Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops

Background:The evaporative fraction(EF)represents an important biophysical parameter reflecting the distribution of surface available energy.In this study,we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning(ML)classes of algorithms:the linear regression(LR),regression tree(RT),support vector machine(SVM),ensembles of tree(ETs)and Gaussian process regression(GPR)to predict the EF at daily time step.The adopted methodology consisted of three main steps that include:(i)selection of the EF predictors;(ii)comparison of the different classes of ML;(iii)application,cross-validation of the selected ML algorithms and comparison with the observed data.Results:Our results indicate that SVM and GPR were the best classes of ML at predicting the EF,with a total of four different algorithms:cubic SVM,medium Gaussian SVM,the Matern 5/2 GPR,and the rational quadratic GPR.The com-parison between observed and predicted EF in all four algorithms,during the training phase,were within the 95%confidence interval:the R^(2)value between observed and predicted EF was 0.76(RMSE 0.05)for the medium Gaussian SVM,0.99(RMSE 0.01)for the rational quadratic GPR,0.94(RMSE 0.02)for the Matern 5/2 GPR,and 0.83(RMSE 0.05)for the cubic SVM algorithms.Similar results were obtained during the testing phase.The results of the cross-validation analysis indicate that the R^(2)values obtained between all iterations for each of the four adopted ML algorithms were basically constant,confirming the ability of ML as a tool to predict EF.Conclusion:ML algorithms represent a valid alternative able to predict the EF especially when remote sensing data are not available,or the sky conditions are not suitable.The application to different geographical areas,or crops,requires further development of the model based on different data sources of soils,climate,and cropping systems.

Guest editorial-soil erosion assessment, tools and data: A special issue from the Global Symposium on soil Erosion 2019

This special issue on soil erosion assessment,tools and data creation,consolidation and harmonization presents advances in soil erosion research with a focus on new tools that are being used to assess soil erosion rates.This publication includes eleven selected contributions presented at the Global Symposium on Soil Erosion(GSER,15-17 May 2019,Rome,Italy)dealing with erosion indicators'improvement,the use of remote sensing,nuclear techniques and geochemical fingerprinting as promising methods to assess soil losses,management practices that reduce soil erosion in vineyards and olive groves plantations and their modelling,and national and regional erosion assessments.