Drinker Technology as an Example of Improving Production Efficiency1

01998 Applied Poultry Science, Inc

DRINKER TECHNOLOGY AS AN EXAMPLE OF IMPROVING PRODUCTION EFFICIENCY'

Primarv Audience: Industrv Analvsts. Producers. Researchers

jective of this study is to evaluate the effect of DESCRIPTION OF PROBLEM adopting new technology, along with other facThe United States broiler industry is reported to be the most technologically developed in the world [l]. The industry has refined all facets of production to take advantage of areas where production efficiency can be enhanced. This has led to vertical integration, large technologically advanced broiler growing houses, sophisticated control of diseases, and highly mechanized rendering and packaging facilities, among other things. The fundamentals of the industry are in place. In order to realize higher levels of efficiency, however, and to stay ahead of competing broilerproducing countries, the United States broiler industry will have to depend, at least in part, on the rapid adoption of technology. The ob-

tors, on the efficiency of poultry production. The current study focuses on the Delmarva region, which is made up of counties in Delaware, Maryland, and Virginia on the Delrnarva Peninsula. This region is the fifthlargest poultry production area in the United States [ 2 ] .In 1995,623million broiler chickens were produced on the peninsula with a total processed and delivered value estimated at $1.5 billion. In Delaware alone, the poultry industry accounts for two-thirds of the total farm income. The new technology evaluated in this study is the water dispensing equipment, commonly called drinkers. Currently, the best technology in drinkers is called the nipple

No. 1642 in the Journal Series of the Delaware Agricultural Experiment Station. 2 To whom correspondence should be addressed 1 Published as Paper

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K. MICHEL, C. GEMPESAW2,J. PESEK, J. BACON, and H. TlLMON Delaware Agricultural Experiment Station, Department of Food and Resource Economics, College of Agriculture and Natural Resources, University of Delaware, Newark, DE 19717 Phone: (302) 831-2511 F a : (302) 831-3651

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MICHELetul.

The two variables chosen to represent broiler production efficiencygains from better technology were weight gain per day and percent mortality. If efficiencies are gained from the new technology, mainly in the area of decreased disease, then it is theorized that weight gain per day will increase, producing a larger and more profitable chicken. Correlated with a decrease in disease would also be a decrease, in flock mortality. This would be a cost savings for the grower and thus a gain in efficiency. The combination of these two factors should result in more efficient and profitable broiler production.

grower had adopted the new drinker technology; this is the period of full implementation. In order to analyze the effect of the new drinking technology, two regression models were estimated. As noted previously, efficiency gains are represented in this study by changes in weight gain/bird/day and the total percentage of mortality in each flock. These two variables, which were the dependent variables, were hypothesized to be a function of various factors in broiler production and the changes in technology over the 10-yr time span of the data. The two regression models were estimated using ordinary least squares. The factors measured against weight gain and mortality in these models were the cost of litter cleanouts, the payment to growers per 1,OOO chickens placed, the average cost of propane gas to heat the buildings, the percentage of flocks placed that were not broilers, the size of the flock placed, the adjusted kilocalories fed to the birds, the percentage of farm condemnations per grower, the cost of water medication to keep the birds healthy, and two variables representing the periods of transition to the new technology and full implementation of the technology. DATA DESCRIPTION Before the regression results are discussed, it is helpful to examine the descriptive statistics of the variables used in the statistical models. The means, standard deviations, minimums, and maximums of thevariables are listed in Table 1. The mean weight gai4day (WEIGHT) of the chickens included in this data set is 0.097 Ib. Not included in the regression, but part of the data set, is the average age of the chickens at slaughter. This number is 49.7 days. Multiplying the average daily weight gain by the average number of days of growth produces a bird with an average weight of 4.82 Ib. The birds grown on the Delmarva Peninsula are typically larger than those grown in the rest of the country. In 1994, the average chicken produced in the United States was grown for 42 days and achieved a weight of 4.18 lb [l]. Average mortality in the United States poultry industry is generally from 3 to 5%. In this data set the average mortality (MORTALITY) was 5.7% with a minimum of 0.0 and

MATERIALS AND METHODS The data for this study covered a period of approximately 10 yr. The nature of the research required data from three overlapping periods: before adoption of nipple drinkers; transition to the new drinker technology; and full implementation of the new technology. A Delmarva poultry integrator gathered data from over 400 poultry contract growers and supplied the data to the Department of Food and Resource Economics at the University of Delaware. The pooled time series-cross sectional data covered the period from January 1986 to mid-February 1996. Prior to January 1990, there were no nipple drinkers in any of the production facilities for which data were collected. Between January 1990 and January 1992, nipple drinkers were being installed. This is the transition period. After January 1992, every

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drinker. Before the nipple drinker, various watering methods were used that involved standing water that could be splashed onto the litter. Wet litter can pack down and become solid, inducing conditions with high levels of ammonia from packed feces, a greater level of foot damage, and a greater likelihood of disease [3]. Because it does not allow for water spillage, the nipple drinker is more sanitary and thus, it is hypothesized, can increase production efficiency by cutting down on disease. It is also hypothesized that with reduced water spillage on the litter, the costs of maintaining a clean floor of litter will be lower and efficiency and profits can increase. Excellent references on the technical discussion of nipple drinkers can be found in Carpenter et uf. [4], Lee and Zimmermann [5], Vest [6], and Carr

JAPR DRINKER TECHNOLOGY

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VARIABLE*

MEAN

STANDARD DEVIATION

MINIMUM

MAXIMUM

WEIGHT

0.097

0.005

0.083

0.119

MORTALITY

5.772

1.751

0.825

20.133

0.0022

LI?TER

0.0018

0.020

0.00004

PAYMENT

175.009

36.824

117.540

339.300

PROPANE

17.476

7.144

0.00

86.000

PLACED KILOCAL

I I

44.323.740

I

20.739.150

2.901.260

I

98.278

I I

5.966.670 2.359.630

1 1

119.756.520 3.305.000

0.889

0.400

0.210

4.198

MED

0.016

0.022

0.00

0.160

a maximum of 20.133%. The variable LITTER represents the litter cost per pound of bud moved in each flock. The mean cost is $0.00226 with a standard deviation of $0.0018. This represents the cost of cleaning out litter to maintain a sanitary and productive growing environment. The lower the litter costs, while keeping other variables such as weight gain and mortality constant, the more efficient the grower. It is hypothesized that the litter cost will decrease with the introduction of the new drinker technology. The mean payment to the growers per 1,OOO chickens placed (PAYMENT) is $175.01 with a standard deviation of $36.82. In most parts of the U. S., contract payment is typically set on a per pound basis. In the Delmarva region, however, most contracts are paid on a per thousand bird basis. It is hypothesized that as production efficiency increases in the form of healthier and heavier birds, the payment to the grower should increase as well. The average cost of propane consumption (PROPANE) is $17.48 with a standard deviation of $7.14. Costs are likely to increase with inflation over any time series data set, but the more costs are contained relative to other variables, the more efficient a production facility will be. It is hypothesized that as production efficiency increases the average cost spent on propane consumption should fall. The average number of chicks placed with each grower (PLACED) is approximately 44,323 birds, representing almost two houses. The standard deviation for this variable is

20,647 birds, or approximately one house. It is known in the United States that as the poultry industry has advanced, larger flocks of birds have been grown in larger houses to capitalize on economies of scale. It is hypothesized that a better drinker technology will allow more birds to be placed in the houses, enabling the grower to sell more birds at the end of the growing cycle. The variable KILOCAL is a measure of feeding efficiency in a poultry production facility. It stands for the adjusted kilocalories (kcal) in the feed and is an indication of the energy contained within the feed. Younger buds receive fewer kcal and mature birds need more kcal. The mean for this data set is 2,901.26 kcal with a standard deviation of 98.27 kcal. It is hypothesized that an increase in technological efficiency may contribute to an increase in feeding efficiency. This will allow a lower percentage of money to be spent on feed, the single largest cost in producing poultry. When a grown bird reaches the processing plant, it is either processed and sent out for retail or condemned due to some problem. Problems can include disease or ammonia irritation caused by improper management on the farm,damage sustained during transportation to the plant, and damage due to improper handling once the bird has reached the processing plant. Any problems caused by improper management on the farm can be charged back to the grower. The variable CONDEMN stands for this event. The mean

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CONDEMN

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MICHEL et al.

VARIABLE^

for 20% of the data points in the data set; 26% are from the time of transition to the new technology; and 54% are from the period of full implementation.

RESULTS AND DISCUSSION The results for the model measuring weight gain are presented in Table 2. This model postulates weight gain/bird/day as the dependent variable. The parameter estimates of the explanatory variables are the partial slope coefficients corresponding to the effect each variable has on the average weight gain of the birds per day. The R2 of 83% indicates that the explanatory variables explain the changes in the dependent variable very well, considering that cross-sectional data sets do not generally produce models with high R2 results. This means that the explanatory variables account for 83% of the variation in weight gain/bird/day. The first variable, MORTALITY, was found to have an increasing effect on the daily average weight gain of the birds. As long as total growout mortality is within bounds, this may occur because as more chickens die there is more room in the poultry house for the remaining birds, resulting in a more favorable environment for gaining weight. The net effect to the farmer may be close to zero as the gain

PARAMITER ESIlMATE

STANDARD ERROR

Intercept

0.123548

0.003159

MORTALITY

0.000143***

0.000053

-0.015619

LITITR PAYMENT PROPANE NOTBROIL

I I I

0.043802

O.ooOo37*** *

I

O.OoooO5

I

O.ooo092** *

I I

O.ooOo16

I I

O.O04%I6** * *

PLACED

-2.327 E-'*'* *

0.000570 0.0

KILOCAL

-0.000013*'**

0.0000009

CONDEMN

-0.001356** * *

0.000221

MED

0.005833'

0.003377

N2

O.O01306** * *

0.000224

N3

o.m3247** * *

0.oO02.50

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for this variable is 0.889% (less than 1%).This is a reasonable number in the industry. Because the new drinking technology is hypothesized to reduce disease, ammonia irritation, and foot problems in the birds, it follows that the condemnation percentage at the processing plant charged back to the grower will decrease, resulting in better production efticiency. Another cost for the grower is the cost of medication (MED) to keep down disease and mortality. The mean cost of MED is $0.016, less than 2e/lb of bird moved. It is hypothesized that as the new technology decreases the incidence of disease, the cost of medication will also decrease, and this will result in better production efficiency. Three variables included in the models but not in Table 1 are NOTBROIL, N2, and N3. These variables are dummy variables (having the data points of 0 and 1) and thus are not adequately described by means and standard deviations. Over the data set only 11% of the birds raised are not broilers (NOTBROIL), meaning that a vast majority of the data points deal with broilers. Note that those growers who produced birds that were not broilers did not begin doing so until 1992. The other variables (N2 and N3) have to do with the technology periods. The period before nipple drinkers were installed accounts

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fect on the daily weight gain of the birds being grown. Evidently the birds’ ability to gain weight and grow bigger is diminished as they become more crowded. Thus, the more birds the grower has to take care of, the higher the chance that management mistakes will lead to reduced weight gain. KILOCAL, the adjusted kcal fed to the birds, is a measure of feed efficiency. The older the birds are, the more kcal, or units of energy, are fed to them. The result of this model, that an increase in the adjusted kcti1 fed to the birds is accompanied by a decrease in average daily weight gain, is contrary to what is normally thought to take place. The variable CONDEMN, representing the percentage of farm condemnations per grower, is an indicator of grower efficiency. The fewer the condemnations of a grower’s birds, the more healthy birds the grower is producing. In this model an increase in farm condemnations is accompanied by a decrease in average weight gain per day. This may occur because heavier birds are in general healthier and less vulnerable to disease than underweight birds. MED, the variable representing the cost of water medication charged to the grower, has a significant relationship with the dependent variable. It shows that an increase in the cost of water medication is accompaniedby an increase in the average weight gain per day. Birds kept free of diseases are likely to gain weight faster. The variable N2, which represents the transition period to nipple drinker technology, is significant in this model and positive. This shows that during the transition to the new technology the average daily weight gain increases. This indicates an increase in efficiency for the growers. An even greater increase in weight gain is seen with the variable N3, the time period of full implementation of the new drinker technology. While it is hypothesized that a new, clean technology like the nipple drinker will increase poultry production efficiency, not all new technologies are beneficial. This result confirms that the nipple drinker technology does indeed have a positive effect on production efficiency. The second regression model estimated measures the effect of production variables on the total grow-out mortality per flock, an-

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in payment for heavier chickens is canceled out by the cost of higher mortality. The variable LITTER, the litter cost in dollars per pound of bird moved weighted by the number of flocks grown by the ith grower, is the only explanatory variable found to be insignificant in explaining the dependent variable. Given its insignificance, the effect of LITTER on weight gain cannot be determined. PAYMENT, the variable representing the payment in dollars to the ifhgrower per 1,OOO chickens placed, has a positive effect on the average weight gain of birds per day. Although it would be more appropriate to use payment per pound to measure its effect on weight gain, the absence of the payment per pound data precludes this analysis. However, it should be noted that the variable PAYMENT has an important positive effect on weight gain, indicating that more efficient growers receive higher payments. This may also indicate that efficient growers who make more money from their production process than the average grower tend to put profits back into production and, perhaps, realize greater efficiencies. One of the costs for poultry growers, especially in an area like the Delmarva Peninsula which experiences cold weather in winter, is energy costs for heating. In this data set energy costs are measured by the variable PROPANE, the average cost in dollars of propane consumption for the ith grower. In this model an increase in spendingon propane has a significant effect on an increase in weight gain, indicating that warm, comfortable birds are likely to grow more efficiently than buds in an inhospitable environment. As noted in the data description, the average weight of chickens grown on the Delmarva Peninsula is greater than the average weight of chickens grown in the United States as a whole. In addition, many roasters are grown in the Delmarva region. Roasters are grown longer and weigh more than broilers. Not surprisingly, the result of this model indicates that an increase in the number of birds that are not broilers (NOTBROIL) has a significant correspondence to an increase in weight gain per day. The effect of the number of birds placed in a house (PLACED) has the exact opposite effect. As the number of buds placed increases, there is a significant and negative ef-

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grower who experiences a lower mortality rate. The variable representing propane consumption (PROPANE) in this model does not produce significant results. Its effect on mortality therefore cannot be determined. NOTBROIL, the variable representing the percentage of birds in each flock that are not broilers, reveals interesting results for this model. As the percentage of birds that are not broilers increases, the percentage of total grow-out mortality also increases. Further examination of the data set shows that the average mortality rate for the NOTBROIL data set was 6.7596,which was higher than the average mortality rate for broilers and supports these results. The variable PLACED also yields an interesting result. The negative coefficient means that as the number of birds placed increases, there is an accompanying decrease in mortality. This is a good thing for the growers, but at some point too many birds will be placed and mortality will likely increase. For this data set, however, newer, larger, and more technologically advanced growing houses were added over the 10-yr time span, allowing for increased placement and a concomitant decrease in mortality. The variable KILOCAL, representing feed efficiency, produces significant results in

TABLE 3.Model II results (Effect on mortality)

*WEIGHT=Average weight gain/day in Ibs; LITTER=Litter cost per pound of bird moved in each flock; PAYMENT= Payment to growers per 1,OOO birds placed; PROPANE= Ener costs; NOTBROIL= Nonbroiler birds; PLACED = Number of birds placed per grower; KILOCAL = Adjusted Ea1 as a measure of feed efficiency; CONDEMN= Bird condemnations at processing resulting from im roper grower management; MED = Medication costs; N2 =New nipple drinker technology transition period; N3 = &w nipple drinker technology fully implemented. ****significantat0.001 level: ***~imificant at 0.01 level: **significant at 0.05level; 'Significant at 0.101evel;R '=

.a.

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other measure of efficiency. The results are presented in Table 3. The degree to which the explanatory variables explain the changes in the mortality is rather good for a crosssectional data set, with an R2 of 44%. In the first model daily average weight gain increased as mortality increased. In the second model the same effect is seen: an increase in weight gain (WEIGHT) is accompanied by an increase in mortality. As explained previously, this may reflect that the remaining chickens can gain more weight because more space is left in the growing houses. That the same effect occurred in both models confirms the results. An increase in the cost of litter per pound of bird moved (LITTER) has a significant and negative effect on total grow-out mortality. A cleaner environment is beneficial for the growing birds, so one in which more is spent on keeping it clean should experience lower mortality rates. The effect on mortality of an increase in the payment to growers per 1,OOO birds placed (PAYMENT) is both significant and negative. This means that as payments increase, mortality decreases. This would be expected because a grower with high mortality may have less healthy birds in general and may therefore receive less in payment for his flock than a

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tality is both significant and negative. When the drinkers have been installed in all of the houses, total grow-out mortality decreases. This result supports Model I (effect on weight gain per day) in showing that the change to nipple drinker technology does indeed have a positive effect on poultry production efficiency. This study focused on a new drinker technology that was adopted on the Delmarva Peninsula. The methodology applied in our study, however, can be used to evaluate the effect of any new technology and other factors on the efficiency of poultry production. The study revealed many significant results and supported the notion that enhanced technology can have a positive effect on production efficiency. As global competition in the poultry industry grows, the importance of technology grows with it. Generally, the lowest cost producers have an advantage over higher cost producers. A recent study, found that Brazil has the lowest cost of production among nations and that the United States has the second lowest cost [l].In order to stay competitive it will be important for the United States poultry industry to seek efficiencies in technology to keep costs down. Those countries that are able to produce chickens in the most efficient way will have an advantage over others. The appropriate use of technology, as has been demonstrated in this study, is an important factor in enhancing efficiency and should therefore be considered an important factor in any poultry production facility today.

CONCLUSIONS AND APPLICATIONS 1. Daily average weight gain per bird and total grow-out mortality rates were used as measures of the gains in efficiency that producers experienced over the 10-yr data set. Because technology was not the only variable affecting these measures, other production variables were also included in the regression models. Most of the production variables included in the regression models were found to have a significant relationship with the dependent variables. 2. The significant variables contributing to the largest increases in daily average weight gain were the cost of medication, the percentage of birds placed that were not broilers, and the implementation of technology. The full implementation of technology had a larger effect than the transition period of technology. A few variables contributed to a decrease in weight gain per day, but their effect was smaller than the positive effect from other variables.

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this model. As the energy in the food given to the birds increases, there is an accompanying decrease in the percentage of total grow-out mortality. This may occur because buds fed properly will be less vulnerable to diseases. The relationship between the variable representing the percentage of farm condemnations (CONDEMN) and total grow-out mortality is signifcant and positive. Farm condemnations are condemnations done at the processing plant that are charged back to the grower because they are generally caused by poor growing conditions. For example, too much ammonia in the growing house produces an inferior bud. A grower who experiences an increase in farm condemnations might also experience an increase in grow-out mortality. The variable MED, representing the cost of medication in the water, shows a result which is positive and significant at the 10% level. As the cost of medication increases, there is an increase in the percentage of total grow-out mortality. The positive relationship between increased medication and increased mortality may reflect disease outbreaks: higher medication costs incurred in treating the disease occur at the same time as an increase in mortality due to the disease. The variable N2, representing the transition phase of the drinker technology, produces curious results in this model. During the transition to the new technology, the regression results show a positive impact on percentage of total grow-out mortality. This increase in mortality may reflect growers’ errors in judgement while learning to use the new technology. When the drinker technology is fully implemented (N3), however, the change in mor-

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The variables contributing most to a decrease in mortality were the full implementation of drinker technology, an increase in the cost of cleaning out the litter (which is assumed to mean that the houses were cleaned out more often), and an increase in the amount paid to , OO birds placed. It is signifcant that the largest contributor to decreased producers per 1O mortality is the variable representing the full implementation of the new technology. This bears out the hypothesis that the technological improvement really does contribute to improved efficiency of production. A new technology, such as nipple drinkers, along with other production variables utilized in an efficient way, can have a critical effect on the improved efficiency of a poultry production facility.

1. Henry, R a n d G. RolhweU, 1995.The World Poultry Industry. The World Bank and Intl. Finance Cop., Washington, DC.

5. Lee, R and N.G. Zlmmermann, 1987. Effects of enclosed nipple drinkers vs. dome waterers on broiler performance. Poultry Sci. 66(Suppl):132.

2. Delmarva Poultry Industry, Inc., 1996. Voice of Delmam’s Poultry Industry. Pamphlet. Georgetown, DE.

6. Vest, LR,1986. Management of closed water systems for poultry. Poultry Sci. 65(Suppl):139.

3. Appleby, M.C., B.O. Hughes, and H A Elson, 1992. Poultry Production Systems: Behavior, Management, and Welfare. C.A.B. Intl., Oxford, England. 4. Carpenter, G.H., Rk Peterson, W.T.Jones, K.R Daly, and W.A. Hypes, 1992. Effects of two nipple drinker types with different flow rates on the productive performance of broiler chickens during summerlike growing conditions. Poultry Sci. 71:145&1456.

7. Carr, LE, 1984. Comparison of three drinker systems for broilers. Pages 1-20 in: Proc. Amer. SOC.Agric. Engineers 1984 Winter Conference, paper number 844520. St. Josephs, MI.

ACKNOWLEDGEMENT The authors gratefully acknowledge the assistance of the editor and two anonymous journal reviewers for their suggestions in improving the manuscript.

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REFERENCES AND NOTES