African Crop Science Conference Proceedings 2013 Vol. 11: 943 - 954
In order to develop basis for tactical or strategic decision making towards agricultural productivity improvement in
Tanzania, a new approach in which crop models could be used is required. Since most crop models have been developed elsewhere,
their adaptation, improvement and/or use outside their domain of development requires a great deal of data for estimating model
parameters to allow their use. Cultivar specific parameters for maize varieties in Tanzania have not been determined before and
consequently, crop modelling approaches to address biophysical resource management challenges have not been effective. An overall
objective of this study was to evaluate DSSAT (v4.5) Cropping System Model (CSM) using four adapted maize cultivars namely
Stuka, Staha, TMV1 and Pioneer HB3253. The specific objectives were; to determine maize crop growth and development indices
under optimum conditions, to estimate maize cultivar parameters, and to evaluate DSSAT CSM for simulating maize growth under
varied nitrogen fertilizer management scenarios. The results indicate that maize cultivars did not differ significantly in terms of the
number of days to anthesis, maturity, or grain weight except final aboveground biomass. Also there was no difference between
variables with respect to growing seasons. The cultivar specific parameters obtained were within the range of published values in the
literature. Model evaluation results indicate that using the estimated cultivar coefficients, the model simulated well the effects of
varying nitrogen management as indicated by the agreement index (d-statistic) closer to unity. Also, the cultivar coefficients which are
difficult to measure physically were sensitive to being varied indicating that the estimated values were reasonably good. Therefore,
it can be conclude that model calibration and evaluation was satisfactory within the limits of test conditions, and that the model fitted
with cultivar specific parameters that can be used in simulation studies for research, farm management or decision making.