Critical control points for profitability in the cow-calf enterprise

Miller, A J

Abstract

Financial, economic, and biological data from cow-calf producers participating in the Illinois and Iowa Standardized Performance Analysis programs were analyzed. Data were collected from 1996 to 1999; each herd-year represented one observation. The database consisted of 225 commercial herd observations (117 Iowa; 108 Illinois) and ranged from 20 to 373 cows. Analyses were conducted on financial and economic costs of production. Each observation was analyzed as the difference from the mean for that given year to eliminate environmental and cattle cycle effects. The dependent variable used as an indicator of profit was return to unpaid labor and management per cow (RLM). Independent variables were feed, operating, depreciation, capital, hired labor costs, calf weight, calf price, cull weight, cull price, weaning percentage, calving distribution, herd size, and investment. Family labor was used in the economic analysis. All financial factors analyzed were correlated to RLM (P

calf weight, cull weight, and cull price. A financial prediction equation using eight variables accounted for 82% of the variation among farms. For both economic and financial analyses, feed cost accounted for over 50% of the variation among farms. In the financial regression model, depreciation cost was the second critical factor accounting for 9% of variation in RLM followed by operating cost (5%). Calf weight was the fourth indicator of RLM in the financial model (5%). Cost factors accounted for more variation in RLM than production, reproduction, or producer-controlled marketing factors. Feed cost was the most critical control point, as it accounted for 50% of the variation in profit among the herds.

(Key Words: Cow-Calf, Economics, Profitability, Standardized Performance Analysis.)

Introduction

Identifying practices to enhance profitability is an objective of any effective business manager. According to Harris and Newman (5), breeding objectives over the last century progressed from being predominately based on visual appearance to criteria involving performance. Selection for improved biological performance has led to dramatic increases in growth rates of beef cattle, but has not necessarily led to increased profitability among commercial cow-calf producers (1). Therefore, the transition from selection based on performance criteria to selection based on economics is untested. There is an inconsistency in the definition of profit in agricultural enterprises. Yet, profit is the most fundamental measure of business success (12). Using the Farm Financial Standards procedures (4), the appropriate definition of "farm or ranch profit" is the "net farm or ranch income from operations minus the value of unpaid family labor and management."

To position beef cow herds as sustainable business entities, production practices that maximize profit must be identified. In 1994, a Standardized Performance Analysis (SPA) program was implemented in Illinois and Iowa to provide cow-calf producers with an evaluation tool to measure the biological, financial, and economic performances of their operations. This program was designed in accordance with the Integrated Resource Management-SPA (IRM-SPA) Guidelines as set forth by the National Cattlemen's Beef Association (16). Research in farm and ranch profitability has been significantly hindered because of the lack of actual financial and economic costs of production data. Research has shown how specific management strategies (e.g., cross-breeding systems, feeding systems, reproductive performance, health practices) affect profit. A beef production system is a highly complex combination of many biological and economic factors. A producer must view the beef-cattle operation in its entirety and understand how its component parts interact with one another to ultimately affect profitability (1). Bruce et al. (2) utilized a computer simulation of various management factors and identified annual cost of maintaining a cow as the most influential factor determining profit, followed by calf sale price and weaning weight. Hughes (7) analyzed averages from farm business records of herds in North Dakota and reported that total feed costs, followed by selling price of calves and number of cows in the herd, were the three most important factors explaining variation in profit.

This study analyzed actual cowcalf enterprise data to identify specific management factors that influence profitability. In addition, a database of actual financial, economic, and biological production information for Midwestern cow-calf producers is presented.

Materials and Methods

Data collected from cow-calf producers who participated in the Illinois or Iowa SPA program were used in this study. Data were collected for 1996 through 1999 calendar years; each herd-year represented one observation. Data were collected using the SPA Beef Cow Business Record program developed by Iowa State University, in accordance with the SPA guidelines, developed by the IRM. Coordinating Committee of the National Cattlemen's Beef Association (16). Data were from the cowcalf enterprise only. Allocations for equipment and inputs shared with other livestock or farming enterprises required a percentage allocation to the cow herd by each producer. Excluded from the final data set were purebred seedstock producers, herds with less than 20 cows, and one herd with more than 2,000 cows. Producers who were involved with the program more than 1 yr may be included multiple times. This resulted in a final database of 225 observations (117 from Iowa; 108 from Illinois) from 126 different producers who operated commercial beef herds with a range of 20 to 373 cows. In the stepwise regression analysis, 164 observations were utilized, as 61 were missing weaning percentage data.

Two analyses were conducted: one utilized financial cost of production data; the other utilized economic data. Financial costs were defined as cash-flow costs and included debt service and hired labor; economic costs reflected the opportunity cost of inputs and included a charge for invested capital (rather than principal and interest payments) and the value of family and operator labor (11).

To alleviate the influence of factors that are beyond a manager's control (e.g., cyclical differences in calf price and yearly variations in weather), arithmetic means were developed within each year. Each observation was analyzed as the difference from the mean for that given year.

Stepwise linear regression analyses were conducted according to the procedures of SPSS (15). Pearson correlation coefficients were calculated to determine linear associations between variables (15). Because of lack of significance (P>0.05), hired labor, family labor (economic analysis only), investment (both cost basis and market basis), cull weight, cull price, and calving distribution were excluded from the final stepwise regression models. All variables included in the financial and economic models were included at a significance level of P

The dependent variable in both the financial and economic models as an indicator of profit was return to unpaid labor and management per cow (RLM). Total annual cow cost was excluded from the analysis as an independent variable, as previous research (2) and preliminary analysis of this data set indicated that it would be the overriding factor influencing profit. To allow for a better understanding of how management factors may influence profit, total cost was broken down into the five factors: feed cost (total annual feed cost including pasture cost), operating cost, depreciation cost, capital charge, and hired labor. In the economic analysis, family labor (family and operator labor charge per cow) was included. Investment (total capital investment per cow) on a market value basis was used as an independent variable in the economic analysis; the financial analysis used investment on an actual cost basis. Production factors evaluated as independent variables in both models were calf BW (average BW of feeder calves sold), cull BW (BW of breeding stock sold per cow), weaning percentage, and calving distribution (percentage of calves born in the first 42 d of the calving season). Calf price (price per 45.4 kg feeder calves sold) and cull price (price per 45.4 kg breeding stock sold) were also evaluated. Herd size (number of cows in the herd based on January 1 inventory) was also analyzed as an independent variable. All variables were calculated according to SPA guidelines (16).

Results and Discussion

Means, SD, minimal values, and maximal values are presented in Table 1 for all variables. The average herd size was 97 cows. Herd size distribution is shown in Table 2. Most of these herds were part of a multi-- enterprise farming operation.

During the 4-yr period studied (1996-1999), the average cow-calf enterprise had a negative $19.91 financial RLM per cow. When economic costs were included, the RLM per cow decreased to a negative $80.69. The means by year indicate that 1996 was the lowest profitability year; then, profitability trended upward through 1999. Based on calf price data, the period of evaluation included the bottom of the most recent calf price cycle (3).

Herds had an average financial total annual cow cost of $327.77. This was lower than SPA data reported for Texas [$356.59; (14)], North Dakota [$367.00; (8)], and Colorado [$504.00; (9)]. When comparing these data, it is important to note that the figure reported from Texas differs from this data set in that it was adjusted for noncalf revenue. The North Dakota data utilized a fair market value for harvested feeds, as opposed to a financial cost of production. The Colorado figure was significantly higher as a result of the inclusion of one herd with exceptionally high annual cow cost. The total economic cost from this study ($446.82) was similar to Texas [$446.68; (14)] and slightly lower than Colorado [$510.00; (9)].

Simple linear correlations among the factors used in developing the financial prediction equation are displayed in Table 3. Correlations among some independent variables must be considered in interpreting the final regression analysis results. All financial independent variables analyzed were significantly correlated to RLM (P

The prediction equation based on financial measurements is displayed in Table 5. Results of this analysis indicated that there are eight measurements capable of explaining over 82% of the farm-to-farm variation in RLM. The prediction equation based on economic measures is shown in Table 6. In both equations, the same eight independent variables were significant (P

Feed Cost. When calculated on a financial cost basis, 63% of total annual cow cost was feed cost. Feed cost in the financial analysis included the financial cost of producing raised hay and pasture, as well as the cash cost of purchased feedstuffs. In the economic summary, feed cost was $34.00 per cow higher because economic feed costs were calculated using the economic cost of production (e.g., a fair market value was assigned to owned land, equipment, and labor) for raised hay and pasture, along with the cash costs of purchased feed. There was more than $1.00/d per cow difference in feed cost from the high-cost producer to the low-cost producer. Feed cost ($205.44) was similar to that reported in North Dakota [$216.00; (8)], but higher than reported in Texas [$157.64; (14)].

Financial and economic feed costs were positively correlated (P

The financial regression analysis (Table 5) indicated that a $1.00 increase in feed cost would need to result in nearly 1 additional kg calf BW or an increase of 0.5% in weaning percentage, while keeping all other factors the same, to break even.

Operating, Depreciation, and Capital Cost. Operating and depreciation costs were calculated in the same manner in both the financial and economic analyses. These two expenses accounted for an additional $100.00 per cow in the average herd (Table 1).

Financial capital charge accounted for the debt service of loans pertaining to the cow-calf enterprise. In the economic analysis, capital charge provided a return to the fair market value of invested capital. On average, capital charge was higher ($50.89) in the economic summary than in the financial summary ($10.26). This relatively small amount of financial capital expense indicated that most producers have little debt in their cow-calf enterprise.

Operating costs, depreciation costs, and capital charges were all negatively correlated (P

In the financial regression model (Table 5), depreciation cost was the second critical factor explaining almost 9% of the herd-to-herd variation in RLM followed by operating cost (5%). The unstandardized coefficients and their SE for depreciation and operating costs indicated that there was essentially a 1;1 reduction in RLM for each dollar spent. Therefore, these costs do not increase economic return by improving production. Lawrence and Strohbehn (11) showed that a $1.00 increase in operating cost resulted in a larger than $1.00 increase in total annual cost, suggesting a correlation between operating cost and other costs unaccounted for in their equation. Capital charge showed a $1.38 reduction in RLM for every dollar spent, indicating that it was correlated with other factors that increased expenses. Capital charge was correlated with feed cost, operating cost, and investment (P

Even though depreciation cost was calculated the same in the financial and economic analyses, as an independent variable, it had a greater impact on the economic regression model than on the financial regression model and had a larger slope. This was because of the correlation of depreciation cost to other factors unaccounted for in the models. Depreciation cost was responsible for 12% of the herd-to-herd variation in RLM in the economic model. Each $1.00 increase in depreciation cost in the economic equation resulted in a negative $1.19 RLM. Depreciation expense may be a function of investment in equipment and structures or breeding stock. If this expense was because of equipment, spreading this cost over more cows would be a logical management alternative. The correlation between depreciation cost and herd size (P

Herd Production and Marketing. The average price of feeder calves sold for the entire database was $77.22/ 45.4 kg (cwt), with a low of $62.58/ cwt in 1996 and a high of $86.43/cwt in 1999. Calf price data over these years were similar to those reported by Cattle-Fax (3) for 1996 to1999. Cattle-Fax data indicated a choice 205-kg steer (about 16 kg lighter than the average calf BW in this data set) was worth $64.10/cwt in 1996, $88.93/cwt in 1997, $87.50/cwt in 1998, and $92.22/cwt in 1999. These data indicated that the 4-yr study included the bottom of the most recent cattle cycle and occurred during the liquidation phase. The upward trend seen in calf prices agreed with the upward trend seen in RLM (Table 1), indicating that calf price as a function of the cattle cycle certainly impacted profitability. In this study, individual observations were calculated as a difference from the yearly mean, in an attempt to gauge differences in producer-controlled marketing factors.

Calf BW had a greater impact on RLM in both the financial and economic regression models than did calf price. Calf BW was the fourth indicator of RLM in the financial model (Table 5) and was similar in magnitude to operating cost, explaining about 5% of the herd-to-herd variation. Results of both the financial and economic equations indicated that each additional kg of calf BW would be expected to return about $1.18 ($0.54 per pound). This was intermediate to results reported by Hughes [$0.41; (6)] and Bruce et al. [$0.60; (2)]. The $0.54 was lower than the $0.77 average calf price reported for this data set for two main reasons. First, as calves get heavier, the sale price per kilogram decreases (6), which agreed with the negative correlation observed for calf BW and calf price. Second, based on an 83% average weaning percentage, 17% of the cows that had costs were not marketing calves. This was the same reason that an increase of $1.00/cwt in calf price only resulted in an increased return of $3.40 per cow in the financial regression equation, similar to the $3.68 reported by Bruce et al. (2).

Cull BW was correlated with cull price and weaning percentage (P

Reproductive Efficiency. The average producer had a weaning percentage of 83%. This was similar to data reported in Colorado [84%; (9)] and Texas [82.9%; (14)]. A SD of 8% indicated that 68% of the herds had a weaning percentage between 75 and 91%. Thus, there was less variation in the reproductive performance of these herds than in the cost data. McGrann (13) reported only a two percentage point (84% vs. 82%) difference for weaning percentage between producers in the high net income quartile and those in the low net income quartile of the Texas SPA database.

Weaning percentage was correlated (P

McGrann (13) reported that calf BW weaned per exposed female accounted for 7% of net income differences between herds. Combining weaning percentage and calf BW values from this study resulted in a similar outcome.

An evaluation of calving distribution was made by summarizing data for the percentage of calves born in the first 42 d of the calving season. The average for all observations was 81% with an SD of 14% (Table 1). Calving distribution was positively correlated (P

Investment. Investment measured on a market basis ($2,350) and on a cost basis ($1,560) was somewhat lower than investment figures reported in Texas [$3,355 and $2,276; (14)]. This was because of the enterprise analysis approach taken by this program. Producers often utilize equipment and structures in other farming enterprises. Allocating a percentage of equipment to other enterprises effectively lowers the investment for the cow-calf enterprise and may illustrate the complementary economic relationship of multiple enterprises.

Investment measured on a cost basis remained constant across years. Investment measured on a market-- value basis tended to increase from 1996 to 1999, as would be expected because commercial cow values increased. Investment was not a significant variable in either regression model. However, it was highly correlated with a number of the significant cost variables. Depreciation was highly correlated (P

Herd Size. Economies of scale have often been reported to exist in the cow-calf enterprise (10). Investment was negatively (P

Herd size was negatively correlated with family labor (P

The negative coefficients in the prediction equations for herd size (-0.17 financial; -0.32 economic) suggested advantages for a smaller producer (Tables 5 and 6), if they are able to manage the first factors. This might be difficult given the negative correlations observed among herd size and feed cost, operating cost, depreciation cost, and capital charge, as well as the positive correlation observed between calf price and herd size. It does indicate that small producers who manage these factors may have an advantage over large producers in matching their operations to their resources. These data indicated that economies of scale exist primarily in the form of reduced feed and operating costs.

Implications

The average cow-calf producer operating in Illinois or Iowa from 1996 through 1999 had a negative RLM. Cost factors were far more influential in driving RLM than production, reproduction, or producer-controlled marketing factors. Of these cost factors, feed cost had the largest impact. Smaller producers were not competitive in feed, operating, or investment costs and should hire custom machine operators rather than own leveraged equipment. Larger producers also received higher prices for their calves. The large herd-- to-herd variation seen for many cost factors indicates that many producers can dramatically improve their profitability by finding ways to lower production costs. As producers focus attention on factors that affect the profitability of the operation, feed cost was the most critical control point because it was responsible for over 50% of the herd-to-herd variation in profit.

Literature Cited

1. Bourdon, R. 1999. The Systems Concept of Beef Production. Beef Cattle Handbook. BCH-1330. MidWest Plan Service, Ames, IA.

2. Bruce, L. B., R. C. Torell, and H. S. Hussein. 1999. Profit predictions in cow-calf operations: Part 2, influence of major management practices. J. Prod. Agric. 12:647.

3. Cattle-Fax. 2000. Cattle Marketing Information Service, Englewood, CO.

4. Farm Financial Standards Task Force. 1991. Recommendations of the Farm Financial Standards Task Force: Financial Guidelines for Agricultural Producers. Performance Management Resources, Ltd. Denver, CO.

5. Harris, D. L., and S. Newman. 1994. Breeding for Profit: Synergism Between Genetic Improvement and Livestock Production (A Review). J. Anim. Sci. 72:2178.

6. Hughes, H. 1991. Economics of reproduction efficiency in beef cow herds. In Proc. Annu. Conv. Am. Assoc. Bovine Pract. p 67, 71.

7. Hughes, H. 1991. Financial performance of North Dakota's beef cow enterprises-The critical success factors. Proc. Annu. Conv. Am. Assoc. Bovine Pract. p 100.

8. Hughes, H. 1999. North Dakota's IRM Databank Averages. North Dakota State University Extension. North Dakota State Univ., Fargo, ND.

9. Lankister, W. L., R. D. Green, P. H. Gutierrez, N. L. Dalsted, J. 0. Green, and R. E. Taylor. 1999. Identification and management of critical control points in the cow-calf enterprise for achieving and maintaining consistency and low cost of production: Summary of integrated resource management-standardized performance analysis results. Prof. Anim. Sci. 15:83.

10. Langemeier, M. R., J. M. McGrann, and J. Parker. 1995. Economies of size in cow-calf production. Agri-Pract. 16:16.

11. Lawrence, J. D., and D. R. Strohbehn. 1999. Understanding and Managing Costs in Beef Cow-Calf Herds. Iowa Beef Center. Iowa State Univ., Ames, IA.

12. McGrann, J. M. 1998. Cow-calf enterprise financial analysis using standardized performance analysis (SPA). IRM-SPA Handbook. Texas Agricultural Extension Service, Texas A&M University.

13. McGrann, J. M. 1999. Cost Effective Decisions Needed: Cow-Calf SPA Results For Texas--1991-98. Texas Agricultural Extension Service, Texas A&M University.

14. McGrann, J. M., and J. Parker. 1999. Factors influencing profitability of the cowcalf operation. IRM-SPA Handbook. Texas Agricultural Extension Service, Texas A&M University.

15. SPSS. 1998. SPSS 8.0 for Windows User's Guide. Prentice-Hall, Inc., Upper Saddle River, NJ.

16. USDA. 1992. Guidelines for Production and Financial Performance Analysis for the Cow-Calf Producers: Cow-Calf SPA. Extension Service, USDA, Washington, DC.

A. J. MILLER*,1, D. B. FAULKNER*, PAS, R. K. KNIPE*, D. R. STROHBEHN^, D. F. PARRETT*, and L. L. BERGER*, PAS

*Department of Animal Sciences, University of Illinois, Urbana, IL 61801 and ^Department of Animal Science, Iowa State University, Ames, IA 50011

1To whom correspondence should be addressed: aj-millr@uiuc.edu

Copyright American Registry of Professional Animal Scientists Dec 2001
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