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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

has been cited by the following article:

• TITLE: Machine Learning Mapping of Soil Apparent Electrical Conductivity on a Research Farm in Mississippi

  AUTHORS: Reginald S. Fletcher

  KEYWORDS: Spatial Variability, Machine Learning, Electrical Conductivity, Mapping, Data Mining

  JOURNAL NAME: Agricultural Sciences, Vol.14 No.7, July 21, 2023

  ABSTRACT: 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.