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Modeling the response of growing broiler chickens

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Title:
Modeling the response of growing broiler chickens methodology for the evaluation of alternate feed ingredients under a variety of environmental and economic situations
Creator:
Fattori, Thomas Richard, 1950-
Publisher:
[s.n.]
Language:
English

Subjects

Subjects / Keywords:
Broiler production ( jstor )
Commercial production ( jstor )
Confidence interval ( jstor )
Datasets ( jstor )
Economics ( jstor )
Farms ( jstor )
Liveweight gain ( jstor )
Meats ( jstor )
Peanuts ( jstor )
Recommendations ( jstor )
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bibliography ( marcgt )
non-fiction ( marcgt )

Notes

Funding:
Florida Historical Agriculture and Rural Life
Statement of Responsibility:
by Thomas Richard Fattori.

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Marston Science Library, George A. Smathers Libraries, University of Florida
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Florida Agricultural Experiment Station, Florida Cooperative Extension Service, Florida Department of Agriculture and Consumer Services, and the Engineering and Industrial Experiment Station; Institute for Food and Agricultural Services (IFAS), University of Florida
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17886157 ( OCLC )
AFA2028 ( NOTIS )

Full Text
MODELING THE RESPONSE OF GROWING BROILER CHICKENS:
METHODOLOGY FOR THE EVALUATION OF ALTERNATE FEED INGREDIENTS
UNDER A VARIETY OF ENVIRONMENTAL AND ECONOMIC SITUATIONS
By
THOM1AS RICHARD FATTORI
A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF MASTER OF SCIENCE
UNIVERSITY OF FLORIDA 1987




MODELING THE RESPONSE OF GROWING BROILER CHICKENS:
METHODOLOGY FOR THE EVALUATION OF ALTERNATE FEED INGREDIENTS
UNDER A VARIETY OF ENVIRONMENTAL AND ECONOMIC SITUATIONS
By
THOMAS RICHARD FATTORI
A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF MASTER OF SCIENCE
UNIVERSITY OF FLORIDA 1987




To Maisha, Jonathan and Benjamin with the future in mind.




ACKNOWLEDGMENTS
The author wishes to express his most sincere appreciation to his major advisor, Dr. F. Ben Mather, for his guidance throughout the author's Master of Science program. Dr. Mather's steadfast interest and support of the selected coursework and research project ensured the completion of this learning experience.
Special appreciation is extended to Dr. Joseph H. Conrad and Dr. Peter E. Hildebrand for their encouragement, technical advisement and tolerance in guiding me throughout the research program. Without their continued and open confidence in the author and the program, the motivation to venture out of the traditional circuit would have been lost.
Sincere gratitude is extended to Dr. D. Comer for her advice and guidance in the analysis of the experimental data and to Dr. Nelson Ruiz for his technical advice and personal encouragement.
Sincere appreciation is expressed to the author's parents, Mr. and Mrs. L. A. Fattori, for their support and vote of confidence in the decision to return to school.
The author wishes to extend his deepest appreciation to his wife Maisha for her care and support of their home and family which freed the many hours needed to complete this program. Without her patient understanding this endeavor would have never been completed.
iii




TABLE OF CONTENTS
Page
ACNOLDGENT ............. iii
LIST OF TABILES .................****...... *........... vi
ABSTRACT .... .. ...... .......... .... ..... ..... *. **** ** xi
CHAPTERS
Problematic Situation to ....... .. 0 ......... .... 2
Researchable Problem ..... .. ..... .. 3
Hypotheses ............ ..0..6. .. .... *... .. ...... ....*** ** 4
Experimental Objectives .. .. .... .. .. .. .. .... 5
Research Emphasis ....*q*..t..***.*.** 6
II LITERATURE REVIEW ........... go*.. ..o.. 8
Experimental Design .**...* ............ ........ 8
Relevance to Farming Systems Research and Extension to 10
Environmental Considerations .......... 0.............. 12
III MATERIALS AND METHODS ... ... ... .. .. to ....... 17
Experimental Methods and Procedures .............. 17
Feed Form, Formulation and Treatments ......... 18
Simulation of a Farm Environment ............. 22
Environmental Factors ....................... .. 23
IV PARTITIONING BROILER GROWERS INTO
HOMOGENEOUS PRODUCTION ENVIRONMENS .................. 28
Introduction .... ......... **** *** *** *. ... ... 28
Materials and Methods ..................... ... 29
Results and Discussion *............... 33
iv




V ESTIMATION OF A RELIABLE BROILER PRODUCTION FUNCTION
WHEN USING ALTERNATE FEED INGREDIENTS ................ 66
Introduction .... ........... .. .. .. .. ... 66
Data and Methods .... .. ............ .... .. ...... ..... 67
Results and Discussion ............................... 70
VI ECONOMIC ANALYSIS OF BROILER PRODUCTION
WHEN USING ALTERNATE FEED INGREDIENTS ................ 87
Introduction ............... ........ ... 87
Materials and Methods ................................ 88
Results and Discussion ........................ ......... 102
VII SUMMARY AND CONCLUSIONS ............................. 120
Problematic Situation ................................ 120
Partitioning Environments ............ 0.............. 121
Modeling the Response ........ .................... .... 124
Economic Significance .......... ..... ........ ........ 126
APPENDIX EXPERIMENTAL DATA .............................. 130
REFERENCES ................ ....... o................... o........ 135
BIOGRAPHICAL SKETCH ............ ...... .............. .......... 140
V




LIST OF TABLES
Table Pg
3-1. Composition of the experimental diets ................... 19
3-2. Composition of the vitamin-mineral premix ............... 20
3-3. Nutrient values used to calculate the diets .......... 21
3-4. Calculated amino acid profile of the diets ... a.......... 22
3-5. Averaged weekly range of temperatures by replication .... 26
3-6. Mortality and culls by environment, sex and feed form .. 27
4-1. Broiler gain response to protein treatments for
pellet form at weeks 4, 6, and 8 in kg/pen (complete 4-2. Broiler gain response to protein treatments for
mash form at weeks 4, 6, and 8 in kg/pen (complete 4-3. Linear equations for modified stability analysis
calculated from broiler gain (complete data set) ........ 37
4-4. Broiler gain response to protein treatments for pellet
form at weeks 4, 6, and 8 in kg/pen (partitioned data
set) .. .... .......................... ...*. ................. 42
4-5. Broiler gain response to protein treatments for mash
form at weeks 4, 6, and 8 in kg/pen (partitioned data
4-6. Distribution of confidence intervals of broiler gain
to protein treatments for pellet form at weeks 4, 6,
and 8 in kg/pen (complete data set) ..................... 48
4-7. Distribution of confidence intervals of broiler gain to
protein treatments for mash form at weeks 4, 6, and 8
in kg/pen (complete data set) .. ........................... 49
vi




Table Page
4-8. Distribution of confidence intervals of broiler gain to
protein treatments for pellet form at weeks 4, 6, and 8
in kg/pen (partitioned data set) ........................ 57
4-9. Distribution of confidence intervals of broiler gain to
protein treatments for mash feed at weeks 4, 6, and 8
in kg/pen (partitioned data set) ............00........... 59
4-10. Ranking of the graphed distribution of confidence
intervals to protein treatments for feed form and
environments .............. 0................ .....o 63
5-1. Estimation of the coefficients of regression for
broiler gain to protein treatments and feed form from
3 to 10 weeks and under varying temperatures (estimates
+ standard error) *.............. ...... 71
5-2. Regression analysis of variance for broiler gain to
protein treatments and feed from at 3 to 10 weeks and
under varying temperatures ..............~. 71
5-3. Estimation of the coefficients of regression for broiler
feed intake to protein treatments and feed form from 3
to 10 weeks and under varying temperatures (estimates +
standard error) .. .... ........... ...... .... ... ...... ... 72
5-4. Regression analysis of variance for broiler feed intake
to protein treatments and feed form from at 3 to 10
weeks and under varying temperatures ..... ..**....... 72
5-5. Estimates of the coefficients of regression for broiler
gain when fed milo and peanut meal in mash and pellet form from 1 to 10 weeks and under varying temperatures
(estimates + standard error) ............................ 73
5-6. Regression analysis of variance for broiler gain when
fed milo and peanut meal in mash and pellet form from
1 to 10 weeks of age and under varying temperatures ..... 73
6-1. Optimum production for varying economic and
environmental sitations in mash or pelleted form kg
of gain per square meter ...... 0.................... 110
6-2. An example of recommended feeding programs for
environment-specific broiler growers at a fixed
economic situation .................... 115
vii




LIST OF FIGURES
Figure Page
3-1. Broiler house pen arrangement showing good and poor
environments, completely randomized blocks of eight feed
treatments representing a growers farm and examples of the location of feed treatments. M and P indicate mash
and pellet form, respectively ........... ......... 24
4-1. Broiler gain response to protein treatments for mash
and pelleted feed form at weeks 4, 6, and 8 (complete
data set) ............................ ... 36
4-2. Broiler gain response to protein treatments for mash
form at 4 weeks of age as influenced by environment ..... 38
4-3. Broiler gain response to protein treatments for pellet
form at 4 weeks of age as influenced by environment 38
4-4. Broiler gain response to protein treatments for mash
form at 6 weeks of age as influenced by environment ..... 39
4-5. Broiler gain response to protein treatments for pellet
form at 6 weeks of age as influenced by environment 39
4-6o Broiler gain response to protein treatments for mash
form at 8 weeks of age as influenced by environment 40
4-7. Broiler gain response to protein treatments for pellet
form at 8 weeks of age as influenced by environment .. ... 40
4-8. Broiler gain response to protein treatments for pellet
form at 4, 6, and 8 weeks based on good and poor
environments ............. o...o ... 60.* ... 0 ............... 46
4-9. Broiler gain response to protein treatments for mash
form at 4, 6, and 8 weeks based on good and poor
environments ....................... 47
4-10. Distribution of confidence intervals for broiler gain
to protein treatments for mash feed at week 4.
Complete data set (a), poor environment (b), and good
environment W ........ 0.000 ............................ 50
viii




Figure Page
4-11 Distribution of confidence intervals for broiler gain
to protein treatments for pellet feed at week 4.
Complete data set (a), poor environment (b), and good
environment (c) ....................... *** ** *** ** *** .. 51
4-12. Distribution of confidence intervals for broiler gain
to protein treatments for mash feed at week 6.
Complete data set (a), poor environment (b), and good
environment (c) to. .....** .. ... .... 52
4-13. Distribution of confidence intervals for broiler gain
to protein treatments for pellet feed at week 6.
Complete data set (a), poor environment (b), and good
environment (c) .............******e**** 53
4-14. Distribution of confidence intervals for broiler gain
to protein treatments for mash feed at week 8.
Complete data set (a), poor environment (b), and good
4-15. Distribution of confidence intervals for broiler gain
to protein treatments for pellet feed at week 8.
Complete data set (a), poor environment (b), and good
5-1. Broiler gain response surfaces to protein treatments for
pellet feed in a good environment by weeks of age ....... 75
5-2. Broiler gain response surfaces to protein treatments for
pellet feed in a poor environment by weeks of age .... 76
5-3. Broiler gain response surfaces to protein treatments for
mash feed in a good environment by weeks of age ..... 77
5-4. Broiler gain response surfaces to protein treatments for
mash feed in a poor environment by weeks of age ..*.,..... 78
5-5. Predicted broiler gain response surface with milo and
peanut meal inputs for pellet form in a good
environment ..... ******* ******* .... .. .. .. 81
5-6. Predicted broiler gain 'response surface with milo and
peanut meal inputs for pellet form in a poor
environment ...***.****........ ..... 82
5-7. Predicted broiler gain response surface with milo and
peanut meal inputs for mash form in a good environment ..83
ix




Figure Pg
5-8. Predicted broiler gain response surface with milo and
peanut meal inputs for mash form in a poor environment .. 84
6-1. Broiler gain isoquants derived from milo and peanut meal
feed inputs for pellet form in a poor environment ....... 91
6-2. Protein treatment lines depicting a fixed proportion of
milo and peanut meal, and broiler gain isoquants for
pellet form in a poor environment ....................... 93
6-3. Isocost lines based on a fixed price ratio of 2.5:1
(price of PM to price of milo) and corresponding gain
isoquants for pellet form in a poor environment ......... 99
6-4. Least-cost expansion path based on a fixed price ratio
of 2.5:1 for pellet form and in a poor environment ...... 104
6-5a. Location of maximum production and maximum profit at a
fixed price ratio of inputs; least-cost feeding program
for pellet form in a poor environment ................... 107
6-5b. Location of maximum production and maximum profit at
a fixed price ratio of inputs; time isoquants (age)
derived for pellet form in a poor environment ........... 108
6-6a. Broiler feeding program at a fixed price ratio;
pellet form in a good environment ....................... 112
6-6b. Broiler feeding program at a fixed price ratio; time
isoquants (age) derived for pellet form in a good
environment ........................ ........ 113
6-6c. Broiler feeding programs at a fixed price ratio;
comparison of the programs developed for good and
poor environments of milo and peanut meal in pellet
form .................................................... 114
6-7a. Mash form in a good environment; broiler gain isoquants
derived from milo and peanut meal feed inputs ........... 116
6-7b. Mash form in a good environment; derived time
isoquants (age) ........... ........ .. *...... ..... 117
6-8a-. Mash form in a poor environment; broiler gain isoquants
derived for milo and peanut meal feed inputs ............ 118
6-8b. Mash form in a poor environment; derived time
isoquants (age) ......................................... 119
x




Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science
MODELING THE RESPONSE OF GROWING BROILER CHICKENS:
METHODOLOGY FOR THE EVALUATION OF ALTERNATE FEED INGREDIENTS
UNDER A VARIETY OF ENVIRONMENTAL AND ECONOMIC SITUATIONS
By
Thomas Richard Fattori
August, 1987
Chairman: F. Ben Mather
Major Department: Poultry Science
If world broiler production is to meet its share of demand for
animal protein then more efficient ways to utilize feed resources and more appropriate feeding technologies suited for specific environments must be found. A methodology rooted in the farming systems approach to technology generation was tested through a simulation study on the broiler gain response to alternate feed ingredients under a variety of environmental and economic situations.
The use of modified stability analysis combined with the graphic frequency distribution of confidence intervals was demonstrated to be an efficient tool for 1) quantifying the differences between broiler growers due to environment (temperature), 2) partitioning growers into homogeneous production environments (good and poor environments), 3) detecting stability of technologies (protein levels) across
xi




environments, 4) evaluating trends in production due to protein treatment levels and 5) identifying discrepancies in the hypothesized versus actual response to treatments in a good or poor environment.
Procedures described in the use of response surface methodology were followed so a reliable estimate of the broiler gain response function to the alternate feed ingredients milo and peanut meal could be defined. A technical model was developed to describe the biological response of broilers (live-weight gain) to varying levels of dietary protein and feed form at specific ages and in good and poor environments. Then, an efficiency model was established describing broiler gain as a function of cumulative milo and peanut meal intake, feed form and environment. The models demonstrated that 96 and 98% of the variation in live-weight gain was explained by the terms in the technical and efficiency models, respectively.
The estimated model for efficiency was then used to determine the economic significance of different feeding programs recommended for particular environments. Broiler gain isoquants were derived from the experimental data and were considered to be representative of the underlying biological relationship of broiler gain as a function of the cumulative intake of milo and peanut meal while exposed to different environmental temperatures. Economic analysis was then performed by superimposing a variety of economic considerations on this estimated biological response. It was demonstrated how a schedule of recommendations that optimize the use of feed resources in the production of broiler gain could be established for varying environmental and economic situations.
xii




CHAPTER I
INTRODUCTION
Growth of the world's poultrymeat sector was a remarkable 62 percent for the ten year period covering 1974 to 1984. In volume terms this expansion translated into an additional million tons of poultry meat in the world marketplace each year (Anon., 1986). Over the past 20 years the production of poultrymeat and eggs in developing countries has grown faster than for any other major food. Between 1970 and 1980, beef, sheep, goat, and milk production increased less than three percent per year. Pork production rose four percent while poultrymeat and eggs increased by nearly six and four percent per year, respectively (Krostitz, 1984). Reasons for the rise in poultry meat production are attributed to declining prices for poultry products relative to other animal products, population and income growth, and urbanization. All those trends are expected to continue into the future. Between now and the year 2000 poultry products are the most likely source of animal protein to keep pace with demand in the developing countries of the world.




2
Problematic Situation
To meet this agenda world broiler producers, especially
developing country producers, must find more efficient ways to utilize available feed resources and develop feeding technologies suited for specific achievable environments (Fetuga, 1983). The underproduction of cereals and tubers and the resulting human food shortages have made the use of agro-industrial by-products an attractive proposition in many developing countries. Even if the use of these by-products results in some depression in rate of live-weight gain and poorer feed efficiency, the true test of their potential is when they are examined in relation to their overall economic advantage of possible reductions in feed cost and acceptable profits for the producer.
Numerous review and scientific articles describing the
constraints on poultry production in developing countries emphasize that feed quality and quantity are the major constraints to increased production (Ikpi and Akinwumi, 1981; Rao, 1982; Leong and Jalaludin, 1982; Ballard, 1985; Adeyemo, 1986; Randall, 1985; and Wilson, 1986).
Feed costs are the major component of total production costs for broilers. In the U.S. feed costs represent over 60 percent of total production costs. In developing countries the percentage is likely to be higher.
The major objectives of broiler producers are to optimize
live-weight gain per unit of feed input (biological response) and to maximize profit (economic returns). Together these objectives imply that optimum nutrient levels in a broiler feeding program are an




3
economic as well as biological question. This optimization problem is especially complex in developing countries where wide fluctuations in availability and cost of feed ingredients as well as the price received for broiler meat occur not only rapidly but in an unpredictable fashion.
One of the greatest challenges to poultry researchers in the
developing countries of the world is to find new feeding technologies that are more appropriate to a complex and highly variable local production environment. They typically work in a research environment with a great deal more socio-economic as well as agro-biological variability than is found in the temperate, developed countries of the world.
Although the basis for most feeding recommendations comes from a general pool of knowledge, poultry consultants generally agree that the best recommendations often are those that are custom-fit to the immediate situation. This is especially true when the producer operates in an environment of high variability.
Researchable Problem
To assist the broiler producer in making production decisions, especially in matters concerning substitution rates of alternative feed ingredients, a methodology is needed that is capable of generating technical recommendations that are more efficient in their application and more appropriate for the clients in need of their use. Feeding recommendations are more efficient if they consider a range of




4
possible economic situations that broiler growers could experience and more appropriate if they are made for environment-specific growers.
Hypotheses
Hypothesis 1
If the differences in environment on broiler growers' farms can be characterized and quantified then these farms can be partitioned into homogeneous production environments. The purpose of such a partitioning is to increase the efficiency of feeding recommendations by formulating feeds specific to particular production environments.
Hypothesis 2
If the functional form of the broiler gain response to alternate feed ingredients (in this case milo and peanut meal) can be defined mathematically (modeled) then predictions of broiler gain from these in gredients can be made. A model that describes the efficiency of alternate feeds in broiler production could be estimated for a range of feed treatments (protein levels) and include temperature as a variable in the production process. This model can then be used to predict gain for a particular environment (temperature) and for a wide range of milo and peanut meal combinations.
Hypothesis 3
If the estimated model proves to be an accurate description of
the broiler gain response to the alternate ingredients then this model




5
can be used in the derivation of a range of feeding programs for varying economic and environmental situations. By developing appropriate feeding programs for environment-specific growers the efficient use of feed resources and profit from production are maximized.
Experimental Objectives
The research project had the following experimental objectives.
Regarding Hypothesis 1
1. Demonstrate how levels of gain from all treatments can be
averaged into an environmental index providing a means for quantifying the differences in broiler growers' environments.
2. Illustrate how broiler growers can be partitioned into relatively homogeneous production environments.
3. Illustrate how the stability of different feed technologies (protein levels) is affected by environment.
4. Identify production trends from different feed technologies over time.
5. Identify a need to recharacterize the production environment if an environment by treatment interaction is not consistent with the hypothesized interaction.




6
Regarding Hypothesis 2
1. Estimate a reliable experimental production function
(efficiency model) of the broiler gain response to the alternate feed ingredients milo and'peanut meal, under varying environmental situations.
2. Develop a biological model of the broiler gain response to protein treatments with age and under varying environmental situations.
Regarding Hypothesis 3
1. Derive a range of least-cost and least-time feeding programs when feeding milo and peanut meal in mash or pellet form.
2. Demonstrate how the optimal protein concentration of a diet can be determined for environment-specific growers in response to changing feed ingredient costs and broiler meat prices.
Research Emphasis
The main emphasis of this research project concerned the testing of the prescribed methodology for evaluating alternate feed ingredients under varying economic and environmental situations rather than the specific results obtained. When an accurate description of the nutrient profile of a feed ingredient is not known due to variability in storage, humidity, processing or in the product itself, then potential errors in feed formulation may result, especially if these feed ingredients are fed under varying environmental situations.




7
Therefore, nutritional considerations in this study were allowed to be consistent with circumstances such as these in order that they reflect a situation common to a developing country.
Clearly, the client for whom this research endeavor was conducted is the broiler producer and/or researcher world-wide, with emphasis on problems associated with developing countries where the factors that impact on broiler production are more varied and difficult to manage.




CHAPTER II
LITERATURE REVIEW
Experimental Design
Recent studies conducted in Nigeria (Olomu and Offiong, 1980a and 1980b; and Onwudike, 1983) address the problem of a shortage of research on the effects of protein and energy on the performance of broiler chickens in the tropics. Also, they recognize the importance of investigating the use of alternate feed ingredients (peanut meal, wheat middlings and fish meal) as well as the cost/benefit implications of using these ingredients for the local poultry producer. These references, as well as many others from developing countries, use the traditional approach of research by evaluating the effect of one variable (protein or energy) while holding all other variables constant. The shortcoming of such an experimental design is in the failure to account for such variability as feed quality, temperature, water quality and management practices all of which researchers acknowledge have a significant influence on broiler production. Also, the relative inefficiencies of these designs in the application of economic analysis needs to be underscored.
Morton and Wyllie (1983) explained that often in the tropics no theory exists as to how the birds should or do respond to treatments
8




9
such as protein and energy levels from alternate feeds. Therefore, researchers should explore those empirical relationships which will give production responses over a wide range treatments. Experimental designs incorporating production functions or response surfaces are the most useful in the exploration of likely agro-economic situations (Dillon, 1966 and 1977; Heady and Dillon, 1961; Heady and Shashanka, 1983).
Response surface methodology (RSM) was described by Roush et al. (1979) as the integration of experimental strategy, mathematical methods and statistical inference. Together these concepts can be used to efficiently make empirical explorations of the relationships among variables that are of interest to the broiler producer. This approach is different from the more traditional procedure of varying one factor and holding the rest constant in that it results in the simultaneous solution of several factors to find the quantitative levels which will give the most desirable (economic) response.
Roush (1982) applied the RSM technique in the investigation of
protein levels for broiler starter and finisher diets and demonstrated the power of this methodo logy in the determination of the optimal biological response and economic returns of a range of potential feeding programs for broilers. Furthermore, by using a three dimensional central composite design (Cochran and Cox, 1957) along with the RSM technique, far more scientific information can be generated and evaluated with an equivalent amount of facilities, animals, money and trained personnel than by using traditional complete factorial designs. This point is particularly relevant to




10
developing countries where such resources are a normal constraint on research programs. Since the objectives of Roush (1982) were to quantitatively examine the combination of starter and finisher protein levels and the time of ration change to produce optimal biological and economic response for broiler males, environment as a variable that affects production was not considered. The broilers were confined to battery brooders on raised wire floors and did not e xperience the environmental pressures (temperature, humidity, management, water quality, etc.) that would be characteristic of a real broiler growout
farm.
Relevance to Farming Systems Research and Extension
The Farming Systems Research and Extension (FSR/E) approach to technology generation and promotion is viewed by many as the state of the art in applied agricultural research and development (Hildebrand, 1986). One of the tools that is often used in an FSR/E program is modified stability analysis (MSA) (Hildebrand, 1984; Hildebrand and Poey, 1985). MSA is a design and analysis technique that incorporates the variability among broiler growers into the experimental methodology. Often in developing countries there is a great deal of variability among broiler growers due to differences in management, practices, housing design, altitude, ambient temperature and other environmentala" factors. MSA is used because of its value in making production oriented recommendations to specific producers that have been characterized and then classified by their common or more




homogeneous environment. Environment can then be treated as a continuous quantifiable variable whose range, as in the case of a broiler study, is the range of cumulative live-weight gains for a number of feed treatments at a particular location. This measure is related to environment by simple linear regression.
The FSR/E approach suggests that a targeted region, for which and perhaps in which research is to be conducted, be identified as a research domain (Hildebrand, 1986). Within this research domain the biophysical or agro-cliznatic attributes will span a range of production environments that will impact on broiler production in different ways. In the U.S. a research domain is likely to be identified as the region around a vertically integrated broiler producer and encompasses all the growers associated with that integrator. Lacy and Benoff (1986) have demonstrated that by examining the production records of 250 broiler growers for a year the 20 top (good) and 20 bottom (poor) performers are differentiated by their ability to control the broiler environment.
These findings imply that the agro-socioeconomic conditions of an individual grower has significant economic implications for both the grower and the integrator. Lacy and Benoff (1986) reported that the differences between broiler production on the good and poor farms, in terms of feed conversion, final body weight, livability, uniformity and condemnations, were all of significant economic importance.
Therefore, the FSR/E approach suggests that the integrators research domain should be partitioned into recommendation domains (Hildebrand, 1986) where the partitioning defines a group of growers




12
as having roughly homogeneous production environments. The purpose of this partitioning of the research domain into one or more recommendation domains is to enable the integrator to develop more appropriate feeding programs for environment-specific growers. The development of environment-specific feeding programs would add to the efficient use of feed resources and the profits of both the grower and integrator.
Environmental Considerations
Temperature and Production
House temperature is a principal factor in the matrix of
environmental variables that affects the economic optimization of broiler production. Charles (1986), in his review of*the literature on temperature for broilers, found a bewildering variety of possible conclusions concerning brooding and finishing temperatures, a ir speed, humidity, nutrition, sex and temperature fluctuations. He reported that sex by temperature interactions could be expected due to differences in growth rate and fat deposition. Methods of production such as stocking rate and housing design will also interact with temperature and make prediction of an optimum temperature for growth difficult. Charles et al. (1981) found that both weight gain and feed intake were depressed by increasing temperature in the range of 15 to 270C (60 to 800F). Also, Harris et al. (1974) noted that commercial broilers are not grown at constant temperatures but are subjected to variations in house temperature that are directly related to diurnal




13
changes in outside air temperature. The general conclusion being that depressions in broiler performance cannot entirely be prevented by dietary formulation, although, as increasing temperatures restrict intake, more concentrated diets are used in an attempt to meet nutrient requirements.
Choice of Feed Ingredients
Grain sorghum (milo). Milo is used extensively as a direct
replacement for maize in poultry feeding in many developing countries. It is an important food crop in arid and semi-arid tropics and is believed to have originated in Africa about 5000 years ago (Olentine, 1986). Kramer and Matz (1969) reported that milo is the third most utilized cereal grain in the world.
An early study on the replacement value of milo for corn (Harms
et al., 1958) found that the substitution of milo for corn in the diet of broilers did not have a significant affect on feed efficiency at the rates utilized in the experiment. However, a decrease in body weight gain was obtained as yellow corn was replaced by milo.
New varieties of milo that are being developed in Africa were examined by Okoh et al. (1982) for amino acid composition and tannin content. Major differences among varieties were observed for lysine and tannin. The primary dietary role of milo, like that of corn, is to supply energy. The proximate analysis of the milo samples investigated by these researchers indicate that milo has a similar nutrient profile to that of corn. However, milo is slightly higher in energy and protein content than corn. Although, the protein content




14
is slightly higher the efficiency of utilization of this protein will depend on the amino acid balance and tannin content of each variety. They concluded that the suitability of most of the varieties for poultry feeds in Africa was likely to be affected by their high tannin content. Antongiovanni et al. (1980) reported that two high protein varieties of milo, although rich in protein, were limited by a severe decrease in the concentration of the limiting amino acid lysine. Broiler feeding trials conducted by Mohamedain et al. (1986) noted that food intake of milo diets was reduced as compared to maize due in part to the presence of tannins which, being orally astringent, reduce palatability and thus intake. Tannins in milo are also known to reduce protein digestibility and to inhibit the activity of various enzyme systems. However, a high tannin level is desirable for wild bird resistance in the fields.
When one considers the growing importance of milo in terms of
available quantities in areas of the world where other cereal grains are relatively scarce, weighed against the inherent variability in nutrient profile, tannin content and lysine limitations, a need is indicated for research that explores the feeding potential of this cereal in light of its availability and economic advantages over corn.
Peanut meal (PM). In general, attempts to utilize peanut meal as a source of vegetable protein for growing chickens have met with favorable results when the rations were fortified with methionine and lysine (Douglas and Harms, 1959). Driggers and Tarver (1958) reported that up to half the soybean meal can be replaced by peanut meal depending upon the level of fish meal and supplemental lysine used.




15
Without the fishmeal the diets required methionine supplementation for a growth response comparable to that of a corn-soybean meal diet.
Peanut meal is the major protein supplement in poultry diets in India and many other subtropical countries. Singh and Prasad (1979) found that the replacement of peanut meal in the diets of growing chickens by sunflower meal improved growth rate and feed efficiency. Diets based on sunflower meal needed only four percent fish meal supplementation as compared to eight percent needed with peanut meal for equal weights in broilers to 70 days of age. The amino acid profile of the peanut meal was complemented by the sunflower meals higher sulfur amino acid and lysine content in much the same way as the previously reported supplementation of fish meal or synthetic lysine and methionine.
It is interesting to note that Douglas and Harms (1959) reported a significant sex by peanut meal treatment interaction and they concluded that the slower growing females were more tolerant than males to low protein levels when using peanut meal in the diets.
Feed Form. To attain the greatest level of efficiency in the allocation of feed resources the possible economic advantages of pelleting feed were examined in the simulation study. A summary of 14 research reports (Jones, 1979) comparing the value of pellets relative to mash, showed that in all cases either growth rate and/or feed conversion was improved by pelleting. The reason or reasons for the improvement in production is not fully understood. Dymsza et al. (1955), using rations containing 5, 10 and 15 percent fiber, showed that pelleting and crumbling concentrated the nutrients relative to




16
mash and counteracted some of the adverse effects of low energy diets when fed to turkey poults. Broiler studies conducted by Newcombe and Summers (1985) reported that when cellulose was added to the basal diet, birds fed on crumbles performed better than those fed mash. These researchers attributed this difference to poor palatability associated with the flour-like texture of the mash diets. Additional research on the possible advantages of pelleting when feeding high fiber diets with low palatability in the tropics was conducted by Abdelsomie et al. (1983) where pelleting was shown to significantly improve both growth rate and feed efficiency but not carcass yield. Huile-Shen et al. (1985) concluded that steam pelleting enhanced the utilization of lower energy diets and could be an important economic consideration in parts of the world where dietary energy is in short supply.




CHAPTER III
MATERIALS AND METHODS
Experimental Methods and Procedures
Live-weight gain, feed intake and house temperature data from a ten-week broiler response study were collected during the summer months (Aug. 1 Oct. 10, 1986) at the University of Florida Poultry Science research station. Individual birds of a commercial strain of feather sexed broiler chicks (Cobb Cobb) were assigned at random to 64 pens subject to including 16 chicks in each pen and maintaining a 1:1 sex ratio. Protein treatments were specified in terms of four isocaloric diets containing 16, 19, 22, and 25% crude protein. The four protein treatments were fed in both mash and pellet form in a four by two factorial arrangement for a total of eight feed treatments and replicated eight times. Chicks were placed on experimental diets at one day of age and fed ad libitum those same respective diets to ten weeks of age.
Observations were recorded on the basis of a fixed time approach where records were maintained on the cumulative live-weight and feed consumed per pen on a weekly basis through ten weeks after the beginning of the experiment. These records included 640 observations on each of the production criteria. Data on cumulative live-weight
17




18
gain (G) in kilograms per pen, as well as cumulative milo and peanut meal (PM) consumption in kilograms per pen were converted to kilograms per square meter of pen space so that different size farms could use the data in the evaluation of the economic implication. The experimental unit was a pen (2.3225 square meters) with each weighing of feed or birds an observation.
Feed Form, Formulation and Treatments
The Brill feed formulation program (Brill, Inc., 1985) was used to balance four protein treatment diets given the alternate feed ingredients milo, peanut meal and wheat middlings. Composition of the diets is detailed in Table 3-1. Wheat middlings, salt, limestone, dicalcium phosphate, vitamin-mineral premix, methionine, lysine and coccidiostat were all held at constant levels for all treatments. Composition of the vitamin-mineral premix is shown in Table 3-2. Vegetable fat (corn oil) was varied between 0.5 and 5.5%, to formulate the four isocaloric diets (2860 kcal ME/kg) for the 16, 19, 22 and 25% crude protein treatments. Therefore, the principal changes in dietary composition were due to changes in milo and peanut meal concentrations enabling the evaluation of the efficiency of these ingredients in producing live-weight gain in broilers.
Ingredient composition files in the Brill formulation system were updated to values taken from standard industrial feed composition tables (Table 3-3).




19
Table 3-1. Composition of the diets
Ingredient1 16% 19% 22% 25%
Milo 69.98 60.71 51.44 42.17
Peanut meal (solvent) 14.00 21.73 29.45 37.18
Wheat middlings 10.00 10.00 10.00 10.00
Vegetable fatz 0.77 2.32 3.86 5.41
Dicalcium phos.3 2.00 2.00 2.00 2.00
Limestone 1.40 1.40 1.40 1.40
Salt 0.30 0.30 0.30 0.30
Vit.-min.4 0.50 0.50 0.50 0.50
Meth.,dry D-L 0.40 0.40 0.40 0.40
Lysine-L 0.55 0.55 0.55 0.55
Coban 0.10 0.10 0.10 0.10
Calculated values
Protein (%) 16 19 22 25
ME (kcal/kg) 2860 2860 2860 2860
1 Ingredient levels expressed as g/lOOg of diet.
2 Corn oil (8,800 Kcal/kg).
3 Contains 22% Ca and 18.5% P.
4 Standard University of Florida chick microingredient premix.




20
Table 3-2. Composition of the vitamin-mineral premix
Vit.-min. Broiler Improved1 Units2
---- --------- --
Vitamin A 6,600 6,600 IU
Vitamin D3 2,200 2,200 ICU
Vitamin E 0.0 11.0 IU
Vitamin K3 2.2 2.2 mg
Riboflavin 4.4 4.4 mg
Niacin 39.6 59.6 mg
Pantothenic acid 13.2 13.2 mg
Choline chloride 499.0 998.8 mg
Vitamin B12 22.0 22.0 mcg
Biotin 0.0 0.11 mg
Folic acid 0.0 1.0 mg
Pyridoxine(82.2%) 0.0 3.0 mg
Ethoxyquin 125.0 125.0 mg
Manganese 60.0 60.0 mg
Iron 50.0 50.0 mg
Copper 6.0 6.0 mg
Cobalt 0.1980 0.1980 mg
Iodine 1.1 1.1 mg
Zinc 35.0 35.0 mg
1 Added vitamin E, biotin, folic acid and pyridoxine.
2 Vitamin and mineral concentrations are expressed as their
activity per kilogram of finished feed.
3 Menadione Dimethylpyrimidinol Bisulfate (MPB).




21
Table 3-3. Nutrient values used to calculate the diets
NutrientI Milo Peanut meal Wheat middlings
Protein 9.30 45.00 16.00
Fat 2.80 1.20 4.00
Calcium 0.02 0.15 0.10
Total phos. 0.30 0.63 0.85
Arginine 0.29 4.80 1.10
Lysine 0.19 1.60 0.75
Methionine 0.13 0.45 0.26
Meth. + cyst. 0.27 1.15 0.63
Tryptophan 0.09 0.46 0.23
Threonine 0.30 1.44 0.59
Crude fiber 2.50 12.00 7.50
ME(kcal/kg) 3,300 2,750 1,606
1 Nutrient values expressed as g/lOOg of ingredient.
Amino Acid Profile
The amino acid profile of each protein treatment was examined for deficiencies and presented in Table 3-4. Threonine was found to be deficient in all treatments when expressed as a percent of total weight and as a percent of protein for a particular treatment. This amino acid is the first limiting amino acid after lysine and methionine supplementation of the peanut meal and milo diets used in this study.




22
Table 3-4. Calculated amino acid profile of the diets 16% 19% 22% 25%
NRC % of
Amino acid Requ. Prot. al b2 a b a b a b
Arginine 1.44 5.0 1.08 6.75 1.48 7.79 1.88 8.55 2.28 9.12 Lysine 1.05 4.0 0.88 5.50 1.00 5.26 1.12 5.09 1.24 4.96
Methionine 0.50 2.0 0.57 3.56 0.60 3.16 0.62 2.82 0.64 2.56
Meth.+cyst. 0.85 3.6 0.82 5.13 0.88 4.63 0.95 4.32 1.02 4.08
Tryptophan 0.23 1.0 0.18 1.13 0.22 1.16 0.26 1.18 0.30 1.20 Threonine 0.80 3.5 0.47 2.94 0.55 2.89 0.64 2.91 0.72 2.88
1 Calculated level of amino acids expressed as g/100g of diet.
2 Calculated level of amino acids expressed as a g/lOOg of protein
in a particular treatment.
Simulation of a Farm Environment
A broiler house containing 64 pens (2.3225 sqm/pen) simulated a broiler integrator's geographical production area and was considered a research domain. Within this research domain a completely randomized block of four protein treatments (16, 19, 22 and 25% crude protein) each in mash and pellet form were used (eight feed treatments) as shown in Figure 3-1. These eight pens simulated a growers farm on which research was conducted.
To simulate the effects of different environments the lower wall flaps and the roof vents associated with the eight pens (one environment) in each of the four corners of the building were closed so that these four environments would experience higher ambient




23
temperatures (poor environments) as compared to the four environments with opened wall flaps and roof vents (good environments) (Fig. 3-1).
The resulting differential in live-weight gain between the poor (hot) and good (cool) environments enabled a partitioning of the research domain into environment-specific recommendation domains.
Environmental Factors
Management
Broiler management was held constant over all pens so that the effects of ambient temperature on broiler gain could be estimated. Standard broiler management practices concerning the feeder, water, litter, brooder lamps and house lighting practices were performed as recommended in a standard broiler service manual (Cobb, Inc., 1984).
Vaccination Program
Day-old chicks were vaccinated subcutaneously against Marek's disease. A commercial Newcastle-Bronchitis live virus vaccine (B-type, B-strain) was administered at 14 days of age as a coarse spray. At five weeks of age Newcastle-Bronchitis live virus vaccine was administered in the drinking water.
Medication Program
Starting at seven days of age, when broilers on the low protein treatments experienced signs of nutritional deficiencies (perosis) a commercial vitamin stress pack with electrolytes was added to the




24
BROILER HOUSE
20 9 7
19 21 _''-_"'" 58
18 22 ...59
17 -23 77r,'-'. ,, '- ,-- 554 59
.... :,-,.-., -,, ....60
16 24 53 ,.,;,- 6 1-:'....-"... .
15 25 52 62
14 26 51 63
13 27 50 ( 1
12 28 49 65
11 29 48 66
10 22%M 30 25%M 47 C7
9 16%P 31 19%M 46' 68
8 16%M 32 25%P 45 69
7 19%P 33 22%P 44 70
6 34 43 71
5 35 ,- 42 72
I ? ,: ,. \- >,- .,,.
3~%M" 37 ~ -'
238 -9 "- 5
,-,_ .. ,., ,, ,,,..- -..,- .;
GOOD ENVIRONMENT W
% PROTEIN, FEED FORM
F,7.7!777 POOR ENVIRONMENT
, % PROTEIN, FEED FORM
E
Figure 3-1. Broiler house pen arrangement showing good and poor
environments, completely randomized blocks of eight feed
treatments representing a growers farm and examples of the location of feed treatments. M and P indicate mash
and pellet form, respectively.




25
water. This medication was administered to all pens in the broiler house and was continued until 21 days of age at which time an improved vitamin-mineral premix was used in the feed (Table 3-2).
At 18 days of age a generalized respiratory infection became apparent throughout all pens. It was believed to be a typical reaction to the vaccination program administered four days earlier, but, as a precaution a commercial brand of the antibiotic erythromycin was administered in the drinking water for five days.
Temperature
Two maximum-minimum thermometers were placed at bird level in each completely randomized block of pens. Temperature data was recorded daily and the range of temperatures (maximum minus minimum) was computed and then averaged for the week. The average weekly temperature range (AWTR) of ambient temperature for each replication (grower's environment) is reported in Table 3-5.
The simulated "good" production environments (cooler
temperatures) ranged from a 12.21 to 18.46 AWTR for the ten-week period. The "poor" environments (hot temperatures) ranged from a 9.36 to 14.61 AWTR. The overall AWTR for all replications and for the entire study period was 14.12. A higher AWTR value indicated that those pens (good environment) cooled down to a lower temperature than those in the poor environments since the average maximum temperatures were similar. The highest AWTR values recorded were in the good environments on the south side of the broiler house.




26
Table 3-5. Average weekly temperature range (AWTR) in the house by
replication
Rep. Wk-1 Wk-2 Wk-3 Wk-4 Wk-5 Wk-6 Wk-7 Wk-8 Wk-9 Wk-10 avg.
Ri 13.68 13.14 9.36 13.61 9.50 13.29 13.50 15.54 14.93 15.71 13.23
R2 15.89 16.64 12.68 15.93 12.21 14.86 15.36 17.93 17.57 18.46 15.75
R3 16.15 16.32 11.57 15.57 11.71 14.54 15.01 17.43 17.04 18.18 15.35
R4 14.29 13.93 10.21 13.32 9.86 11.93 12.93 14.61 14.50 16.11 13.17
R5 13.75 14.14 10.50 13.32 10.32 12.29 12.18 14.57 13.75 16.11 13.09
R6 15.95 15.93 12.07 15.25 11.57 13.79 13.71 16.25 14.61 16.50 14.56
R8 13.43 12.89 9.68 11.89 11.04 12.96 12.64 15.07 13.32 14.75 12.77
avg.14.79 14.77 11.02 13.91 10.86 13.40 14.15 16.27 15.45 16.61 14.12
Therefore, temperature values used in the modeling exercise were 14.0 AWTR representing an average environment, a 10.0 AWTR for a poor (hot) environment and an 18.0 AWTR for a good (cool) environment.
Mortality
Mortality was recorded daily and feed intake data were adjusted when deaths occurred. Birds that would be considered culls in a typical broiler operation were culled from the experiment on those days when body-weights and feed intake were taken. This was often due to cases of severe perosis. The total number of culls and mortality by feed treatment and sex are shown in Table 3-6.




27
Table 3-6. Mortality and culls by environment (e), sex and
feed form
Feed treatment
16% 19% 22% 25%
(e) Sex M1 p2 M p M p M p
---------Mort. poor males 0 1 1 1 0 0 2 2
good males 0 2 0 2 3 1 0 3
poor females 2 1 1 1 1 0 0 1
good females 0 1 1 1 1 0 0 1
Culls poor males 5 7 5 5 2 5 0 1
good males 1 13 7 5 2 2 0 2
poor females 2 6 2 2 1 0 0 0
good females 4 1 2 3 2 0 0 1
Total males 6 23 13 13 7 8 2 8
Total females 8 9 6 7 3 1 0 2
---- ---- -------- -Total treatment 46 39 19 12
1 Mash.
2 Pellet.




CHAPTER IV
PARTITIONING BROILER GROWERS
INTO HOMOGENEOUS PRODUCTION ENVIRONMENTS Introduction
Broiler producers throughout the world realize that farms and farm managers are not created equal and yet averaged data from research stations are commonly used as the basis for making feeding recommendations to individual broiler growers. If the decision makers of a vertically integrated broiler production system have the objective of improving the efficiency or quality of feeding recommendations to individual contract growers, then a method for partitioning the growers into groups with homogeneous production environments is required. The purpose of this chapter is to demonstrate the use of modified stability analysis combined with a frequency distribution of confidence intervals (Hildebrand, 1984; Hildebrand and Poey, 1985) as a tool for meeting that objective.
Variability among growers is due to differences in the overall environment each grower provides for the broiler. Environment encompasses those agro-biological and socio-economic factors that impact on broiler production. For example, under the socio-economic factors to be considered, the differences among growers may range from management ability, education, experience, and age to the type of 28




29
equipment used on the farm. Likewise, under the agro-biological factors, the differences may range from feed form (mash or pellet), bird density, and breed to house temperature.
It is important to note that the following methodology is as
relevant to large broiler integrators in the U.S. as it is to smaller integrators in developing countries or even regional consultants for independent growers in those countries where the industry is not vertically integrated.
Materials and Methods
Modified Stability Analysis and Broiler Production
Modified stability analysis is used to examine the need to
partition the research domain into environment-specific recommendation
-domains (Hildebrand, 1986). It is an analysis technique that, as used here, enables accounting for variability among broiler growers. By averaging the results of all experimental feed treatments on a grower's farm into an environmental index (e), a means for measuring and quantifying the effect of all the factors that influence the gain response of broilers to a feed treatment can be achieved. This quantified variability found on different growers' farms then becomes a practical means for partitioning the research domain.
To conceptualize the application of modified stability analysis to broiler production, imagine an integrated broiler producer with growers dispersed over an area with a radius of 50 miles. Consider this area a research domain, where the integrator desires to




30
investigate the efficiency of the feeding recommendations currently practiced by all growers within this study area. All growers are feeding four protein treatment feeds in two forms (mash or pellet). No other changes are made from the growers' usual practices. The only constants on the farm are those factors that are controllable by the integrator, feed treatments, strain of bird and medication programs. The broilers on each grower's farm will be exposed naturally to different environmental conditions, such as water quality, house temperature, equipment type, housing design and management in general. If the average production of broiler meat (gain) from the four protein treatments is high for whatever reason then this grower's farm is classified as a "good" environment for broilers. A grower's farm for which production of broiler meat is low for whatever reason is classified as a "Poor' environment for broilers.
Broiler live-weight (gain) for each of the four protein
treatments can be related to environment by simple linear regression.
(Gi) a + b (ei) (1)
where (G) gain from feed treatment i, and
(e) environmental index for farm j, equal to the average gain of all feed treatments at each grower's farm.
The environmental index (e) becomes a continuous variable whose range is the range of cumulative average live-weight gains from the experiment.




31
By fitting the linear equation independently for each treatment
level, then plotting the broiler gain response to environment for each treatment on the same graph, it is possible to visually compare treatments across a range of environments (e).
Frequency Distribution of Confidence Intervals and Broiler Production
A graphic distribution of confidence intervals can be used to evaluate the variability in the results of each protein treatment within each partition of the research domain. These partitions are tentatively considered recommendation domains until the analysis is complete.
The confidence intervals are calculated for a 50, 60, 80, 90, 95, and 99% level of confidence from the equation:
(G) + ta S / n (2)
Where (G) = mean treatment gain for the partitioned group of farmers, and
t = table value of t,
a = the level of confidence,
S1 n = standard error of the mean.
Simulation of an On-Farm Broiler Response Study
Live-weight gain, feed intake and house temperature data from a ten week broiler response study were collected during the summer months (Aug. 1 Oct. 10, 1986) at the University of Florida Poultry




32
Science research station. Individual birds of a commercial strain of feather sexed broiler chicks (Cobb Cobb) were assigned at random to 64 pens subject to including 16 chicks in each pen and maintaining a 1:1 sex ratio. Protein treatments were specified in terms of four isocaloric diets containing 16, 19, 22, and 25% crude protein. The four protein treatments were fed in both mash and pellet form in a four by two factorial arrangement for a total of eight feed treatments and replicated eight times-. A broiler grower's environment was then represented by a randomized block of eight pens (four protein levels by two forms of feed).
To simulate the effect of different environments the lower wall flaps and the roof vents associated with the eight pens (one environment) in each of the four corners of the building were closed so that these four environments would experience a higher house temperature as compared to the four environments with opened wall flaps and roof vents (Fig. 3-1, pg. 24). The entire building simulated a research domain, with four growers anticipated to be in a 11good" environment and four growers in a "poor" environment. This simulation could be representative of farms such as those having different housing designs, different elevations or proximity to large bodies of water.
Of all the variables considered in the environmental matrix, house temperature is of major importance due to its effect on feed intake and thus live-weight gain.




33
Results and Discussion
The simulation study was designed and conducted to test a number of hypotheses one of which was the hypothesis that broiler gain response to different feed treatments and feed forms would vary depending upon environmental conditions. The treatment averages for all growers (complete data set) from the broiler response study are presented in Tables 4-1 and 4-2 for weeks 4, 6, and 8.
Examination of the graphed broiler response surface to averaged feed treatments from the combined data set (Fig. 4-1) gives no indication of a need to partition the complete data set (research domain) into more homogeneous production environments.
Partitioning the Growers by Production Environment
Application of modified stability analysis begins by averaging all treatments tested on each grower's farm. This value is expressed as the environmental index (e), as shown in Tables 4-1 and 4-2. The broiler gain data for each treatment and for the 8 growers were fit to equation (1) by simple linear regression and presented in Table 4-3. By plotting individual broiler gain data points from the broiler response study with its corresponding environmental index value (e), along with the simple linear regression functions for each protein level, a means for observing treatment by environmental interactions can be made (Fig. 4-2 to 4-7).




34
Table 4-1. Broiler gain response to protein treatments for pellet
form at weeks 4, 6, and 8 in kg/pen (complete data set)
---- -- --- ----- ---- -- ----- --------Protein (%)
Broiler -- --- Environmental
grower index (e)
No. 25% 22% 19% 16% avg.
------------ ------- --------Week 4 1 15.400 12.227 9.540 5.919 10.772
2 14.841 13.178 10.753 6.546 11.330
3 14.917 13.263 10.014 6.039 11.058
4 13.614 12.988 10.118 9.169 11.472
5 13.735 13.596 9.252 6.550 10.783
6 15.843 13.595 11.693 7.332 12.116
7 14.629 15.144 11.028 8.521 12.331
8 15.301 14.230 9.732 5.386 11.162
-----------avg, 14.785 13.528 10.266 6.933 1-1.378
Week 6 1 23.846 21.811 17.291 11.395 18.586
2 24.831 24.420 19.446 11.679 20.094
3 26.254 23.438 19.206 10.247 19.786
4 24.521 22.592 17.047 15.554 19.929
5 22.745 21.108 16.355 12.888 18.274
6 27.340 24.641 20.810 14.961 21.938
7 23.596 26.094 18.529 15.905 21.031
8 26.175 25.405 17.887 10.320 19.947
-------- ---------- ---------avg. 24.194 23.689 18.321 12.869 19.948
-- ------ ------------- ---------- ------ -- -----Week 8 1 33.296 27.886 24.996 15.495 25.418
2 33.429 34.505 27.136 16.789 27.965
3 36.904 33.828 27.738 15.337 28.452
4 34.196 31.287 23.832 20.929 27.561
5 28.120 31.978 20.130 19.038 24.817
6 39.841 34.325 29.216 22.086 31.367
7 32.921 38.769 25.654 23.180 30.131
8 35.745 36.490 24.912 15.845 28.248
avg, 34.307 33.634 25.452 18.587 27.995
---- ---------- ----- --- ---------- -




35
Table 4-2. Broiler gain response to protein treatments for mash
form at weeks 4, 6, and 8 in kg/pen (complete data set)
---- -------- ---- -- ---------- --- ----Protein
Broiler Environmental
grower index (e)
No. 25% 22% 19% 16% avg.
----------------------------Week 4 1 13.273 12.416 7.635 7.722 10.262
2 14.461 13.159 10.105 7.303 11.257
3 15.136 13.219 9.623 8.255 11.558
4 13.875 13.644 9.809 7.106 11.109
5 13.231 12.680 11.416 8.275 11.401
6 13.251 13.091 11.235 8.413 11.498
7 15.271 13.156 11.125 8.776 12.082
8 13.621 12.528 10.870 6.503 10.881
------ --- ------ -------avg, 14.015 12.987 10.227 7.794 11.256
---------
Week 6 1 24.012 23.545 13.902 14.902 18.895
2 25.212 24.551 17.829 13.904 20.374
3 26.231 23.568 18.643 15.519 20.990
4 23.838 23.629 18-.478 13.095 19.760
5 23.504 22.095 20.197 16.086 20.471
6 24.755 23.854 20.806 16.121 21.384
7 26.704 23.991 19.529 16.809 21.758
8 25.753 22.585 20.289 12.827 20.364
- ---- -- ----- --------------------avg, 25.001 23.477 18.709 14.810 20.499
----------
Week 8 1 33.137 34.370 20.687 19.811 27.001
2 35.437 35.526 24.604 20.219 28.947
3 35.021 33.432 27.193 22.494 29.535
4 34.166 34.219 25.978 19.665 28.507
5 33.404 31.545 25.536 23.169 28.414
6 34.677 33.304 30.611 22.931 30.381
7 37.504 34.087 29.069 23.800 31.115
8 35.793 32.235 30.185 17o387 28.900
avg. 34.892 33.590 26.733 21.185 29.100
---- ---- -- -




36
40
............. mash
pellet 35-......... Wk-8
35*
.**.
30
z
w
.*
-25 *' Wk-6
9 ,,**q
0
S20
LU.*
15 ,. ------ wk-4
....**
10
16 19 22 25
CRUDE PROTEIN, (%)
Figure 4-1. Broiler gain response to protein treatments for mash and
pelleted feed form at weeks 4, 6, and 8 (complete data
set).




37
Table 4-3. Linear equations for modified stability analysis
calculated from broiler gain completet data set)
-------- -- -- -----Protein
Treatment a b R
--------------------------------------- --------Pellet Form
-------------------------------------------------Week 4 16% -10.130 1.500 .66
19% 4.267 1.277 .89
22% 2.620 .959 .64
25% 11.777 .264 .20
Week 6 16% -6.653 .979 .45
19% -1.789 1.008 .82
22% .282 1.173 .79
25% 8.160 .840 .64
Week 8 16% -2.535 .755 .52
19% -2.236 .989 .60
22% 3.916 1.062 .70
25% .856 1.195 .76
Mash Form
Week 4 16% -2.463 .911 .63
19% -8.708 1.682 .72
22% 8.304 .416 .54
25% 2.867 .990 .40
Week 6 16% -9.177 1.170 .70
19% -18.955 1.837 .76
22% 20.525 .144 .17
25% 7.607 .849 .66
------------ ---- ----
Week 8 16% -8.500 1.020 .58
19% -34.347 2.099 .81
22% 33.761 -.006 .01
25% 9.086 .887 .80
--------------




16 -38 25% CP
z 1 ?"'l ----*-il,,*- 22% CP
14
16 12CP
S........... .
......... ...................
<: ..................... 1 % C
10.5 11 11'.5 12
ENVIRONMENTAL INDEX (e), KG
Figure 4-2. Broiler gain response to protein treatments for mash
form at 4 weeks of age as influenced by environment.
15 2 5 %------- -- *.... 22% CP
-" 19% CP
10..... .""" -- .
z 16% CP
I ---------------------------------.............. .
19.1.5 12
ENVIRONMENTAL INDEX (e), KG
Figure 4-3. Broiler gain response to protein treatments for pellet
form at 4 weeks of age as influenced by environment.




39
I25% CP
25
z .-I..... 2 % C
---- ----22% OP
19% CP
20I
2 ... .. ......
V 5 .. ........ .. .... ... .. 1 c
.oLOI.oI .-.j6-C
19 20 21 22
ENVIRONMENTAL INDEX (e). KG
Figure 4-4. Broiler gain response to protein treatments for mash
form at 6 weeks of age as influenced by environment.
25% CP
22% CP
z 2 1 5
20
ZI
< 15 16% CP
.................... ......... ..............
. .. ...1.. ..
... .. ... .. --F "' .o
10.I
18 19 20 21 22
ENVIRONMENTAL INDEX (e). KG
Figure 4-5. Broiler gain response to protein treatments for pellet
form at 6 weeks of age as influenced by environment.




40
25% CP
35
-- -- i- =Ir- ---2 % C
UJ 19% CP
0 300 *
25
25 ....... 16% CP
< .,..,.... .......................
....-o- '20 ........... *
27 28 29 30 31
ENVIRONMENTAL INDEX (e). KG
Figure 4-6. Broiler gain response to protein treatments for mash
form at 8 weeks of age as influenced by environment.
40 25% CP
o '-"22% CP
Z w
0
Z 30 "
30 J.......1 9% C P
,.,.--16 % C P 25... .'-................... .......
2C '- .............
20
15
26 29 30 32
ENVIRONMENTAL INDEX (e), KG
Figure 4-7. Broiler gain response to protein treatments for pellet
form at 8 weeks of age as influenced by environment.




41
The advantages of partitioning the growers in the research domain into homogeneous production environments (recommendation domains) is clearly to develop more appropriate feeding recommendations for environment-specific growers. Where, the greater the difference between a good and poor broiler grower's environment the greater the need to partition the environments. The development of environmentspecific feeding programs will contribute to the optimization of the use of feed resources in the production of broiler live-weight gain.
For example, tentative partitioning of the research domain can be made for week 4, pellet form (Fig. 4-3), by declaring those growers above the environmental index e = 11.5 as "good" growers and those lower than e = 11.5 as f1poor" growers. If the spread of (e) is wide the broiler integrator may partition the research domain into 3 recommendation domains. For growers above e = 11.5, below e = 11 and .between e = 11 and e = 11.5
If the integrator averaged data from all of his growers by not
partitioning the research domain, those growers within the e = 11 to 11.5 range would benefit from the recommended feeding practices. Those growers above 11.5 would be feeding a protein level higher than they would require and those growers below e = 11 would not be feeding adequate protein levels to meet the maximum growth requirement.
In this case, the complete data set was partitioned into 11good" and "poor" broiler production environments as differentiated by the open or closed wall flaps and roof vents, as shown in Tables 4-4 and 4-5. This partitioning was based on the assigned environmental groups as designed in the simulation study.




42
Table 4-4. Broiler gain response to protein treatments for pellet
form at weeks 4, 6, and 8 in kg/pen (partitioned data set)
Protein
Broiler --- Environmental
grower index (e)
No. 25% 22% 19% 16% avg.
---- ---- ------------------------Week 4
poor 1 15.400 12.227 9.540 5.919 10.772
4 13.614 12.988 10.118 9.169 11.472
5 13.735 13.596 9.252 6.550 10.783
8 15.301 14.230 9.732 5.386 11.162
poor avg. 14.513 13.260 9.661 6.756 11.047
good 2 14.841 13.178 10.753 6.546 11.330
3 14.917 13.263 10.014 6.039 11.058
6 15.843 13.595 11.693 7.332 12.116
7 14.629 15.144 11.028 8.521 12.331
good avg. 15.058 13.795 10.872 7.110 11.709
---- ----- --- ----Week 6
---------poor 1 23.846 21.811 17.291 11.395 18.586
4 24.521 22.592 17.047 15.554 19.929
5 22.745 21.108 16.355 12.888 18.274
8 26.175 25.405 17.887 10.320 19.947
poor avg. 24.322 22.729 17.145 12.539 19.184
---------- ---------- ------ -- ------ -----good 2 24.831 24.420 19.446 11.679 20.094
3 26.254 23.438 19.206 10.247 19.786
6 27.340 24.641 20.810 14.961 21.938
7 23.596 26.094 18.529 15.905 21.031
good avg. 25.505 24.648 19.498 13.198 20.712
Week 8
---------- -------poor 1 33.296 27.886 24.996 15.495 25.418
4 34.196 31.287 23.832 20.929 27.561
5 28.120 31.978 20.130 19.038 24.817
8 35.745 36.490 24.912 15.845 28.248
poor avg. 32.839 31.910 23.468 17.827 26.511
good 2 33.429 34.505 27.136 16.789 27.965
3 36.904 33.828 27.738 15.337 28.452
6 39.841 34.325 29.216 22.086 31.367
7 32.921 38.769 25.654 23.180 30.131
good avg. 35.774 35.357 27.436 19.348 29.479
- ---- -- --------




43
Table 4-5. Broiler gain response to protein treatments for mash form
at weeks 4, 6, and 8 in kg/pen (partitioned data set)
Protein
Broiler --- Environmental
grower index (e)
No. 25% 22% 19% 16% avg.
- ------- ---- ---Week 4
poor 1 13.273 12.416 7.635 7.722 10.262
4 13.875 13.644 9.809 7.106 11.109
5 13.231 12.680 11.416 8.275 11.401
8 13.621 12.528 10.870 6.503 10.881
poor avg* 13.500 12.817 9.933 7.402 10.913
good 2 14.461 13.159 10.105 7.303 11.257
3 15.136 13.219 9.623 8.255 11.558
6 13.251 13.091 11.235 8.413 11.498
7 15.271 13.156 11.125 8.776 12.082
good avg. 14.530 13.156 10.522 8.187 11.599
--- -- -- ---- -- --------------Week 6
poor 1 24.012 23.545 13.902 14.120 18.895
4 23.838 23.629 18.478 13.095 19.760
5 23.504 22.095 20.197 16.086 20.471
8 25.753 22.585 20.289 12.827 20.364
poor avg* 24.277 22.964 18.217 14.032 19.873
good 2 25.212 24.551 17.829 13.904 20.374
3 26.231 23.568 18.643 15.519 20.990
6 24.755 23.854 20.806 16.121 21.384
7 26.704 23.991 19.529 16.809 21.758
good avg. 25.726 23.991 19.202 15.588 21.127
---- ---- ------------- ------- ---Week 8
---- ---- -------- ------ -poor 1 33.137 34.370 20.687 19.811 27.001
4 34.166 34.219 25.978 19.665 28.507
5 33.404 31.545 25.536 23.169 28.414
8 35.793 32.235 30.185 17.387 28.900
poor avg. 34.125 33.092 25.597 20.008 28.206
good 2 35.437 35.526 24.604 20.219 28.947
3 35.021 33.432 27.193 22.494 29.535
6 34.677 33.304 30.611 22.931 30.381
7 37.504 34.087 29.069 23.800 31.115
good avg. 35.660 34.087 27.869 22.361 29.995
- ------------ -- --------




44
Stability of Treatments
An indication of the relative stability of protein treatments
across a range of environments can be seen in Figures 4-2 to 4-7. For example, in Figure 4-3 the regression line describing the 25% protein treatment as a function of the environmental index (e) is almost horizontal (slope = .264). This indicates that at four weeks of age the 25% protein treatment, pellet form, produced a stable gain response across all environments. Whereas, the regression line describing the 16% protein treatment produced a relatively unstable response (slope 1.5) across all environments. The level of gain from the 16% treatment in the good environment (e > 11.5) was nearly 7.11 kg per pen and the gain from the poor environment (e < 11.5) was only 6.76 kg per pen, which.suggests a relatively strong 16% protein treatment by environment interaction.
The relative stability of a protein treatment in varying
environments has practical implications in the making of feeding recommendations. In this study the implications are two fold; first, high protein (22 and 25%) treatments can help "overcome" the effects of poor environments (temperature) and secondly, caution should be advised when feeding low protein levels (16 and 19%) in poor environments.
Broiler Gain Response to Varying Environments
Differences in the predicted broiler gain response for good and poor broiler production environments can be visualized by graphing the average gain response to protein treatments for the good and poor




45
environments (Fig. 4-8 and 4-9). The impact of environment (e) on gain becomes evident in this visual representation and further examination indicates that a) the differences between good and poor environments were greater for pellet than for mash form, b) with age and in poor environments the gain response curve becomes more cubic in form and less qu adratic and c) with age the differences between poor and good environment becomes greater.
The distribution of confidence intervals for gain were calculated for the complete data set (all growers), each feed form, ages 4, 6, and 8 weeks, and presented in Tables 4-6 and 4-7. Graphing the distribution of confidence intervals for the four treatments by mash and then pellet form, taking care to set the axis to equal intervals, enables an overlay of the graphs which gives a preliminary indication of a greater gain response to pellet form at 22 and 25% protein and mash being greater than pellet at 16% protein (Fig. 4-10a to 4-15a).
Overlaying graphs for similar feed forms at different weeks
illustrates the general trends in broiler response to protein levels and feed form. But, as the graphed gain response to the protein treatments from the complete data set (Fig. 4-1) failed to discern variability due to environment, neither do the graphs of the corresponding confidence intervals (Fig. 4-10a to 4-15a).
In contrast, the variability due to environment can be discerned with the following approach. Treatment averages for the good and poor growers representing the partitioned research domain (Tables 4-4 and 4-5) are graphed (Fig. 4-5 and 4-6) and a visual inspection of the magnitude and form of the curves from these partitioned environments




46
40
GOOD ENVIRONMENT
,S; ',- -s -- Wk-8
30 -: POOR ENVIRONMENT
LI)25- Wk-6
E6
20
15- Wk-4
10
16 19 22 25
CRUDE PROTEIN, (%)
Figure 4-8. Broiler gain response to protein treatments for pellet
form at 4, 6, and 8 weeks based on good and poor
environments.




47
40
GOOD ENVIRONMENT
35- Wk-8
,30- : POOR ENVIRONMENT.
Z0
2s5.-0 .:: .0""0 Wk-6
oooo~o: 0 0
00000 0
20 0 0
Wk-4
1.:
1 0Go 0 0
16 19 22 25
CRUDE PROTEIN, (%)
Figure 4-9. Broiler gain response to protein treatments for mash
form at 4, 6, and 8 weeks based on good and poor
environments.




48
Table 4-6. Distribution of confidence intervals of broiler gain to
protein treatments for pellet form at weeks 4, 6, and 8
in kg/pen (complete data set)
Probability of a larger value (a)
0.50 0.40 0.20 0.10 0.05 0.01
-- ---------------------- -- -- ------ -------Protein .711 .896 1.415 1.895 2.365 3.499
---------Week 4
16% 7.265 7.351 7.594 7.818 8.037 8.567
6.601 6.515 6.272 6.048 5.829 5.299
19% 10.474 10.528 10.679 10.819 10.957 11.288
10.058 10.004 9.853 9.713 9.575 9.244
22% 13.746 13.803 13.962 14.110 14.254 14.602
13.310 13.253 13.094 12.946 12.802 12.454
25% 14.982 15.033 15.177 15.310 15.440 15.754
14.588 14.537 14.393 14.260 14.130 13.816
Week 6
16% 15.190 15.289 15.567 15.824 16.075 16.682
14.430 14.331 14.053 13.796 13.545 12.938
19% 19.261 19.404 19.807 20.180 20.544 21.424
18.157 18.014 17.611 17.238 16.874 15.994
22% 23.675 23.726 23.870 24.003 24.133 24.447
23.281 23.230 23.086 22.953 22.823 22.509
25% 25.296 25.373 25.588 25.787 25.982 26.453
24.706 24.629 24.414 24.215 24.020 23.549
- ---- ----- --
Week 8
-------------------16% 21.748 21.895 22.306 22.686 23.058 23.956
20.622 20.475 20.064 19.684 19.312 18.414
19% 27.560 27.775 28.379 .28.937 29.483 30.802
25.906 25.691 25.087 24.529 23.983 22.664
22% 33.907 33.990 34.221 34.435 34.645 35.151
33.273 33.190 32.959 32.745 32.535 32.029
25% 35.245 35.337 35.595 35.834 36.067 36.631
.34.539 34.447 34.189 33.950 33.717 33.153




49
Table 4-7. Distribution of confidence intervals of broiler gain to
protein treatments for mash form at weeks 4, 6, and 8 in
kg/pen (complete data set)
Probability of a larger value (a)
0.50 0.40 0.20 0.10 0.05 0.01
--------- --- ---------Protein (%) .711 .896 1.415 1.895 2.365 3.499
-- --- -- --- -- -------Week 4
16% 7.988 8.039 8.180 8.311 8.440 8.749
7.600 7.549 7.408 7.277 7.148 6.839
19% 10.541 10.623 10.852 11.065 11.272 11.774
9.913 9.831 9.602 9.389 9.182 8.680
22% 13.090 13.117 13.192 13.262 13.330 13.494
12.884 12.857 12.782 12.712 12.644 12.480
25% 14.227 14.282 14.437 14.580 14.720 15.058
13.803 13.748 13.593 13.450 13.310 12.972
------------- -- ------- ---- -------
Week 6
16% 13.453 13.605 14.031 14.425 14.811 15.742
12.285 12.133 11.707 11.313 10.927 9.996
19% 18.689 18.785 19.054 19.303 19.546 20.133
17.953 17.857 17.588 17.339 17.096 16.509
22% 24.131 24.245 24.568 24.866 25.158 25.862
23.247 23.133 22.810 22.512 22.220 21.516
25% 25.306 25.408 25.694 25.958 26.217 26.842
24.522 24.420 24.134 23.870 23.611 22.986
--- -- ---- -------
Week 8
------------ ---------------16% 19.381 19.588 20.168 20.704 21.229 22.495
17.793 17.586 17.006 16.470 15.945 14.679
19% 26.148 26.329 26.837 27.307 27.767 28.878
24.756 24.575 24.067 23.597 23.137 22.026
22% 34.468 34.685 35.294 35.857 36.408 37.738
32.800 32.583 31.974 31.411 30.860 29.530
25% 35.164 35.388 36.013 36.592 37.159 38.527
33.450 33.226 32.601 32.022 31.455 30.087
---- ---- -- ----




50
50- A
60
70
80
90 ..
ot~ .. .
100 -.
z 50- B
B
W
w .i
S 60
W 70.so -***
900
Lu 80- .' :;.
,,=, 9oU
z 100 ,
0
0 50- C
60
70
80
90 X* .. r
100 I I '
5 10 15
GAIN (G), KG/PEN 16% CP 19% CP 22% CP 25% CP
Figure 4-10. Distribution of confidence intervals for broiler gain to
protein treatments for mash feed at week 4. Complete
data set (a), poor environment (b), and good environment
(c).




51
50
A
60
70
80
\ \
90
100 I I
Z 50- B
260
LL~
W 700
O
S 80
z
W 90z 100
0
o 50- C
6070
80- K
90
100- I I I
1 5 10 15
GAIN (G), KG/PEN 16% CP 19% CP 22% CP 25% CP
Figure 4-11. Distribution of confidence intervals for broiler gain to
protein treatments for pellet feed at week 4. Complete data set (a), poor environment (b), and good environment
(c).




52
50- A
607080 90.
. 100 I
z 50- B
U.]
L- 60L.
U.
W 700
S80
W 90
OLMO o C
6070
8090 100
100 I I I I I
5 10 15 20 25 30
GAIN (G), KG/PEN 16% CP l19% CP 22% CP 25% CP
Figure 4-12. Distribution of confidence intervals for broiler gain to
protein treatments for mash feed at week 6. Complete
data set (a), poor environment (b), and good environment
(c).




53
50 A
60
70
80
90 .:
100 '
Z 50- B LU
S60
LL
W 70
O
0 4
w 80 ... t" **t'-. .
z
Wj 90 z 100- ,
.0
OI
0 50- C 60 70 80
90 -.
100 i I ''' I '
5 10 15 20 25 30
GAIN (G), KG/PEN 16% CP 19% CP 22% CP 25% CP.
Figure 4-13. Distribution of confidence intervals for broiler gain to protein treatments for pellet feed at week 6. Complete data set (a), poor environment (b), and good environment
(c).




54
50- A
6070
80
90
S100
Z 50- B
0 6060 ...
LL
O 70-w 80- -, o ,
mw 90
0
2100 0 50- C
60
70 .
80
90 .
- ... .
100- I /
10 20 30 40
GAIN (G), KG/PEN S16% CP 19% CP 22% CP 25% CP
Figure 4-14. Distribution of confidence intervals for broiler gain to
protein treatments for mash feed at week 8. Complete
data set (a), poor environment (b), and good environment
(c).




55
50 A
60
70
80
90
100 I I I
z 5o- B
LU
_ 60 L14
1.1..
W. 70O
w 80
z
W 90z 100 '
0
0 50 C4
6070
80 90
100 I I
10 20 30 40
GAIN (G), KG/PEN 1: 16% CP 19% CP 22% CP 25% CP
Figure 4-15. Distribution of confidence intervals for broiler gain to
protein treatments for pellet feed at week 8. Complete data set (a), poor environment (b), and good environment
(c).




56
may be compared to the curves generated from the complete data set (Fig. 4-1). Obvious visual differences in these graphs reveal the masked effects of environment on broiler gain and indicate the degree of error if recommendations were made from averaged data. Comparison of the graphs for the poor and good environments revealed a potential means for generating more appropriate feeding recommendations for each environment. By comparing the differences between the poor and good environments for any age, a means for quantifying the technical potential for the poor environment can be established. Further investigation, through farm characterization can then be made to isolate those factors that are contributing to the grower's good or poor performance.
Examination of the graphs in Figures 4-8 and 4-9 will enable
comparison of feed form in different environments, providing a means for refining feeding recommendations for specific environments.
Support for the above mentioned findings was then found in the analysis of confidence intervals by partitioned environment. Visualization of differences in the broiler gain response surface to treatments and feed form under different environments can be made by calculating the distribution of confidence intervals for good and poor environment (Tables 4-8 and 4-9) and then graphing these values for the different feed forms for week 4, 6 and 8 (Fig. 4-l0b,c to 4-15b,c).




57
Table 4-8. Distribution of confidence intervals of broiler gain to
protein treatments, for pellet form at weeks 4, 6, and 8
in kg/pen (partitioned data set)
Probability of a larger value (a)
0.50 0.40 0.20 0.10 0.05 0.01
---------Protein .765 .978 1.638 2.353 3.182 5.841
------------ -Week 4
poor 167. 7.398 7.577 8.130 8.730 9.426 11.657
6.114 5.935 5.382 4.782 4.086 1.855
19% 9.800 9.839 9.959 10.089 10.240 10.724
9.522 9.483 9.363 9.233 9.082 8.598
22% 13.587 13.679 13.961 14.267 14.622 15.760
12.933 12.841 12.559 12.253 11.898 10.760
25% 14.884 14.987 15.307' 15.654 16.056 17.346
14.142 14.039 13.719 13.372 12.970 11.680
------------------good 16% 7.523 7.638 7.995 8.381 8.828 10.264
6.697 6.582 6.225 5.839 5.392 3.956
19% 11.137 11.211 11.440 11.688 11.976 12.899
10.607 10.533 10.304 10.056 9.768 8.845
22% 14.146 14.244 14.547 14.875 15.256 16.476
13.444 13.346 13.043 12.715 12.334 11.114
25% 15.262 15.319 15.497 15.689 15.912 16.627
14.850 14.793 14.615 14.423 14.200 13.485
Week 6
poor 16% 13.407 13.648 14.396 15.207 16.147 19.163
11.671 11.430 10.682 9.871 8.931 5.915
19% 17.388. 17.455 17.664 17.891 18.154 18.997
16.902 16.835 16.626 16.599 16.136 15.293
22% 23.450 23.650 24.272 24.946 25.726 28.231
22.008 21.808 21.186 20.512 19.732 17.227
25% 24.871 25.024 25.498 26.011 26.607 28.516
23.773 23.620 23.146 22.633 22.037 20.128
good 16% 14.221 14.506 15.388 16.344 17.452 21.007
12.175 11.890 11.008 10.052 8.944 5.389
19% 19.864 19.966 20.283 20.625 21.022 22.296
19.132 19.030 18.713 18.371 17.974 16.700
22% 25.067 25.184 25.546 25.937 26.392 27.849
24.229 24.112 23.750 23.359 22.904 21.447
25% 26.131 26.305 26.845 27.430 28.108 30.283
24.879 24.705 24.165 23.580 22.902 20.727




58
Table 4-8. Cont.
Week 8
Probability of a larger value (a)
0.50 0.40 0.20 0.10 0.05 0.01
----------------Protein .765 .978 1.638 2.353 3.182 5.841
poor 16% 18.826 19.104 19.966 20.900 21.983 25.455
16.828 16.550 15.688 14.754 13.671 10.199
19% 24.343 24.587 25.342 26.160 27.108 30.150
22.593 22.349 21.594 20.776 19.828 16.786
22% 33.263 33.640 34.808 36.072 37.539 42.243
30.557 30.180 29.012 27.748 26.281 21.577
25% 34.103 34.455 35.545 36.726 38.096 42.488
31.575 31.223 30.133 28.952 27.582 23.190
good 16% 20.827 21.238 22.514 23.896 25.499 30.639
17.869 17.458 16.182 14.800 13.197 8.057
19% 28.000 28.157 28.643 29.170 29.781 31.741
26.872 26.715 26.229 25.702 25.091 23.131
22% 36.234 36.478 37.234 38.054 39.004 42.051
34.480 34.236 33.480 32.660 31.710 28.663
25% 37.013 37.357 38.426 39.584 40.926 45.231
34.535 34.191 33.122 31.964 30.622 26.317




59
Table 4-9. Distribution of confidence intervals of broiler gain to
protein treatments for mash form at weeks 4, 6, and 8 in
kg/pen (partitioned data set)
Probability of a larger value (a)
0.50 0.40 0.20 0.10 0.05 0.01
------------------ ---------Protein .765 .978 1.638 2.353 3.182 5.841
--------- ----------------Week 4
poor 167. 7.695 7.777 8.029 8.303 8.621 9.639
7.109 7.027 6.775 6.501 6.183 5.165
19% 10.572 10.750 11.301 11.898 12.590 14.810
9.294 9.116 8.565 7.968 7.276 5.056
22% 13.032 13.092 13.277 13.478 13.711 144458
12.602 12.542 12.357 12.156 11.923 11.176
25% 13.617 13.650 13.751 13.860 13.987 14.394
13.383 13.350 13.249 13.140 13.013 12.606
---------- --------good 16% 8.427 8.494 8.701 8.926 9.186 10.021
7.947 7.880 7.673 7.448 7.188 6.353
19% 10.823 10.906 11.166 11.447 11.773 12.818
10.221 10.138 9.878 9.597 9.271 8.226
22% 13.176 13.181 13.199 13.217 13.239 13.308
13.136 13.131 13.113 13.095 13.073- 13.004
25% 14.883 14.982 15.287 15.617 16.000 17.229
14.177 14.078 13.773 13.443 13.060 11.831
Week 6
---------- ---------- -----poor 16% 14.597 14.755 15.242 15.771 16.383 18.348
13.467 13.309 12.822 12.293 11.681 9.716
19% 19.362 19.681 20.669 21.739 22.980 26.961
17.072 16.753 15.765 14.695 13.454 9.473
22% 23.250 23.330 23.577 23.844 24.154 25.149
22.678 22.598 22.351 22.084 21.774 20.779
25% 24.662 24.769 25.101 25.461 25.878 27.215
23.892 23.785 23.453 23.093 22.676 21.339
------------------------------ -------------------good 16% 16.062 16.194 16.604 17.047 17.561 19.209
15.114 14.982 14.572 14.129 13.615 11.967
19% 19.690 19.826 20.247 20.703 21.232 22.929
18.714 18.578 18.157 17.701 17.172 15.475
22% 24.149 24.192 24.328 24.476 24.646 25.194
23.833 23.790 23.654 23.506 23.336 22.788
25% 26.069 26.165 26.461 26.782 27.155 28.349
25.383 25.287 24.991 24.670 24.297 23.103




60
Table 4-9. Cont.
Week 8
Probability of a larger value (a)
0.50 0.40 0.20 0.10 0.05 0.01
Protein .765 .978 1.638 2.353 3.182 5.841
poor 16% 20.919 21.173 21.959 22.810 23.798 26.965
19.097 18.843 18.057 17.206 16.218 13.051
19% 27.083 27.497 28.780 30.169 31.780 36.946
24.111 23.697 22.414 21.025 19.414 14.248
22% 33.634 33.785 34.253 34.760 35.348 37.233
32.550 32.399 31.931 31.424 30.836 28.951
25% 34.582 34.709 35.103 35.530 36.025 37.612
33.668 33.541 33.147 32.720 32.225 30.638
good 16% 22.945 23.108 23.612 24.159 24.792 26.824
21.777 21.614 21.110 20.563 19.930 17.898
19% 28.858 29.134 29.987 30.911 31.983 35.421
26.880 26.604 25.751 24.827 23.755 20.317
22% 34.476 34.585 34.921 35.285 35.707 37.060
33.698 33.589 33.253 32.889 32.467 31.114
25% 36.145 36.280 36.698 37.152 37.677 39.363
35.175 35.040 34.622 34.168 33.643 31.957
-------- -- ----




61
Table 4-10 compares the ranking of the visual differences in the graphed distribution of confidence intervals in three ways:
1. Comparison of feed form and protein treatment within the combined data set (overlay Fig. 4-10a with 4-11a; 4-12a with 4-13a; 4-14a with 4-15a).
Examination of the ranking from this comparison would lead to the conclusion that with the 16 and 19% protein level the mash was superior to pellets whereas at the 22 and 25% protein levels pellet was superior to mash. Also, mash was more stable (narrower confidence intervals) than pellets for all treatments except 19% protein. This discrepancy may be explained by a review of the mortality records, which indicated the deads and culls from the 19% treatment were contributing to the instability in earlier weeks. With their removal there was increased stability as compared to the mash treatment.
2. Comparison of feed form and treatment within a specific
environment (overlay Fig. 4-10b,c with 4-1lb,c; 4-12b,c with 4-13b,c; 4-14b,c with 4-15b,c).
The scores noted in this comparison trial (Table 4-10), reveal an obvious difference in the response of broiler gain to environment at the 19% protein level, where pelleted feed in the good environment is superior to mash. Stability scores demonstrate a similar response to treatments in the partitioned environment as it did in the complete data set.
3. Comparison of specific environments and treatments by feed form (overlay all b Fig. with all c Fig., e.g.4-10b with 4-10c).




62
Table 4-10 demonstrates that in regard to mean broiler gain,
there is a consistent beneficial effect of a good environment over a poor one for both feed forms. Stability of mash was clearly superior in the good environment wherethe pellets produced a discrepancy in the 16 and 19% pellet treatments as explained earlier. The 16% pelleted treatment experienced heavy mortality at later ages.
Application of the Methodology
The broiler integrator should ask the following questions. Can the differences between the good and poor growers be quantified? Can these different production environments (good and poor) be characterized in such a way as to be able to distinguish those factors that impact on broiler production? What are the costs and benefits ,for recommending more accurate feeding programs to good and poor growers? What are the costs and benefits to the integrator for aiding the poor growers to improve production in their environments?
Although the simulation study clearly demonstrated the ability
and advantages of interpolating results to partition a research domain by environment, care should be taken when extrapolating results for environments beyond the range of data.
In order to enhance the predictive capability of the procedure the broiler integrator needs to incorporate and complete a detailed characterization of a full range of grower environments within the research domain. Data from existing production records along with the detailed characterization would allow for the simulation and testing on-station of a preliminary model. The researcher/integrator's




63
Table 4-10. Ranking of the graphed distribution of confidence
intervals to protein treatments for feed form and
environments
1) Comparison of feed form and protein treatment within the combined
data set.
MEANS STABILITY
Week 16% 19% 22% 25% 16% 19% 22% 25%
M P M P M P M P M P M P M P M P
4 *-* *6 ._.* *
8 *
2) Comparison of feed form and protein treatment within an environment.
4 poor *
6 poor *
8 poor 4 ** *
4 good ** *
6 good *
8 good *
3) Comparison of particular environments and treatments by feed form. Week 16% 19% 22% 25% 16% 19% 22% 25%
pr gd pr gd pr gd pr gd pr gd pr gd pr gd pr gd
4 mash ..
6 mash _8 mash .__4
4 pellet __ *
6 pellet 4__4 *
8 pellet *
* denotes higher treatment mean or greater stability.
*-_* denotes similar treatment means or stability. M = mash, P = pellet, gd = good and pr = poor.




64
confidence in the accuracy of the characterization process and the results of the simulation study should enable research station results to be tested on-farm. A methodology for the scientific evaluation of treatments on-farm would have to be determined so that production costs to the integrator would be minimized.
Movement of research on-farm could be step-wise, where the initial testing would be on the extremes in environment. If the methodology proves to be sensitive in its ability to distinguish between the differences in treatments due to environment and the benefits of partitioning the research domain outweigh the costs, then the on-farm research program could be expanded. This expansion should include an adequate number of growers that span a wide range of environments so that confidence in the data set in terms of stability in the estimate of the treatment means is sound.
This simulation study emphasized the differences between growers based on house temperature. Modified stability analysis enables the partitioning of the research domain by the sum of all uncontrollable factors (e.g. management, water quality, temperature, etc.) expressed as an environmental index (e). Therefore, research conducted on growers farms and managed by the farmers provides a more accurate means for testing a proposed technology and a realistic way to evaluate the effect of farm differences which arise from socio-economic factors as well as agro-biological influences on the broiler production process,
Results from the characterization, simulation and on-farm research will enable the integrator to make important production




65
decisions based on knowledge generated from the application of scientific methodologies.




CHAPTER V
ESTIMATION OF A RELIABLE BROILER PRODUCTION FUNCTION
WHEN USING ALTERNATE FEED INGREDIENTS Introduction
Before the optimum biological response and economic returns from a particular broiler feeding program can be evaluated, a reliable estimate of the broiler gain production function must be established. This response function is a mathematical expression or approximation that defines the way in which live-weight gain (dependent variable) changes as a result of changes in one or more independent variables, such as feed ingredients, temperature, feed form, feed protein level and broiler age. Numerous investigations have been conducted where the functional form of the broiler gain response to corn-soybean meal diets have been established (Dillon, 1977; Heady and Dillon,1961; Heady and Shashanka, 1983; Hoepner and Freund, 1964; Naiyana and Anderson, 1982; Pesti, 1982; Pesti and Fletcher, 1984; Roush, 1982). Often, the empirical model chosen for approximating the broiler response is a second order polynomial and is fitted to the experimental data by least square regression methods. Typically, corn-soybean meal studies were analyzed under the assumption that satisfactory levels of all nutrients and environmental conditions required for growth were given as adequate and constant.
66




67
If an accurate description of the nutrient profile of a feed ingredient is not known due to variability in storage, humidity, processing or in the product itself, then potential errors in feed
formulation may result, especially if these feed ingredients are fed under varying environmental situations.
Therefore, under conditions typical to a developing country,
where the use of agro-industrial by-products as alternate feeds for broiler production, and the environment (temperature) in which the broilers are grown are both highly variable in their composition, then it is advisable to estimate the functional form of the broiler gain response under these specific conditions so that, accurate feeding recommendations can be made.
Data and Methods
Experimental Data
The source of data for this analysis was from a broiler response study conducted during the summer months (Aug.1-Oct.1O,1986), at the University of Florida Poultry Science research station. A commercial strain of feather sexed broiler chicks (Cobb Cobb) were assigned at random to 64 pens subject to including 16 chicks in each pen and maintaining the 1:1 sex ratio. Feed treatments were specified in terms of four different isocaloric diets containing 16, 19, 22, and 25% crude protein. The four protein treatments were fed in both mash and pellet form in a four by two factorial arrangement for a total of eight feed treatments and replicated eight times. To simulate the




68
effect of different environments the lower window flaps and roof vents that cover the eight pens in each of the four corners of the building were closed so these four environments (poor) would experience higher am bient temperature as compared to the four environments (good) with opened window flaps and roof vents. The experimental unit was the total live-weight of the survivors in one pen (2.3225 square meters) and expressed as live-weight gain (G) per pen.
Observations were made on live-weight gain and feed intake each week through 10 weeks of age, as summarized in Appendix A. There were 640 observations on gain, measured in kg/pen of live-weight; 640 observations on feed intake, measured in kg/pen of feed treatment; and 1120 observations on temperature, where the maximum and minimum temperatures were recorded daily, then combined into 80 observations on average weekly temperature ranges (AWTR), for each of the eight environments over the ten-week period.
Fitting the Broiler Production Function
Three forms of the broiler gain response were estimated:
(1) Gain (G) =f(protein, temperature, feed form, age)
(2) Intake (I) = f(protein, temperature, feed form, age)
(3) Gain (G) = f(milo, peanut meal, temperature, feed form)
Evaluation of the Biological (Technical) Response
Model (1) was estimated so that an evaluation of the impact of feed protein level, fed in different forms and under different




69
environmental temperatures, on broiler live-weight gain could be made at different ages in the broiler production process.
Model (2) was estimated so that a comparison of the terms that
are significant in the broiler gain model can be examined in relation to the significant terms in the intake model.
Evaluation of the Efficiency of Feed Inputs
Model (3) was estimated so that an evaluation of the efficiency of milo and peanut meal, fed in different forms and under different environmental temperatures, on broiler live-weight gain could be made at different ages in the broiler production process.
The functional form for both the broiler gain response and intake as the dependent variables was estimated in terms of the independent variables protein, temperature, feed form, and age in model (1) and
(2); and milo, peanut meal, temperature and feed form in model (3). This was accomplished by using the interactive multiple linear regression program in the statistical analysis package Statpak (Northwest Analytical, 1986).
Partitioning the Broiler Gain Response by Environment
Analysis of the graphic representation of the broiler gain
response to alternate feeds after partitioning the complete data set into more homogeneous environments, indicates that the functional form of the broiler gain response will differ between environments, feed form and age, as explained in Chapter 4, pg




70
The procedure used here was first, to estimate both broiler
production function models with the full set of independent variables, secondly, to partition the data set by feed form and broiler age, and thirdly, to fix the temperature variable by incorporating a constant temperature value into the appropriate coefficients (parameters).
This procedure was useful for the testing of the significance of feed form in the complete model as well as to reduce the number of independent variables to two so that a three dimensional graphic representation of the broiler response could be produced. All figures presented here (Fig. 5-1 to 5-8) were developed using the G3D option of the SASIGRAPI program of the SAS statistical package (Council and Helwig, 1981) and then plotted on a Versetec plotter.
Results and Discussion
Data on live-weight gain, cumulative feed intake, cumulative milo and peanut meal intake, and feed efficiency results for 3 to 10 weeks of age appear in Appendix A.
Technical Response
The coefficients of determination CR-squared) for 3 to 10 week live-weight gain (Table 5-1) and 3 to 10 weeks of broiler feed intake (Table 5-3) indicate that these equations (models) account for approximately 96 percent of the variation observed in the simulation study. The estimates of the coefficients (parameters) for all variables and interactions tested for both gain and intake (combined




71
Table 5-1. Estimation of the coefficients of regression for broiler
gain from 3 to 10 weeks production when fed various
protein levels, in mash or pellet form and under varying
temperatures (estimates + standard error)
Performance Gain Gain Gain
parameters (combined) (pellet form) (mash form)
Intercept -11.313 +1.993** -10.162 +3.0377** -11.152 +2.502**
Age 39.967 +6.956** 47.056 +10.641** 32.367 +8.769**
Temperature 0.513 +0.152** 0.480 +0.232* 0.548 +0.191**
Age x temp. -0.080 +0.022** -0.095 +0.232** -0.066 +0.028**
Age x protein -6.028 +1.045** -7.224 +1.600** -4.827 +1.317**
Age x protein2 0.327 +0.052** 0.391 +0.079** 0.263 +0.065**
Age x protein3 -0.006 +0.001** -0.007 +0.001** -0.005 +0.001**
Age x form 0.296 +0.057** Feed form x
protein3 0.001 +0.001**
Coefficient of
determination (R2) 0.959 0.942 0.961
* Significant (P<.025).
**Significant (P<.01).
Table 5-2. Regression analysis of variance for broiler gain from 3 to
10 weeks production when fed various protein levels, in
mash or pellet form and under varying temperatures
Source Sum of Degrees of Mean
squares freedom squares F
1. Gain (combined mash and pellet)
Due to regression 53,739 8 6,717 1,195**
Residual 2,826 503 5.619
Total 56,565 511 110.69
2. Gain (pellet form)
Due to regression 26,579 6 4,430 673**
Residual 1,637 249 6.576
Total 28,217 255 110.65
3. Gain (mash form)
Due to regression 27,189 6 4,531 1,015*
Residual 1,112 249 4.465
Total 28,300 255 110.98
** Significant (P<.01).




72
Table 5-3. Estimates of the coefficients of regression for broiler
feed intake from 3 to 10 weeks when fed various protein
levels, in mash or pellet form and under varying
temperatures (estimates + standard error)
Performance Intake Intake Intake
parameter (combined) (pellet form) (mash form)
Intercept -5.236 +4.340** -6.016 +5.931** -1.872 +5.730**
Age 43.878 +15.143** 63.212 +20.778** -6.315 +2.225**
Temperature -1.478 +0.330** -1.342 +0.453** -1.608 +0.438**
Age x temp. 0.283 +0.049** 0,223 +0.067** 0.341 +0.064**
Age x protein -6.813 +2.275** -10.076 +3.122** 0.950 +0.200**
Age x protein2 0.384 +0.002** 0.561 +0.154** -0.016 +0.005**
Age x protein3 -0.007 +0.002** -0.010 +0.003**
Age x form -0.669 +0.125**
Feed form x
protein3 0.001 +0.001**
Coefficient of
determination (R2) 0.966 0.969 0.971
* Significant (P<.01).
Table 5-4. Regression analysis of variance for broiler intake from 3
to 10 weeks production when fed various protein levels, in
mash or pellet form and under varying temperatures
Source Sum of Degrees of Mean
squares freedom squares F
1. Intake (combined mash and pellet)
Due to regression 385,175 8 48,147 1,808**
Residual 13,395 503 26.631
Total 398,570 511 779.98
2. Intake (pellet form)
Due to regression 192,435 6 32,073 1,279*
Residual 6,244 249 25.074
Total 198,679 255 779.13
3. Intake (mash form)
Due to regression 193,617 6 38,723 1,653*
Residual 5,854 249 23.415
Total 199,471 255 782.24
"- Significant (P<.01).




73
Table 5-5. Estimates of the coefficients of regression for broiler
gain from 1 to 10 weeks of age when fed milo and peanut
meal, in mash or pellet form and under varying
temperatures (estimates + standard error)
Performance Gain Gain Gain
parameters (combined) (pellet form) (mash form)
Intercept 0.727 + 0.113** 0.849 + 0.163** 0.658 + 0.121**
Milo 0.465 + 0.070** 0.487 + 0.026** 0.447 + 0.017**
Peanut meal 1.428 + 0.045** 1.593 + 0.064** 1.280 + 0.047**
Milo2 -0.005 + 0.001** -0.006 + 0.001** -0.004 + 0.001*
Peanut meal2 -0.017 + 0.001** -0.019 + 0.001** -0.014 + 0.001** Temp.x PM -0.021 + 0.001** -0.038 + 0.005** -0.008 + 0.004**
Temp.xPMxMilo 0.001 + 0.001** 0.001 + 0.001** 0.001 + 0.001*
Coefficient of
determination (R2) 0.989 0.989 0.994
SSignificant (P<.01).
Table 5-6. Regression analysis of variance for broiler gain from 1 to
10 weeks of age when fed milo and peanut meal, in mash and
pellet form and under varying temperatures
Source Sum of Degrees of Mean
squares freedom squares F
-------------------- --- -------------1. Gain (combined mash and pellet)
Due to regression 92,879 6 15,480 9,456**
Residual 1,036 633 1.637
Total 93,915 639 146.97
2. Gain (pellet)
Due to regression 45,744 6 7,624 4,508**
Residual 529 313 1.69
Total 46,273 319 145.06
------ -----------3. Gain (mash)
Due to regression 47,307 6 7,884 8,324**
Residual 296 313 0.947
Total 47,604 319 149.23
- Significant (P<.1).-------------------------------SSignificant (P<.01).




74
data set) were significant (P<.01). The regression analysis of variance for broiler live-weight gain (Table 5-2) and broiler feed intake (Table 5-4) show a significant F value (P<.01) indicating that the models are relatively precise descriptions of the relations defined by the production function. This result is in agreement with other studies (Pesti and Fletcher, 1984; Arraes, 1983 as cited in Pesti and Fletcher, 1984) where the inclusion of age as a continuous variable improved the fit for protein intake.
The significant effect of the interaction of feed form with age or protein on both gain and intake was used as the basis for dividing the combined data set into separate mash and pellet data sets. All estimated coefficients remained significant (P<.O1) after separation of the data, with the exception of the temperature coefficient in pellet form and on gain (P<.025). Also, the interaction of age with the cubic effect of protein on intake (mash form) was not significant (P>.05).
The models predicting gain for mash and pellet form, (Gm) and
(Gp), were then used to generate three dimensional graphs of the broiler live-weight gain response to protein treatment at 3 to 10 weeks of age and for good and poor environments (Fig. 5-1 to 5-4). It can be seen from these figures that the response curves became increasingly cubic in form as the flock aged. The characteristics of a cubic form are a slight depression of the response on the 19 and 25% protein treatments. This result was in contrast to the reports by Pesti (1982) and Heady (1983) when feeding corn-soybean meal diets. These three dimensional plots suggest that the nutrient profile of the




75
GRIN = KG/SQM AGE = WEEKS PROTEIN = PERCENT
GAIN
17.98
12.77
7.56
2.35 25
9N2
7 PROTEIN
19
3 16
Figure 5-1. Broiler gain response surfaces to protein treatments for
pellet feed in a good environment by weeks of age.




76
GRIN KG/SQM AGE WEEKS PROTEIN PERCENT
GRIN
19.60
13.63
7.65
1.68 1 25
9
22
7 PROTEIN
AGE6 19
4
3 16
Figure 5-2. Broiler gain response surfaces to protein treatments
for pellet feed in a poor environment by weeks of age.




77
GAIN = KG/SOM AGE = WEEKS PROTEIN PERCENT
GAIN
18.06
13.03
\N 0
7.99
2.95 25
I[
8 '22
7 PROTEIN
3 16
Figure 5-3. Broiler gain response surfaces to protein treatments
for mash feed in a good environment by weeks of age.




78
GRIN z KG/SUM RGE :WEEKS PROTEIN :PERCENT
GAIN
L8.45
12.86
7.31
1.75 425
8 22
7 PROTEIN
Ro 19
4
Figure 5-4. Broiler gain response surfaces to protein treatments
for mash feed in a poor environment by weeks of age.




79
alternate feeds used in this study decreased at a more rapid rate between 22 and 19% protein than was found between similar treatments using corn-soybean meal diets. This was especially true for the pelleted treatments in poor environments. It was also possible to see how the gain response to 25% protein decreased relative to 22% protein as the flock aged. This indicated that protein requirements decreased with increasing age, as previously reported by Roush (1982). Also, it appears that compensatory growth on the 22% protein treatment was superior to all other treatments probably due to the well balanced nutrient profile of this feed treatment.
Efficiency Model
The coefficient of determination (R-squared) for the 3 to 10 week live-weight gain data, mash, pellet and combined data sets (Table 5-5) indicates that these models account for approximately 99 percent of the variability observed in the broiler study. The estimates of the coefficients for the linear, quadratic and interaction terms, were all significant (P<.O1) in the mash, pellet and combined data set for the equations describing live-weight gain.
The regression analysis of variance for the gain models all had significant F values (P<.01) indicating that broiler live-weight gain can accurately be described by the models estimated in this study.
The significant effect of temperature with milo and peanut meal was to be expected since feed intake was shown to be significantly affected by temperature in the technical model. The significance of the interaction of temperature with peanut meal suggests that the gain




80
response to protein derived from peanut meal was affected by temperature.
The (Gm) and (Gp) models (mash and pellet form of model 3) as a function of milo and peanut meal intake under varying environments (temperature) were used to generate three dimensional response surfaces (Fig. 5-5 to 5-8). As with the technical model temperature was held constant at 10 and 18 F (AWTR) representing good and poor environments, respectively. Inspection of the plotted broiler gain response surfaces gives a clear view of the diminishing returns of live-weight from both peanut meal and milo.
Characteristics of the Response
The quadratic broiler gain response to corn soybean meal diets reported by Dillon (1977), Heady and Dillon (1961), Heady and Shashanka (1983), Pesti and Fletcher (1984), was the basis of a new least-cost feed formulation computer program for the broiler industry (Pesti et al., 1986). The diets used in those studies were well balanced for all nutrients which enabled a relatively good response to low protein treatments.(16 and 19%). In this study, the imbalance of nutrients at the low protein levels provoked a rapid decrease in the gain response between the 22 and 19% protein treatments resulting in a cubic rather than a quadratic gain response curve. Still, it was possible to model the gain response and to accurately predict broiler gain for a range of protein treatments and environments. These
results show that under circumstances often found in developing countries, where the nutritional profiles of feed ingredients have not




81
ALL UNITS (KG/SQMtJ
GAIN
22.51
20.25 17.99 15.73
13.48 11.22
8.96 6.70
4.44
2.18 2 .53
34.4
31. P 1858
28.8
25.9 /, 15.62
23. t 12.67
20.3 e PEANTML 7L 9?.7d 14.7
6.77
9.0 3.81
6.2
3. 4 0.86
Figure 5-5. Predicted broiler gain response surface with milo and
peanut meal inputs for pellet form in a good
environment.




82
ALL UNITS (KG/SOQM)
GAIN
19.32
17.40 15.48
13.56
11.64 9.72
7.80 5.88 3.96
2.04 21.53
34.421.53
31.6 18.58
28.8
25.9 15.62
23.1 12.67
20.3 PEANUTML
d 9.72
14.7
6.77 9, 3.81
6.2
3.44 0.86
Figure 5-6. Predicted broiler gain response surface with milo and
peanut meal inputs for pellet form in a poor
environment.




83
RLL UNITS (KG/SQMJ
GAIN
19.90 17.99 16.08
14.17 12.25
10.34 8.43 6.52
4.61
2.70 21.53
31.6 18.58
28.8
25.9 15.62
23.1 12.67
' 20.3 PERNUTML
L m a 9.72
14.7
6.77
9.0 3.81
6.2
3.440.86
Figure 5-7. Predicted broiler gain response surface with milo and
peanut meal inputs for mash form in a good environment.




84
RLL UNITS (KG/SQM)
GRIN
19.11 17.29 15.47
13.65 11.83 10.02 8.20 6.38 4.56
2.74 21.53
31.6 18.58
28.8
25.9 15.62
23.1 12i.67
20.3 PERNUTML
7L. 9.72
14.7
6.77 11.9
9.0 3.81
6.
3. 40.86
Figure 5-8. Predicted broiler gain response surface with milo and
peanut meal inputs for mash form in a poor environment.




85
been well defined, the modeling of the broiler gain response can lead to efficient feeding recommendations.
The researcher should identify what nutritional deficiencies
caused the low gain response on the low protein treatments. When this is known correction of the deficiency through supplementation of the feed with ingredients that provide adequate amounts of the deficient nutrients is recommended if the supplements are available and economical.
Compensatory growth after protein restriction has been
demonstrated with broilers (Deaton et al., 1973; Moran, 1979; Pesti and Fletcher, 1984) when fed corn-soybean meal diets. Observations in those studies were made at three and seven weeks of age. In the present study, observations were made weekly on broilers fed a single diet through ten weeks of age as well as having used alternate feed ingredients. Diminishing rates of live-weight gain from the 25% protein treatments were noted as exceeding diminishing rates for 22% protein. This was attributed to compensatory growth of birds fed the 22% protein which was surprising since this treatment had a higher mortality rate than the 25% protein treatment and since experimental unit was based on the total live-weight of the survivors.
Pesti and Fletcher (1984) suggested that changes in dietary protein content should be considered whenever food is mixed for a particular farm. Also, other variables such as temperature (Charles, 1981) and sex need to be considered in the development of economic models that are employed to predict broiler gain. These recommendations are supported by the findings of this study, where




86
compensatory growth from the 22 relative to the 25% protein treatment was evident. Decreasing protein requirements with increasing age would suggest a need to reformulate diets to lower protein levels as feed is mixed for a particular farm. House temperature at a particular farm will influence the rate at which protein levels could be decreased with increasing age. When feeding alternate feed ingredients caution should be exercised as protein levels fall below the 22% protein level because the nutritional quality of these diets appears to decline rapidly.




CHAPTER VI
ECONOMIC ANALYSIS OF BROILER PRODUCTION
WHEN USING ALTERNATE FEED INGREDIENTS Introduction
Profitable broiler production is often constrained by
uncontrollable changes in such factors as cost or availability of inputs, price of product, feed quality, and environment. Therefore, to be efficient the broiler manager is required to make decisions concerning the allocation of limited resources to a number of production alternatives. This implies that the correct decision concerning the use of feed resources in a feeding program that optimizes broiler gain will differ depending on the grower's environmental and economic situation.
Constraints on broiler production such as high temperature and expensive feed inputs are a concern to broiler producers world wide. This is especially true in developing countries where the variability in environmental and economic conditions are more extreme and production decisions need to be made constantly. Broiler producers could benefit greatly from a schedule of feeding recommendations based upon local economic conditions that account for the variability among Srowers due to differences in environment.
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