United States certified organic and conventional dairy farms are compared on the basis of economic, financial, and technological measures using dairy data from the 2016 USDA Agricultural Resource Management Survey. A stochastic production frontier model using an input distance function framework is estimated for U.S. dairy farms to examine technical efficiency and returns to scale (RTS) of farms of both systems and by multiple size categories. Financial and economic measures such as net return on assets and input costs, as well as technological adoption measures are compared by system and size. For both systems, size is the major determinant of competitiveness based on selected measures of productivity and RTS.
PurposeIn recent years, socially disadvantaged farmers and ranchers have increased their usage of nontraditional lending nearly converging to levels of usage observed for nonsocially disadvantaged groups. The purpose of this research is to explore explanations for this trend in lending utilization by socially disadvantaged farmers and ranchers by examining factors that influence credit usage and credit choice.Design/methodology/approachA multinomial logit is used to estimate the probability of loan choice given characteristics of the producer and farm.FindingsWhile not a causal analysis, the results suggest that farm characteristics, which differ between socially disadvantaged and nonsocially disadvantaged producers, are associated with a lower likelihood of credit usage by an average socially disadvantaged farmer. For those that have loans, socially disadvantaged producers exhibit higher debt-to-asset ratios and lower current ratios, characteristics that are typically associated with higher than observed probability of usage of loans other than nontraditional. Socially disadvantaged producers also have lower value of assets which is associated with a higher probability of nontraditional loan usage.Originality/valueThis research is among the first to examine loan usage of socially disadvantaged producers using nationally representative data.
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