Olivares Barlin O, Vega Andrés, Calderón María A Rueda, Rey Juan C, Lobo Deyanira, Gómez José A, Landa Blanca B
Doctoral Program in Agricultural, Food, Forestry Engineering and Sustainable Rural Development, Rabanales Campus, University of Cordoba, Carretera Nacional IV, km 396, 14014 Cordoba, Spain.
Faculty of Agricultural Sciences, National University of Cordoba, Av. Haya de la Torre s/n, Cordoba 5016, Argentina.
Plants (Basel). 2022 Aug 8;11(15):2070. doi: 10.3390/plants11152070.
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.
在过去几十年里，委内瑞拉中部香蕉产区香蕉枯萎病（BW）的发病率呈上升趋势。这种疾病被认为是由真菌 - 细菌复合体以及特定土壤特性的影响所致。然而，到目前为止，对于与香蕉枯萎病高发病率相关的土壤特性尚无共识。本研究的目的是使用监督方法确定可能与香蕉枯萎病发病率相关的土壤特性。在连续两年（2016年和2017年）收集了委内瑞拉与香蕉植株地块相关的土壤样本，这些地块的香蕉枯萎病发病率低（n = 29）和高（n = 49）。在这些土壤上，测定了16种土壤变量，包括砂、粉砂和黏土的百分比、pH值、电导率、有机质以及钾、钠、镁、钙、锰、铁、锌、铜、硫和磷的有效含量。威尔科克森检验确定了两组香蕉枯萎病发病率之间土壤变量存在显著差异。此外，应用正交最小二乘判别分析（OPLS - DA）和随机森林（RF）算法来寻找能够区分香蕉枯萎病发病率高或低的地块的土壤变量。OPLS - DA模型显示数据拟合良好（R2Y：0.61，p值<0.01），并具有良好的预测能力（Q2：0.50，p值<0.01）。随机森林对受试者工作特征（ROC）曲线的分析表明，锌、铁、钙、钾、锰和黏土的组合能够准确区分84.1%的香蕉地块，灵敏度为89.80%，特异性为72.40%。到目前为止，这是第一项将这六种土壤变量确定为可能与委内瑞拉湖相起源土壤中香蕉枯萎病发病率相关的新指标的研究。
