Impact of cowpea breeding and storage research in Cameroon - IITA

Impact of cowpea breeding and storage research in Cameroon
5.5
Impact of cowpea breeding and storage
research in Cameroon
F. Diaz-Hermelo, A. Langyintuo, and J. Lowenberg-DeBoer 1
Abstract
The Bean–Cowpea Collaborative Research Support Program (CRSP) has research
programs focused on beans and cowpea that span the full range from producer to
consumer. CRSP researchers work on postharvest technologies, such as improved
storage and value-added processing, as well as on genetic improvement, agronomic
practices, and field integrated pest management. The methodologies for economic
impact assessment of this research are well developed for either production-related
work or postharvest technology, but a framework for an integrated assessment of
research, which includes both production and postharvest, is not available. The
objective of this paper is to summarize the results from a theoretical model of
impact assessment with a combined production and storage assessment and to apply
that combined model to an empirical example based on Bean–Cowpea CRSP
breeding and storage research on cowpea in northern Cameroon, West Africa. From
a methodology perspective, this study has shown that the interaction of production
and storage technologies can have important consequences for impact assessment.
Higher yields and reduced storage losses are two ways to have more usable products.
The sum of their separate benefits is often greater than the estimate of combined
benefits. The bias introduced by separate estimation is greatest when the
demand for the product in period two is elastic and when the storage loss reduction
is large. Combined estimation should be used when the bias is likely to be large,
but simpler, separate estimates are still useful for other situations. For the Cameroon
cowpea example, separate estimation has little effect on impact estimates. With
conservative estimates of cowpea area and new technology adoption, both combined
and separate estimates, the baseline IRR is about 5% and the net present value
(NPV) about US$200 000. With a real opportunity cost of capital of about 4%, the
Cameroon project is about breakeven from just the adoption in Cameroon alone.
The real gains from the research are coming from extension of the cowpea storage
technologies into other areas of West and Southern Africa.
Introduction
The Bean–Cowpea Collaborative Research Support Program (CRSP) has research programs
focused on beans and cowpea that span the full range from producer to consumer.
CRSP researchers work on postharvest technologies, such as improved storage and valueadded
processing, as well as on genetic improvement, agronomic practices, and field
integrated pest management. The methodologies for economic impact assessment of this
research are well developed for either production related work (Alston et al. 1995; Masters
et al. 1996) or postharvest technology (Fuglie 1995), but a framework for an integrated
assessment of research which includes both production and postharvest is not available.
1. All authors are at Purdue University, West Lafayette, Indiana, USA.
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Cowpea postharvest and socioeconomic studies
One alternative would be to apply the production and postharvest impact assessment
methods separately and sum the benefits. Unfortunately, this simple approach may lead
to over- or underestimating benefits because production and postharvest technology may
have complementary or offsetting effects. The objectives of this paper are to summarize
the results of an integrated production and storage theoretical model and to apply that
integrated model to Bean–Cowpea CRSP breeding and storage research on cowpea in
northern Cameroon, West Africa.
An example of the potential problem with separate impact assessments of production
and postharvest research is provided by the CRSP work in Cameroon that focused
on breeding for higher yields and resistance to postharvest pests (Murdock et al. 1997).
Both the higher yields and reduced storage losses could lead to increases in the effective
supply and, depending on elasticities, to lower producer and consumer prices. Separate
assessment of impact for production and postharvest may tend to overestimate benefits,
especially for producers. Typically, higher yields result in consumer gains through lower
prices and producer gains from greater productivity, in spite of the lower prices. Reduction
in storage losses mainly benefits consumers and may have negative results on producers
because less needs to be produced to satisfy consumer demand in subsequent periods.
With combined yield increases and reduction in storage losses, the price effects are greater
than with either effect alone. With reduced storage losses, the value of the extra grain
produced through genetic improvement may be less than it would have been without the
storage research.
CRSPs are programs funded by the US Agency for International Development (USAID)
to support research and technology transfer on specific agricultural problems through
collaboration between the US and developing country institutions. CRSPs are organized
to focus on a specific commodity or resource management problem and are among the
longest running USAID programs. The Bean–Cowpea CRSP was created in 1980 to work
on beans and cowpea in Africa, Latin America, and the US. The CRSP cowpea storage
project in northern Cameroon started in 1987 with a partnership between Purdue University
and the Institute for Agricultural Research for Development (IRAD) of Cameroon.
The Cameroon project includes both nonchemical storage technologies and breeding for
storage pest resistance.
Impact assessment overview
As research budgets shrink, impact assessment has become more important for donors and
administrators. Donors want to know if their resources are used effectively and administrators
want information that will help them guide allocation of research resources into
productive areas. Several methods have been used to assess the economic impact of
research, including economic surplus, econometric methods, and mathematical programming
models (Alston et al. 1995). The economic surplus approach is most widely used
because it is relatively simple to implement in spreadsheets and flexible in terms of data
requirements. It is almost the only method used for assessment of the economic impact
of agricultural research in developing countries where data are often inadequate for other
approaches.
Typically, financial tools are borrowed to summarize net benefits over time. A time
series of net benefits is created by subtracting research and transfer costs from the benefits
estimated using economic surplus techniques. From this time series an internal rate of
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Impact of cowpea breeding and storage research in Cameroon
return (IRR) or net present value (NPV) is estimated to determine if the research is a good
investment. Essentially, agricultural investment in research is treated just as any other
investment. A positive NPV means that the investment returns more than the opportunity
cost of capital. The IRR can be compared with returns to other research investments.
Economic impacts are only one of the many types of research impact that might be
tracked. Others include environmental, health, social, and nutritional impacts. Most projects
include economic impact assessment in their plans because:
• it is less costly than other types of impact assessment
• economic measures (in particular IRR) are comparable across a wide variety of projects
• use of financial tools allows it to deal with a series of costs and benefits over time
Most economic impact assessments have focused on production research (Echeverria
1990; Alston 1991). Masters et al. (1996) provide a practical guide for doing this kind of
assessment in a spreadsheet format. The functional form of supply and demand curves, the
nature of the supply shift, has been the subject of considerable debate in academic circles.
Most analyses use linear supply and demand with parallel shifts as the simplest alternative.
Akino and Hayami (1975) propose constant elasticity power functions and pivotal shifts.
Lindner and Jarret (1978) and Rose (1980) showed that the type of shift has important
consequences for the distribution of benefits between consumers and producers.
Assessment of the benefits of postharvest research is a more recent phenomena. Fuglie
(1995) used a framework developed by Muth (1964) and Alston (1991) to estimate the
benefits from potato storage research in Tunisia. Fuglie specified a two-period model with
linear supply and demand, and changes in storage costs and storage losses. Production
occurs in period one; consumption occurs in both periods. The model could be easily
modified to accommodate more than two periods. Processing technology can use a similar
model that is specified in terms of multiple markets. Instead of the storage costs and
losses, the processing model parameters would include processing cost and efficiency. The
authors are not aware of any model that combines production and postharvest impacts of
research in a common framework.
Integrated model
In a two-period linear model based on the work of Fuglie (1995), Diaz-Hermelo and
Lowenberg-DeBoer (1999) show that the potential bias in the economic surplus estimate
due to separate estimation of the impact of production and storage innovations is related
to the changed weighted sum of the demand and supply elasticities:
1 –∆E/EE’ = – (E’–E)/EE’
where:
E = –η 1
q 1
– η 2
q s
(P 1
/P 2
)ρφ + ε = the weighted sum of supply and demand
elasticities, E > 0
E’ = –η 1
q 1
– η 2
q 2
(P 1
/P 2
) ρ (φ – α) 2 + ε = weighted sum of supply and demand
elasticities when innovations reduce storage losses, E’ > 0
P i
= price of product for period i
ρ = (1 + r) = opportunity cost of capital factor
r = six months opportunity cost of capital, r > 0
φ = 1/(1–δ) = storage loss factor
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Cowpea postharvest and socioeconomic studies
δ = proportional storage losses, 0 δ 1
η 1
= elasticity of demand period 1, η 1
< 0
η 2
= elasticity of demand period 2, η 2
< 0
ε = elasticity of supply, ε > 0
q 1
= proportion of total quantity consumed in period 1, q 1
, = 0
q 2
= proportion of total quantity consumer in period 2, q 2
, = 0
q s
= proportion of total quantity stored in period 1, qs = 0
α = storage loss reduction with innovation, α, = 0
Dias-Hermelo and Lowenberg-DeBoer call this (–∆E/EE’) the bias factor. The
importance of the weighted elasticity term (E) can be better understood by noting that
it is the denominator of the economic surplus estimation formula. The bias factor is
always non-negative and it is largest when first period elasticity of demand and supply
elasticity are small relative to the second period demand elasticity term. When supply
or first period demand is very elastic, the bias factor dwindles to insignificance. For
example, with the baseline parameters in Table 1, if the storage loss reduction almost
eliminates storage loss, the bias factor is 6% for a second period demand elasticity of
–0.8. If the consumption share of the second period is increased to 30% without changing
other parameters, the bias factor is 14% at the second period demand elasticity of
–0.8. Table 1 values indicate that the bias factor can be important for some plausible
parameter values.
Table 1. Values of the bias factor (–∆/EE’) for some representative values of other
parameters.
Storage loss reduction factor α
3.0 0.50 0.70 1.00
Baseline
η 2
–0.4 0.01 0.02 0.02 0.03
–0.8 0.02 0.03 0.04 0.06
–1.2 0.03 0.05 0.06 0.08
–1.6 0.04 0.06 0.08 0.10
Higher consumption share in second period 30%
η 2
–0.4 0.03 0.05 0.06 0.08
–0.8 0.06 0.09 0.12 0.14
–1.2 0.09 0.13 0.16 0.19
–1.6 0.12 0.17 0.20 0.24
Elastic first period demand η 1
= –10
η 2
–0.4 0.01 0.02 0.02 0.03
–0.8 0.02 0.03 0.05 0.06
–1.2 0.03 0.04 0.07 0.09
–1.6 0.04 0.07 0.09 0.12
*Given the baseline storage loss factor, φ = 2, α = 1 indicates zero storage loss.
**Baseline parameter values are: first period demand elasticity, η 1
= –0.4; consumption share
period one, q 1
= 0.60; storage share = 0.40; consumption share period two, q 2
= 0.20; price period
1, P 1
= 1; price period 2, P 2
= 4.8; discount rate r = 0.2; storage loss, δ = 0.5; discount factor, ρ = 1.2;
storage loss factor, φ = 2.
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Impact of cowpea breeding and storage research in Cameroon
The theoretical model developed by Diaz-Hermelo and Lowenberg-DeBoer (1999)
shows that when production and storage innovation occur together and impact assessment
is done separately, the results tend to overestimate impact if the storage innovation
reduces storage losses. Numerical examples indicate that the bias in impact assessment
results can reach 20% for some plausible parameter values.
Cameroon example
From the beginning of the Purdue/IRAD effort, storage research has included both breeding
for resistance to storage pests and postharvest storage technology (Purdue/IRA 1987).
The concept was that postharvest technologies would be developed and extended rapidly
as an intermediate term response, while longer-term genetic resistance to storage pests
would be added to the portfolio of strategies.
The Bean–Cowpea CRSP has been collaborating with IRAD since 1982. Before 1998
IRAD was known as the Institute for Agricultural Research (IRA). Initially, the CRSP
focused on field pest management and agronomy, under the leadership (on the US side)
of the University of Georgia (UGA). On the Cameroon side, leadership is in the hands of
the IRA Cowpea Section at the experiment station at Maroua in the Far North Province.
The UGA/IRA collaboration was part of the effort that led to the release of the varieties
Vya in 1986, and BR1 and BR2 in 1987. Vya is a selection from a traditional variety from
the Kaélé area of the Far North Province. BR1 and BR2 are closely related varieties with
genetics from the International Institute of Tropical Agriculture (IITA). BR1 and BR2
show some bruchid resistance.
Impact assessment showed that cowpea research at IRAD from 1979 to 1987, including
the CRSP effort, had IRR under baseline assumptions of 15% annually (Sterns and
Bernstein 1993). The research benefits were largely linked to the widespread planting of
BR1 and BR2 in monocrop by farmers looking for a cash crop as an alternative to cotton.
Based on interviews with key informants in the region, Sterns and Bernstein (1993)
estimated adoption of improved varieties to be 25% of cowpea area in 1990. Adoption of
these varieties was facilitated by SODECOTON, the Cameroon cotton parastatal, which
sold seed and cowpea pesticides.
In 1987, the focus shifted to cowpea storage under the leadership of Purdue University.
Storage research started with a study of indigenous cowpea storage methods (Wolfson
1990). Five general techniques were identified, which were pursued in on-station and
on-farm research: ash storage, solar heating, hermetic storage, oils, and botanicals. In
1988 and 1989, cowpea lines from Cameroon and IITA were screened for resistance to
storage insects, a solar heater for disinfecting cowpea was tested at the experiment station
in Maroua, and improved methods were developed for ash storage (Murdock and Shade
1988; Murdock et al. 1989). Resistance screening included the search for nondehiscent
pods that formed a barrier to insects, as well as for harder seed coats that were more difficult
for bruchids to penetrate. Pod resistance is potentially important in Cameroon, because
Wolfson’s research showed that farmers often store cowpea in pod form for several months
before threshing. A form of hermetic storage was added to the technology portfolio in
1990 in the form of triple bagging (Purdue/IRA and TLU/NCRE 1990).
In 1990 and 1991, on-farm testing of promising postharvest storage technologies was
begun in four villages in the Maroua area (Purdue/IRA and TLU/NCRE 1990). Those
technologies were:
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Cowpea postharvest and socioeconomic studies
• solar heater disinfestations
• triple plastic bags
• ash storage
It should be noted that neither solar disinfestation nor hermetic storage of grains are
novel ideas. The contribution of the Purdue/IRA team was to adapt these ideas to the materials
and constraints of Cameroonian farmers. Extension materials were developed for each
technology (Kitch and Ntoukam 1991 a, b; Ntoukam and Kitch, undated) and translated
into French and Fulfuldé. In 1992–1993, the storage technologies were demonstrated
in 100 villages by agents of SODECOTON and MINAGRI, the Cameroonian national
agricultural extension agency (Purdue/IRA 1992; Purdue/IRA 1993). The storage technologies
became a standard part of the farmer training offered by private and government
extension organizations in northern Cameroon (Lowenberg-DeBoer, 1995, 1996, 1997).
The techniques were featured in a radio program on cowpea storage in French, Fulfuldé,
and other local languages. The program was produced by SODECOTON in 1995.
Economic studies indicated that the IRA/CRSP storage technologies were potentially
profitable for some producers (Schultz 1993; Kamuanga and Kitch 1994; Lowenberg-
DeBoer 1994). Use of the ash technique was limited primarily by the quantity of ash
required. Access to materials for the solar heater and triple bagging was identified as
another constraint. Both plastic sheets and bags are readily available in the market in
Maroua, but not necessarily available in rural areas. SODECOTON facilitated access to
solar heaters by selling pre-cut solar heater kits through their village agents in 1995.
Lowenberg-DeBoer (1994) concluded that the triple bagging and solar heater technologies
were competitive with those of storage insecticides, especially for storage periods
longer than three months. He also noted that given the high opportunity cost of capital
in northern Cameroon and throughout rural West Africa, many farmers would continue
to sell at harvest. They have urgent need for cash for family and business purposes, and
cannot afford to store, regardless of the technology.
Merchants are very active in cowpea storage, but most of the extension effort on cowpea
storage has focused on farmer storage. Oumarou (1999) found that merchants in Maroua
have storage for 25 000 to 30 000 t of cowpea. In most cases, this cowpea is stored using
insecticides. Most of this cowpea is shipped out of the region, to the cities of southern
Cameroon, to Nigeria, and increasingly to Gabon and Congo.
Plant breeding research focused on the development of storage pest resistance continued
through the early 1990s. In 1994, 1995, and 1996, varieties were evaluated on-station
during the growing season by farmers known in their communities as master cowpea
growers (Kitch et al. 1998). Two varieties were identified; they have the breed codes of
24–130 and 2–38 and have been given the names Lori Niébé and CRSP Niébé, respectively
(IRAD 1999). In the 1997 and 1998 seasons they were tested in on-farm trials. In 1999,
multiplication for seed dissemination started.
Cowpea area and production
There are two primary sources of statistics on cowpea area and production: the Provincial
Agroeconomic Survey and Agricultural Planning Service (SPEAPA) and the National
Directorate of the Agricultural Census (DEAPA). SPEAPA statistics are based on extension
agents’ reports. DEAPA data were drawn from a random sample of farm households.
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Impact of cowpea breeding and storage research in Cameroon
The DEAPA effort was funded by a USAID project that ended in the early 1990s. The
shortcomings of each data source are discussed by Lowenberg-DeBoer (1994, 1995).
Both DEAPA and SPEAPA data show substantial year-to-year variation in area and
production. There is some indication of an upward trend in yields in both the DEAPA
and SPEAPA data, which confirm anecdotal reports, but when regressed on a linear trend
line the estimates are not statistically significant. The best fitting regression model is a
quadratic trend (R 2 = 65%), but that results in unrealistically high cowpea area by the
end of the study period (more than 1 million ha). In an alternative regression, changes in
cowpea area are not well explained by prices from the previous year. As a compromise,
the baseline analysis uses the last DEAPA observation (1992) as the area and yield estimate
for the period 1993–2015. This incorporates the increase in area and yield that had
occurred up to that time, but does not include later increases. The 1991 gap was filled with
the mean for the data period. Sensitivity tests will be done using DEAPA and SPEAPA
average area and yield.
Adoption
Adoption of storage technologies can be estimated using data from a survey in 1994
(Lowenberg-DeBoer 1994) and interviews with key informants in the region after that
time (Lowenberg-DeBoer 1995, 1996). The new varieties Lori Niébé (24-130) and CRSP
Niébé (2-38) are at the beginning of the seed dissemination process, so there are no adoption
data. Their adoption potential will be estimated based on previous experience with
Vya, BR1, and BR2.
The 1994 data collection used a modified rapid rural appraisal design. Six villages
were chosen randomly from a list of all villages in the Départements of Diamaré, Mayo
Danay, Mayo Kani, Mayo Sava, and Mayo Tsanaga in the Far North Province and the
Département of Mayo Louti in the North Province. This covers the principal cowpea
growing areas of North Cameroon.
One village per département was chosen randomly from a list of all villages. None of
the villages chosen had been exposed to cowpea storage demonstrations. Discussions were
held in a village meeting context, thus the unit of observation is the village. In addition,
case studies were done on two villages with long-term cowpea extension efforts, to gain
a perspective on adoption when information on the techniques was available. Details of
the data collection methods and results are given by Lowenberg-DeBoer (1994).
In the randomly selected villages, about 23% of cowpea area was planted with the
improved varieties Vya, BR1, and BR2. About 13% of 1991 cowpea area was planted
to BR1 and BR2. About 10% was planted to Vya. BR1 and BR2 are grown mainly as
monocrops. Vya is grown primarily intercropped with sorghum. The 23% estimate is
very close to the 25% improved variety area estimated earlier by Sterns and Bernstein
(1993) by informal interviews. This suggests that the use of these improved varieties has
reached a plateau.
Farmers in the case study villages were well informed about CRSP storage techniques.
In Douroum, Mayo Tsanaga, about 20% of the harvest was stored with the improved ash
method, 7–10% of farmers used triple bagging, and 5% the solar heater. In Gatouguel,
Mayo Louti, farmers had used the eight solar heaters supplied by IRA until they were in
tatters. They said that about 50% of cowpeas produced in the village had been treated when
the solar heaters were available. At the time of the study they were eager to buy plastic to
replace the original heaters, but this was not available in the village.
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Cowpea postharvest and socioeconomic studies
The fall of 1994 and 1995 was a period of major extension activity in cowpea storage.
SODECOTON sold about 300 heaters in the first few months of 1995. The National
Agricultural Extension and Training Project (PNVFA) disseminated the technology to
approximately 1500 villages in the Far North Province where it has a regular presence.
Programs in French and local languages were aired on local radio.
Interviews with key informants in the fall of 1995 indicated that about 5% of farmers
were using CRSP technologies, mainly the solar heater (Lowenberg-DeBoer 1995). Triple
bagging is not widely used in rural areas because of the lack of bags. The solar heater
was used on about 90% of the farms’ cowpea production. Interviews in 1996 and 1997
indicated that the use of the solar heater continued to rise, but that storage insecticide use
was growing rapidly (Lowenberg-DeBoer 1996, 1997). These insecticides are readily
available at low cost from SODECOTON or from Nigeria border markets. By 1977 it
was estimated that 70% of cowpeas stored was treated with insecticides and the use of
the CRSP technologies, primarily the solar heater, appeared to have reached a plateau of
about 10% of cowpea production.
For the impact analysis a logistic function was used to estimate adoption for both the
postharvest technologies and varieties. The baseline scenario used a plateau of 10% for
postharvest technologies and 13% for varieties. A sensitivity test will be done on a plateau
of 25% for both innovations.
Prices
There is no regular system for reporting cowpea price data in northern Cameroon. Sterns
and Bernstein (1993) documented monthly prices for the period 1985–1990. Lowenberg-
DeBoer (1995, 1996) obtained some monthly price data for the period 1992–1995. Since
September 1997, the Purdue/IRAD project has collected cowpea price and quality data
monthly, starting with two markets in the Maroua area and later extending the collection
to two additional markets (Langyintuo and Lowenberg-DeBoer 1999).
For the model outlined by Diaz-Hermelo and Lowenberg-DeBoer (1999), prices are
needed for two periods, harvesttime and after storage. Cowpea harvest typically starts in
October. Cowpeas are all hand-harvested by picking pods in the field. Harvesting continues
through early December. Many producers store cowpea in pods on a raised storage
platform called a danki (Wolfson 1990). Danki storage may last for two to three months.
Threshing may occur any time between the beginning of harvest and the following March.
For the model it was assumed that newly threshed cowpea might be marketed any time
from October to March and the price (P1) was the simple average price from the data for
those months. Stored grain was assumed to be stored for six months and sold at the simple
average price for the period April to September (P2).
Period one and two prices were estimated as the simple average for the periods October
to March, and April to September in the available data. Gaps were filled with the average
price for the data period, 193 real 1998 FCFA for P1 and 236 real 1998 FCFA for P2.
Elasticities
For the purpose of the analysis, cowpea supply is the cowpea grain produced in the
three northern provinces of Cameroon—Far North, North, and Adamoua. The demand
is the sum of local demand for consumption and demand for export outside the northern
Cameroon region. For the analysis, cowpea purchased for export in period 1 is treated as
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Impact of cowpea breeding and storage research in Cameroon
consumed; only cowpea stored for local consumption is considered by the storage model.
Thus storage by merchants in Maroua awaiting export is treated in this analysis as part
of the first period demand.
There are no statistics on the proportion of cowpea retained for local consumption
in the storage period. Cowpea market studies indicate that a large portion of cowpea
production in northern Cameroon is exported and most of the cowpea for export is purchased
around harvesttime. The baseline scenario will assume that 80% of cowpea grain
is either consumed locally or purchased for export in the first period. That leaves 20% of
the cowpea stored, and given estimates (described below) of 52% storage losses prior to
the introduction of CRSP technologies, about 10% of the total production is available for
local consumption in the second period. A sensitivity test will be done assuming 40% of
cowpea is stored.
Empirical estimates of the supply and demand for cowpea in northern Cameroon are
yet to be made. This analysis used the logic outlined by Masters et al. (1996) to determine
elasticity parameters. He states that the typical supply elasticity is in the range of 0.2 to
1.2, and demand in the range of –0.4 to –10. The demand elasticity of –10 would occur
when the product is traded in a larger international market. The lower end of the range
would be for food crops with only local markets.
The cowpea market in northern Cameroon is open to international trade, but Lowenberg-
DeBoer (1994) observed that this trade occurred mainly around harvesttime. Later in the
year, markets in northern Cameroon were dominated by local demand. This observation
explains the high and volatile prices in the April to September period. The baseline scenario
in this analysis assumes that the first period has a very elastic demand (–1.0) because of
international trade and low elasticity in the second period (–0.40) when local demand
dominates. For the supply side, a midrange estimate of 0.7 will be used. Sensitivity tests
will be done on both demand and supply elasticities.
Storage losses
For the baseline model it was assumed that one hole per grain was sufficient to cause a
merchant to sort out that grain. While that damaged grain may still have value as livestock
feed, this value is much less than that for human consumption. No salvage value for damaged
grain was included in the model.
The number of holes per grain after a given storage period was estimated using data from
on-farm tests of CRSP storage technologies in 1990–92 (Purdue/IRA 1990; Purdue/IRA
1992 supplemental). The on-farm storage tests all ran for about three months. A logistic
function of time similar to that used by Schultz was employed (Schultz 1993) to obtain
estimates of losses after six months of storage. Unfortunately, no insecticide treatments
were used in the on-farm trials; it was assumed that damage levels with insecticide treatment
would be similar to that of the solar disinfestations.
The initial loss parameter (δ) was estimated as the weighted average loss given the
distribution of storage techniques estimated by Wolfson (1990) at the time when the
project started. This required lumping techniques into categories for which loss estimates
were available. The percentage in each category was ash, 24%; insecticide, 23%; herbs or
“nothing”, 48%; hermetic storage, 2%; heating, 2%. Ash loss was estimated by data on
the CRSP ash technique (11% after six months), even though the indigenous technique is
often less reliable because a lower proportion of ash is used. Because the CRSP trials did
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Cowpea postharvest and socioeconomic studies
not include insecticide or heating treatments, insecticide loss and heating are measured by
data on the solar disinfestations (4% after six months). Hermetic storage loss is estimated
with data from the triple bagging treatment (4% after six months).
Each of these loss estimates assumes that cowpea put into storage has an initial damage
level like that of Vya after three months on a danki in pod form (4.3%). Vya is typical of
many local cowpea varieties that are not particularly resistant to bruchids in pod form.
While it is clear that the actual storage situation is much more complicated than this
set of simple categories and loss rates, it is argued that the weighted average loss (52%)
is a reasonable estimate. Insecticides and ash are not always used correctly and higher
losses may result. In particular, a lower proportion of ash may be used (often because
of a lack of an adequate quantity of ash) or the second insecticide treatment after three
months may be omitted. Some Cameroonian botanicals may reduce storage losses, even
though that has not yet been proven in the laboratory. Thus, ash and insecticide losses may
be somewhat higher than estimated and the herbs and “nothing” category losses may be
lower, but these offset each other.
If the baseline loss parameter (δ) is 52%, the model loss parameter (φ) is 2.09. The loss
reduction for solar heating (α) is 1.04 (φ) –1/(1–0.043)).
Pod resistance is a characteristic of the newly developed varieties Lori Niébé and CRSP
Niébé. This would affect the damage that occurs before the cowpeas are threshed. For the
model, on-farm trial data with BR1 and BR2 were used. After three months on a danki
these varieties showed only 0.8% damage, compared to the 4.3% with Vya. The 0.8%
was then used as the starting damage in the six-month storage estimates. This is done by
recalibrating the logistic function used to estimate losses to pass through an initial level
of 0.8%, instead of 4.3%. With the 0.8% initial damage, the weighted average loss (δ) is
50% and an alpha parameter (α = 0.10) is 10%.
Storage costs
Storage costs were estimated based on data provided by Lowenberg-DeBoer (1994). All
technologies except ash and triple bagging were assumed to store grain in woven sacks,
valued at 250 FCFA for those that can hold 80 kg. Ash is stored in large clay pots called
cannari commonly used to hold water in northern Cameroon. New pots are valued at
1500 FCFA and can hold about 50 kg of cowpea when mixed 50/50 with ash. They are
assumed to last for five years. Plastic bags are valued at 150 FCFA/bag and are assumed
to be used for one season because of the risk that reused bags may have small holes.
Insecticide is valued at 175 FCFA/packet with one packet treating 80 kg seed every three
months. The solar heater is composed of two sheets of plastic, one clear the other black,
valued at 3500 FCFA and given a useful life of two years. The estimate used the high
volume estimate outlined by Lowenberg-DeBoer in which 4000 kg seed are treated per
year per solar heater. For lack of data, labor is assumed to be the same for all technologies
at 100 FCFA/hour.
The interest cost on the durable investment is annual, while the interest on variable
cost in the model (r) is semiannual.
Using the estimates, the initial nominal cost of storage is 6049 FCFA/t or 7117 in 1998
FCFA. The nominal cost for the solar heater is 4166 FCFA/t or 4902 in 1998 FCFA. Thus
the storage cost reduction is estimated at 2215 FCFA in 1998 FCFA.
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Impact of cowpea breeding and storage research in Cameroon
Yield of improved varieties
Yields of improved varieties Lori Niébé and CRSP Niébé in station trials are documented
by Kitch et al. (1998) and Purdue/IRAD (1997). In these on-station trials, Lori Niébé and
CRSP Niébé were statistically significantly different from a commonly used variety at
the 5% level. In general, improved varieties yield less in farmers’ fields than under the
almost ideal conditions of the research station. The on-farm yield was estimated at 80%
of the station yield. This results in a simple average yield improvement of 0.02 t/ha or
about 2%. These data suggest that the main benefit of the new varieties is the storage pest
resistance, not increased yield.
Improved yield always comes at some cost, even if that cost is difficult to calculate.
Increase in seed cost due to the introduction of these varieties will probably be negligible.
These are varieties and once farmers have the variety, they can easily save their
own seed or get from friends and relatives. Increased yields remove more nutrients from
the soil, but in northern Cameroon, farmers rarely plant cowpea with fertilizer. In the
long run, a greater need for fertilizer should be expected from increased yield, but in the
variety extension period of 2000–2015 it is unlikely that there will be a major move in
that direction. Increased yield requires more labor and even if that is family labor it has
an opportunity cost. Abdoulaye (1995) estimates that the harvest rate is about 28 kg/day
in Niger. With an eight-hour day and a cost of 100 FCFA/hour, this is an added cost of
about 29 FCFA/kg.
Opportunity cost of capital
Opportunity cost of capital in West Africa can be very high compared to levels in industrialized
countries, often rising to over 100% annually for small-scale informal sector
enterprises (Vijverberg 1991; Lowenberg-DeBoer et al. 1994). Lowenberg-DeBoer (1994)
presents case study evidence that female cowpea street food vendors earn these high returns
on their very limited capital.
One hypothesis is that opportunity costs of capital are highest for low resource people
with very little capital and drop as capital levels increase. The economic logic suggests that
very poor people will seek out the higher return activities for their very limited capital. As
they acquire more money, they exhaust those high return activities and invest in somewhat
lower return larger scale enterprises. As capital levels become large, the opportunity cost
approaches the level in the formal sector.
Calculations suggest that returns to cowpea storage vary substantially from year to
year, but since 1985 they have averaged from 15 to 25% annually depending on exactly
how the calculation is done. This suggests that cowpea storage is an activity undertaken
by those with relatively large amounts of capital, not the poor, and may explain why a
large part of cowpea storage is handled by merchants in Maroua.
As a conservative estimate of the opportunity cost of capital, the average real cost
of formal sector capital in Cameroon (15% annually) was used for the parameter “r” in
equation 1. A sensitivity test was done with the 50% annual cost of capital suggested by
Lowenberg-DeBoer et al. (1994). It should be noted that the opportunity cost of capital in
the model is defined on a six-month basis, so in the baseline analysis r = 0.15/2 = 0.075.
For the NPV calculation, the real cost of long-term capital to the principal donor, the US
government, was used. It was estimated as the real interest rate of 4.22% on long-term
US government bonds from 1985 to 1998.
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Cowpea postharvest and socioeconomic studies
Research and extension costs
Allocation of research and extension costs is one of the most difficult parts of economic
impact assessment. The allocation of such resources is seldom as clear as the theory suggests.
A key problem is the cumulative nature of science. Scientific discoveries today
are built on the research of the past and are the foundation of future work. An additional
problem is the broad responsibility of most public sector agricultural research organizations.
A team, such as the Purdue/IRAD group, is seldom able to focus exclusively on a
narrow set of problems, but must respond to producer and consumer concerns on a wide
range of issues.
In allocating costs, the cumulative nature of science is dealt with following the logic
suggested by Masters et al. (1996). All costs related to decisions that were already made
when the CRSP and IRA decided to initiate cowpea storage research are considered sunk
costs and not charged to the project. Thus, costs incurred by IITA in developing the genetic
materials that became part of the parentage of the new CRSP varieties are not charged
to the Purdue/IRAD project. Similarly, costs incurred by CRSP and other organizations
in developing and extending BR1 and BR2 are not charged to the storage project even if
BR1 and BR2 were among the parents of the new varieties, and even if the extension of
BR1 and BR2 created a precedent that other new varieties can follow.
The principal use in allocation of costs is that the intellectual debt to previous researchers
is acknowledged, but the cost of previous research is not added in. Similarly, benefits
of the project for future research are discussed, but not deducted from the research and
extension costs. The benefits of the “sweet cowpea” variety that was discovered during
the development of the new varieties are not included in this analysis.
The diverse responsibilities of many agricultural researchers are often more difficult
to deal with than previous research and extension. Often there is no good way to allocate
time and resources. Field trials may be done at the same site and cared for by the same
technicians. A computer and software purchased by one organization is also used incidentally
to analyze data from a related effort. For example, after the CRSP collaboration
with IRAD was refocused on storage, some effort continued on the previous agronomy
research, especially in the first few years of the storage effort. For this analysis all costs
incurred by CRSP in Cameroon and for Cameroon, and by IRAD for CRSP collaboration
are treated as being for development of the postharvest technologies and the new varieties.
It is acknowledged that this probably overstates the cost of research, but given the lack of
information on researcher time and resource allocation it is the only practical solution.
Bean–Cowpea CRSP expenditures are taken from accounting records at Purdue University
and at the CRSP Management Office at Michigan State University. The CRSP amounts
represent two categories of expenditure: those in Cameroon by CRSP and by IRAD and
those made in the US explicitly for IRAD. A typical example of the “for Cameroon”
expenditure is the purchase of scientific equipment in the US for shipment to Cameroon.
Expenditure for short-term training is included. Long-term training typically has objectives
beyond the development of specific technology and is not included in research costs for
this analysis. The CRSP is a collaboration with benefits expected on both sides. The US
costs are assumed to be paid for by US side benefits. The IRAD costs are those reported
by the Institute to the CRSP as their contribution to the overall cowpea effort.
In Cameroon, the Purdue/IRAD team worked closely with the National Cereals
Research and Extension (NCRE) project. This occurred in particular with the on-farm
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Impact of cowpea breeding and storage research in Cameroon
testing of the postharvest storage technology. The NCRE allocation of funds to those
joint activities was estimated with the help of Dr Emanuel Atayi, former NCRE team
leader (Emmanuel Atayi, Personal Communication), and with information from NCRE
plans of work for 1988–89, 1991, 1992, 1993, and 1994.
Extension of the postharvest technologies and new varieties was done in collaboration
with several private and public organizations, especially the National Program for
Agricultural Extension and Training (PNVFA), which is the Ministry of Agriculture’s
extension project; Support Service for Grassroots Initiatives in Development (SAILD),
an NGO with mainly Austrian support; Africa, Women and Development (AFP), an
NGO focusing on women’s issues; and SODECOTON, the Cameroon cotton parastatal.
For each of these extension efforts, cost estimates are based on interviews with
administrators in the organization.
Economic surplus
The economic surplus calculations follow the method outlined by Masters et al. (1996),
modified by the need to combine storage and yield increasing innovations as outlined
by Diaz-Hermelo and Lowenberg-DeBoer (1999).
Table 2 shows IRR and NPV results. It indicates that the investment in research
and extension was only slightly above the real cost of capital (4.22%) when only the
benefits in Cameroon are considered. Transfer of CRSP storage technologies to Benin,
Niger, Nigeria, Senegal, and other countries (Lowenberg-DeBoer 1999) is adding
substantial benefits with mostly extension costs. Estimation of these spillover benefits
is an important topic, but beyond the scope of this analysis.
Separate estimation yields an IRR and NPV only slightly higher. This is consistent
with the bias estimates in Table 1. The baseline parameters of period 2 demand
elasticity (η 2
) and the reduction in storage loss (α) are such that the bias in separate
estimation is minimized. In addition, the potential for bias only occurs when the new
varieties are introduced after 2000. Thus, the bias occurs mainly toward the end of
the analysis period when the NPV effect would be reduced.
Sensitivity testing shows that for most parameter values the conclusions are the
same concerning both the benefits of the cowpea research and extension effort, and
the effects of separate estimates. A lower first period demand elasticity (η 1
= –0.4)
or higher opportunity cost of capital (r = 0.25) have little impact on the results.
Alternative values of the cowpea area, supply elasticity, and proportion consumed
in the second period result in higher benefits. If the supply elasticity is near the lower
end of the range (ε = 0.2), the grain from increased yields and that saved by storage
loss reduction are more valuable, resulting in an NPV of over US$800 000 and an
IRR of 7.70. Similarly, if the proportion of cowpea consumed in the second period
is higher (q s
= 0.4), NPV and IRR are increased because more grain is stored and the
lower costs and value of reduction of storage losses are multiplied by a larger quantity.
With cowpea area estimated with the quadratic trend that gives the best statistical fit,
IRR rises to 21%.
Higher, long-term adoption plateaus (M = 0.25) increase the economic impact.
If postharvest technologies have a plateau of 0.25 instead of 10%; the NPV rises to
US$1.4 million and IRR to 10%. Raising the new variety yield plateau to 0.25 has a
smaller effect, because the yield gain is small.
419

420
Table 2. Summary comparison of combined and separate estimation, ex-post and sensitivity analysis.
Combined Separate Difference
Relative NPV
IRR IRR difference IRR
NPV (%) Annual NPV (%) Annual NPV (%) (%) Annual
Baseline values 100 469 4.74 6072 103 214 4.75 6 238 2745 2.73 0.01 166
Sensitivity analysis
η 1
= –0.4 88 131 4.68 5326 92 525 4.70 5592 4394 4.99 0.02 266
η 2
= –10.00 628 261 0.08 –37 970 –588 763 0.44 –35 582 39 498 6.29 0.36 2387
q s
= 0.4 1 456 093 10.20 88 000 1 461 715 10.22 88 340 5623 0.39 0.01 340
r = 0.25 102 198 4.75 6,176 104 870 4.76 6 338 2672 2.61 0.01 161
Supply elasticity = 0.2 866 055 7.70 52 341 875 404 7.73 52 906 9349 1.08 0.03 565
Adoption plateau = 0.25 349 294 5.83 21 110 358 461 5.87 21 664 9167 2.62 0.04 554
Two holes/grain –626 172 0.32 –37 843 –625 126 0.33 –37 780 1046 0.17 0.01 63
P2 higher 113 552 4.80 6863 115 743 4.82 6995 2191 1.93 0.01 132
SPEAPA avg † –110 673 3.62 -6689 –107 677 3.64 –6508 2996 2.71 0.02 181
DEAPA avg ‡ –656 382 –0.07 –39 669 –654 508 –0.06 –39 556 1874 0.29 0.02 113
Cowpea postharvest and socioeconomic studies
†
SPEAPA average yield, which is higher than DEAPA average yield.
‡
with DEAPA average yield.

Impact of cowpea breeding and storage research in Cameroon
At some relatively extreme values of the parameter ranges the IRR drops below the cost
of capital and the NPV is negative. If second period demand is very elastic (η 2
= –10) or
if it assumed that two holes constitute a loss, the IRR is less than the opportunity cost of
capital and the NPV is negative. The difference between separate and combined estimates
is largest when the second period demand elasticity is large (η 2
= –10), but even then it
is still less than a one percentage point difference in the IRR. The NPV is also negative
when either the SPEAPA or DEAPA average area and yield are used.
Given the information on elasticities, partitioning of the economic surplus into producer
and consumer surplus is not particularly reliable, but it can provide some insight
into potential distributional effects. Producer surplus in the separate estimate of storage
impacts is negative every year after the postharvest technologies are introduced. In effect,
reducing storage loss substitutes “saved” grain for produced grain. As defined in the model,
producers only gain by producing and hence they lose when less total grain is needed. Of
course cowpea producers are also cowpea consumers who gain every year. The profits for
storage are built into the storage cost calculation. Because of the high demand elasticity
in the first period, most of the impact due to yield gains from the new varieties goes to
producers. In the combined estimation, producer surplus is positive after the new varieties
are introduced because the production benefit from higher yield outweighs the producer
surplus declines from reduced storage loss.
Conclusions
From a methodological perspective we have shown that the interaction of production
and storage technology can have important consequences for impact assessment. Higher
yields and reduced storage losses are two ways to have more usable product. The sum of
their separate benefits is often greater than the combined estimate of benefits. The bias
introduced by separate estimation is greatest when the demand for the product in period
two is elastic and when the storage loss reduction is large. Combined estimation should
be used when the bias is likely to be large, but a simpler separate estimate is still useful
for other situations.
For the Cameroon cowpea example, separate estimation had little effect on impact
estimates. With conservative estimates of cowpea area and new technology adoption, both
combined and separate analyses estimate the baseline IRR at about 5% and the NPV at
about US$200 000. With a real opportunity cost of capital of about 4%, the Cameroon
project is about breakeven from just the adoption in Cameroon alone. The real net gains
from the research are coming from the extension of cowpea storage technologies into
other areas of West Africa.
As with any empirical impact assessment, the Cameroon example has numerous limitations.
The cowpea area, production, yield, and price data provide a general indication
of conditions in northern Cameroon, but they are not perfect. In particular, the data on
trends in area and production were hard to interpret. The postharvest adoption estimates
were based mainly on interviews with key informants. The new variety adoption estimates
were made with the assumption that they would follow a path similar to those of BR1
and BR2. The analysis assumed linear supply and demand throughout. Changes in grain
quality due to the new varieties are not included in benefit calculations. The focus was
on economic impact. Environmental and human health benefits from the reduction in use
of storage chemicals were not assessed.
421

Cowpea postharvest and socioeconomic studies
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