Experimental data collection and analysis

The ORYZA2000 model is calibrated and evaluated using well-designed, well-implemented, and well-documented pot and field experiments. Experimental details are given in some sample protocols listed below:  

In these experiments, data are analyzed statistically. Analysis of variance (ANOV) and covariance (ANACOV) show the factors that influence the variation of measured parameters. Such factors could be used as indicators of the minimum number of data sets for summarizing the experimental results. For example, if interactions among treatment factors are not significant but single-factor treatment effects are significant, the experiment results may be summarized into different treatment groups. In this case, it is good to tabulate the treatment means as well as standard deviation of measurements for easy reference.

Coefficient of variation (CV) expresses the degree of experimental error (indicating the degree of precision with which the treatments are compared) and is considered a good index of the reliability of the experiment. The CV varies greatly with the type of experiment, the crop grown, and the character measured. For example, acceptable range of CV for grain yield of transplanted rice in variety trials is 6-8%, fertilizer trials is 10-12%, and pesticide trials is 13-15% (Gomez and Gomez 1984).

At least two independent data sets are needed for model calibration and evaluation: one data set which is under potential production situation (i.e. one treatment where there is ample supply of water and nutrient and there is no pests and disease infestation) for model calibration and the remaining data sets (i.e. other treatments) for model evaluation.