Economic Impacts of Integrated Agricultural Research

African Development Review, Vol. 25, No. 1, 2013, 30–41

Economic Impacts of Integrated Agricultural Research for Development (IAR4D) in the Sudan Savanna of Nigeria* Adeolu Babatunde Ayanwale, Adolphus Adekunle Adekunle, Akinboye Adebayo Akinola and Victoria Adeyemi Adeyemo**

Abstract: This paper assesses the potential economic impacts of Integrated Agricultural Research for Development (IAR4D) conceived to address observed low productivity, prevailing poverty level, slow growth and general underperformance of the agricultural sector associated with the traditional agricultural and rural development (ARD) approach. The economic surplus analysis suggested that IAR4D research and extension, with respect to maize production, could achieve returns ranging from 30 to 38 per cent and a maximum adoption of 25 to 50 per cent for the conservative and baseline scenario, respectively. Similarly, with the same range of maximum adoption, the approach could yield returns ranging from 22 to 29 per cent for the conservative and baseline scenario, respectively in millet production. However, the same range of adoption could result in 29 to 37 per cent for the conservative and baseline scenario, respectively in sorghum production. The estimated benefits are sensitive to expected adoption rates but much less so to changes in research and extension costs. However, the estimates indicate that the production of all the crops was socially profitable under the IAR4D option. Our results were consistent with earlier economic analyses which showed that IAR4D was more productive, profitable and acceptable to farmers than the conventional Research for Development (R&D) approach.

1. Introduction The widely acknowledged reason for the observed low productivity, prevailing poverty level, slow growth and general underperformance of the agricultural sector in the sub‐Saharan Africa sub‐region has been largely ascribed to institutional laxities especially in the formulation and dissemination of agricultural research (FARA, 2008). The poor performance of the traditional agricultural and rural development (ARD) approach is manifest in low adoption rates of technologies, poor linkages among agricultural value‐chain actors and the pervasive unprofitability of farm enterprises in SSA. It has been hypothesized that these indicators of unsatisfactory ARD performance are traceable to the organization of research and development as a linear process (Mugerwa, 1998; Daane and Booth, 2004). This configuration of ARD actors limits interaction with researchers and timely intervention in research process and direction. The above‐mentioned reasons led to the conception of Integrated Agricultural Research for Development (IAR4D) as a way of addressing the dissatisfaction with traditional approaches for organizing ARD in sub‐Saharan Africa and getting the agricultural sector out of the woods (Anandajayasekeram, 2011). IAR4D was designed to transform this configuration by embedding research within an innovation system comprising all actors in agricultural value chains. Within such a system — a network configuration — innovation does not follow a linear path that begins with research, moves through the processes of development, transfer, diffusion, adoption, production, and ends with successful introduction and use of new products and processes; rather, it tends to involve continuous feedback between different stages (Dantas, 2005), thus drawing on the knowledge of all relevant actors at each stage. The network configuration facilitates timely interaction and learning, and aims at generating innovations (rather than research products per se). In this regard,

*The authors wish to acknowledge the Forum for Agricultural Research in Africa (FARA) for providing funds and technical support for this study. **Adeolu Babatunde Ayanwale (corresponding author), Department of Agricultural Economics, Obafemi Awolowo University, Ile‐Ife, Nigeria, e‐mail: [email protected] or e‐mail: [email protected]. Adolphus Adekunle Adekunle, Forum for Agricultural Research in Africa, Accra, Ghana. Akinboye Adebayo Akinola and Victoria Adeyemi Adeyemo, Department of Agricultural Economics, Obafemi Awolowo University, Ile‐Ife, Nigeria. © 2013 The Authors. African Development Review © 2013 African Development Bank. Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

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Economic Impacts of Integrated Agricultural Research for Development

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innovation refers to the activities and processes associated with the generation, product distribution, adaptation and use of new technical and institutional/organizational knowledge. It therefore adds value to products of research, thus catalysing the achievement of development impact (FARA, 2008). The purpose of this study is to estimate the likely economic impacts of the IAR4D concept in the Sudan Savanna of the Kano‐ Katsina‐Maradi Pilot Learning Site (KKM PLS) of the Sub Saharan Africa Challenge Programme (SSA CP). The study involved the evaluation of the farm level economic impacts of the adoption and operation of Innovation Platforms (IPs) and translating those farm level impacts into an estimate of the economy‐wide impacts. In conducting this study we used information from an extensive baseline data collected across the Sudan Savanna Taskforces (TF) in the PLS combined with focus group discussion sessions with major stakeholders in each Innovation Platforms (IP) at each TF.

1.1 IAR4D Concept and Design A multiple‐treatments experimental design was used as obtained in the typical SSA CP proof of concept design. The design compares household‐ and community‐level outcomes under: (i) IAR4D, (ii) the conventional approach, and (iii) no intervention. In other words, the SSA CP experiment comprises three treatments carried out in three blocks (the PLS) and nine repetitions (three per block — the taskforces). Following White and Chalak (2006) the set of counterfactuals was taken to be the set of all possible states of the world with outcomes taking different values under different possible states of the world. An intervention was also defined as the move from one possible state to another. So there are as many counterfactuals as there are possible states of the world. However, under the SSA CP we limit ourselves to comparing outcomes under IAR4D and under only two other possible states, namely: the conventional approach and under non‐intervention. Innovation Platforms was evaluated at the district, local government area or commune level, because it is conceptualized that the innovation process is best organized through geographically decentralized sites. The geographical area of influence of the IP is conceptualized to be mostly within district/local government areas/ commune jurisdictional boundaries because of clustering of activities and interactions among government administration units, public research and extension organizations, farmers, farmers’ organizations, NGOs, agricultural input suppliers and output marketing firms, credit and finance organizations, and service providers.

The treatment communities consist of organizations and farm households in areas where IAR4D was practised. The non‐treatment communities consist of similar organizations and households in other sites. The PLS was zoned into development domains — areas with comparable development potential. The development domains used by the SSA CP are based on two factors that usually have the largest influence on agriculture‐driven development, namely agro‐climatic potential and access to markets. The development domains combined with population data were used to target areas most likely to provide the highest returns on the SSA CP’s investment. They also provide a basis for stratifying the PLS in order to capture its variation and to delineate similar domains from which comparable sites was selected. Research sites (districts, communes, local government areas) were allocated to IAR4D and non‐IAR4D treatments through stratified random sampling. The strata within which the randomization was carried out are four development domains delineating the combination of market access potential and agro‐climatic potential. Each IAR4D treatment site (district, commune, local government area) had a corresponding counterfactual site randomly selected from the same stratum as the IAR4D site. Taskforces typically spread IAR4D treatment sites across various strata in order to investigate the performance of the approach across a wide range of conditions. Each taskforce established four Innovation Platforms in four separate districts/communes/local government areas. Thus, each taskforce worked in eight sites. Within IAR4D and non‐IAR4D sites, focal villages were also selected randomly. The focal villages were screened prior to implementation of IAR4D to establish whether or not they have had conventional ARD or IAR4D‐ type of projects in the past 2–5 years. Villages were classified into two types: (a) ‘Clean’ villages that have neither had IAR4D nor conventional projects in the last 2–5 years; and (b) Conventional ARD villages that have had projects identifying, promoting and disseminating technologies in the past 2–5 years. © 2013 The Authors. African Development Review © 2012 African Development Bank

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A.B. Ayanwale et al.

IAR4D was introduced in ‘clean’ villages within the IAR4D sites. The SSA CP’s hypotheses were tested by determining whether outcomes differ among households in the IAR4D, ‘clean’ and conventional ARD villages.

2. Methodology 2.1 Area of Study: Sudan Savanna The Sudan is the name given to a geographic region to the south of the Sahara, stretching from Western to Eastern Africa. The name derives from the Arabic meaning or ‘land of the Blacks’ (an expression denoting West and Central Africa). The Sudan extends in some 5,000 km in a band several hundred kilometres wide across Africa. It stretches from the border of Senegal, through southern Mali (formerly known as French Sudan when it was a French colony), Burkina Faso, southern Niger and northern Nigeria, southern Chad and the western Darfur region of present‐day Sudan. In the north of the region lies the Sahel, a more arid Acacia savanna region which in turn borders the Sahara desert to the north, and the Ethiopian Highlands in the east (called al‐Habašah in Arabic). In the south‐west lies the West Sudanian savanna, a wetter, tropical savanna region bordering the tropical forest of West Africa. In the centre is Lake Chad, and the more fertile region around the lake; while to the south of there are the highlands of Cameroon. To the south‐east is the East Sudanian savanna, another tropical savanna region, bordering the forest of Central Africa. This gives way further east to the Sudd, an area of tropical wetland fed by the water of the White Nile. The Sudan Savanna is characterized by the coexistence of trees and grasses. Dominant tree species are often belonging to the Combretaceae and Caesalpinioideae, some Acacia species are also important. The dominant grass species are usually Andropogoneae, especially the genera Andropogon and Hyparrhenia, on shallow soils also Loudetia and Aristida. Much of the Sudanian Savanna region is used in the form of parklands, where useful trees, such as shea, baobab, locust‐bean tree and others are spared from cutting, while sorghum, maize, millet or other crops are cultivated beneath. The main constraints to agricultural production in the Sudan Savannah include limited adoption of improved technologies, land degradation, diseases, insect pests, Striga infestation, and lack of labour‐saving technologies for field operations and processing. These constraints are compounded by market‐related and policy‐related constraints such as limited access to credit; low farm‐gate prices; inadequate supply, high cost and low quality of inputs; poor access to output markets, and weak linkages between producers, agro‐industry and markets on the market side and, on the policy side, by conflicts arising from access to community resources and utilization especially between farmers and pastoralists. Ineffective extension systems and lack of policy incentives also constrain agricultural intensification. The Sudan Savanna subproject was led by IITA (International Institute for Tropical Agriculture). Its areas of intervention include the Sudan Savanna zones of Katsina and Kano States in Nigeria. This sub‐project works on cereal‐legume‐livestock issues in the two states with special focus on the production to consumption value chains. The actual choice of cereal and legume depends on the comparative advantages of each of the regions due to rainfall in the north‐south gradient.

2.2 Data Collection Method The IAR4D approach was formally introduced to farmers in the Sudan Savanna of KKM PLS in 2004. The approach was embraced and adopted in the area through the facilitation of International Institute of Tropical Agriculture (IITA), Institute of Agricultural Research (IAR), Zaria in bringing various stakeholders of agriculture together at the IP level. Using the approach, key crops were planted in Sudan in the year 2008. The study used plot level data as well as survey data collected in 2008 from a sample of 1,800 farming households in each of the agro‐ecological zone of the KKM PLS. The survey data enabled the researchers to track the rate and pathway of adoption of IAR4D option as well as perceived constraints to adoption since the initial dissemination. In addition, we interviewed other stakeholders and researchers to gain expert opinions on technical aspects of the approach. Baseline reports written by researchers and submitted to FARA also provided points of reference for the surveyed villages. To assess the potential economic benefits from adoption of the IAR4D approaches, we 1. estimated the yield gains and the unit production cost reduction; © 2013 The Authors. African Development Review © 2013 African Development Bank

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2. defined the socioeconomic domains of cereals production for extrapolation to other areas; 3. examined the adoption pathway; and 4. used an economic surplus model and information from the first three steps, along with secondary data, to evaluate the potential economic impacts of the IAR4D approach. Stakeholder fora (consisting of farmers, agro‐allied companies, seed producers, etc.) were established in 2004 in the Sudan Savanna by the FARA in collaboration with the IITA and IAR, Nigeria and Ministry of Agriculture, Niger. Demonstration trials were conducted in collaboration with all the relevant stakeholders at the IP levels. In each IP demonstrations were done in four local government areas. Five villages each were chosen from each of the local government areas. For easy comparison, equal numbers of villages were chosen from areas where there was some R&D and no R&D. All the farmers in the IAR4D and R&D have similar socio‐economic characteristics and were exposed to the same improved crop and livestock varieties.

2.3 Variable Measurement and Data Analysis Yield Gains and Unit Cost Reduction Using the on‐farm trial data presented in Table 1, estimates of the yield gain and unit cost reduction effects of the IAR4D approach on crop production based on different agro‐ecological zones can be derived as follows: 1. The average maize yield for adopters of IAR4D option in Sudan Savanna was 1.96 t/ha, an increase of about 47 per cent over the R&D approach yield of 1.33 t/ha. However, the unit variable costs of maize production under the two approaches were US$151/t and US$212/t for IAR4D and R&D options, respectively. 2. In the same agro‐ecological zone, the average millet yield for adopters of IAR4D approach was 1.84 t/ha, an increase of about 51 per cent over the R&D option yield of 1.22 t/ha. The unit variable costs of millet production were US$145/t and US $180/t, respectively. 3. Similarly, the average sorghum yield for users of IAR4D approach was 1.18 t/ha, an increase of about 30 per cent over the R&D approach of 0.91 t/ha. The unit costs of sorghum production were US$248/t and US$309/t for the IAR4D and R&D approaches, respectively. The share of arable lands in the Sudan Savanna was established following Sowunmi and Akintola (2010) and data obtained from the FAOSTAT database. Total arable land for Sudan Savanna was 7,845,582 ha (26 per cent of total arable land). The land under the IAR4D approach was 2,353,674 ha (assuming 30 per cent coverage). Moreover, the shares of land for major crops were subsequently obtained (25 per cent maize, 14 per cent rice, sorghum 31 per cent and millet 30 per cent).

2.4 The Adoption Pathway Household survey data were used to project the adoption patterns of the IAR4D approach over time. We assumed that adoption started in 2006 and nearly 10 per cent of the sample households in the pilot villages adopted by the end of the year. Adoption picked up in 2008 and by the end of 2009 about 30 per cent of the households had adopted. We assumed that by the end of 2010,

Table 1: Crop yield gains and unit cost reduction under IAR4D and R&D options Agro‐ecological zone Sudan Savanna

Crop Maize Millet Sorghum

R&D IAR4D R&D IAR4D R&D IAR4D

Yield (t/ha)

Cost (US$/ha)

Unit cost (US$/t)

1.33 1.96 1.22 1.84 0.91 1.18

282 296 219 267 282 293

212 151 180 145 309 248

© 2013 The Authors. African Development Review © 2012 African Development Bank

Unit cost reduction US$/t)

Unit cost reduction (as a proportion of price)

60

0.50

35

0.29

61

0.51

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Figure 1:

Map of Sudan Savanna Taskforce

Source: Wikipedia, the free encyclopedia (en.www.wikipedia.org/wiki/Wikipedia).

Figure 2:

Projected adoption of IAR4D option



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about 50 per cent would have adopted the IAR4D option. The estimates on adoption rates were used to extrapolate the ceiling adoption rates that can be expected across each of the agro‐ecological zones in the KKM. Since the household survey was undertaken in an area where adoption had occurred and was occurring, the percentage of farmers who would adopt IAR4D approach in 2010 was assumed to be the ceiling rate of adoption — 50 per cent. However, it was assumed that for total coverage of each of the agro‐ecological zones the ceiling rate of adoption of the approach would only be reached in 15 years, as opposed to the five years it took the project villages in the pilot villages to reach this ceiling. In other words, it would take the whole ecological zones about three years to achieve the rate of adoption that the project villages achieved in one year. The adoption rates in the target villages from 2006 to 2010 and the assumed adoption lag for coverage of each of the agro‐ecological zones were used to estimate the parameters of the logistic function needed for predicting adoption rates in to other areas in the zones from 2012 to 2035, as follows: Ait ¼

Ci 1 þ eðaþbtÞ

ð1Þ

where Ait is the percentage adoption of the ith IAR4D approach in the tth year; Ci is the adoption ceiling of the ith technology; b is the rate of adoption; and a is the constant intercept term. The adoption pathway for the IAR4D option was predicted using the following logistic function that was estimated using the survey data: At ¼ ð1þe50 ð4:11:1tÞ Þ (see Figure 2).

Supply Shift The unit cost reduction as a proportion of product price discussed earlier represents the maximum supply shift (K) — that is, given 100 per cent adoption — and translates into the actual annual supply shift (Kt) when multiplied by innovation adoption at time t (At). That is, the annual supply shift is the product of cost reduction per ton of output as a proportion of product price (K) and technology adoption at time t (At). Indeed, the standard supply‐and‐demand diagram demonstrating shifts in the supply curve due to adoption of a new technology represents research benefits for one year. A successful research investment will yield benefits over a number of years. As the level of adoption increases, there will be further shifts in the supply curve, and corresponding changes in benefits.

Estimating Research Benefits The potential benefits of a technical intervention can be measured ex ante as well as ex post. Following Alston et al. (1995), a number of studies have applied the economic surplus model to estimate research benefits (Kristjanson et al., 2002; Okike, 2002; Bantilan et al., 2005). The essence of the economic surplus model is that an improved technology, such as the IAR4D approach, reduces the cost of production of each kilogram of output, leading to a shift in the supply curve to the right, an increase in the quantities supplied and traded, and a drop in prices in a competitive market. When this happens, although the selling price is reduced, smallholder producers may benefit from the reduced production costs and from selling larger quantities of maize produced at these lower costs, while consumers benefit from lower purchase prices. Two scenarios are always presented: the closed and the open economy models. Assuming a closed economy model implies that the adoption of the IAR4D option increases the supply of crops. This study used a partial equilibrium, comparative static model of a closed and an open economy and the simple case of linear supply and demand with parallel shifts. A review of research benefits by Alston et al. (1995) revealed that most studies have used the assumption of linear supply and demand curve. Alston et al. (1995) argue that when a parallel shift is used the functional form is largely irrelevant, and that a linear model provides a good approximation to the true (unknown) functional form of supply and demand. A hypothetical case is illustrated in Figure 3. The supply of any given crop before the technical intervention of IARD approach is denoted by S0. The demand for maize is denoted by D. The supply of maize shifts to S1 following adoption, changing the equilibrium price and quantity before intervention from P0 and Q0 to a new equilibrium price and quantity, P1 and Q1. The change in consumer surplus is the area represented by P1P0AB and the change in producer surplus is the area covered by P1BCE. The change in total surplus is the sum of consumer and producer surpluses, which can be shown to be equal to I1I0AB. © 2013 The Authors. African Development Review © 2012 African Development Bank

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Figure 3: Estimating changes in producer and consumer surplus P

S0

D P0 P1 I0 I1 0

A C Q0

S1 B

Q1

In a closed economy, economic surplus measures can be derived using formulas presented in Alston et al. (1995): (1) economic surplus (ES) ¼ P0Q0Kt(1 þ 0.5Zth); (2) consumer surplus (CS) ¼ P0Q0Zt(1 þ 0.5Zth); and producer surplus (PS) ¼ (Kt − Zt) P0Q0(1 þ 0.5Zh), where Kt is the supply shift representing the product of cost reduction per ton of output as a proportion of product price (K) and technology adoption at time t (At), both of which have been presented and discussed earlier; P0 represents pre‐research price (US$/ton); Q0 is quantity of maize in tons; h is the price elasticity of demand; and Zt is the relative reduction in price at time t, which is calculated as Zt ¼ Kt"/(" þ h), where " is the price elasticity of supply. Similarly, Alston et al. (1995) show that, in a small open economy, change in economic surplus is equal to change in producer surplus and can be calculated as ES ¼ PS ¼ PwQ0Kt(1 þ 0.5Kt"), where Pw is the real world price.

Prices and Price Elasticities In view of the fact that cereals are a highly tradable commodity on regional as well as international markets, however, the base model uses the open economy framework, and average international maize prices for 2006–2010 adjusted for shipping and insurance were used in valuing the research benefits. The average real international crop prices were estimated based on FAOSTAT database. There is no reliable estimate of cereals and legumes supply elasticity for West Africa. Following Alston et al. (1995) in such situations, the price elasticity of cereals and legumes supply was assumed to be 1. Given that the crops are important staples for most households in West Africa, the (absolute) price elasticity of demand for cereals was assumed to be 0.4.

Research and Extension Costs The total costs for research, development and extension of IAR4D approaches from 2004 to 2013 were obtained from the IAR4D approach management at the FARA’s IAR4D project management’s office at the IITA. The research costs included the annual salary of the IAR4D taskforce and other implementation teams; the annual operational expenses required to set up various IPs and sustaining them as well as other costs involved to undertake the approach including packaging, and diffusion of IAR4D option; and the annual overhead costs at the FARA. The annual extension costs associated with the large‐scale dissemination of the approach in each agro‐ecological zone estimated at US$1.5 million for each crop for the expected 15 years – from 2013 until the adoption of IAR4D approach reaches the ceiling of 50 per cent.

3. Results This section presents and discusses the base model results of the benefit–cost analysis for the IAR4D approach in the zone. The benefits and costs of IAR4D, research and extension in relation to priority crops of each agro‐ecological zone were arrayed on a yearly basis from 2006 to 2035, and a discount rate of 5 per cent was applied to calculate the net present value of benefits from IAR4D research and extension efforts as total discounted net benefits of total discounted costs. The internal rate of return was also calculated as the discount rate at which the net present value is zero and can be compared to the opportunity cost of funds. The © 2013 The Authors. African Development Review © 2013 African Development Bank

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Table 2: Streams of market‐based costs and benefits for research and technology transfer of IAR4D due to maize in Sudan Savanna, Nigeria (closed economy model) Benefits (US$ million) Year

Present producer surplus

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 Total Annual Rate of return Benefit–Cost ratio

0 0 0 0 0 0 0 0 0 0 1.20 3.16 7.22 12.86 17.25 19.02 19.11 18.54 17.77 16.96 16.16 15.40 14.66 13.97 13.30 12.67 12.06 11.49 10.94 10.42 9.93 9.45 9.00 6.17 8.17 306.88 8.77

Cost (US$ million)

Net benefits (US$ millions)

Present consumer surplus

Present total surplus

R&E

NPV

0 0 0 0 0 0 0 0 0 0 0.48 1.26 2.89 5.14 6.90 7.61 7.64 7.42 7.11 6.78 6.46 6.16 5.87 5.59 5.32 5.07 4.83 4.60 4.38 4.17 3.97 3.78 3.60 6.17 3.27 126.45 3.61

0 0 0 0 0 0 0 0 0 0 1.69 4.42 10.10 18.01 24.15 26.62 26.75 25.96 24.88 23.74 22.63 21.55 20.53 19.55 18.62 17.73 16.89 16.09 15.32 14.59 13.90 13.23 12.60 12.34 11.43 433.33 12.38

0.50 0.48 0.45 0.43 0.41 0.39 0.37 0.36 0.34 0.32 0.92 0.88 0.84 0.80 0.76 0.72 0.69 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9.65 0.28

−0.50 −0.48 −0.45 −0.43 −0.41 −0.39 −0.37 −0.36 −0.34 −0.32 0.77 3.54 9.27 17.21 23.40 25.90 26.07 25.96 24.88 23.74 22.63 21.55 20.53 19.55 18.62 17.73 16.89 16.09 15.32 14.59 13.90 13.23 12.60 12.34 11.43 423.68 12.11 38% 44

Source: Estimate from Data Analysis (2010).

benefit–cost ratio was also calculated as total discounted benefits divided by total discounted costs. Sensitivity analyses were also conducted to test the sensitivity of the estimated benefits and rates of return to changes in the values of key parameters as well as model assumption. An important assumption underlying the analyses of potential benefits and rates of return is that promotional efforts target only one crop at a time. Table 2 presents the estimated benefits and costs, respectively, accruing to the IAR4D approach. With the bulk of the research expenditure made before 2014, the net present values of benefits were negative during the early years of development and © 2013 The Authors. African Development Review © 2012 African Development Bank

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validation of the approach. With adoption picking up from 2015, annual benefits quickly offset the research and extension costs and benefits begin to level off as the upper ceiling of adoption is reached after 2025. The results in Table 2 show that Sudan Savanna gains an estimated US$423 million — equivalent to US$12 million per year — from adoption of the IAR4D approach for maize production. Out of these benefits, present producer surplus was about US$306 million (about 60 per cent) — equivalent to annual benefits of about US$9 million. However, an annual consumer benefit due to maize production as a result of adoption of IAR4D was about US$4 million. The results demonstrate that IAR4D research and extension yields a rate of return of 38 per cent and a benefit–cost ratio of 44 to 1. The estimated rate of return is higher than the

Table 3: Streams of market‐based costs and benefits for IAR4D transfer due to millet in Sudan Savanna, Nigeria (closed economy model) Benefits (US$ million) Year




Cost (US$ million) R&E

Net benefits (US$ millions) NPV

0 0 0 0 0 0 0 0 0 0 0.23 0.59 1.34 2.38 3.18 3.50 3.51 3.41 3.26 3.11 2.97 2.83 2.69 2.56 2.44 2.33 2.22 2.11 2.01 1.91 1.82 1.74 1.65 1.57 1.50 56.86 1.62

0 0 0 0 0 0 0 0 0 0 0.79 2.06 4.70 8.33 11.13 12.24 12.29 11.92 11.42 10.90 10.39 9.90 9.43 8.98 8.55 8.14 7.75 7.39 7.03 6.70 6.38 6.08 5.79 5.51 5.25 199.03 5.69

0.50 0.48 0.45 0.43 0.41 0.39 0.37 0.36 0.34 0.32 0.92 0.88 0.84 0.80 0.76 0.72 0.69 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9.65 0.28

−0.50 −0.48 −0.45 −0.43 −0.41 −0.39 −0.37 −0.36 −0.34 −0.32 −0.13 1.18 3.86 7.53 10.37 11.52 11.60 11.92 11.42 10.90 10.39 9.90 9.43 8.98 8.55 8.14 7.75 7.39 7.03 6.70 6.38 6.08 5.79 5.51 5.25 189.38 5.41 29% 20

2004 0 2005 0 2006 0 2007 0 2008 0 2009 0 2010 0 2011 0 2012 0 2013 0 2014 0.56 2015 1.47 2016 3.35 2017 5.95 2018 7.95 2019 8.74 2020 8.78 2021 8.51 2022 8.16 2023 7.79 2024 7.42 2025 7.07 2026 6.73 2027 6.41 2028 6.11 2029 5.82 2030 5.54 2031 5.28 2032 5.02 2033 4.78 2034 4.56 2035 4.34 2036 4.13 2037 3.94 2038 3.75 Total 142.16 Annual 4.06 Rate of return Benefit–Cost ratio Source: Estimate from Data Analysis (2010).


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prevailing market interest rates and confirms that adoption of the approach generates a stream of benefits in excess of the research and extension expenditures. The estimated benefit: cost ratio of 44 to 1 indicates, on the other hand, that each dollar invested in IAR4D research and extension generates 44 dollars’ worth of additional maize output. As an agricultural approach option, IAR4D appears to have lower potential for generating productivity gains and economic benefits in excess of the expenditures in millet compared with maize in the Sudan Savanna. The results in Table 3 show that it generates estimated gains of US$189 million — equivalent to US$5 million per year. The average annual present producer surplus and present consumer surplus are US$4.1 million and US$1.6 million, respectively in the Sudan Savanna. The results further

Table 4: Streams of market‐based costs and benefits for IAR4D Transfer due to sorghum in Sudan Savanna, Nigeria (closed economy model) Benefits (US$ million) Year




Cost (US$ million) R&E

Net benefits (US$ millions) NPV

0 0 0 0 0 0 0 0 0 0 0.37 0.97 2.21 3.93 5.26 5.79 5.82 5.65 5.41 5.16 4.92 4.69 4.47 4.25 4.05 3.86 3.67 3.50 3.33 3.17 3.02 2.88 2.74 2.61 2.49 94.23 2.69

0 0 0 0 0 0 0 0 0 0 1.29 3.39 7.74 13.76 18.42 20.28 20.37 19.76 18.94 18.07 17.22 16.41 15.63 14.88 14.18 13.50 12.86 12.25 11.66 11.11 10.58 10.07 9.59 9.14 8.70 329.81 9.42

0.50 0.48 0.45 0.43 0.41 0.39 0.37 0.36 0.34 0.32 0.92 0.88 0.84 0.80 0.76 0.72 0.69 0.00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9.65 0.28

−0.50 −0.48 −0.45 −0.43 −0.41 −0.39 −0.37 −0.36 −0.34 −0.32 0.37 2.51 6.90 12.96 17.66 19.56 19.68 19.76 18.94 18.07 17.22 16.41 15.63 14.88 14.18 13.50 12.86 12.25 11.66 11.11 10.58 10.07 9.59 9.14 8.70 320.16 9.15 35% 33

2004 0 2005 0 2006 0 2007 0 2008 0 2009 0 2010 0 2011 0 2012 0 2013 0 2014 0.92 2015 2.42 2016 5.53 2017 9.83 2018 13.16 2019 14.48 2020 14.55 2021 14.12 2022 13.53 2023 12.91 2024 12.30 2025 11.72 2026 11.16 2027 10.63 2028 10.13 2029 9.64 2030 9.18 2031 8.75 2032 8.33 2033 7.93 2034 7.56 2035 7.20 2036 6.85 2037 6.53 2038 6.22 Total 235.58 Annual 6.73 Rate of return Benefit–Cost ratio Source: Estimate from Data Analysis (2010).


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demonstrate that in millet production, IAR4D approach research and extension yields a rate of return of 29 per cent and a benefit– cost ratio of 20 to 1. The estimated rate of return is much higher than the prevailing market interest rates and confirms that the adoption of the IAR4D option generates a stream of benefits in excess of the research and extension expenditures. The estimated benefit–cost ratio of 20 to 1 indicates that each dollar invested in IAR4D research and extension generates 20 dollars’ worth of additional food. Nevertheless, IAR4D appears to have higher potential for generating productivity gains in excess of the expenditures in sorghum compared with millet in the Sudan Savanna. The results in Table 4 show that it generates estimated gains of US$320 million — equivalent to US$9 million per year. The average annual present producer surplus and present consumer surplus are US $6.7 million and US$2.7 million, respectively, in the Sudan Savanna. The results further demonstrate that in sorghum production, IAR4D approach research and extension yields a rate of return of 35 per cent and a benefit–cost ratio of 33 to 1. The estimated rate of return is much higher than the prevailing market interest rates and confirms that the adoption of IAR4D option generates a stream of benefits in excess of the research and extension expenditures. The estimated benefit–cost ratio of 33 to 1 indicates that each dollar invested in IAR4D research and extension generates 33 dollars’ worth of additional food.

3.1 Sensitivity of Results to Changes in Key Parameters A sensitivity analysis was undertaken to evaluate the robustness of the estimated benefits with respect to model assumptions and certain parameter values. Apart from the model assumption (closed economy), the analysis focused on assessing the effects of: (1) halving the expected adoption rates, and (2) doubling the extension costs. Indeed, expected adoption and extension costs are the two most important parameters with less certain values. The adoption rates observed in the IAR4D approach serve as a good basis for extrapolation, but these may not apply strictly in all the agro‐ecological zones. While the IAR4D research costs are actual investments by the FARA, the extension costs are not. Research benefits are also sensitive to the price elasticity of supply, but this is mainly the case when the value of the supply shift associated with unit cost reduction is approximated as experimental yield gains (i.e. horizontal supply shift) divided by supply elasticity (Alston et al., 1995). In this study, the supply shift was derived on the basis of unit cost reductions calculated directly from the detailed partial budgets presented in Table 5. The price elasticity of demand, on the other hand, influences the distribution of benefits between producers and consumers and not the total benefits. The results of the sensitivity analysis are presented in Table 5. In the Sudan Savanna of Nigeria, for maize, halving the expected adoption rates reduces the present value of benefits by more than half, from US$402 million to US$191 million, but only reduces the rate of return from 38 per cent to 30 per cent, indicating that the IAR4D approach would still be socially profitable. Doubling the extension costs has much less effect on the estimated benefits and rate of return — here, the benefits are reduced to US$393 million and the rate of return to 30 per cent. Similarly, for millet, halving the expected adoption rates reduces the present value of benefits by more than half, from US$189 million to US$89 million, but only reduces the rate of return from 29 per cent to 22 per cent, indicating that the IAR4D approach would still be socially profitable. Doubling the extension costs has much less effect on the estimated benefits and rate of return — here, the benefits are reduced to US$178 million and the rate of return to also 22 per cent. In the same vein, halving the adoption rate in sorghum production also reduced the present value of benefits by more than

Table 5: Sensitivity of the results of the IAR4D approach to changes in the values of key parameters Agro‐ecological zone Sudan Savanna

Crop Maize

Millet

Sorghum

Key parameter change

Net present value (US$ million)

Rate of return (%)

B:C ratio

Baseline Halving adoption rate Doubling extension costs Baseline Halving adoption rate Doubling extension costs Baseline Halving adoption rate Doubling extension costs

402 191 393 189 89 178 384 184 373

38 30 30 29 22 22 37 29 29

43 20 21 20 9.2 9.3 40 19 19

Source: Data Analysis (2010). © 2013 The Authors. African Development Review © 2013 African Development Bank


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half, from US$384 million to US$184 million, but only reduces the rate of return from 37 per cent to 29 per cent, but reduces the rate of return from 37 per cent to 29 per cent. The results compare with the findings of Akinola et al. (2009), Okike (2002), and Bantilan et al. (2005) that indicates that the positive impacts of investment in innovations outweighs the costs incurred in prosecuting them.

4. Conclusion The estimated benefits are sensitive to expected adoption rates but much less so to changes in research and extension costs. However, the estimates indicate that the production of all the crops was socially profitable under the IAR4D option. Our results were consistent with earlier economic analyses which showed that IAR4D was more productive, profitable and acceptable to farmers than the conventional Research and Development (R&D) approach. Overall, while the potential economic gains are considerable, realization of these gains depends on the efficiency and effectiveness of extension, co‐operation and understanding among the stakeholders as well as input supply and output marketing systems. Therefore, policies and institutions that will enhance the adoption of this approach as alternative to the conventional R&D should be pursued vigorously. Moreover, concerted extension efforts are needed to stimulate adoption of IAR4D option, using extensive participatory demonstrations, and because the IAR4D option is knowledge‐intensive, considerable technical advice is also needed to get farmers on board. Aggressive capacity building of farmers and other stakeholders on the concepts via field days, extension activities, etc., should also be encouraged.

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