Abstract:
Contemporary agriculture undergoes enormous
challenges in order to compensate the increasing food
demand of the rapidly growing global population. Crop
intensification is crucial to address that rising food
requirement whilst focusing on the potential environmental
impacts of intensive agriculture. Crop models are deployed
in crop intensification process ensuring sustainability
through virtualization of interactions between climate,
soil, management practices and genotypes. Among the
crop models, Agricultural Production Systems Simulator
(APSIM) which is comprised of sub models capable of
predicting crop yields and phonological parameters
through integration of underlying factors. Present study
evaluates the applicability of APSIM-maize sub model in
yield prediction under Sri Lankan conditions. The study
was undertaken in collaboration with CIC Grains (Pvt.)
Ltd. for the existing maize fields in Siyambalanduwa area.
The model was validated using the growth parameters
viz., plant height and number of leaves, for 2016/17 maha
cropping season. With the secondary soil, climate and
management data, the model simulated growth parameters
at weekly intervals and were compared statistically with
the observed data. Plant height and number of leaves were
predicted by the model with a strong correlation with
observed data with calculated r2 values of 0.82 and 0.99.
Student’s paired t-test showed no significant difference
where P value was 0.73 and 0.67 at 95% confidence level.
Positive modelling efficiency of 0.60 for plant height
and 0.79 for number of leaves affirmed that the model
predictions were acceptable. The model achieved RMSE
of 33.61 for plant height with a standard deviation of
73.3and RMSE value of 1.15 with a standard deviation
of 5.51. However, the model overestimated the yield for
2016 (observed yield=5.24 mt/ha, Predicted yield=6.27
mt/ha) and 2015 (observed yield=3.85mt/ha, predicted
yield=5.22 mt/ha by 21%. This discrepancy could have
been aroused due to the generalization of the soil data
for the entire cultivated area under the study.
