Machine Learning Algorithms for Soil Analysis and Crop Production Optimization: A review

International audienc

Using parallel random forest classifier in predicting land suitability for crop production

International audienceIn this paper, we present an optimized Machine Learning (ML) algorithm for predicting land suitability for crop (sorghum) production, given soil properties information. We set-up experiments using Parallel Random Forest (PRF), Linear Regression (LR), Linear Discriminant Analysis (LDA), KNN, Gaussian Naïve Bayesian (GNB) and Support Vector Machine (SVM). Experiments were evaluated using 10 cross fold validation. We observed that, parallel random forest had a better accuracy of 0.96 and time of execution of 1.7 sec. Agriculture is the main stream of food security. Kenya relies on agriculture to feed its population. Land evaluation gives potential of land use, in this case for crop production. In the Department of Soil Survey in Kenya Agriculture and Livestock Research Organization (KALRO) and other soil research organizations, land evaluation is done manually, is stressful, takes a long time and is prone to human errors. This research outcomes can save time and improve accuracy in land evaluation process. We can also be able to predict land suitability for crop production from soil properties information without intervention of a soil scientist expert. Therefore, agricultural stakeholders will be able to efficiently make informed decisions for optimal crop production and soil management

Soil carbon, multiple benefits

In March 2013, 40 leading experts from across the world gathered at a workshop, hosted by the European Commission, Directorate General Joint Research Centre, Italy, to discuss the multiple benefits of soil carbon as part of a Rapid Assessment Process (RAP) project commissioned by Scientific Committee on Problems of the Environment (SCOPE). This collaboration led to the publication of the SCOPE Series Volume 71 "Soil Carbon: Science, Management and Policy for Multiple Benefits"; which brings together the essential scientific evidence and policy opportunities regarding the global importance of soil carbon. This short communication summarises the key messages of the assessment including research and policy implications. (Résumé d'auteur

Soil carbon,multiple benefits

In March 2013,40 leading experts from across the world gathered at a workshop, hosted by the EuropeanCommission, Directorate General Joint Research Centre, Italy, to discuss the multiple benefits o fsoil carbon as part of a Rapid Assessment Process (RAP) project commissioned by Scientific Committee on Problems of the Environment (SCOPE). This collaboration led to the publication of the SCOPE Series Volume 71 “Soil Carbon:Science, Management and Policy for Multiple Benefits”; which brings together the essential scientific evidence and policy opportunities regarding the global importance of soil carbon.This short communication summarizes the key messages of the assessment including research and policy implications. & 2014ElsevierLtd.JRC.H.5-Land Resources Managemen