A SYSTEMS APPROACH TO AGRICULTURAL RESEARCH RESOURCE
ALLOCATION IN DEVELOPING COUNTRIES
.Paper prepared for Conference on Resource Allocation and Productivity
in International Agricultural Research, Airlie House, Virginia, U.S.A.
January 26-29, 1975
CENTRO INTERNATIONAL DE AGRICULTURE TROPICAL
Apartado Aereo 6713
Cali, Colombia, S.A.
research priorities and allocate available research resources, the research
manager needs information on the probable impact of alternative research ef-
forts on social and economic goals and the cost associated with each research
This paper suggests a systems approach to the collection and analysis
of such information. -/ The first part of the.paper is dedicated to a brief
discussion of means and ends in agricultural research. Then follows the pre-
sentation of a model for data collection and analysis and the paper termina-
tes with a discussion of some of the information generating efforts currently
under way in CIAT.
MEANS AND ENDS IN AGRICULTURAL RESEARCH
A clear understanding of the distinction between final and immediate
goals on the one hand and means to reach these goals on the other is essential
to fully appreciate the need for improved management tools for the research
manager and to assure that such tools are relevant for establishing research
priorities. Furthermore, a clear definition of these concepts greatly facil-
itates the communication among disciplines on relative benefits of alternative
research efforts. It is quite obvious,that increased production is not a final
1/ A discussion of the systems approach in general may be found in a number
of books including: Stanford L. Optner (Editor), Systems Analysis, Penguin
Modern Management Readings, Middlesex, England, 1973, C. W. Churchman, R.L.
Achoff and E. L. Arnoff, Introduction to Operations Research. John Wiley &
Sons, New York, 1957; A. W. Wymore, Systems Engineering Methodology for Inter-
disciplinary Tearas. To be published by John Wiley & Sons, New York, 1974.
Preston C. Hammer,(Editor),Advances in Mathematical Systems Theory Penn.
State University Press, College Park, 1969. The usefulness of this approach
for research resource allocation is discussed in: John L. Dillon, The Economics
of Systems Research. Paper presented to Agricultural Systems Research Conference,
Massey University, 20-22 November, 1973.
goal of agricultural research but rather a means to reach some final goal
such as increased farm incomes or improved nutrition. It is equally clear
that improved income distribution, although it may be a final development
goal, does not serve as a working objective for the plant breeder or ento -
mologist. Nevertheless it is amazing how often these concepts are misused
both in written and verbal communication and in actual decision making on
research resource allocation.
Figure 1 outlines the potential outcomes of applied agricultural pro-
duction.research. Successful applied agricultural research produces knowledge
and/or improved material, e.g. seed. The knowledge and improved material may
be fed back into the research process for further work, or it may be released
to the farmer as new technology. There are three, and only three, potential
direct contributions of such technology: (1) increasing technical efficiency
of at least one resource ; (2) changing characteristics and composition of
products and developing new products ; and (3) reducing production risk. Any
other contribution will be indirect it must come about as a consequence of
one or more of the three direct contributions. There are three potential re-
sults of the above direct contributions: (1) changing the composition and
quantity of the aggregate supply of food, feed and fiber; (2) changing the
composition and quantity of the aggregate resource demand, e.g. increased or
decreased employment, and (3) changing the composition and quantity of aggre-
1/ Technical efficiency is a measure of output per unit of input where both
output and input are expressed in physical terms, e.g.production/ha.
2/ Developing plant types more apt for mechanization and improving the amino
acid composition in the protein of a certain crop are examples of this
kind of research contribution.
Figure 1. Illustration of the potential outcomes and
implications of agricultural research
gate domestic farm consumption. Any of these results may contribute to the
achievement of national development goals through changes in:(l) farm income
and its distribution among groups of farmers, (2) relative resource earnings,
(3) consumer real income and its distribution among consumer groups, (4) for-
eign exchange earnings, (5) human nutrition, and (6) any other element of
the national development goals.
In Viewing agricultural research and its potential outcomes and impli-
cations as a process, the confusion on means and ends can be eliminated. The
first level of outcomes (marked by (1) in Fig. 1) is clearly a set of means,
except when research is carried out for its own sake. The second level pe -
presents the working objectives for the agricultural production scientist.
For the research manager and society as a whole, however, this level expresses
alternative approaches to reach the goals shown in the fourth level. The
third level shown in Figure 1 represents the vehicle by which activities meet-
ing the scientist's working objectives are influencing the achievement of the
final goals. Changes in product supply, input demand and domestic farm con -
sumption are not themselves goals but means to reach some final goals.
Three conclusions may be drawn from the above discussion:
1. The working objectives for the-agricultural production scientist
must be expressed in terms of technical efficiency, desired product character-
istics and/or production risk. The specific working objectives and the most
effective technology to reach these objectives should be determined on the
basis of national development goals. Concurrence between the technology spe-
cification received by the scientist and the technology which results in ma-
ximum contribution to the achievement.of social goals is the responsibility
of the research manager.
2. The research'manager needs an information system for research re-
source allocation capable of (a) translating national development goals into
working objectives for the agricultural production scientist and (b) helping
the production scientist select the most effective technology to reach the
3. Confusion of ends and means, e.g. (a) using increased production
of a certain crop as the ultimate goal of certain research or (b) using im-
proved income distribution as the working objective for the scientist, are
likely to lead to a considerable waste of scarce research resources.
A SUGGESTED INFORMATION SYSTEM
An effective information system for allocation of resources'in applied
agricultural production research must be capable of providing the research
manager with reliable information that will enable him to establish and peri-
odically review research priorities in such a way as to maximize the expected
research contribution to the achievement of national development goals. The
system must be relatively simple to operate. It should provide a frame of
reference-within which project priorities can be established and individual
projects can be accepted or rejected without large time delays. Extreme care
must be taken to avoid a system that imposes heavy bureaucratic procedures
on the production scientist.
The cost of collecting, processing and analyzing the data must not be
excessive. The complexity of the system could range from certain gross pre-
dictions based on secondary data to a complete systems simulation model for
the economy. While the former is unlikely to provide sufficient reliable
information the latter would probably be too costly to operate.-
Assuming that agricultural research and efforts to improve its con-
tribution to the achievement of development goals compete for the same re-
sources, the decision as to how much should be allocated to one versus the
other must be based on the, same principles as those used to allocated re -
sources among alternative agricultural research activities.
Before the data requirements and the analytical model are discussed
it may be'useful to illustrate a series of steps necessary to translate na-
tional development goals into working objectives for the scientist and tech-
As shown in figure 2, it is essential that the development goals be
clearly specified. The changes in product supply, input demand and domestic
farm consumption expect to meet some or all of these goals should be iden -
tified. Then the researchable problems which solution is expected to accom-
4/ls suh hu
plish such changes must be identified.- assume, as an example, that one of
society's goals is to increase protein intake among protein deficient groups
of the population.
3/ Although expected benefits from the latter might exceed costs, it is likely
that the cost differential between a complete simulation model for the economy
and a somewhat simpler and more selective model exceeds the difference in bene-
fits, i.e. marginal costs exceeds marginal benefits. However, if a systems
simulation model of the whole economy is needed for other purposes such as pu-
blic policy and planning it may be feasible to include the components necessary
for our present purpose at low additional costs.
4/ At this point no attempt should be made to quantify the expected contribu-
tions to development goals.
Figure 2. Outline of the major steps needed to translate national
development goals into working objectives and technology specification
Specification of national
development goals (1)
Changes in product supply, input demand
and domestic farm consumption expected (2)
to meet goals
Identification of relevant researchable problems (3)
Identification of alternative technologies
to solve problems (4)
estimation of time Estimation of research Estimation of research (5)
rements for research t I
recent for research and adoption costs and adoption probabilities
nation of impact on Estimation of impact on i Estimation of impact on (6)
tic farm-consumption product supply and prices input demand and prices
SEstimation of contribution to achievement
F .n.41 an lo I (7
It may be expected that among other activities increased production of
grain legumes, animal products and high protein cassava may make a contri-
bution. The researchable problems limiting production of these commodities,
e.g. a certain disease in field beans, the non-availability of a high protein
cassava variety, etc., should then be identified.
It is importantthat the problems limiting the achievement of establi-
shed objectives be identified independently of possible solutions,i.e. a
"technology free specification of the problem". For example, if the problem
-- -'II ---"---Hn^^----^
Ji' one of low yields, it should be expressed in terms of the factors causing
low yields, e.g. lack of insect resistance, rather than specifying the pro -
blem as one of developing an insect resistant variety. As such, the technology
free specification of the problem implicitly provides a measure of the poten-
tial value of assembling technology to solve a particular problem. The tech -
nology free specification of the problem has to identify the farmer's needs,
and convert these needs into a specification of the parameters and constraints
that must be satisfied by the technological innovations.
When the relevant researchable problems are identified, the alterna-
tive technologies expected to solve the problems should be specified. Then
the cost, probability and time requirements of research and farm adoption
should be estimated for each proposed technology. Based on these estimates
as well as the nature of the problem, the structure and performance of the
production sector and the input and product market relationships it is now
possible to estimate the impact of solving each of the problem on product
.supply, input demand and domestic farm consumption. The last step before
specifying the scientist's working objectives and the technology to be deve-
loped refersto a..quantitative estimation of the contribution of alternative
research efforts to the achievement of national development goals.
Based on the broad framework presented above it is now possible to
specify the data requirements.
Data Requirements and Sources
This section discusses the general classes of data needed and their
possible sources. An exact specification of data requirements is not attemp-
ted.- Four sources of data are discussed: (1) the farm sector, (2) the mar-
ket sector, (3) the research sector, and (4) the government.
Farm Sector Data
Allocation of resources in applied agricultural research is frequently
made without sufficient knowledge about the existing problems and their rela-
tive economic importance in the production process. The communication between
the farm sector and the research institute is often deficient and the demands
at the farm level for problem solving research frequency are not well known
by the researchers.
Farmers in most developing countries, maybe with the exception of
large commercial ones and members of efficient producer associations, tend
to be unable to communicate their research needs directly to the research
institutes because of institutional and social barriers. Because of that si-
tuation, research is often irrelevant to the actual farm problems and research
results are not adopted.
A system is urgently needed that will provide a continuous flow of in-
5/ A more complete discussion of data requirements may be found in: Per
F'. Pinstrup-Andersen Toward a Workable Management Tool for Resource Alloca-
tion in Applied Agricultural Research in Developing Countries. Revised
version of paper presented at the Ford Foundation Meeting for Program
Advisors in Agriculture, Ibadan, Nigeria, April 29-May 4, 1974.
formation to the research manager on the potential gains in production, pro-
ductivity and risk obtainable from such research activities as (1) developing
resistances to existing diseases and insects, (2) improving cultural practi -
ces, (3) improving plant types, and (4) changing plant response to nutrients.
Furthermore, information is needed on the farmers' demand function for new
technology and how it may be changed, in order to focus on the development of
technology with a high probability of adoption.
Such a system might be built on a continuous feed back of information
from the farmer through the extension service to the research agency as is
the case in Denmark and certain other countries. Unfortunately, such an in-
formation feed back does not exist in most developing countries, maybe with
the exception of some of the integrated rural development projects.
Although such feed back may develop on a national scale, it is not
likely to do so in the near future. In the meantime, the necessary informa-
tion may best be obtained through organized surveys including field observa-
tions. In addition to these surveys it may be necessary to carry out control-
led experiments on the yield reducing effect of the various researchable pro-
blems. While field surveys will provide information on area affected by each
of the researchable problems and some indication of the yield depressing im-
pact, controlled experiments on yield losses will provide more exact infor-
mation on yield reducing effect and together the two data sources provide a
sound basis for estimating production and productivity impact of each of the
researchable problems. The impact on risk would be estimated from survey
data on past appearance and severity.of the problems, (pests, climate, etc.)
and the resulting yield variance.
Market Sector Data
Information on the structure and performance of product and input
markets is essential to predict the contribution of alternative research
efforts to achievement of development goals.
Existing and expected future product demand relationships may be
very unfavorable to the expansion of the supply of certain commodities while
favorable to the expansion of others. In the case of new products or dras -
tic changes in traditional products it is important to predict consumer pre-
ferences either before research is initiated, or at as early a stage in the
research as possible. Although a certain change in a traditional product
makes it "better" using some objective measure such as nutritional value, it
is quite possible that the consumer finds it unacceptable. A considerable
number of cases could be cited where"good" products have been developed
through research, only to find that they were unacceptable to the consumer.
Had the consumer preferences been checked out at an earlier stage, a consi-
derable amount of research resources might have been saved.
Instead of allocating research resources to fit existing product
market relationships it is frequently possible to change the market relation-
ships to fit the research results. Consumer preferences may be changed, new
markets may be found, etc. It is important to predict how these relationships
would behave -in the case of supply expansions in order to recommend adequate
public policy measures aimed at facilitating the necessary changes.
The impact of new technology on input demand will depend on the parti-
cular technology developed. Hence, before the decision is made on the type
of technology to develop, information should be obtained on existing and ex-
pected future input supply relationships.
Research sector data
Data are needed to estimate research costs and time requirements
and the likelihood of achieving the desired results. Although the outcome
of research usually cannot be predicted with great precision because of the
very nature of research, it is argued here that efforts to utilize the prior
knowledge of the scientists in a formal manner to make some, at least crude4
predictions as to outcomes is likely to greatly improve the efficiency of
the allocation of resources in applied agricultural research.
Government sector data
Developent goals may be classified under three general headings: 1)
growth, 2) equity, and 3) security. Although the specific development goals
may differ considerably among countries, all three of these types of goals-
are usually found.
The development goals must be clearly defined and, if possible, the
socially acceptable trade-offs among them should be specified.
At present, most research managers have little information on these
issues, and research priorities are at best based exclusively on the ob-
jectives of increasing production and productivity.
The Analytical Model
Figure 3 outlines an analytical model for an information system for
resource allocation in applied agricultural research. The figure outlines
the relationships determining the expected contribution of alternative re-
search efforts to the achievement of selected development goals. Only those
relationships believed to be most important are included in the model.
L~~ 'i ~;tl"`
Figure 3. Flow diagram for analytical model for an informtton system for
resource allocation in applied agricultural research
O Aritth tic operation points.
The following social goals are considered:
1. Economic growth.
2. More equitable income distribution.
3. Increased productive employment.
4. Increased net incomes to small farmers.
5. A more even cash flow to farmers.
6. Improved human nutrition.
7. Higher degree of self sufficiency in basic foods.
8. Increased foreign exchange earning.
The model may be changed to accommodate a different set of goals.
The model includes 53 arithmetic operation points. Each point esti-
mates the quantitative casual effect of a change in one variable on another.
The information output from the model to be used in decision-making is shown
by The contribution of new technology to the achievement of development
goals depends heavily on existing public policy. Hence, existing policy should
be clearly specified and it may be useful to apply the model for alternative
SELECTED CIAT ACTIVITIES
The remainder of the paper discusses some of the current CIAT efforts
aimed at developing and field testing simple methodologies for generating the
information most needed by the research manager. While the information ob -
tained from these efforts is expected to be useful for CIAT and the national
research agencies in the countries where the empirical testing is carried
out, the primary purpose of the work is to develop simple methodologies for
use by national research agencies in Latin America.
The CIAT work are discussed under three headings: (1) Single commodi-
ty analyses, (2) Iulti-commodity analyses, (3) A systems engineering metho-
dology for small farms. The discussion is limited to selected illustrative
projects. A description of all the CIAT activities in this area is beyond
the scope of this paper.
Single Commodity Analyses
This type of work is relevant to a situation where a decision has been
made to research a specific commodity either indefinitely or for a certain
minimum time period. While the amount of research resources allocated to a
certain commodity may be gradually increased or decreased over time, low mo-
bility of research resources may not permit rapid and large changes in rel -
ative research emphasis among commodities. Hence, the single commodity anal-
ysis may be appropriate at least for the short run.
In the case of a single commodity, information is needed on the commo-
dity itself as well as its interaction with other commodities both in produc-
tion and consumption. The current CIAT single commodity data collection and,
analysis focuses on the farm sector.
The single commodity approach attempts to identify the factors asso-
ciated with low productivity in a specific crop. It then proceeds to (1)
identify researchable problems expected to improve productivity and produc-
tion, (2) estimate the impact of solving each of the problems on producti-
vity and production,,(3) estimate the research and adoption probabilities,
costs and time requirements for each problem and each technology, (4) esti-
mate the impact of alternative research efforts on product supply, input de-
mand, domestic farm consumption, farm sector income and its distribution on
farm size. Such projects are currently under way for maize, cassava and
beans. Basic data are collected from agro-economic surveys and agro-biolo-
The agro-economic survey attempts to transmit to the research manager
the farm level demand for applied agricultural research, through establishing
a direct link between the farm and the research agency. The survey describes
the production process and focuses on identifying factors limiting production
and productivity and estimating their relative importance. Although highly
interrelated, these factors may be classified as (1) agro-biological, (2)
socio-economic, and (3) institutional. Given the purpose of the survey, em-
phasis is placed on agro-biological and related economic factors.
Most of the data related to the agro-biological factors are obtained
from direct observation in the farmers' fields. The occurrence and severity
of disease and insect damage, mineral deficiencies and weed occurrence are
noted. Furthermore, existing cropping systems, cultural practices, soil qual-
ity, availability of water, plant type and general plant development are des-
cribed. The farmer's perception of the agro-biological problems are compared
to the field observations and his attitudes toward solutions to the problems
(new technology) are sought. 6
6/ Emphasis is placed cn obtaining some indication of the farmer's objective
function including the relative importance of income, risk and home consump-
'tion to help identify technology with high expected rate of adoption.
Data are sought on (1) the use of purchased inputs such as chemical
fertilizers and insecticides, (2) labor use and production costs by produc-
tion activity, and (3) gross and net revenues obtained from the crop.
Information is sought on certain aspects of input and product market
relationships, as well as the availability and use of credit and technical
The data are collected by a small team of agronomists and economists.
After having received an intensive training course in diagnosing farm level
production problems, the team makes periodic visits (normally 3 4 visits)
to a selected sample of farmersthroughout a complete crop cycle. About half
of the time on each farm is spent in the field collecting data on agro-biologi-
cal issues while the other half is used to interview the farmer.
Training of the field team is one of the most critical issues in assur-
ing high quality data from the agro-economic survey. Making a correct diag-
nosis in the field, e.g. distinguishing among the symptoms of certain diseases,
insect damage, mineral deficiencies, etc. in most cases requires considerable
expertise. Before initiating the agro-economic survey, the agronomists on
the CIAT field teams spend a certain amount of time with each disciplinary
group on the relevant CIAT commodity team supplemented in some cases with
training from professionals from national research and extension agencies.
Most of this initial training takes place in the field.
The agro-economic survey provides an estimate of the area affected
by each of the problems identified. Furthermore, it gives an indication of
the yield depressing effect. However, it is frequently difficult to estimate
the yield impact from survey data with a great deal of accuracy. Hence, con-
trolled experiments are carried out to help quantify the impact of the problems
The work described above is in its preliminary stages and no signifi-
cant contribution to research resource allocation can yet be identified. It
may be expected, however, that the direct participation of the CIAT agricul -
tural production scientists in project planning and training of field agrono-
mists as well as communication with these agronomists when they are back at
the research station and the distribution of preliminary project findings may
have been of some value to the scientists in planning their future research.
However, more time is needed to terminate the first round of data collection
and analysis before the real value of these efforts for research resource al-
location can be established.
If these initial effects prove to be useful, the methodology and exper-
ience gained will be made available to interested national research agencies.
Currently, the possibility of carrying out agro-economic. surveys for cassava
in Brazil and Thailand are being discussed, and funds have been assured to pro-
vide technical assistance for two similar projects for beans in Latin America.
As opposed to the analysis described above, the multi-commodity approach
assumes that the choice of commodities for research and the relative priority
among those commodities are not determined a prior. Hence, in addition to the
data collected for a single commodity, information is needed on the relative
contribution to development goals of research on alternative commodities.
In this area, CIAT is currently undertaking a project which initial
objective is to develop and test a methodology to estimate the impact on human
nutrition of increasing the production of each of a number of foods. The em -
pirical testing is currently being done for the city of Cali, Colombia. In
addition to the impact on human nutrition, the project provides information
of the impact of alternative production expansions on consumer real income by
income strata and could be extended to include the impact on farm sector in -
comes and distribution.
The methodology is based on a simulation model using as basic data a
set of price elasticity matrices (one for each of five income strata)as well
as current food prices, quantities consumed and protein and caloric intakes.
The model is expected to facilitate the estimation of the impact of alterna-
tive research efforts on human nutrition. The model form a part of the analy-
tical model shown in Figure 3, estimating the coefficients indicated in the
Figure by arithmetic operation points numbers 20, 21, 28, 29, 36, 37, 45,
46, 47, 48 and 53.
A Systems Engineering Methodology for
This approach centers on the farmer and is considered complementary
to the commodity oriented approach discussed above. As opposed to the model
in Figure 3, this approach focused primarily on farmer goals. It involves
the development_of _agenejral_cssofmodels for the small farm where the
small farm system is defined as one in which the farm family and other living
on the farm assemble individual enterprises into production, consumption, mar-
keting systems in which biological and physical factors interact with social,
political and economic systems. Such systems engineering models of the small
farm help explaining the dynamic behavior of the farm system as a function of
the input and output relationship with the external systems (the biological,
ecological and institutional environment) and makes it possible to identify
the most effective agricultural technologies required to stimulate changes in
the performance of the individual farm systems. In particular, by being cen-
tered on the farm as a system, it is hoped that these models will identify the
principal limitations to the generation of well-being, income and marketable
agricultural surpluses in what we call a technology free specification of the
problem, i.e. a specification of the problem independent of possible technolo-
gies for its solution. The advantages of using this approach for research re-
source allocation were discussed earlier.
The systems engineering methodology for small farms is currently being
applied by the Small Farm Systems Program of CIAT in its collaborative work
with the Institute of Agricultural Sciences and Technology of Guatemala (ICTA).
Before the expected utility of this methodology for research resource alloca-
tion is discussed, the overall structure of the models is briefly described.
The collaborative project is being carried out in an agrarian zone in
a Southern Coastal Region of Guatemala. Figure 4 shows the principal activi-
ties of the agricultural cycle for that zone. A schematic representation of a
general model for the small farm system is'presented in Figure 5 while Figure
6 is a reduced version which is currently being utilized for the study of the
farm system in the zone. The behavior of the small farm system is being stu-
died as a function of the principal inputs for the system: credit, prices,
availability of machinery, availability of labor and climate. This is a limi-
ted set of input factors and the principal concern at this time is to under -
tand the behavior of the small farm system in the presence of the climatologic
risk and the interaction of this risk with other inputs.
The farmers in this zone currently utilize almost no modern factors of
production and it is speculated that this situation is due primarily to risk
aversion. Delays in the credit system and the lack of confidence in the support
prices create a situation in which institutional factors do not help absorb the
risk. There are serious delays in availability of machinery and a seasonal.
labor shortage exists due to competition with the large plantations. The pri-
mary purpose of the model is to analyze whether in fact the dynamic interactions
of institutional and climatological factors are the principal limitations to
production and farm incomes.
Sub-system Z1 in figure 6 denominated CASH has as its principal function
to account and allocate the cash flow to the different activities of the fami-
ly, including the purchase of family consumption goods, factorsof production
and payments to credit. It is in this sub-system that farmers' decision crite-
ria are studied.
Figure 4. Cropping Cycle on Some Farms in Southern Guatemala
Limitations in the
Credit and Machi-
ON THE FARM
HIRED LABOR ON
-pH, N, P, K
Schematic Diagram of "A General Model" for Small Farm Systems
. CREDIT PAYMENT
Reduced Model of a
Farm System in Southern Guatemala
Sub-system Z4, which is CROP PRODUCTION is linked to the external
inputs of machinery and climate and to the CASH and SOIL sub-systems. The
evaluation of alternative technological alternatives for production is
carried out within this sub-system. The FAMILY CONSUMPTION sub-system re-
presents the need for on-farm consumption of the various products! on the
farm as well as for the purchase of food stuffs and non-foods. This sub-
system helps in estimating the family nutritional situation.
The technical coefficients used in the model are the best estimates
on the behavior of each of the sub-systems as have been provided by tech -
nical experts. The structure of the model was derived from information
gathered through frequent visits to the zone by the members of the CIAT
Small Farm Systems Program and represents the synthesis of the insight that
is available on the behavior of small farms in that zone.
A number of agronomic experiments and a socio-economic survey are
presently being carried out by the CIAT Small Farm Systems Program to test
the technical coefficients presently available and to test the behavior and
predicting ability of the model.
It is not suggested that this model as it presently stands repre -
sents the total reality of agriculture in the zone. The purpose of develop-
ing and utilizing the model is to illustrate some of the principal structural
relationships in the physical, biological and economic environment and to de-
mostrate the possible utility of such a model.
The Model as a Research Guide
It is expected that this model will be useful for estimating the likely
outcomes of alternative research and public policies and institutional changes.
With specific reference to the likely outcomes of alternative extension and
research policies the model evaluates a number of proposed technological pack-
ages as shown in Figures 7, 8, 9 and 10.
These packages are evaluated with respect to their expected impact on
family nutrition, family income, risk (as measured by income and production
variance) and labor utilization. Preliminary results from this work are shown
in Figures 7, 8, 9 and 10.
Figure 7 and figure 8 present production trajectories generated by the
model over a simulated five-year period. Each production trajectory is iden-
tified with the production package which was simulated. Figure 7 presents the
production under the assumption that prices fluctuate between US$70 and US$120
per metric ton throughout the year as is presently the case in the zone. Fi -
gure 8 presents the production trajectories under support price. Comparison
of the graphs indicates that price stability can be a means by which the adop-
tion of technological packages is stimulated.
Figures 9 and 10 are the net family income trajectories for some of
these technological packages under the two sets of price assumptions. Figure
9 is illustrative of the risk that is involved under a situation of unstable
prices and unstable weather conditions. In particular two of the so called
"production packages" are so costly that when the risks are taken into consi-
deration-they would generate negative net income for at least one year. Tra-
ditional or subsistence farmers cannot tolerate this kind of risk. Another
salient feature of the four graphs is that the traditional production package
which utilizes few modern factors of production, produces the lowest yields
Figure 7. Simulated Maize Production Trajectories with Prices
Varving from US$70 to US$120.ton
Ii / .
I / ./
"..- ..--. Herbicides
- .. -. .. --- |Herbicides
Simulated Maize Production Traiectories With
Prices Fixed at US$120/Ton
2 3 4
Figure 9. Simulated Annual Net Income From Maize Production 29
With Prices Varying from US$70 to US$120/ton.
/ -- -* -' Herbicides
90. / -.
so. / I ,,~
"70.'1 "1'...... Insecticides
40" / Fertilizers
40 Imp. Var.
20 / .. -- Insecticides
\\ aImp.. Var.
10 \ / Deep Plowing
o. i ,.
0 1 2 3 4 5
Figure 10. Simulated Annual Net Income From Maize Production
With Prices Fixed At US$120/ton
19--0 .1 Herbicides
180 / -
150 / Insecticides
140 /\ /" Imp. Var.
I\ / Traditional
110 \ \ .\ / /.
90 =\ Herbicides
80 -* Fertilizers
60 \ '
0 1 2 3 4 5 Years
but tends to be better in terms of net income than some of the more compli-
cated production packages. The traditional system has the lowest income
variance. A comparison of figures 9 and 10 would illustrate the potential
value of an effectively functioning market and price support system.
It appears that the package expected to make the largest contribution
to the income goal is that referring to the use of herbicides. Prior to this
finding, ICTA did not have any work planned on weed control for that zone.
However, as a result of the finding, a professional has now being sent for
training in weed control and the collaborative CIAT-ICTA work for the coming
agricultural season will involve extensive research on weed control methods
and the economic evaluation of different weed control techniques.
In addition to this immediate although preliminary outcome of the
model, it is expected that further utility to decision making on research
resource allocation will be achieved through sensitivity analysis of the mo-
del. This involves the estimation of the response of the system to variation
of the parameters and coefficient. The experimental work will be focused on
measuring with precision those technical coefficients which appear to be
sensitive to variation. If, for example, the model were to indicate sensi-
tivity for insect damage, intensive research on insect control would be re-
commended. On the other hand, if the model is not sensitive to variation in
these technical coefficients, such research would take a lower priority. Thus
the model can be utilized to establish research priorities both in the far -
mer's field and the experiment station. As more general and regional models
are developed, it is expected that analyses can be made to help the allocation
of resources not only within agricultural research, but to trade-offs
that exist between investment in research and investment in institu-
tional services such as credit and marketing.
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