In a long-term experiment set up in 1960 on a medium heavy loamy soil, the effect of 5 crop production factors in increasing maize yields was studied in 7 treatment combinations. The factors studied were soil cultivation, fertilisation, plant density, variety and weed control. All the factors had a minimum and an optimum value. When examined over 35 years of the long-term experiment the crop production factors were found to contribute in the following ratios (%) to an increase in maize yield: fertilisation 30.7, variety 30.0, plant density 20.3, weed control 16.3, soil cultivation 2.7. It was established that an optimum combination of crop production factors led not only to an increase in yield quantity, but also to improved yield stability, due to a reduction in yield fluctuation. The interpretation of the significant year x treatment interactions observed in the variance analysis models of long-term crop production experiments or series of experiments can be regarded as a new method of stability analysis. Both the variance and regression methods of stability analysis have contributed to the characterisation of the stability of experimental treatments in various environments. Among the variance methods, ecovalence, stability variance and the yield stability index (calculated using Kang's STABLE model) gave a good description of the contribution of the experimental treatments to the treatment x year interaction. The ''b'' coefficient of linear regression analysis provided a satisfactory characterisation of the stability of the treatments in different environments, while the distance between the straight lines expressed the yield differences between the treatment pairs.