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Spatial field variations in soybean (
Glycine max
[L.] Merr.)
performance trials a
ff
ect agronomic characters
and seed composition
J. Vollmann
a,
*, J. Winkler
b
, C.N. Fritz
a
, H. Grausgruber
a
, P. Ruckenbauer
a
aAgronomy and Plant Breeding Department, University of Agricultural Sciences Vienna, Gregor Mendel-Str. 33, A-1180 Vienna, Austria bSaatzucht Gleisdorf GmbH, A-8200 Gleisdorf, Austria
Accepted 4 August 1999
Abstract
The objective of this study was to investigate field trends in agronomic characters of soybean (Glycine max[L.]
Merr.) performance trials. Field experiments were arranged as lattice designs, and for each plot within an experiment, an environmental covariate representing the spatial trend was calculated using neighbour analysis. Correlations between environmental covariates of different characters were then used to analyse the degree of similarity in their spatial trends. Grain yield, seed protein content, and seed size were more influenced by spatial variations than time to flowering, time to maturity, and oil content. Significant correlations between environmental covariates were found, which demonstrate the similarity of trend patterns in different characters within particular experiments. In field trials exhibiting a high degree of spatial heterogeneity, correlations between environmental covariates of grain yield and protein content were highly positive. This indicates that field conditions, which promote grain yield, would also enhance protein content. Moreover, the observed trend patterns considerably affected the calculation of phenotypic coefficients of correlation between grain yield and seed protein content. The results suggest that spatial analysis should be applied to all characters of interest, when field conditions are not homogeneous. © 2000 Elsevier Science B.V. All rights reserved.
Keywords:Grain yield; Neighbour analysis; Seed protein content; Soybean; Spatial heterogeneity
1. Introduction such as water and nitrogen content ( Kirda et al., 1988; Mulla et al., 1992; Wade et al., 1996), Heterogeneity within experimental fields, which element concentration (Berndtsson and Bahri, may affect yield and other characters of a crop, 1995), organic carbon content (Ball et al., 1993), can often be seen in large agronomic experiments. or soil physical properties (Becher, 1995). In plant This field heterogeneity or spatial variation usually breeding trials, spatial variation affects the ranking indicates the presence of soil fertility gradients, of genotypes (Brownie et al., 1993; Stroup et al., which might be due to trends in soil parameters 1994) and broadens the experimental error vari-ance (Ball et al., 1993; Brownie et al., 1993; Helms et al., 1995; Vollmann et al., 1996a). This could * Corresponding author. Tel.:+43-1-47654-3309;
cause a decreased response to selection and a fax:+43-1-47654-3342.
E-mail address:[email protected] (J. Vollmann) reduced precision of statistical testing procedures.
Moreover, estimates of heritability and other of correlation between agronomic and seed quality characters was investigated.
genetic parameters might be biased by field hetero-geneity (Rosielle, 1980; Magnussen, 1993; Helms et al., 1995). In agronomic trials, treatment effects
2. Materials and methods could be masked by spatial variations, thus
prohib-iting the identification of the most favourable
2.1. Experiments
agronomic treatment (Scharf and Alley, 1993). In order to monitor and control spatial field
During the present study, seven different soy-variation statistically, a number of different
con-bean performance trials ( Table 1), which had been cepts and procedures, such as incomplete block
grown at Raasdorf, Lower Austria (10 km east of designs, neighbour analysis, trend analysis, or
Vienna, Austria, 48°12∞N, 16°32∞E), between 1995 correlated error models, have recently been
dis-and 1997, were analysed for spatial field variations cussed and evaluated comparatively using yield
in different agronomic characters. The soil type at data (e.g. Brownie et al., 1993; Stroup et al., 1994;
the experimental site was classified as a chernozem Grondona et al., 1996; Gleeson, 1997). Although
of alluvial origin. Each performance trial was substantial effects of spatial heterogeneity on grain
originally planted as a generalized lattice design in yield have been demonstrated, spatial methods of
two replications at a plot size of 5.5×1.25 m with analysis are rarely being considered for characters
four rows per plot. The trials designated EXP1, other than grain yield. In wheat, field trends in
EXP2, and EXP5 in Table 1 comprised genotypes seed size, test weight, seed protein content, element
of soybean maturity groups 0–000 ( Fehr and concentration of seed, and plant height have been
Caviness, 1980), which were F
2-derived lines in
reported from production fields and from plot F
5or F6generations from crosses between
high-trials (Rosielle, 1980; Mulla et al., 1992;
yielding and high-protein parents. The trials desig-Berndtsson and Bahri, 1995). In soybean trials,
nated EXP3, EXP4, EXP6, and EXP7 involved characters such as seed size, seed protein and oil
F
5-derived breeding lines of soybean maturity
content, symbiotically fixed nitrogen and fibrous
group 00 in F
7 or later generations, which had
root area have been adjusted for spatial
hetero-been pre-selected for yield performance in earlier geneity in order to enhance the precision of
experi-experiments. mental results (Herridge and Rose, 1994;
In all experiments, soybean seeds were Pantalone et al., 1996; Rosielle, 1980; Rebetzke
inoculated with Nodular-G (Serbios, Badia et al., 1998). In barley, environmental trends have Polesine, Italy), a commercial preparation of been observed among scores of powdery mildew Bradyrhizobium japonicum ( Kirchner) Jordan, in in an experiment on disease control (Hackett order to promote nodulation and symbiotic
nitro-et al., 1995). gen fixation. Mineral nitrogen was applied at a
Despite the numerous reports on spatial vari- rate of 30 kg ha−1 prior to sowing. In addition, ability in field trials, the related effects of spatial phosphorus and potassium were provided prior to heterogeneity on different agronomic characters seed bed preparation at rates of 70 kg ha−1P
2O5
within the same experiment have never been con- and 140 kg ha−1K
2O, respectively.
sidered explicitly, and trends occurring in several
plant traits have not been compared, although 2.2. Data collection they might be caused by variations in the same
Table 1
Experimental designs of seven different soybean performance trials grown in three years, and the efficiency (%) of lattice analysis as
an indication of the presence or absence of effects of spatial variations on agronomic and seed compositional characters
Design/character Experiment
1995 1996 1997
EXP1 EXP2 EXP3 EXP4 EXP5 EXP6 EXP7
Lattice designa 10×6 6×6 5×5 5×5 10×5 5×5 5×5
Number of entries 60 36 25 25 50 25 25
Lattice efficiency (%)
Time to flowering 100.0 101.1 100.0 100.0 100.0 100.1 100.0
Time to maturity 168.6 104.0 104.4 100.0 114.1 121.4 114.6
Reprod. phaseb 136.2 110.8 104.5 100.0 100.4 101.7 118.3
Plant height 235.6 117.7 106.0 124.3 104.7 100.0 100.0
Grain yield 198.3 210.9 143.3 402.8 135.6 100.6 108.7
Protein content 405.0 180.3 140.3 147.7 109.3 104.9 100.0
Oil content 197.6 116.7 100.0 133.3 107.8 106.2 109.6
Protein plus oilc 250.7 157.2 110.0 123.1 100.0 102.0 100.3
Seed size 314.8 204.7 129.3 150.3 103.7 122.6 100.0
aGeneralized lattice design: Number of incomplete blocks×number of plots per block. bDuration of reproductive phase (R1–R8 in days).
cSum of protein and oil content.
maturity) developmental stages, respectively, (RCB) designs in order to compare the two designs. Moreover, the efficiency (%) of lattice according to Fehr and Caviness (1980).
For determination of protein and oil content, a analysis relative to randomised complete blocks (Cochran and Cox, 1957) was calculated to com-25 g sample of dry seeds from each plot was finely
ground and scanned by near-infrared reflectance pare the degree of spatial variation in the different characters within each experiment, as detectable spectroscopy (NIRS ) using an InfraAlyzer model
450 spectrophotometer and IDAS calibration soft- by lattice analysis.
For the application of neighbour analysis, indi-ware (Bran and Luebbe, Norderstedt, Germany).
Seed protein content, oil content, and the sum of vidual plot residuals were calculated for each experimental plot and for each character as: protein and oil content were expressed in g kg−1
on a dry matter basis. Seed size [weight of 1000
e
ij=xij−x:i, seeds (g)] and grain yield (kg ha−1) were expressed
on an 8%seed moisture basis. wheree
ijdenotes the plot residual,xijthe observed character expression of genotypeiin replicationj, and x:
i the arithmetic mean of genotype i. Plot
2.3. Statistical analysis
residuals of particular neighbour plots (Fig. 1) were then used to calculate environmental covari-Experimental data were subjected to a lattice
model of ANOVA, and the F-test was applied to ates for each experimental plot. Following the designation introduced by Stroup et al. (1994) for examine the statistical significance of genotype
differences for the characters investigated. The long and narrow plots, longitudinal neighbour covariates were referred to as east–west ( EW ) generalized lattices were also regarded as
random-ised complete block designs with additional restric- covariates. Neighbour plot covariate EW1 was calculated as the arithmetic mean of the eastern tions within replications (Cochran and Cox, 1957).
boundaries between neighbouring plots falling in different blocks (Brownie et al., 1993).
Generalized lattice designs were analysed using the PLABSTAT software program ( Utz, 1988). Plot residuals and covariates from neighbour plots were calculated from the raw data using a spread-sheet program, Corel Quattro Pro (Corel, Orem, UT ) and the appropriate field layout information. Combined analysis of variance and covariance as well as the adjustment of entry means by neighbour covariates were carried out using the GLM pro-cedure and the LSMEANS statement of the SAS program (SAS Institute, 1988). Phenotypic and genetic coefficients of correlation between charac-ters were also calculated with PLABSTAT; as there is no adequate test of significance available for Fig. 1. Schematic representation of the neighbour analysis
examining genetic coefficients of correlation methods applied. In EW1, EW2 and EW3, the residuals from
(Thomas and Tapsell, 1985), the standard errors 1, 2 or 3 neighbour plots, respectively, are used as an
environ-mental covariate to correct the plot value of a test plot for of the coefficients were used as an indicator of
field trends. their significance.
3. Results method proposed by Papadakis (1937). Covariates
EW2 and EW3 were calculated from plot residuals
of two ( EW2) or three ( EW3) plots ( Fig. 1) at As an indication of the presence of spatial field trends detectable by lattice analysis, the efficiency each side of a test plot, respectively, because the
use of two or more neighbour plots for describing of lattice designs is presented in Table 1 for different soybean performance experiments and for a local trend could improve the efficiency of
analy-sis in particular experiments ( Vollmann et al., all characters investigated. Differences in the effi -ciency roughly demonstrate that distinct characters 1996a). For border plots missing particular
neigh-bours, covariates were calculated without the resid- were affected by spatial field variations to a clearly different extent: time to flowering, time to matu-uals of those plots. Subsequently, a combined
analysis of variance and covariance was carried rity, the duration of the reproductive phase, and oil content were generally less influenced by spatial out according to the model:
variations than grain yield, protein content, and
x
ij=m+ti+bEWij+eij, seed size. Moreover, differences in lattice efficiency can also be recognized between different years: wherex
ijdenotes the character expression of
geno-typeiin replicationj(=plot value),mis the overall lattice efficiencies were higher in the 1995 and 1996 trials than in 1997, which suggests that seasonal mean value,t
i denotes the effect of genotype i,b indicates the regression coefficient of the EW
ij effects influenced the magnitude of spatial varia-tions at a given experimental site.
covariate used, ande
ijrepresents the random error
term. For each analysis of variance, one additional Comparative results of different ANOVA models for controlling spatial variations in grain degree of freedom was allocated to the regression
coefficient because residuals were calculated from yield, protein and oil content, and seed size are summarized in Table 2 using the EXP2 experiment genotype means as concomitant covariates (Pearce
and Moore, 1976). Block effects were ignored as an example. The highest residual error mean squares and CVs were obtained when the random-when applying the neighbour analysis, because the
Table 2
Comparison of various ANOVA models for the control of spatial variation in four characters of the EXP2 experiment
ANOVA model Grain yield Protein content Oil content Seed size
MSEa P(F)b CV,% MSEa P(F)b CV,% MSEa P(F)b CV,% MSEa P(F)b CV,%
RCBc 175 126 0.001 17.5 421 0.179 7.0 145 0.001 5.3 74.0 <0.001 5.5 6×6 latticed 83 023 <0.001 12.0 234 0.038 5.2 124 <0.001 4.9 36.7 <0.001 3.9 EW1e 71 751 <0.001 11.2 334 0.181 6.2 131 <0.001 5.0 46.2 <0.001 4.4 EW2e 77 168 <0.001 11.6 272 0.040 5.6 137 <0.001 5.2 36.3 <0.001 3.9 EW3e 86 096 <0.001 12.3 263 0.061 5.5 126 <0.001 4.9 33.5 <0.001 3.7
aResidual error mean square. bProbability value of entry-H
0fromF-test. cRandomized complete block design.
dFor the lattice design, the effective error MS is presented instead of the residual error MS, which only covers the intra-block error. eNeighbour analysis using EW1, EW2, or EW3, respectively, as neighbour covariate.
error mean squares were drastically reduced by lattice or neighbour analysis in all characters except oil content, which seemed to be less affected by spatial heterogeneity in this experiment. In seed protein content, significant genetic differences between entries could only be detected by lattice or EW2-based neighbour analysis, as revealed by the respective F-tests. Correspondingly, residual error mean square was similarly reduced after lattice or neighbour analysis of different characters in the other experiments investigated (results not shown).
The EW2 residuals of the EXP2 experiment were further utilized to visualize field trends in four different characters ( Fig. 2): Rather similar patterns of trend can be recognized for grain yield, protein content, and seed size, whereas the trend lines of oil content show a pattern that seems to be opposite to those found in the other characters. The view of trend similarities between grain yield and other seed characters of the same experiment is further supported by the significant and close correlations between the respective EW2 residuals (Fig. 3). Apart from EXP2, similar correlations between EW2 residuals of different characters were detected in other experiments grown in different seasons ( Table 3). Positive correlations between EW2 residuals were found between grain yield and protein content, and between grain yield and seed
Fig. 2. Field trends of grain yield, protein content, oil content size. This suggests that soil properties within and seed size in each of the two replications grown in two heterogeneous fields, which improve grain yield consecutive blocks of 36 plots of the EXP2 experiment, as
for spatial variations. Genetic coefficients of corre-lation were rather similar for each of the three methods of statistical analysis, although the esti-mated levels of significance tended to be higher after lattice or EW2 adjustment than after RCB analysis. Considerable differences were found between particular phenotypic coefficients of corre-lation after an adjustment of field data. The pheno-typic coefficient of correlation between grain yield and protein content was low (r=−0.18) and statis-tically not significant, when calculated from unad-justed genotype mean values after RCB analysis. However, when using lattice- or EW2-adjusted means, the correlation between grain yield and seed protein content was clearly negative ( Table 4). An even more drastic influence of data adjustment on the relationship between grain yield and protein content was found in the EXP1 experiment. In this case, the estimate of the genetic correlation between yield and protein content was
r
g=−0.70, whereas the correlation between EW2
residuals of the two characters was r=+0.69 (Table 3). The apparent lack of a significant phe-notypic correlation between the two characters [Fig. 4(a)] might be due to a complete balancing of the negative genetic correlation by the positive correlation between EW2 residuals, which describe the environmental variation. After adjusting geno-typic mean values by EW2 values and lattice analysis, which were the most efficient procedures in terms of a reduction of residual error mean Fig. 3. Relationships between EW2 neighbour residuals of grain square for grain yield and protein content, respec-yield and seed characters for the EXP2 experiment. tively, the phenotypic correlation between the two
characters clearly changed to negative [Fig. 4(b)]. Negative correlations between EW2 residuals were
always found between grain yield and oil content, and between protein and oil content. These
correla-tions between environmental covariates were sta- 4. Discussion tistically significant even in the EXP5 experiment
( Table 3) as well as in the two other 1997 experi- The results of the current investigation clearly demonstrate that soil heterogeneity in soybean ments (data not shown), although spatial field
variations were only present at a lower degree in performance trials may simultaneously influence agronomic characters such as grain yield, plant the 1997 season according to the respective lattice
efficiencies computed ( Table 1). height, seed size, and protein and oil content. This fact and its consequences have not been considered Both the phenotypic as well as the genetic
coefficients of correlation between different charac- in recent reviews on methods of controlling spatial variations in breeding experiments (Brownie et al., ters of the EXP2 experiment are presented in
varia-Table 3
Coefficients of correlation (r) between EW2 residuals from a neighbour analysis of different characters in three performance trials grown in different years
Character Time to maturity Grain yield Protein content Oil content Protein plus oila
EXP1
Time to maturity –
Grain yield +0.47** –
Protein content +0.33** +0.69** –
Oil content −0.18 −0.45** −0.34** –
Protein plus oila +0.26** +0.51** +0.89** +0.13 –
Seed size +0.29** +0.41** +0.48** −0.05 +0.48**
EXP2
Time to maturity –
Grain yield +0.09 –
Protein content −0.28* +0.83** –
Oil content +0.21 −0.57** −0.48** –
Protein plus oila −0.18 +0.61** +0.85** +0.07 –
Seed size +0.51** +0.80** +0.58** −0.36** +0.44**
EXP5
Time to maturity –
Grain yield +0.43** –
Protein content +0.04 +0.29** –
Oil content +0.06 −0.31** −0.84** –
Protein plus oila +0.17 −0.02 +0.37** +0.24 –
Seed size +0.65** +0.53** +0.14 −0.02 +0.21*
aSum of protein and oil content.
tions in grain yield only. However, variations in side of a test plot was often more efficient than using only one nearest neighbour for covering a soil parameters have been reported from
pro-duction fields, which affected different plant field trend ( Table 2), which is in agreement with earlier findings ( Vollmann et al., 1996a,b). parameters such as element concentrations in grain
(Berndtsson and Bahri, 1995) or grain yield and Negative correlations between soybean yield and protein content within different populations protein content of wheat (Mulla et al., 1992) in a
correlated manner. Therefore, the available meth- have been well established by numerous breeding studies (e.g. Leffel and Rhodes, 1993; Wilcox and ods of spatial analysis could be applied to any
character of interest, when statistically detectable Cavins, 1995). For this reason, the unexpected finding of a highly positive relationship between soil trends are present in one trait. Although
spatial trends were very similar for different traits the field trends ( EW2 residuals) of grain yield and protein content ( Table 3 and Fig. 3) deserves of the same experiment in the present study ( Figs. 2
and 3), a different statistical model might be most additional consideration. As genotype effects are omitted through the calculation of neighbour cova-adequate for each of the traits in order to reduce
the residual error mean square ( Table 2). In gene- riates, the correlation between EW2 residuals of grain yield and protein content has to be regarded ral, however, lattice analysis and the methods of
neighbour analysis were far more efficient than the as a purely environmental correlation (Bos and Caligari, 1995). Therefore, a variation in environ-randomised complete block analysis in modelling
spatial variations. Moreover, in regular field plots mental parameters such as nitrogen availability from mineralization, symbiotic N
2 fixation and
of long and narrow shape, the use of two or three
Table 4
Phenotypic (above diagonal ) and genetica(below diagonal ) coefficients of correlation (r) between different characters of the EXP2 experiment at three ways of analysis of field data
Character Time to maturity Grain yield Protein content Oil content Protein plus oilb Seed size
RCB (unadjusted)c
Time to maturity – +0.80** −0.20 +0.38* +0.12 +0.50**
Grain yield +0.96++ – −0.18 +0.34* +0.11 +0.61**
Protein content −0.41+ −1.10+ – −0.51** +0.64** −0.12
Oil content +0.51++ +0.79++ −0.79++ – +0.33 +0.35*
Protein plus oil +0.24 −0.29 +0.14 +0.49+ – +0.18
Seed size +0.51++ +0.61++ −0.50+ +0.54++ +0.17 –
Lattice adjustmentd
Time to maturity – +0.78** −0.41* +0.41* −0.02 +0.48**
Grain yield +0.83++ – −0.49** +0.42* −0.10 +0.56**
Protein content −0.53++ −0.73++ – −0.40* +0.61** −0.34*
Oil content +0.48++ +0.62++ −0.44++ – +0.48** +0.35*
Protein plus oil −0.04 −0.13 +0.54++ +0.51++ – −0.03
Seed size +0.49++ +0.57++ −0.46++ +0.43++ −0.06 –
EW2 adjustmente
Time to maturity – +0.83** −0.33 +0.31 −0.04 +0.51**
Grain yield +0.92++ – −0.42* +0.32 −0.12 +0.54**
Protein content −0.53++ −0.96++ – −0.46** +0.56** −0.33*
Oil content +0.38++ +0.56++ −0.72++ – +0.48** +0.35*
Protein plus oil −0.11 −0.38 +0.20 +0.53+ – −0.00
Seed size +0.51++ +0.57++ −0.61++ +0.45++ −0.12 –
aSignificance of a genetic coefficient of correlation is expressed as being greater than once (+) or twice (++) its standard error. bSum of protein and oil content.
cCorrelations based on unadjusted entry means from the randomized complete block analysis. dCorrelations based on entry means adjusted by lattice analysis.
eCorrelations based on entry means adjusted by neighbour analysis using the EW2 neighbour covariate.
and protein content of a soybean crop (Soldati, correlation between grain yield and protein content is evident after appropriate adjustment of the field 1995), could explain the positive correlation
between the trends in grain yield and protein data for spatial trends [Fig. 4(b)].
The empirical results obtained from the current content in all experiments investigated.
The effects of spatial variations on the results investigation of different soybean trials demon-strate that spatial variations can affect various of performance trials have been characterized as
influencing the ranking of genotypes (Stroup et al., agronomic characters, revealing similar patterns of trend in each of the traits, and affecting the 1994), thus reducing selection efficiency, and
inflating residual error variance, which reduces the estimates of phenotypic correlation between traits. Different statistical methods such as lattice analysis power of statistical tests and biases the estimates
of heritability as well as other parameters (Ball and various neighbour or trend analysis techniques are available for an efficient monitoring of field et al., 1993). In the case of spatial variations in
several characters, coefficients of phenotypic corre- variations and for adjusting treatment means. Apart from covering fertility trends in yield trials, lation ( Table 4) and estimates of correlated
response to selection can also be biased. Using the adjustment for spatial trends in all agronomic characters of interest could be useful in different unadjusted field data, the relationship between
yield and protein content [Fig. 4(a)] would suggest fields of agronomic and plant breeding research, e.g. in selecting for seed quality characters such as that a selection for one character would not
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