The effects of soil management on soil losses from olive plantations in southern Spain were evaluated using the Revised Universal Soil Loss Equation (RUSLE), a review of published experiments, and preliminary results of an on-going ®eld trial. Experimental data were used to parameterize the RUSLE for olive orchards under various soil management regimes. The predictions agreed qualitatively with the data available, and the model provided a simple way to assess the effects of soil management on erosion. Our results showed that no-tillage caused the greatest soil loss, while cover crops showed the least. Tillage and planting following contours proved only partially effective and did not reduce soil erosion as much as protective crops. One scenario studied suggests that, on slight to moderate slopes, land transformed from row crops to olive orchards may remain below the maximum tolerable soil erosion limit, if a cover crop is included between the trees. A scenario for marginal olive orchards located on steep slopes suggests that effective erosion control could only be achieved with a cover crop system that would have the side-effect of reducing the yield of rain-fed olives. Quantifying the effects of soil management on soil erosion in olive orchards is uncertain because very few experimental results are available. Further research that monitors soil loss in carefully selected long-term experiments at different scales and follows the changes in key soil parameters is urgently required to develop effective erosion control policies.

Three different management systems were compared in an olive grove on a Vertic soil, near the city of Cordoba, Spain. Rainfall, runoff and soil loss were recorded from experimental plots of 6 £ 12 m for three years. Results indicated that the no-tillage system, which was kept weed-free with herbicides, gave the largest soil loss (8.5 t ha 21 yr 21 ) and average annual runoff coefficient (21.5%), due to increased soil compaction, particularly outside the canopy projection area. A system that used a grass cover gave the lowest soil losses (1.2 t ha 21 yr 21 ) and average annual runoff coefficient (2.5%) due to the protective effects of the cover and increased soil aggregate stability. The third system, conventional tillage, gave intermediate results, with a soil loss of 4.0 t ha 21 yr 21 and an average runoff coefficient of 7.4%. The search for alternative soil management to conventional tillage should consider occasional light tillage to establish a grass cover that would keep both soil erosion and runoff losses to a minimum.

No abstract

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.