SFMToolbox: an ArcGIS Python Toolbox for Automatic Production of Maps of Soil Fertility

Velamala, Ranga Rao; Pant, Pawan Kumar

doi:10.7494/geom.2023.17.2.105

Artykuł - szczegóły

Tytuł artykułu

SFMToolbox: an ArcGIS Python Toolbox for Automatic Production of Maps of Soil Fertility

Autorzy

Velamala Ranga Rao , Pant Pawan Kumar

Treść / Zawartość

Pełne teksty:

Pobierz

Identyfikatory

DOI

10.7494/geom.2023.17.2.105

Warianty tytułu

Języki publikacji

Abstrakty

SFMToolbox is an ArcGIS Python toolbox developed in ArcGIS Desktop (ArcMap) to perform preprocessing tasks for the automatic creation of maps of soil fertility parameters. Through SFMToolbox, users can automatically produce 12 soil fertility parameter maps as a batch at one time. It is easy to use, where users can only provide input; the output files are automatically created from the name of the sample point and saved in the defined workspace. During the execution of the tools, various processes, such as Inverse Distance Weighted (IDW) – a technique of interpolation, reclassification, adding color, merging, projection, area calculation, and legend are done automatically for all 12 parameters at the same time. The SFMToolbox was validated as part of the following case study: village – Kashipur, tehsil – Balrampur, district – Balrampur, state – Uttar Pradesh, Country – India. The results show that the user can quickly generate maps and save time, improve accuracy, and reduce human intervention and ensure uniformity among maps. This toolbox also applied to Cycle II data from the Government of India’s Soil Health Card (SHC) scheme and timely produced 12-parameters soil nutrient maps for 630 districts in a uniform format. The toolbox may be used by public and private organizations to make timely decisions on agricultural and environmental issues.

Słowa kluczowe

ArcGIS GIS Python ArcPy ArcMap soil fertility toolbox soil health card

Wydawca

AGH University of Science and Technology Press

Czasopismo

Geomatics and Environmental Engineering

Rocznik

2023

Tom

Vol. 17, no. 2

Strony

105--145

Opis fizyczny

Bibliogr. 55 poz., rys., tab.

Twórcy

autor

Velamala Ranga Rao

velamala.ranga@gmail.com

Soil and Land Use Survey of India, Department of Agriculture and Farmers Welfare, Ministry of Agriculture and Farmers Welfare, Government of India, New Delhi, India

https://orcid.org/0000-0002-6171-2293

autor

Pant Pawan Kumar

pwnpant5@gmail.com

Soil and Land Use Survey of India, Department of Agriculture and Farmers Welfare, Ministry of Agriculture and Farmers Welfare, Government of India, New Delhi, India

https://orcid.org/0000-0002-7144-5483

Bibliografia

1. Mahendra S.: Soil management in relation to sustainable food production. Journal of the Indian Society of Soil Science, vol. 58 (supplement), 2010, pp. 65–72.
2. Rashid A., Ryan J.: Micronutrients constraints to crop production in soils with Mediterranean-type characteristics: A review. Journal of Plant Nutrition, vol. 27(6), 2004, pp. 959–975. https://doi.org/10.1081/PLN-120037530.
3. Yang Y., Zhang S.: Approach of developing spatial distribution maps of soil nutrients. [in:] Li D. (ed.), Computer and Computing Technologies in Agriculture, Volume I: First IFIP TC 12 International Conference on Computer and Computing Technologies in Agriculture (CCTA 2007), Wuyishan, China, August 18–20, 2007, IFIP Advances in Information and Communication Technology, vol. 258, Springer, New York, pp. 565–571. https://doi.org/10.1007/978-0-387-77251-6_62.
4. Shit P.K., Bhunia G.S., Maiti R.: Spatial analysis of soil properties using GIS based geostatistics models. Modeling Earth Systems and Environment, vol. 2, 2016, 107. https://doi.org/10.1007/s40808-016-0160-4.
5. Barman D., Sahoo R., Kalra N., Kamble K., Kundu D.: Homogeneous soil fertility mapping through GIS for site specific nutrient management by QUEFTS model. Indian Journal of Soil Conservation, vol. 41(3), 2013, pp. 257–261.
6. Reddy A.A.: Impact Study of Soil Health Card Scheme. National Institute of Agricultural Extension Management (MANAGE), Hyderabad-500030, 2017.
7. Havlin J.L., Tisdale S.L., Nelson W.L., Beaton J.D.: Soil Fertility and Nutrient Management: An Introduction to Nutrient Management. 8th ed. Pearson, Upper Saddle River 2014.
8. Leena H.U., Premasudha B.G., Panneerselvam S., Basavaraja P.K.: Pedometric mapping for soil fertility management – A case study. Journal of the Saudi Society of Agricultural Sciences, vol. 20(2), 2021, pp. 128–135. https://doi.org/10.1016/j.jssas.2020.12.008.
9. EnviStats India 2019. Volume II – Environment Accounts. Government of India, Ministry of Statistics and Programme Implementation, National Statistical Office (Social Statistics Division), 2019. https://mospi.gov.in/web/mospi/envistats-india-2019-vol.ii-environment-accounts- [access: 31.12.2021].
10. Soil Health Card (SHC). https://www.soilhealth.dac.gov.in/ [access: 7.01.2022].
11. de Paz J.-M., Sánchez J., Visconti F.: Combined use of GIS and environmental indicators for assessment of chemical, physical and biological soil degradation in a Spanish Mediterranean region. Journal of Environmental Management, vol. 79(2), 2006, pp. 150–162. https://doi.org/10.1016/j.jenvman.2005.06.002.
12. Labib M., Nashed A.: GIS and geotechnical mapping of expansive soil in Toshka region. Ain Shams Engineering Journal, vol. 4(3), 2013, pp. 423–433. https://doi.org/10.1016/j.asej.2012.11.005.
13. Reetz H.F., Rund Q.B., Murrell S.: GIS in nutrient management – A 21st century paradigm shift. [in:] Twenty-fourth annual ESRI International User Conference: proceedings: GIS serving our world, Sound of Knowledge, San Diego 2004.
14. Iftikar W., Chattopadhyaya G.N., Majumdar K., Sulewski G.D.: Use of village-level soil fertility maps as a fertilizer decision support tool in the red and lateritic soil zone of India. [in:] The Proceedings of the International Plant Nutrition Colloquium XVI, Department of Plant Sciences, UC Davis, 2010. https://escholarship.org/uc/item/7642k8hr [access: 27.12.2021].
15. Sen P., Majumdar K., Sulewski G.D.: Importance of spatial nutrient variability mapping to facilitate SSNM in small land holding systems. Indian Journal of Fertilisers, vol. 4(11), 2008, pp. 43–50.
16. Strahler A.: Introducing Physical Geography. 5th ed. John Wiley and Sons, 2010.
17. Antle J.M., Jones J.W., Rosenzweig C.E.: Next generation agricultural system data, models and knowledge products: Introduction. Agricultural Systems, vol. 155, 2017, pp. 186–190. https://doi.org/10.1016/j.agsy.2016.09.003.
18. Kumar A., Srivastava L.K., Mishra V.N., Banwasi R.: Major and micro nutrient status of rice-chickpea grown in soils of Chhattisgarh Plain region of India. Indian Journal of Agricultural Research, vol. 51(1), 2017, pp. 1–8. https://doi.org/10.18805/ijare.v0i0.7009.
19. Ransom K.M., Nolan B.T., Traum J.A., Faunt C.C., Bell A.M., Gronberg J.A.M., Wheeler D.C. et al.: A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA. Science of the Total Environment, vol. 601–602, 2017, pp. 1160–1172. https://doi.org/10.1016/j.scitotenv.2017.05.192.
20. Papadopoulos A., Kalivas D., Hatzichristos T.: GIS modelling for site-specific nitrogen fertilization towards soil sustainability. Sustainability, vol. 7(6), 2015, pp. 6684–6705. https://doi.org/10.3390/su7066684.
21. Kumar M.V., Bakiyathu S.B., Kannan P., Mahendran P.P.: Delineation and geographic information system (GIS) mapping of soil nutrient status of sugarcane growing tracts of Theni district, Tamil Nadu. African Journal of Agricultural Research, vol. 10(33), 2015, pp. 3281–3291. https://doi.org/10.5897/AJAR2013.7251.
22. AbdelRahman M.A.E., Natarajan A., Srinivasamurthy C.A., Hegde R.: Estimating soil fertility status in physically degraded land using GIS and remote sensing techniques in Chamarajanagar district, Karnataka, India. The Egyptian Journal of Remote Sensing and Space Science, vol. 19(1), 2016, pp. 95–108. https://doi.org/10.1016/j.ejrs.2015.12.002.
23. Banerjee K., Paul R., Dash B.B.: Soil resource mapping and its nutrient status in two blocks of Koraput District, Odisha using GIS technology. International Journal of Engineering Research & Technology (IJERT), vol. 4(3), 2016, pp. 1–5.
24. Ren Y., Chen J., Chen L., Zhang H., Zhang K.: Research and implementation of a universal workflow model to evaluate the soil fertility based on OGC Web Service. Geo-spatial Information Science, vol. 21(4), 2018, pp. 346–357. https://doi.org/10.1080/10095020.2018.1519350.
25. Kashiwar S.R., Kundu M.Ch., Dongarwar U.R.: Assessment and mapping of soil nutrient status of Sakoli tehsil of Bhandara district of Maharashtra using GIS techniques. Journal of Pharmacognosy and Phytochemistry, vol. 8(5), 2019, pp. 1900–1905.
26. Biradar B., Jayadeva H.M., Channakeshava S., Geetha K.N., Sannagoudar M.S., Pavan A.S., Prakash K.N.: Assessment of soil fertility through GIS techniques and thematic mapping in micro-watershed of Hassan, Karnataka. Journal of Pharmacognosy and Phytochemistry, vol. 9(4), 2020, pp. 3218–3228.
27. Pratibha T.D., Saikia B., Raju P.L.N.: Land use planning using geospatial technology and soil health card data for a micro watershed in sub tropical humid region of Meghalaya. Agricultural Research & Technology: Open Access Journal, vol. 25(2), 2020, 556301.
28. Li Q., Yang J., Guan W., Liu Z., He G., Zhang D., Liu X.: Soil fertility evaluation and spatial distribution of grasslands in Qilian Mountains Nature Reserve of eastern Qinghai-Tibetan Plateau. PeerJ, vol. 9, 2021, e10986. https://doi.org/10.7717/peerj.10986.
29. Stefanidis S.P., Chatzichristaki C.A., Stefanidis P.S.: An ArcGIS toolbox for estimation and mapping soil erosion. Journal of Environmental Protection and Ecology, vol. 22(2), 2021, pp. 689–696.
30. Naghibi S.A., Hashemi H., Pradhan B.: APG: A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies. Geoscience Frontiers, vol. 12(6), 2021, 101232. https://doi.org/10.1016/j.gsf.2021.101232.
31. Rigol-Sanchez J.P., Stuart N., Pulido-Bosch A.: ArcGeomorphometry: A toolbox for geomorphometric characterisation of DEMs in the ArcGIS environment. Computers & Geosciences, vol. 85 (part A), 2015, pp. 155–163. https://doi.org/10.1016/j.cageo.2015.09.020.
32. Dysarz T.: Development of RiverBox – An ArcGIS toolbox for river bathymetry reconstruction. Water, vol. 10(9), 2018, 1266. https://doi.org/10.3390/w10091266.
33. Ramírez-Cuesta J.M., Mirás-Avalos J.M., Rubio-Asensio J.S., Intrigliolo D.S.: A novel ArcGIS toolbox for estimating crop water demands by integrating the dual crop coefficient approach with multi-satellite imagery. Water, vol. 11(1), 2019, 38. https://doi.org/10.3390/w11010038.
34. Higgins Ch.D.: Accessibility toolbox for R and ArcGIS. Findings, May, 2019, pp. 1–8. https://doi.org/10.32866/8416.
35. Bíl M., Andrášik R., Sedoník J., Cícha V.: ROCA – An ArcGIS toolbox for road alignment identification and horizontal curve radii computation. PLoS ONE, vol. 13(12), 2018, e0208407. https://doi.org/10.1371/journal.pone.0208407.
36. Nowak M.M., Pędziwiatr K.: Dataset and GIS toolbox for modeling potential tree belt functions. Data in Brief, vol. 20, 2018, pp. 326–332. https://doi.org/10.1016/j.dib.2018.08.005.
37. Favre C., Fahland D., Völzer H.: The relationship between workflow graphs and free-choice workflow nets. Information Systems, vol. 47, 2015, pp. 197–219. https://doi.org/10.1016/j.is.2013.12.004.
38. Yang Ch., Chen N., Di L.: RESTFul based heterogeneous geoprocessing workflow interoperation for sensor web service. Computers & Geosciences, vol. 47, 2012, pp. 102–110. https://doi.org/10.1016/j.cageo.2011.11.010.
39. ESRI: Publishing Geoprocessing Services Tutorial. https://help.arcgis.com/en/arcgisdesktop/10.0/pdf/publishing-geoprocessing-services-tutorial.pdf [access: 19.02.22].
40. Fiannacca P., Ortolano G., Pagano M., Visalli R., Cirrincione R., Zappalà L.: IG-Mapper: A new ArcGIS® toolbox for the geostatistics-based automated geochemical mapping of igneous rocks. Chemical Geology, vol. 470, 2017, pp. 75–92. https://doi.org/10.1016/j.chemgeo.2017.08.024.
41. Ortolano G., Visalli R., Godard G., Cirrincione R.: Quantitative X-ray Map Analyser (Q-XRMA): A new GIS-based statistical approach to mineral image analysis. Computers & Geosciences, vol. 115, 2018, pp. 56–65. https://doi.org/10.1016/j.cageo.2018.03.001.
42. Ortolano G., Zappalà L., Mazzoleni P.: X-Ray Map Analyser: A new ArcGIS® based tool for the quantitative statistical data handling of X-ray maps (Geoand material-science applications). Computers & Geosciences, vol. 72, 2014, pp. 49–64. https://doi.org/10.1016/j.cageo.2014.07.006.
43. Ortolano G., D’Agostino A., Pagano M., Visalli R., Zucali M., Fazio E., Alsop I., Cirrincione R.: ArcStereoNet: A new ArcGIS® toolbox for projection and analysis of mesoand micro-structural data. ISPRS International Journal of Geo-Information, vol. 10(2), 2021, 50. https://doi.org/10.3390/ijgi10020050.
44. ESRI: What is ArcPy? 12.02.2010. https://www.esri.com/arcgis-blog/products/arcgis-desktop/analytics/what-is-arcpy/ [access: 28.12.2021].
45. Dutta S., Patra D., Shankar H., Alok Verma P.: Development of GIS tool for the solutation of minimum spanning tree problem using PRIM’s algorithm. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-8, 2014, pp. 1105–1114. https://doi.org/10.5194/isprsarchives-XL-8-1105-2014.
46. Alam Z.R.: Utilizing GIS in the development of detailed distribution urban drainage models. Toronto Metropolitan University, Toronto 2014 [M.A.Sc. thesis]. https://doi.org/10.32920/ryerson.14652438.v1.
47. Dahal K.R., Chow T.E.: A GIS toolset for automated partitioning of urban lands. Environmental Modelling & Software, vol. 55, 2014, pp. 222–234. https://doi.org/10.1016/j.envsoft.2014.01.024.
48. Walsh S.J., Page Ph.H., McKnight S.A., Yao X., Morrissey T.P.: A reservoir siting tool for North Carolina: System design & operations for screening and evaluation. Applied Geography, vol. 60, 2015, pp. 139–149. https://doi.org/10.1016/j.apgeog.2015.03.015.
49. ESRI: Introduction to arcpy.mapping. https://desktop.arcgis.com/en/arcmap/latest/analyze/arcpy-mapping/introduction-to-arcpy-mapping.htm [access: 10.01.2022].
50. ESRI: Guidelines for arcpy.mapping. https://desktop.arcgis.com/en/arcmap/latest/analyze/arcpy-mapping/guidelinesforarcpymapping.htm [access: 12.12.2021].
51. Toms S., O’Beirne D.: ArcPy and ArcGIS – Second Edition: Automating ArcGIS for Desktop and ArcGIS Online with Python. Packt Publishing, Birmingham 2017 [Kindle edition].
52. LGD: Local Government Directory: Complete directory of Land Regions/Revenue, Rural and Urban Local Governments. https://lgdirectory.gov.in/ [access: 17.06.2022].
53. ESRI: How IDW works. https://desktop.arcgis.com/en/arcmap/10.3/tools/3d-analyst-toolbox/how-idw-works.htm [access: 17.01.2022].
54. The Pennsylvania State University (PSU): Limitations of Python scripting with ArcGIS. https://www.e-education.psu.edu/geog485/node/288 [access: 16.02.2022].
55. Pimpler E.: Tutorial – Automating the Production of a Map Series with Arcpy. Geospatial Training Services, 26.01.2021. https://geospatialtraining.com/tutorial-automating-the-production-of-a-map-series-with-arcpy/ [access: 28.01.2022].

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023)

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-e97a9cf4-d6f5-429e-a53c-013d1cecc435