Amir Dadras Moghaddam 1, Sadrollah Ramezani 2,3, Seyed Mehdi Hosseini 1, Ahmad Vandaki 4
1University of Sistan and Baluchestan, Department of Agricultural Economics, Zahedan, Iran
2University of Sistan and Baluchestan, Zahedan, Iran
3Zanjan University of Medical Sciences, Zanjan Pharmaceutical Biotechnology Research Center, Zanjan, Iran
4University of Sistan and Baluchestan, Department of Economy, Zahedan, Iran
Moghaddam, A. D., Ramezani, S., Hosseini, S. M. & Vandaki, A. (2020). The modeling of effective factors on Qaenat saffron yield using GFA and ANFIS. Bulg. J. Agric. Sci., 26 (4), 719–725
Saffron is one of main strategic crops in Iran that have exclusive position in non-oil export products of the country. There is special weather condition in Qaenat state for saffron production and so saffron allocated high proportion of agricultural products in this region. In current research, the modelling of affecting factors on saffron yield using Genetic function approximation and adaptive neuro-fuzzy inference system in Qaenat state was done.
The statistical samples were taken using coincidence sampling among 120 saffron growers’ in Qaenat state at 2017. Results are shown that cultivation area, related education, total sale, product price, labor cost, irrigation and harvest cost are most effective factors on saffron yield. Price elasticity coefficient was higher in comparison with other factors that show growers reaction to price was higher than others. Labor cost elasticity coefficient was higher among saffron production inputs that means increase in labor cost have more portion in increasing of yield compared than other inputs. Results of comparison between genetic function approximation and adaptive neuro-fuzzy inference system showed that genetic function approximation was predicted in lower error compared than adaptive neuro-fuzzy inference system.