Abstract:
TABLE 1 Once the production unit has been specified it may then be defined in terms of specific population factors. The following are examples of specific population factors (singular and interacting) in a type III production unitGrowth per head. Calf liveweight growth per cow mated. Viability per calf conceived. Cow mortality per age group per birth. Milk production per sire group. Although the discussion here is at the population level of organisation, this approach could be used to deal with problems on a lower level (tissue, cellular, molecular) of organisation. III. ECOSYSTEM ANALYSIS Qnce the problem has been defined in terms of specific population factors the ecosystem may be analysed. The discussion of the analysis will be based on the hypothetical example of weaner liveweight per cow mated (i.e. type III production unit) in the far north of the Northern Territory. Briefly, this area is characterised by its tropical nature with distinct wet and dry seasons, a relatively low plane of nutrition, high effective temperatures and high ectoparasite burdens. The expression required is: Specific factors can be ascribed for the non-genetic factors e.g. energy, water and protein (nutrition), temperature and humidity (climate), ticks (disease), temperament (psychic) and dingoes (predatory). Each factor will occupy a single row and interactions can occur betweenall factors, e.g. energy and protein. The above expression may be called a growth matrix, MG. Similarly MF is the fertility (conception) matrix and Ms is the survival matrix. Further, the cows and calves are in two distinct ecological situations (nutrition, disease immunity, etc.) Ms = f' (M c, MI,) where MC is the cow survival matrix and ML is the calf survival matrix. However this partitioning will not be done to any extent here. To evaluate the means in the model: is the number of weaners; log x2 = P2 - log n2, where P2 is the population fertility phenotype and n2 is the number of cows;' 437 which contributes to various components in the matrix. These experiments should be designed so that a comprehensive, but also economic, range of combinations of limiting factors is studied, preferably in factorial designs. It is necessary in each experiment to attempt to define or describe those factors which are held constant e.g. breed, heat stress, etc. when studying the effect of ticks upon growth. Secondly, by an understanding of the physiological processes associated with the limiting factors estimates may be made of the variance components. Another possibility is that simulation techniques may be feasible. IV. PROBLEMS OF TIME, SPACE AND POPULATION An ecosystem remains constant for only one point in time and thus, in practice, it must be evaluated as a dynamic system. To carry out comprehensive measurements of the ecosystem over a period of time (e.g. season, year) would be an uneconomic, if not impossible task. However, it is often possible to define and describe the ecosystem at certain time intervals and, together with other experimental information, to extrapolate with a reasonable degree of accuracy. For example, periodic assessment of the availability of energy, protein and water, ticks, temperatures, etc., together with experimental knowledge of their effects on animals of Bos taurus and B. indicus breeds in North Australia may enable the relative operation of the limiting factors on growth to be evaluated. Two important aspects of time are the rhythmical nature of changes in many situations (Cloudsley-Thompson 196 1) and various lag phenomena, e.g. equilibration of body weight with energy and nitrogen intakes (Weech, Woolstein and Gottsch 1937). Problems of space (e.g. land husbandry system) involve considering two or more distinct and often very different ecosystems. Thus, comparisons of growth data from similar populations in different districts mean little unless some assessment is made of the relative contribution of the major limiting factors in each district. Space and time may be confounded thus making deductions difficult. This follows from the fact that each space has its own peculiar ecology and consequently its own time series. Populations present problems primarily because of the complexity of their limiting factors. Firstly, at a given point of time each individual in a population has its own unique elements in the matrix and thus no two groups of animals can be exactly the same. Similar groups of animals differing ain space over a time period will therefore differ in numerous minor and major ways. Secondly, each of the population factors is a complex in itself, e.g. growth consists of accretions of minerals, proteins, water, etc. arranged in the anatomy in various ratios and proportions which vary between animals and over time (Wallace 1948; Butterfield 1966). To summarize, the theory is proposed that time, space and population are relative concepts in the expression of population (and therefore production) factors. The corollary is that in experiments it is necessary to examine the relationships between the space, time and population involved. Thus, when examining experimental results it is necessary to consider such factors as the different origin of the groups of animals used, as past history can have significant effects on performance. Results from an experiment are strictly correct only for that particular population .439