Again, Wolf and Sumner find no evidence of a bimodal dairy industry using Farm Cost and Return Surveys of dairy farms for the years 1989 and 1993. In MacDonald et al. , they suggest that larger dairies tend to have lower costs per cow, which allows them to capture greater economies of scale. The cost-minimizing efforts of individual dairy farms will influence the specific farm management choices that the farm makes, as only the individual farm has a true sense of where it sits on its long-run average cost curve. Some of these management decisions include the dairy’s strategy to capture economies of scope, through sales diversification, or vertically integrate to minimize input and production costs. Sumner and Wolf find that vertical integration has little influence on the farm size and that the tendency for farms in the Pacific and South to have larger herd sizes remains true, even when accounting for the levels of vertical integration. The farm’s choice to incorporate different management strategies reflects the incentives and constraints that the farm faces, i.e., influences of geographic location and capital. Other influences on management choices by dairies are in part due to different environmental regulations in each state that impact the average cost of production for dairy farms. There has been a significant amount of agricultural economic research on dairy farm size with respect to their risk management and technical efficiency. Tauer finds that smaller dairies in New York do have a high average cost of production than dairies with larger herd sizes, grow trays but that these higher costs are due to inefficiencies and efficient small dairies are competitive with the larger dairies. Tauer and Mishra examine whether differences in technology or efficiency characterize the higher cost that smaller dairy farms face and find that using a frontier cost of production analysis show that inefficiencies in smaller dairies characterize the higher costs, not technological differences.
There has also been significant analysis in farm structure changes of the dairy industry. Zimmermann and Heckelei utilize a Markov Chain Model on dairies in the European Union to characterize farm size change and find that regional characteristics such as off-farm opportunities and unemployment rates are significant in relation to dairy farm size change. They also find that high milk prices slow down farm size change due to high milk prices correlation to uncertainty and price volatility leading to a decrease in investment. Wolf details how dairy farms in Michigan have increased their use of risk management tools from 1999 to 2011 and find that the use of such risk management tools was positively correlated with measures of dairy farm size. This research also discusses how age related to risk management adoption with younger dairy farmers being less likely to utilize the risk management tools. Wolf outlines characteristics of dairy farm size change across time Beyond management decisions influencing or being correlated with the farm size and farms’ decision to exit, previous economic literature has hypothesized about the possible influences of operator characteristics, like human capital , the number of female operators, the age of operators, or other farm operator characteristics on farm size. Sumner and Leiby find that human capital positively influences the size of the farm, and this is hypothesized to be due to increasing opportunity costs for dairy farmers with high levels of human capital. Dairy farmers that have the possibility of making more money elsewhere will do so, therefore it seems likely that dairy farms with sufficient returns, which tend to be found on larger dairy farms, will attract high human capital management. Another aspect of the previous research related to farm size and the dairy industry is farm exits. There have been several studies of individual farm movement across farm size groups and characterization of exits.
Most of this literature, however, has been limited to regions or states. Macdonald et al. finds that in 2016 about 40 percent of dairy farms with at least 2,000 milk cows did not have positive net returns and that the share of dairies that did not have positive net returns increased as herd size decreased. However, they do note that negative returns in the dairy industry are seen as temporary lows by dairy operators, so they do not serve as a direct indication of an expected exit from the industry. Other reasons for exits from agriculture, or dairy specifically, include increased suburbanization of previously agricultural land, driving land prices up, and strong local economies, opening off-farm employment opportunities for farm operators. As outlined in Sumner and Leiby and Sumner , the human capital element remains prevalent through economic explanations of farm exit. Of course, age of the farm operators plays key role. Macdonald et al. discuss the role of the advanced age of many dairy farmers and the fact that many dairy farms are family-run, suggesting that there will be an increase in exits as more farmers choose to retire. Furthermore, the study relates the probability of exit to farm size, finding that not only does the age of the operator increase the likelihood of exit, but the smaller the farm size also increases the probability of exit. This section discusses the sample used in this analysis and details changes in the COA questions that are relevant to this analysis. The research utilizes COA data from 2002, 2007, 2012, and 2017 for six select states: California, Idaho, New Mexico, New York, Texas, and Wisconsin. The results presented have gone through a disclosure review process and no data on individual/farm-specific is specific to individual farms and instead characterizes them more generally. Although the COA is federally mandated, it does not collect data on every U.S. farm and as such weights responses to create the most accurate sample that reflects the true U.S. farm sample. As discussed in Chapter 2, I use a specific definition of a commercial dairy in order to capture dairies with significant engagement with the dairy industry.
A commercial dairy for the purposes of this analysis is defined as a farm with at least 20 milk cows on the farm as of December 31 of the Census year and the farm must have dairy or milk sales revenue above the dollars of milk sale revenue that would have been generated by 30 milk cows. The survey questions asked of farmers and ranchers by the COA change slightly every Census round, although most remain the same across time. Below are descriptions of question changes for relevant variables to the analysis. First, in 2002 and 2007, farms were asked for the total amount of dairy sales in that year, but in 2012 and 2017, this question was dropped and replaced with the total amount of milk sales. Furthermore, whether the dairy farm had any level of organic production was only asked in 2007, 2012, and 2017. Second, operator characteristic questions have become more detailed over the years and allowed more information about operators to be collected. In 2002, 2007, and 2012, the COA asked detailed operator characteristic questions about up to three operators, but only one operator was identified as the principal operator. In 2017, the COA expanded its detailed operator questions to include up to four operators and allowed for up to four operators to be identified as principal operators. In this Chapter, pruning cannabis the operators for which the number per farm is limited and detailed information is provided will be referred to as the “core operators.” There is other no limit to the number other operators listed per farm and only the gender of each such operator and the total number per farm are provided in the Census. The COA has three potentially relevant farm size variables for dairy farms, the number of milk cows, the value of farm production, and the value of milk or dairy sales. I utilize all three in this chapter. However, I focus particular attention on the number of milk cows for the kernel density graphs. I characterize the distributions of number of milk cows per commercial dairy farm using two approaches. One approach is to fit a non-parametric distribution by year, and by state for each year to the data on milk cow herd size per farm. The other approach is to fit two commonly used parametric distributions to characterize dairy farm size distributions for the national and individual states over census years. One aim of my thesis is to characterize the farm size distribution of dairy farms and fitting parametric density functions serves as a starting point for characterizing and analyzing dairy size distribution. As explained above, there is previous literature that utilizes parametric distributions to characterize farm size and this research provides evidence that commonly used distributions do not fit well with the U.S. commercial dairy industry. It is common in farm size analysis to fit parametric density functions to characterize farm size distribution . I create kernel density plots for the herd size distribution by state across the years and then find and fit two common parametric density functions to the distribution.
This section will be structures as follows: a brief overview of the mathematics used in fitting parametric density functions. There are three steps to fitting the parametric density function to the farm size variables. First, I hypothesize based on the kernel density plots what distributions seem reasonable. For this analysis I use the lognormal and the exponential function, as those are two common distributions used in farm size literature and are likely shapes for most farm size distributions. Lognormal is the typical selection, as it is referenced in Gibrat’s Law. The exponential distribution was selected because it can account for the same skewed shape but has more flexibility. Second, I estimate the parameters of interest needed to form that distribution in order to create an estimated distribution of random numbers that follow the specific distribution. For this analysis, the measures of farm size, the number of milk cows for each farm, are random variables x1, x2, x3, …, xn, where n is the sample size of farms, for which the joint distribution depends on distribution parameters. For example, using the lognormal the parameters are the mean and variance, and there are two related parameters for the exponential distribution. From there, we can calculate the estimates of these parameters to create a different distribution with those same parameters and compare them to the actual distribution of the number of milk cows. Some estimated parametric distributions appear to have slight irregularities, this is due to the number of observations and the impose parameters. This section will summarize the resulting farm size graphs and detail the trends across time and states. Overall, when looking at the six select states together commercial dairy farm distributions have shifted towards larger dairies. In 2002, there was a clear peak in the number of farms with less than 200 milk cows, but the peak falls significantly from 2002 to 2017 . Whereas farm size distribution shows a clear increase in the farms with larger herd sizes in 2017. Although this graph gives interesting detail about the trends in herd size for the U.S. overall it is mostly characterized by Wisconsin and New York which have a significantly larger share of the number of commercial dairies and tend to have smaller herd sizes relative to other states. This graph clearly shows that there remains a large share of dairies that have a herd size of less than 200 milk cows, despite the relative shift in herd size. Moving to state-specific trends, overall California dairies have had larger herd sizes than other states, such as New York or Wisconsin across all years . California had a peak in the share of dairies with less than 1,000 milk cows from 2002 to 2017, but the peak fell significantly between 2007 and 2012. There was a clear shift in 2012 with an increase in the 1,000 to 2,000 milk cow herd size in 2012 and then another shift in 2017 in the 2,000 to 3,000 milk cow herd size. This documents a clear movement of California dairies towards larger herd sizes and a decrease in smaller herd sizes. Idaho had a large peak in commercial dairies with less than 500 milk cows in 2002 and then a significant drop in that peak in 2007 with smaller subsequent decreases in 2012 and 2017 .