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SUPPLY RESPONSE WITHIN THE FARMING SYSTEM CONTEXT

WEEK 2: DAY 4

POLICY REFORM AND TECHNICAL CHANGE

by Bruno Henry de Frahan, Catholic University of Louvain

CONTENTS

1. INTRODUCTION

2. IDENTIFYING FACTORS THAT INFLUENCE TECHNICAL CHANGE

3. ESTIMATING THE IMPACT OF AGRICULTURAL RESEARCH AND EXTENSION:A REVIEW OF METHODOLOGIES

3.1. Types of studies assessing the impact of agricultural research3.2. Objectives of studies assessing the impact of agricultural research3.3. Ex-post evaluations3.4. Ex-ante evaluations

4. ASSESSING THE NEED FOR POLICY REFORM AND INSTITUTIONALRESTRUCTURING TO INDUCE TECHNICAL CHANGE

4.1. Ex-post evaluations4.2. Ex-ante evaluations

REFERENCES

LIST OF TABLES

Table 1. Policies and their impact on technical changeTable 2. Rate of return to investments in agricultural research in sub-Saharan AfricaTable 3. Economic values of the scenarios

LIST OF FIGURES

Figure 1. Value of inputs saved in SchultzFigure 2. Hybrid maize, perfectly elastic supply in GrilichesFigure 3. Hybrid maize, perfectly inelastic supply in GrilichesFigure 4. Poultry supply shift resulting from the use of new inputs in PetersonFigure 5. Rice supply shift due to breeding research in Akino and Hayami

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1. INTRODUCTION

The first section of this chapter introduces the variables which encourage technical changeand, thereby, enhance factor productivity. This section concludes that policies are importantand that policy reform can play a critical role in encouraging technical change.Breakthroughs in agricultural research are identified as having the most significant impact ontechnical change, and so consequently the second section reviews methodologies forevaluating ex-post and ex-ante investments in agricultural research. These evaluations focusspecifically on the contribution of agricultural research to growth in agricultural productionand income and are used to help organise research priorities. Case studies examined in thethird section indicate how investments in agricultural research can be associated with otherinvestments and policies to have an impact on technical change and, hence, agriculturalgrowth.

2. IDENTIFYING FACTORS THAT INFLUENCE TECHNICAL CHANGE

Technical change in agriculture is largely driven by investments in research and extension. Itis, however, also conditioned by other micro- and macro-policy factors such as prices fortraded and non-traded inputs and outputs, specific agricultural policies that influence theseprices, incentives and disincentives to adopt improved agricultural techniques, andinvestments in infrastructure(Anderson 1994).

Consequently, macroeconomic and agricultural policy reform, efficient institutionalarrangements and investments in infrastructure have been identified as the key elementsdriving the sort of technical change that has a positive impact on agricultural productivity andcan stimulates agricultural growth in developing countries, particularly those in sub-SaharanAfrica (SSA). New agricultural technologies have been adopted by farmers only in those fewAfrican countries where agricultural research and extension systems have worked reasonablywell, where adequate road infrastructure has facilitated trade in farm inputs and products andwhere agricultural product and input prices have not been excessively distorted bygovernment policy. Successful examples include hybrid maize in Kenya and Zimbabwe, thecotton-maize package in francophone West Africa, rubber in Côte d'Ivoire and tobacco inseveral African countries (Cleaver, 1994). Unfortunately, examples of successful technicalchange have been rare in Africa. However, as many African countries undergo policyreforms to reduce price distortions (which have rendered technical change in agriculture bothunprofitable and risky), eliminate public-sector monopolies for input supply, loosen foreignexchange and licensing restrictions on the import of farm inputs, strengthen agriculturalresearch and extension and promote the production and trade of high-value commodities suchas cereals, fruits, vegetables and livestock products, the prospects for African agriculture lookbrighter.

It is now widely accepted that technical change in the African farm sector is the outcome of aset of strongly interlinked and complementary policies and institutional arrangements thateither generate improved technology or manage to get improved technology used. Table 1summarises these policies and institutional arrangements. Policies that have an impact ontechnical change and, therefore, factor productivity can be divided into sector-specific (direct)and economy-wide or macro (indirect) policies (Ingco and Mitchell 1994). Empirical studiesin eighteen developing countries have shown that economy-wide policies generally have hada negative effect on agricultural incentives, particularly through their adverse effect on the

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terms of trade between the agricultural and non-agricultural sectors. Although indirect, thisnegative effect has often dominated the effects of sector-specific policies, whether the latterwere positive or negative (Krueger, Schiff and Valdés 1988 cited in Ingco and Mitchell 1994,p. 484).1

Macroeconomic and sector-specific policies can influence price and non-price incentives fortechnical change. Macroeconomic policies that affect price incentives for technical changeinclude trade and exchange rate policies that can distort prices across sectors of the wholeeconomy. Other macroeconomic policies include interest and wage rate policies as well ascapital and labour market regulations, both of which affect the cost of capital relative to thecost of labour and, therefore, alter incentives to adopt capital versus labour saving technicalchanges. Fiscal policies may affect interest rates and, therefore, the cost of and access tocapital and investment for technical change.2 Macroeconomic policies can also influencenon-price incentives for technical change: public investment in transport systems (roads andrailways) increase access to farm inputs, which may embody technological innovations, andtrade in agricultural products. Secure land tenure can encourage the adoption of agriculturaltechnologies that requires investments. Education and training policies have an effect onhuman capital formation and, therefore, on technology generation and adoption.Macroeconomic policies tend to have a larger impact on technology adoption than ontechnology generation although, according to the induced innovation paradigm, these policiescan also affect technology generation by altering relative factor costs. The fact that thesepolicies have been lacking can, to a certain extent, explain the limited technical change andpoor agricultural growth record in SSA.

As with macroeconomic policies, sector-specific policies can create both price and non-priceincentives for technical change. Specific agricultural market price interventions, includingproducer and consumer price supports, import substitution protection and targeted farm creditprogrammes are examples of sector-specific policies that can influence technical change.These policies directly influence technology adoption and indirectly influence technologygeneration through their effect on the terms of trade. Sector-specific policies can also createnon-price incentives for technical change such as investments in agricultural research andextension and other public investments, including irrigation schemes, improved access towater, manufacturing capacity for fertiliser and other major agricultural inputs, and storagefacilities. Investments in agricultural research are the backbone of technology generationwhile investments in extension and rural infrastructure set the stage for technology adoption.Traditional arguments for public investment in research are reviewed below.

In the economic literature, the main justification for public-sector investment in agriculturalresearch rests on the assumption that there is a "market failure" in the private production andfunding of research (Alston, Norton and Pardey 1995).3 That is, the market does not providethe private sector with incentives to support the quantity and mix of research that would bebest from the point of view of society. This market failure arises when the private sectorcannot appropriate all the benefits from its investments in research, in other words when it is 1 In these studies, direct policies mainly include producer price supports and protection from import-substitutionwhile indirect policies include exchange rate and industrial protection policies.2 In developing countries, large fiscal deficits push up interest rates raising the user cost of capital to farmers.Since the adoption of new technology usually requires an increase in net investment or net capital accumulation,a more costly access to credit tends to limit the rate of technology adoption and technological progress.3 The following five paragraphs are drawn from Alston, Norton and Pardey (1995, pp. 12 - 14 and pp. 16 - 17)and complement Section 3 of Day 13 "Agricultural research and extension" of Module ... "..." of this thematicfield..

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practically impossible or very costly to exclude individuals from using the research outcomes.In this particular situation the research becomes somewhat of a public good. When theprivate benefits from incremental investment in research are less than the social benefits, theresult is an underinvestment in research from society's point of view. This situation justifiesgovernment intervention to correct the market failure.

Governmental intervention is most effective when it supports research that has a high socialpayoff and in which the private sector has relatively little incentive to invest. Consequently,the public sector has often supported basic research. This type of research often cannot betransferred immediately into new technologies and generate gains which can be capturedeasily by the private sector. The private sector itself has historically concentrated much of itsown research efforts in the development of seed, machinery and chemicals for which patentsand licenses can generally be easily obtained and enforced. Thus private investors avoidmany of the "free-rider" problems that occur with other types of investment.4

The frequent existence of size and scope economies in research means that the same researchoften is more efficiently carried out by a large and diversified organisation than a number ofsmaller ones. However, the natural monopoly power of a large and diversified organisationthreatens the competitive structure of the market for innovations, therefore justifying somepublic control. In the U.S. land-grant university system, public-sector involvement inagricultural research has been used to maximise the complementarities between research,higher education and extension.

Note that when government intervention is warranted, the form of intervention need notnecessarily be the use of government revenue to fund research or conduct research in a public-sector institution. For example, when underinvestment in research is due to a "free-rider"problem among producers, it might be fairer and more efficient for the government to createan institution to carry out research on behalf of producers using funds collected by taxingoutput. This type of solution has been used in Uruguay to support research on rice and inColumbia for research on coffee. Government-funded public sector research in agriculture is,however, more easily justified in poorer developing countries where market failures inresearch associated with transaction costs, intellectual property rights problems or otherdistortions are likely to be more severe. Unfortunately, in these countries the opportunity costof general government revenues is relatively high which lends some justification to supportfrom the international community.

The justifications for public intervention cited above are the normative circumstances underwhich government should be involved in funding or conducting agricultural research. Actualdecisions concerning the allocation of resources to research are, however, subject to politicaland economic forces which determine the demand for particular types of research (Alston,Norton and Pardey 1995). Analysts charged with informing decision makers must be awareof these pressures. Producers, consumers, owners of factors of production and even scientistsand administrators are all potential beneficiaries of research. However, which group actuallybenefits from research depends on many factors, including the nature of the research-inducedtechnical change, the particular characteristics of the market for the commodity, the incentivestructure in the research system, the country's trade status with respect to the commodity,price policies, etc.

4 Note, however, that the private sector is unlikely to fund research in self-pollinated crops because the benefitscannot be appropriated.

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Using an empirical study in the Philippines, Ingco and Mitchell (1994) assess the relativecontribution of price incentives ("getting prices right") and non-price incentives to technicalchange and agricultural growth and conclude that both price and non-price incentives areimportant. They demonstrate that the performance of the agricultural sector depends on thewhole economic environment, including not only sector-specific and economy-wide priceincentives but also the level of private and government investment in irrigation, roads,research and extension. The responsiveness of economic agents to the market opportunitiescreated by removing price distortions increases when investment is made in these factors ofproduction.

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Table 1. Policies and their impact on technical change

POLICY TYPE OF POLICY TYPE OF IMPACT ON TECHNICALCHANGE

1. Macroeconomic policies1.1. Price incentives • Trade and exchange rate policies

• Monetary policies• Interest & wage rate policies• Capital and labour market regulations• Fiscal policies

Indirect but powerful impact on technologyadoption and agricultural growth

1.2. Non-price incentives • Public infrastructural investments (roads,railways, etc.)

• Land tenure legislation• Education & training policies

idem

2. Sector-specific policies2.1. Price incentives • Specific agricultural market price interventions

• Targeted farm credit programmesDirect impact on technology adoption & indirectimpact on technology generation

2.2. Non-price incentives • Agricultural research• Agricultural extension• Public infrastructural investments (irrigation,

input supply, storage, etc.)

Direct powerful impact on technology generationDirect impact on technology adoptionIdem

3. ESTIMATING THE IMPACT OF AGRICULTURAL RESEARCH ANDEXTENSION: A REVIEW OF METHODOLOGIES

In addition to economic and agricultural policy instruments, agricultural research is used toattain agricultural-sector goals. In many circumstances, economic growth, incomedistribution and food security are the stated objectives of agricultural research (Alston,Norton and Pardey, 1995).5 Growth in agricultural production is essential to improve welfareand stimulate overall economic development in many developing countries. Agriculturalresearch, through its influence on productivity, has the potential to be a major source ofgrowth in agricultural production and income. The results and impact of agricultural researchare rarely evenly distributed among different income groups, regions, producers andconsumers. Due to its impact on technology and institutions, agricultural research can eitherreduce or worsen problems of food insecurity, the long-run unsustainability of farmingsystems and natural resource depletion. But the use of public-sector agricultural research topursue non-efficiency objectives such as food security and income distribution should becarefully assessed. Considering more than one objective greatly adds to the cost of decision-making and research management, and there are better policy instruments to meet non-efficiency objectives. Because economic arguments support the singular objective ofeconomic efficiency for agricultural research, evaluation methodologies reviewed here areonly those based on the efficiency criterion. These methods, however, can be used to informdecision makers about the cost of biasing the research portfolio to pursue specific non-efficiency objectives.

3.1. Types of studies assessing the impact of agricultural research6

Impact studies can be separated into ex-post and ex-ante studies (Figure 1). Ex-post studiesexamine the impact of past agricultural research while ex-ante studies examine alternativeresearch programmes and assess their potential impact.

Both ex-ante and ex-post studies can provide a rate of return (ROR) to research investmentsand/or a descriptive analysis.

5 As pointed it out by Alston, Norton and Pardey (1995), environmental objectives are also frequently stated butcan be considered as falling under growth, distributional and security objectives. For example, environmentalconcerns often arise when measures of growth fail to include the external costs associated with environmentaldamage or when the distribution of benefits to future generations are jeopardised.6 These first two sections are drawn from notes prepared by Crawford for a workshop on "Méthodes pourl'évaluation de l'impact économique de la recherche économique" presented in Sénégal in 1991.

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Among ROR studies, one can distinguish between studies using the economic surplusapproach, where agricultural research shifts the supply curve and generates a change ineconomic surplus, and those using the production function approach, where agriculturalresearch is incorporated as a factor of production. The economic surplus approach gives anaverage rate of return for the research activity which can be expressed either as a net presentvalue (NPV), a benefit/cost ratio (B/C ratio) or an internal rate of return (IRR). Theestimated coefficient of the independent variable "investment in agricultural research" of theproduction function gives the marginal rate of return, expressing the benefit obtained fromthe last Ecu invested in the research activity. In principle, the optimal allocation of fundsamong different research activities should be based on marginal rates of return.

Finally, the studies using the economic surplus approach can in turn be separated into thoseusing the "index number" (i.e., an indicator of productivity gain) approach and those usingthe standard benefit-cost analysis with the implicit hypotheses of a completely elastic demandand a completely inelastic supply.

3.2. Objectives of studies assessing the impact of agricultural research

The objectives of ex-post studies include the following:

1. To assess the impact of a specific innovation, such as:• an improved vegetal or animal variety,• an improved practice for producing, harvesting, transforming or storing an

agricultural product. 2. To assess the impact of a research programme which includes several research activities.

For example, a research programme on rainfed cereals can include research activities onmillet, maize, sorghum, etc. A research programme on maize can include researchactivities on varietal improvement, agronomy, post-harvest protection, etc.

3. To assess a set of interventions to develop a given sub-sector. Such an assessment is

useful because it is hard in practise to isolate the contribution of agricultural research fromother complementary interventions such as extension, credit, agricultural prices, transportor exogenous factors such as rainfall, world prices, etc. Note, however that the productionfunction approach enables the contribution of each factor to be disentangled.

4. To assess several types of impact:

• the economic impact at different levels (farm, region and country) expressed interms of ROR,

• the distribution of the economic impact among different population groups orwithin the household,

• the impact on the environment as well as on health, nutritional level, humanresources, social and political relationships.

5. To highlight factors that have had a positive or negative impact on the research or the

implementation of research results. These factors can be within or outside the researchinstitution or programme. They can include administrative and financial issues as well asthe skill-level and motivation of the research staff. The identification of key factors can

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lead to recommendations to improve the efficiency of agricultural research orcomplementary interventions.

The objectives of ex-ante studies include the following:

1. To assess the potential economic impact of research activities in view of justifying relatedinvestments.

2. To rank research priorities by identifying the relative advantages and probabilities of

success of different research activities. 3. To identify the optimal combination of research activities by using modelling techniques.

3.3. Ex-post evaluations7

3.3.1. The economic surplus or "index number" approachThe first major quantitative evaluation of agricultural research investments was done bySchultz (1953). He estimated the value of inputs saved through more efficient productiontechniques compared to the cost of research and development. He calculated that output perunit of input was at least 32% higher in 1950 than in 1910 or that to produce 1950 outputwith 1910 techniques would require an additional expense of US$ 10 billion in agriculturalinputs. He actually calculated the increase in consumer surplus resulting from a parallel shiftof the supply curve, given a completely elastic supply curve S2 and a completely inelasticdemand curve D1 (Figure 1).

Since Schultz's work there have been many economic surplus research evaluation studies,most at the commodity level. Griliches (1958) estimated the loss in net economic surplus ifhybrid maize were to disappear. He assumed that the adoption of hybrid maize shifts thesupply curve downward S' and estimated returns for the extreme cases of perfectly priceelastic (Figure 2) and perfectly price inelastic (Figure 3) supply curves, given a unitarydemand price elasticity. In figure 2, the change in economic surplus (∆ES) is tantamount tothe change in consumer surplus and equals the areas (E+F):

∆ES = KP1Q1[1+K/(2ε)]

where K=∆P/P1 and ε is the absolute value of the demand price elasticity.

In figure 3, the change in economic surplus equals the areas (A+B-A+C):

∆ES = KP1Q1[1+K/(2ε)]

where K=∆Q/Q1, an indicator of productivity gain that corresponds to the percentage shift inthe supply curve.

7 This section is drawn from Norton and Davis (1981), an excellent and comprehensive review ofmethodologies for evaluating ROR to agricultural research.

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Peterson (1967) estimated the change in economic surplus for poultry research eliminatingthe supply and demand elasticity restrictions. In figure 4, when price and quantity move fromP1 to P2 and from Q1 to Q2, respectively, the change in economic surplus equals the areas(A+B+C+E+G+(-A-B+H+I+J)) = (C+E+G+H+I+J) ≈ (I+J+K+L+E+G-D):

∆ES = KP1Q1 + K2P1Q1/(2ε) - K2P1Q2(P1/P2)[εη/( ε+η)][(ε-1)/ε]2/2

where ε is the absolute value of the demand price elasticity, η is the absolute value of thesupply price elasticity and K the percentage shift in the supply curve (Q1-Q'2)/Q1.

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An internal rate of return is estimated by comparing the change in economic surplus to thecosts of research and extension.

Early ROR studies also include Schmitz and Seckler (1970) who extended the model toaccount for labour released with the introduction of a mechanical tomato harvester; Ayer andSchuh (1972) who altered the model to make Brazilian cotton supply dependent on theprevious year's price; and Akino and Hayami (1975) who estimated the social benefit fromrice-breeding research in Japan by including distributional effects of rice import policies.This latter study has served as a model for many other ROR studies and, therefore, is brieflydescribed below.

Assuming market equilibrium and no rice imports, the change in economic surplus due toresearch equals the area ABO, adding the change in consumer surplus (PnBCP0 + ABC) andthe change in producer surplus (AOC - PnBCP0) (Figure 5). If the government kept the riceprice at P0, the change in economic surplus would be equals to the change in producersurplus. Without the increased production due to research, Japan would have to import rice ata cots of ACQ'nQ0 to keep the price at P0. Akino and Hayami provide formulas for estimatingthe relevant areas.

PnBCP0 = P0Q0K(1+η){1- K(1+η)ε/[2(ε+η) - K(1+η)ε/2}/(ε+η)

ABC = P0Q0[K(1+η)]²/[2(ε+η)]

AOC = KP0Q0

ACQ'nQ0 = (1+η)KP0Q0

where K = (Q0 - Qn)/Q0.

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Scobie and Posada (1978) used the economic surplus approach to study technical change inColombian rice production among different producer and consumer groups. Duncan (1972),using new pasture technologies, estimated the benefits of research on an input for theproduction of another commodity.

As already mentioned, the benefit-cost analysis approach is drawn from the economic surplusapproach with the hypotheses of a completely elastic demand and a completely inelasticsupply. The first hypothesis is correct for a small open economy. In this kind of analysis, thecosts include the additional costs for adopting the innovation in addition to the costs of theresearch investment. Those additional costs are not included in the more general economicsurplus or "index number" approach because the supply shift implicitly takes them intoaccount, the supply curve being the sum of the individual marginal cost curves.

The nature of the supply curve shift is a controversial issue. Lindner and Jarrett (1978)hypothesised that some innovations will generate divergent, some others convergent andothers parallel supply shifts. Biological and chemical innovations tend to generate adivergent supply shift. However, mechanical and organisational innovations result in aconvergent supply shift because the adoption of these types of innovations affect differentlythe average costs of marginal and inframarginal farms (i.e., the more profitable, lower costfarms) and, consequently, the shift of the industry supply curve. Rose (1980), however,argued that the rent components in supply price make it difficult to link specific farms withparticular points on the supply curve. Nevertheless, the assumption concerning the nature ofthe supply shift is important since a divergent shift results in fewer benefits to producers thaneither parallel or convergent shifts.

Although the size of the shifter K determines net benefits, it is the demand elasticity whichaffects the distribution of the benefits between consumers and producers. The more elasticthe demand curve, the more likely producers gain from adopting technical change. If demandelasticity is larger than supply elasticity in absolute terms, producers receive a larger share ofthe benefits than consumers.

The annual data needs of this approach include:

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• the value of the additional production resulting from the adoption of the innovation, whichdepends on yield increase, area increase and price change, and/or the value of thereduction in production costs,

• the costs of agricultural research and complementary interventions (e.g., extension) andadditional production costs due to the adoption of the innovation,

• the time lag between the investment in research and the actual adoption of the innovationby farmers,

• the demand and supply price elasticities of the commodity or commodity group for the"index number" approach.

3.3.2. The production function approachThe basic model used in the production function approach is:

Q = A Π Xiβi Π Rt-j

αt-j eν,

where:Q is the value of agricultural output,A is a shift factor,Xi is the ith conventional production input,Rt-j is expenditure on research (and extension) in the t-jth year,βi is the production coefficient of the ith conventional input,αt-j is the partial production coefficient of research (and extension) in the t-jth year,ν is a random error term.

A major source of variation in the production function approach is the length and shape of thetime lag reflecting the impact of research expenditures on output. Early studies, such as thepioneering work by Griliches (1964), used either a single year's lagged expenditure or asimple average of the two previous years. More recent studies (Evenson 1967, Fishelson1971 and Cline and Lu 1976) use an inverted 'V' or 'U'-shaped distribution with a mean lag ofsix to seven years.

The above specification is estimated mainly with cross-section data to either aggregate outputor commodity groups. Time-series studies often display an alternative specification:

P = AWγEε Π Rt-jαt-j eν,

where:P is a productivity index of agricultural output,W is a weather index,E is measure of the education level of farmers,γ and ε are productivity coefficients for the associated inputs.

The major reasons for using a productivity index are high intercorrelation problems withtime-series data for conventional production inputs and the general lack of sufficient data forthe important conventional inputs. Most studies used a Cobb-Douglas specification.

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Evenson (1967) first used this model to calculate the marginal product of research in theUnited States. Cline and Lu (1976) updated and refined Evenson's work for aggregateagricultural output and for ten production regions.

Limitations of this model include:• the need for an adequate data series for a 20 to 40 year period (note that demand and

supply price elasticities are not needed here),• the difficulty in obtaining data on production inputs such as labour, machinery and

chemical applications by commodity,• the consideration of research as an exogenous variable, a generally questionable

assumption.

3.4 Ex-ante evaluations8

According to Norton and Davis (1981), ex-ante studies of research assessment can beclassified into four groups:

1) the scoring or weighted criteria model, under whose heading they include congruenceanalysis, the domestic resource costs (DRC) ratios approach and checklists as specialcases;

2) the expected economic surplus approach, with benefit-cost analysis as a special case;

3) the mathematical programming approach;

4) simulation techniques.

The scoring or weighted criteria model is carried out in four steps. First, a set of possibleresearch programmes is agreed upon. Second, evaluation criteria are selected and receive aweight. Third, each possible research programme is reviewed, with each criterion receiving ascore. Fourth, scores and weights are multiplied so as to produce a combined score for eachresearch programme. This model is generally conducted with the help of surveys, paneldiscussions or the delphi technique. A good application of this approach can be found inNorton, Ganoza and Pomareda (1989), a report presenting the results of a preliminaryanalysis of agricultural research priorities for the Gambia.

Congruence analysis and DRC ratios are special cases of this model in the sense that all theweight in these techniques is placed on the value of production or on comparative advantage,respectively. Checklists contain a list of questions, but no specific weight is given to theanswers.

The expected economic surplus approach was already introduced in the previous section. Itmeasures society's gains from a shift of supply induced, for example, by the use of aninnovation by some producers. The supply shift is often associated with a shift of demand 8 This section is drawn from Henry de Frahan (1990). The literature review on this topic presented here is notexhaustive. Studies consulted but not included in this review are: Anderson and Parton (1983); Barker (1986);Binswanger and Ryan (1977); Davis, Oram, Ryan (1987); Greig (1981); Pasour and Johnson (1982); Pinstrup-Andersen (1979); Pinstrup-Andersen and Franklin (1977); Schuh and Tollini (1979); Shumway (1983, 1977,1973); Shumway and McCracken (1975).

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induced, for example, by a population or income increase. This approach provides measuresof changes in total net social surplus, in consumer surplus and in producer surplus. Theestimation of these changes depends on the assumptions made about demand and supplyelasticities, the magnitude and the type (divergent, parallel, or convergent shifts) of thesupply curve shift, and the shape of the demand and supply curves (Norton and Davis, 1981).

The benefit-cost (BC) approach is based on the concept of discounted net cash flow. Itcompares the time-valued estimate of the net returns from the innovations generated in aresearch programme as farmers adopt them, with the time-valued costs of the researchprogramme itself (Bottomley and Contant, 1988). Similar to the economic surplus approach,it estimates an average rate of return to proposed agricultural research.9 The BC approach,however, focuses more on the estimation of the producer surplus, postulating a perfectlyelastic demand curve and a vertical supply curve (Norton and Davis 1981).

Mathematical programming is an optimization technique which involves maximizing amultiple-goal objective function subject to the resource constraints of the research system.The result is the recommendation of a research portfolio. This technique is based on the sameconcept as the weighted criteria technique: in both techniques, attaching weights to a set ofgoals is exogenous to the programme and must be done prior to the analysis. Much moresophisticated than the simple weighted criteria approach, mathematical programmingquantifies the trade-offs among goals (Norton and Pardey 1987).

Simulation techniques are used on models that include the demand and supply sides of thefarm sector as well as the alternative technologies the research system might generate andproducers might eventually adopt. By simulating changes in supply and demand to meet pre-identified goals, the models predict relative contributions and costs of alternative researchactivities. Flexibility is the major advantage of this technique. However, to be useful, themodels need to be sophisticated and this in turn necessitates more data (usually primary data)and a higher level of analytical capacity (Norton and Pardey 1987).

For Norton and Pardey (1987), the availability of data, the resources and time frame formaking decisions, and the economic importance of the allocation decision should guide thechoice among these four techniques. Reviewing several studies using these four techniques,Norton and Pardey (1987) conclude that the weighted criteria and the expected economicsurplus techniques, including the benefit-cost technique, have the best potential forapplication in selecting research priorities in developing countries. They recommend theexpected surplus technique when the number of commodities under consideration is small,research outputs are relatively easy to quantify, and time and resources are available. Theycite several advantages to this approach vis-à-vis the weighted criteria technique:

1) The internal rates of return (IRR) to research on different commodities or research areascan be calculated and, therefore, compared to each other or to other alternative publicinvestments.

9 The average rate of return is expressed as an internal rate of return (IRR), a benefit/cost ratio (B/C ratio), or a netpresent value (NPV). The production function (PF) approach, which would provide a marginal rate of return, is notan appropriate tool for ex-ante research project evaluations because it requires a high degree of disaggregation tohave some value at the project level, it requires more data (usually secondary data) than the economic surplusapproach, and it is not able to incorporate uncertainty (Norton and Davis 1981, Norton and Pardey, 1987).

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2) The distribution of benefits between producers and consumers can be estimated.

3) Spillover effects can be included as benefits.

4) When the research goals are efficiency and income distribution, individual criteria donot need to be weighted in the expected surplus approach, while they do in the weightedcriteria approach. However, efficiency and distributional goals must be weighted inboth approaches.

5) Economic policies can be included explicitly.

The expected economic surplus technique, however, presents some disadvantages vis-à-visthe weighted criteria approach (Norton and Pardey 1987):

1) It is more difficult to apply this approach when research areas, such as socio-economics,basic research and systems or multidisciplinary research, are included in the set ofinvestment alternatives because these research areas are not commodity-specific.

2) This approach requires a higher level of expertise in economics and is more difficult foradministrators and decision makers to comprehend.

3) This approach cannot incorporate some criteria, such as private-sector incentives,because the criteria used in the economic surplus technique are economic efficiency andincome distribution.

4) This approach requires less involvement on the part of administrators and decision-makers than the weighted criteria approach where decision-makers must explicitlychoose among divergent goals.

5) This approach cannot be applied to a long list of commodities or research areas becauseof time or data limitations.

Bottomley and Contant (1988) also compare the priority-setting methods for agriculturalresearch. They add a detailed procedure to conduct the benefit-cost analysis. It includes thesequential estimation of eight variables of a research programme: the annual cost of research,research duration, probability of research success, on-farm implementation costs, on-farmbenefits, rate of adoption, adoption ceiling, and life of the innovation. Because muchuncertainty surrounds the estimations of these variables, sensitivity tests are used. Accordingto Bottomley and Contant (1988 p. 88), the advantage of benefit-cost analysis is to be able to"chart the course of events descriptively."

The analysis of the five major commodity research and extension programmes in Peruprovides a thorough application of the expected economic surplus technique (Norton, Ganozaand Pomareda 1987). Reviewing this analysis, Norton and Pardey (1987) proposed usingfirst a weighted criteria procedure to narrow down the list of alternative commodities to beincluded in the expected economic surplus analysis.10 This analysis provided an original way 10 Janssen and Lynam (1988) provide a good example of how the understanding of supply-demand linkages helpedidentify the components of a research project that were most critical for successful technology generation and

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of measuring the effects of alternative pricing policies on both the total level and distributionof research benefits. Projections of yields, costs, and adoption rates were, however,simplified in this application because the estimates were based on technologies that had beenrecently released or on technologies for which experimental results were already available(Norton, Ganoza and Pomareda 1987).

This technique has been applied to examining the growth and distributional effects oftechnical change along with the direct and indirect effects of research (Ramalho de Castroand Schuh 1977), as well as the benefits of research under alternative domestic pricing andinternational trade policies (Edwards and Freebairn 1984, Alston, Edwards, and Freebairn1988, Norton, Ganoza and Pomareda 1987).

Araji, Sim, and Gardner (1978) estimated the returns to investment in current research andextension programmes for nine agricultural commodities in the western region of the U.S.They were also interested in estimating the specific contribution of cooperative extension toresearch effectiveness. Sharing the same interest, Lu (1980) used a procedure where he variedthe expected adoption curve according to the levels of research and extension expenditures.Martínez and Saín (1983) used Lu's procedure to estimate the returns to the methodologicalinnovation of on-farm research in Panama.

Methods of dealing with the stochastic nature of research payoffs vary among studies. Fishel(1970) used a Monte-Carlo sampling procedure to generate subjective probabilitydistributions of costs and values. Easter and Norton (1977) used sensitivity analysis to showthe variations in probabilities of success, expected yield increases, product prices, and lagsbetween research expenditures and availability of results. Scobie (1984) showed how toestimate a distribution of expected benefits or, alternatively, of expected rates of return. Hereported that Greig (1979) used the additional information provided by the variance of returnsto compare two poultry research projects. First, he elicited subjective estimates of theprobability distributions of the main parameters of his model by questioning researchscientists. Second, using these distributions and drawing repeated samples with a Monte-Carlo technique, he derived a histogram of the present value of each research programme.The same technique is described in Reutlinger (1984) and Pouliquen (1983).

In 1981, Norton and Davis concluded that scoring models, simulation models and ex-antebenefit-cost models are the most helpful in identifying and quantifying factors affectingprogress in given research lines. However, these authors suggested that three areas neededfurther methodological work: 1) the evaluation of non-commodity research, 2) the analysis offactors affecting progress in given research lines, and 3) the study of private-publicinteraction in agricultural research, including transmission of research results to farmers.Since then, with the exception of Norton, Ganoza and Pomareda's study in Peru, Bottomleyand Contant's publication and Henry de Frahan's doctoral research, no important contributionhas been made to the procedure for conducting ex-ante evaluations of agricultural research.

4. ASSESSING THE NEED FOR POLICY REFORM AND INSTITUTIONALRESTRUCTURING TO INDUCE TECHNICAL CHANGE application. They show that market development should receive priority over the generation of productiontechnology in the Atlantic coast region of Colombia.

12

4.1. Ex-post evaluations

The quantitative methods presented in the previous section are useful not only in assessingthe return to investments in agricultural research but also in showing the extent to whichpolicy and institutional factors associated with agricultural research are key for technicalchange. Recent ex-post evaluations of agricultural research conducted in sub-Saharan Africaby Michigan State University (MSU) identify policy and institutional variables in each of thesix countries surveyed (Table 2). The results of the evaluations are compared andsummarised in a paper by Crawford (1993) which is used here to illustrate the policy andinstitutional factors that affect the impact of agricultural research programmes and, hence,technical change.

The empirical studies conducted by MSU quantitatively evaluate several types of impacts:• the economic impact of increasing agricultural productivity,• and the contribution of improved technology to food security (e.g., Senegal).These studies also include a qualitative analysis of impacts such as:• the distribution of benefits between producers and consumers,• the impact of technological innovation on women (e.g., Uganda),• and the reinforcement of human and institutional capacity.

4.1.1. MethodsThe economic value of these impacts was estimated with the economic surplus approach inall the country studies except in the Kenya study where a long enough series of data wasavailable to use the production function approach. Investments for which RORs werecalculated varied among the studies. In the Kenya study investments related to agriculturalresearch alone, while in the other country studies they included agricultural research andextension.11

The observed impacts and benefits of agricultural research also depend on variables such asthe adoption of the technological innovation and the yield increase, which in turn depend oninvestments in extension and other complementary interventions. Consequently, it isnecessary to incorporate these investments with investments in agricultural research andmeasure a ROR to agricultural research and its complementary interventions together.12

Evaluation methods also differ according to the length of the period considered for the releaseand the use of the technological innovations, the types of costs considered (e.g., publicfertiliser subsidy costs and external research costs) and the price adjustments made in theeconomic analysis (e.g., exchange rate overvaluation or other market distortions). Thesedifferences in estimation methods affect the size of the ROR and should be taken into accountwhen comparing reported RORs.

The empirical studies conducted by MSU pinpointed several key policy and institutionalvariables:• economic policy, such as discrimination against the private sector,

11 Investments in the Zambia study included agricultural research, extension and policy.12 From an analytical point of view, it is also difficult to separate ex-post the effect of agricultural research fromthat of other complementary interventions except with the production function approach.

13

• agricultural policy, such as import barriers and/or subsidies for agricultural inputs,agricultural credit access, agricultural price policy,

• support and organisation of public institutions, such as the national agricultural researchsystem (NARS), agricultural input suppliers, extension services, marketing services,processing services,

• infrastructure, such as road networks,• and political stability.

4.1.2. ResultsMost of those ex-post evaluations as well as some others available for sub-Saharan Africashow acceptable ROR, that is they are higher than a typical target rate of 10-12% (Table 2).Investments in agricultural research in sub-Saharan Africa seem, therefore, to be profitable.This result is also found in studies conducted outside of Africa, some of which show a RORexceeding 30% (Evenson 1990). As a result, additional investments in agricultural researchwould appear to be justified.

As Crawford (1993)notes, such conclusions must be qualified since conditions thatcontributed to a high ROR may not recur in the future. It is not certain that the nexttechnological innovation will be as significant as, for example, hybrid maize in Zambia. It isalso not certain that the NARSs will be as effective as in the past considering the currentfinancial and human constraints they face.

RORs found in the MSU empirical studies varied according to the commodity focus ofagricultural research in addition to the differences in the estimation methods underlinedabove. Some commodities like maize in Kenya or Zambia had a higher capacity than othercrops for genetic improvement and yield response to agronomic conditions such as goodfertility and improved cultivation practices. For such commodities, yield increases andadoption rates were particularly high and this was reflected in high ROR to agriculturalresearch investments. In countries with a more difficult climate and limited and variablerainfall, such as Niger, finding varieties or agronomic practices capable of raising agriculturalproductivity represents an enormous challenge. This partly explains the low ROR in Niger.Some commodities have a higher market value than others because of their role in householdfood strategies and in the national economy. This higher value leads to higher ROR. Forexample, the early-maturing cowpea variety released in Senegal and Cameroon contributed tohousehold food security at a critical period and this raised the benefits inputted to agriculturalresearch. The improved maize variety released in Mali led to the substitution of importedmaize for locally cropped maize, which resulted in an economic value higher than thefinancial value. These examples show how much RORs to agricultural research, includingtechnical change, depend on the local agro-climatic environment and the co-ordination ofresearch objectives with local needs and market forces.

The empirical studies reviewed here show that, in addition to the performance of the NARSin developing new appropriate technologies, policy and institutional factors have stronglyinfluenced the ROR to agricultural research and technical change (Crawford 1993).

Agricultural policies, such as fertiliser subsidies in Kenya and Zambia, agricultural credit inZambia and price policy in Mali and Zambia, have played an important role in providingincentives for technical change in these countries. Discontinuity in these policies can have adetrimental effect on technical change, as has been the case in Mali.

14

The performance of organisations involved in the transfer of new technologies through inputdistribution, extension and marketing is critical too. The ability of public developmentagencies in Mali and Cameroon and a private agency in Zambia to multiply and distributeimproved seeds contributed to technical change in these countries. Similarly, the inability ofdevelopment agencies in Niger and Uganda to carry out these functions impeded technicalchange in these countries. Well-functioning markets, both for agricultural inputs and outputs,and infrastructure for transportation and processing, such as that in Zambia, have beeninstrumental as well.

15

Table 2. RORs to investments in agricultural research in sub-Saharan Africa

Author(s)Year

ofpublica

tion

Country Commodity Period ROR (%)

Monares 1984 Rwanda Potatoes 1978-1985 40Norgaard 1988 Africa Cassava 1977-2003 B/C=149:1aWorld Bank 1988 Burkina Faso,

Côted'Ivoire,Togo

Cotton Until 1985 11-41

Karanja 1990 Kenya Maize 1955-1988 40-60Mazzucato 1991 Kenya Maize 1955-1988 58-60Mazzucato& Ly

1992 Niger Cowpea&Millet/Sorghum

1975-19911975-2006

<02-21b

Boughton &Henry de Frahan

1994 Mali Maize 1969-19911962-1991

135c

54cSchwartz, Sterns& Oehmke

1992 Senegal Cowpea 1981-1986 31-92d

Sterns &Bernsten

1993 Cameroon Cowpea

Sorghum

1979-19921979-19981979-1998

3151

Howard et al. 1993 Zambia Maize 1978-19911978-19911978-2001

<0e

90-103f

96-106fLaker-Ojok 1993 Uganda Sunflower

Maize

Soja

1985-19961985-20061985-19961985-20061985-19961985-2006

3138<033<06

Source: Crawford, 1993.a Benefit/cost ratio.b Depending on assumptions about yield, adoption rate, etc.c Including the costs of extension.d The 92% rate is obtained when the value of the early cowpea variety includes its value interms of household food security.e Including the costs of seed multiplication, production, research, extension, and the real costsof maize programme subsidies.f Without the real costs of maize programme subsidies.

16

4.2. Ex-ante evaluations

Ex-ante evaluations of investments in agricultural research can also be a useful tool toidentify the set of policy and institutional factors that may affect technical change in the nearfuture. To illustrate this point, an ex-ante evaluation of investments in a farming systemsresearch (FSR) programme in the semi-arid area of Mali is described below. This studyshows that the ROR to investment in this research programme crucially depends on thepolicy, institutional and marketing environment and that key complementary interventionscan be ranked according to priorities (Henry de Frahan 1995).

4.2.1. Objectives and methodsThe main objectives of the ex-ante evaluation was to design an agricultural researchprogramme for the North-eastern semi-arid area of Mali and examine the financial andeconomic feasibility of such a programme. The research programme planned for this areawas a farming systems research or on-farm research programme, i.e., an applied researchprogramme which, on one hand, would adapt on-station research (OSR) results to local andspecific conditions and constraints faced by farmers and, on the hand, would orient on-stationresearch according to the problems identified at the farm level.

The results available from on-station research and local agro-climatic and socio-economicconstraints were used to help design the farming systems research programme for the area.The research programme notably sought to develop and propose to farmers of the area fourtechnological innovations for millet, cowpea, peanuts and sesame. The technicalcharacteristics of those innovations were identified a priori and adjusted according to farmagro-climatic and socio-economic specificities. Farms with similar agro-climatic and socio-economic specificities were grouped into "recommendation domains".

The technological innovations were first evaluated in financial terms. The innovations with amarginal rate of return below 40% and a marginal return per person day below theopportunity cost of labour were eliminated from the research programme.13 The innovationswith an unstable financial return considering realistic changes in prices, yields and productioncosts were eliminated to account for risk aversion behaviour among farmers.

The expected diffusion paths of the remaining profitable and stable innovations across thearea were estimated on the basis of the diffusion paths that had occurred for animal traction inthe same area. The parameters of the animal traction diffusion paths were estimated with anordinary least-squares regression, using a logistic function representing the cumulativegrowth in the percentage of farmers who had adopted animal traction between 1966 and1988.14 Those parameters were then adjusted to take into account the characteristics of theinnovations and the recommendation domains.

13 The marginal rate of return is the ratio of incremental net income to incremental costs and reflects theadditional net income earned by the additional capital and labour invested in the new practice. The marginalreturn per person day is the incremental return to the incremental person-days of labour used in the new practice.It isolates the effect of additional labour from other factors of production, such as capital and land.14 This logistic is characterised as follows: P(t) = K/[1 + exp(-a-bt)],where K is the long-run upper limit on diffusion, the slope 'b' is a measure of the rate of acceptance of theinnovation and the intercept 'a' reflects aggregate adoption at the start of the estimation period and thus positionsthe curve on the time scale (Henry de Frahan 1995).

17

The economic surplus approach was used in the economic evaluation of the researchprogramme. This method was used to measure the change in economic surplus arising fromthe adoption of the financially profitable innovations developed by the research programme.As explained above, this method gives an average rate of return which can be expressed interms of an internal rate of return. The measure of the change in economic surplus is reducedto the measure of the change in producer surplus, assuming a perfectly elastic demand curvefor the agricultural products considered. This hypothesis is reasonable since it is not expectedthat the research programme would be able to reverse the food situation in the area from netdeficit to net surplus. With the additional hypothesis that the regional supply curve for theagricultural products considered is perfectly inelastic to price (the observed price elasticitiesfor supply are low in Mali), the estimation of the change in producer surplus is equivalent tothe estimation of the increased net incomes accruing to farm households as a result of thetransfer and adoption of technological innovations developed and tested by the researchprogramme. Input and output market prices were adjusted to their economic value. Allmonetary transfers due to subsidies, taxes, or interest rate and exchange rate controls wereremoved.

4.2.2. ResultsThe on-farm research programme identified was not economically profitable. With a capitalopportunity cost of 12%, the present value of the incremental farm net benefits amounted to$US 0.94 million while the present value of the research programme costs amounted to $US2.80 million. This gave a negative net present value of $US 1.86 million and a low internalrate of return of 2%. The economic value of the research programme was, however,undervalued by this measure because some research areas that the research programme couldhave developed were not included in the economic value of the project. Some possibleexternal effects of the programme were not included in the economic value of the project,particularly the reduction of food aid and out-migration due to increases in farm income.

The economic value of the on-farm research programme was very sensitive to variations inproject costs, yields and prices of agricultural products. A change in one of these threecomponents led to a more than proportional change in the value of the research programme.To a lesser extent, the value of the research programme was also sensitive to changes in thetime taken to release innovations, incremental farm costs, diffusion parameters and the life ofthe innovations.

This sensitivity analysis implied that the economic value of the research programmedepended on a set of prerequisites that were not met. If on-station research were to providemore appropriate technical recommendations and vegetal materials, the on-farm researchprogramme would be more likely to develop innovations with higher and more stable yields,and release them more rapidly. If marketing costs were lower, farm gate prices ofagricultural would be higher and more stable across seasons and farm gate prices ofagricultural inputs would be lower. If the public organisations responsible for extension,credit and input distribution were more efficient, the transfer and diffusion of thetechnological innovations released from the on-farm research programme would be faster.Finally, if customs duties and other taxes on farm products were reduced, the adoption of thetechnological innovations would be facilitated. In sum, the economic value of the on-farmresearch programme critically depended on the performance of:

18

1. on-station research in generating improved technological components from which on-farmresearch could draw,

2. the marketing system in reducing marketing margins and seasonal price variations forinputs and outputs,

3. the institutional setting in creating a conducive environment for the transfer oftechnological innovations.

A set of scenarios was simulated and evaluated to indicate the extent to which animprovement in the policy, institutional and marketing environment that complemented orpreceded the on-farm research programme could increase the economic value of the researchprogramme.

4.2.3. Interactions between the research programme and its policy, institutional andmarketing environmentAn improvement in the policy, institutional and marketing environment was simulated withthree types of interventions. First, investments complementary to the research programmewere considered in addition to on-station research. These investments would release milletand rice varieties that had higher yields and were more appropriate to local conditions. Basedon expert opinion, yield increases of 20% for millet and 30% for rice were likely and,therefore, simulated. The cost increases associated with these yield increases were alsosimulated.

Second, investments to make extension and credit services more effective were considered.These investments would improve the extension and diffusion of the technologicalinnovations, and help organise farmers' associations to contract purchases and access formalcredit. Yield increases of 5%, input cost decreases of 10% and a borrowing rate cut of 50%were simulated as well as the additional costs associated with these investments.

Third, an improvement in the agricultural product marketing system and a tax reduction wasconsidered. The improvement in the marketing system was simulated by increasing the farmgate price of agricultural products by 10% and including the costs associated with thisimprovement.15 Taxes were reduced by removing all taxes and subsidies on agriculturalinputs and outputs. The simulation included, therefore, the elimination of administrative feesfor importing and exporting, export taxes and inputs subsidies.

These three categories of interventions were first evaluated individually, with the exceptionof investments in on-station research, and then in association with each other and theinvestments in on-farm research.16 The return to investments in extension and credit services,on one hand, and the return to the marketing and fiscal improvements, on the other hand,were estimated on the basis of the acceleration of the diffusion of the currently available

15 Two areas for market improvement were considered to increase prices received by farmers. First, theelimination of export restrictions and the promotion of new agricultural outlets would prevent producer pricesfrom falling precipitously during surplus periods. Second, providing training to farmers' associations to exploitthe market information system, contract purchases, store and unstock agricultural products would give farmersgreater collective bargaining power.16 Based on the finding that on-station research for semi-arid environments has been unsuccessful without on-farm research components (Matlon 1985), returns to on-station research were only estimated when this researchwas associated with on-farm research.

19

agricultural technologies.17 Similarly, the return to investments in on-farm researchassociated with one, two or three complementary interventions was estimated on the basis ofthe acceleration of the diffusion of the technological innovations released due to the on-farmresearch programme. For every scenario, the additional costs associated with thecomplementary interventions were included.

Table 3 reports the economic values of the simulations. Investing solely in improvingextension, input delivery and credit supply (E&C) was not profitable, unless the yield effectdue to extension increased to 14%. The low profitability of the agricultural technologiescurrently available for this area does not justify improvements in extension and creditservices. In contrast, promoting improvements in the marketing system and reducing taxes(P) was profitable and resulted in an IRR of 18%. The returns to the promotion of theseimprovements were, however, probably underestimated since direct and induced effects ofthese improvements on other areas of the agricultural sector and on other sectors of theregional economy were not considered.

Combining the on-farm research programme (FSR) with any other intervention was profitableand yielded similar IRRs ranging between 13 and 15%. Among those three combinations,combining the on-farm research programme with investments in the extension and creditservices (FSR-E&C) was preferable when the scenarios were considered mutually exclusivesince the net benefits are the highest. However, when a higher preference was accorded tolong term objectives rather than to short term objectives by using a lower discount rate, thenet benefits from the combinations of the on-farm research programme and the on-stationresearch programme (FSR-OSR) became higher than those for the other two types ofcombinations. This suggests that, in the short run, the constraints in the technology transfersystem are greater than the lack of results from on-station research.

Combining the on-farm research programme with two other interventions was shown also tobe profitable with IRRs ranging between 18 and 24%. Among those three combinations,combining the on-farm research programme with the on-station research programme and theimprovements in the marketing system and fiscal policy (FSR-OSR-P) was preferable sincethe net benefits were the highest. Finally, the complete combination of the on-farm researchprogramme with the other three interventions (FSR-OSR-E&C-P) resulted in an IRR of 26%,the highest IRR among all the combinations that were evaluated. With an IRR higher than theopportunity cost of capital, this combination should be preferred since the net benefits of thiscombination are the highest. These simulations show that the returns to a researchprogramme crucially depend on the policy, institutional and marketing environment sincereturns rose from an IRR of 2% to an IRR of 26%.

The calculation of the magnitude of the interactive effects between the on-farm researchprogramme and the other three interventions confirmed that the economic impact of the on-farm research programme depended on the performance of the technology transfer system,the marketing system and the policy framework. Table 3 shows the interaction effects.18 The

17 As in the original scenario, the parameters of the diffusion curves were adjusted according to agriculturalpractices and the recommendation domains. The farm budgets included the changes in technical coefficientsand prices due to the suggested complementary interventions.18 The interactive effect was first estimated for a combination of two investments according to the simple rulethat the interactive effect due to a combination of two investments is equal to the net benefits of the combinationof the two investments taken together less the net benefits generated by the two investments taken alone. In the

20

interactive effects between the on-farm research programme and one of the threeinterventions were important. 19 The highest interactive effect was observed when theinvestments in the technology transfer system were combined with on-farm research (FSR-E&C). When a second intervention was combined with on-farm research, the highestinteractive effects were observed when one of the two interventions consisted in improvingthe marketing system and the policy framework (FSR-E&C-P and FSR-OSR-P). Thissuggests that implementing this intervention will provide important economic benefits. Thesize of the interactive effects helped identify the sequence of the interventions when all theinterventions complementary to the on-farm research programme could not be simultaneouslyimplemented because of limited financial and human resources.

Under the assumption that human and financial constraints might be gradually removed overtime, it is possible to consider the staggering the public investments. If improving thefunctioning of the agricultural product marketing system and reforming fiscal policy ispolitically acceptable in the short run, these should be the first changes implemented becauseof their large and immediate impact. Because market conditions would facilitate technologytransfer and adoption, investing in on-farm research would be an advisable second step. Thethird step in this series would be to invest in additional on-station research to take advantageof the strong complementarity between on-farm research and commodity and disciplinaryresearch.20 This third step should be taken as soon as human and financial resources areadequate because of the long lead-times in research. However, on-farm research couldalready begin adaptive research on the results already available from on-station research andthe collection of information and data that could improve the relevance of on-station researchefforts. The last step in the investment series would be to invest in the technology transfersystem. Improving the input and credit supply functions could come earlier in the sequenceof investments, but improving the extension function should be delayed until the researchsystem is able to generate improved technologies ready for extension.

same way, the interactive effects were successively estimated for combinations of three and four investments. Itwas assumed that the interactions of additional on-station research with improvements in the technology transfersystem or with improvements in the marketing system and fiscal policy are nil on the basis that on-stationresearch needs to be complemented by on-farm research to have some impact.

19 The interactive effect between the on-farm research and the on-station research was calculated on the basisthat the net present value of investing in on-station research is nil when not associated with on-farm research(see footnote 16).20 The interactive effect between on-farm research and additional on-station research was three times larger thanthe additional investment in on-station research. This large interactive effect underscored the importance ofassociating additional on-station research with on-farm research.

21

Table 3. Economic values of the scenarios

Scenario(a) Incremental FarmNet Benefit (b)

Public InvestmentCosts (b)

Project NetBenefits (b)

IRR (%)(c)

InteractiveEffects (b)

FSRE&C-PE&CPFSR-PFSR-OSRFSR-E&CFSR-OSR-E&CFSR-E&C-PFSR-OSR-PFSR-OSR-E&C-P

94044232039134543654370474310140110131038118773

28046246359611143918362838607406571847429433

-1864-1823-15572314477428832734529556399340

1.7NANA18.313.513.914.918.224.221.726.2

NA-497NANA208026064304-75525982586-392

(a) FSR = Farming Systems ResearchE&C = Extension, credit and input supplyP = Marketing and fiscal policyOSR = On-station research

(b) Present value in thousands of $US at a 12% discount rate.(c) IRR is undefined when the annual incremental net benefits are negative every year of the project life.Source: Henry de Frahan, 1995.

22

4.2.4. ConclusionsIn sum, the major finding of this feasibility analysis was that on-farm research alone is not themost effective means to increase farm productivity in the north-eastern area of Mali.Improving the functioning of the agricultural marketing system and reforming fiscal policyappeared to be the most important pre-conditions for positive and significant returns to theresearch programme. Investments in on-farm research, on-station research and the technologytransfer system could then follow sequentially. If these pre-conditions cannot be met in theshort term, then an alternative pattern of investments would be first to invest simultaneouslyin on-farm research and on-station research, then promote improvements in the marketingsystem and fiscal policy, and lastly strengthen the technology transfer system with, however,the possibility of improving the input and credit supply earlier in the sequence of investments.

The simulation results of this study confirmed the need to pay more attention to the internaland external linkages to research. First, the strong complementarity between on-farmresearch and on-station research called for removing institutional and training barriersbetween on-station researchers and on-farm research practitioners. Second, the strongcomplementarity between marketing improvements and policy reform, on one hand, andagricultural research, on the other hand, confirmed the need to incorporate economics andother social-science input into the agricultural research process. Third, the strongcomplementarity between improvements in the technology transfer system and agriculturalresearch called for strengthening linkages between the regional development agencies thathandle extension, input marketing and credit, and agricultural research.

23

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by Bruno Henry de FrahanResearch Unit of Agricultural EconomicsCatholic University of LouvainPlace Croix du Sud, 2/15B-1348 Louvain-la-NeuveBelgium