aistand south~ern afrkca - (PDF, 101 mb) - USAID

ATLN and at ILCA. Collaboration with field staff
was extremely helpful in obtaining the information
required and the use of local ATLN
infrastructure (computer at some sites, vehicle,
office) permitted smooth operation. Discussions
iith NARS, government officials and extension
services, ATLN field staff and ILCAstaffetc were
most fruitful at all stages. The main problem
was that the economic component was grafted
onto the main biological research project and
basically limited to one man's work. The lack of
provision for training of local scientists in the
collection and analysis of economic daca was
apparent. These problems need to be addressed
in future.
Methodology
Through application of a herd model, proper
phasing ofcosts and benefits could be accounted
for. The period simulated by the model (10 years)
waE sufficient to reveal the dynamic effects of the
production system, yet rot too long to risk
making certain assumptions. In a corresponding
study, Jahnke (1974) used a 30-year projection
to examine the economics of introducing trypanotolerant
cattle into the Central African Republic.
Changes in socio-economic environ- ment and
technical developments outdated control methods
and modified expected benefits. In this case, the
assumptions turned out to be far from reality:
instead of the 128,000 head of trypanotolerant
cattle projected for 1985, the population actually
dropped to 7400 head by 1984 (Shaw and Hoste,
1987). The political chaos in the country was
mainly responsible for this. At the same time
there was, however, a considerable influx of
pastoralists with zebu cattle (under trypanocidal
coverage) (Shaw and Hoste, 1987).
This was the first economic study on the
subject based on empirical quantitative data on
livestock productivity. These data were in fact
quite unique for African village conditions,
regardless of the focus on trypanosomiasis. The
long period over which biological data had been
collected using standardised research
techniques has culminated into a large data base
that is extensive enough to generate a means
applicability for individual herds or heid groups,
thus allowing a comparison of inter-herd
variation. The collection period was long enough
(three to four years) to obtain meaningful
estimates of reproduction and lactation yields,
which are often poorly documented, if at all (e.g.
previous estimates of lactation yields in The
Gambia were as much as four times lower than
those actually recorded). The mean actual milk
off-take in the two Gambian sites was 373 kg for
Gunjur and 413 kg for Keneba (Itty et al, 1992),
whereas earlier estimates were 69.3 kg (high
trypanosomiasis risk) and 98.9 kg (low risk)
(Clifford, 1977). Some previous studies
examined only liveweight gains (Wilson et al,
1976 and 1986; Logan et al, 1984), which is
justified in the case of beefranches. Results show
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that the value of milk accruing to the relevant
participant (state or producer) appears to largely
determine the level of returns. in any case, the
majority of the producers sell animals when cash
is needed and not after a certain period of
fattening. This questions the relevance of
liveweight gains to such producers.
Rigorous application of social-level analyais
using shadow prices and private-level analysis
using market prices revealed major constraints,
aq illustrated by The Gamhian case study of
Keneba. The difference in social and private
value of herd labour and milk were principally
responsible for the wide divergence in results.
The internal rate of return was 46% in the social
analysis, compared with 26% in the private
analysis. Herding costs appeared to be an
important factor in increasing private profitability,
as a largr Ghare of the milk went to the
herders as part of their remuneration. A possible
explanation for the high remuneration is offered
by information collected during the RRA; this
indicated that cattle owners were crop farmers,
who are less knowledgeable about cattle than the
Peul herders. The share contract provided the
herders with incentives as they had a stake in
the system (risk sharing) and they reduced
supervision costs (time consuming and difficult
because of the limited expertise of owners).
Share contracts were widespread and similar
arrangements were practised at the other
Gambian site, in COte d'Ivoire and also in Kenya.
Identification of such major constraints was only
feasible through on-farm research in villages.
The use ofa shadow exchange rate to account
for a possible overvaluation of the local currency
was very important when comparing tsetse and
trypanosomiasis control methods. If the
overvaluation was not accounted for, methods
requiring large amounts offoreign exchange (e.g.
aerial spraying) appeared too cheap relative to
methods requiring more local inputs (labour
intensive ground spraying or traps and targets).
Since socio-economic criteria were not
included in the selection of the herds or in the
experimental design, test herds may therefore
not necessarily be rep.esentative for the site as
they were generally larger than average and, in
the case of C6te d'Ivoire, often belonged to
affluent absentee owners. Furthermore, a socioeconomic
protocol was not established from the
beginning of the network. Limitations appeared
as there were no time series of inputs used and
prices; hence price variations or trends based on
historical data and sensitivity analysis using
standard er''ors could not be simulated.
Biological data were limited to milk and meat
although in Ghibe (Ethiopia), for instance, the
principal purpose of keeping cattle was for
animal traction. In many other sites croplivestock
interactions are of prime importance.
These indirect benefits of trypanosomiasis
control could not be quantified because data
collection was not planned; investigation of these