methods of stability analysis contributed to the characterisation of the stability of the experimental treatments in various environments. © 2000 Elsevier Science B.V. All rights reserved.
Keywords
:
Long-term experiment; Crop rotation; Maize; Wheat; Fertilisation; Yield response; Stability analysis
1. Introduction
In recent decades the interest shown in long- term experiments has increased worldwide, since
suitable indicators of sustainable agriculture yield trends, parameters characteristic of the quality of
the ecosystem, capable of serving as an early warning system, can only be obtained in such
experiments Barnett et al., 1995.
Crop sequences represent a systems approach in crop production research, enabling the available
natural resources to be preserved and more effi- ciently utilised. In crop rotation experiments a
monoculture is generally compared to various crop sequences. The fact that in most cases the
yields of the cultivated crops are higher in crop rotation, as compared with a monoculture under
identical conditions, is explained by the rotation effect. This rotation effect has been demonstrated
irrespective of whether the crop rotation contains legumes or non-leguminous plants.
The benefits of crop rotation for land and water resource protection and productivity have been
identified, but many of the rotation factors, pro- cesses and mechanisms responsible for increased
yield and other benefits need to be better under- stood. Increased nitrogen supply is sometimes re-
sponsible,
but improvements
in soil
water availability, soil nutrient availability, soil struc-
ture, soil microbial activity and weed control, decreased insect pressure and disease incidence,
and the presence of phytotoxic compounds andor growth-promoting substances originating from
crop residues have also been identified as con- tributing factors Karlen et al., 1994. Currently,
no amount of chemical fertilizer or pesticide can fully compensate for crop rotation effects.
The analysis of crop rotation data is generally more complicated due to yearly replications, cy-
cles and crop orders, and correlational errors than that of 1-year experiments. The evaluation of
several decades of data series requires a specific order of data processing and biometric analysis
annual and combined analysis of variance, trend calculations, simulation models. A detailed dis-
cussion of the special problems encountered dur- ing the biometrical analysis of crop rotation
experiments was given by Yates 1954. Further valuable details are found in the papers of Patter-
son 1953 and Cady 1991.
The first step in the biometrical analysis of long-term crop rotation data is annual variance
analysis. The aim of this preliminary analysis is to determine whether all the experimental years can
be included in a single combined analysis, or whether it is necessary either to transform the
data or to divide the experimental years into more or less homogeneous groups. If the variances are
homogeneous on the basis of the Bartlett test, combined analysis can be carried out using the
data of the various experimental years. Emphasis continues to be put on the examination of the
treatment × year interaction and the analysis can be extended to cycles and series Cady and Ma-
son, 1964.
The significant treatment × environment inter- actions observed in the variance analysis of long-
term experiments are difficult to interpret using traditional analysis of variance due to the com-
plexity of the factors influencing the environment, but can be interpreted simply using stability anal-
ysis. The treatment × environment interactions can be partitioned into variation associated with
site fixed variation and variation associated with yearly fluctuations within a location random
variation. Treatments with less variation at a location over years are judged to be more stable
Lin and Binns, 1988. Specific cropping systems can be considered as fixed environmental effects.
The notion of stability implies that there is a random, unpredictable element in the perfor-
mance of a cropping system. The larger this ran- dom component, the smaller the stability of a
system. The mean fixed, i.e. systematic effect
and the variance random element are the two main parameters describing the response pattern
of a cropping system Piepho, 1998. Different approaches to stability analyis differ in how the
random term is further partitioned.
The measurement of yield stability over time involves at least three components: 1 the rela-
tionship of yield with the local environment, 2 the average yield level and 3 the variability of
the yield Mead et al., 1986. Univariate stability measures are divided into two broad categories
that use either variance or regression methods. The most important variance parameters are the
within-treatment mean square s
2
, the coefficient of variation CV, the ecovalence W
2
Wricke, 1962 and the stability variance s
2
Shukla, 1972. The two latter parameters measure the
contribution of the treatment to the treatment × environment interaction Callaway and Francis,
1993. There are several approaches that combine the mean and variance of a system. Recently
Kang 1993 developed a yield stability YS statistic that combines yield and stability of per-
formance into a single selection criterion. It is often found that variance is related to the mean.
For this reason, the square root of the variance is usually standardised by the mean, which leads to
the coefficient of variance CV.
A common approach to stability analysis is to regress the performance of the system onto an
environmental index computed as the mean of all observations in an environment. The index may
be taken as a measure of the productivity of an environment. Regression techniques used to de-
velop stability parameters are based on linear slope and deviation from that slope. Systems
where the regression has a relatively large slope show an above-average response to improved en-
vironmental conditions, as indicated by the envi- ronmental index. The regression approach was
first suggested by Yates and Cochran 1938, fol- lowed later by Finlay and Wilkinson 1963 and
Eberhart and Russell 1966. Piepho 1997 dis- cussed the regression method in a mixed model
context. A stable system has been defined as one that changes least in response to changes in the
environment. Finlay and Wilkinson 1963 sug- gested that slopes with b B 1.0 indicated better
adaptation to poor environments, while genotypes with b \ 1.0 are best used in superior environ-
ments. A cropping system with an estimate of b equal to unity shows an average response to envi-
ronmental conditions, as measured by the envi- ronment mean. But it is not obvious why a system
with an average response should be particularly stable Dyke et al., 1995. Several statistical and
biological limitations of the regression method were reported by Crossa 1990, Guertal et al.
1994 and Piepho 1998.
Alternatively, stability may be assessed in terms of the risk of a system falling below a certain level
or the risk of one system being outyielded by another Piepho, 1998. In many situations, e.g. in
subsistence farming, risk considerations are more important than the concern about variability
alone.
In agricultural research the analysis of yield stability has been largely confined to multienvi-
ronmental trials of crop cultivars. The idea of applying methods of stability analysis to cropping
systems is not novel Willey, 1979; Mead and Riley, 1981. Hildebrand 1984, Raun et al.
1993 and Guertal et al. 1994 employed stability analysis in long-term experiments to evaluate fer-
tilisation treatments. Piepho 1998 emphasizes that methods for comparing the stability of culti-
vars can also be used for comparing different agronomic treatments in general, of which culti-
vars are but a special case.
The aim of the present paper was a to evalu- ate the effect of various crop sequences and fertil-
isation treatments on maize and wheat yields in comparison with maize and wheat monocultures
on the basis of almost 40 years of data, and b to use conventional analysis of variance as well as
the variance and regression methods of stability analysis to characterise the effect of experimental
treatments on yield stability.
2. Materials and methods
