Open-source and free tools are readily available to the public to process data and assist producers in making management decisions related to agricultural landscapes. On-the-go soil sensors are being used as a proxy to develop digital soil maps because of the data they can collect and their ability to cover a large area quickly. Machine learning, a subcomponent of artificial intelligence, makes predictions from data. Intermixing open-source tools, on-the-go sensor technologies, and machine learning may improve Mississippi soil mapping and crop production. This study aimed to evaluate machine learning for mapping apparent soil electrical conductivity (ECa) collected with an on-the-go sensor system at two sites (i.e., MF2, MF9) on a research farm in Mississippi. Machine learning tools (support vector machine) incorporated in Smart-Map, an open-source application, were used to evaluate the sites and derive the apparent electrical conductivity maps. Autocorrelation of the shallow (ECas) and deep (ECad) readings was statistically significant at both locations (Moran’s I, p 0.001); however, the spatial correlation was greater at MF2. According to the leave-one-out cross-validation results, the best models were developed for ECas versus ECad. Spatial patterns were observed for the ECas and ECad readings in both fields. The patterns observed for the ECad readings were more distinct than the ECas measurements. The research results indicated that machine learning was valuable for deriving apparent electrical conductivity maps in two Mississippi fields. Location and depth played a role in the machine learner’s ability to develop maps.
References
[1]
Bourennane, H., Nicoullaud, B., Couturier, A. and King, D. (2004) Exploring the Spatial Relationships between Some Soil Properties and Wheat Yields in Two Soil Types. Precision Agriculture, 5, 521-536. https://doi.org/10.1007/s11119-004-5323-z
[2]
Corwin, D.L., Lesch, S.M., Shouse, P.J., Soppe, R. and Ayars, J.E. (2003) Identifying Soil Properties That Influence Cotton Yield Using Soil Sampling Directed by Apparent Soil Electrical Conductivity. Agronomy Journal, 95, 352-364. https://doi.org/10.2134/agronj2003.3520
[3]
Corwin, D.L. and Lesch, S.M. (2005) Apparent Soil Electrical Conductivity Measurements in Agriculture. Computers and Electronics in Agriculture, 46, 11-43. https://doi.org/10.1016/j.compag.2004.10.005
[4]
Veronesi, F. and Schillaci, C. (2019) Comparison between Geostatistical and Machine Learning Models as Predictors of Topsoil Organic Carbon with a Focus on Local Uncertainty Estimation. Ecological Indicators, 101, 1032-1044. https://doi.org/10.1016/j.ecolind.2019.02.026
[5]
Khaledian, Y. and Miller, B.A. (2020) Selecting Appropriate Machine Learning Methods for Digital Soil Mapping. Applied Mathematical Modelling, 81, 401-418. https://doi.org/10.1016/j.apm.2019.12.016
[6]
Adhikari, K., Smith, D.R., Collins, H., Hajda, C., Acharya, B.S. and Owens, P.R. (2022) Mapping Within-Field Soil Health Variations Using Apparent Electrical Conductivity, Topography, and Machine Learning. Agronomy, 12, Article No. 1019. https://doi.org/10.3390/agronomy12051019
[7]
Pereira, G.W., Valente, D.S.M., Queiroz, D.M. de, Coelho, A.L. de F., Costa, M.M. and Grift, T. (2022) Smart-Map: An Open-Source QGIS Plugin for Digital Mapping Using Machine Learning Techniques and Ordinary Kriging. Agronomy, 12, Article No. 1350. https://doi.org/10.3390/agronomy12061350
[8]
Pouladi, N., Møller, A.B., Tabatabai, S. and Greve, M.H. (2019) Mapping Soil Organic Matter Contents at Field Level with Cubist, Random Forest and Kriging. Geoderma, 342, 85-92. https://doi.org/10.1016/j.geoderma.2019.02.019
[9]
Wadoux, A.M.J.-C., Brus, D.J. and Heuvelink, G.B.M. (2019) Sampling Design Optimization for Soil Mapping with Random Forest. Geoderma, 355, 113913. https://doi.org/10.1016/j.geoderma.2019.113913
[10]
Wadoux, A.M.J.-C., Minasny, B. and McBratney, A.B. (2020) Machine Learning for Digital Soil Mapping: Applications, Challenges and Suggested Solutions. Earth-Science Reviews, 210, Article ID: 103359. https://doi.org/10.1016/j.earscirev.2020.103359
Henderson, B.L., Bui, E.N., Moran, C.J. and Simon, D.A.P. (2005) Australia-Wide Predictions of Soil Properties Using Decision Trees. Geoderma, 124, 383-398. https://doi.org/10.1016/j.geoderma.2004.06.007
[13]
Grimm, R., Behrens, T., Märker, M. and Elsenbeer, H. (2008) Soil Organic Carbon Concentrations and Stocks on Barro Colorado Island—Digital Soil Mapping Using Random Forests Analysis. Geoderma, 146, 102-113. https://doi.org/10.1016/j.geoderma.2008.05.008
[14]
Wang, B., Waters, C., Orgill, S., Gray, J., Cowie, A., Clark, A. and Liu, D.L. (2018) High Resolution Mapping of Soil Organic Carbon Stocks Using Remote Sensing Variables in the Semi-Arid Rangelands of Eastern Australia. The Science of the Total Environment, 630, 367-378. https://doi.org/10.1016/j.scitotenv.2018.02.204
[15]
Chagas, C.S., de Carvalho Junior, W., Bhering, S.B. and Calderano Filho, B. (2016) Spatial Prediction of Soil Surface Texture in a Semiarid Region Using Random Forest and Multiple Linear Regressions. Catena, 139, 232-240. https://doi.org/10.1016/j.catena.2016.01.001
[16]
Vaysse, K. and Lagacherie, P. (2017) Using Quantile Regression Forest to Estimate Uncertainty of Digital Soil Mapping Products. Geoderma, 291, 55-64. https://doi.org/10.1016/j.geoderma.2016.12.017
[17]
Dharumarajan, S., Hegde, R. and Singh, S.K. (2017) Spatial Prediction of Major Soil Properties Using Random Forest Techniques—A Case Study in Semi-Arid Tropics of South India. Geoderma Regional, 10, 154-162. https://doi.org/10.1016/j.geodrs.2017.07.005
[18]
Forkuor, G., Hounkpatin, O.K.L., Welp, G. and Thiel, M. (2017) High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models. PLOS ONE, 12, e0170478. https://doi.org/10.1371/journal.pone.0170478
[19]
Song, Y.-Q., Zhao, X., Su, H.-Y., Li, B., Hu, Y.-M. and Cui, X.-S. (2018) Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale. Sensors, 18, Article No. 3086. https://doi.org/10.3390/s18093086
[20]
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. and Kwabena, N.A. (2017) Soil Nutrient Maps of Sub-Saharan Africa: Assessment of Soil Nutrient Content at 250 m Spatial Resolution Using Machine Learning. Nutrient Cycling in Agroecosystems, 109, 77-102. https://doi.org/10.1007/s10705-017-9870-x
[21]
Viscarra Rossel, R.A., Chen, C., Grundy, M.J., Searle, R., Clifford, D. and Campbell, P.H. (2015) The Australian Three-Dimensional Soil Grid: Australia’s Contribution to the GlobalSoilMap Project. Soil Research, 53, 845-864. https://doi.org/10.1071/SR14366
[22]
Bou Kheir, R., Greve, M.H., Abdallah, C. and Dalgaard, T. (2010) Spatial Soil Zinc Content Distribution from Terrain Parameters: A GIS-Based Decision-Tree Model in Lebanon. Environmental Pollution, 158, 520-528. https://doi.org/10.1016/j.envpol.2009.08.009
[23]
Fletcher, R.S. (2022) Temporal Comparisons of Apparent Electrical Conductivity: A Case Study on Clay and Loam Soils in Mississippi. Agricultural Sciences, 13, 936-946. https://doi.org/10.4236/as.2022.138058
[24]
WorldClimate. Stoneville, Mississippi Climate—38756 Weather, Average Rainfall, and Temperatures. http://www.worldclimate.com/climate/us/mississippi/stoneville
[25]
Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcseprd1464818
QGIS Development Team (2021) QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org
[28]
Moran, P. (1948) The Interpretation of Statistical Maps. Journal of the Royal Statistical Society: Series B (Methodological), 10, 243-251. https://doi.org/10.1111/j.2517-6161.1948.tb00012.x
[29]
Guevara, M., Olmedo, G.F., Stell, E., Yigini, Y., Aguilar Duarte, Y., Arellano Hernández, C., Arévalo, G.E., Arroyo-Cruz, C.E., Bolivar, A., Bunning, S., Bustamante Cañas, N., Cruz-Gaistardo, C.O., Davila, F., Dell Acqua, M., Encina, A., Figueredo Tacona, H., Fontes, F., Hernández Herrera, J.A., Ibelles Navarro, A.R., Loayza, V., Manueles, A.M., Mendoza Jara, F., Olivera, C., Osorio Hermosilla, R., Pereira, G., Prieto, P., Ramos, I.A., Rey Brina, J.C., Rivera, R., Rodríguez-Rodríguez, J., Roopnarine, R., Rosales Ibarra, A., Rosales Riveiro, K.A., Schulz, G.A., Spence, A., Vasques, G.M., Vargas, R.R. and Vargas, R. (2018) No Silver Bullet for Digital Soil Mapping: Country-Specific Soil Organic Carbon Estimates across Latin America. Soil, 4, 173-193. https://doi.org/10.5194/soil-4-173-2018
