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Development of new cultivars requires extensive genetic knowledge, trained personnel, and significant financial resources, so it is crucial for breeders to focus on the attributes most preferred by the key supply chain stakeholders such as consumers and producers. The purpose of this paper is to identify which attributes generate the highest total revenue or social surplus, information that breeders can take into account as they allocate resources to focus on attributes in their breeding programs.

This study used mail-in and online surveys to collect consumer and producer choice experiment data, and then employed mixed logit models to analyze and simulate individual producer and consumer willingness to pay (WTP) for the apple attributes.

Based on the simulation results, this study derived the supply and demand curves and the market equilibrium prices and quantities for each apple attribute. Based on the WTP analysis for both consumer and producer, this paper found the highest equilibrium price and welfare for apples come from crispness, followed by flavor.

The authors propose a framework to estimate the equilibrium prices and quantities of a product based on the results of choice experiments. The framework can be easily adapted to understand any countries’ producer and consumer preferences for certain products.

The purpose of this paper is twofold, first: to define the profile of adopters of labor-enhancing technologies (e.g. platforms) identifying factors – such as operations size, mix of fruits grown, apple operation location, principal operators socio-demographics – and second: to estimate the efficiency threshold for platform adoption during apple harvesting to be financially feasible considering future increases in farm labor wages.

The authors conducted a mixed-mode survey in January-February 2010. Data were analyzed using a bivariate probit model, considering that the decision to adopt platforms was related with the orchard planting system. The authors conducted simulation scenarios to estimate the efficiencies – harvest – platforms must achieve in order to be economically feasible.

In total, 11 percent of the 316 apple operations covered by the survey used platforms. Orchard operations most likely to invest in planar structures are relatively large, produce high-value varieties, use organic systems, and have relatively young and educated operators. Similarly, operations producing high-value fruit such as “Honeycrisp” and controlled or patented varieties and relatively large operations are more likely to invest in platforms. The results of the comparison of the cost of harvesting apples using platforms vs ladders under several production assumptions indicate that platforms must increase labor productivity by at least 13 percent in order to be adopted by the industry.

This study caveat is the lack of inclusion of production and marketing uncertainties in the estimation of future apple harvest costs. Further research to deeper analyze these issues is needed.

The authors present information on the profile of mechanization adopters, so extension educators and engineers could concentrate efforts on them to increase adoption levels. In addition the authors provide a threshold of efficiencies for harvest platforms associated with cost savings compared to manual harvest.

Enabling the adoption of mechanization technologies by specialty crop industries would decrease the dependence on labor, decreasing labor uncertainties and facilitating the production of high quality produce to satisfy the needs of consumers. Second, it will end an era of importing poverty, given that the specialty crop industry has long benefited from seasonal migrant workers. It will improve rural American communities to shorten pools of farm workers, giving them access to permanent jobs with higher salaries.

The contribution of this study is to improve understanding of the degree of mechanization, financial feasibility of current existing technologies, and barriers to greater mechanization by the Washington apple industry. Given the similar labor challenges faced, in general, by the US specialty crop agriculture, results could be applicable to the entire industry.

The purpose of this paper is to use prediction markets to forecast an agricultural event: United States Department of Agriculture's number of cattle on feed (COF). Prediction markets are increasingly popular forecast tools due to their flexibility and proven accuracy to forecast a diverse array of events.

During spring 2008, a market was constructed comprised of student traders in which they bought and sold contracts whose value was contingent on the number of COF to be reported on April 18, 2008. During a nine‐week period, students were presented three types of contracts to forecast the number of COF. To estimate forecasts a uniform price sealed bid auction mechanism was used.

The results showed that prediction markets forecasted 11.5 million head on feed, which was about 1.6 percent lower than the actual number of COF (11.684 million). The prediction market also fared slightly worse than analysts' predictions, which on average suggested there would be about 11.795 million head (an over‐estimate of about 1 percent).

The contribution of this study was not to provide conclusive evidence on the efficacy of using prediction markets to forecast COF, but rather to present an empirical example that will spark interest among agricultural economists on the promises and pitfalls of a research method that has been relatively underutilized in the agricultural economics literature.