- Email: [email protected]
A quantitative framework for livestock development planning: Part 1—The planning context and an overview
Agricultural Systems 12 (1983) 231 249
A Quantitative Framework for Livestock Development Planning: Part 1--The Planning Context and an Overview David Hallarn Department of Agricultural Economics and Centre for Agricultural Strategy, University of Reading, Earley Gate, Reading, Great Britain
J. A. Gartner & J. P. Hrabovszky United Nations Food and Agriculture Organization, Via delle terme di Caracalla, Rome, Italy
SUMMARY This paper presents an overview oJthe nature and application oJa simple quantitative j~'amework for livestock development planning. This, hopeJully, provides a useful tool Jor use in one typical planning situation-the estimation of resource requirements, the evaluation oJ resource constraints and the tracing of the implications of alternative deuelopmen~ programmes in the achievement o/" specified production targets. Subsequent papers will present detailed descriptions oJ various components of the framework and a case stud); oj" its application.
INTRODUCTION From comparatively low levels, meat and milk consumption in some developing countries is currently rising at the rate of 6-7 ~o per year (FAO, 1979). Prompted by rising incomes and populations, such rates of increase are expected to continue. To keep pace with growth in demand, the output of livestock products in developing countries would need to increase by an average of 4.7 ~o compared with 3.6 % for crops (FAO, 1979). Whilst expansion of the number of livestock will contribute to the necessary increase in output, its achievement will be primarily consequent upon major improvements in productivity. Productivity, reflected in those parameters of livestock production systems such as mortality rates, fertility rates, milk yields and carcass weights, is typically at extremely low 231 Agricultural Systems 0308-521X/83/$03-00 V~Applied Science Publishers Ltd, England, 1983. Printed in Great Britain
232
Dat'id Ha/lain, J. A. Gartner, J. P. Hrabovszky
levels in developing countries relative to those common in developed countries: developing countries have two-thirds of the world's population of cattle, buffalo, sheep and goats yet account for only one-third of the world's meat output and only one-fifth of the world's milk output from these species (Fitzhugh e t al., 1978). The reasons for this low productivity are manifold but amongst the factors to which it might be attributed are the following: (i)
Traditional 'unimproved' breeds are prevalent, although in many situations these may, in fact, be the most appropriate. (ii) The incidence of diseases and parasites is widespread and their effects are exacerbated by frequently meagre veterinary resources. (iii) The quantity and quality of livestock feed available is often limited. Livestock production is, and will continue to be, primarily dependent upon grazing resources, with other feed resources such as crop by-products and residues often underutilised due to a lack of integration between livestock and crop production systems. Indeed, there is often antagonism between pastoralists and cultivators. Grazing availability itself can be subject to significant seasonal variations and periodic crises resulting from drought, for example. In some areas excess pressure upon grazing land from overstocking has led to further degradation of the range. In Africa, it is estimated that 700 m ha of potential grazing land are lost through tsetse infestation (FAO, 1981). Nevertheless, the world's grasslands are potentially capable of supporting a substantially greater livestock population" advanced technology has been applied to less than 8 ..... Jo of total permanent pasture and meadow (Fitzhugh e t al., 1978), while poor management results in feed losses of up to 50 !~Jl,each year (Blair Rains & Kassam, 1980). Removal of such constraints upon livestock productivity and the consequent expansion of livestock production will typically involve the active involvement of government in, for example, the areas of provision of veterinary services, extension and institutional change. In most circumstances an efficient allocation of scarce resources, in terms of longrun social benefits, rather than private short-run benefits, between alternative livestock development programmes is unlikely to be achieved without planning. Furthermore, development programmes for the livestock sector cannot be designed independently of consideration of its
A quantitatit'eJramework Jor livestock de~'elopment planning: Part 1
233
inter-relationships with other sectors of agriculture. Livestock production competes with other sectors for resources such as land, but also complements them, being, for example, the provider of draught power for crop production and the consumer of crop-residues and by-products as feed. Attempts to raise production and productivity in the livestock sector should, therefore, form part of a comprehensive programme aimed at agriculture as a whole and its ancillary activities such as marketing and processing. Again, some form of sectoral plan is the obvious means of promoting consistency between programmes for the various sectors concerned. Those aspects specific to the livestock sector should normally establish consumption and production targets to be achieved, the consequent requirements, in terms of quantity and productivity, for resources and hence the programmes and policies needed to ensure that these resources are available in sufficient quantity and that productivity is raised to appropriate levels.
Q U A N T I T A T I V E M O D E L S FOR LIVESTOCK D E V E L O P M E N T PLANNING Quantitative models can assist in the formulation of livestock development plans in many ways. The two basic r61es of most interest here are: (i)
(ii)
The quantification of resource requirements contingent upon specified production targets and hence the quantification of current and future constraints upon their achievement. Investigation of the likely effects of alternative development programmes via simulation experiments.
Models can also contribute indirectly to the planning process. For example: (i)
(ii)
The need to ~fill' a formal model with data throws information gaps into sharp relief, thus guiding future data collection exercises towards the most critical areas. Consistency with planning for associated sectors is promoted through the need to be constantly aware of the boundaries of the system being modelled and the division between endogenous and exogenous variables.
234
David Hallam, J. A. Garmer, J. P. Hrabovszky
(iii) Models can provide a framework for the compilation and collation of otherwise disparate expert knowledge and so maximise its productive value. A range of alternative types of model is available to assist the livestock planner. In practice, the distinctions between model types may be arbitrary and more apparent in their application than in their specification. Levine (1982) draws a useful distinction, however, between 'biological' and 'economic' livestock models. Biological models of animal production tend to be the preserve of animal scientists and usually involve the description of the links between feed available to livestock, their actual intake and consequent weight change, and, finally, their fertility. The Texas A and M University (TAMU) Cattle Model (Sanders & Cartwright, 1979a,b) is broadly in this class. This is a monthly simulation model based upon a detailed description of the biological processes of reproduction, growth, production and mortality. Its initial objectives were to determine optimal genotypes and management practices for given feed resources. The endogenous variables are herd productivity parameters--growth rates, milk yields, mortality rates, and so on--determined by the interaction of the genetic potential of cattle, the feed resource and the management system. Although initially developed and applied in the US context, the model has since been applied in a number of Latin American and African countries. In particular, the TAMU model has formed the basis for much of the modelling activity of the International Livestock Centre for Africa (e.g. ILCA, 1978). The typically highly-disaggregated and detailed nature of the biological models makes them suitable vehicles for the analysis of micro-level management, genotype and feed changes. For national level livestock planning exercises, however, their value is relatively limited. The extensive and detailed data requirements cannot normally be satisfied without significant investment in field research, while the computational burden involved at individual herd level can become prohibitive where aggregation to a national basis is necessary. 'Economic models' are the more common domain of agricultural economists and planners. Such models usually simulate the time path of herd/flock size and composition on the basis of different assumptions concerning management strategies or technical performance. Prices are sometimes grafted on to the analysis to allow the calculation of the relative profitabilities of alternative strategies. 'Economic' models might, in fact, be further subdivided into 'management' models applied at farm
A quantitative framework.[or livestock development planning." Part I
235
level and usually incorporating prices, perhaps in a formal linear programming framework, and 'planning' models applied at national or sector level. The latter may involve truly economic considerations based upon valuations of products and resources, or may be simply "population' or 'herd' models. It is 'planning' models which are the concern of this paper.
DATA NEEDS A N D PROBLEMS IN LIVESTOCK DEVELOPMENT PLANNING The collection and compilation of all relevant information should be the first stage in the creation of a sectoral plan (FAO, 1970a). For the livestock sector this involves building up as detailed a picture as possible of its current and future state, and its environment. The range of 'relevant information' is wide, from aggregated 'background' information concerning the place of the livestock sector in the national economy to detailed measurements concerning specific livestock systems. It normally includes: (i)
The r61e and importance of the livestock sector in the overall agricultural sector and the national economy--employment, land use, contribution to GNP, export earnings. (ii) The current and future position of other sectors within agriculture, particularly crop production, as a source of livestock feed and a competitor for resources. (iii) The current and planned provision of infrastructure--road and rail networks, marketing facilities, water supplies. (iv) The current and future demand for livestock products. (v) A catalogue of the various production systems within the livestock sector--their socio-economic objectives, technologies, outputs, size in terms of number of people and livestock, level of output, potential for change. (vi) The size, age-sex composition and growth rate of livestock populations in each system. (vii) The current and expected future productivity of livestock in each system--carcass weights, milk yields, egg yields, fertility rates, mortality rates, ages at maturity, slaughter-ages, longevity. (viii) The current and future availability of feed resources--the quantity and quality of roughages and concentrates.
236
David Hallam, J. A. Gartner, J. P. Hraboz'szky
Needless to say, in practice, the planner normally has to contend with gaps in the necessary information set, and insufficient detail and inaccuracy in what is available. Gaps in the information set can be bridged by assumptions based upon 'expert opinion'. Expert opinion is not infallible, however. Thus, it was not until detailed surveys had been undertaken (Adams, 1974; Hunting Technical Services, 1976) that the widely held view that Baggara herds contained an excessive number of unproductive males was challenged. The same surveys also revealed that cattle numbers in Western Sudan had been falling since 1973 while the Veterinary Service continued to assume an annual rate of growth of 317o (Adams, 1974). Where information is available it is frequently insufficiently detailed. While detailed analyses of the nutritive value of most crops and concentrates are available the quality of a country's grazing resources is typically inadequately documented. With respect to herd structures, the most detailed that can normally be hoped for is perhaps the sort of two-way classification recommended for the 1970 World Census of Agriculture (EAO, 1970b), which divided cattle into those under, and those over, 2 years of age. This is not usually adequate for the operation of a herd growth model, although herd composition can, of course, be inferred from knowledge of herd productivity. The apparent general presumption of inaccuracy in statistical information is unfortunately well supported, although the extent of inaccuracy in any one statistic can rarely be measured. In some countries there has never been a census of agriculture and hence no benchmark measurements of animal numbers are available, or, where census results are available, they may be too historic to be of value. Livestock numbers can be estimated indirectly from, for example, numbers passing through markets, or numbers slaughtered. The value of such indirect methods depends crucially upon the accuracy of market or slaughter statistics, of course, and upon the markets or abattoirs having a known constant proportion of national offtake. Where livestock numbers are estimated from taxation returns a significant downward bias is to be expected: Van Raay (1975), for example, suggests that livestock numbers in Northern Nigeria were underestimated by up to one-third, whilst, for the Sudan, Abdallah (1970) recommended that official estimates be doubled. Inaccurate livestock population statistics can be perpetuated by the c o m m o n practice of estimating each year's figure as an assumed percentage increase on the previous year's. Meat output statistics usually cover only 'official' channels involving government inspection. Slaughter outside these
A quantitative.[ramework J~r lit'estock detelopment planning: part 1
237
channels is largely unrecorded. Milk output rarely passes through official channels, typically being consumed in the producer's household or village. Published statistics are rarely accompanied by an adequate amount of information concerning their origins precise definitions of concepts, the data collection methodology, for example. Interpretation can, therefore, be problematic. It is not often known, for example, at what time of year a particular livestock inventory was made. In the case of sheep and goats where mortality rates can be as high as 50 ',~ofor young stock, and male survivors are slaughtered at an early age, significantly different populations will be recorded depending upon the time of year. In the absence of periodic censuses most information concerning livestock numbers and productivity must be derived from surveys. These can give very detailed information but its applicability may be limited geographically or temporally. Different surveys, even of the same system, can yield quite different estimates of key productivity parameters (Dahl & Hjort, 1976). This is partly due to the general problem of lack of uniformity in definitions adopted in different studies: offtake, for example, can be defined variously as purely commercial offtake, or total offtake including local consumption, and may or may not include slaughter of old or dying animals. It could (and has) been argued that, given the deficiencies of data available for the majority of developing country livestock systems, any attempt at formal modelling is unlikely to be productive. Certainly, it is the case that there are large gaps in the data set and that where data exist they are frequently suspect. It is also the case that relatively marginal variations in the values of key parameters can produce significant changes in results. Such data problems are widely recognised, however, and mechanical interpretation of results derived from formal models employing suspect data can only be considered irrational. Given a pragmatic attitude, cognisant of the validity of data inputs, models can still yield practically useful results. Furthermore, the likely consequences of potential error in input data can be investigated and predicted by sensitivity analyses. USER C O N S I D E R A T I O N S A N D THE A P P R O A C H TO MODELLING Model building and model usage tend to be the tasks of separate individuals or groups. It is important, therefore, that the model builder
238
David Hallarn, J. A. Gartner, J. P. Hrabot,szky
should be fully familiar with the model user's requirements, objectives and capabilities and design his models accordingly. A sentiment sometimes expressed in connection with model building for development planning is that models should be kept ~simple'. The rationale for this view is threefold: the quantity and quality of necessary data are normally severely limited; computational capacity is frequently restricted and 'simple' models encourage user confidence. Whilst the significance of the data constraint cannot be minimised, it is, as noted above, a major benefit of model building that it exposes information gaps in sharp relief, providing a guide to productive data collection exercises. The importance of computational constraints is easily" overstated, particularly since micro-computers have become so widely and cheaply available. It is usually the t,olume, rather than the complexity, of computations which can present problems, so even worksheets could provide a solution, albeit a laborious one. The final consideration relating to user confidence is, however, an important one. The active involvement of the planner at all stages in the application of the models--and hence control over their outcomes---can increase his confidence in their use. The construction and application of models is partly an educational process. Model users will frequently have had little previous experience of such tools and it is important that they should gain confidence. An attempt should be made, therefore, wherever possible, to employ a transparent methodology, embodying commonly understood technical concepts and emphasising ease of computation. The cost of this may, however, be some sacrifice of descriptive detail. Underlying the question of ~simplicity' of models is the issue of the appropriate degree of aggregation. Conventionally, the level of aggregation of a model should be appropriate to the policy-maker's or planner's domain. It is desirable that models should include explicitly and independently all parameters open to influence. 'Simple' models embodying 'black box' compound parameters may therefore be the reverse of what is really needed.
A QUANTITATIVE F R A M E W O R K FOR LIVESTOCK DEVELOPMENT PLANNING The primary purposes of the models described in this and subsequent papers are twofold: first, to provide a means of identifying and
A quantitatil'eJ~'amework for lirestock derelopment planning: Part 1
239
quantifying resource requirements (livestock numbers, herd composition and feed thus far) and constraints upon the achievement of specified levels and composition of livestock product demand and, secondly, given the low productivity per animal in most developing country livestock systems, to provide a means of investigating the effects of development programmes (with regard to health, breed, management or feed, for example) aimed at changing those parameters of the system in which low productivity is manifested. The 'quantitative framework' referred to in the title comprises three types of model, in fact.
(i) 'Demand-driven' models These quantify the livestock population size and structure necessary to achieve specified production targets on the basis of alternative assumptions concerning livestock productivity. The 'conventional' approach to livestock modelling, whereby time paths for animal population size and structure, and product output are traced from some base period, is likely to be of relatively limited value for this purpose. In the determination of resource requirements, such models are rather cumbersome in use, usually involving iterative procedures to reach the desired conclusions. More appropriate would be some sort of inputoutput model with which the resource requirements of a given 'bill of goods' can be estimated. At the same time, to be a useful planning tool, the model should allow the identification of constraints upon livestock production and the investigation of the effects upon them of programmes aimed at improving productivity. The solution of such models will be demand driven and will be in the opposite direction to that of the conventional livestock model. These demand-driven models determine the size and composition of the livestock population required within each production system to meet target demands for livestock products. Requirements for other inputs such as labour, housing capacity, marketing facilities, and so on, can be determined from livestock numbers by the application of appropriate input-output type coefficients of proportionality. The models also specify the sources of output within each system: how much of a beef production system's output stems from actual slaughter-stock and how much from culled breeders, for example. The component relationships of these models describe the physical production processes involved, and their parameters reflect productivity
240
Dat'id Hallam, J. A. Garmer, J. P. Hrabot'szky
in the form of fertility rates, mortality rates, carcass weights, milk yields, and so on. Values of these productivity parameters form the majority of the input data to the models. In any one 'solution' of the models for a given target year the productivity parameters' values are fixed but they can obviously be varied from one target year to another to represent technical improvements, for example, or indeed varied for the same target year to obtain alternative solutions or to trace implications of specific development programmes.
(ii) Feed accounting models These quantify the feed requirements of each livestock production system associated with the specified production targets and confront these requirements with feed availabilities to each system, according to feed allocation rules, to obtain a feed balance sheet. Feed availability can be regarded as the single most important constraint upon increasing livestock production and raising livestock productivity: ' . . . the principal cause of poor animal performance is malnutrition; nutritionally inadequate food results in low conception and low calving rates, low weaning weights and slow growth' (Blair Rains & Kassam, 1980). Investigation of the existence and severity of actual and potential feed constraints is therefore an essential aspect of livestock development planning. This is facilitated by the construction, usually for a 1-year period, of a feed balance sheet confronting feed requirements and availabilities and hence quantifying feed deficits and/or surpluses. Such feed balance sheets are most usefully presented on a livestock production system basis with sub-balances indicating the feed deficit or surplus within each system. In this way, the livestock planner is made aware of exactly where feed constraints are binding and hence which production targets are unlikely to be achieved. Balance sheets in terms of feed resources, rather than production systems, are less instructive in this context.
(iii) Herd growth models These are of the conventional type, tracing the expansion of a herd/flock from a given base period towards the target year and hence investigating the feasibility of achieving target population sizes by the target year. One
A quantitative framework for livestock development plann&g: Part 1
241
major constraint upon the achievement of production targets is the inability to expand herds or flocks rapidly enough to reach their required size by the target year. This is a particularly significant potential problem in the case of the larger ruminants due to their relatively lengthy reproductive cycle. In spite of apparently low commercial offtake rates, low fertility rates and high mortality rates limit many developing country herds to a slow rate of expansion and, in some cases, an actual decline. First calvings at ages exceeding 3 years, calving rates of around 0.6 and mortality rates for young stock of 20 ~o are not unusual. It is therefore of some importance to the livestock planner to investigate the expansion possibilities of herds charged with production targets significantly in excess of their current production levels. It is necessary to trace the time path of herd size and structure towards the target year, and also the time path of production. By estimating meat and milk output each period and comparing the target year's predicted outputs with the target production, the extent of the herd expansion constraint can be assessed as the discrepancy between the two. Whilst the demand-driven models can specify target population size, they cannot specify the time path of the current population towards it because of their essentially static nature. Many simple 'population models' which will perform this function are available, however. Indeed, most previous herd-level livestock modelling work has been based upon models of this type. Models of varying degrees of complexity, both discrete and continuous, unconstrained and constrained, have been developed (see Murdie, 1976 for a simple review). In fact, the actual time path of population and production are of secondary importance to the purpose at hand: the major interest lies in whether or not populations can reach their target levels (given the demand-driven models) by the target year. Attention may therefore be focused upon relatively simple discrete population models.
The basic unit of analysis is the individual livestock production system. Specification of the models on this basis is desirable given the different demands on resources of different systems using the same species and the individual system orientation of most development programmes. Representations of the complete livestock sector of a country are built up as aggregations of the models of its component systems. It is only within the feed accounting model that the whole sector is explicitly simultaneously considered. This modular approach has certain advantages: it allows the consideration of any one system in isolation or any
242
Dat'id Hallam, J. A. Gartner, J. P. Hraborszky
combination of systems within a country, thus facilitating the analysis of system-specific programmes without the need to involve the whole sector; it facilitates the description and analysis of the evolution of a livestock sector through time which typically involves changes in the balance between the various production systems; it eases the computational burden of model solution since an aggregate picture can be derived gradually--a valuable feature where computational constraints are severe. It will not normally be necessary to consider all livestock systems in each country in a formal sense. Where the number of animals concerned is limited due to consumer resistance or religious beliefs, for example, and/ or no increasing contribution to overall livestock production is anticipated, then the systems concerned can be largely neglected. 'Largely' because it is important that they should not be overlooked in the feed balance analysis: in combination, they may absorb an appreciable proportion of available feed. In these situations estimates might be made of likely animal numbers involved in target years and feed allocations made as appropriate. The classification and identification of livestock systems is obviously an essential pre-requisite for application of the models. The models can support any level of disaggregation in system classification provided that sufficiently accurate detailed data inputs can be obtained. In practice there is little point in disaggregating beyond the point where there are significant differences between systems in terms of productivity or where the systems have significantly different implications for resource use or appropriate development programmes. The actual separation of systems in any particular situation will be largely the responsibility of the planner's expert judgement since generalisation is impossible. The criteria for demarcation of systems usually relate to internal homogeneity but external heterogeneity in terms of such factors as species, outputs, feed base, degree of commercialisation, development potential, and nature of response to external stimuli (Humphrey, 1980). Two levels of classification of relevance to model specification can be distinguished. At a fundamental level, certain gross archetypal systems can be defined primarily upon the basis of the animal concerned and the product: beef cattle production or buffalo milk production, for example. Separate models must be specified for each of these basic systems. Within one particular basic system it will normally be possible to identify several sub-systems at a more detailed level in terms of the classification factors
A quantitative framework for livestock development planning: Part 1
243
mentioned above: within, say, sheep production, for example, one might distinguish nomadic and transhumant systems. Such variations do not warrant the construction of separate models, but can be described using the same basic model with different values given to the parameters. Relatively few basic models are required to describe the world's livestock systems, but these will embrace a multiplicity of sub-system descriptions differentiated by the values given to their parameters.
THE P L A N N I N G CONTEXT A N D THE OPERATION OF THE MODELS The typical livestock planning context in which the framework is expected to be of assistance is broadly as follows. The planner will be faced with demand targets for livestock products, derived perhaps on the basis of assumptions concerning future population and income growth and the associated demand elasticities (see, for example, Hallam (1981) for a discussion of the methods and problems involved). The following questions must then be answered, and the framework should aid the planner to do so: (i)
What are the alternative ways of meeting the demand targets? What will be the self-sufficiency ratio, and how will domestic production be divided up between the various systems capable of contributing to it? (ii) What are the implications of the above alternatives for resource usage ? Are the targets achievable with current resources? If not, what is the extent of the resource constraint? What additional resources and/or improvements in productivity are needed, and hence what development programmes need to be undertaken? The answers to these questions cannot, of course, be arrived at for the livestock sector independently of similar considerations with respect to other sectors of agriculture, particularly crop production, plans for the agricultural sector's ancillary industries and, indeed, for the economy as a whole. Interdependence between livestock and crop production is both competitive and complementary: both compete for the same limited resources of land, sometimes labour, and development funds; crop production systems generate feed for livestock--livestock production generates inputs into crop production in the form of draught power and
244
David Hallam, J. A. Gartner, J. P. Hrabovszky
manure for fertiliser. Interdependence of this extent warrants a fully simultaneous treatment of both livestock and crop production plans, at least informally. The development of livestock production must also be consistent with planned or expected development of its ancillary activities: the expansion of the livestock sector is likely to yield little benefit without the provision of additional or improved facilities for slaughtering, processing, cold storage, or rail and road transport. Finally, the Government's wider economic, social and political objectives will also impinge upon, and partly determine, the nature of plans for the livestock sector. Objectives in terms of equity or employment, for example, might determine whether smallholder or large commercial enterprises are encouraged. Perhaps the most important task for the planner is to determine how generalised livestock product demand targets will be divided between home production and net imports, how home production will be allocated between the various systems and what productivity levels will be achieved in each of those systems. These decisions together form what might be termed the 'livestock production plan'. This livestock production plan is both the starting point and the end point of the whole livestock planning exercise. Faced with a collection of generalised demand targets for livestock products, the planner's first priority is to allocate these between systems in the design of a trial livestock production plan. As described earlier, this will be in accordance with his view of their relative development potential and wider planning objectives at sectoral and macro-economic levels. Individual system production targets thus derived 'drive' the demanddriven models to determine necessary livestock population sizes and structures. The feed allocation models quantify the consequent feed balances, and the herd growth models check the feasibility of achieving target populations over the planning time horizon. Initially, solutions might be obtained on the assumptions of unchanged productivity and resource availability, providing a yardstick against which constraints can be measured and adjustments for their removal tested. The emergence of either feed deficits or growth constraints will lead the planner to a choice between alternative strategies. A number of alternatives are open to him: (i) (ii)
To import livestock products, i.e. change the self-sufficiency ratio for livestock products. To reduce demand targets for livestock products.
A quantitative.[ramework.]or licestock development planning: Part l
245
(iii)
To change the livestock production plan to increase productivity by shifting the balance of production between systems or improve the productivity of individual systems. (iv) To import any necessary additional feed, i.e. change the selfsufficiency ratio for crops. (v) To increase domestic crop production to provide additional feed in the form of concentrates. (vi) To reduce domestic crop production targets to release additional land for grazing. (vii) To change the crop production plan to increase the productivity of domestic crop production. (viii) To improve the productivity of grazing land, i.e. increase carrying capacity.
The precise adjustments most appropriate in any particular instance obviously depend upon the specific circumstances. Hence there is little possibility for the incorporation of generally applicable formal adjustment procedures into the operation of the models. Any adjustments made to the livestock production plan will also have repercussions beyond the limits of the livestock sector: again, therefore, it will be necessary to consider the alternatives available in the context of wider sectoral or economy-wide objectives. Once a course of action is decided upon, and a second-round livestock production plan drawn up, a second application of the models yields information concerning the revised version and so the iterative process continues until a final production plan is arrived at. The actual operating procedures for the modelling framework are summarised in Fig. 1. Operation of the models is expected to be interdisciplinary in nature, drawing upon the knowledge and expertise of a variety of specialists--livestock, crop and range scientists, veterinarians, planners, economists--to provide the necessary information and iterative--with trial solutions being obtained under alternative productivity and resource availability assumptions throughout the agricultural sector. Each possible amendment to the livestock production system or assumed productivity implies a course of action and hence normally a potential development programme: a decision to raise the productivity of a system may be based, for example, on the assumption that a health programme could be undertaken. The determination of production plans and the policy framework is simultaneously achieved in the iterative
246
David Hallam, J. A. Gartner, J. P. Hraborszky
*
7
I
I I I "41-
II
I Evaluation of xx.~._J HERD GROWTHI/~/\' feed constraint/v 91
[
MODELS }
~'~Evaluation of \ growth constraint/-- -KEY
Formal models
I
0
Data inputs Basic computational flow
Fig. 1.
-
-
-
-
Informal expert judgements and decisions Results Iterative flows, ~ - - revisions to information inputs
Operational flow diagram.
A quantitatireframework jbr lirestock derelopment planning. Part 1
247
process. The final production plan, therefore, not only determines the allocation of production targets between systems, but also, in effect, the required policy framework to achieve them. It will be based upon assumptions concerning the adoption and probability of success of development programmes in influencing the livestock sector in the required way.
CONCLUSION The models discussed here can only be considered the first stage in the creation of a comprehensive methodology for livestock development planning. Their obvious defects indicate the directions for refinement and further development. Resource requirements for the achievement of production targets are currently limited to livestock and feed. The inclusion of other resources, such as labour, housing, marketing facilities--should present no major problems, however. The current division of variables into endogenous and exogenous might be revised. In particular, those productivity parameters currently defined as exogenous input data might be related to more basic determinants such as breed, health status, management system and feed quantity and quality. The impact of v.ariations in such factors is currently examined through direct manipulation of the value of key productivity parameters after the appropriate 'manipulation' has been specified by 'expert opinion'. The endogenisation of these productivity parameters and the formal representation of their dependence upon the more fundamental factors of, say, breed, health, management and feed, is a potentially valuable extension. The determination of resource requirements would then be in terms of variables explicitly open to the influence of development programmes. Whilst such an extension might be made in principle, there are many conceptual and practical difficulties obstructing it and, typically, these can only be resolved by either an excessively detailed, and possibly unworkable, approach or one which is so grossly generalised and aggregated that it is likely to prove equally unhelpful. Related to the issue of which variables might be considered endogenous and which exogenous in any model is the direction of the causal chain implicit within that model. The modelling framework presented here is purely recursive, considering one-way flows from production through to animal numbers and feed resources via various productivity parameters.
248
David Hallam, J. A. Gartner, J. P. Hrabot'szky
Interdependence and two-way chains of influence are not recognised. Thus, for example, not only does the number of livestock required determine feed requirements but feed available is a determinant of livestock requirements: feed deficiencies limit productivity and hence increase livestock requirements. Such interdependencies are not easily handled at this level of aggregation in any formal sense. Nevertheless, it is essential that the livestock planner be aware of them and their significance when operating the modelling framework.
ACKNOWLEDGEMENT This work was funded under an FAO contract with the Centre for Agricultural Strategy, University of Reading.
REFERENCES Abdallah, Z. M. (1970). Marketing of livestock in the Sudan. Unpublished PhD thesis, Department of Agricultural Economics, University of Reading. Adams, M. E. (1974). The supply of cattle from Southern Darfur: 1. Herd structure, cattle numbers, offtake and market statistics. Sudan Journal oj Veterinary Science and Animal Husbandry, 15, 49 56. Blair Rains, A. & Kassam, A. H. (1980). Land resources and animal production. In: Land resources jor populations of the future, Rome, FAO. Dahl, G. & Hjort, A. (1976). Haeing herds." Pastoral herd growth and household economy. Stockholm Studies in Social Anthropology 2. Stockholm, Department of Social Anthropology, University of Stockholm. FAO (1970a). Introduction to agricultural planning, Rome, FAO. FAO (1970b). Worm census of agriculture, Rome, FAO. FAO (1979). Agriculture: Toward 2000, FAO Conference 20th session. Rome, FAO. FAO (1981). Agriculture." Toward 2000, Rome, FAO. Fitzhugh, H. A., Hodgson, H. J., Scoville, O. J., Nguyen, T. D. & Byerly, T. C. (1978). The role of ruminants in support oJ man, Morrilton, Arkansas, Winrock International. Hallam, D. (1981). Long-term demand projections--Methods and problems. In: Grassland in the British Economy (Jollans, J. L. (Ed.)), CAS Paper 10. Reading, Centre for Agricultural Strategy. Humphrey, J. H. (1980). The classification oJ world lit'estock systems. AGA/ MISC/80/3, Rome, FAO.
A quantitative framework for livestock development planning: Part 1
249
Hunting Technical Services (1976). Savanna development project phase II. Annex 3. Livestock and range resources. ILCA (1978). Mathematical modelling oj livestock production systems: Application of the TA M U beef cattle production model to Botswana, Addis Ababa, ILCA. Levine, J. M. (1982). Modelling livestock systems under tropical conditions with special reference to Indonesia. In: Livestock in Asia." lssues and policies (Fine, J. C. & Lattimore, R. G. (Eds)), Ottawa, IDRC. M urdie, G. (1976). Population models. In: Mathematical modelling (Andrews, J. G. & McLone, R. R. (Eds)), London, Butterworths. Sanders, J. O. & Cartwright, T. C. (1979a). A general cattle production systems model. I. Description of the model, Agricultural Systems, 4, 217 27. Sanders, J. O. & Cartwright, T. C. (1979b). A general cattle production systems model. II. Simulation of animal performance. Agricultural Systems, 4, 289 309. Van Raay, H. G. T. (1975). Rural planning in a Savanna region. Rotterdam, Rotterdam University Press.
A Quantitative Framework for Livestock Development Planning: Part 1--The Planning Context and an Overview David Hallarn Department of Agricultural Economics and Centre for Agricultural Strategy, University of Reading, Earley Gate, Reading, Great Britain
J. A. Gartner & J. P. Hrabovszky United Nations Food and Agriculture Organization, Via delle terme di Caracalla, Rome, Italy
SUMMARY This paper presents an overview oJthe nature and application oJa simple quantitative j~'amework for livestock development planning. This, hopeJully, provides a useful tool Jor use in one typical planning situation-the estimation of resource requirements, the evaluation oJ resource constraints and the tracing of the implications of alternative deuelopmen~ programmes in the achievement o/" specified production targets. Subsequent papers will present detailed descriptions oJ various components of the framework and a case stud); oj" its application.
INTRODUCTION From comparatively low levels, meat and milk consumption in some developing countries is currently rising at the rate of 6-7 ~o per year (FAO, 1979). Prompted by rising incomes and populations, such rates of increase are expected to continue. To keep pace with growth in demand, the output of livestock products in developing countries would need to increase by an average of 4.7 ~o compared with 3.6 % for crops (FAO, 1979). Whilst expansion of the number of livestock will contribute to the necessary increase in output, its achievement will be primarily consequent upon major improvements in productivity. Productivity, reflected in those parameters of livestock production systems such as mortality rates, fertility rates, milk yields and carcass weights, is typically at extremely low 231 Agricultural Systems 0308-521X/83/$03-00 V~Applied Science Publishers Ltd, England, 1983. Printed in Great Britain
232
Dat'id Ha/lain, J. A. Gartner, J. P. Hrabovszky
levels in developing countries relative to those common in developed countries: developing countries have two-thirds of the world's population of cattle, buffalo, sheep and goats yet account for only one-third of the world's meat output and only one-fifth of the world's milk output from these species (Fitzhugh e t al., 1978). The reasons for this low productivity are manifold but amongst the factors to which it might be attributed are the following: (i)
Traditional 'unimproved' breeds are prevalent, although in many situations these may, in fact, be the most appropriate. (ii) The incidence of diseases and parasites is widespread and their effects are exacerbated by frequently meagre veterinary resources. (iii) The quantity and quality of livestock feed available is often limited. Livestock production is, and will continue to be, primarily dependent upon grazing resources, with other feed resources such as crop by-products and residues often underutilised due to a lack of integration between livestock and crop production systems. Indeed, there is often antagonism between pastoralists and cultivators. Grazing availability itself can be subject to significant seasonal variations and periodic crises resulting from drought, for example. In some areas excess pressure upon grazing land from overstocking has led to further degradation of the range. In Africa, it is estimated that 700 m ha of potential grazing land are lost through tsetse infestation (FAO, 1981). Nevertheless, the world's grasslands are potentially capable of supporting a substantially greater livestock population" advanced technology has been applied to less than 8 ..... Jo of total permanent pasture and meadow (Fitzhugh e t al., 1978), while poor management results in feed losses of up to 50 !~Jl,each year (Blair Rains & Kassam, 1980). Removal of such constraints upon livestock productivity and the consequent expansion of livestock production will typically involve the active involvement of government in, for example, the areas of provision of veterinary services, extension and institutional change. In most circumstances an efficient allocation of scarce resources, in terms of longrun social benefits, rather than private short-run benefits, between alternative livestock development programmes is unlikely to be achieved without planning. Furthermore, development programmes for the livestock sector cannot be designed independently of consideration of its
A quantitatit'eJramework Jor livestock de~'elopment planning: Part 1
233
inter-relationships with other sectors of agriculture. Livestock production competes with other sectors for resources such as land, but also complements them, being, for example, the provider of draught power for crop production and the consumer of crop-residues and by-products as feed. Attempts to raise production and productivity in the livestock sector should, therefore, form part of a comprehensive programme aimed at agriculture as a whole and its ancillary activities such as marketing and processing. Again, some form of sectoral plan is the obvious means of promoting consistency between programmes for the various sectors concerned. Those aspects specific to the livestock sector should normally establish consumption and production targets to be achieved, the consequent requirements, in terms of quantity and productivity, for resources and hence the programmes and policies needed to ensure that these resources are available in sufficient quantity and that productivity is raised to appropriate levels.
Q U A N T I T A T I V E M O D E L S FOR LIVESTOCK D E V E L O P M E N T PLANNING Quantitative models can assist in the formulation of livestock development plans in many ways. The two basic r61es of most interest here are: (i)
(ii)
The quantification of resource requirements contingent upon specified production targets and hence the quantification of current and future constraints upon their achievement. Investigation of the likely effects of alternative development programmes via simulation experiments.
Models can also contribute indirectly to the planning process. For example: (i)
(ii)
The need to ~fill' a formal model with data throws information gaps into sharp relief, thus guiding future data collection exercises towards the most critical areas. Consistency with planning for associated sectors is promoted through the need to be constantly aware of the boundaries of the system being modelled and the division between endogenous and exogenous variables.
234
David Hallam, J. A. Garmer, J. P. Hrabovszky
(iii) Models can provide a framework for the compilation and collation of otherwise disparate expert knowledge and so maximise its productive value. A range of alternative types of model is available to assist the livestock planner. In practice, the distinctions between model types may be arbitrary and more apparent in their application than in their specification. Levine (1982) draws a useful distinction, however, between 'biological' and 'economic' livestock models. Biological models of animal production tend to be the preserve of animal scientists and usually involve the description of the links between feed available to livestock, their actual intake and consequent weight change, and, finally, their fertility. The Texas A and M University (TAMU) Cattle Model (Sanders & Cartwright, 1979a,b) is broadly in this class. This is a monthly simulation model based upon a detailed description of the biological processes of reproduction, growth, production and mortality. Its initial objectives were to determine optimal genotypes and management practices for given feed resources. The endogenous variables are herd productivity parameters--growth rates, milk yields, mortality rates, and so on--determined by the interaction of the genetic potential of cattle, the feed resource and the management system. Although initially developed and applied in the US context, the model has since been applied in a number of Latin American and African countries. In particular, the TAMU model has formed the basis for much of the modelling activity of the International Livestock Centre for Africa (e.g. ILCA, 1978). The typically highly-disaggregated and detailed nature of the biological models makes them suitable vehicles for the analysis of micro-level management, genotype and feed changes. For national level livestock planning exercises, however, their value is relatively limited. The extensive and detailed data requirements cannot normally be satisfied without significant investment in field research, while the computational burden involved at individual herd level can become prohibitive where aggregation to a national basis is necessary. 'Economic models' are the more common domain of agricultural economists and planners. Such models usually simulate the time path of herd/flock size and composition on the basis of different assumptions concerning management strategies or technical performance. Prices are sometimes grafted on to the analysis to allow the calculation of the relative profitabilities of alternative strategies. 'Economic' models might, in fact, be further subdivided into 'management' models applied at farm
A quantitative framework.[or livestock development planning." Part I
235
level and usually incorporating prices, perhaps in a formal linear programming framework, and 'planning' models applied at national or sector level. The latter may involve truly economic considerations based upon valuations of products and resources, or may be simply "population' or 'herd' models. It is 'planning' models which are the concern of this paper.
DATA NEEDS A N D PROBLEMS IN LIVESTOCK DEVELOPMENT PLANNING The collection and compilation of all relevant information should be the first stage in the creation of a sectoral plan (FAO, 1970a). For the livestock sector this involves building up as detailed a picture as possible of its current and future state, and its environment. The range of 'relevant information' is wide, from aggregated 'background' information concerning the place of the livestock sector in the national economy to detailed measurements concerning specific livestock systems. It normally includes: (i)
The r61e and importance of the livestock sector in the overall agricultural sector and the national economy--employment, land use, contribution to GNP, export earnings. (ii) The current and future position of other sectors within agriculture, particularly crop production, as a source of livestock feed and a competitor for resources. (iii) The current and planned provision of infrastructure--road and rail networks, marketing facilities, water supplies. (iv) The current and future demand for livestock products. (v) A catalogue of the various production systems within the livestock sector--their socio-economic objectives, technologies, outputs, size in terms of number of people and livestock, level of output, potential for change. (vi) The size, age-sex composition and growth rate of livestock populations in each system. (vii) The current and expected future productivity of livestock in each system--carcass weights, milk yields, egg yields, fertility rates, mortality rates, ages at maturity, slaughter-ages, longevity. (viii) The current and future availability of feed resources--the quantity and quality of roughages and concentrates.
236
David Hallam, J. A. Gartner, J. P. Hraboz'szky
Needless to say, in practice, the planner normally has to contend with gaps in the necessary information set, and insufficient detail and inaccuracy in what is available. Gaps in the information set can be bridged by assumptions based upon 'expert opinion'. Expert opinion is not infallible, however. Thus, it was not until detailed surveys had been undertaken (Adams, 1974; Hunting Technical Services, 1976) that the widely held view that Baggara herds contained an excessive number of unproductive males was challenged. The same surveys also revealed that cattle numbers in Western Sudan had been falling since 1973 while the Veterinary Service continued to assume an annual rate of growth of 317o (Adams, 1974). Where information is available it is frequently insufficiently detailed. While detailed analyses of the nutritive value of most crops and concentrates are available the quality of a country's grazing resources is typically inadequately documented. With respect to herd structures, the most detailed that can normally be hoped for is perhaps the sort of two-way classification recommended for the 1970 World Census of Agriculture (EAO, 1970b), which divided cattle into those under, and those over, 2 years of age. This is not usually adequate for the operation of a herd growth model, although herd composition can, of course, be inferred from knowledge of herd productivity. The apparent general presumption of inaccuracy in statistical information is unfortunately well supported, although the extent of inaccuracy in any one statistic can rarely be measured. In some countries there has never been a census of agriculture and hence no benchmark measurements of animal numbers are available, or, where census results are available, they may be too historic to be of value. Livestock numbers can be estimated indirectly from, for example, numbers passing through markets, or numbers slaughtered. The value of such indirect methods depends crucially upon the accuracy of market or slaughter statistics, of course, and upon the markets or abattoirs having a known constant proportion of national offtake. Where livestock numbers are estimated from taxation returns a significant downward bias is to be expected: Van Raay (1975), for example, suggests that livestock numbers in Northern Nigeria were underestimated by up to one-third, whilst, for the Sudan, Abdallah (1970) recommended that official estimates be doubled. Inaccurate livestock population statistics can be perpetuated by the c o m m o n practice of estimating each year's figure as an assumed percentage increase on the previous year's. Meat output statistics usually cover only 'official' channels involving government inspection. Slaughter outside these
A quantitative.[ramework J~r lit'estock detelopment planning: part 1
237
channels is largely unrecorded. Milk output rarely passes through official channels, typically being consumed in the producer's household or village. Published statistics are rarely accompanied by an adequate amount of information concerning their origins precise definitions of concepts, the data collection methodology, for example. Interpretation can, therefore, be problematic. It is not often known, for example, at what time of year a particular livestock inventory was made. In the case of sheep and goats where mortality rates can be as high as 50 ',~ofor young stock, and male survivors are slaughtered at an early age, significantly different populations will be recorded depending upon the time of year. In the absence of periodic censuses most information concerning livestock numbers and productivity must be derived from surveys. These can give very detailed information but its applicability may be limited geographically or temporally. Different surveys, even of the same system, can yield quite different estimates of key productivity parameters (Dahl & Hjort, 1976). This is partly due to the general problem of lack of uniformity in definitions adopted in different studies: offtake, for example, can be defined variously as purely commercial offtake, or total offtake including local consumption, and may or may not include slaughter of old or dying animals. It could (and has) been argued that, given the deficiencies of data available for the majority of developing country livestock systems, any attempt at formal modelling is unlikely to be productive. Certainly, it is the case that there are large gaps in the data set and that where data exist they are frequently suspect. It is also the case that relatively marginal variations in the values of key parameters can produce significant changes in results. Such data problems are widely recognised, however, and mechanical interpretation of results derived from formal models employing suspect data can only be considered irrational. Given a pragmatic attitude, cognisant of the validity of data inputs, models can still yield practically useful results. Furthermore, the likely consequences of potential error in input data can be investigated and predicted by sensitivity analyses. USER C O N S I D E R A T I O N S A N D THE A P P R O A C H TO MODELLING Model building and model usage tend to be the tasks of separate individuals or groups. It is important, therefore, that the model builder
238
David Hallarn, J. A. Gartner, J. P. Hrabot,szky
should be fully familiar with the model user's requirements, objectives and capabilities and design his models accordingly. A sentiment sometimes expressed in connection with model building for development planning is that models should be kept ~simple'. The rationale for this view is threefold: the quantity and quality of necessary data are normally severely limited; computational capacity is frequently restricted and 'simple' models encourage user confidence. Whilst the significance of the data constraint cannot be minimised, it is, as noted above, a major benefit of model building that it exposes information gaps in sharp relief, providing a guide to productive data collection exercises. The importance of computational constraints is easily" overstated, particularly since micro-computers have become so widely and cheaply available. It is usually the t,olume, rather than the complexity, of computations which can present problems, so even worksheets could provide a solution, albeit a laborious one. The final consideration relating to user confidence is, however, an important one. The active involvement of the planner at all stages in the application of the models--and hence control over their outcomes---can increase his confidence in their use. The construction and application of models is partly an educational process. Model users will frequently have had little previous experience of such tools and it is important that they should gain confidence. An attempt should be made, therefore, wherever possible, to employ a transparent methodology, embodying commonly understood technical concepts and emphasising ease of computation. The cost of this may, however, be some sacrifice of descriptive detail. Underlying the question of ~simplicity' of models is the issue of the appropriate degree of aggregation. Conventionally, the level of aggregation of a model should be appropriate to the policy-maker's or planner's domain. It is desirable that models should include explicitly and independently all parameters open to influence. 'Simple' models embodying 'black box' compound parameters may therefore be the reverse of what is really needed.
A QUANTITATIVE F R A M E W O R K FOR LIVESTOCK DEVELOPMENT PLANNING The primary purposes of the models described in this and subsequent papers are twofold: first, to provide a means of identifying and
A quantitatil'eJ~'amework for lirestock derelopment planning: Part 1
239
quantifying resource requirements (livestock numbers, herd composition and feed thus far) and constraints upon the achievement of specified levels and composition of livestock product demand and, secondly, given the low productivity per animal in most developing country livestock systems, to provide a means of investigating the effects of development programmes (with regard to health, breed, management or feed, for example) aimed at changing those parameters of the system in which low productivity is manifested. The 'quantitative framework' referred to in the title comprises three types of model, in fact.
(i) 'Demand-driven' models These quantify the livestock population size and structure necessary to achieve specified production targets on the basis of alternative assumptions concerning livestock productivity. The 'conventional' approach to livestock modelling, whereby time paths for animal population size and structure, and product output are traced from some base period, is likely to be of relatively limited value for this purpose. In the determination of resource requirements, such models are rather cumbersome in use, usually involving iterative procedures to reach the desired conclusions. More appropriate would be some sort of inputoutput model with which the resource requirements of a given 'bill of goods' can be estimated. At the same time, to be a useful planning tool, the model should allow the identification of constraints upon livestock production and the investigation of the effects upon them of programmes aimed at improving productivity. The solution of such models will be demand driven and will be in the opposite direction to that of the conventional livestock model. These demand-driven models determine the size and composition of the livestock population required within each production system to meet target demands for livestock products. Requirements for other inputs such as labour, housing capacity, marketing facilities, and so on, can be determined from livestock numbers by the application of appropriate input-output type coefficients of proportionality. The models also specify the sources of output within each system: how much of a beef production system's output stems from actual slaughter-stock and how much from culled breeders, for example. The component relationships of these models describe the physical production processes involved, and their parameters reflect productivity
240
Dat'id Hallam, J. A. Garmer, J. P. Hrabot'szky
in the form of fertility rates, mortality rates, carcass weights, milk yields, and so on. Values of these productivity parameters form the majority of the input data to the models. In any one 'solution' of the models for a given target year the productivity parameters' values are fixed but they can obviously be varied from one target year to another to represent technical improvements, for example, or indeed varied for the same target year to obtain alternative solutions or to trace implications of specific development programmes.
(ii) Feed accounting models These quantify the feed requirements of each livestock production system associated with the specified production targets and confront these requirements with feed availabilities to each system, according to feed allocation rules, to obtain a feed balance sheet. Feed availability can be regarded as the single most important constraint upon increasing livestock production and raising livestock productivity: ' . . . the principal cause of poor animal performance is malnutrition; nutritionally inadequate food results in low conception and low calving rates, low weaning weights and slow growth' (Blair Rains & Kassam, 1980). Investigation of the existence and severity of actual and potential feed constraints is therefore an essential aspect of livestock development planning. This is facilitated by the construction, usually for a 1-year period, of a feed balance sheet confronting feed requirements and availabilities and hence quantifying feed deficits and/or surpluses. Such feed balance sheets are most usefully presented on a livestock production system basis with sub-balances indicating the feed deficit or surplus within each system. In this way, the livestock planner is made aware of exactly where feed constraints are binding and hence which production targets are unlikely to be achieved. Balance sheets in terms of feed resources, rather than production systems, are less instructive in this context.
(iii) Herd growth models These are of the conventional type, tracing the expansion of a herd/flock from a given base period towards the target year and hence investigating the feasibility of achieving target population sizes by the target year. One
A quantitative framework for livestock development plann&g: Part 1
241
major constraint upon the achievement of production targets is the inability to expand herds or flocks rapidly enough to reach their required size by the target year. This is a particularly significant potential problem in the case of the larger ruminants due to their relatively lengthy reproductive cycle. In spite of apparently low commercial offtake rates, low fertility rates and high mortality rates limit many developing country herds to a slow rate of expansion and, in some cases, an actual decline. First calvings at ages exceeding 3 years, calving rates of around 0.6 and mortality rates for young stock of 20 ~o are not unusual. It is therefore of some importance to the livestock planner to investigate the expansion possibilities of herds charged with production targets significantly in excess of their current production levels. It is necessary to trace the time path of herd size and structure towards the target year, and also the time path of production. By estimating meat and milk output each period and comparing the target year's predicted outputs with the target production, the extent of the herd expansion constraint can be assessed as the discrepancy between the two. Whilst the demand-driven models can specify target population size, they cannot specify the time path of the current population towards it because of their essentially static nature. Many simple 'population models' which will perform this function are available, however. Indeed, most previous herd-level livestock modelling work has been based upon models of this type. Models of varying degrees of complexity, both discrete and continuous, unconstrained and constrained, have been developed (see Murdie, 1976 for a simple review). In fact, the actual time path of population and production are of secondary importance to the purpose at hand: the major interest lies in whether or not populations can reach their target levels (given the demand-driven models) by the target year. Attention may therefore be focused upon relatively simple discrete population models.
The basic unit of analysis is the individual livestock production system. Specification of the models on this basis is desirable given the different demands on resources of different systems using the same species and the individual system orientation of most development programmes. Representations of the complete livestock sector of a country are built up as aggregations of the models of its component systems. It is only within the feed accounting model that the whole sector is explicitly simultaneously considered. This modular approach has certain advantages: it allows the consideration of any one system in isolation or any
242
Dat'id Hallam, J. A. Gartner, J. P. Hraborszky
combination of systems within a country, thus facilitating the analysis of system-specific programmes without the need to involve the whole sector; it facilitates the description and analysis of the evolution of a livestock sector through time which typically involves changes in the balance between the various production systems; it eases the computational burden of model solution since an aggregate picture can be derived gradually--a valuable feature where computational constraints are severe. It will not normally be necessary to consider all livestock systems in each country in a formal sense. Where the number of animals concerned is limited due to consumer resistance or religious beliefs, for example, and/ or no increasing contribution to overall livestock production is anticipated, then the systems concerned can be largely neglected. 'Largely' because it is important that they should not be overlooked in the feed balance analysis: in combination, they may absorb an appreciable proportion of available feed. In these situations estimates might be made of likely animal numbers involved in target years and feed allocations made as appropriate. The classification and identification of livestock systems is obviously an essential pre-requisite for application of the models. The models can support any level of disaggregation in system classification provided that sufficiently accurate detailed data inputs can be obtained. In practice there is little point in disaggregating beyond the point where there are significant differences between systems in terms of productivity or where the systems have significantly different implications for resource use or appropriate development programmes. The actual separation of systems in any particular situation will be largely the responsibility of the planner's expert judgement since generalisation is impossible. The criteria for demarcation of systems usually relate to internal homogeneity but external heterogeneity in terms of such factors as species, outputs, feed base, degree of commercialisation, development potential, and nature of response to external stimuli (Humphrey, 1980). Two levels of classification of relevance to model specification can be distinguished. At a fundamental level, certain gross archetypal systems can be defined primarily upon the basis of the animal concerned and the product: beef cattle production or buffalo milk production, for example. Separate models must be specified for each of these basic systems. Within one particular basic system it will normally be possible to identify several sub-systems at a more detailed level in terms of the classification factors
A quantitative framework for livestock development planning: Part 1
243
mentioned above: within, say, sheep production, for example, one might distinguish nomadic and transhumant systems. Such variations do not warrant the construction of separate models, but can be described using the same basic model with different values given to the parameters. Relatively few basic models are required to describe the world's livestock systems, but these will embrace a multiplicity of sub-system descriptions differentiated by the values given to their parameters.
THE P L A N N I N G CONTEXT A N D THE OPERATION OF THE MODELS The typical livestock planning context in which the framework is expected to be of assistance is broadly as follows. The planner will be faced with demand targets for livestock products, derived perhaps on the basis of assumptions concerning future population and income growth and the associated demand elasticities (see, for example, Hallam (1981) for a discussion of the methods and problems involved). The following questions must then be answered, and the framework should aid the planner to do so: (i)
What are the alternative ways of meeting the demand targets? What will be the self-sufficiency ratio, and how will domestic production be divided up between the various systems capable of contributing to it? (ii) What are the implications of the above alternatives for resource usage ? Are the targets achievable with current resources? If not, what is the extent of the resource constraint? What additional resources and/or improvements in productivity are needed, and hence what development programmes need to be undertaken? The answers to these questions cannot, of course, be arrived at for the livestock sector independently of similar considerations with respect to other sectors of agriculture, particularly crop production, plans for the agricultural sector's ancillary industries and, indeed, for the economy as a whole. Interdependence between livestock and crop production is both competitive and complementary: both compete for the same limited resources of land, sometimes labour, and development funds; crop production systems generate feed for livestock--livestock production generates inputs into crop production in the form of draught power and
244
David Hallam, J. A. Gartner, J. P. Hrabovszky
manure for fertiliser. Interdependence of this extent warrants a fully simultaneous treatment of both livestock and crop production plans, at least informally. The development of livestock production must also be consistent with planned or expected development of its ancillary activities: the expansion of the livestock sector is likely to yield little benefit without the provision of additional or improved facilities for slaughtering, processing, cold storage, or rail and road transport. Finally, the Government's wider economic, social and political objectives will also impinge upon, and partly determine, the nature of plans for the livestock sector. Objectives in terms of equity or employment, for example, might determine whether smallholder or large commercial enterprises are encouraged. Perhaps the most important task for the planner is to determine how generalised livestock product demand targets will be divided between home production and net imports, how home production will be allocated between the various systems and what productivity levels will be achieved in each of those systems. These decisions together form what might be termed the 'livestock production plan'. This livestock production plan is both the starting point and the end point of the whole livestock planning exercise. Faced with a collection of generalised demand targets for livestock products, the planner's first priority is to allocate these between systems in the design of a trial livestock production plan. As described earlier, this will be in accordance with his view of their relative development potential and wider planning objectives at sectoral and macro-economic levels. Individual system production targets thus derived 'drive' the demanddriven models to determine necessary livestock population sizes and structures. The feed allocation models quantify the consequent feed balances, and the herd growth models check the feasibility of achieving target populations over the planning time horizon. Initially, solutions might be obtained on the assumptions of unchanged productivity and resource availability, providing a yardstick against which constraints can be measured and adjustments for their removal tested. The emergence of either feed deficits or growth constraints will lead the planner to a choice between alternative strategies. A number of alternatives are open to him: (i) (ii)
To import livestock products, i.e. change the self-sufficiency ratio for livestock products. To reduce demand targets for livestock products.
A quantitative.[ramework.]or licestock development planning: Part l
245
(iii)
To change the livestock production plan to increase productivity by shifting the balance of production between systems or improve the productivity of individual systems. (iv) To import any necessary additional feed, i.e. change the selfsufficiency ratio for crops. (v) To increase domestic crop production to provide additional feed in the form of concentrates. (vi) To reduce domestic crop production targets to release additional land for grazing. (vii) To change the crop production plan to increase the productivity of domestic crop production. (viii) To improve the productivity of grazing land, i.e. increase carrying capacity.
The precise adjustments most appropriate in any particular instance obviously depend upon the specific circumstances. Hence there is little possibility for the incorporation of generally applicable formal adjustment procedures into the operation of the models. Any adjustments made to the livestock production plan will also have repercussions beyond the limits of the livestock sector: again, therefore, it will be necessary to consider the alternatives available in the context of wider sectoral or economy-wide objectives. Once a course of action is decided upon, and a second-round livestock production plan drawn up, a second application of the models yields information concerning the revised version and so the iterative process continues until a final production plan is arrived at. The actual operating procedures for the modelling framework are summarised in Fig. 1. Operation of the models is expected to be interdisciplinary in nature, drawing upon the knowledge and expertise of a variety of specialists--livestock, crop and range scientists, veterinarians, planners, economists--to provide the necessary information and iterative--with trial solutions being obtained under alternative productivity and resource availability assumptions throughout the agricultural sector. Each possible amendment to the livestock production system or assumed productivity implies a course of action and hence normally a potential development programme: a decision to raise the productivity of a system may be based, for example, on the assumption that a health programme could be undertaken. The determination of production plans and the policy framework is simultaneously achieved in the iterative
246
David Hallam, J. A. Gartner, J. P. Hraborszky
*
7
I
I I I "41-
II
I Evaluation of xx.~._J HERD GROWTHI/~/\' feed constraint/v 91
[
MODELS }
~'~Evaluation of \ growth constraint/-- -KEY
Formal models
I
0
Data inputs Basic computational flow
Fig. 1.
-
-
-
-
Informal expert judgements and decisions Results Iterative flows, ~ - - revisions to information inputs
Operational flow diagram.
A quantitatireframework jbr lirestock derelopment planning. Part 1
247
process. The final production plan, therefore, not only determines the allocation of production targets between systems, but also, in effect, the required policy framework to achieve them. It will be based upon assumptions concerning the adoption and probability of success of development programmes in influencing the livestock sector in the required way.
CONCLUSION The models discussed here can only be considered the first stage in the creation of a comprehensive methodology for livestock development planning. Their obvious defects indicate the directions for refinement and further development. Resource requirements for the achievement of production targets are currently limited to livestock and feed. The inclusion of other resources, such as labour, housing, marketing facilities--should present no major problems, however. The current division of variables into endogenous and exogenous might be revised. In particular, those productivity parameters currently defined as exogenous input data might be related to more basic determinants such as breed, health status, management system and feed quantity and quality. The impact of v.ariations in such factors is currently examined through direct manipulation of the value of key productivity parameters after the appropriate 'manipulation' has been specified by 'expert opinion'. The endogenisation of these productivity parameters and the formal representation of their dependence upon the more fundamental factors of, say, breed, health, management and feed, is a potentially valuable extension. The determination of resource requirements would then be in terms of variables explicitly open to the influence of development programmes. Whilst such an extension might be made in principle, there are many conceptual and practical difficulties obstructing it and, typically, these can only be resolved by either an excessively detailed, and possibly unworkable, approach or one which is so grossly generalised and aggregated that it is likely to prove equally unhelpful. Related to the issue of which variables might be considered endogenous and which exogenous in any model is the direction of the causal chain implicit within that model. The modelling framework presented here is purely recursive, considering one-way flows from production through to animal numbers and feed resources via various productivity parameters.
248
David Hallam, J. A. Gartner, J. P. Hrabot'szky
Interdependence and two-way chains of influence are not recognised. Thus, for example, not only does the number of livestock required determine feed requirements but feed available is a determinant of livestock requirements: feed deficiencies limit productivity and hence increase livestock requirements. Such interdependencies are not easily handled at this level of aggregation in any formal sense. Nevertheless, it is essential that the livestock planner be aware of them and their significance when operating the modelling framework.
ACKNOWLEDGEMENT This work was funded under an FAO contract with the Centre for Agricultural Strategy, University of Reading.
REFERENCES Abdallah, Z. M. (1970). Marketing of livestock in the Sudan. Unpublished PhD thesis, Department of Agricultural Economics, University of Reading. Adams, M. E. (1974). The supply of cattle from Southern Darfur: 1. Herd structure, cattle numbers, offtake and market statistics. Sudan Journal oj Veterinary Science and Animal Husbandry, 15, 49 56. Blair Rains, A. & Kassam, A. H. (1980). Land resources and animal production. In: Land resources jor populations of the future, Rome, FAO. Dahl, G. & Hjort, A. (1976). Haeing herds." Pastoral herd growth and household economy. Stockholm Studies in Social Anthropology 2. Stockholm, Department of Social Anthropology, University of Stockholm. FAO (1970a). Introduction to agricultural planning, Rome, FAO. FAO (1970b). Worm census of agriculture, Rome, FAO. FAO (1979). Agriculture: Toward 2000, FAO Conference 20th session. Rome, FAO. FAO (1981). Agriculture." Toward 2000, Rome, FAO. Fitzhugh, H. A., Hodgson, H. J., Scoville, O. J., Nguyen, T. D. & Byerly, T. C. (1978). The role of ruminants in support oJ man, Morrilton, Arkansas, Winrock International. Hallam, D. (1981). Long-term demand projections--Methods and problems. In: Grassland in the British Economy (Jollans, J. L. (Ed.)), CAS Paper 10. Reading, Centre for Agricultural Strategy. Humphrey, J. H. (1980). The classification oJ world lit'estock systems. AGA/ MISC/80/3, Rome, FAO.
A quantitative framework for livestock development planning: Part 1
249
Hunting Technical Services (1976). Savanna development project phase II. Annex 3. Livestock and range resources. ILCA (1978). Mathematical modelling oj livestock production systems: Application of the TA M U beef cattle production model to Botswana, Addis Ababa, ILCA. Levine, J. M. (1982). Modelling livestock systems under tropical conditions with special reference to Indonesia. In: Livestock in Asia." lssues and policies (Fine, J. C. & Lattimore, R. G. (Eds)), Ottawa, IDRC. M urdie, G. (1976). Population models. In: Mathematical modelling (Andrews, J. G. & McLone, R. R. (Eds)), London, Butterworths. Sanders, J. O. & Cartwright, T. C. (1979a). A general cattle production systems model. I. Description of the model, Agricultural Systems, 4, 217 27. Sanders, J. O. & Cartwright, T. C. (1979b). A general cattle production systems model. II. Simulation of animal performance. Agricultural Systems, 4, 289 309. Van Raay, H. G. T. (1975). Rural planning in a Savanna region. Rotterdam, Rotterdam University Press.