Analysis of Factors Affecting Canola Plantation Development in Tabriz and Marand Counties, Iran

This study identifies and analyzes factors influencing canola plantation development in Tabriz and Marand Counties. The Censored Model was used to analyze cross-sectional data collected from 372 farmers using a questionnaire. Due to the weakness of the Tobit model in separating factors affecting the adoption decision of farmers and factors affecting the rate of adoption, the Heckman Model was employed to separate the contributions made by these factors. The results of estimated Probit model in the first stage of the Heckman Approach showed that machinery ownership had an important effect on canola adoption, as a 1% increase in machinery ownership had led to 0.158% increase in canola adoption probability. Contact with extension agents, farm income proportion, education, and farmers’ experience influenced canola plantation probability positively, and the age and number of fragmentations had a negative impact on it. The significance of inverse Mill’s ratio indicates that the factors affecting the decision to start planting and the amount of canola plantation are not the same. The Heckman’s second step estimation results indicated that the loan amount, canola relative benefit, and family labor had a positive effect, and that machinery cost and farm distance from the road had a negative effect on canola acreage. Relative benefit was the most effective element, as 1% increase in relative benefit results in a 0.342% increase in canola plantation

Analysis of Adoption of Biological Control practices in Tomato Farms of Jiroft County Using Duration Analysis

The human need for food, during recent decades has increased dependence on pesticides and chemical pesticides. Due to the destructions effect of chemical toxins, adoption of bio and non-bio technologies Identical with the sustainable agriculture such as pest control by natural enemies, is taken into consideration by agriculture researchers. so, the process of adopting biological control technology is investigated in the farms of tomato in Jiroft County during the period of 2010 until 2014. Why some farmers are faster to adopt this technology is investigated using duration analysis, which allows the timing of an event to be explored in a dynamic framework. The empirical results highlight the negative importance of age variable, and positive effect of farm size and attitude to control biologic. In this study due to the use of survival analysis model it was possible to evaluate the effect of time dependent variables include product price and years of knowledge about control biologic on speed of adoption. Therefore, it became clear that if in a year the price of the product is increase the probability of adoption is increased as well as if the farmer has been informed about biological control technology earlier the technology adoption rate increases

Analysis of Factors Affecting Canola Plantation Development in Tabriz and Marand Counties, Iran

This study identifies and analyzes factors influencing canola plantation development in Tabriz and Marand Counties. The Censored Model was used to analyze cross-sectional data collected from 372 farmers using a questionnaire. Due to the weakness of the Tobit model in separating factors affecting the adoption decision of farmers and factors affecting the rate of adoption, the Heckman Model was employed to separate the contributions made by these factors. The results of estimated Probit model in the first stage of the Heckman Approach showed that machinery ownership had an important effect on canola adoption, as a 1% increase in machinery ownership had led to 0.158% increase in canola adoption probability. Contact with extension agents, farm income proportion, education, and farmers’ experience influenced canola plantation probability positively, and the age and number of fragmentations had a negative impact on it. The significance of inverse Mill’s ratio indicates that the factors affecting the decision to start planting and the amount of canola plantation are not the same. The Heckman’s second step estimation results indicated that the loan amount, canola relative benefit, and family labor had a positive effect, and that machinery cost and farm distance from the road had a negative effect on canola acreage. Relative benefit was the most effective element, as 1% increase in relative benefit results in a 0.342% increase in canola plantation