Relationship of Microbial Activity with Soil Properties in Banana Plantations in Venezuela

Barlin O. Olivares (), Juan C. Rey, Guillermo Perichi and Deyanira Lobo
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Barlin O. Olivares: Programa de Doctorado en Ingeniería Agraria, Alimentaria, Forestal y del Desarrollo Rural Sostenible, Campus Rabanales, Universidad de Córdoba, Carretera Nacional IV, km 396, 14014 Cordoba, Spain
Juan C. Rey: Unidad de Recursos Agroecológicos, Instituto Nacional de Investigaciones Agrícolas (INIA-CENIAP), Av. Universidad vía El Limón, Maracay 02105, Venezuela
Guillermo Perichi: Facultad de Agronomía, Universidad Central de Venezuela, Av. Universidad, Maracay 02105, Venezuela
Deyanira Lobo: Facultad de Agronomía, Universidad Central de Venezuela, Av. Universidad, Maracay 02105, Venezuela

Sustainability, 2022, vol. 14, issue 20, 1-16

Abstract: The present work aims to analyze the relationship of microbial activity with the physicochemical properties of the soil in banana plantations in Venezuela. Six agricultural fields located in two of the main banana production areas of Venezuela were selected. The experimental sites were differentiated with two levels of productivity (high and low) of the “Gran Nain” banana. Ten variables were selected: total free-living nematodes (FLN), bacteriophages, predators, omnivores, Phytonematodes, saturated hydraulic conductivity, total organic carbon, nitrate (NO 3 ), microbial respiration and the variable other fungi. Subsequently, machine learning algorithms were used. First, the Partial Least Squares-Discriminant Analysis (PLS-DA) was applied to find the soil properties that could distinguish the banana productivity levels. Second, the Debiased Sparse Partial Correlation (DSPC) algorithm was applied to obtain the correlation network of the most important variables. The variable free-living nematode predators had a degree of 3 and a betweenness of 4 in the correlation network, followed by NO 3 . The network shows positive correlations between FLN predators and microbial respiration (r = 1.00; p = 0.014), and NO 3 (r = 1.00; p = 0.032). The selected variables are proposed to characterize the soil productivity in bananas and could be used for the management of soil diseases affecting bananas.

Keywords: soil microbes; microbial respiration; organic carbon; Phytonematodes; soil; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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