Modelling village chicken production systems

Modelling is increasingly accepted as a tool for understanding the complex interactions of the various parts of farming systems, and also as a guide to resource-use decisions for sustainability (Pandey and Hardaker, 1995). However, most of the livestock model structures developed are too complex for smallholder farming systems of Africa like village chicken production systems. Modelling village chicken production systems is complex because of the subsistence nature of the production system, where reliable input-output data are limited. Furthermore, effective use of computer-based models in decision-making in village chicken production systems requires an understanding of the socio-economic aspects of the system, which is an area not adequately addressed in the literature (Kitalyi, 1996). Given these circumstances, computer-based models for village chicken production systems should be simple and flexible.

Johnston and Cumming (1991) describe a simple computerized mathematical representation of a mixed bird population, compartmentalized into subpopulations by age and disease status, which is probably the first attempt to build models for village chickens. The model was termed a “Markov chain model” or state-transitional model and was based on techniques used for other animal production systems. Assigning probabilities to time- and disease-related movements, simulated flock characteristics such as death rates, offtake and composition or structure were drawn, which were similar to those observed in the field. The authors described a model of chicken population and safe yield that showed a decreasing capacity of a population undergoing increasing rates of growth to sustain offtake. In this model, an increasing net population growth rate was described where food supplies were being exploited, with an increasing reproduction rate, which would reach a maximum where the population replenishment and death rates, the food supply and the standing population are all in balance. These models can be used to study the effect of interventions on economic efficiency and resource use.

Modelling village chicken production systems should take into account other factors. The chickens are exposed to full variability of the environment because they are not confined most of the time; therefore nutrition, reproduction, disease epidemiology and production of the village chickens are influenced by day-to-day conditions. Under these circumstances, the time factor is important in modelling. Interpretation of quantitative longitudinal data should be supported by qualitative data, taking into account seasonal differences. The chickens are not segregated by age or sex, which may increase the volume of data to be collected. Data deficiencies may be apparent in village chicken production systems because of the subsistence nature of the production system.

Village chicken production models can be used to identify key data collection variables; to study features of the production system; to study the relationships among the key variables of the farming system affecting chicken production; to determine responses to interventions; to explore future scenarios (e.g. What are the future effects of effective control of Newcastle disease?); and to support decision-making, particularly in relation to policy and institutional reforms.

In the present study a spreadsheet model initiated by Rushton (1996b) has been used to describe input-output relationships in the village chicken production system.

**Model building blocks**

The main parameters of the model, calculations and output worksheet are based on the factors described in Figure 17. The first building block constitutes the main parameter worksheet and contains the initial flock data. This block accounts for sex and age factors, where the age is set following the life cycle of village chickens. The egg data cover three weeks, and harvested eggs (sales or consumption) are taken at week 1. All eggs transferred to weeks 2 and 3 are assumed to be in incubation and will hatch in 21 days. Chick data cover week 4 to week 8, when the chicks are transferred to the growers group. The growers include males and females, in a 50:50 ratio. The growing period is estimated to last until week 24, when all the growers are transferred to the hens and cocks group.

The other input parameters are hen data, flock losses and flock offtake. The hen data set contains hen production coefficients: the percentage of hens laying eggs, the number of eggs laid per day, the percentage of eggs eaten or sold and the percentage of eggs incubated in the hatch. The losses data set contains data on all losses, either through predators, death or theft. The offtake data set includes eggs and birds sold or consumed.

The first building blocks are used to calculate the flock numbers and weekly totals, and then are used to compute the flock output. At this stage, another input data set that can contain input requirements or costs can be added to study the input-output relationships.

**Calculations**

In the flock number building block, the population of
each category was calculated for each week (W*n*) using
the following formula:

(EO_{Wn} = MH_{W(n-1)} * pMH-inlay * eggs laid per day)-(MH_{W(n-1)} * pMH-inlay * eggs laid per day * egg-offtake),

where

EO_{Wn} = total number of week 1 eggs (week is based on
life cycle of the chickens) at any week in period (*n*),
i.e. time of data collection, assumed to cover week
1 to week 48;

MH_{W(n-1)}= total number of mature hens in the previous
week;

pMH-inlay = proportion of mature hens in lay, calculated on monthly basis.

The same formula was used to calculate the other categories. However, at the transition period, i.e. transfers from one category to another (egg to chicks, chicks to growers and growers to replacement stock), calculations were made so that the replacements were included:

Chick 4_{Wn} = E2_{W(n-1)} * (MH_{W(n-1)} * pMH-inlay * eggs
laid per day * pEggs incubated in that hatch);

Grower 9_{Wn} = C7_{W(n-1)}-(C7_{W(n-1)} * pC7-losses_{W(n-1)})-(C7_{W(n-1)} * pC-offtake_{W(n-1)};

MH_{Wn} = MH_{W(n-1)} - (MH_{W(n-1)} * pMH-losses_{W(n-1)})-(MH_{W(n-1)} * pMH-offtake_{W(n-1)}) + FG23-(FG23 *
FG23-losses_{W(n-1)}) - (FG23 * FG23-offtake_{W(n-1)}),

where

FG = fully grown.

Annexes 9 and 10 show a sample page of the initial
flock and the flock number worksheet. Using the
summation function, weekly data were drawn for each
flock category. The weekly worksheet was then used to
draw flock output, which can be used to study the flock
structure changes (Figures 18 and 19). As indicated
above, the data on input requirements or costs can be
added to the model (Figure 20). Setting the data input
parameters at different levels as a result of improvement
interventions, the model can be used to predict input
requirements and relate them to available resources.

**FIGURE 17
Conceptual structure of a computer-based model for village chicken production systems**

**FIGURE 18
Flock structure of chicks and growers**

**FIGURE 19
Flock structure of hens, cocks and total bird population**

This model can be used as a planning tool. Input parameters can be manipulated to fit planned interventions or expected output, and their effect on flock input-output can be predicted using the model.

Data sets collected in village poultry surveys can be used to validate the model. In the present study, there were no adequate data to test the model. There is a need for more farm-based research on appropriate data collection techniques. Key questions are: What are the critical production indicators? By whom and how should data be collected? What are the appropriate data management techniques?

**FIGURE 20
Energy requirement for different flock categories**

This simple model is based on data that can be collected directly from the field. It will facilitate data management as data can be stored directly in the spreadsheet. The model can be used in studying flock dynamics and in planning village chicken improvement technologies.