The instruments encompass abroad spectrum of questions pertinent to production practices, social demographics,individual attitude, beliefs, perceptions, as well as the characteristics of farms. The organic production in the survey may take the form of the USDA certified, certification exempt, or transitioning farms. The interview were conducted by trained personnel following the well-established procedures, which insures the veracity of data collected. However, it is also in evidence that some self-selection biases occurred due to the fact that the higher level of education were associated with organic producers and they were inclined to finish the survey retained in the sample, which make the organic operations in the sample high than the overall percentage in Georgia farms in 2012 Census of Agriculture.The defect may constrain the effort to reach a general extrapolation beyond the survey data.
In Table 1, the variables covered in the survey and the corresponding preliminary statistics were reported to provide a profile of small farmers in the Southern region of states. We approached farmers’ choice of organic farming and potential factors of influence with the help of the logit regression model. After comparative study on the logit, the probit, and the linear probability models, being alike in ways in analyzing categorical data , we first exclude the linear probability model for its bias and inefficiency. The logit and probit models are arguably equivalent, only many investigators prefer the former for easy interpretation of parameters. For the sake of comparability, we used the logit model hereof. Since rich documents related to the logit model are readily available in the literature, the authors just present a model brief in the context of this investigation, rather than a thoroughgoing model discussion in the coming section. As usual, the most challenging part of modelling is associated with model selection among many alternatives.
In this study, we adopted the approach of Purposeful Selection of Variables , which usually retains important confounding variables and result potentially in a slightly richer model. We beganour model fitting by a univariate analysis of all variable relevant. Any variable with a significant univariate test at 0.25 level was selected as a candidate for the multivariate analysis. In an iterative process, covariates are removed from the model if they are non-significant and not a confounder. Significance is evaluated at the 0.1 level and confounding as a change in any remaining parameter estimate greater than 15% as compared to the full model. A change in a parameter estimate above the 15% indicates that the excluded variable was important in the sense of providing a needed adjustment for one or more of the variables remaining in the model. At the end of this iterative process of deleting, refitting,and verifying, the model contains significant covariates and confounders. Then,we took into account of any variable not selected for the original multivariate model and added them back one at a time, with all significant covariates and confounders retained earlier. In such a way, other variables which, by themselves,were not significantly related to the outcome but became an important contributor in the presence of other variables will be included in the final model.
The marketing channel is identified as a key factor on farmers’ conversion decision.We were attentive to the long list of marketing channels, including farmers market, roadside stands, directly to consumers, wholesale markets, processors, restaurants, food stores, and schools. The access to farmers market was retained in the model as a significant factor. Producers who sell on farmers market had a26% increment of probability in converting to organic farming in comparison with those with no access. Similarly, the accessibility reduced the likelihood of farmers remaining in conventional farming by 19.6%. The impact is the largest among all influential variables, which implies the vital role of the farmers market for organic products. Farmers market could relate organic products to other value-added attributes such as freshness and locally produced, which enable an easy claim on premium price. In addition, farmers market tends to tolerate the unstable and inconsistent supply in both quantity and quality of organic products.The absences of other market channels in the model may not necessarily mean they are less importance, rather it likely reflected a reality that they were perceived by organic farmers as inaccessible or less accessible at the time of our survey. Finding reliable buyers was identified as a major barrier from a long list of potential ones including price premium, distance to organic markets, handling costs, and competition with non-organic products, and access to capital through lenders. The result seemed contradict the claim on a great demand for organic products. The solution to the puzzle is that existence of a great demand for organic products showed an unbalanced spatial structure and could not be extrapolated into areas not adjacent to metropolis.