Identification of the Main Soil Variables Correlated with Banana Productivity

The application of conventional agricultural techniques and high-cost inputs has not been sufficient to reverse the trend toward reduced banana yields in Venezuela. This has also been linked to the worsening of soil properties in banana plantations. This chapter aims to identify the main edaphic variables most correlated to banana productivity in Venezuela and explore the development of an empirical correlation model to predict this productivity based on soil characteristics. Six agricultural fields located in two of the main banana production areas of Venezuela were selected. Additionally, a productivity index (PI) based on three different biometric data on plant productivity was proposed (see Chap. 2 ). Thus, five multiple linear regression models were estimated, using the stepwise regression method. Subsequently, a performance analysis was used to compare the prediction quality range and the error associated with the number of soil variables selected for the proposed models. The selected model included the following soil variables: Mg, penetration resistance, total microbial respiration, bulk density, and omnivorous free-living nematodes. These variables explain the PI with an R2 of 0.55, the mean absolute error (MAE) of 0.8, and the root mean squared error (RMSE) of 1.0. The regression model obtained allowed the development of an easy-to-implement methodology for the rest of the banana areas of Venezuela and Latin America, which when further validated, it would represent a tool for sustainable land management.