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This study investigates the economic outcomes of organic farming controlling for the four major aspects of a cropping system, including climate, genotypes, management and soil. Considering possible variations in treatment responses, this study also presents empirical evidence of heterogeneous treatment effects associated with spatial agglomeration or farm covariates.

Rice farm households data taken from the 2015 Agriculture Census is merged with township-level seasonal weather data, crop suitability index and average income per capita in Taiwan. To address the selection bias problem, the authors apply the Probit-2SLS instrumental variable (IV) method in the binary treatment model under homogeneous and heterogeneous assumptions.

It is found that organic farming leads to a significantly positive effect on rice farms' economic performances in terms of cost reduction and profit growth. This positive treatment effect is more sizable with spatial agglomeration. Furthermore, the treatment effect of organic farming is found to vary with the farm characteristics such as farmland area and the number of hired workers.

Two important implications for the promotion of sustainable agri-food production are inferred: (1) establishing organic agriculture specialized zones may benefit rural development; (2) providing economic incentives to small farms to expand their scale may be a more effective policy means to promote sustainable agri-food production.

The findings in this study complement the body of knowledge by drawing insights from the agriculture census data and providing profound evidence of the heterogeneous outcomes of organic farming due to spatial clustering and farm covariates.

The Old Farmer Pension Program (OFPP) represents Taiwan’s long-standing efforts aiming at improving farm household income and well-being; however, how effective the pension program is in terms of achieving the policy agenda has remained unclear. The paper aims to discuss this issue.

Based on data drawn from the Survey of Family Income and Expenditure during 1999–2013, two identification strategies are used to examine the effect of OFPP. First the authors apply the Blinder-Oaxaca decomposition to address the concern if the program reaches the socially/economically disadvantaged farm households. The second identification strategy involves using the static and dynamic decomposition approaches to identify the major factors contributing to farm household income inequality and the redistribution role of the OFPP.

Results from the Blinder-Oaxaca decomposition indicate that about 60 percent of the income gap can be eliminated if the pension recipients’ socio-economic characteristics are the same as the non-recipient group, suggesting it is the disadvantaged group that receives the old farmer pension. Moreover, the results suggest the significant contributions of household investments in health and human capital as well as diversification toward nonfarm activities, to income inequality among Taiwan’s farm households. Results from the dynamic decomposition suggest that the first-wave adjustment of the OFPP enlarges farm household income inequality, the following two waves of adjustment, however, plays an equalizing role.

This study adds to the literature by providing a methodological refinement promoting the view that it calls for the use of the dynamic (change) decomposition framework to investigate the inequality-enlarging or inequality-equalizing role each income determinant plays.

The purpose of this paper is to present an integrated analysis of determining factors of farmers’ genetically modified (GM) technology adoption behavior, with a special emphasis on information acquisition, knowledge accumulation, product attributes and technology traits.

Extending the expected profit maximization framework into a random utility model which accommodates joint decisions of information acquisition and technology adoption, the authors use the full information maximum likelihood method to yield both consistent and efficient estimates. The model is applied to a field survey collecting a sample of 141 randomly selected bananas farmers.

The empirical results indicate information acquired through social network will increase the probability of adoption. Knowledge accumulation as depicted by education and farming experience is found to play a role in farmers’ technology adoption, whereas disease-resistant technology trait and flavor-enriching product attribute of GM bananas also appear to be important determinants for GM seeds adoption in Taiwan.

Empirical evidence supports significance of technology traits and product attributes in farmer's GM technology adoption, suggesting the close collaboration between industry, government and academia is the key to successful commercialization of GM crops.

Understanding the determinants of farmers’ GM technology adoption can serve as the basis for promoting new biotechnology, and thus can facilitate the establishment of tenable solutions to food security issues.

This paper is the first attempt to incorporate information acquisition into the behavioral analysis of GM technology adoption. The present study also extends previous literature by considering influential factors related to both consumers’ and producers’ preferences in modeling technology adoption.