Joint International Conference on Long-term Experiments ...

optimum. The factors at a minimum were tillage in Treatment 3, fertilisation in
Treatment 4, plant density in Treatment 5, variety in Treatment 6 and weed control in
Treatment 7.
The experimental results were evaluated using a variety of methods. The
cumulative yield analysis method (Sváb 1981) designed for the evaluation of long-term
experiments was used to compare the cumulative yield-enhancing effect of individual
production factors compared with the minimum combination. Analysis of variance was
performed by using the “General Analysis of Variance” procedure of the GenStat 9
program. Analysis was conducted separately for each year and pooled together
afterwards. Stability analysis on the experimental treatments was carried out using
single-variable (variance and regression parameters) and multi-variable (AMMI)
methods. Among the variance parameters, the ecovalence (W²), the stability variance
(σ²) and the yield stability (YS) parameters were calculated using the STABLE model
proposed by Kang (1995), where the covariant was the annual rainfall quantity from
April to September. The regression method of stability analysis was applied as
described by Finlay and Wilkinson (1963). The year × treatment interaction was
investigated by means of AMMI analysis, a combination of analysis of variance and
principal component analysis (Crossa 1990).
RESULTS AND DISCUSSION
Annual analysis of variance indicated that the effect of the experimental treatments was
significant at the P=0.1% level every year. Combined ANOVA over the 42 years
demonstrated that both the year effect and the treatment effect were significant, but the
MS values showed that the treatment effect was more than 16 times as strong as the
year effect (Table 1).
Table 1. Analysis of variance of the long-term experiment (1960-2001)
Source df SS MS F-value
Total 1175 8540
Environments (Years) 41 2091 51.0 92.5 ***
Replication 3 16.6 5.54 10.1
Year (replication) 123 67.8 0.55
Treatments 6 5054 842.3 643.9 ***
Residuals 1002 1311 1.31
The cumulative yield-enhancing effect of the production factors compared with the
minimum combination is illustrated for the years 1960–2001 in Figure 1. It is clear from
the figure that under Hungarian conditions deep tillage only had a slight effect, ranging
from 2.4–3.0% over the period in question. A pronounced increase in the fertiliser effect
was observed in the long-term experiment, from 18.6% in the early years to 30.7%. The
effect of an increase in plant density on maize yields changed little over the 42 years,
having values of 19.9–23.3%. Apart from fertilisation, the factor with the greatest
influence on maize yields was the genotype, the effect of which increased from 26.6%
to 32.6% over the years. The effect of weed control on the maize yield ranged from 23–
22