C. Hermans
Introduction
The Centre for World Food Studies (CWFS) conducts research related to food policies and hunger. The core of this work is the formulation of national models which describe food production, the functioning of markets and government interventions. The models are multidisciplinary in that they pertain to the physical factors and economic causalities that, together, determine a country's food system. The purpose of the models is to help in formulating national food policies (CWFS, 1984).
The physical factors that determine production are studied by CWFS–Wageningen(CWFS–W). A crop production model simulates crop yields, taking into account soil type, climate and soil moisture content. The effects of fertilizers, weed, pest and disease control measures and harvest losses are also analysed.
The Department of Tropical Animal Husbandry of the Agricultural University of Wageningen and CWFS–W work together on modelling of livestock in mixed farming systems in South and Southeast Asia, where both institutions are involved in research projects. This model (SOWTV) quantitatively describes and identifies the different components of the livestock system and their interrelationships, and their interactions with factors outside the system.
The livestock model is used to investigate the consequences of different interventions on the production of the livestock system. Interventions considered include changes in animal husbandry practices, i.e. changes in feed quality or quantity, kinds of feed, animal breed and animal type; changes in marketing conditions, i.e. supply and demand, prices, and imports; and changes in development strategies, such as the development of the livestock sector vis-a-vis the crop production sector.
It was decided to model at the farm level, which requires treatment and monitoring of each animal, the study of the production processes, the investigation of the interactions, between livestock and other farm enterprises and the study of the consequences of the farmer's decisions on the livestock enterprise. Once the processes and interrelationships are understood at the farm level, the farm-level knowledge can be aggregated to the regional or national level.
The model is linked to the crop production model of CWFS–W to analyse interrelationships between primary and secondary production, but can also function as a separate entity.
This paper describes a model of the livestock component of a mixed farming system that has been developed by the Department of Tropical Animal Husbandry and MS–W. The characteristics of the Southeast Asian livestock system are given. The boundaries of the model are explained, and the Kahn model, which was used as a basis for this model, is introduced. Finally, the different modules of the model are discussed.
Crop and animal production are closely interrelated in the farming system of Southeast Asia. Animal traction and manure play essential roles in land preparation and maintenance of soil fertility, while agricultural products and wastes from the farm constitute the main constituents of the animals' diets. Thus, the animals complement and support crop production.
The system also comprises a wide variety of species: cattle and buffaloes, sheep, goats and poultry. Figure 1 illustrates the most important components of the integrated farming system: crops and their products and byproducts, animal husbandry with livestock products and byproducts, the family and the market.
Figure 1. Schematic representation of interactions within the agricultural sector.
The land use in a country gives a first indication of the type of livestock system. India, Bangladesh and Thailand grow annual or perennial crops on a large part of the land (57, 69 and 35%, respectively), while not more than 5% of the land is available for pasture (FAO, 1983). Most of animals' rations will consist of crop byproducts, mainly low-quality crop residues. Most of these byproducts are consumed by cattle and buffaloes. Crop residues such as straw are not readily eaten by sheep and goats (Table 1) (Dolberg, 1983), which consume better quality resources and thus graze pasture land and road sides. From the distribution of land to pasture and crops, and the demands made by the different species on feed, it is obvious that the number of cattle and buffaloes greatly exceeds the number of sheep and goats in these entries.
Table 1. Comparative performance of small and large ruminants on a roughage1 diet in Egypt.
|
Body weight |
Intake of dry matter |
change in body weight2 | |
| Animal species |
kg |
kg/day |
g/kg W 0.75/day |
g/day |
Cattle |
259 |
7.8 |
121 |
+0.58 |
Buffaloes |
230 |
8.9 |
151 |
+0.65 |
Sheep |
40 |
0.6 |
38 |
–0.03 |
Goats |
24 |
0.4 |
38 |
–0.04 |
Source: E1–Naga, not dated (reproduced from Dolberg, 1983)
1 The diet DM was composed of 50% corn stover (ammonia treated) plus rice and faba bean straws (not clear whether they were treated). In addition the animals received minerals.
2 Change in body weight was recorded over the week in which the intake of the animals was recorded, which followed two weeks of adjustment to the diet.
The same holds for Java, although in Indonesia as a whole the situation is quite different (FAO, 1983). Pasture land is relatively more abundant, reflected in the relatively larger sheep and goat population compared with the cattle and buffalo population. Moreover, the relatively small number of cattle and buffaloes in Thailand and Indonesia can be explained partly by the lactose intolerance of the population. Milk is generally an unwanted and unimportant product. The limited feed resources are used to produce draught power and meat (Crotty, 1980).
Between 1974 and 1982 the livestock population in the Far East increased by between 6 and 31% for cattle, buffaloes, pigs, sheep, goats, chickens and ducks (FAO, 1983) (Table 2). Small ruminants and poultry showed the largest increase during this period.
Table 2. Increases (%) in population number of the animals from 1974–1976 to 1982 in some Southeast Asian countries.
Animal |
Far East |
Thailand |
Indonesia |
India |
Bangladesh |
Cattle |
6 |
7 |
4 |
1 |
38 |
Buffaloes |
7 |
8 |
5 |
4 |
49 |
Pigs |
21 |
3 |
21 |
30 |
|
Sheep |
25 |
22 |
29 |
4 |
8 |
Goats |
22 |
1 |
10 |
4 |
54 |
Chicken |
31 |
16 |
23 |
6 |
53 |
Ducks |
30 |
16 |
38 |
39 |
Calculated from data in FAO (1983).
The changing emphasis on particular types of livestock may partly be explained by social factors:
a. There is, generally speaking, a positive relationship between the size of the holding and the animal species kept. Smaller farms keep mostly goats and poultry, while larger farms keep draught animals.
b. The increasing population causes increased fragmentation of holdings, and thus favours an increase in the number of small animals. The number of cattle and buffaloes decreases when population pressure increases.
c. The growing human population and higher incomes increase the demand for livestock products. This increased demand can only be met by an increased animal population.
As explained above considerable differences in feed production potential, animal species and animal numbers exist within the integrated farming system among the different countries. Considerable variation also exists in production potential of the different animal species, distribution of the animals over the farms, seasonal variation in the feed production, etc.
Not all the components of the integrated farming system-crops and their products and byproducts, animal species and their products and byproducts, the family and the marke—are included in the SOWTV model. Research is focused on the animal components and the relationships between feed supply and livestock performance. The family, the market, crop production and policy decisions outside the farm unit are considered as exogenous factors. The interactions between the components of the farming system are strongly reduced. Until now, only one species, cattle, has been considered, but other species will be included later. Buffaloes probably do not differ from cattle and will overlap in use of feed resources, feed intake and animal production. Sheep, goats and poultry only partly utilise the same feed resources as cattle and buffaloes.
The supply of crop byproducts for feed is considered as a fixed input, and effects of draught power and dung on crop production are not yet described in the model. These products are available output. Similarly, the interactions between the livestock and the family are not taken into account. It is taken for granted that enough human labour is available to do the work involved in keeping the animals. Also the effect of the availability of animal products—meat, milk, draught, manure—on the family's behaviour are not considered. The animal products are the technical output of the system without an economic evaluation. The same holds for non-farm inputs. All products, except the animal feed, are considered to be available in non-limiting amounts. These simplifications obviously affect the assumptions of the integrated farming system. However, they allow us to concentrate on the livestock aspects with their most simple link to crop production: available feed.
The subjects that are included in the model are:
a. Feed aspects, including availability (quantity and quality), intake and conversion of feed energy into animal products (meat, milk, traction, manure).
b. Herd dynamics, including mortality, reproduction and purchase or sale.
c. Feedback between herd dynamics and feed availability.
The model of Kahn (1982) was used as a starting point. This model is a version of the Texas A & M University (TAMU) model (Sanders and Cartwright, 1979a; 1979b) that has been adapted to African conditions. The model calculates animal performance on the basis of specific feed resources and genetic potentials for animal production. Animal performance is calculated on an individual basis. The randomly occurring discrete events—mortality, conception and calf sex—are treated stochastically, and for every time step, the biological status of the animals (growth, reproduction, mortality) is recalculated according to the intake of energy and its utilisation (Kahn and Spedding, 1983).
However, the Kahn model had to be modified for various reasons.
In the remainder of this paper, the different modules of the Kahn model, the modifications implemented and the problems encountered during the modelling exercises are discussed.
In essence, the general structure of the Kahn model is maintained. The state-variable approach is employed. At each time step each state variable is recalculated from the situation at the beginning of the time step and the energy exchanges and production during the time step. Where Kahn uses a single animal, or, in the case of suckler cows, the cow-calf entity as the calculation unit, the SOWTV model uses a single animal in all cases.
The time step in the SOWTV model is 30 days, but, as in the Kahn model, any time step from one day upwards can be used.
A random number generator is used to preserve the integer quality of the herd. Discrete events, such as mortality or reproduction, occur with an exogenously-defined probability. A random number is generated and compared with that probability. If the generated random number is less than or equal to the probability, the event will occur. Otherwise, it will not. Indeed, the simplicity of the integer approach is achieved at the expense of random variation between replicate model runs (Kahn and Spedding, 1983). In the case of small herds, the variation between replicate runs is too large to be useful for mean herd composition calculations. However, replicate runs have a value in risk assessment.
Within the replication loop in the SOWTV model, the various calculation modules are treated separately. For all animals, the following modules are successively passed through: allocation of a feed ration to each animal, calculation of the animal's energy and nitrogen requirements, calculation of the theoretically possible intake, calculation of the real intake, and calculation of the animal's production in terms of meat, milk, manure and offspring. The herd-dynamics module then generates pregnancy and mortality probabilities.
The Kahn model describes a grazing herd of female cattle. In addition to a seasonally-dependent pasture component, provisions are made for a seasonal allocation of supplementary feed to heifer-replacers and calves. An option is also included to supplement according to physiological group (calf, lactating cow, pregnant cow)
The climate of Southeast Asia is suitable for year-round production of various crops. Crop residues are the main animal feed in this region. Different residues are available during different seasons, and the feed allocation module in the SOWTV model has been made more flexible to allow for this.
Nine animal classes are distinguished:
Fifteen different feeds can be allocated. A distinction is made between farm-produced feeds (mainly roughages and crop byproducts) and purchased feed. In the model, available roughage is given in total amount per month per farm. This amount is distributed over the different animal classes, according to a distribution factor defined for each class. Purchased feeds are given in absolute amounts per animal per day. In addition, for every feed, an indication is given as to whether it can be saved for the following time-step, what percentage is wasted while eating, and the sequence in which it is fed.
To allow calculation of feed intake, the crude protein content, the digestibility of the dry matter and the energy content of the digestible dry matter are given for the different feeds. These values are given as monthly averages.
These structural modifications allow greater flexibility in the combination of feed sources, their availability during the year and their allocation over the different animal categories than the original. Kahn model. The module is valid in a mixed farming system, but can also be used in any system in which the amount of feed available to the animal is known, i.e. intensive or extensive production systems, including grazing systems.
The output of the feed allocation module, i.e. the amounts of different feeds available to each individual animal from which it can select, is used as input for the feed intake module.
The feed intake regulation in Kahn's model is based on two concepts: the existence of a physical control operating on most roughages, and the existence of a physiological control associated with highly digestible diets.
The idea underlying physical control of feed intake is the assumption that undigested material in the digestive tract (ballast) restricts the rate of passage of feed through the digestive tract and thus restricts feed consumption. Following this theory, dry matter intake per kg liveweight is inversely proportional to the non-digestible fraction when digestibility is less than 67%, while the faecal dry matter output per kg liveweight remains constant (Conrad et al, 1974). Kahn (1982) uses a value for faecal dry matter output of 0.0093 kg/kg liveweight per day, but this value gradually increases in the case of lactating cows to a maximum value of 0.0116 in the fifth month of lactation, and decreases gradually to the basic value at the end of lactation.
Analysis of data on sheep by Ketelaars (1984), however, showed considerable variation in faecal dry matter output as shown in Table 3. Therefore, using a constant faecal dry matter output to predict the feed intake, as done by Kahn (1982) and also by Sanders and Cartwright (1979a; 1979b) and Konandreas and Anderson (1982) seems very inaccurate.
Table 3. Digestibility (%) of the dry matter and corresponding minimum and maximum values of faecal dry matter output (kg DM/kg LWT/day) for sheep.
|
Faecal DM output | ||
|
DM digestibility |
||
|
% |
Minimum |
Maximum |
|
70 |
0.00590 |
0.01079 |
|
60 |
0.00572 |
0.01343 |
|
50 |
0.00425 |
0.01177 |
|
40 |
0.00440 |
0.00914 |
According to Conrad et al (1974) and Kahn (1982) physiological control becomes important when the feed is highly digestible (more than 67%). Feed intake will then be restricted by the animal's potential to absorb and utilise digestible nutrients, or, stated differently, intake is regulated by energy requirements. Kahn (1982) calculated the physiological limit for dry-matter intake as 2.5% above the calculated requirements for maintenance, traction, lactation, gestation, growth or weight gain. However, she also found that intake predictions obtained in this way were not very accurate.
It may be concluded that intake predictions based on a combination of physical and physiological control can be criticised on different points, and, therefore, remain questionable.
An alternative system was developed by Ketelaars (1983). According to his analysis, feed intake of growing cattle can be explained from the relative availability of energy and protein in the feed, and the capacity of the animal to utilise energy and protein in a certain ratio. The data used to develop that theory pertained to both temperate and tropical cattle fed temperate as well as tropical grasses and legumes. This conceptual model has been developed further for sheep (Ketelaars, 1984). At any level of intake of nitrogen the maximum amount of energy the animal can consume— given the feed digestibility— and convert into animal product (meat, milk, draught) can be calculated from:
In this calculation assumptions have to be made about the efficiency of conversion of metabolisable energy into net energy, of digestible energy into metabolisable energy, and of ingested nitrogen into animal product. Also the energy content of the digestible dry or organic matter has to be estimated. The nitrogen/energy ratio or the animal product (g N/MJ NE, NE = net energy) defines the slope (b) or the maximum intake versus nitrogen intake line. The intercept (a) of the maximum intake curve with the energy intake axis is defined by this slope and the maintenance requirements. The maximum intake curve can be visualised as:
Do max (g DOM 0.75) = a + b * IN (g N/kg 0.75)
where Do max = maximum digestible oceanic matter intake
IN =nitrogen intake
The maximum intake is very sensitive to the maintenance requirements and the composition of the animal product (Ketelaars, 1984).
Whether the animal will eat this maximum possible amount of energy is co-determined by feed availability. If the animal has less feed available than it can consume, feed intake will equal feed availability. In the SOWTV model the animal selects the ration it will ingest, according to predefined criteria (nitrogen content of the feed), from the feeds available. For these calculations the animal's energy and nitrogen requirements have to be known. How these requirements for the different products are defined is explained below.
The TAMU (Sanders and Cartwright, 1979a; 1979b), ILCA (Konandreas and Anderson, 1982) and Kahn (1982) models use energy availability and requirements to explain animal performance. For these calculations ARC (1980) standards are used, with some slight modifications if necessary; if model predictions are unsatisfactory, they can often be brought into line by exchanging one or more functions for others that are equally-well documented.
The SOWTV model uses the method of Ketelaars (1984) to calculate feed intake; thus, animal production is predicted not only from energy, but from both energy and protein availability.
One reason for not applying the ARC standards for tropical breeds is that an analysis of data from Bangladesh showed that if ARC feed requirements are used to explain animal production, predicted feed intake is below even the maintenance requirements. In reality, the animals did grow and produce.
In the following paragraph, the requirements applied in the model for the different animal functions are discussed.
Maintenance: Accurate estimates of the maintenance requirements of animals are crucial in the calculation of their maximum voluntary feed intake. In the literature no consensus exists on the value or the method of calculation of these requirements. Wallach et al (1984) compared 14 methods for calculating energy requirements for maintenance in grazing sheep and found that widely differing approaches are used. One approach independently evaluates fasting energy loss and the efficiency of utilisation of feed energy for maintenance. A second approach determines maintenance requirements from indoor feeding trials. Short-terms or short- and long-term trials were often performed on mature and non-producing animals.
In calculating maintenance requirements, some authors include the heat production associated with muscular activity, in addition to the fasting heat production. Some also include the loss of energy in the urine.
Calculations of the fasting heat production almost always include a term proportional to metabolic weight (animal weight raised to the power 0.73 or 0.75) . However the animal weight used is not always the same. Some authors use empty body weight, others use weight including gut fill. Sometimes, an age factor is included, a distinction is made between suckling or ruminant animals of the same weight, or an effect of sex or a lactation factor is included.
The calculations of the energy costs of muscular activity are also not unequivocal. Sometimes it is assumed that the cost of activity is included in the fasting heat production, sometimes a fixed level of activity is added, or the level of activity varies. Factors that are included separately or in combination are: standing, changing position, walking, climbing, ruminating and eating. As a result, estimates of the maintenance requirements differ according to the system used.
A first rough comparison has been made of six different methods for calculating maintenance energy requirements of cattle: ARC (1980), Sanders & Cartwright (1979a: 1979b), Siebert and Hunter (1977), Levine et al (1981), Levine and Hohenboken (1981), Konandreas and Anderson (1982) and Kahn (1982). The equations are given in Table 4, and the results obtained with these equations are given in Table 5. The differences between the lowest and the highest values in each column range from 28 to 64% for the different animals.
Table 4. Equation for calculating maintenance energy requirements for cattle.
|
Source |
Requirement |
Unit |
|
ARC (1980) |
(COF* (LWT/1.08)0.67 + 0.0043*LWT)/KM |
MJ ME/day |
|
Sanders and Cartwright (1979) |
0.465*LWT0·75 (no activity included) (quoted by Kahn, 1982) |
MJ ME/day |
|
Konandreas and |
(0.376*LWT 0.73 + 0.0021*LWT*D)/KM |
MJ ME/day |
|
Siebert and |
((12150–8.8*AGE + 0.0045*AGE2)* |
kJ ME/day |
|
Levine et al |
C*ELAC2*LLAC2*LGEST* |
kcal ME/day |
|
Kahn (1982) |
COF* (LWT/1.08)0.67/KM + ACI*0.012*LWT |
MJ ME/day |
| LWT | liveweight (kg). | |
| KM | efficiency of conversion of ME to NE. | |
| D | walking distance (km/day) · | |
| AGE | age in days. | |
| ME/DM | metabolisable energy concentration in the dry matter (MJ/kg). | |
| COF | coefficient, 0.53 for female animals, 0.67 for male animals. | |
| C | 0.955 to modify maintenance requirements during early rainy season on native savanna or to early and,/or late rainy season on molasses. | |
| ELAC2 | 1.4 to modify maintenance requirements of cows during the first 100 days of lactation. | |
| LLAC2 | 1.32 to modify maintenance requirements of cows during the remainder of the lactation period after the first 100 days. | |
| LGEST | 1.05 to modify maintenance requirements of cows during the last trimester of pregnancy. | |
| ACI | activity factor | 0: no activity; 1: moderate activity; 2: high activity. |
Table 5. Maintenance energy requirements (MJ ME) of cows, calculated with the equations of Table 4.
|
LWT |
(kg) |
25 |
50 |
100 |
100 |
200 | |
|
Source |
Age |
(days) |
109 |
365 |
730 |
1095 |
1095 |
|
|
Maintenance energy (W ME) | ||||||
| ARC (1980)1,6 |
6.56 |
10.49 |
16.83 |
16.83 |
27.03 | ||
|
Sanders and Cartwright (1979) |
5.20 |
8.74 |
14.70 |
14.70 |
24.73 | ||
|
Konandreas and Anderson (1982)2,6 |
5.95 |
9.92 |
16.56 |
16.56 |
27.69 | ||
|
Siebert arid Hunter (1977)3 |
5.13 |
7.34 |
10.57 |
10. 30 |
17. 38 | ||
|
Levine et al (1981)3,5 |
5.26 |
7.62 |
11.14 |
10.87 |
16.93 | ||
|
Kahn (1982)4 |
6.55 |
10.48 |
16.79 |
16.79 |
26.96 | ||
|
Orskav (1981)7 |
4.47 |
7.52 |
12.65 |
12.65 |
21.27 | ||
1 COF = 0.53
2 D=2 km/hour
3 ME/DE = 1.50 Mcal/kg
4 ACI = 0.5
5 C = ELAC2 = LLAC2 = LGEST = 1
6 KM = 0.68
7 Estimates for Bengali cattle
Although further study is necessary, no reason was found to prefer one of the calculation methods in our model. Therefore, the maintenance energy value given for Bengali cattle (0.4 MJ ME/kg 0.75) by Ørskov (1981) is used. It can be seen in Table 5 that this value is within the range of the other estimates. This value is used for female animals: For male animals, it is increased by 26%, as proposed by ARC (1980).
For the time being, the value given by Preston (1972) for protein requirements is adapted: 0.5 g N/kg 0.75. No comparison was made with other estimates reported in the literature.
Traction: Few data on nutrient requirements for animal traction are reported in the literature, and little attention is paid to this subject in the different models. In Kahn's model the animals can perform 'light' work for 2 hours per day or no work at all. Two hours of 'light' work per day requires 10 MJ ME. Only animals older than 4 years, lactating for more than 60 days, or during the first 180 days of pregnancy are allowed to work. Because animal traction is one of the important production goals in Southeast Asia, it was felt that this subject should be investigated more thoroughly.
Mainly cattle and buffaloes are used for traction in Southeast Asia. Buffaloes have a lower heat tolerance than cattle, and can be used only during cool morning hours if no bathing facility is available. Cattle are stronger than buffaloes in the dry season, i.e they can be used for longer periods and travel faster (Rufener, 1971).
Immature cattle (less than 3 years old) and pregnant cows are unsuitable for work (Gill, 1981). In villages around Noakhali (Bangladesh), cattle less than 100 kg are never used for work (Hermans, 1984). Fertility and lactation problems arise if cows are used for draught (Gill, 1981; Groenewold, 1983; Jabbar, 1983). Lactation is more severely affected by draught work than is fertility. On working days milk production losses amount to 20–30%. Fertility is 6-7% lower in working animals (Goe, 1983).
Training for work starts when the animals are between 2.5 and 4 years old (Goe, 1983; Howard, 1980; Starkey, 1982; Nourrissat, 1965; FAO, 1972). In Senegal animals are used for work until they are 14 to 15 years old (Nourrissat, 1965) .
The number of hours per day an animal works depends on its sex and physiological status. The literature does not show much variation. Bullocks work for 5–6 hours per day (Gill, 1981; FAO, 1972) in one single session, interrupted by reasonable rest periods, or half in the morning, half at the end of the day. Female animals work for 2–4 hours per day (Goe, 1983). Our survey in Bangladesh showed that bullocks did not work for more than 5 hours per day, cows work at most for 3.9 hours per day, while lactating animals never work more than 2.8 hours per day. N'Dama cattle in Sierra Leone are used for 4 hours per day (Starkey, 1982). The number of days animals are used during the year varies between 50 and 200 (Sarker, 1981). Nourrissat (1965) stated that the animals are used for 350 hours per year. Assuming a working day of 5 hours, this means that the animals are used for 70 days. Rough calculations on the Noakhali data (Hermans, 1984) show that the bullocks are used for 520 hours per year or about 100 days per year.
The number of hours needed to plough one hectare is also variable and depends on the type of plough and the type of soil. With a traditional plough, between 22.2 and 33.3 hours would be necessary to plough 1 hectare (Sarker, 1981; Sarker & Farouk, 1982), while 14.3 to 28.6 hours are needed with an improved plough.
The walking rate of the animals differs according to species and category. Buffaloes walk at 2.5 to 3.2 km/h (Goe, 1983) 1983), or 2.9 to 3.2 km/h (Smith, 1980). Generally they are known to walk slower than cattle. Bullocks work at a walking speed of 2.2 to 3.1 km/h (Smith, 1980; Howard, 1980), 2.5 to 4.0 km/h (Goe, 1983) or 2.2 to 3.6 km/h (Lawrence, 1984) . Howard (1980) makes a distinction between the speed for ploughing (2.5 km/h) and the speed for the roundabout (2.2 km/h). Cows walk about 2.5 k4/h (Smith, 1980; Howard, 1980) or 2.5 to 3.5 km/h (Goe, 1983). Other authors do not differentiate between animal categories and give walking rates between 2.5 km/h and 5–6 km/h for draught animals (Sarker, 1981; Sarker and Farouk, 1982; FAO, 1972).
The tractive effort cattle can produce is directly proportional to body weight up to 500 kg (Hussain, 1981). An effort of 10% of the animals bodyweight is frequently given (Rivière, 1978; Howard, 1980). However, other sources show more variation: 18 – 22% of body weight for animals of 200 to 275 log (Sacker, 1981); 10 –13% of body weight for cows of 400 to 600 kg (Howard, 1980); 9–12% of body weight for bullocks of 500 to 900 kg (Howard, 1980) . An increase in tractive effort reduces the speed of the animals (Goe, 1983).
The power developed by the animals is the result of the tractive effort and the walking rate. According to Singh arid Marcellor (1975), small bullocks develop 224 watts (W) and large buffaloes 746 W, while most bullocks develop between 373 and 522 W. Information for cattle in Bangladesh gives 128 W for bullocks and 124 W for cows (Hussain, 1981).
The power necessary to pull the plough varies between 256 and 336 W for traditional ploughs and between 240 and 331 W for improved ploughs (Sacker and Farouk, 1982). Two animals would thus be necessary to deliver the power, resulting in less power developed per animal, since animals hitched as a team incur a loss of energetic efficiency (Goe, 1983).
The nutrient requirements of draught animals vary according to age, sex, breed, species, tractive effort and duration of work. The tractive effort depends on species and rate and type of work. Age, sex and breed are known variables of the animal. Estimates have to be made of the tractive efforts of the different animal categories and the duration of work.
Feed requirements given in the different references are calculated according to different principles. Rivière (1978) and FAO (1972) make a distinction between light, medium and sustained work. For these types of work, the animals need a surplus of 0.5, 1 and 1.5 times maintenance needs above maintenance requirements. However, no definition is given of light, medium and sustained work. Hrabavszky (1983) states that working animals need 30% feed above maintenance.
ARC (1980) and Mathers (1980) estimate the energy needed for walking at 2 Joules (J) /meter walked per kg liveweight. Mathers (1980) defined the energy requirements for pulling a plough at 33 J/kg pulled per meter for Brahman cattle, and at 26 J/kg pulled per meter for buffaloes.
The literature gives conflicting information for the protein requirements of draught animals. Most sources suggest that no excess protein is needed provided the maintenance requirements are met (Smith, 1980; Rivière, 1978; Goe, 1983). Other references suggest an additional 13.6 g digestible crude protein per hour worked (Smith, 1981), or 250 g digestible crude protein daily for maintenance and traction for a 300 kg working bullock (FAO, 1972).
The following assumptions are incorporated in the model. The model calculates the required metabolisable energy for draught work, depending on the force delivered by the animals its walking speed, the working time, a factor for soil characteristics, plough characteristics, and an efficiency factor for traction.
Fixed parameters in the model are:
1. Tractive efforts produced by cattle:
–12% of body weight for males
–9% of body weight for females
2. Maximum time animals are used for traction:
–5 hours per day for males
–4 hours per day for non-pregnant, non-lactating cows
–2 hours per day for lactating cows
3. Walking speed: 3 km/hour
4. Time needed to plough 1 hectare: 22 hours
5. Efficiency factor for traction: 0.30.
Variation is brought into the model via:
1. The number of hectares that have to be ploughed seasonally;
2. The number and category of animals (bulls, lactating cows or dry cows) that can be used for work;
3. A soil- and plough-specific parameter; and
4. Choice between single or teamwork.
In the model, preference is given to male animals to do the work. Only in case of shortage of tractive power, dry cows are used, and ultimately lactating cows. If these categories are not able to execute the work, it is assumed that farmers hire animals. No feed requirements are included for hired animals.
Because it is assumed that no supplementary protein above maintenance is required for draught work, maximum feed intake calculations do not take into account a possible increased intake due to draught work. This is in accordance with the calculation procedure of Ketelaars (1984), which needs a protein to energy ratio of the product.
Growth: In the model of Kahn (1982), weight gain and weight loss are the result of a positive or negative balance between energy intake and energy expenditure. Kahn uses two equations for the translation of the energy balance into a weight change:
The equations used are modified ARC (1980) equations.
Because of the feed intake calculation method used, energy and protein requirements for body-weight gain or loss are needed in the SOWTV model. These are calculated from data in ARC (1980), which give the protein and fat content per kg body-weight gain for animals of 50–500 kg. These protein and fat contents have been modified, according to breed (small–large), sex (female–male) and daily gain. Because 1 kg of protein contains 23.6 MJ NE, and 1 kg of fat 39.3 MJ NE, the energy content of 1 kg liveweight gain can be calculated. Using linear regression, a straight line is derived, giving the composition of the liveweight gain, at any given liveweight, for small breeds with a mean daily growth rate of about 50 g:
where BODYCF is the composition of 1 kg liveweight gain of a female;
BODYCM is the composition of 1 kg liveweight gain of a male; and
LWT is liveweight
These equations define the slope of the maximum energy intake as a function of the nitrogen intake.
Pregnancy: To calculate energy requirements for pregnancy, Kahn (1982) uses the ARC (1980) equations, modified for birth weight and for the difference between actual and simulated gestation period. A correction factor is also introduced to minimise errors due to large time-steps. The requirements depend on the number of days of pregnancy.
In the SOWTV model, energy and protein requirements are derived from data given by ARC (1980). The protein and fat content per kg liveweight gain due to pregnancy is calculated from the composition of the body weight of a new-born calf, the deposition of nutrients in the foetus at full term, the time course of nutrient deposition in the foetus and the gravid uterus during pregnancy and the weight of the foetus and the gravid uterus. The protein and fat content allow calculation of the energy and nitrogen content of 1 kg of liveweight gain, and also the ratio of nitrogen to energy during pregnancy (Table 6). The ratio of nitrogen to energy determines the slope of the maximum energy intake line as a function of the nitrogen intake (Ketelaars, 1983; 1984).
Table 6. Composition of the foetus and gravid uterus during different stages of pregnancy.
|
Days from conception | ||||||
|
141 |
169 |
197 |
225 |
253 |
281 | |
|
Weight of foetus + |
5.50 |
8.88 |
13.36 |
19.33 |
26.97 |
36.40 |
|
Protein content |
0.051 |
0.059 |
0.072 |
0.087 |
0.107 |
0.124 |
|
Fat content |
0.007 |
0.007 |
0.009 |
0.012 |
0.016 |
0.021 |
|
Energy |
1.479 |
1.668 |
2.289 |
2.525 |
3.154 |
3.752 |
|
Nitrogen |
0.008 |
0.009 |
0.012 |
0.014 |
0.017 |
0.020 |
|
g N/MJ NE |
5.41 |
5.4 |
5.24 |
5.54 |
5.39 |
5.33 |
Lactation: According to ARC (1980), the energy required for lactation equals the energy content of the milk yield, divided by a conversion efficiency coefficient. The lactation potential, defined by Kahn (1982) is a function of the genetically defined maximum potential daily milk yield, the number of days to peak yield, the lactation stage and the age of the animal. The potential for mobilising body tissue to meet lactation requirements in case of energy deficit is also included.
Kahn's calculation of the lactation potential and tissue mobilisation is adopted in our model. These subjects still need further verification. Energy and protein requirements for lactation are defined in a similar way to requirements for pregnancy and growth. Milk composition, in terms of protein and energy value, tends to be constant for a given breed (Ketelaars, 1984). From the fat content of the milk the energy content can be deduced through the formula of Tyrell and Reid, given by ARC (1980): E (kJ/kg) = 40.6 * F (g/kg) + 1509.0. Finally, the nitrogen/energy ratio can be calculated and this ratio is again used to define the slope of the line giving the maximum energy intake as a function of the nitrogen intake.
To estimate an animal's production—traction delivered, milk, growth and offspring— a comparison is made between its intake and its requirements as specified in terms of nitrogen and energy. Although the ultimate intention is to define production as a function of the limiting factor—either energy or nitrogen—until now only energy availability is used to determine production.
Priorities for the various production alternatives as defined by Kahn (1982) have been retained in this model. This implies that maintenance requirements have to be met before draught-work requirements and they in turn have to be met before growth requirements are considered. In addition, pregnancy and lactation requirements have priority over growth requirements. If an animal is pregnant and lactating, pregnancy requirements are considered more important.
The dynamics of a herd are basically determined by its mortality rate and its reproduction rate. Consequently, expulsion (culling or sale) or purchase can be practiced to restore the balance in the herd size.
Kahn's model (1982), as well as the TAMU model (Sanders and Cartwright, 1979a; 1979b), deal with herd dynamics. In these models the effects of nutrition on reproduction and mortality are defined separately from the effects of other environmental and internal factors.
The reproduction equations in Kahn's model (1982) are based on results of Wiltbank et al (1962; 1964) and Dunn et al (1969), who investigated the effects of pre- and post-partum energy intake on reproductive performance of different breeds of different ages. The equations calculate:
CCYC = CFW 0.1 * CFDW 0 1 * CFA 0.5; and
where CFT is a correction factor for time since calving, CFW a correction factor for body condition, CFDW for daily weight loss, CFM for immaturity, CFL for lactation stage and CFA for age.
Two of the equations were again validated using results of Wiltbank et al (1962; 1964) and Dunn et al (1969) and one was validated with the results of Kahn and Lehrer (1984).
No evidence is found in the literature to substantiate these equations. Wiltbank et al (1962; 1964) and Dunn et al (1969) used Hereford and Angus and Hereford cattle, respectively, of different ages and found indications that energy intake before calving determines the onset of oestrus following calving: a low energy intake causes a delay in the onset of oestrus. Holness et al (1980) also found a significant effect of the level of nutrition on the duration of post-partum anoestrus for Afrikaner and Mashona cattle. In the latter case the animals were fed from early pregnancy to mid-breeding season on either a high or low level of nutrition, and it could not be confirmed whether just the feeding level before parturition was important. Some authors mention breed differences under given experimental conditions (Dunn et al, 1969; Holness et al, 1980). However, comparison of the results of various authors for one breed shows that considerable differences also exist within a breed (Table 7).
Table 7. Days from calving to first oestrus.
|
Breeds |
Days |
Source |
|
Afrikaner |
102.2 ± 6.2 |
Holness et al 1980 |
|
Mashona |
69.7 ± 6.8 |
Holness et al |
|
Friesian |
63.8 |
Donkin 1980 |
|
Hereford |
70.8 ±11.7 |
Wiltbank et al 1964 |
|
Hereford |
51.7 ± 8.2 |
Wiltbank et al 1962 |
|
Experimental conditions | ||
|
Hereford |
||
|
low nutrition before calving, high |
65 |
Wiltbank et al (1962) |
|
post-partum |
49 |
Wiltbank et al (1964) |
|
Mashona & Afrikaner |
||
|
high nutrition |
77.6 + 5.4 |
Holness et al (1980) |
|
low nutrition |
98.4 ± 6.8 |
Holness et al (1980) |
According to Wiltbank et al (1962; 1964) and Dunn et al (1969), the post-partum level of energy intake influences the conception rate, i.e. a low energy intake results in a low conception rate. The authors are not sure whether this response is a result of energy intake per se or of body condition. Holness et al (1980) conclude that the level of nutrition does have a significant effect on calving rate and thus on conception rate, but it must be noted that the feeding level in their experiments did not differ before and after calving. They also deduced from their data that total conception rate increased with increasing post-partum body-mass, indicating that the ability to conceive is a function of body weight. Richardson et al (1975) came to the same conclusion in their experiments with Nkone and Afrikaner cattle. Ward (1968) observed the existence of a critical weight for Mashona cattle below which conception did not take place. Again, there were breed differences in conception rate and subsequent calving rate under given environmental conditions (Table 8).
Table 8. Mean calving rate of different breeds under different environmental conditions.
|
Breed |
Calving |
Source |
|
Afrikaner & Mashona |
89 |
Holness et al (1980) |
|
Nkone & Afrikaner |
94 |
Richardson et al (1975) |
|
Afrikaner & Mashona |
44 |
Holness et al (1980) |
|
Nkone & Afrikaner |
78 |
Richardson et al (1975) |
|
Nkone & Afrikaner |
69 |
Richardson et al (1975) |
There is conflicting evidence about the effect of weight changes prior to the breeding season on fertility. Richardson et al (1975) found a relationship between body-weight change and fertility as measured by calving rate (Figure 2), and suggest that this effect may be explained by the effect of a critical weight. Some cows may have been so heavy relative to the critical weight that they could suffer severe weight losses and still be above the critical weight during the mating season, while other cows of a very low weight would be below the critical weight for conception even without any additional losses.
Figure 2. Relationship between bodyweight change from autumn peak to mid-mating and subsequent calving rate
Although we do not deny that the various factors discussed above influence fertility, insufficient data are available about the additional effects of the various factors to allow their quantification. Therefore it was decided to simplify the fertility module of Kahn (1982). In a first approximation a minimum value is introduced for age, post-partum interval and weight at which conception can occur. At the same time the probability of conception is estimated from the literature, and this determines the calving rate.
These simplifications restrict the applicability of the model. The Kahn (1982) model investigates the effects of the feed situation on the reproduction rate of the herd via animal characteristics. The SOWTV model cannot do this, because, in this model, reproduction is independent of animal characteristics, except for those mentioned above.
The mortality module suffers from problems similar to those of the reproduction module. In the model of Kahn (1982), mortality is described by a basic mortality rate, modified by a factor for weight index, an age factor, a post-partum interval factor and a seasonality factor. It is even more difficult to validate the separate effects for mortality because 'experiments generally are not designed to elucidate mortality thresholds' (Kahn, 1982).
De Vaccaro (1974) states that mortality is influenced by breed, season of birth, birth weight and management practices. The data presented in her article represent the combined effect of the various factors. In an experiment with Brahman Shorthorn crossbred heifers, Taylor et al (1982) found that feed supplementation significantly reduced death rate. It appeared impossible to deduce the influence of individual factors from these data.
Because quantification of the individual factors influencing mortality was not possible, a simplified description was introduced. In the model mortality rates are related to age and only a minimum critical value for weight is introduced to eliminate very thin and weak animals.
Figure 3 and Table 9 show the evolution in herd size resulting from the simplified calculation method. Variation in the conception rate causes small differences in animal numbers. A 5% lower or higher conception rate increases or reduces the time necessary to double the herd number by one year (from 9 to 10 years, and from 9 to 8 years respectively). A variation in mortality rate causes considerable variation in herd size. The doubling time of the herd size increases by more than 200% when mortality rate increase by 5%. This means that mortality data are crucial in animal production models since they influence the ultimate model output decisively, i.e. other animal characteristics and even feed characteristics become of minor importance. The same conclusion holds for the age at first calving.
Figure 3. Evolution of the herd size under different experimental conditions
Table 9. Annual herd growth (%) and doubling time (years) as affected by different fertility (PPREG) and mortality (PMORT) rates and age at first calving.
|
PPREG |
PMORT |
Age at first calving (yr) |
Doubling |
Herd growth | ||
|
SOWTV |
72 |
standard1 |
>3 |
9 |
8.5 | |
|
Low reprod. |
67 |
Standard |
>3 |
10 |
7.4 | |
|
High reprod. |
77 |
Standard |
>3 |
8 |
9.6 | |
|
High mortality |
72 |
+ 5% |
>3 |
12 |
3.4 | |
|
Low mortality |
72 |
– 5% |
>3 |
6 |
13.7 | |
1 Standard mortality rates.
|
Age (yr) |
0 1 |
2 |
3 4 |
5 |
6 7 8 9 10 |
11 12 |
|
PMORT |
14.4 7 |
7 |
7 5 |
5 |
5 10 20 33 |
49 90 |
In Table 10 the annual herd growth (%) and the doubling time (years) are given for ages at first calving varying between 2 and 5 years old. The annual herd growth varies between 3.3 and 12.3%, and the doubling time varies between 7 and 22 years.
Table 10. Annual herd growth (%) and doubling time (years) of a herd for various ages at first calving.
|
PPREG |
PMORT |
Age at first |
Doubling |
Herd growth |
|
72 |
Standard1 |
>3 |
9 |
8.5 |
|
72 |
standard |
>2 |
7 |
12.3 |
|
72 |
standard |
>4 |
12 |
5.7 |
|
72 |
standard |
>5 |
22 |
3.3 |
1 Standard mortality rates
|
Age (yr) |
0 |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
|
PMORT |
14.4 |
7 |
7 |
7 |
5 |
5 |
10 |
10 |
20 |
33 |
49 |
90 |
Farmers' decisions on feeding, breeding and management practices influence livestock production. In the SOWTV model, feeding practices are explicitly described in the feed allocation module. Breeding practices are partly included in the herd dynamics module.
In a separate management module, decisions with respect to weaning, culling and buying of animals and the period during which lactating animals are hand milked can be imposed. In the SOWTV model, weaning takes place according to two criteria, adopted from Kahn (1982): the age and/or the weight of the calf. Culling and buying decisions differ for different countries, regions or populations. Social factors (e.g. wedding, status of the farmer), religious factors (e.g. an Islamic holiday, such as Eid E1 Adha, on which people are obliged to sacrifice an animal) and economic factors (income) influence culling and buying practices. For every modelling effort, culling and buying practices have to be studied, quantified and introduced in the model.
A final option included in the SOWTV management module is the farmer's decision to stop milking a cow if a specified lactation period is exceeded, if milk production is less than a specified level, or if pregnancy is advanced beyond a specified period.
The model generates data on the animal's feed intake, requirements, actual production, reproduction and mortality events and herd composition. These calculations require input data on feed characteristics (type, account and quality of the feed) and animal characteristics (genetic potential for growth and milk production of the breed, traction characteristics).
One of the future steps in the SOWTV livestock modelling research will comprise a sensitivity analysis of the model for the various parameters and estimates used. There is considerable quantitative variation documented in the literature for score of the parameters used, while no criteria are available with which to judge the accuracy of the chosen value. In this respect, the influence of the estimated mortality rates and the age at first calving on herd composition and herd number has already been mentioned. Other parameters that may be considered are the nitrogen/energy ratio of the various animal products, the estimates of maintenance requirements, the values for weaning age, stopping of hand milking, walking rate and tractive effort of the animals, etc. The results of such a sensitivity analysis are essential for correct interpretation of the model output. Another topic in the development of the SOWTV model is the validation of the different modules of the model, and the validation of the model as a whole. This requires a detailed set of input and data for the different modules as well. as for the entire model. Quite often it is difficult to get access to these data. Presently an attempt is being made to validate the intake and production module of the model with data from Bangladesh. These data comprise daily records on the animal's ration, its milk production, traction produced and reproductive status, and weekly information on the animal's growth. The first results of this validation will be discussed at the workshop.
Contributions to the research reported in this paper were made by Prof. H Bakker, Mr B Brouwner and Mr K Slingerland of the Department of Tropical Animal Husbandry of the Agricultural University, Wageningen; and by Mr E van Puffelen of the Centre for World Food Studies, Wageningen.
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Question – Are you assuming that food supplies will increase proportionally to maintain the herd? .
Answer– We assume that the animal gets the same amount of feed every day, so the feed supply is unlimited. But the number of animals in the region is determined by the available feed resources as determined by a regional planning model.
Question – You said that the probability equations for fertility and mortality are not sufficiently scientifically-based and are not applicable to your situation. Did you try to run your model and compare the results with actual data? I feel that you probably would get a more representative picture of the situation if you did take into consideration the effects of weight changes and condition on reproduction and mortality.
Answer – I don't think so. We tried to quantify the information that was available and we found that it was not possible. For instance, regarding mortality rate, everybody agrees that weaker animals are more likely to die than stronger animals, but how do you quantify this? In certain experiments you find a mortality or reproduction rate for a particular animal or breed and a particular feeding system. But when we used the same equation or the same quantification for another set of data or another set of experiments in the literature, there was no consensus at all, except that the absolute weight is more important than weight change. So we have a fixed critical weight index that determines performance, but it is not related to weight change or condition.
Question– Do you use the same level of production throughout the year?
Answer – The production level does not change with the season but if we use the model for another situation or another region, we try to find experimental results in the literature for that particular situation. If there are no new data we can run the model with the available information but also with other mortality or reproduction rates to make it clear that the results are just estimates and that they are closely related to the input data.
Question–What is the range in liveweight change within one year in mature cows?
Answer –In Bangladesh the weight change is about 20–25 kg, but the maximum weight of the animals is not more than 150 kilos.
Question – So, the animals may be within the range of liveweights at which neither reproduction nor mortality are affected?
Answer – I do not know. We have not been able to find a relation or an influence, but there may be one.
Question –Do you fix the reproduction and mortality rates in advance, independently of the feeding situation?
Answer – When dealing with a different feeding situation, we put a higher or a lower reproduction rate into the model, instead of the model producing that figure.
Question– But then are you not defeating the whole purpose of the model? Must not the model tell you which parametres are going to change if you change the feeding system?
Answer – We cannot investigate that with the model as it is constructed now. That is one of the limitations of the model, but we prefer not to say something, rather than say something about that which we do not know.
Question – So you only model growth and milk production?
Answer – We also model the herd dynamics.
Statement – But herd dynamics is a result of those fixed variables. so you have got two separate models, a feed model and a herd dynamics model, and you use them side by side.
Reply - If you mean that the two of them do not interact, you are right. The two fit together in one model and we run them together.
Question - Is the main purpose of the model to get an estimate of herd increase when there are no limitations to feed production? If so, how will you use the information that you generate?
Answer – The reason that we included herd dynamics in the model is that we then have an idea of how many animals a farmer can sell, how many he can buy. In Bangladesh animal traction is important. A farmer needs two male animals. We can look up the herd dynamics file, and see how many male animals we have at any moment. If we have too few, we have to buy, or if there are too many, we can sell. We just try to imitate the situation on the farm and adjust the herd size of the farmer. The model is not meant to be used to study effects of management interventions.
The model must be able to provide activity tables for the economists of CWFS in Amsterdam. The ultimate goal of the model is to get predictions on management and farming practices, but until now these possibilities are very limited.
Question – If the main purpose is to get activity tables for the economic model, would it not be better to get them directly from the farm statistics in Bangladesh?
Answer – This model is able to generate input/output data for livestock systems, which are used in regional LP programmes by CWFS.
If an animal is meant to grow by 1 kilogram a day, or if it is being used for traction for two hours a day, you need a set of inputs, including feed resources. Another set of activity tables is generated for cropping activities, such as for growing rice, maize, wheat. Those also go into a linear programming model, together with the regional resources. This generates a feasible development pattern over a number of years. The link between cropping and animal husbandry is in the linear programming model which is used to calculate the amount of rice, maize etc. that is grown each year. This determines the amount of crop byproducts produced which influences the number of animals that can be kept, and whether the herd can be allowed to increase.
By linking up with other interest groups, the model has assumed a multipurpose function, not only generating data for this linear programming model, but also analysing effects of management changes on a typical farm. I do not know whether this is the optimum way of developing and using such a model.
Comment –It probably would be if there was more certainty about the parameters that have been discussed here today. Most of the discussion seems to add more uncertainty to the parameter values, so that one wonders how much precision one can get with that sort of model.
Reply – I agree completely. When we use these herd or animal production models, some of the parameters are so critical that the results have to be checked in the region itself.
Comment – Mr J. Gartner of PAO is doing this sort of work in the Agriculture Towards 2, 000 project, but uses a much simpler estimate of food requirements. It starts with an estimate of the future demand for animal products and then determines the prospects for the necessary increase in animal population.
Reply – We are cooperating with FAO in crop production modelling and on the problems of animal husbandry in Thailand. All the statistics on crops, crop residues and pastures indicate that it is impossible to feed the animals that are reported to be there. So, we do not know what they are living on or whether they are there at all. That is a common problem with rural statistics.
