Productive performance of small peri-urban farms using self-organizing maps and data envelopment analysis

Abstract

In this paper, we aim to analyze the productive performance of plots cultivated by family farmers. We use an alternative Data Envelopment Analysis (DEA) approach to calculate the relative efficiency of such plots. Notwithstanding, DEA's basic assumption includes the homogeneity of the production units under analysis. Herein, as the chemical composition of the soil varies considerably among plots, directly influencing their fertility, and, thus, their productivity levels, the plots shall be grouped into homogeneous clusters first. For that, we use SelfOrganizing Maps, based on their comparative advantages to other methods. Then, the relative efficiencies of plots within each cluster will be assessed using separate DEA models. Nonetheless, a direct comparison among the scores of plots from different clusters is not feasible, because the relative efficiency of a production unit can solely be compared to those inserted in the same set of analysis. Hence, to overcome such inconvenience, we further apply a technique that allows compensating for the non-homogeneity of plots. The results indicate that, when countervailing the effects of the chemical composition of the soil, the plots with favorable conditions do not necessarily present better productive outcomes.

Using a Self-Organizing Map to Explore Local Weather Features for Smart Urban Agriculture in Northern Taiwan

“…An increasing number of SOM applications were also adopted to assist with agricultural decision making when considering the weather information from recent years [36]. For agricultural applications, some data-driven models were developed to identify land covers for agricultural control and management and provide information for production systems to better manage their crops according to the specific conditions on farms [37][38][39][40]. Time series of climatic and agro-climatic indices were used to examine the signs of climate changes in rainfall, temperature, and agricultural drought to identify potential impacts on the agricultural water balance [41].…”