Agricultural Economics and Development

Agricultural Economics and Development

An Investigation of Factors Affecting the Marketing Margins Using Spatial Regression: A Case Study of Neyshabour Plain in Iran

Authors
Mohammad Reza Kohansal 1
hadi rafiei 2
1 Professor, Department of Agricultural Economics, Ferdowsi University of Mashhad, Mashhad, Iran
2 Former PhD Student in Agricultural Economics, Ferdowsi University of Mashhad, Mashhad, Iran
10.30490/aead.2019.95471
Abstract
Inefficiency of marketing networks of agricultural products and the price differences between sale price by farmers and purchase price by final consumers are the main issues in the marketing of agricultural products. This study aimed mainly at investigating the factors affecting the marketing margins in Neyshabour plain of Iran. For this purpose, within the geographically weighted regression with adaptive cores (GWR-A), the upgraded version of ‘Mark Up Model’ was used for all crops. Statistics and information were obtained through interviews and questionnaires completed by 366 farmers in 2016. Moran test results showed that there was a positive spatial autocorrelation on the marketing margin of crops. The results of GWR model indicated a positive impact of marketing costs and retail prices in most areas is positive and a negative impact of cultivated areas on the marketing margins. Also, the coefficients of the Mark Up Model were estimated regionally and with respect to farmer’s location. Finally, a number of suggestions were made, including: reforming the marketing network in order to reduce the margin of the market, using statistical methods based on the location of data in similar studies, reforming the market system and the sale of products based on the development of professional commerce companies, implementing the special information plans related to the market information on agricultural products as well as the implementation of specific cultural and social programs.
Keywords
Marketing
Marketing Margin
Mark Up Model
Geographically Weighted Regression (GWR)
Neyshabour (Plain)
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