These aspects will have to be addressed in the Regulations if there is a specific intention of including the role of Animal Scientists in the control and use of veterinary medicines.

It is also recommended that parts of Section 31 and 33 be amended to include recognition of the South African Council for Natural Science Professionals as a statutory body.

A course in Pharmacology has been introduced for Animal Science students at the University of Pretoria. It is believed that passing a course such as this would be an essential step if Animal Scientists are to be licensed to handle Veterinary Medicines, so many of us will have to go back to school.

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The CPM Dairy Feeding Program

One still hears stories of legends that formulated feeds for dairy cows with a pencil and paper. Back then the number of variables that one had to contend with was limited to TDN, crude protein, crude fibre and minerals. Things have changed. It would not be too difficult to make a list of 50 plus variables that can be taken into account when formulating a dairy feed today. The result is that it has become impossible to formulate accurate, cost effective diets for the modern dairy cow without the use of computers and sophisticated software.

I had the opportunity to spend time in October learning about the new CPM-Dairy modelling software. The course was presented by Dr. Chalupa from Pen State. CPM-Dairy is a combined effort by researchers at Cornell University, the University of Pennsylvania and the W.H. Miner Agricultural Institute. The software was released in October 1998 and contains a modified NRC model and an up-dated version 3 of the Cornell Net Carbohydrate and Protein System (CNPS).

The responses of animals (production, metabolism, and reproduction) as measured under experimental conditions can be expressed in the form of equations. A model is a collection of these different and often complicated equations. The model endeavours to describe the animal’s response to a set of circumstances by taking as many variables into account as possible. The model used was developed over a period of 18 years on the basis of research on factors that influence cattle requirements and feed utilisation.

The program has a number of sub-models that describe inputs and calculate digestion, nutrient metabolism and requirements, ration evaluation and nitrogen balance. These are described in Table 1.

The work that has gone into CPM-Dairy is monumental and extremely impressive. The program is Windows based and the layout is well put together and easy to use. The reports that can be generated are very useful and take into account the practical implications of feeding cows. Some of the more interesting aspects of CPM-Dairy are:

1. Rumen model.

When feeding ruminants one is always feeding the bacterial population in the rumen and the animal itself. In fact one could say that a model that does not accurately describe rumen fermentation would never work. CPM-Dairy has a very complicated rumen model that describes the supply of bacteria (microbial protein) to the small intestine. It assumes that there are basically two types of bacteria, those that ferment structural carbohydrate and those that ferment non-structural carbohydrate. The amount of bacteria produced in the rumen will depend on the total amount fermentable carbohydrate in the diet. The model revolves around maximising rumen fermentation that in turn supplies the cow with the cheapest, best quality protein available. A guideline given by Dr. Chalupa is that a diet should contain at least 45% fermentable carbohydrates in order to maximise rumen output. Having used CPM-Dairy to do some formulations with local roughage I have once again been reminded just how crucial roughage quality is to cost effective diets. It is not easy to get to 45% fermentable carbohydrate with Eragrostis curvula!

2. Energy and protein values.

In order to do least cost formulation ingredients have to be assigned an ME value and the assumption is made that these values are additive. This is of course not strictly true. The ME of any ingredient is a function of the animal it is being fed to and of the other ingredients in the diet. The first thing you notice when you start with CPM-Dairy is that none of the ingredients in the library have an ME value assigned to them. The model actually describes the amount of energy that a given diet will yield based largely on the dynamics of rumen fermentation as well as digestion in the small intestine i.e. the better a feed is fermented and digested the more energy it will yield. The energy yielded by the diet is then compared to the animal’s requirement. At first it takes some getting used to working this way round, but as you do it you realise that it makes much more sense and the answers you get are more realistic.

Another unusual aspect of the model is that ingredients are not given one by-pass protein value. The amount of by-pass protein in any ingredient is not only a function of the ingredients chemical properties. Rumen fermentation and turnover will have a significant impact on an ingredient's by-pass protein content. The model therefor calculates different by-pass values depending on DMI and how fermentable the diet is.

3. Laboratory analyses of ingredients.

The answers the model gives are very sensitive to the correct description of feed ingredients. This is undoubtedly the biggest, and extremely significant, limitation to the use of CPM-Dairy in South Africa. According to Dr. Chalupa the following 12 values are needed for each ingredient in order to run the model: Dry matter, Crude protein, Soluble protein, NDF, NDF protein, ADF protein, Ash, Sugar, Starch, Silage acids, 30 hour NDF in vitro digestibility and minerals. The question as to whether local laboratories can supply these values needs to be asked. This appears to be a problem in the USA where the model was developed. A survey of 17 laboratories showed that even though all 17 claimed to use the Van Soest method to determine NDF, all 17 had a different procedure! The only way to overcome this hurdle locally is a great deal of co-operation within the industry. Is this possible? If not, then the impact of an extremely powerful tool on local dairy production could be negligible.

 

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Steve Leeson and John Summers’ long awaited book entitled "Broiler Breeder Production" has arrived. It represents a comprehensive review of all aspects of breeder production. Most poultry prod-production publications pay scant attention to broiler breeders and what data is covered if often of little relevance to the modern industry. Drs Leeson and Summers, who together have 70 years experience in this specialised industry, have set about redressing this shortcoming.

The book provides an up to date look at all aspects of broiler breeder production and management, starting with specialised genetic programs as developed by the primary breeders and ending with two chapters on very practical, hands-on aspects of breeder management. There is extensive coverage of health management, feeding systems, environmental control, lighting programs and all aspects of male and female reproduction. Together with extensive tabular material and line diagrams, there are numerous photographs that fully depict critical aspects of breeder management.

The cost of the book is US$ 47.00 with an additional US$ 10.00 for postage and packaging. The book is available directly from the publisher or alternatively we will have a few copies locally.

Email: unilee@sentex.net

http://www.sentex.net/~unilee

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Evaluating Broiler Performance

Evaluating broiler performance is complex. Over time we have moved from a simple measurement of bird weight, to weight for age, mortality, FCR and more recently to a Production Efficiency Factor (PEF). The advantage of using PEF is that all of the factors mentioned above are considered simultaneously and it gives us a reasonable idea of overall technical efficiency. For this reason it is often used as the basis for contract grower payments and the payment of management bonuses. The equation that we use to calculate PEF is as follows:

Liveability % x Mass (kg) x 100

The obvious shortcoming of using the PEF system is that it does not take any financial aspects into consideration. I was struck by just how poor the system was when trying to evaluate the results of one of the trials that we conducted last year.

What we need is a system that takes all of the performance parameters used in calculating PEF, together with some financial information. It must also take the major production economic principles into consideration as well. The principle that I am particularly concerned about is that where capacity is not limited then our objective should be to maximise the return per unit of production (per bird), but where capacity is limited then the objective must be to maximise the return per unit time. A calculation of the return per square meter of broiler housing per unit time (day) would achieve this goal as follows:

Whilst in the short term the age in days would not impact on the profitability, it would play a role if it allowed the cycle time to be changed.

Secondly, in this example I have used a live bird price. If the price were to be determined using the value of the final product minus the processing costs, this model could take the cost benefit of slaughtering larger birds into consideration.

I have used the results from a trial to illustrate just how we can be mislead by using PEF. In the trial three energy levels were tested against one another, High (H), Medium (M) and Low (L). The amino acid levels were kept much the same across all treatments as summarised below.

The results of the trial are summarised in the table.

It was of interest that the FCR changed by approximately 25 point per 0.1 MJ of dietary ME. This is in exact agreement with figures published in the literature.

From this set of data it would be easy to assume that the High energy diet had outperformed the low energy diets by about 16%. By applying the formula developed above a different picture emerges

From these results it can be seen that the difference in financial terms is only in the order of 8%, which is a very different scenario to conclusion drawn from using PEF as a yardstick. In the real world I would expect the value of the meat sold to increase as the birds got heavier. Because this was a trial, all the birds were slaughtered at 42 days, so the impact of age was nullified. The other factor that would play an important role in commercial practice would be the stocking density that was kept constant during this trial.

The scenario above may well change if the cost of highly dense ingredients such as FishMeal, Acid Oil and Full Fat Soya were to increase, or if the cost of ingredients such as Bran and Sunflower Oilcake were to decrease.

By using "company" standards to create a benchmark, it would be possible to index results against the benchmark and pay producers based on these results rather than on PEF values.

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The Effect of Temperature on the Farrowing House

"The inability of sows to consume sufficient feed to meet their nutritional needs is a common problem in many countries, but especially in hot climates. Sows with low feed intakes metabolise their body tissue to support milk production. If this happens, piglet growth and weaning weight is reduced and subsequent reproduction is impaired" (Dr DJA Cole and Dr WH Close).

So too was the case on a large piggery in Natal. The farm was effected with depressed weaning weights, ranging from 8.0 kg – 6.2 kg at 28 days.

Research was carried out on the farm, taking into consideration the weekly weaner weights and the daily minimum and maximum temperatures (making use of the mean temperature as the daily trend). The study was taken back to 1 January 1998 and calculated forward to 30 September 1999, i.e. 21 months of data.

The results showed that there was a direct response between the temperatures and the weaner weights. The graph shows that as the temperature increased over summer, so the weaner weights decreased, in converse to this, as the temperature decreased over winter, so the weaner weights increased. It therefore shows that the high temperature within the farrowing house will affect the sow and in turn the weaner weights.

This could be corrected by several management and husbandry procedures to reduce the effects of high temperatures, such as wallows, sprinklers and water drippers. Similarly, increasing air movement around the sow by maximising ventilation rate is very effective. However, it is imperative that the increase rate of air movement is directed towards the sow and not the vulnerable piglets.

In conclusion, lactation is a critical period in the life of the pig. Inappropriate nutrition, through the changes in metabolic and physiological status influences not only short and long term reproduction, but also the subsequent growth and efficiency of the growing pig. Thus every effort must be made to maximise the sow’s appetite during lactation and thus it is essential to minimise the effects of heat by all means possible.

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A New Way to Specify Lysine in Pig Grower Diets

Inaccurate dietary protein (lysine) levels are expensive. Too much lysine in the feed means elevated feed costs, while too little lysine would not sustain the potential lean growth rate. According to American Nutritionist Dr Jim Pettigrew* farm differences in lysine requirement are caused by differences in the rate of protein accretion, energy intake, and dietary energy density. Recently the American National Research Council is (NRC) has published the tenth edition of Nutrient Requirements of Swine.

In this edition integrated models where utilised to generate situation-specific estimates of nutrient requirements. Mathematical equations here provided that allow the user to create tables of nutrient requirements for pigs of a specific body weight and level of productivity.

The pig’s daily lysine requirement is for body maintenance as well as protein accretion. The pig’s maintenance requirement for lysine is a function of body weight and is derived by a mathematical calculation. The requirement for protein accretion is the product of 2 numbers, i.e. the amount of protein accreted (deposited) and the amount of absorbed lysine required to support the accretion of each gram of body protein.

In order to calculate the lean growth rate the following information is needed:

	The fat-free lean meat content of the carcass at slaughter (measured at the abattoir).
	The fat-free lean meat content at the beginning of the growing period (there is an equation available for this).
	The time spent in the growing department).

the commercial lean growth rate of pigs is calculated by subtracting the carcass lean at the start of the growing period from the carcass lean at slaughter. The mean lean growth rate is than calculated by dividing the total lean growth by the number of days pigs spent in the growing department.

Once the mean lean growth rate is known it can be converted into a lean growth curve which shows the lean growth rate at each stage of growth. Protein accretion is estimated indirectly from the widely available estimates of carcass lean growth, helped by a strong and consistent quantitative relationship which says there is 1 gram of empty-body protein accretion for every 2.55 grams of carcass fat-free lean gained. The protein accretion curve for pigs with an average lean growth of 325g per day is shown in figure 1.

According to the NRC (1998) 0.12g of true digestible lysine is needed per gram of protein accreted. The lysine requirement for protein accretion is determined by multiplying the whole body protein accretion (as shown in figure 1) by 0.12 and adding this to the maintenance requirement to obtain the total daily lysine requirement. This is illustrated in Figure 2.

 

Measuring feed disappearance:

Feed formulation requires that the target lysine level be expressed as a percentage of the diet rather than as grams per day. We can convert the g per day target lysine intake into a percentage if we know how much feed is consumed. The common method to determine feed disappearance is by direct measurement from a sample of pens.

 

 

In this method hand feeding is required and growth and feed intake should be determined on a 2 or 3 weekly basis. Direct measurement of feed disappearance however includes both feed consumed and feed wasted and it is prudent to make some allowance for feed wastage.

 

Putting the two together:

By dividing the target lysine level by the measured feed intake the dietary lysine requirement can be calculated. This is shown in figure 3.

 

Figure 3 illustrates the lysine requirement curve for pigs with a lean growth rate of 325g per day and a feed intake of 2.2kg per day. The lysine levels of typical commercial diets (grower @10g/kg total lysine fed from 30 to 60kg followed by a finisher @ 8.5g/kg total lysine) are also shown in figure 3 to illustrate the following:

	In this example pigs are underfed during the initial 4-week growing period, while they are overfed during the last 4 weeks of their growing period.
	By using a higher density 1st stage grower, feeding the current grower during the second month (50 – 75kg) and feeding a cheaper finisher, a significant improvement in performance and cost of production should be realised.

 

Important notes:

	Modelling cannot estimate a lysine requirement higher than the level fed to the pigs whose performance was measured – it tells us only the amino acid intake needed to reach the measured level of performance.
	The calculated requirements are appropriate over a limited range of conditions. They would for instance be different between summer and winter, they would be different for each sex and be influenced by significant changes in health, genetics and stocking density.

*American Nutritionist Dr Jim Pettigrew is the principal of Pettigrew Consulting International. He participated in the review of swine nutrient requirements published recently by the US National Research Council. Dr Jim Pettigrew visited South Africa at the end of October 1999 where he attended a workshop on the modelling of animal production.

He also addressed pig producers meetings in Pietersburg and Middleburg on behalf of NuTec.

 

Walter Scharlach

 

 

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