Predicting Livestock Farmers’ Attitudes towards Improved Sheep Breeds in Ahar City through Data Mining Methods

Jabraeil Vahedi, Masoumeh Niazifar, Mohammad Ghahremanzadeh (), Akbar Taghizadeh, Soheila Abachi, Valiollah Palangi and Maximilian Lackner ()
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Jabraeil Vahedi: Department of Agricultural Economics, Faculty of Agriculture, University of Tabriz, Tabriz 51666-16471, Iran
Masoumeh Niazifar: Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz 51666-16471, Iran
Mohammad Ghahremanzadeh: Department of Agricultural Economics, Faculty of Agriculture, University of Tabriz, Tabriz 51666-16471, Iran
Akbar Taghizadeh: Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz 51666-16471, Iran
Soheila Abachi: College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, MO 62511, USA
Valiollah Palangi: Department of Animal Science, Faculty of Agriculture, Ege University, 35100 Izmir, Turkey
Maximilian Lackner: Department of Industrial Engineering, University of Applied Sciences Technikum Wien, 1200 Vienna, Austria

World, 2024, vol. 5, issue 4, 1-17

Abstract: Sheep breeding is one of the most important economic activities in Ahar City, Iran. However, due to traditional production techniques, livestock farmers face the problem of low productivity. To address this issue, traditional breeds can be replaced with improved and high-yielding ones; in the first stage, this requires the acceptance of these new sheep breeds by the region’s ranchers. This research aimed to evaluate the attitudes of the livestock breeders of Ahar City towards the improved breeds of sheep and the influential factors. We collected data through in-person interviews using a simple random sampling method, surveying 100 sheep breeders in Ahar. The breeders were categorized into three groups based on their attitudes towards improved breeds: negative, indifferent, and positive. Next, we employed data mining-based methods, including multilayer perceptron neural networks, random forest, and random tree algorithms. These helped identify essential variables affecting ranchers’ attitudes. The results showed that several factors contribute to the ranchers’ philosophy, with the number of sheep sold in the past year and the total sheep ownership being the most significant ones. Comparing statistical evaluation criteria, we found that the random tree algorithm outperformed other methods in predicting and classifying livestock farmers, achieving a prediction accuracy rate of 86% for a sample of 100 farmers. Based on our findings, promoting training courses and raising awareness about the benefits of breeding new sheep breeds, along with providing facilities and credits based on economic conditions, can foster a positive attitude among herders. Increasing the number of sheep owned and improving marketing strategies can further enhance this positive outlook.

Keywords: prediction; data mining; sheep; attitude; machine learning (search for similar items in EconPapers)
JEL-codes: G15 G17 G18 L21 L22 L25 L26 Q42 Q43 Q47 Q48 R51 R52 R58 (search for similar items in EconPapers)
Date: 2024
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