Over the last few decades, a growing incidence of Banana Wilt (BW) has been detected in the banana-producing areas of the central zone of Venezuela. This disease is thought to be caused by a fungal–bacterial complex, coupled with the influence of specific soil properties. However, until now, there was no consensus on the soil characteristics associated with a high incidence of BW. The objective of this study was to identify the soil properties potentially associated with BW incidence, using supervised methods. The soil samples associated with banana plant lots in Venezuela, showing low (n = 29) and high (n = 49) incidence of BW, were collected during two consecutive years (2016 and 2017). On those soils, sixteen soil variables, including the percentage of sand, silt and clay, pH, electrical conductivity, organic matter, available contents of K, Na, Mg, Ca, Mn, Fe, Zn, Cu, S and P, were determined. The Wilcoxon test identified the occurrence of significant differences in the soil variables between the two groups of BW incidence. In addition, Orthogonal Least Squares Discriminant Analysis (OPLS-DA) and the Random Forest (RF) algorithm was applied to find soil variables capable of distinguishing banana lots showing high or low BW incidence. The OPLS-DA model showed a proper fitting of the data (R²Y: 0.61, p value < 0.01), and exhibited good predictive power (Q²: 0.50, p value < 0.01). The analysis of the Receiver Operating Characteristics (ROC) curves by RF revealed that the combination of Zn, Fe, Ca, K, Mn and Clay was able to accurately differentiate 84.1% of the banana lots with a sensitivity of 89.80% and a specificity of 72.40%. So far, this is the first study that identifies these six soil variables as possible new indicators associated with BW incidence in soils of lacustrine origin in Venezuela.

Accurate predictions of crop production are critical to developing effective strategies at the farm level. Knowing banana production is due to the need to maximize the investment–profit ratio, and the availability of this information in advance allows decisions to be made about the management of important diseases. The objective of this study was to predict the number of banana bunches from epidemiological parameters of Black Sigatoka (BS), using random forests (RF) for its ability to predict crop production responses to epidemiological variables. Weekly production data (number of banana bunches) and epidemiological parameters of BS from three adjacent banana sites in Panama during 2015–2018 were used. RF was found to be very capable of predicting the number of banana bunches, with variance explained as 70.0% and root mean square error (RMSE) of 1107.93 ± 22 of the mean banana bunches observed in the test case. The site, week, youngest leaf spotted and youngest leaf with symptoms in plants with 10 weeks of physiological age were found to be the best predictor group. Our results show that RF is an efficient and versatile machine learning method for banana production predictions based on epidemiological parameters of BS due to its high accuracy and precision, ease of use, and usefulness in data analysis.

The objective of this study was to analyze the relationships between the Visual Evaluation of Soil Structure (VESS) and soil properties that make its adoption widely used in agriculture. For this, 25 papers related to the VESS (2009-2022) were considered where the information of the authors, the soil property: bulk density (BD), total soil porosity (TP), soil penetration resistance (PR), macroporosity, microporosity, soil organic carbon (SOC), and mean weight diameter of stable aggregates (MWD). The sample size (n=120 cases), and the correlation coefficient were extracted. To measure this relationship, the correlation coefficient of all studies was contemplated. A meta-analysis was carried out to obtain a global measure of the correlation between the VESS and soil properties across the studies. In the first instance, a fixed effects model was fitted for the correlation (effect size, known as the response variable in the meta-analysis). Subsequently, a random effects model was fitted for the correlation, followed by a subgroup analysis according to soil properties, due to the presence of high heterogeneity between studies. The overall effect (average correlation coefficient) of the fitted random effects model was 0.31 with a confidence interval (CI) of [0.22;0.41]. The heterogeneity between studies was high ( =94.0%) and statistically significant p<0.001. Although it is moderate, the average correlation in practical terms may be sufficient. There were significant differences in some average correlations of some categories, such as BD, PR, TP, and microporosity. The weights reported for the BD, SOC, and PR were 29.7%, 16.4%, and 14.2%, respectively. This result evidenced that the greatest contribution to the global effect of correlation between VESS and soil properties is provided by BD.