The adoption of technologies, management practices, and production systems in U.S. milk production

Jeffrey Gillespie 0 Richard Nehring 2 Isaac Sitienei 1 0 Department of Agricultural Economics and Agribusiness, Louisiana State University Agricultural Center , Martin D Woodin Hall, Baton Rouge, LA 70803, USA 1 Department of Agricultural Economics and Agribusiness, Louisiana State University , Martin D Woodin Hall, Baton Rouge, LA 70803, USA 2 Economic Research Service , 1800 M St. NW, Washington, DC 20036, USA Adoption rates of 19 dairy technologies, management practices, and production systems (TMPPS) are estimated for the U.S. for 2005 and 2010 and, in cases where data are available, 1993 and 2000. Logit models are estimated to determine types of farms most likely to use each TMPPS. TMPPS experiencing the greatest increases in adoption have been automatic take-offs, the internet, breeding technologies, and USDA certified organic production; recombinant bovine somatotropin experienced a reduction in usage between 2005 and 2010. Factors influencing TMPPS usage include farm size, tenure, and diversification; farmer age and education; and region of the U.S. where the farm is located. - Background Over the past two decades, the U.S. dairy industry has experienced significant structural change that has been accompanied by increases in the adoption of productivityinfluencing technologies, management practices, and production systems (TMPPS). A number of factors can be attributed to changes in TMPPS usage by dairy farmers, including the capacity of some TMPPS to allow for realization of benefits associated with economies of size and/or improved efficiency, as well as changing consumer tastes and preferences for milk produced under specific production systems. The research presented in this paper adds to past literature on dairy technology adoption by analyzing the drivers that have influenced the use of TMPPS in the U.S. dairy industry, estimating new 2010 aggregate adoption estimates for these TMPPS, and analyzing the adoption diffusion of these TMPPS over the past two decades. There have been large changes in numbers of dairy farms, total milk production, and milk production per cow in the U.S. over the past 20-years. From 1991 to 2010, total milk production increased by 30%, the number of farms decreased by 66%, and milk production on a per-cow basis increased by 42%, showing significant changes in the industry structure and productivity over the period. Clearly, average farm size increased along with cow productivity. A major contributor to this structural change has been the adoption of TMPPS that have allowed for greater economies of size and increased cow productivity. Farmers generally adopt a TMPPS if it serves to increase farm profit and fits within the farms resource constraints. In deciding whether to adopt a TMPPS, the farmer must consider whether it would change output quantity, output price, and/or cost of production. For example, use of recombinant bovine somatotropin (rbST) serves to increase milk produced per cow, but in areas where non-rbST premium milk prices are provided, a lower effective price per unit is received for milk produced under the technology. Furthermore, adoption would serve to alter production costs. In addition to the costs and returns associated with a TMPPS, farmers must consider whether adoption can occur given land, labor, capital, and credit constraints. For example, adoption of a pasture-based system may not fit within the farms resource constraints if insufficient land is available for grazing or the land is in a higher-value use. A number of studies have addressed factors influencing the adoption of various combinations of TMPPS in dairy production, most using limited dependent variable models to assess factors influencing usage. Table 1 describes each TMPPS and provides a brief summary of previous work for each. The most recent comprehensive study addressing TMPPS usage in U.S. dairy production was Khanal et al. (2010). The present study differs from theirs in several important ways. First, they analyzed 11 TMPPS; we analyze a fuller set, 19. Second, their analysis used mean comparison tests to compare usage on the basis of five farm and farmer demographic drivers, while we use multivariate analysis (logit) to analyze usage with 16 drivers, including farm, farmer demographic, and regional variables. Thus, we were able to fully account for the influence of adoption drivers used in the logit regression models. Third, their analysis focused on adoption drivers in 2000 and 2005 while ours focuses on the more recent 2010 data. Fourth, our analysis uses the full set of USDA Agricultural Resource Management Survey (ARMS) and Farm Costs and Returns Survey (FCRS) data, dairy versions, since 1993 to examine aggregate adoption over a 17-year period. Though this period is not sufficient to examine full diffusion of most technologies from introduction to equilibrium, it provides insight into TMPPS use patterns over an extensive period of diffusion. The analysis of technology adoption and its determinants in agriculture has a rich history. In an early classic analysis of the technology adoption process, Griliches (1957) examined the diffusion of hybrid corn in the U.S., showing that technology diffusion followed an S-shaped, logistic curve from introduction to equilibrium. This shape implies that adoption diffusion starts off rather slowly, speeds up, and eventually levels out. He further showed strong influences of region on adoption rates, suggesting that identical adoption diffusion curves do not exist under all conditions. Cochrane (1958) discussed the agricultural treadmill, addressing how early technology adopters generally benefit the most economically, with late adopters being forced to either adopt or exit production. The analysis of technology adoption in agriculture increased throughout the 1960s and 1970s. By 1985, Feder et al. (1985) had reviewed the extensive literature on technology adoption in developing countries, discussing the major adoption drivers. The literature addressing technology adoption in agriculture has expanded since then, with a large body of work conducted on adoption determinants, the extent of which will not be fully discussed here. A substantial amount of this work has dealt with the U.S. dairy industry, much of which is discussed within this paper with respect to each of the TMPPS. Table 1 Technologies, management practices, and production systems analyzed in the study Computerized and/or Automated Technologies Computerized feed delivery system Computerized milking system Provides specific feed ration to an individual or group of cows, depending upon cows lactation phase. Typically used with total mixed ration designed to meet the animals full nutritional needs. At least compiles computerized milking data from milker, but may also refer to an automatic milking system or fully automated robotic system. Data provided useful for making individual cow decisions (Gillespie et al., 2009a). Provides n (...truncated)