Joint International Conference on Long-term Experiments ...

EFFECT OF VARIOUS CROP PRODUCTION FACTORS ON THE YIELD
AND YIELD STABILITY OF MAIZE IN A LONG-TERM EXPERIMENT
Zoltán Berzsenyi – Dang Quoc Lap
Agricultural Research Institute of the Hungarian Academy of Sciences, Martonvásár
ABSTRACT
In a long-term experiment set up in 1960 on a medium heavy loamy soil, the effect of
five crop production factors in increasing maize yields was studied in seven treatment
combinations. The factors studied were soil cultivation, fertilisation, plant density,
variety and weed control. All the factors had a minimum and an optimum level. Yield
data recorded over 42 years were evaluated using analysis of variance and stability
analysis. The highest yield (8.59 t ha –1 ) was obtained when all the production factors
were optimum and lowest (2.09 t ha –1 ) when these factors were at a minimum. When
only one factor was at a minimum and all the other factors were optimum the following
yields were obtained (t ha –1 ): soil tillage: 8.32, fertilisation: 5.21, genotype: 4.98, plant
density: 6.31, weed control: 7.01.The crop production factors contributed to the increase
in maize yield in the following ratios (%): fertilisation 30.6, variety 32.6, plant density
20.2, weed control 14.2, soil cultivation 2.4. The highest value of CV%, expressing the
deviation of the yield averages, was obtained when all the production factors were at the
minimum level (45.7%) and when weed control or fertilisation were at a minimum
(36.6% and 34.8%, respectively), while the lowest value was recorded when all the
factors were optimum (19.5%). The significant treatment × year interaction could be
attributed principally to treatments in which weed control, fertilisation, genotype or all
the factors were at a minimum. The regression 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. The AMMI (Additive Main Effect and
Multiplicative Interaction) model proved to be a valuable approach for understanding
agronomic treatment x environment interactions and assessing yield stability.
Key words: long-term experiment, maize, production factors, stability analysis, AMMI
model
INTRODUCTION
Long-term experiments are often conducted to compare the long-term effects (e.g.,
sustainability) of various treatments on one or more response variables. Measurements
made on the plots are generally taken each year in the case of crop yield and other plant
measurements. These data are ultimately subjected to some kind of statistical analysis,
often with the goal of understanding something about the potential for different
cumulative effects of treatments over time. As a result, the time x treatment interaction
becomes the focus of an analysis.
It is generally accepted that measurements made in field experiments may be
influenced by uncontrollable environmental factors. Aside from random variation there
is also a predictable element, the long-term average of yield in the experiment. The
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