Agriculture, Macmillan (India), Bangalore, India, 653–660
Seyedmohammadi J, Esmaeelnejad L, Shabanpour M (2016) Spatial variation modelling of groundwater electrical conductivity using geostatistics and GIS. Model Earth Syst Environ 2:1–10. https://doi.org/10.1007/s40808-016-0226-3
Sharma JC, Chaudhary K (2007) Vertical Distribution of Micronutrient cations in relation to soil characteristics in lower Shiwaliks of Solan district in North-West Himalayas. J Indian Soc Soil Sci 55(1):40–44
SHC (2022) Soil Health card. Available via https://soilhealth.dac.gov.in/ (accessed 17 July 2021)
Shukla A, Tiwari P, Prakash C (2014) Micronutrients Deficiencies vis-a-vis Food and Nutritional Security of India. Indian J Fert 10(12):94–112
Shukla AK, Behera SK, Lenka NK, Tiwari PK, Prakash C, Malik RS (2016) Spatial variability of soil micronutrients in the intensively cultivated Trans-Gangetic Plains of India. Soil Tillage Res 163:282–289. https://doi.org/10.1016/j.still.2016.07.004
Shukla AK, Behera SK, Singh VK, Prakash C et al (2020) Pre-monsoon spatial distribution of available micronutrients and sulphur in surface soils and their management zones in Indian Indo-Gangetic Plain. PLoS One 15(6):e0234053. https://doi.org/10.1371/journal.pone.0234053
Shukla AK, Behera SK, Tripathi R, Prakash C, Nayak AK, Kumar PS, Chitdeshwari T, Kumar D, Nayak RK, Babu PS, Katkar RN, Subbarayappa CT, Moharana KC, Patra AK, Srinivasarao C, Chaudhari SK, Rao AS, Singh AK, Das S (2021) Evaluation of spatial spreading of phyto-available sulphur and micronutrients in cultivated coastal soils. PLoS One 16(10):e0258166. https://doi.org/10.1371/journal.pone.0258166
Singha C, Swain KC (2022) "Assessing The Spatial Variability of Soil Nutrients Prediction Using GIS-based Interpolation Techniques," 2022 IEEE World Conference on Applied Intelligence and Computing (AIC), Sonbhadra, India, 2022, pp. 757–763. https://doi.org/10.1109/AIC55036.2022.9848951
Smith D (1996) Soil pH preferences of agricultural plants. In: Handbook of reference methods for plant analysis. CRC Press, pp 133–146
Subbaiah PV, Krishna RY, Mrudhula KA, Kaledhonkar MJ (2022) Assessment of soil properties in Krishna western delta ecosystem of Guntur district of Andhra Pradesh. Indian J Ecol 49(6):2093–2098
Sun B, Zhou S, Zhao Q (2003) Evaluation of spatial and temporal changes of soil quality based on geostatistical analysis in the hill region of subtropical China. Geoderma 115(1–2):85–99. https://doi.org/10.1016/S0016-7061(03)00078-8
Thombe SV, Badole WP, Chaure PR (2020) Study of soil fertility and correlation of soil properties of selected villages under Jalyukt Shivar in Nagpur district. Int J Appl Res 6(7):241–244
Tiruneh GA, Alemayehu TY, Meshesha DT, Vogelmann ES, Reichert JM, Haregeweyn N (2021) Spatial variability of soil chemical properties under different land-uses in Northwest Ethiopia. PLoS One 16(6):e0253156. https://doi.org/10.1371/journal.pone.0253156
Tiwari A, Ajmera S (2021) Land Suitability Assessment for Agriculture Using Analytical Hierarchy Process and Weighted Overlay Analysis in ArcGIS ModelBuilder. In: Pathak KK, Bandara JMSJ, Agrawal R (eds) Recent Trends in Civil Engineering. Lecture Notes in Civil Engineering, vol 77. Springer, Singapore. https://doi.org/10.1007/978-981-15-5195-6_56
Tripathi R, Nayak AK, Shahid M, Raja R, Panda BB, Mohanty S, Kumar A, Lal B, Gautam P, Sahoo RN (2015) Characterizing spatial variability of soil properties in salt affected coastal India using geostatistics and kriging. Arab J Geosci 8:10693–10703. https://doi.org/10.1007/s12517-015-2003-4
Tunçay T, Kılıç S, Dedeoğlu M, Dengiz O, Oğuz Başkan O, Bayramin İ (2021) Assessing soil fertility index based on remote sensing and gis techniques with field validation in a semiarid agricultural ecosystem. J Arid Environ 190:104525. https://doi.org/10.1016/j.jaridenv.2021.104525
Uthman QO, Kadyampakeni DM, Nkedi-Kizza P, Kwakye S, Barlas NT (2022) Boron, Manganese, Zinc Sorption and Leaf Uptake on Citrus Cultivated on a Sandy Soil. Plants 11(5):638. https://doi.org/10.3390/plants11050638
Velamala RR, Pant PK (2023) SFMToolbox: an ArcGIS Python Toolbox for Automatic Production of Maps of Soil Fertility. Geomat Environ Eng 17(2):105–145. https://doi.org/10.7494/geom.2023.17.2.105
Vijaykumar M, Haroon ARM (2013) Nutrient status of sugarcane growing soils of Theni district, Tamil Nadu - A soil series based study. Asian J Soil Sci 8(2):385–389
Wang S (2010) A cyberGIS framework for the synthesis of cyberinfrastructure, GIS, spatial analysis. Ann Assoc Am Geogr 100(3):535–557. https://doi.org/10.1080/00045601003791243
Wang J, Fu BJ, Qiu Y, Chen LD (2003) Analysis on soil nutrient characteristics for sustainable land use in Danangou catchment of the Loess Plateau, China. Catena 54(1–2):17–29. https://doi.org/10.1016/S0341-8162(03)00054-7
Wang R, Zou R, Liu J, Liu L, Hu Y (2021) Spatial Distribution of Soil Nutrients in Farmland in a Hilly Region of the Pearl River Delta in China Based on Geostatistics and the Inverse Distance Weighting Method. Agriculture 11(1):50. https://doi.org/10.3390/agriculture11010050
Wanghe K, Guo X, Wang M, Zhuang H, Ahmad S, Khan TU, **ao Y, Luan X, Li K (2020) Gravity model toolbox: An automated and open-source ArcGIS tool to build and prioritize ecological corridors in urban landscapes. Glob Ecol Conserv 22:e01012. https://doi.org/10.1016/j.gecco.2020.e01012
Warrick AW (1998) Spatial variability. In: Hillel D (ed) Environmental soil physics. The hypothesis of data normality was verified using the Shapiro-Wilk test. Academic Press, Cambridge, MA, pp 655–675
Wei X, Hao M, Shao M, Gal WJ (2006) Changes in soil properties and the availability of soil micronutrients after 18 years of crop** and fertilization. Soil Tillage Res 91:120–130. https://doi.org/10.1016/j.still.2005.11.009
Williams WA, Jensen ME, Winne JC, Redmond RL (2000) An Automated Technique for Delineating and Characterizing Valley-Bottom Settings. In: Sandhu SS, Melzian BD, Long ER, Whitford WG, Walton BT (eds) Monitoring Ecological Condition in the Western United States. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-4343-1_10
Yadav KK, Mali N, Kumar S, Surya J, Moharana PC, Nogiya M, Meena R (2022) Assessment of Soil Quality and Spatial Variability of Soil Properties Using Geo-Spatial Techniques in Sub-Humid Southern Plain of Rajasthan, India. J Indian Soc Soil Sci 70(1):69–85. https://doi.org/10.5958/0974-0228.2022.00004.4
Yadav TC, Singh YP, Yadav SS, Singh A, Subhash PT (2023) Spatial Variability in Soil Properties, Delineation Site-specific Management Division Based on Soil Fertility Using Fuzzy Clustering in Gwalior, Madhya Pradesh, India. Int J Plant Soil Sci 35(6):49–76. https://doi.org/10.9734/ijpss/2023/v35i62840
Yageta Y, Osbahr H, Morimoto Y, Clark J (2019) Comparing farmers’ qualitative evaluation of soil fertility with quantitative soil fertility indicators in Kitui County, Kenya. Geoderma 344:153–163. https://doi.org/10.1016/j.geoderma.2019.01.019
Yan R, Jiaying C, Lankang C, Haitao Z, Ke Z (2018) Research and implementation of a universal workflow model to evaluate the soil fertility based on OGC Web Service. Geo Spat Inf Sci 21(4):346–357. https://doi.org/10.1080/10095020.2018.1519350
Yang Y, Zhang S (2008) Approach of Develo** Spatial Distribution Maps of Soil Nutrients. In: Li D (ed) Computer and Computing Technologies In Agriculture, Volume I. CCTA 2007 The International Federation for Information Processing, vol 258. Springer, Boston, MA
Yasrebi J, Saffari M, Fathi H, Karimian N, Emadi M, Baghernejad M (2008) Spatial variability of soil fertility properties for precision agriculture in Southern Iran. J Appl Sci 8:1642–1650. https://doi.org/10.3923/jas.2008.1642.1650
Zhang SL, Huffman T, Zhang XY, Liu W, Liu ZH (2014) Spatial distribution of soil nutrient at depth in black soil of Northeast China: A case study of soil available phosphorus and total phosphorus. J Soils Sediments 14:1775–1789. https://doi.org/10.1007/s11368-014-0935-z
Zhu AX, Zhao FH, Liang P, Qin CZ (2021) Next generation of GIS: must be easy. Ann GIS 27(1):71–86. https://doi.org/10.1080/19475683.2020.1766563
Acknowledgements
We would like to express our gratitude to the Chief Soil Survey Officer, Soil and Land Use Survey of India (SLUSI), Department of Agriculture and Farmers Welfare, Ministry of Agriculture and Farmers Welfare, Government of India, for providing the necessary resources to carry out this study.
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Velamala, R.R., Pant, P.K. SFM_MB Toolbox: a new ArcGIS toolbox for building spatial distribution maps of soil fertility using model builder in ArcMap of ArcGIS, a case study. Arab J Geosci 17, 46 (2024). https://doi.org/10.1007/s12517-023-11843-x
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DOI: https://doi.org/10.1007/s12517-023-11843-x