Interestingly, in 2007 there was an increase in the number of dairies with a milk cow herd size between 500 to 1,000, but then a subsequent decrease the following year. In 2017 there was a clear increase in the number of commercial dairies with a milk cow herd size between 1,500 and 2,000. New Mexico had one of the more unique herd size distributions with no clear peak in the smaller herd size ranges . From 2002 to 2007, there was a clear drop in the density of commercial dairies with less than 1,000 milk cows and a relative increase in the density of commercial dairies with 1,000 milk cows. Then in 2012, there was a shift towards commercial dairies with more than 2,000 milk cows and a downward shift in commercial dairies in the 500 to 1,000 milk cow herd size range. This trend continued in 2017 with even further shifts in each direction. From 2002 to 2017, New York has seen a slight decrease in the smaller herd sizes and a little increase in the larger herd sizes . In Texas, the most distinct trend was a significant drop in the density of commercial dairies with herd sizes of less than 500 milk cows between 2012 and 2017 . There has previously been a trend of decreases in this herd size range, but these follow a similar pattern as compared to most other states. However, in other states, drying room there was not such a significant drop. In 2017, there was an increase in commercial dairies with more than 1,000 milk cows.
There was a significant decrease the commercial dairies in Wisconsin with less than 100 milk cows from 2007 to 2012 and then again from 2012 to 2017 . In 2017, there was an increase in commercial dairies with a herd size of between 150 and 200 milk cows. Wisconsin’s dairy industry is characterized by a significant number of smaller dairies and few dairies with large milk cow herd sizes. Across the states, there is a trend of consolidation with few commercial dairies and an increase in the number of dairies with larger herd sizes. Despite the decrease in the number of farms in each state, the number of milk cows increased in some states and broadly remained relatively stable . California had a 6.7% increase in the number of milk cows from 2002 to 2017, but Idaho had a 55% increase. The number of milk cows in New Mexico and Wisconsin both remained roughly the same. There was a 6% decrease in the number of milk cows in New York and number of Texas grew by more than 70%. Neither of the two parametric distributions fit the national data well. In particular, both the lognormal and exponential distributions failed to capture the very high mode at the low herd size in 2002. The herd sizes in California did not fit any distribution well in 2002 or 2017 . Idaho has a large peak in the smaller ranges that is well above either the lognormal or the exponential distribution in 2002 . The herd size does fall significantly when looking at the Idaho herd size distribution in 2017, this does somewhat follow a lognormal pattern, but not very well . New York follows a similar pattern with the smaller herd size peak being significantly higher than either the lognormal or the exponential peaks in 2002 or 2017 .
As we saw across years in Texas, the herd size shifted dramatically. In 2002, the herd size distribution slightly resembled a lognormal trend but had definite deviations and in 2017 did not follow any distribution well . Wisconsin follows a similar pattern to New York with no clear distribution trend in 2002 or 2017, but with significantly high peaks in the lower herd size range that deviate from the distributions.As explained above there are several possible influences, but given the Census data, I have chosen the following variables: characteristics of the operators , farm sales diversification across commodities, and share of farm operators who have off-farm employment. I also account for state fixed effects and Census year fixed effects. Clearly sales diversification and off farm work are jointly determined with dairy farm size, so I do not claim to be measuring a causal impact in the regressions presented discussed in this section. The aim here is to discuss statistical relationships between these characteristics and the farm size measures because although they cannot be thought of as directly influencing farm size the relationship between such measures is of interest and allows for discussion about the characteristics of the U.S commercial dairy. The age of the operator is likely to influence the size of the dairy operation because it is likely that as an operator gets older and remains in the dairy industry as a dairy operator, they expand their business. Since most dairy farm operators enter the industry when they are young, age is likely to be highly correlated dairy farm experience and often with specific experience at a specific farm in a particular location. Therefore, it is reasonable to suggest that age is heavily correlated with on-farm experiences which is a form of human capital. High level of human capital at the farm level could be hypothesized to be attributed to a farm’s success and growth.
The trend of increasing farm size as the age of the operator increases is likely to occur until they reach the age of retirement, maybe decreasing slightly as they get closer to retirement age. Table 4.3 shows the share of dairy operators by age range, state, and year. We can see that the average age of dairy farm operators is increasing for both female and male operators. Based on the information available, I include the following variables in my analysis: the average age of operators and maximum age of any one operator . There are no COA questions directly asking about the farm’s level of sales diversification . However, I created a variable intended to capture sales diversification by taking the share of milk or dairy sales divided by total sales revenue. This gives an idea of the level of sale diversification on the dairy farm with dairies with little to no sale diversification being near one and those with significant sales diversification with lower values. I also included the share of operators that have off farm employment . These are not clear independent variables, as there appears to simultaneity bias between sales diversification and other variables. For the farm size variables, of the individual farm at time , are the dependent variables including Cowsit number of milk cows , TMDit total sales revenue from dairy or milk, and TVPit total value of production. Table 4.5 shows the regression results for Equation 1 with the mean age selected as the age variable. First starting with the farm size number of milk cows, I find that the coefficient on the mean age variable is not significant. A 1% increase in the share of operators with off farm employment suggests a decrease in the number of milk cows by 30.8%. Whereas a 1% increase in sales diversification corresponds with an increase of 107% in the number of milk cows. Now looking at the farm size variable total milk or dairy sales, vertical farming units the mean age variable is now significant. A year increase in the mean age of dairy operators relates to a decrease of 0.1% in the total milk or dairy sales. Sales diversification level has a relatively strong relationship with a 189% increase in the total milk or dairy sales given a 1% increase in the level of sales diversification. Finally, when we consider the total value of production as the farm size variable, a year increase in the mean age of dairy operators corresponds to a decrease in the total value of production by 0.1%. Also, a 1% increase in the share of operators with off farm employment relates to a decrease in the total value of production by 32% and a 1% increase in sales diversification suggests a decrease the total value of production by 39.3%.
Between 2002 and 2017, there has been a significant change in the dairy industry with distinct shifts in herd size towards larger farms and a decrease in the share of dairies with smaller herd sizes. This result differs greatly by state. California and New Mexico increased the number of commercial dairies with more than 1,000 milk cows. New York and Wisconsin started with much smaller herds in 2002 and the increases in herd size but tended to see large decreases in the number of commercial dairies with smaller herd sizes. The lognormal or exponential distributions fit neither the national distribution nor any of the states herd size distribution well. Neither maximum operator age nor average operator age had a very strong relationship with farm size. Both the degree of farm diversification across non-dairy commodities and the share of operators with off farm employment were highly correlated with the farm size measures. Future research on this topic could explore the relationship of individual farm size over time across Census years to examine individual farm growth. Moreover, there is much more to explore when looking at the farm characteristics on farm size, including exploring relationships between vertical integration and other measures of human capital with farm size. Dairy farms have long been run by men, with relatively few women acknowledged as farm operators. Women have played a substantial role on farms, even when their contribution was often not classified as contributing to the farm operation or management. With the rapidly changing dairy industry, it is important to document the validity of assumptions we have about the demographics of farm operators. Successful farms have high quality management, and women have become a crucial part of the supply of farm management expertise. Based on recent U.S. Department of Agriculture Census of Agriculture data, there appears to be both an increase in the share of female dairy farmer operators and an increase in the share of dairies with at least one female operator. There are two confounding factors that influence these statistics, but fundamentally it implies that farms that have been successful have tended to include female operators. Furthermore, the current data support the previously held assumption that there are a significant number of dairies that are run by spouses with a large share of female farm operators married to a principal operator. Understanding the correlation between the presence and the share of female operators, as well as operations run by spouses on farm size provides insight to a previously limited section of agricultural economics literature. Furthermore, by providing evidence and understanding of dairy farm management demographics this research is able to add to discussions about the future of the dairy industry and a better understanding past patterns. Very little agricultural economics literature has addressed the intersection of gender and agricultural industry in developed countries, but there has been some work on this topic for developing countries . It was not until 1978 that the COA even asked about the gender of the farm operators. Historically, being a farm operator has been thought of as a male profession with the work done by women on farms tending not to be labeled as farm management. Interest in the role of women on farms is prevalent across several disciplines with some sociology and anthropology research on women in agriculture claiming that women farmers tend to run smaller farms and adopt more sustainable practices than their male counterparts . There has been no agricultural economics research on the role and impact of female operators in agriculture for the dairy industry, specifically. An increase in the share of commercial dairy farms with a female operator suggests that farms that have not exited, during a trend of consolidation, are likely to have a female operator as compared those with only male operators. However, the increase in shares of women may also reflect a change in the practice of reporting to data collectors in addition to a change in actual farm practices. This chapter explores the hypotheses that the presence of a female operator on the dairy farm may indicate that the dairy farm is more adaptable or more open to change in management practices.