Critical Control Points For Profitability In Sheep Production (continued)

Keywords

Introduction

Materials and
Methods

Results and
Discussion

Conclusions

References

Project
Background

Animal science research has typically focused on changing the biological production parameters of the animal to enhance production. Maximum production has been the goal, with little or no analysis of the profitability of the increased production (Heady and Dillon, 1961). Since increased physical products do not automatically generate increased total profit, this research provides an important economic component to producers' decision-making process.

In North Dakota, farm profitability is often measured through the checkbook of the producer and cash flow frequently becomes the driving force behind most producer decisions. Many farmers in the state do not analyze their individual enterprise profit centers, but rather depend on a measure of the amount of cash on hand at the end of the year to determine the success or failure of the farm or ranch total business. This frequently leads to incorrect management decisions since the profit contribution of each profit center is unknown. Furthermore, which management practice in each enterprise is most responsible for profit in each profit center is unknown.

Net profit is a function of many parameters. An almost infinite number of production practices and management decisions, as well as many external forces, affect final profit results from an enterprise. The question then becomes which factors should a producer focus his management attention on to make the best use of his limited management time.

There is speculation among researchers about the most important profit criteria. Animal scientists may focus on the production factors: lambing rate per ewe, weaning weight of lambs, and lamb death loss. Someone with an accounting background may focus on gross income, total cost of production, and net farm income. An economist may look at earned returns to labor, management, and equity capital as well as marketing decisions as crucial to the profitability of the flock. A producer however, cannot measure and analyze every possible production or financial parameter.

A producer and his/her family brings three resources to each enterprise on a farm: equity capital, unpaid family and operator labor, and management. The common factor among all three resources is that they are limited. Focusing management attention on non-critical profit parameters in the sheep profit center comes at a cost. The cost is loss in potential profits in another profit center due to limited management time.

It is important to identify the critical control points (CCP) of operating a profitable sheep enterprise so managers can focus their limited management time and skills on those factors most likely to affect the bottom line of the sheep profit center. This insures that finite management resources are allocated most efficiently for all enterprises on the farm.

This study identified the critical control points of operating a profitable sheep profit center. This was accomplished by estimating the statistical relationship of various measurable financial and production criteria to net cash profit. In this study, net cash profit was defined as the return to unpaid family labor, management, and equity capital. Cash costs of acquisition were used for all inputs measured.

Specific Objectives of This Study
The primary objective of this study was to identify the critical control points (CCP) for profitability in a sheep production enterprise. Knowing these CCPs allows producers to focus their management efforts on those areas most likely to affect flock profitability. This information is also available to record-keeping systems designers, allowing them to create information management systems that gather CCPs needed for profitable decision making.

The second objective of this study was to generate statistical relationships explaining each of the identified critical control points. This allows producers to understand the underlying production relationships that are critical to the CCPs.

Materials and Methods

Data Used
This study uses data from a group of North Dakota producers who were enrolled in a sheep producer education project from 1988 through 1994. The data cover 1989 through 1993. First-year data were not collected as producers had not been trained in data collection techniques. The education program terminated in 1994.

Livestock production data were collected and analyzed for each flock using the North Dakota Sheep Production Testing Program (Haugen, 1981). Producers kept production records on individual animal performance. As part of the education program, assistance was provided on weigh days for lambs and also in completing input sheets for computer processing.

Financial data were collected for the computer program SHEEPBUD (Nudell and Hughes, 1996). Client financial records ranged from shoeboxes full of receipts to computerized accounting programs. Producers were assisted in data collection, and economic data input was done on site with each client.

Not all clients who participated in the educational program agreed to maintain all records. Some kept only performance records, others kept only financial records, and still others kept both financial and performance records. Not every producer who began the program finished, and some did not start to keep records until they had been in the program for a year or two. Thus, the data set is a pooled set containing both cross-sectional and time-series data.

Data used in this study are from those producers who completed the SHEEPBUD records. Most of these producers also completed the performance testing records. Ninety-six records were used in the final analysis. Information from the North Dakota Sheep Production Testing Program and SHEEPBUD were stored in a computer database.

Flock performance data included a unique identifying number for each ewe and all of her lambs, birth date of the lambs, sex of the lambs, weaning date, and weight for each lamb. Lambs that died were recorded along with the date of death and if known, the cause of death. The data set also included optional data including sire identification for both ewes and lambs, breed information, and producer comments about the ewe or lambs. Financial data collected include approximately 150 input parameters covering both cash and opportunity costs. Measurements include variable and fixed costs, land use data, debt payments, and all revenue data.

Thirty-four production and financial factors were recorded in the database. An additional seven variables were calculated from the raw data and were included in a database. To test for non-linearity, nearly all variables were squared and cubed and tested for inclusion in the model.

Because of the large number of potential explanatory variables, stepwise regression was used to search for independent variables that explain the variation in profitability. The intent of this regression exercise was to determine which of the variables the stepwise regression procedure would identify as "significant" in the model and also to see which variables were "not significant" in predicting net profit.

After the stepwise equation was completed, the information gained was used to test multiple factors against net profit. Several equations were tried with a goal of increasing the model's efficiency, measured by the number of variables used, without sacrificing the predictive power of the model, measured by the calculated R-square.

Management Parameters Selected
The model identified four critical control points in the profit equation. These four CCPs were regressed against other variables in the data set to identify a subset of management factors affecting the four main critical control points. A stepwise regression procedure was again used for each parameter, and individual equations were derived for the four critical control points with a goal of finding an efficient¹ equation with high predictive power.

¹Efficiency in this case is defined as an equation having fewer defining variables and still maintaining good predictive power.

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Table of Contents – Fall 1998