Modelling deficit irrigation of wheat in Zimbabwe.
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Date
1993
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Abstract
Wheat is grown in Zimbabwe during the relatively dry, cool winter with irrigation. On most large-scale farms,
land resources exceed irrigation water resources. Consequently, the
efficient use of water is of prime concern. This has led to the
development and adoption of deficit irrigation techniques, with
the aim of maximizing net financial returns per unit of applied
water rather than per unit land area. This often requires that
less water be applied than that required for maximum yields,
which implies that water deficits are allowed to develop in the
crop. Although the basic principles of deficit irrigation are
known, there exists no systematic procedure for advising
farmers on whether or not to, or how to, employ such a
management option in Zimbabwe. This research was therefore
undertaken to develop an interactive computer programme that
would assist farmers in determining optimum irrigation
strategies for wheat.
The CERES-Wheat version 2.10 crop simulation model (WHV21)
was chosen as the basis for this programme. In order to
validate and modify, where necessary, WHV21, a series of field
experiments were conducted at a number of wide-ranging
locations in Zimbabwe during the period 1986 to 1992. These
included sowing date x cultivar, sowing date x seeding rate,
growth analysis and irrigation experiments. In all, 122 data
sets were collected, of which 47 were used for model validation
and 75 used for calibration and modification of WHV21.
The initial validation of WHV21 showed that the model gave
biased and imprecise predictions of phenological development,
particularly under deficit-irrigated conditions. The simulation
of tillering was poor and the model tended to over-predict dry
matter accumulation and under-predict leaf area indices. The
yield component and grain yield predictions were also generally
imprecise. On the other hand, for most data sets, the simulated
soil water contents were similar to measured soil water
contents. These inconsistencies prompted a revision of the
phenological and growth subroutines of the model.
In the phenological subroutine, new thermal time durations
and base temperatures (Tb ) for all growth phases were determined
from regressions of the rate of phasic development on mean air
temperatures. For growth phases one, two and three, a Tb of 4°C
was established, whereas for growth phases four and five, a Tb
of 3°C was used. The revised model included the prediction of
leaf emergence (as apposed to leaf appearance) and first node
appearance (Zadoks growth stage 31). In order to hasten plant
development under conditions of soil water deficit stress,
daily thermal time was made to increase whenever the actual
root water uptake declined below 1.5 times the potential plant
evaporation. These changes improved the prediction of crop
phasic development: for example, the Index of Agreement for the
prediction of physiological maturity was improved from 0.643
with WHV21 to 0.909 with the revised version.
Many changes were made to the growth subroutine, inter alia:
1. the extinction coefficient in the exponential photosynthetically
active radiation (PAR) interception equation was
reduced from 0.85 to 0.45; 2. an allowance was made for the
interception of PAR by the wheat ears during growth phases four
and five, with the statement IPAR=1-EXP(-O.004*TPSM), where
IPAR is the proportion of PAR intercepted and TPSM is the
number of tillers m-2; 3. the area to mass ratio of leaves was
increased from 115 cm2 g -l to 125 cm2 g -l during growth phase two
and this was allowed to decrease under conditions of water
deficit stress; 4. tiller production during growth phase one
was made a function of daily thermal time, total daily solar
radiant density and plant density, moderated by high air
temperatures and a new soil water deficit factor that takes the
dryness of the surface soil layer into account; 5. a cold
temperature routine was added to reduce kernel numbers whenever
the exposed minimum air temperature decreased below 0°C during
the period ear emergence to the start of the linear kernel
growth phase (cold temperatures during anthesis occasionally
cause reductions to kernel numbers in Zimbabwe); and 6. the
kernel growth rate was gradually increased during growth phase
four, and the rate of kernel growth was increased under
conditions of water deficit stress during growth phase five.
The modifications made to the growth subroutine of WHV21
improved predictions of tillering, ear density, yield
components and yield on the independent validation data set.
The modified model (WHVZIM22) was used to evaluate wheat
sowing date and irrigation strategies on ten-year sets of
weather data from representative locations in Zimbabwe. The
results indicated that the highest yields were obtained with
sowings during the latter half of April and the early part of
May at all tested locations. Yields were greater for each
sowing date and irrigation regime at the high altitude (1480 m)
location than at warmer, lower altitude locations. The response
of wheat yield to irrigation application was typically
curvilinear, particularly on the soil with a high water holding
capacity. Maximum yields were attained with the application of
400 to 500 mm (net) water. Soils with low water holding
capacities produced lower mean yields than soils with a high
water holding capacity. Maximum financial returns tended to
occur with the application of less water than that required for
maximum yields, particularly on the soil with a high water
holding capacity. However, the variance of financial returns
increased with reductions in the amount of water applied.
These simulation results corroborated field observations
and, taken together with the improved predictive ability of
WHVZIM22 over WHV21, provided sufficient justification to use
the revised model as a basis for the development of a pre-season
irrigation optimization computer programme. This
programme seeks the intraseasonal irrigation regime that
maximizes the total gross margin for a particular soil,
cultural and weather scenario, within the constraints of land
and water availability. The programme is written in Microsoft
QuickBASIC 4.00 and can generate an optimized irrigation regime
within 4 to 5 minutes when executed on an IBM AT-compatible
80486 computer running at 25 MHz.
It is envisaged that the programme would be used as a
pre-season management tool, but the literal application of the
results in the field is not recommended in view of the fact
that the WHVZIM22 model has a number of inherent limitations
and is therefore not a perfect predictor of crop growth and
yield. The optimum irrigation solution generated by the
programme simply provides a basis from which a farmer can plan
irrigation management strategies. The actual intraseasonal
irrigation schedule would necessarily depend on the real-time
crop, soil and weather conditions.
Description
Thesis (Ph.D.)-University of Natal, Pietermaritzburg, 1993.
Keywords
Wheat--Irrigation--Zimbabwe., Irrigation efficiency--Zimbabwe., Irrigation--Computer simulation., Irrigation--Mathematical models., Theses--Crop science.