ESCRITURA FINAL

The control independent variables are those variables added to the model to allow for more accurate comparison across farms. These variables are also hypothesized to account for variation of profitability; however, these variables are either static or cannot be readily manipulated without major changes to each operation. Also, these variables are not tied into the financial structure of the operation, which is the main focus of this analysis. The first of the control independent variables is the soil productivity rating (spr). This is an index value out of 100 that measures the quality of the soil. A positive coefficient is expected for this variable in each of the three profit measures (nfiperacre, ROA, ROE), as one would expect a higher spr to result in increased crop yields. This assumption would result in higher revenues and higher net farm income. Mishra et al. (1999) found that soil productivity was statistically significant to increase net farm income of limited resource farms, as we have hypothesized here for the pure grain farms in Illinois. Again, this variable is relevant to grain production entities in this analysis. The second control variable is the number of hundreds of tillable acres (hundsofacrtil). This is included as an economy of scale measure. The bigger the farmer is, the larger the economies of scale. Hence, it is expected that the operator will receive inputs more cheaply by buying in bulk and less equipment costs per acre given bigger, more efficient equipment on average. As an operation lowers the costs per acre, higher profitability overall is expected, hence, a higher ROA, ROE, and nfiperacre. This positive coefficient expectation is in concurrence of Purdy,

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Langemeiser, and Featherstone’s (1997) research where they found that increases in farm size had a positive effect on financial performance while not significantly increasing risk. However, the results of Garcia et al. (1980) indicate that farm size is not a major factor in determining profitability.

The last set of control variables were established to control for variation in the data that are specific for each year. Each year in agriculture is typically very different from the others, and this fact is exemplified within the FBFM dataset for these corn and soybean farms. Furthermore, the average marketing prices per year at the end of Chapter 3 show one aspect that creates major variation of returns from year-to-year. Other things such as yields, precipitation, severe weather events, and government policies have major effects on the profitability. These factors need to be accounted for so they do not affect the significant or magnitude of the other independent

variables in the regression analysis. To account for these problems dummy variables were created for every year except one. The year (2009) that corresponds to the missing dummy variable acts as a baseline for comparative analysis of returns across years.

These three models (nfiperacre, ROA, ROE) will be applied to the entire dataset as well as subsample datasets for the time period 1998-200, 2002-2004, and 2007-2009. The interactions and relationships of these variables will be analyzed in the next two chapters. Chapter 5 provides summary statistics and means analysis, which show changes in the dependent and independent variables among different groupings of other independent variables. Chapter 6 provides

regression analysis that explains the variation of the dependent variable in terms of the structure and control independent variables. The results of the regression analysis shows if the

expectations come to fruition and what are the main determinants of profitability for Illinois pure grain operators.

44 4.3 Summary

The three models corresponding to different profitability measures are analyzed in this

chapter. These three models are separately applied to the dataset spanning 1996-2009 and then to each of the time period models. These time periods correspond to a depressed pricing period (1998-2000), a “normal” pre-ethanol pricing period (2002-2004), and a post-ethanol pricing period (2007-2009). The independent variables are justified in the chapter as well as

expectations of them on each profit measure. The econometrics used for regression analysis are discussed in Chapter 6, as well the results of the regressions. The next chapter presents

descriptive statistics of the variables discussed in this chapter for the entire period and within each of the three-year time periods.

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5 DESCRIPTIVE STATISTICS ON PURE GRAIN FARMS

This chapter outlines descriptive statistics for the dataset that is focused on pure grain farms. A description of how each variable is constructed and a summary of the main variables used in the regression analysis are presented in this chapter. Also, changes of these variables across time are also analyzed in this section, as well as the time periods from 1998-2000, 2002-2004, and 2007-2009. Furthermore, changes in these variables across difference groups of debt-to-asset ratio, farm size, and land tenure within different time periods are also analyzed here. The tables referenced in the text are presented at the end of this chapter. Additional descriptive statistics are provided in more depth for the pure grain farms in Appendix A. Furthermore, descriptive

statistics for all farm types combined are presented in Appendix B. 5.1 Overall Statistics for Pure Grain Farms

Table 5.1 provides a description of the composition of each variable. The equation used to calculate each variable is provided if it is applicable. For instance, the return on assets (ROA) is calculated by adding interest expenses to each farms net farm income and then dividing by total assets. Also, the units of each variable are stated in Table 5.1. Many of these variables have been divided by a common measure, such as assets, to provide a relative number that can be analyzed across farms.

Table 5.2 shows summary statistics of all pure grain farms throughout Illinois. The first three variables (ROA, ROE, and nfiperacre) are used to assess the returns of these crop production entities. The results indicate the average return on assets (ROA) was 11% for these farms from 1996 to 2009. While this seems like a fairly large return, the standard deviation is also quite large. The average return on equity is also 11% for these farms from 1996-2009. The standard