A Decision Support Tool for Buying Farm Tractors, Based on Predictive Analytics

Luis Ruiz-Garcia and Patricia Sanchez-Guerrero
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Luis Ruiz-Garcia: Departamento de Ingeniería Agroforestal, ETSIAAB, Universidad Politécnica de Madrid, Av. Puerta de Hierro, 2, 28040 Madrid, Spain
Patricia Sanchez-Guerrero: Departamento de Ingeniería Agroforestal, ETSIAAB, Universidad Politécnica de Madrid, Av. Puerta de Hierro, 2, 28040 Madrid, Spain

Agriculture, 2022, vol. 12, issue 3, 1-26

Abstract: Data science can help farmers when making a decision about tractor purchase. Buying a tractor represents a big investment for farmers, and price is one of their main concerns. This study presents the development of a web-based decision support tool (DST) that calculate the price of new and second-hand tractors, for the purpose of providing the decision-maker some information that will lead him to the final decision. The tool makes use of different algorithms based on predictive analytics methodologies. The dataset has information about 227 different observations of new tractors and 1003 of second-hand tractors, from different European countries. During the study, the prices of new and used tractor were modeled, testing parametric and non-parametric regression models with different segmentations and predictor variables. Non parametric models includes regression trees, support vector machines, ensembles of regression trees, Gaussian process, and neural networks. In both cases, for predicting the prices of new and second-hand tractors, adjusted R 2 higher than 0.99 were achieved. The models developed were implemented in the DST which is fully operative, available in Internet, and free to use.

Keywords: farm tractors; data science; data economy; decision support tool (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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