While the demand for pollination services have been increasing, continued declines in honey bee, Apis mellifera L. (Hymenoptera: Apidae), colonies have put the cropping sector and the broader health of agro-ecosystems at risk. Economic factors may play a role in dwindling honey bee colony supply in the United States, but have not been extensively studied. Using data envelopment analysis (DEA), we measure technical efficiency, returns to scale, and factors influencing the efficiency of those apiaries in the northern Rocky Mountain region participating in the pollination services market. We find that, although over 25% of apiaries are technically efficient, many experience either increasing or decreasing returns to scale. Smaller apiaries (under 80 colonies) experience increasing returns to scale, but a lack of available financing may hinder them from achieving economically sustainable colony levels. Larger apiaries (over 1,000 colonies) experience decreasing returns to scale. Those beekeepers may have economic incentivizes to decrease colony numbers. Using a double bootstrap method, we find that apiary location and off-farm employment influence apiary technical efficiency. Apiaries in Wyoming are found to be more efficient than those in Utah or Montana. Further, engagement in off-farm employment increases an apiary's technical efficiency. The combined effects of efficiency gains through off-farm employment and diseconomies of scale may explain, in part, the historical decline in honey bee numbers.

The objective of the study was to estimate the current level of technical efficiency and to identify the factors affecting technical efficiency of intercropped pineapple production in Kurunegala district. A questionnaire based survey was carried out to collect the data from eighty pineapple growers. The data were analyzed by using the stochastic frontier production function approach. The pineapple production was determined as a function of land extent, plant density, labour, fertilizer, agrochemicals and worth of assets. The land extent, plant density, labour and fertilizer had significant effect on pineapple production in Kurunegala district. The technical inefficiency was regressed as a function of season, age, education, number of family members, land, plant density, ownership, experience, occupation, off farm income and constraint index. The technical efficiency was significantly affected by season, ownership, experience, off farm income and a constraint index. The mean technical efficiency of pineapple production was eighty five percent. The study reveals that there is a possibility, for further increase of productivity.

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This study was carried out to analyze the present level of technical efficiency of rubber smallholders in Gampaha district and primary data were collected from 100 smallholder rubber farmers. Data Envelopment Analysis (DEA) was utilized to estimate technical efficiency levels. A double censoring Tobit model was estimated to assess factors affecting technical efficiency. Farmer specific and tapper specific factors were considered as covariates in the Tobit model. Findings reveal that, levels of human capital of owner farmers as well as latex extractors account for variations in technical efficiency levels. This highlights the importance of directing extension services not only towards the owner farmers, but also towards hired latex extractors to reap benefits of the present price hikes in the rubber sector.

Agricultural markets, compared to other sectors, are typically characterized by uncertainty and high price fluctuations. High price volatility in livestock markets leads to inefficient resource allocation and production planning. Expert price forecasts are not always affordable for all market players, so readily available public forecasts have risen in popularity. This study uses accuracy-based testing methods to evaluate the accuracy of the United States Department of Agriculture (USDA) livestock price forecasting by utilizing the World Agricultural Supply and Demand Estimates (WASDE) quarterly data for slaughter cattle, hogs, and broilers. The study also employs a vector error correction (VEC) model to compare USDA price forecasts. Results suggest that the USDA forecast was more accurate than the competing VEC model across three sectors, suggested by low RMSE and MAE. The beta efficiency test results showed that USDA price forecasts were efficient for all three price series, whereas VEC forecasts were biased for hogs and broiler prices. The findings of the study also confirm that USDA price forecasts are biased for cattle prices with a tendency to repeat past forecast error in all three markets. Results from the forecast encompassing tests showed that USDA cattle and broiler forecasts captured the information contained in VEC forecasts. However, because the hog prices did not show any improvement over time, there is room for improvement of the USDA price forecasts. Overall, results suggest that USDA price forecasts for slaughter cattle, hogs and broilers provide useful information to the market. However, the results also indicate that USDA price forecasts reduce forecast error by economically significant levels.