RA 00110.pdf - OAR@ICRISAT

ing a relatively good year in the Sahel and Sudan
savanna, the two major millet producing areas.
On-Farm Performance of
New Millet Cultivars
Between 1977 and 1984, more than 3000 millet
entries were screened by 1CRISAT millet improvement
researchers in Burkina Faso. Since 1981, cultivars
showing the greatest promise in on-station
trials have been advanced to on-farm, researchermanaged
trials in villages in the Sahel and Sudan
savanna zones. If performance at this stage of testing
was encouraging, the cultivars were advanced to
closely-monitored, farmer-managed tests for additional
evaluation. During 1982-84, four promising
millet cultivars were advanced to this stage: Souna 3,
I K M V 8101, I K M V 8201, and I K M V 8202. However,
once they were evaluated in the more stressed
environments, all cultivars had a significantly different
performance to that observed on-station.
Yield Stability
The brief description of land-use patterns and management
in the W A S A T stressed the high degree of
microvariability and the importance of risk. Because
of farmers' risk aversion, the probability of wide
adoption of a new millet cultivar will be greater to
the extent that it has stable yield superiority, compared
to locals, over a range of physical and management
environments. Millet's role as the riskreducing
crop sown on the poorest land only emphasizes
the importance of stability for that crop.
A commonly employed technique to compare
yield stability across cultivars, which can be applied
to data drawn from a large number of test sites, is to
regress the grain yield of each cultivar at each site
against the mean yield of all cultivars at each site
(Hildebrand 1984). The mean site yield then represents
a type of environmental index. A site (in this
case a particular farmer's test block) where yields are
low, due either to management or the physical site
characteristics, is considered a poor environment,
and vice versa.
We modified the standard approach by fitting the
following regression model
where
Y ikj = yields for the elite cultivar i and the control k
a t location j ,
j = the average yield of all cultivars at location j,
and
X i = a dummy variable for elite cultivar i.
The regressions were fitted separately to data
from zero fertilizer plots, and to data from test plots
which received either 100 kg ha' -1 NPK (14:23:15)
(1981-82) or 100 kg ha" 1 NPK (14:23:15) and 50 kg
ha -1 urea (1983-84) (Fig. 2). The number of observations
at each level of fertilizer were as follows: Souna
3 (Sahel, 1982)-24; I K M V (Sudan savanna, 1983)-
18; I K M V (Sahel, 1983)-20; I K M V (Sahel, 1984)-40.
Results for 1984 are not presented due to extreme
low yields caused by severe drought conditions.
Results for the zero fertilizer plots show that over
most environments all test cultivars yielded lower
compared to local controls. Under fertilized conditions,
the results were more mixed, with one selected
cultivar (Souna 3, Sahel, 1982), projected to be
superior but still low-yielding in better environments.
Most importantly, none of the entries tested
performed better than locals over the range of environments
in any region.
Response to Improved Management
Enhanced responsiveness to inputs is a common
breeding objective aimed to increase.the profitability
and thus the use of modern inputs. To compare
responses to fertilizer and plowing for the improved
and local cultivars, farmers' test data were fitted to
yield function regression models:
Y = a 0 + b 1 X 1 + b 2 X 2 + b 3 X 3 + b 4 X 1 X 2
+ b 5 X1X 3 x b 6 X 2 X 3
where Y = grain yield,
X 1 = dummy variable for the elite cultivar,
X 2 = dummy variable plowing, and
X 3 = dummy variable for fertilizer
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