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"'-'-II In 004..“ owcnufiusuz It is estimated that, on the average, the forage resources were exploited in 1980 at 87 percent of its carrying capacity (Vanpraet, 1983). However, the exploitation is not uniform in all the areas. For example in the peanut basin (central zone) the utilization rate is well over 100 percent; and, even though in the sylvo-pastoral zone the average exploitation rate is below the 100 percent, signs of deterioration are noticeable in some areas, a result of high animal concentration in the dry season. The development of water supply for the livestock, and the control of the major epizooties (rinderpest, botulism, anthrax and pleuropneumonia) had the priority during the colonial period and have been pursued thereafter. Today immunization against major diseases is regularly undertaken. Effort has also been made in the construction of watering points for the livestock. However, the main problem is getting the boreholes to operate regularly. After the drought of 1972, livestock began to gain more attention in agricultural policy. Development agencies have given more interest in this subsector leading to the creation of livestock development agencies: SODESP (Societe de Developpement de l'Elevage dans la Zone Sylvo-Pastorale), PDESO (Projet de Developpement de l'Elevage au Senegal Oriental) and the Bakel Project. These institutions have as main goal the improvement of husbandry methods by providing inputs like feed supplement, veterinary supplies, better utilization of the forage resources by ensuring the the availability of water. Attempts to organize the marketing system of livestock have also been undertaken. With such intervention, the VIth development plan 1981-1985 intends to raise the meat consumption to 15.7 kg per capita by 1985 which would mean an increase in the production of 30,000 tons over the 5 year-period. 1.2. PASTORAL PRODUCTION IN SENEGAL Pastoral production is a livestock production system where people derive most of their income or sustenance from keeping domestic livestock in condition where most of the feed their livestock eat is natural forage rather than cultivated fodder or pastures (Sanford,1983). In Senegal this type of animal production system is operated in the Ferlo region also known as Sylvo-Pastoral zone. It is an area covering some 40,000 square kilometers and receiving an average annual rainfall between 300 and 500 mm, according to the site. This spatial variability is associated with an inter-annual variability which results in heavy losses in the animal population during years of drought. Figure 2 illustrates the distribution of the average annual rainfall with data collected from 5 stations bordering the zone. The data were gathered from 1934 to 1981. The rain falls from july to september, and the rest of the year is very dry, resulting in a dessication of the forage resources and the lowering of its quality. The long dry season also causes some problems of water availability Frequency (:0 12. IL 10 & 31 a ‘ f 150 200 250 300 350 400 450 500 550 600 650 700 8:3,“?11 Figure 2: Distribution of the average annual rainfall in the Ferlo region (1934-1981). 10 for the people and the animals. The water development program started in the 1950's has resulted in the reduction of the distance travelled by the animals, and also has changed the orientation of the transhumance. Herders and animals who used to migrate in the river valley and the peanut basin during the dry season, now stay in the area year round. It is estimated that only 14 percent of the animals do migrate nowadays versus 60 percent before the creation of the boreholes (ACC-GRIZA,1983). With the increase in the animal population and the limited exploitable forage resources (43 boreholes in 30,000 km2), more and more pressure is put on the the rangeland. Furthermore, the variability of the forage production and the availability of water from one area to the next have resulted in an increase in the movement of the animals within the sylvopastoral zone. The animal population is estimated between 450,000 and 550,000 cattle; 350,000 and 450,000 sheep and 200,000 and 300,000 goats (Vanpraet, 1983: SEA, 1978; SODESP, 1984: Anonyme,l982). Exploitation of the rangeland is done on a communal basis and different species of livestock are raised together: cattle, sheep and goat. During the rainy season and the cool period of the dry season (july to february), people are organized in settlements ”rumano" of 3 to 10 households "galle"; and animals exploit pastures up to 5-6 kms around the temporary pounds. With the advance of the dry 11 season, the grazing areas become farther from the settlements and people leave the "rumano" to camp near the dry season pastures while the animals start drinking at the boreholes. The distance walked between the pastures and the watering point increases and reaches in some years 20 kms by the end of the dry season; and the livestock are now obliged to drink once every two days. Estimation of the human population is difficult to make because of the movement of the herders, but from the ACC— AGRIZA report and given estimates of the human density, a population of 80,000 to 100,000 people could be living in the area. The food consumption is composed primarily with cereals (58 percent of the energy and 45 percent of the protein intake); milk comes second with 18 percent of the energy and 29 percent of the protein intake (Vanpraet,l983). The cows are milked in the morning and the evening and the milk is usually fermented. Millet is the staple food, but at the end of the dry season, it is often substituted with rice which becomes more available. The herds and flocks are composed with a majority of females (70 percent) which translates the heavy emphasis on milk production. Production traits are presented in table 3. The milk offtake is estimated at about 30 percent, value that is commonly considered for pastoral areas (Nicholson, 1984); and most of the milk offtake is auto-consumed. What is left is processed into butter and sold. 12 Table 3: Production Traits of Cattle, Sheep, Goat. Cattle Sheep Goat Age of first parturition (years) 4 1 l Parturition rate (p.100) 55 110 120 Liveweight (kg) .birth 20 2.3 2. .maturity 325 40 33 Mortality rate (p.100) .pre—weaning 15 25 30 .post-weaning 6 8 10 .adult 2 3 3 Milk production (kg) 580 45 60 Lactation length (days) 270 150 150 Offtake rate (p.100) 12 25 30 Source:ACC-GRZA(1983),FAO(1977),ILCA(1978),SODESP(undated). The monetary income is derived primarily from the sales of animals (80 percent). The ACC-GRIZA report mentions the variation in the prices of livestock and food during the period 1973 and 1980. It states that the price has trippled for cattle and goat, and doubled for sheep; and during the same period, the price of millet was experiencing similar increases. The traditional barter trade between milk and millet has diminished and there is now a specialisation with butter trade. In recent years, the terms of trade have turned to the advantage of butter. In 1957, 1 liter of butter allowed the purchase of 7.3 kg of millet or 2.6 kg of rice or 1.7 kg of sugar or 113 grs of tea; in 1980, the liter of butter purchases 10 kg of millet or 6 kg of rice or 2 kg of sugar or 300 grs of tea. 13 It is also important to note that there is a negative relationship between the seasonal variation of the price of animals and the price of millet. The price of millet tends to be higher in june—july (end of dry season-beginning of rainy season) when the price of animals is at its lowest level. In june-july the animals have lost weight and are in very bad shape while the supply of millet is very low. In october—november the supply of millet has increased and the animals have regained weight. To improve the production strategies of the pastoralists and integrate the pastoral economy more and more in the national economy, a development agency (SODESP) has been created in 1975. Its main objectives are: 1.To increase meat production; 2.To increase the well-being of herders; 3.To open the pastoral zone to the national economy; 4.To prepare for the appropriate condition for the herders to take progressively in charge their production system. The strategy used to carry out these above objectives is the stratification of the production by: 1.Considering the ferlo zone as a breeding area for the production of young males that should be sold at weaning age (8-12 months); 2.Those young animals are then transferred to growing-out zones in the peanut bassin and the river valley; 3.The grown animals are finally moved to finition centers around the cities. 14 The early removal of young animals from the pastoral area will lessen the pressure on the rangeland. However with the land pressure in the peanut basin, the availability of pasture seems to be very limited if the animals will have to depend on forage resources for their feed. A more intensive use of agricultural by-products may be a solution, but the profitability of such system should be tested. In Senegal like in other sahelian countries, pastoralism is in a Situation of crisis or in other words using the description of Sanford (1979), under pressure. Since the early 1950's pastoralism has undergone many changes; changes in the structural organization of the society, spatial control of the range resources, accessibility to dry season grazing lands, increase in the pastoral and animal populations, shift from a subsistence economy towards a market oriented system. The traditional organization of the pastoral society along clans and the control they had on particular sites in the region have been replaced by a new type of organization where the land has been turned to a public ownership. The increase in animal population and the reduction in the scale of the transhumance have increased the risk of over-grazing. The productivity of the system has not kept pace with the increase in the herding population and that of the demand of animal products from the other part of the population particularly the urban residents. Here is briefly presented the situation of the livestock 15 industry in Senegal and the national strategy to improve this sub-sector, with the objective of increasing the meat production of the country. In the next chapters, we will examine, by using a systems approach, the behavior of the system when subject to different policy options that might be used as alternatives for the improvement of the current production system. CHAPTER TWO: SYSTEMS APPROACH TO PASTORAL PRODUCTION II.1. SYSTEMS APPROACH METHODOLOGY II.1.l. DEFINITIONS A system is a grouping of parts that operate together for a common purpose (Forrester,l968), and capable of reacting as a whole to external stimuli (Speeding,l979). The way of thinking how the differents parts or components of the system are coordinated and work to accomplish a set of goals is a systems approach (Churchman, 1968). This philosophical definition where a system is viewed in a global perspective can be narrowed down in studying the problems the system faces. And systems approach is then defined as a problem solving methodology which begins with a tentatively sets of needs and has as its result an operating system for efficiently satisfying a, perhaps redifined, set of needs which are acceptable or "good" in light of trade- offs among needs and the resource limitations that are accepted as constraints in the given setting (Manetsch and Park,l982). In this process of problem solving, it is important to define: 1.The system's objectives or goals and more specifically the performance measures of the system. The objectives should not be fuzzy but rather precise, and the performance measures should tell us how the system is doing. 2.The system and its environment; the environment being what is outside the system and influence its behaviour and 16 17 performance. The resources of the system on the other hand are what are inside the boundries of the system and are used by the system to achieve the defined goals and objectives. 3.The componants of the system, their functions and goals. The componants are defined in terms of the kind of activities they perform. The performance measure of a componant should be related to the performance of the overall system. 4.The management of the system which will set the goals and allocates the resources and controls the performance of the system. II.1.2. METHODOLOGY The process of problem solving take fig; different phases: l.Feasibility evaluation: for the generation of sets of alternative solutions in order to satisfy the identified needs. The objectives of the system being defined to satisfy those needs. 2.Abstract modeling: for the development of abstract representation of the system explaining the inter- connections between the different componants of the system and their functional relations. 3.1mplementation design: to completly specify the details of the system and/or the management strategy designed in the abstract modeling phase. 18 4.1mplementation: to give physical existence to the desired system. 5.System operation: to provide a validity test of system adequacy. These different steps are operated in an iterative manner. In this work, we will focus on the first two steps: feasibility evaluation and abstract modeling. 11.1.2.1. FEASIBILITY EVALUATION 11.1.2.1.1. Needs analysis: The purpose of this step is to identify the needs the system has to satisfy and from there define the objectives and performance measures of the system. 11.1.2.1.2. System identification: In this phase, the system is defined with its boundaries, different componants, input and output variables and with the parameters which define the system's structure. The input/output relationships between the different components should also be clearly stated. The environment of the system is also defined in terms of its elements that influence the behavior of the system and its performance. 11.1.2.1.3. Problem formulation: Here, an explicit statement of what the system must do in order to satisfy the determined needs, the specific outputs and the performance sought, is developed. 19 11.1.2.1.4. Generation of system alternatives: The different management strategies that should be used in order to achieve the goals set (satisfy the needs defined) are conceptualized in this phase. 11.1.2.1.5. Determination of physical, social and political realizability: The different management strategies defined above are analyzed here in terms of their physical, social and political implications of the application of those strategies. 11.1.2.1.6. Determination of economic and financial realizability: The profitability and the ability of the system to support the financial cost of the new alternatives are tested here. II.1.2.2. ABSTRACT MODELING The information generated in the feability evaluation is used to develop the model representation of the system. The following methodology is used for this modeling process. 1.Construction of causual maps: to represent the inter- connections between the components of the system. 2.Building blocks construction: to provide -an explicit representation of the model components in terms of there inputs/outputs relations; —an explicit definition of the interactions between the components of the model; -a definition of the exogeneous variables and their 20 points of impact upon the system; -a definition of the policy variables and their points of impact upon the system; -an explicit definition of the performance variables to be used to measure the performance of the system. 3.Provision of mathematical equations that translate the functional relations between the variables and parameters within and between the components of the system. 4.Computer programming to translate the the mathematical model into a computer language to solve the equations defined above. 5.Testing by assigning values to variables and parameters to see the behavior of the model and bring further necessary refinements in the model. 11.2. ANALYSIS 9: THE PASTORAL PRODUCTION 11.2.1. FEASIBILITY EVALUATION 11.2.1.1. NEEDS ANALYSIS We have seen in chapter 1 that the objectives defined in the national policy was to increase the meat production in the pastoral zone of the country by increasing the offtake rate through an acceleration of the growth of the animals especially the young males. A need for a conservation and improvement of the range resources is also felt. In addition the economic condition of the herders has also to be improved through the increase of their income. Since the production system is still dominated by 21 subsistence, milk production takes an important place in the strategies developed by herders to satisfy their subsistence needs. The milk extracted for human consumption may in some cases affects the growth of the young animals and therefore the total meat output of the system. The national objective of increasing the meat production may be hampered by the needs of the herders to guarantee their food supply. 11.2.1.2. SYSTEMS IDENTIFICATION The pastoral system is characterized by a communal use of the rangeland with the animals being privately owned by different households. Since the rangeland and the animals are inseparable components in the production system, the area over which the production takes place has been chosen to define the physical boundaries of the system. The scale of the movements of people and animals suggests a regional approach for the study of the pastoral system. In such approach, the animals are considered being evenly distributed in the whole area; there is no restriction in their movements within the boundaries of the system. The migrations of the animals outside the pastoral area are treated as an expansion of the boundaries of the system during the periods of the year those migrations take place. 11.2.1.2.1. SYSTEMS STRUCTURE The different components of the system are difined acncording to the role they perform in the overall system. (1) The animal component: it is composed by cattle, 22 sheep and goat which are exploited for their meat, milk and hides. They transform the forage energy in useful products for the human consumption. (2)The rangeland component: its surface area, forage productivity and the accessibility of the forage determines the availability of the forage resource to the animals. (3) The management component: it is defined by the communal use of the rangeland and the husbandry method practiced: milk offtake, breeding policy. 11.2.1.2.2. SYSTEMS INPUTS The inputs to the system can be identified as: (1) Rainfall: it is an exogeneous, stochastic variable that is determinant in the forage production. (2) Prices: they are exogeneous, policy variables that influence the offtake rates (liveweight and milk) used by the herders. (3) Expenditures pp drinking water supply: the number and location of the boreholes determine the accessibility to the pastures. (4)Expenditures 93 fire control: affect the availability of forage for animal utilization. (5) Expenditures 93 animal health: affect the mortality rate of the animals. (6) Quantity 2f feed supplement. (7) Expenditures pp marketing infrastructure: the organization of the market and the facilities provided will 23 influence the diffusion of the information and enhance the desire of the herders to sell, therefore resulting in an impact in the offtake rate used. II.2.1.2.3. SYSTEMS OUPUTS The outputs measured for the model are the total liveweight offtake and the total milk offtake from the different animal species. These performance measures are considered along with the forage production change rate which measures the influence of the animal pressure of the rangeland. 11.2.1.3. PROBLEM FORMULATION In this present work, we are seeking an understanding of the long term behavior of the pastoral production system with respect to changes in the availability of forage, changes in the animal population and changes in the husbandry method used. The performance measures of the overall system are defined as the total liveweight offtake and the total milk offtake. However, the need for a conservation of the range resource base, suggests that a measure of the range condition be also used along with the performance measures defined above. The measure of range condition is taken as the forage change rate.’ However, the increase of these outputs (milk and liveweight) should be regarded with respect to their economic value. There should be an effective demand for these products to justify their production. The relative 24 prices of the animal products and that of the traded goods used by the herding community is also important in order to assess a real value for the systems outputs. 11.2.1.4. GENERATION OF SYSTEMS ALTERNATIVES The behavior of the system will be tested with different combinations of the following alternatives. 1. Increasing the liveweight offtake rate of the males. 2. Increasing the accessibility and availability of the forage to the animals by improving the water availability and reducing the forage losses through wild fires. 3. Provision of feed supplement during the the period of low forage availability. The different options will be used under a regional setting where the whole pastoral area is considered as one system and under a communal setting considered as a system centered around a borehole. 11.2.1.5. DETERMINATION 9: PHYSICAL AND SOCIAL REALIZABILITY Since this work focuses on the understanding of the behavior of the pastoral production system, the different management alternatives tested are assumed to be feasible. 11.2.1.6. DETERMINATION OF ECONOMIC AND FINANCIAL REALIZABILITY The observations defined above will apply here too. 25 11.2.2. ABSTRACT MODELING 11.2.2.1. PRINCIPLE AND DEFINITIONS A model can be defined as a representation of an object, system or idea in some form other than that of the entity itself (Shannon,l975); and modeling as the process of developing a mathematical representation of the inter- relations between the inputs and the outputs variables (Jaske, 1976). In this process, knowledge about the inputs and its transformation are essential in measuring the output In a livestock production system a simple representation of the system can described as the forage being the input, the animal being the structure of the system where the input is transformed and the outputs constitued by milk, meat, hides. However this basic representation does not tell us how the forage is made available to the animals, nor it gives a description of the activities that take place in the transformation of the input into output, nor it describes the relationships between the animals and the forage. Incorporation of these processes and relationships make the representation of the more realistic but more complex too. Study of the behavior of such complex organisation when subject to environmental and managerial changes is almost impossible without recourse to modeling and simulation, simulation being the process of conducting experiment with a model for the purpose of either understanding the behavior of the system or evaluating various strategies for the operation of the system (Shannon,l975). 26 Our purpose in this study is to develop from the feasibility evaluation, a model that describes the pastoral production system in Senegal, drawing from works that have been done in the modelisation of livestock production. 11.2.2.2. LITERATURE REVIEW ON LIVESTOCK SIMULATION MODELS During the past 25 years, interest in livestock development in Africa has increased and so has been the body of knowledge gathered in the field of animal nutrition, reproduction, genetics and health, and rangeland productivity. Evaluation and projection of the livestock production system in Africa has been a major problem for planners because of the difficult accessibility to the areas where livestock is produced and also because of the nature of the production system which depends on a perpetual movement of people and animals. Even though some knowledge has been gained in animal related disciplines, most of that work has been undertaken in research stations where the conditions of experimentation are very different from the real world. Knowledge about the production in the pastoral area has been scare and the collection of data in such system of production is likely to be expensive. Recourse to modeling and simulation could be a useful tool for planning purposes and also in research. Some of the models developed to study African livestock production or with of acertain interest in the principle they use are presented below. 27 Manetsch et al.(1971) developed a model for the cattle industry in northen Nigeria. The model disaggregates the cattle industry into two sectors: a traditional which uses traditional methods of husbandry and a modern one which is managed using pasture improvement. Projections are made by testing different policy alternatives with variables like marketing, expenditures in tse-tse fly eradication, grazing reserves, land allocated to crop and animal feed crop production. The amount of total digestible nutrients (TDN) available per animal determines the biological performance of the animals. The performance measures of the system are considered as discounted returns, foreign exchange earnings, farm income, nutrient output and income from beef and milk. Picardi (1974) studied the problem of the "commons" in the sahelian region. He developed a model to show how the communal exploitation of the land leads to overstocking, overgrazing and finally the destruction of the resource base (the rangeland) of the livestock industry in the sahel. He includes in his model some elements that contribute to the tragedy of the commons, for example the growth of the herding population leads to an increase in the animal population, which associated with the reduction in the scale of the transhumance, result of the water development policy and the expansion of cropping northwards has increased the pressure to the rangelands. Dahl and Hjort (1976), construct a model to study the rationale for a household to keep large herds. With 28 different calving and mortality rates they project the long term evolution of cattle, sheep, goat and camel herds with respect to the nutritional needs of the household. Their model tests also the effect of drought to the herd dynamics. Shuette (1976) developed a model to simulate the growth in body weight of beef cattle and the reproductive performance of the females as affected by age and body weight. The model is composed of three components: herd demography, nutrition dynamics and reproduction dynamics. The demographic component simulates the aging and weight changes of the different population groups of animals and move animals from one group to another. The nutrition component determines the feed intake and utilization of the energy for maintenance, growth and lactation. The reproduction component simulates the occurence of puberty based on age and weight, post-calving interval to first oestrus, pregnancy rates and the calving rate and time according to body condition. Jaske (1976) constructed a beef cattle entreprise model that allows investigation of alternative management decision making strategies. The model comprises five components: demography, forage growth, feed stock accounting, nutrient impact on growth and reproduction and management decision making. The management component provides the manager to make decisions on on-going operations as well as investment planning. Forage is modeled as a function of rainfall, solar radiation and temperature. The nutrient impact component 29 determines expected weight gains and reproductive performance in relation to forage intake. Graham et a1. (1976) developed a computer program to estimate daily energy and nitrogen utilization for sheep. Inputs information for the model include: intake, protein content and digestibility of the diet, age, empty body weight, fat content and feeding activity of the sheep. Environmental factors like ambiant temperature and wind speed and management factors like time of shearing and mating are also included in the inputs information. Sanders and Cartwright (1979a,b) developed a deterministic model for simulating beef cattle under a wide range of management schemes and environements. The genetics of the animals are specified as production potentials which are reached only if past and present nutritions are adequate. Intake of feed is simulated as function of the weight and physiological status of the animals and the availability, digestibility and crude protein content of the feed. The model comprises a main program that is primarily a herd dynamics submodel and three main subroutines for the simulation of the growth, reproduction and death rates. Sullivan et a1. (1981) used an interfaced forage and beef cattle model to study the catlle production system in East Africa. The model was designed to study the physical linkages between the scarce forage resource and the outputs of cattle: meat and milk. Stochastic weather variables were introduced in the forage submodel to simulate the effects of 30 weather variability in the production pattern. The cattle model used was developed by Sanders and Cartwright (1979a,b). Konandreas and Anderson (1982) constructed a general cattle herd simulation model in which animals are treated as individual entities. The model is divided into five components: forage intake, energy requirements, milk production and growth, mortality and reproduction. The model introduces great details the the factors that affect forage intake in the context of pastoral production systems. The quantity and quality of forage on offer are specified as input to the model. Policy options for weaning, breeding, milking, buying and selling of stock and supplemental feeding are provided by the user to test alternative management strategies. The model was design to study african pastoral production systems characterized by herds of small size. CHAPTER THREE: MODEL DEVELOPMENT 111.1. GENERAL DESCRIPTION 92 THE MODEL The model is divided into two components: a forage component and an animal component. These two parts interact in terms of forage availability which is an input to the animal component, and in terms of forage production change rate which measures the effect of grazing pressure on the rangeland. The forage component is representd with a causual map in figure 3. The forage production is affected by the rainfall the region receives and the condition of the soil. The rain is an exogenous factor that is that is considered in the model as a stochastic variable and the soil condition is affected by the intensity of forage utilisation which is the ratio of quantity of forage removed by the livestock and through other processes (fire, wind, other animals) over the quantity of forage produced at the end of the growing season. The quantity of forage available to the animals is determined by the standing biomass at the beginning of the previous month minus the total quantity removed by the livestock and lost through other processes. Figure 4 presents a causual map that describe the animal performance. The forage availability determines the amount of forage intake which added to the quantity of feed supplement provided gives the total energy intake. The energy is then utilized for the different functions: maintenance, reproduction, lactation and growth. 31 32 Water “Niall availability . ‘3’ ‘ (P) Land accessible for grazing Animal Population \I Potential forage production Forage available Forage losses tothe aninals Total quantity of forage . removed Forage by the animals _ Intake (E) = Emgeneous variable (P) = Policy variable Figure 3: Causual map explaining the forage production component 33 Rxage availability t/ha Ikrageinnake — Touu.Emugy Influx Fa!!quflamym ”rm 1m Growth t . MU) [ Robe Rana laconicni—_ rune Numnrci Navtxrns Nuflxr of I ___ ianhmu. loans 2 fmxal 5 1 ‘ Anumu pqmflathui I Liveweight Total l Total Milk offtake rate liveweight offtake milk offtake offtake rate (9) ' (p) (P): Policy variable Figure 4: Causual map explaining the animal performance 34 The performance of these functions determines the population dynamics and associated with the management strategy (offtake rate, milk offtake) determines the quantity of outputs produced (meat, milk). The output performances are then used with the condition of the rangeland to assess the overall performance of the system. I I I . 2 . THE FORAGE COMPONENT This part is handled stochastichally in order to incorporate in the model the yearly variation of rainfall. The spatial variation has not been taken into account here; the whole area is considered as receiving the same amount of rain. It is assumed that the animals are evenly distributed over the land area accessible for grazing. The total pastoral zone is defined in the model as variable LAND and the area accessible for grazing each month as LACC which is: LACC LAND for the 5 first months of the year beginning in july. (LAND*PLACC)+(LAND*PMIG) for the remaining months of the year during which a certain percent of the animals do migrate out of the pastoral area. PLACC is the percent of land accessible for grazing during the dry season and PIMG is the percent of animals that migrate in period of wet or normal year. In period of dry year that variable is called PMIDY. 35 The percent of land accessible PLACC is related to the availability of water during the dry season. The forage production is determined stochastically using three levels of productivity for dry, average and wet year. With a series of rainfall data for 48 years (1934 - 1981) from 5 stations bordering the pastoral zone we have computed the distribution of the rainfall using with the occurence of dry year being when the amount of rain is less than 0.8*MEAN RAINFALL and a wet year the amount of rain is greater than 1.2*MEAN RAINFALL (Penning de Vries and Djieteye, 1982). With a mean annual rainfall of 400 mm, a dry year is considered dry when the rain is less than 320 mm and wet when there is greater than 480 mm with a respective occurence of 0.3 and 0.15. Therefore a random variable between 0 and l is generated each year and when it is less than 0.3 we have a dry year, when it is greater than 0.85 it is a wet year and between 0.3 and 0.85, we have an average year. Each type of year corresponds to a potential forage production (FPPDY, FPPAY AND FPPWY). The actual forage production (AFPM) is determined for the three months of the growing season (july, august and september) by: AFPM (tons DM) (FPP * (1+FPCR)) for august and september (FPP * (1+FPCR))*JFFPP for july. JFFPP is the fraction of forage production potential for the month of july; the production potential being the standing biomass in august and september. 36 The total quantity of forage available (TFA) each month is given by: TFA (tons DM)= AFPM * LACC for month 1, 2 and 3 t = TFA * (l -( PLR/9)) for month 4 t-l = TFA * (l -( FLR/9))- TQFI for month 5 t-l t-l (TFA * (l -( FLR/9))-TQF1 )*(1+PM1G) for t-l t-l month 6. TFA * (l -( FLR/9))- TQFI for month 7, t-l t-l 8, 9, 10, 11 and 12. where: t is the current month FLR is the forage loss rate (through fire or other factors) and TQFI the quantity of forage consumed by the animals each month. The forage availability month (FAM) is defined as: FAM (t DM/ha) = TFA/LACC The forage production change rate (FPCR) is incorporated in the model in order to account for the effect of forage removal intensity in upon future productions. Following LeHouerou (1978) and Toutain and Lhoste (1978) who estimate that one third of the biomass produced can be removed by the animals and one third lost through other factors (fire, rodents). Therefore one third should be left to protect the 37 soil. However, Penning de Vries and Djieteye (1982) argue that the amount of forage required to protect the soil is not a percent of the biomass but rather an absolute value; furthermore, they state that the former authors based their estimates on observations rather than on theoretical basis. Using the nitrogen balance in the soil as an indicator of the equilibrium of the soil-plant system, Penning de Vries and Djieteye argue that 45 percent of the nitrogen can be removed without disturbing the equilibrium. In the model, we have settled at a middle ground between the two estimates and fix the allowable forage removal intensity (TFFR) at 50 percent of the production at the end of the growing season. The forage production change rate (FPCR) is given by: FPCR (TFFR - FRI)/3 if FRI > TFFR (TFFR - FRI)/20 if FRI > TFFR The 3 and 20 are the forage production change time (FPCT) which is the time necessary for an overstocked area to deteriorate and to restore a deteriorated area to its potential respectively ( Penning de Vries and Djieteye, 1982; Picardi, 1974). The forage removal intensity (FRI) is the ratio of the total quantity of forage removed (QFR) over the total forage available (TFA) at the end of the growing season. The total quantity of forage removed (QFR) is: QFR (t DM) = TQFRA3+ (TFA * FLR) where TQFRA is the amount of forage consumed by the animals 38 during the whole year 12 TQFRA (t DM) =2 TQFI i= 1,..., 12 months i i=1 where TQFI is the monthly animal consumption TQFI (t DM) =2 TQFC. i=1 3 where j represents the 3 species. The forage consumption for each species (TQFC) is the sum of the consumption of the different cohorts (QFC): l4 TQFC (t DM) =2 QFC k = 1,....,l4 cohorts k=1 k and QFC (t DM)= FINT * POP * 30 * CFKT k k k where FINT is the daily forage intake, POP the population of the cohort and CFKT is the convertion factor from kilograms to tons (CFKT = 0.001). Figure 5 represents a flow diagram for the computation of these variables. 111.3. THE ANIMAL COMPONENT This component is divided into two parts: a main program that simulates the performance variables of the animals: reproduction, lactation, weight changes, mortality and a subroutine that computes the animal population and moves the animals from one cohort to the next one. The animal performance variables are modeled using the principle 39 FX=TFFR-FRI ' FL==Emmgelkssas [PLR IHKHF=Ikxagepxodmnfion0.65 and goat } 42 = 1.86/DIGF if age > 1.5 for cattle } > 0.75 for sheep )DIGF>0.65 and goat } where DIGF is the digestibility of the forage. Forage availability month multiplier: (FAMM) This factor corrects for the effect of forage density on forage intake. FAMM FAM/THFA if FAM < THFA = 1 if FAM >/ THFA where THFA is the forage availability under which forage intake is affected. THFA is set at 0.8 t/ha for cattle, 0.75 t/ha for sheep and 0.7 t/ha for goat based on the fact that sheep graze closer to the ground than cattle and goat relie more on browse than cattle and sheep. Distance walked multiplier: DWM It corrects for the effect of animal activity on the forage intake, activity related to time spent walking to the watering point or searching for pasture. DWM = 1 if DW \< TDW l-0.05*(DW-TDW) if DW > TDW where DW is the distance walked each day of the month and TDW is the distance walked over which the forage intake is affected. TDW is set at 14 kms a day. Age multiplier: (AGEM) It adjusts for the effect of aging on forage intake. AGEM = 1 if AGE \< 8 years for cattle \< 5 years for sheep and goat l-0.03*(AGE-8) if AGE > 8 years for cattle 43 = 1-0.03*(AGE-5) if AGE > 5 years for sheep and goat. Sex multiplier: (SM) This factor adjusts for the higher feed intake of young males over the females. SM = l for cattle older than 1.5 years and 0.75 years for small stock. = 1.1 for male cattle \< 1.5 years and male sheep and goat \< 0.75 years old. Physiological status multiplier: (PM) This allows for the correction of the intake for the different biological classes in the herd (young animals, lactating females, pregnant females). PM = 1.10 for lactating females = 0.53 for unweaned animals = 0.53+0.47*(AGE-0.5) for cattle \< 1.5 years old = 0.53+0.47*(AGE+0.25) for small stock \< 0.75 years = 1. for cattle > 1.5 and for small stock > 0.75 years old. (0.00037*STP) = e for pregnant cattle (0.00067*STP) = e for pregnant sheep and goat where STP = average number of days since the beginning of the pregnancy. It is the adjusted stage of pregnany for the cohort according to the rates the animals are in the different stages of the delay that represents the 44 pregnant cohort. Forage intake coefficient: (FIC) It is a parameter specific for Species and physiological status of the animal. FIC = 0.049 for pregnant cattle = 0.054 for lactating cattle = 0.046 for other classes of cattle = 0.028 for pregnant sheep = 0.033 for lactating sheep = 0.027 for other classes of sheep = 0.026 for pregnant goat = 0.0345 for lactating goat = 0.0255 for other classes of goat Expected liveweight: (TWT) For each cohort, the expected liveweight is determined according to the average age of the animals (see section III.3.2.2.). The forage intake is then computed using the formula: 0.73 FINT (kg DM) = FDM*FAMM*DWM*AGEM*SM*PM*FIC*(TWT ) III.3.1.1.2. TOTAL METABOLIC ENERGY INTAKE The total metabolic energy intake (TMEI)is defined as the sum of the energy from forage intake (FIME) plus the energy from feed supplementation (FSME) plus the energy from milk consumption for young animals (MEMC). The metabolic energy from these differents sources of nutrients is given by: 45 FIME (MJ)= l4.6*F1NT*DIGF FSME (MJ)= 14.6*FSI*DIGS MEMC (MJ)= 0.93*MEC*QMAC where FINT = forage intake (kg/day) FSI = quantity of feed supplement (kg/day) QMAC = quantity of milk available for each young (kg/day) MEC = energy content of milk (MJ/kg) DIGF digestibility of the forage DIGS digestibility of the feed supplement The 14.6 factor comes from the fact that gross energy content of tropical forage is at about 18 MJ/kg DM (Minson, 1981) and that of most feed is at about 18 MJ/kg DM since the dominant constituents are carbohydrates with an energy value of 17.5 MJ/kg DM (MAFF, 1975); and the metabolizable proportion of the gross energy is 0.81 (19 percent of the digestible energy is lost through the urines and methane); thus 18*0.81=l4.6 . The 0.93 is the digestibility of milk. The energy content of milk set at 0.35 MJ/kg for cattle, 0.40 MJ/kg for sheep and 0.375 MJ/kg for goat ( Dahl and Horjt, 1976; Nicholson, 1984). The quantity of milk available for consumption is given by: QMAC = ADMY * (1 - MOFT) where 46 ADMY = actual daily milk yield (kg) MOFT = milk offtake rate. III.3.1.2. ENERGY UTILIZATION The metabolic energy intake is allocated to the different biological functions according to the following hierarchy: maintenance and pregnancy satisfied first, in second position comes lactation and growth is satisfied the last. Maintenance and pregnancy are treated as inseparable functions. Functions like reproduction and mortality are also influenced by the nutritional plan through the condition index defined in section III.3.2.2. The metabolic energy intake is converted into net energy with different coefficients of efficiency: -for maintenance, KM = 0.55+0.3*Q (Konandreas and Anderson, 1982; MAFF, 1975) where Q is the metabolizability of the feed offered. Q = 0.81* DIG (Konandreas and Anderson, 1982) where DIG is the digestibility of the feed. -for pregnancy KP = 0.72 (MAFF, 1975) -for lactation KL 0.60 (Konandreas and Anderson, 1982) -for weight gain KG 0.03+0.81*Q -for energy released from body reserves KB = 0.82 -for energy from milk consumption KK = 0.75 47 III.3.1.3. ENERGY REQUIREMENTS III.3.1.3.1. MAINTENANCE Maintenance requirement is estimated using the formulas given in Konandreas and Anderson (1982) and King (1983) who draw their work from Blaxter (1969) and Webster (1978). The metabolic energy requirement for maintenance is defined as: 0.73 ERM ((0.376*AWT )/KM)+(0.0021*AWT*DW) for cattle 0.73 ((0.243*AWT )/KM)+(0.0024*AWT*DW) for sheep and goat where AWT= actual liveweight KM = coefficient of conversion of metabolic energy into net energy; DW =the distance walked daily during the month (km/day) The second part of the equation allows for animal activity. The coefficient 0.0021 in the Konandreas and Anderson's model was maintained here for cattle; and for sheep and goat, we refer to the value given by Clapperton (1964) for sheep. III.3.1.3.2. PREGNANCY For pregnant females, the energy required for pregnancy and the energy required for maintenance are combined into one requirement (ERMP). 0.0106*t ERMP = ERM+1.13*e for cattle (MAFF, 1975) 48 0.0072*t = (1.2+0.05*AWT)*e +0.0024*AWT*DW for small stock (MAFF, 1975) III.3.1.3.3. LACTATION The net energy requirement for lactation depends on the potential daily milk yield (PDMY in kg) and its energy value (MEC in MJ/kg). With an efficiency of conversion of 0.60, the metabolic energy requirement for lactation is: ERL = 1.67*MEC*PDMY III.3.1.3.4. LIVEWEIGHT GAIN The energy in excess from maintenance, pregnancy and lactation is used for weight gain (LWG). The energy value of gain (EVG) is related to the liveweight and the energy in excess (MEE). EVG (MJ/kg) = 6.28+0.3*(MEE*KG)+0.0188*AWT (MAFF, 1975) Since the MEE is equal to (LWG*EVG)/KG , therefore the metabolic energy required for gain (MEG) can be defined as MEG (MJ) = (LWG*(6.28+0.0188*AWT))/((1-0.3*LWG)*KG) For lactating animals the energy in excess of lactation requirement is used with an efficiency equal to that of lactation (KL). III.3.1.3.5. MOBILIZATION Q: BODY RESERVES Body reserves can be mobilized to satisfy the energy deficit for maintenance or lactation. The efficiency of 49 utilization of body reserves for lactation is at about 0.82 (MAFF, 1975) and the same value is used for maintenance and pregnancy. With an energy value of 20 MJ/kg, the net energy available from the mobilization of body reserves (E) is: E (MJ) = 0.82*20*DWL where DWL is the daily weight loss (kg). III.3.2.HERD/FLOCK DYNAMICS The amount of energy received will determine the level of performance of the production traits of the animal: reproduction, growth, lactation and mortality. These traits combined together will determine the dynamics of the herd/flock. 11.3.2.1. DEMOGRAPHY The population in each species is disaggregated into 14 cohorts according to sex, age and physiological status. The cohorts have been indexed as follows: POP(l) = mature breeding females POP(2) = replacement females 3 POP(3) replacement females 2 POP(4) = replacement females 1 POP(S) = weaned females POP(6) female calves POP(7) mature males POP(8) = male-class 3 POP(9) male-class 2 50 POP(10)= male class 1 POP(11)= weaned males POP(12)= male calves POP(13)= pregnant females POP(14)= lactating females A cohort is considered as a delay process with flows interconnecting different delays to represent the overall process of maturation (Jaske, 1976). A distributed delay subroutine (DELAY) (Llewellyn, 1965 ) with proportional loss rate is used. The delay is defined by a linear differential equation: k k-l d y(t) d y(t) a + a + . . .+ a y(t) = x(t) k k-l l k k-l dt dt the unlagged variable where x(t) y(t) the lagged variable The order of the differential equation k defines the order of the delay which is the number of stages the individual entities in the delay go through to accomplish the maturation process. The delay is also characterized by the time the maturation process takes (DEL). A proportional loss rate is applied to the delay to account for the mortalities, offtake, transfer and addition to the cohort. The transfers concern the animals that conceive and are moved to the pregnant cohort. The subroutine DELAY simulates the movement of animals 51 from one cohort to the next one and also updates the rate of passage of the animals to each stage of the delay. This rate of passage is defined as RPOP. Since the delay does conserve flow because of the losses, the population of cohort is computed with the formula: K i POP =-_>: (RPOP * (K /DEL )) i j=l ij i i The proportional loss rate (PLR) is defined as: PLR = TDR +ADDRT +((PREG /POP )*DT) i i i i i where: TDR = total death rate ADDRT = net rate of addition to the herd or flock. TDR = DR +DRST i i i with DR = the natural death rate not the DRST = death rate due to starvation (to be defined later) ADDRT = OFT -(PUR /POP ) i i i i where OFT = offtake rate PUR = number of animals added to the cohort through purchases or gifts PERG = number of animals that get pregnant DT = the time increment (=0.0833) 52 Figure 6 presents a flow diagram of the movement of the animals from cohort to cohort. Each cohort is characterized by an average age which is adjusted to the rates of passage of the animals in the different stages in the delay. Since the maturation process is here function of age, each stage in the delay can be described by an average age and the age of the cohort-delay is given by: AGE =§ (RPOP * AGER ) /$ RPOP i j=l ij ij 3=1 ij where AGER = the average age of the animals in the stage j. For the pregnant cohort, the age is the adjusted average of the ages of the animals entering the cohort. n n AGE =2 (PREG * AGE )/2 PREG 13 i=1 i i i=1 i For the lactating cohort, the average age is taken as AGE = AGE + 0.75 14 13 III.3.2.2. GROWTH The animals in each cohort are described by an average age and an expected average weight (TWT). The expected weight is defined as a function of age and the weight curve can be divided into 2 sections: one with an accelerating slope and one with a decreasing slope (figure 7 ). The equation that describes the weight is in the general form: 53 ROUT(13_)j 4% . DEL(6) E3 11:1.(12 ‘PLR(6)¢' POP(6) K(6) R<12 POP(12)-o PLR(12) ROUT(6) 'ROU'I‘(12) DBMS) DEL(ll’r'opm) mun) PLR(5)- 1209(5) m5) run) ROUNS) ROU'I‘(11) mm mum) ' ‘ ‘ PLR(10) PLR(4) POP(4) K(4) mm PREXSM) ,Rou'rm Rou'rum mum ' ‘ 11149) I pram POP(3) K(3) _ K(9) POP(9) pram FRESH) Rou'rm ROUT(9) 111(2) ,- , , - A ‘ui413)ngLQp PLR(2) POP(2) 1((2) pop(13) “13) me) PLR(8) Rama) KITNZ) 3001113) Raffle) - DEL(1) mLUA) ('7) 913(1) 909(1) m1) 1 POP‘14)K(14) K(7) POP(7) PLR(7) } FRESH) RGINI) RUIN?) Figure 6: Flow diagram for the animal demography 54 Weight Age rtiotlulc In--nulnnllulim TI WI ..----.-------. BWI Figure 7: Expected liveweight curve 55 2 a +b *t+c *t for the section AB where t \< TI; 1 l l TWT 2 a +b *t+c *t for the section BC where TI < t \< TM 2 2 2 where t = age in months TI= the age corresponding to the point of inflection where the curve changes slope TM= the age at maturity in months The coefficients a , b , c , a , b and c are estimated 1 l l 2 2 2 using birth weight (BW), the age corresponding to the point of inflection (T1), the weight at the point of inflection (WI), the age at maturity (TM) and the weight at maturity (WM) 0 a = BW l b = 2*(TM*(W1—BW)-TI*(WM-BW))/(T1*(TM-TI)) l 2 c = (TI*(WM-WI)+TI*(WM-BW)-TM*(WI-BW))/(TI *(TM-TI)) 1 2 2 2 a = (T1 *WM+TM *WI—2*TM*TI*WM)/(TM-TI) 2 2 b = (2*TM*(WM-WI))/(TM-TI) 2 2 c = -(WM-WI)/(TM-TI) 2 For t greater than TM, the TWT is WM. 56 Using this procedure with data provided for BW, TI, WI, TM and WM (table 4 ), the equations that describe the expected liveweight as function of age have been constructed for the sex groups of each species considered. Table 4 :Data points for expected growth curve. Cattle: male 21 18 180 60 350 female l9 18 150 60 300 Sheep: male 2.5 12 25 36 45 female 2.2 12 20 36 35 Goat: male 2.2 12 20 36 35 female 2.0 12 18 36 30 For cattle: 2 Males TWT (kg)= 21+9.57*t-0.04l*t t \< 18 months 2 = 3.06+ll.57*t-0.096*t l860 months 2 Females TWT (kg)= l9+7.4l*t-0.008*t t \< 18 months 2 = -6.12+10.2*t-0.09*t 1860 months For sheep: 2 Males TWT (kg)= 2.5+2.08*t-0.02*t t\<12 months 2 = 2.5*t-0.035*t 1236 months 57 2 Females TWT (kg)= 2.2+1.72*t-0.02*t t\<12 months 2 = l.25+1.9*t-0.03*t 129years For sheep and goat AMY = 0.2+0.53*t-0.09*t2 t\<2.5 years = l 2.55.5 years Once the milk production potential is defined according to the age of the lactating cohort, the lactation curve is used in order to take into account the different stages the animals in the cohort. Assuming that for cattle, 35 percent of the milk is produced during the first two months of lactation, the proportion of the production during the following months are estimated by the formula: 2 2 H(n) = ((N +N+2)*H(l,2)-8)/(2*(N -3*N+2) 2 -(n*(N*H(1,2)-2))/(N -3*N-2) where n is the month of lactation; N is the lactation length; H(l,2) is the proportion of milk produced during the first two months of lactation. 62 This formula is valid if the following relationship between H(1,2) and N holds: 2/N \< H(1,2) \< (4*N—8)/(N2-N-2) or N between 6 and 9 months; therefore applying only for catlle. Thus for the three stages defined in the lactating cohort, the proportion of milk produced during each stage is 0.504, 0.340 and 0.16 respectively. For sheep and goat, assuming a more stable lactation, we have set the proportion of milk production for the three stages of the lactation period to be 0.45, 0.35 and 0.20 respectively. The potential milk production at each stage is given by: PMYl = 0.504*TPMY*AMY } } 0.340*TPMY*AMY } for cattle } } PMYZ = PMY3 = 0.160*TPMY*AMY PMYl = 0.450*TPMY*AMY 1 PMYZ = 0.350*TPMY*AMY 1 for Sheep and goat PMY3 = 0.200*TPMY*AMY 1 where TPMY = average potential milk yield for the breed under consideration: TPMY = 580 kg/lactation for cattle 45 kg/lactation for sheep 60 kg/lactation for goat The potential milk yield (PMY) for the cohort is calculated using the rates at which the animals are in the three stages of lactating cohort: 63 PMY = (3*(RPOP1*PMY1+RPOP2*PMY2+RPOP3*PMY3))/ (RPOP1+RPOP2+RPOP3) where RPOPl, RPOP2 and RPOP3 are the rates. The potential daily milk yield (PDMY) is adjusted with the condition index (CI) of the animals at the end of the previous month. PDMY = (PMY/LACL)*(CI/0.3) if CI < 0.3 = PMY/LACL where LACL = lactation length. III.3.2.4. REPRODUCTION Since information on parturition rate are more available in the litterature than that of conception rate, in the model, we consider conception rate as parturition rate; therefore assuming that there is no abortion and each conception will result in the birth of a new animal. Four cohorts of cattle and five of smallstock are considered as being able to conceive. These are for cattle cohort l, 2, 3 and 14; and for smallstock, cohort l, 2, 3, 4 and 14. In the cohort 14 only animal in the third stage of lactation can conceive. This assumption is made according to the fact that an interval between parturition and next conception of 180 days for cattle and 100 days for smallsock can be considered an upper limit for the animals raised in the system under study. These limits correspond to a calving rate of 0.8 and a kidding or lambing rate of 1.44 . 64 Conception rate depends on age and condition index. The conception rate can be described as function of age by two curvilinear segments AB and CD and a horizontal one BC (figure 9 ). The earliest age for conception (T1) is set at 2.25 years for cattle and 0.83 years for goat and sheep. Reproduction performances are at the maximum between age T2 and age T3 which are set at 4 and 8 years for cattle and 2 and 5 years for smallstock. Conception rate is lower at age (T4) 11 years for cattle and 6.5 years for sheep and goat. The equations that describe the curvilinear segments are: 2 a +b *t+c *t for T1 \< t \< T2 1 1 1 TCR 2 a +b *t+c *t for T3 < t \< T4 2 2 2 and for the horizontal section TCR = CR23 for T2 < t \< T3 where: t = age in years; CR23 = the conception rate for the species between T2 and T3 a , b , c , a , b and c are coefficients to be determined. 1 l l 2 2 2 2 2 a = (T1*(Tl-2*T2)*CR23+T2 *CRl)/(T2-T1) 1 2 b = (2*T2*(CR23-CR1))/(T2-Tl) 1 2 c = (CR1-CR23)/(T2-Tl) 65 Conception rate R23 __-___,,,_ _ _ _._ R1 R4 .-- - -- ' ! I ' 1 I I I I A p---— no Age H_-_----- T3 ....n *3 N #3 h Figure 9: Conception rate curve as function of age 66 2 2 a = (T4*(T4-2*T3)*CR23+T3 *CR4)/(T4-T3) 2 2 b = (2*T3*(CR23-CR4))/(T4-T3) 2 2 c = (CR4-CR23)/(T4-T3) 2 where CR1, CR23 and CR4 are the conception rate for the breed at age Tl, between T2 and T3 and at age T4 respectively. With data provided for CR1, CR23 and CR4 (table 5 ),the following conception rate equation curves have been obtained: 2 Cattle: TCR = -2.l7+l.4*t-0.18*t t\<4 Years = 0.70 48 years 2 Sheep: TCR = -1.10+2.20*t-0.55*t t\<2 years = 1.10 25 years 2 Goat: TCR = -1.34+2.64*t—0.66*t t\<2 years = 1.30 25 years 67 Table 5 : Average conception rates CR1 CR23 CR4 Cattle 0.15 0.70 0.15 Sheep 0.35 1.10 0.35 Goat 0.40 1.30 0.40 The conception rate is then adjusted using the condition index (CI) of the previous month to give the actual conception rate. The adjustment factor (CRM) is defined as: CRM 0.6+l.5*CI if CI \< 0.2 O.833+0.333*CI if 0.2 < CI \< 0.9 3.233-2.333*CI if CI > 0.9 and the actual conception rate (CR) becomes: CR = TCR*CRM III.3.2.5. DEATH RATE DUE IQ STARVATION We introduce here the effect of nutrition on mortality rate to peak up the increase in death rate during period of starvation. Since we do not have data to estimate this increase in mortality, we consider that death occurs if the condition index decreases below 0.5 with the assumption that the animals are normally distributed in each cohort. The annual death rate increases exponentially as the condition index decreases and at CI equal zero the death rate due to starvation reaches 0.5 . Since we are dealing with an 68 aggregated system, a maximum of 50 percent of the animals die when the condition of the cohort is equal 0. The equation that describes that death rate is: A*CI DRST = e -0.5 where A = -l.38643 . III.3.3. ANIMAL PERFORMANCE For the different classes of animal, the energy is used to satisfy maintenance requirements (maintenance + pregnancy for pregnant females) first and if there is an excess, it is used for lactation (lactating females), and there still is some energy left, it will be used for growth. III.3.3.1. LIVEWEIGHT CHANGE FOR PREGNANT FEMALES Maintenance and pregnancy requirements (ERMP) are compared with the total energy intake (TMEI). If TMEI is greater than ERMP, then the excess energy is used for liveweight gain and the weight gain will process up to the maximum allowable daily weight gain (DGMAX). If TMEI cannot satisfy the ERMP, then the animal will loss weight up the maximum allowable daily weight loss (DLMAX). Figure 10 represents the algorithm for the computation of liveweight change for pregnant females. III.3.3.2. MILK PRODUCTION AND LIVEWEIGHT CHANGE FOR LACTATING FEMALES If the energy requirement for maintenance (ERM) is less than Caque energy deficit for manmemmxxeamd pregnancy ~Cmmmue weight loss to cover energy deficit um. , Commie dai_ly weight chuge DWZ==uuum 69 Caque emx£s i=1 j=1 k=1 k k k nuauemu ocwuuuona uOu cofiu0500uo xAEE can oomcuzo unauoz ham anuuuooHc T. u Qflxzuo 883+ .5. u :2 58.. do new may a. £39: «Ha whamum uos xaaouou coguuuouq £30. 316 m... 3:33 83384 u g u QB 05 unmwfls >33 3:959 71 um... 533 no: 8338.. .38 u 95 «Sufi £34. 316 mJREBU 3.3 58 339 >26 33:38 mflxruo flun- adfiB. .8338; _ dummy :ofififlonfinaa mflaamfianSBiu maxim :Mlnfldaor Qfixrbo 3!. 83303 @398 3 wood 330; can Anzac coca ucmfimz Qfizdufludi69fizflflc um... .533 go: 8338.. Sago £038 .5st 3 wood unmams £3 £038 mnhaufizia.mgbonfi 83 339 3350 came \— mflhamfizaahfia ufiofiumc >ouuco moaning E 72 FIME ___J COKE“? . Conmne energy defic1t energy in s for naintenance frcm 'naintenance Coque Cqmnme. weight loss to cover potential daily maintenance deficit weight gain (DWL) (rm) ‘ CGENWE } ‘ Cammne daily weight daily weight N0 cmuge . ‘dmmg um = DWL on: = mt; ‘ YES ' 1 Caque . 90mmme 1 daily weight daily weight cmuge cmuge me = DIMAX M; = m Camxme wenfim.atthe endcxfmbHU1 4“ AWI‘ =AWI‘ mono t t-l fi ' ' 1es and ' 12: A1 orithm for weight changes for ma Figure nog-pregnant, non-lactating females 73 where i represents the species, j, the 12 months of the year and k, the 14 cohorts of each species. DT = time increment (= 0.0833) POP = population OFT = liveweight offtake rate AWT = actual liveweight TMOFT =g g (POP * TAMY * MOFT ) i=1 j=l 14 i where i = the 3 species j = the 12 months of the year POP = population of the lactating cohort l4 TAMY = total milk production during the month MOFT milk offtake rate Figure 13 represents a block diagram for the computation of the system's performances. 74 alt 'IINOF‘T - - p1 = Function estimate of forage intake F2 s Function estimate of metabolic energy of forage F3 = Function utilization of metabolic energy intake Figure 13: Block diagram for the whole system CHAPTER FOUR: SIMULATION AND RESULTS The model is programmed in FORTRAN V. In the program the variable related to each species are preceded by C for cattle, S for sheep and G for goat (Annexes ). The model is used for two different types of system: - a regional system where the whole pastoral area is considered as a single system characterized by migration of a portion of the animal population outside the system during the dry season. - a communal system which is considered as an alternative management strategy where the system is organized around a borehole and the migrations of the animals outside the boundaries of the system is no longer possible. For both cases, the rangeland is communally exploited by the animals in the system. The year starts in the model in july, the beginning of the rainy season; and the model is run for 10 years in each simulation experiment. The time increment is 1 month and each month of the year is characterized by an average day held constant during the month and the performances of the animals during that day are then multiplied by 30 .to give the performance at the end of the month. Since we do not have precise values of the model parameters, we have relied on information drawn from the literature on similar systems of production. For the initial values assigned to the state variables, we have used our own judgement on the basis of information available from 75 76 the particular system under study. Since there is no information about the performance the system, a validation of the model has not been undertaken. A baseline run is done according to what is believed to be the estimated values of the parameters of the system. IV.l. ASSIGNMENT 9E VALUES FOR PARAMETERS AND INITIAL VALUES FOR STATE VARIABLES IV.1.1. THE FORAGE COMPONENT The values assigned to the parameters are presented in table 6 . Table 6 : Values assigned to forage component parameters REGIONAL COMMUNAL LAND (ha) 3000000 70000 PMIG 0.14 0. PMIDY 0.30 0. FPPWY (t DM/ha) 1.60 1.60 FPPAY (t DM/ha) 1.20 1.20 FPPDY (t DM/ha) 0.80 0.80 JFFPP 0.50 0.50 TFFR 0.50 0.50 For the state variable FPCR, it has been initialized at 0. 77 IV.l.2. THE ANIMAL COMPONENT The parameters defined in the model are presented in the following tables: 7, 8, 9 and 10 Table 7 : Values assigned to animal population parameters Cattle Sheep and Goat DEL (years): (1) 8 4 ( 6, 12) 0.75 0.4167 ( 5, 11) 0.75 0.3333 ( 4, 10) 0.75 0.50 ( 3, 9) 0.75 0.75 (13, 14) 0.75 0.4167 ( 2, 8) l. l. (7) 3. 2. K (1) 8 4 (2 to 4) 3 3 (5, 7, 11) 3 2 (6, 12) 4 4 (8 to 10) 3 3 (13, 14) 3 3 The figures in parentheses represent the cohorts. 78 Table 8 : Values assigned to forage digestibility Month Cattle Sheep Goat 1 0.54 0.56 0.58 2 0.60 0.62 0.60 3 0.60 0.62 0.60 4 0.57 0.58 0.58 5 0.55 0.55 0.56 6 0.53 0.53 0.55 7 0.52 0.53 0.53 8 0.50 0.51 0.53 9 0 50 0.51 0.52 10 0.48 0.50 0.52 11 0 46 0.48 0.50 12 0.46 0.48 0.50 Source: Wilson et a1. (1983): Abassa, (1984) 79 Table 9 : Distance walked (kms/day) Cohorts Month .............................................. (6, 12) (5, ll) (1 to 4 and 7 to 10) 1 2 5 8 2 2 5 5 3 2 5 5 4 2 5 6 5 2 6 7 6 2 6 9 7 2 8 10 8 2 8 12 9 2 8 14 10 2 9 14 ll 2 10 16 ---, 80 Table 10: Values assigned to other parameters used in the model """"""""""""""""" EQZEI;""§£;;5""53;E'"’- WCV (6, 12) 0.30 0.30 0.30 (5, 11) 0.27 0.27 0.27 (1 to 5, 7 to 10, 13, 14) 0.25 0.25 0.25 SR 0.50 0.50 0.50 TDW (kms/day) 14 l4 l4 TFA (t/ha) 0.80 0.75 0.70 FIC (l, 12) 0.046 0.027 0.0255 (13) 0.049 0.028 0.026 (14) 0.054 0.033 0.0345 DR (6, 12) 0.10 0.22 0.27 (5, 11) 0.030 0.12 0.14 (l to 4, 7 to 10, 0.020 0.06 0.08 TPMY (kg/lactation) 580 40 60 LACL (days) 270 150 150 MEC (MJ/kg) 3.50 4. 3.75 Figures in parentheses represent the cohorts. 81 Initial values assigned to animal state variable population, age and weights are presented in tables 11, 12, 13. For the variable population, a herd/flock compostion has been used with the proportion of females being around 72 percent for both species. Table 11 : Initial values for cohort populations Cattle Sheep Goat Cohorts ------------------------------------------------- R(l) C(l) R C R C 1 75000 1500 70000 1200 40000 800 2 20000 350 10000 150 5000 100 3 30000 500 25000 400 15000 200 4 40000 750 30000 450 15000 250 5 40000 750 20000 350 12000 250 6 30000 600 25000 500 17000 350 7 15000 350 10000 200 5000 100 8 15000 350 10000 250 8000 125 9 20000 400 20000 350 12000 250 10 30000 600 25000 400 12000 275 11 40000 650 17000 300 10000 200 12 30000 600 25000 500 17000 350 13 65000 1250 60000 1150 45000 800 14 60000 1200 50000 1000 34000 700 82 Table 12 : Initial values for average age (years) of the cohorts 'ESESEE; """"""""""" 8.1221:""éfi;;;';;5'53;£"" 135 """""" 2, 8 3.5 2.5 3, 9 2.625 1.625 4, 10 1.875 1. 5, 11 1.125 0.583 6, 12 0.375 0.208 7 5.75 4. 13 6.50 3.50 83 Table 13 :Initial values for expected weight (TWT) and actual weight (AWT) in kg. ooqmm 10 11 12 l3 14 350 319 279 214 143 63 300 300 315 288 245 193 129 57 280 270 45 44 36 25 16 35 35 41 4O 33 23 15 33 32 35 34 28 20 13 3O 30 12 6.5 29 28 The condition index is set at 0.4 for all the animals in the system. 84 IV.2. RESULTS 9: THE SIMULATION IV.2.1. THE REGIONAL SYSTEM A 2 by 2 by 3 factorial experiment is conducted with - 2 levels of forage availability and accessibility (FAA) defined by the proportion of land accessible for grazing (PLACC) and the forage loss rate (FLR) - 2 levels of liveweight offtake rate (OFT) - 3 levels of feed supplementation (FS) These facors and levels are defined as follows: FAA: PS: The level 1: PLACC = 0.75 FLR =0.25 level 2: PLACC = 1. FLR = 0.10 level 1: current offtake rate (table 14 ) level 2: increase of the offtake rate of the males (table 14 ) level 1: without level 2: 0.0025 kg/kg liveweight (0.003 kg/kg liveweight for lactating females) level 3: 0.0050 kg/kg liveweight (0.006 kg/kg liveweight for lactating females) feed supplement is considered as having 65 percent digestibility and is provided during the second half of the year from january to june. 85 Table 14 : liveweight offtake rate Offtake rate Cattle Sheep Goat 'iéGIIZ'IEECIS?IS?123"""'BT"'""'BT""""'BT"" (l) 0.08 0.20 0.25 (2 to 4) 0.03 0.10 0.15 (11) 0.08 0.15 0.18 (7 to 10) 0.35 0.50 0.60 Level 2: (5,6,12,13,14) 0. 0. 0. (l) 0.08 0.20 0.25 (2 to 4) 0.03 0.10 0.15 (11) 0.60 0.40 0.45 (7 to 10) 0.60 0.75 0.80 E1522”;TEQQZSSQ'EQEEQQE"'ZSZ'ZSESEEQT"'EE; underscored values are the increased liveweight offtake rates. The FAA FAA FAA FAA FAA FAA FAA FAA FAA FAA FAA FAA The 12 combinations are numbered as follows: l-OFT l-OFT l-OFT 1-OFT l-OFT 1-OFT Z-OFT 2-OFT 2-OFT 2-OFT 2-OFT 2-OFT trial l-FS l-FS l-FS 2-FS 1-FS 1-FS l-FS Z-FS Z-FS 2-FS 1 is 86 NHL» 00 2 3 considered as the baseline. The milk offtake rate (MOFT) is held constant in the model and is equal to: MOFT = 0.30 for cattle = 0.20 for goat = 0. for sheep 87 The random variables generated during the 10 year simulation runs give: - 3 dry years (year 4, 6 and 7): - 1 wet year (year 2) and - 6 average years (year 1, 3, 5, 8, 9 and 10) which correspond to probabilities of occurences of 0.30, 0.10 and 0.60 for dry, wet and average year respectively. The values are close to the computed probabilities form the actual data of the distribution of the rainfall in the area which are 0.30, 0.15 and 0.55 respectively for dry, wet and average year. The results of the 10 years simulation runs have been averaged and presented in table 15 and figures 14, 15 and 16. Figures 17, 18 19 and 20 illustrate the yearly evolution of the total liveweight offtake and the total milk offtake. The comparisons of the baseline run with the other combinations of alternatives show that for the total liveweight offtake, increasing the offtake rate alone yields an average production of only 5 percent higher than the baseline output (trial 1 versus trial 4). And the improvement in the forage availability and accessibility alone results in an increase in the liveweight output by 4 percent (1 vs 7). The provisions of feed supplement has raised the production by 27 and 53 percent for the two levels of feed supplementation (1 vs 2 and 1 vs 3). The combinations of the different factors has revealed a synergestic effect in the total liveweight offtake. Table 15: offtake (FPCR) (TMOFT) and 88 forage production Total liveweight offtake (TLWOFT), total change milk rate -0.0221 10 11 12 -0.0345 -0.0423 -0.0183 -0.0300 -0.0361 -0.0014 -0.0108 -0.0170 -0.0007 -0.0066 -0.0115 89 /////////////////////// 7///////////////////T 7//////////////1 ////////////////////////: x//////////////////-. ///////////////; //////////////////////; /////////////////; ///////////////. /////////////////////. //////////////////; zoo.“ 000000000000 11111111111 A-vasaofiv $295 5.4.53 52E.— 3.8. 1% TRIALS ight offtake for 90 /////////////////////A u 7///////////////////A.u r///////////////¥m 7///////////////////////. //////////////////////. //////////////////////2{ m /////////////////. ///////////////////// 2... _____ 314 08‘" 000000000000 1111111111 tal milk offtake for the regional rage to 15: Ave Figure Aéiaonb 3265 Eric 55 3.2. 91 V////A 12 ////////A ////x a //////////////////A 7//////// /////////////////////z z. //////////////// / ////////// A A! !v HP: “G a: ZOEDAOQQ “61‘?! age forage production cha l6: Aver the regional system 92 Increasing offtake rate and forage availability has resulted in a 8 percent higher production (1 vs 10); whereas the combination of offtake rate and feed supplement increased the output by 33 and 58 percent (1 vs 5 and 1 vs 6). Forage availability and feed supplement together yielded a production 35 and 62 percent higher than the baseline level (1 vs 8 and 1 vs 9). The combination of the three factors boosted the total liveweight output from the baseline level by 39 and 65 percent (1 vs 11 and 1 vs 12). For the total milk output we have the same scenario with a percent increase for the baseline level of 4, 4, 24, 44, 7, 30, 48, 32, 49, 35 and 50 respctively for comparisons 1 versus 4, 7, 2, 3, 10, 5, 6, 8, 9, 11 and 12. For the forage production change rate, the increase in the forage availability and accessibility has reduced significantly the rate of deterioration as compared with the baseline situation under which the system has been overstocked. The comparisons of the baseline and the trials where the forage availability has been improved has resulted in a reduction in the deterioration rate of 94, 51, 23, 97, 70 and 48 percent for 1 versus 7, 8, 9, 10, 11 and 12 respectively. The increase in the offtake rate alone has a positive effect on the deterioration of the rangeland with a 17 percent reduction from the baseline situation (1 versus 4); however when associated with the feed supplement, there is an increase in the deterioration rate of 36 and 63 percent 93 (1 vs 5 and 1 vs 6). The provision of feed supplement alone has a detrimental effect in the protection of the range resources with an increase in the deterioration rate of 56 and 91 percent (1 vs 2 and lvs 3). The provision of feed supplement has resulted, in the different trials, in an increase in the population through the reduction in the mortality rate during the period of feed scarcity. The level of liveweight offtake rate used was not enought to offset the growth in animal population. The yearly evolution of the production for the different trials (figures 17 and 18) shows that with the level of offtake rate used in the study, there is no sustained increase in the yearly liveweight output. The quantity of liveweight offtake falls bellow the baseline level at the third year and will finally reach again the baseline production only by the end of the seventh year. This suggests that the productive capacity of the system could not keep pace with the level of liveweight offtake rate used. For the milk production (figures 19 and 20), the yearly output is mainly affected by the occurence of drought (year 4, 6 and 7) through a low nutrition plan and an increase in the mortality of the animals. m0 (mamas: "W 94 an - so - 34 _. :2 - so » zu ' 26- ..-» at IO . _— : 1. ‘\ 18 _ d N a... fl 0 O NI O D 5 mm 0: trial 1 = trial 3 x: trial 5 oz trial 2 -r= trial 4 v= trial 6 Figure 17: Total annual liveweight offtake for the regional system (1) LFHHHHGHT(NUHZKI(TON8) (Thou-until) 95 ..- C d _= trial“? 9: trial 9 X: trial 11 += trial 8 A= trial 10 '3 trial 12 Figure 18: Total annual liveweight offtake for the regional system (2) mama”) 96 3'- 33-1 34-4 32- 30-- IU- 30‘ “- 22* IO- /,4 ll 16 14. / \/ ., ’ d .1 «u: .1 4 .1 a: j 8 3 4 B 8 7 l O 10 a: trial 1 b= tr5:a_l_3 X= trial 5 6: trial 2 +3 trial 4 ‘V: trial 6 Figure 19: Total annual milk offtake for the regional system (1) “(we 15°”) 97 4.0- 38- 804 “- 321 80~ 28- / 88- / “d , 82- /// . R 1 '° 4 / \/ ll 1° 1 u r . 0: trial 7 0: trial 9 (x: trial 11 f= trial 8 = trial 10 v: trial 12 Figure 20: Total annual milk offtake for the regional SYStem (2) 98 IV.2.2.THE COMMUNAL SYSTEM Here a 2 by 3 factorial experiment is conducted with: - 2 levels of liveweight offtake (OFT) - 3 levels of feed supplementation (F8) The same levels defined in the regional model are used here and it considered that the forage accessibility and availability are the same as the level 2 in the regional model. The trials have been numbered as follows: . OFT l-FS l OFT 1-FS 2 OFT l-FS 3 OFT Z-FS 1 OFT 2-FS 2 0‘ U1 uh u N H O The milk offtake rate (MOFT) is held constant in the model and is equal to: MOFT 0.30 for cattle 0.20 for goat 0. for sheep The averaged results of the 10 year simulation runs are presented in table 16 and figures 21, 22, 23, 24 and 25. The comparisons between the baseline trial and the other trials show a 3 percent increase in the total liveweight offtake and milk offtake when the offtake rate alone has been raised (1 versus 4). For the same comparison, there has 99 Table 16: Total liveweight offtake (TLWOFT), total milk offtake (TMOFT) and forage production change rate (FPCR) TRIALS TLWOFT (tons) TMOFT (tons) FPCR ""imm"""-332""""m""265""""""BTBSSZ" 2 516 512 -0.0015 3 588 567 -0.0055 4 405 409 0.0030 5 524 518 0.0009 6 622 566 -0.0016 been a 25 percent improvement in the forage production change rate. Provision of feed supplement alone increases the liveweight output by 31 and 49 percent for 1 vs 2 and 1 vs 3 respectively; the effects on milk production are 27 and 41 percent. However such alternatives do initiate a deterioration of the rangeland at a rate 163 and 329 percent higher than for the baseline where the system was under- stocked. These high deterioration rates as compared with the regional system, are the results of the fact that the communal system is a close system where there is no migration of the animals in periods of drought. The pressure on the land in these periods is very high, causing an acceleration in the degradation of the rangeland. The combination of increase in the offtake rate and the provision of feed supplement boosts the total liveweight 7///////////////A. 7/////////////-. %///////////.m ////////////////////m ///////////////// ______ o o W o 0 0 0 0 o o m 7 a 5 m 2 1 jw 0000000 o 0 w ..... W////////////////z, //////////////// ... ////////////// TRIALS 102 y////////% . W. 3. 2 1 0 1. 2. J 4 5. J A5 :4! "030 ZOEfifigfi flGaOh cha 103 output by 33 (1 vs 5) and 58 percent (1 vs 6) form the baseline. For milk offtake the increases are 29 and 41 percent respectively. These increases in the productions are associated with a deterioration of the land 63 and 167 percent higher than the baseline situation. Figures 24 and 25 show that with the level of increase in the liveweight offtake, the total yearly liveweight output falls below the baseline level after the third year. This suggests, as seen in the regional system, that the productive capacity of the system could not keep pace with the level of offtake. For the milk production, the effect of drought seems to be the most significant (year 4, 6 and 7). IIWHHHGETINHWIKI(TONS) (Thouandn) 104 09- D 08- O 0.7 - I f I I I j T I I 1 8 4 5 O 7 O O 10 TILES g: trial 1 0: trial 3 x: trial 5 += trial 2 a: trial 4 v= trial 6 Figure 24: Total annual liveweight offtake for the communal system m 0mm (TONS) 105 360 800 - 750 - 700 - 850 - 600 - 660 -( 500 - 450 - 400 j 350 - 300 Figure 25: trial 1 trial 2 Total annual milk offtake for the communal system 1 IIARS 9= trial 3 a: trial 4 1 j 7 8 x: trial 5 v: trial 6 10 CONCLUSION The conclusion drawn from this study will be remarks from what we have learned in the process of analysing the pastoral production system in Senegal by using a Systems Approach. The study did not intend to define options that are to be used as part of recommendations for the improvement of the production system; but rather the goals were the analysis of the system as it is affected by the variability in rainfall and the inter—relations that exist between the forage component and the animal component when different policy options are tested. What comes out from the results is the necessity of the combination of the three factors tested to achieve the goal of increasing the total liveweight offtake and milk offtake and at the same time, preserving the range resources. Each factor in itself alone cannot make the system achieve the goal of maximazing the liveweight and milk offtakes and minimizing the negative effect of animal overstocking on the rangeland. The increase in forage availability and accessibility has a positive effect in lowering the range deterioration rate eventhough its effect in increasing the liveweight and milk outputs is very limited. The increase in the liveweight offtake rate alone has had the same effect as the the forage availability and accessibility. However, the provision of feed supplement without the implementation of the other 106 107 options has resulted in significant increases in the levels of outputs produced with an increase in the range deterioration rate. It is only with the combination of the different management options that the goals sought for the system can be achieve, with the combination of the three options yielding the best results. Increasing the liveweight offtake rate may potentially cause a draw back in the adoption of such policy by the herders who will see their revenues from the sells of animals reduced in some Years after the beginning of the new policy. The level of increase should be consistant with the reproductive capacity of the livestock. There is a necessity to improve the availability and accessiblity of the forage resources to the animals if the system has to provide more output and preserve itself from degradation. The length of the simulation runs seems to be short to give significant changes in the forage productivity of the soil and their impact on the performances of the animals, especially when feed supplement is provided without an alternative to lessen the pressure on the rangeland. the defined for the system. The aggregated system used here has allowed the study of the linkage between the forage component and the animal component but did not address the operation of the system at the household level. The model will be greatly improved if it has a means of incorporating the behavior of a household 108 when the environment in which it operates is subjected to different changes. The model did not address the problem of spatial variation of the rainfall which is very important in semi arid areas such as the Ferlo region. This element of variability in the systems inputs should be given great consideration in possible implementations of a communal type system. In a drought year, the animal population in the community may suffer heavy losses if it does not have the possibility of migrating in surrounding areas where there may be exploitable pastures. In the model, the determination of the forage production potential for each year was based on the generation of a random variable and the actual distribution of the annual rainfalls (30 percent dry year, 55 percent average year and 15 percent wet year). However, since the same deviation from the average rainfall is used in the computation of the rainfall distribution, to determine whether a year is dry or wet, in the simulation model, a year considered wet is very wet as compared to a dry year. 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ODI Review # 2. 1976. Sanford S. (1983): Management of pastoral development in the Third World. ODI. 1983. SEA (1978): Le Senegal en chiffres. Societe d'edition africaine, 1978. Shannon R.E. (1975): Systems simulation: the art and science. Prentice Hall, 1975. Shuette M.R. (1976): A dynamic simulation model of growth and female reproduction of beef cattle. MS thesis, MSU 1976. SODESP (1984): Projet de sauvegarde du betail dans la zone sylvo-pastorale. Sodesp, note technique # XXI. SODESP (undated): Presentation du projet de developpement integre de l'elevage dans la zone sylvo- pastorale. Sodesp, note technique # I. Speeding C.R.W. (1979): The biology of agricultural systems. Academic Press Inc. 1979. Sullivan G.M. et a1. (1981): Simulating production systems in East Africa by use of interfaced forage and cattle models. Agric. systems 7 (1981) 245-265. Vanpraet C.L. eds. (1983): Methode d'inventaire et de surveillance des ecosystems pastoraux saheliens. Application au developpement. Acts du colloque tenu a Dakar 1es 16, 17, 18 novembre 1983. Webster A.J.H. (1978): Predictions of the energy requirements for growth in beef cattle. World Rev. Nutr. Diet., 30 : 189-226. World Bank, (1984): World development report, 1984. 113 AN NEXE dd-Adduono 090000 Nu NJ J-uuu-u-uo a. 0 o .0 OOOuuUU'AUUqu')‘JFJFJV" PM'Jva‘wH—w—ouoww JO-‘oauoemdoaoawu not? 000210 .u'oo-v 393.1090 0'.»- 3:1: ~00 U'DU‘JU- “~L DNOUO tau—aunt: so JDunu a p out fififi OUJH 3111') 0€‘0030 0 no 0 C‘oocoz’ogggtsth' fl 0 0:4 3P z°ccnt9'°€0~s~ Orqof‘031q0000cn3.0 "and n 00 0 000000000000 000000000000 00000000000 000 O D O 0- 0° C O ‘3": 2 ’ ’ , 2 ‘1 h u - M q -0 -d 0- mm 3 ‘8 33 D D ’ ’ d 22 M 0" MP9 F - ~~ Z 2 28 D '00- U) M 40400-400 20‘13’ P 09 ~ 0'- *XOD‘ 8-0‘?3\ .0 O O 00%fl3c C‘I‘V~l 05 ‘fl 2 2 2: :.<3 Z)‘KOH -0 0-0-0 0 . \01<<0 > PF Odonfieflaaoficowdwmwwmwwmgmwnnnnnnnnnnnnpoono ~~\\\u C! 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