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Field trials conducted in 2008 and 2009 investigated whether plot size affects incidence of white flower anther injury by tarnished plant bug (
Insecticides remain the chief method for managing tarnished plant bugs (TPB) ((
Adult tarnished plant bug (Heteroptera: Miridae.
The probability of square abscission following TPB feeding is a function of anther size. When anthers are barely visible, the bug feeds on the entire floral bud, but as the square grows, the anthers become large enough for the plant bug to selectively feed just on them, since they are rich in nitrogen. After these larger squares are fed upon, at anthesis the effects of anther injury are apparent as a “dirty” white flower [
A standard, rapid, screening technique historically used in the University of Arkansas cotton breeding program to evaluate the HPR response to the TPB is to monitor WFAI among cultivar lines ([
Therefore, the primary objective of this research was to conduct field trials designed to investigate effects of plot size and cultivar interspersion upon incidence of white flowers with TPB anther injury in HPR experiments. The second objective was to present the results of the research (over two production seasons) while describing the novel design of the experiment. The concepts necessary to complete the statistical analysis of the design are presented.
Results indicate that counts of WFAI associated with plant bug feeding were not significantly different between miniplot and maxplot plantings under similar conditions, while spraying reduced injury. Regardless of the cultivar line assigned to the maxplots or miniplots, WFAI from plant bug feeding in the susceptible frego cotton line was significantly greater than that of the Suregrow (SG) 105 and Deltapine (DP) 393 lines. Details from the perspective of the analyses of the modified Latin square design follow.
Covariance Parameter Estimates—terms with missing standard errors were on boundary where they are essentially equal to zero—2008.
Cov Parm  Subject  Estimate  Standard Error 

MaxCol  1 × 10^{−8}  .  
MaxRow  1 × 10^{−8}  .  
yrow(MiniLine*MaxRow*MaxLine)  0.05489  0.02135  
Spray*MaxCol  1 × 10^{−8}  .  
Spray*MaxCol*MaxLine  1 × 10^{−8}  .  
MaxRow*MaxLine  1× 10^{−8}  .  
AR(1) 

0.01635  0.02386 
Variance  1.3313  0.04266 
Type III Tests of Fixed Effects—2008.
Effect  Num DF  Den DF  F Value  Pr > F 

Spray  2  310.3  3.69  0.0261 
MiniLine  2  220  155.44  <0.0001 
DAP  16  1822  4.58  <0.0001 
DAP*Spray  32  1871  2.06  0.0005 
DAP*MiniLine  32  1855  3.12  <0.0001 
Percent (±Standard Error) of flowers with no anther injury for mini, max and sprayed maxplots across DAP for 2008.
Percent (±Standard Error) of flowers with no anther injury for each cotton line across DAP for 2008 at the Judd Hill study site, comparing the cultivar lines within the miniplots for different days after planting (DAP).
Results in the following year (
Covariance Parameter Estimates—terms with missing standard errors were on boundary where they are essentially equal to zero—2009.
Cov Parm  Subject  Estimate  Standard Error 

MaxCol  0  .  
MaxRow  0  .  
yrow(MiniLine*MaxRow*MaxLine)  0.1028  0.02169  
Spray*MaxCol  9.08 × 10^{−20}  .  
Spray*MaxCol*MaxLine  0.01181  0.02050  
MaxRow*MaxLine  0.02342  0.02046  
AR(1) 

0.04870  0.02121 
Variance  1.3810  0.03926 
Type III Tests of Fixed Effects—2009.
Effect  Num DF  Den DF  F Value  Pr > F 

Spray  2  14.19  40.82  <0.0001 
MiniLine  2  122.2  43.92  <0.0001 
DAP  16  2306  29.48  <0.0001 
DAP*Spray  32  2390  1.78  0.0020 
DAP*MiniLine  32  2390  4.74  <0.0001 
The similarity of findings among the two years provides evidence for the capacity of these insects to find a favorable resource that is dispersed among other host plants, even if these alternative hosts are of the same plant family.
Percentages of WFAI from plant bug feeding in the frego cotton line were significantly greater than in SG 105 and DP 393 in both single row and large plots (
While TPB pest pressure was very low in 2008 and much higher in 2009 (
Significantly higher numbers (
Percent (±Standard Error) of flowers with no anther injury for mini, max and sprayed maxplots across DAP 2009.
Percent (± Standard Error) of flowers with no anther injury for each cotton line across DAP for 2009.
Mean (±Standard Error) numbers of tarnished plant bug nymphs per drop cloth sample observed in weekly samples collected in unsprayed maxplots through the 2008 season.
Mean (± Standard Error) numbers of tarnished plant bug nymphs per drop cloth sample observed in weekly samples collected in unsprayed maxplots through the 2009 season.
Effect of plot size on HPR screenings is a topic of investigation found throughout the literature. A few of these kinds of works are now examined in regards to the goals and design of the experiment employed herein. Considered first is the issue of observer effects on monitoring responses from plots of different sizes. Pascal and Guisan [
Possibly the earliest study examining the effect of plot size on studies of insect resistance in cotton was by Ellington and colleagues [
This study embedded the miniplots within the maxplots to evaluate effects of plots size and interspersion of cultivars with the utilization of only one Latin square design and found that occurrences of WFAI were significantly different from zero regardless of plot size and were of similar trends across the two years (
Working with the velvetbean caterpillar
This study on WFAI considered the interaction, yrow(MaxRow*MaxLine*MiniLine) and was found significant both years (
Effects of plot size on selection criteria for sugarcane clones in another study that did not involve insects, were explored by Jackson and McRae [
Principal results found by Jackson and McRae [
Citing Gauch and Zobel [
In the near future, one can envision the development of a variablecultivar planting system that can embed potential HPR cultivars for TPB as georeferenced miniplots in a sea of conventional cultivars derived as one, two, or three, splitplanter maxplot arrangements for HPR evaluations in commercial production fields. If georeferenced field topography layers such as elevation, slope, soil type, or past yield history zones are also available, then such a study could be quite diverse. The statistical approach employed in this study would be combined with other concepts developed by Willers and Milliken and colleagues [
An analysis where plot size and arrangements of plots were varied over years was described by Ratnadass and colleagues [
The Latin square design was selected as the basic design structure, and then modified through the concepts of (1) the maxplots; (2), the miniplots; and (3) spraying a part of the maxplots, and then the assignment of (4) the MaxLine and (5) the MiniLine treatments to these plot types. This experimental structure was motivated by experiences learned in commercial cotton fields on TPB abundance and interspersion among crop habitat categories mapped through remote sensing analyses by Willers and colleagues [
While the modifications to the Latin square design in this study are similar to work on spatial design of the experiments by Willers and Milliken and colleagues [
Prior to the early 1990s, the statistical software capability to analyze data from an experiment with such a layout as used in this study was nonexistent, or inaccessible to most investigators. As a consequence, the design of the experiment for the primary question of “Are TPB preferences affected by cultivar arrangements among different sizes of plots?” would have been considerably different than the modified Latin square layout presented in this paper. Therefore, this investigation demonstrates that current advances in statistical software (e.g., [
The experiment was conducted at the Cooperative University Research Farm at the Judd Hill Foundation Farm in Poinsett County, near Trumann, in northeast Arkansas. Planting dates were 8 May 2008 and 19 May 2009. A highly susceptible frego line, RBCDHGPIQH197 (frego) was planted along with two standards, DP 393 and SG 105. The field layout (
Plot diagram of the maxplot and miniplot layout.
Assessments for WFAI were made 5 to 7 times weekly during the first 4 weeks of flowering. These weekly counts of the number of WFAI within the first 10 flowers encountered within the miniplots of each maxplot provided the data to evaluate effects of plot sizes and interspersion of cultivars on the proportion of WFAI caused by TPB.
Thus, for the maxsprayed and maxnonsprayed plots, 3 samples of 10 consecutive WF per sample were taken each day counts were made. Counts of WF were fixed to a limit of 10 because (1) flowering rate is cultivar specific; (2) flowering rates vary with changes in DAP and weather; and (3) the miniplots rows are short. This technique of using a fixed count limit for flowers, rather than a fixed distance of row within which to make counts, should mimic the search behaviors of the TPB that also has to respond to these variations in flowering rate and flower density among the cultivars. Using this sample of 10 WF, injury was categorized as either “0” for no anther injury or “1” if anther injury was present (
Picture showing early symptoms of anther injury (
Cotton plants were sampled both years in each maxplot from the squaring period through the physiological cutout period. In cotton, cutout can be defined as when the mean number of nodes above the first white flower position on a sympodial branch equals 5 nodes from the plant terminal. Sampling included measurement of plant height, number of sympodia, and presence or absence of first position squares, flowers, and bolls. In this paper, only the results of analyses for the WFAI are discussed.
Each year population densities were monitored using weekly drop cloth sampling. Numbers of nymphs and adults were recorded. Rows 24 and 25 in the center tier of unsprayed maxplots were used for sampling. Variation in average number of collected insects was analyzed separately for each insect sampling date using ANOVA with StudentNewmanKeuls multiple comparison procedure.
A logistic regression model [
The maxplot design consists of a Latin square [
Analysis of the Maxplot structure.
Source  DF 

MaxRow  2 
MaxCol  2 
MaxLine  2 
Error(MaxPlot)  2 
An insecticide was sprayed in strips consisting of the first 8 rows of each of the MaxColumns and the remaining 20 rows of each of the Max–Columns were not sprayed. This pattern of sprays forms splitplots within the MaxColumns and another stripplot structure along the MaxRows.
The nonsprayed section of each maxplot was separated into two subsections where one was the collection of the nonsprayed miniplots and the other one comprised the remainder of the maxplot. Thus, the spray condition has three levels, designated as “max_sprayed”, “max_non_sprayed” and “mini_non_sprayed”.
There are 27 observations when the sprayer condition is included in the design. The sprayer treatment can be added to the analysis portion presented in
One additional part of the design describes the miniplots, or the subplots with two replications of each miniline centered within the maxlines assigned to each of the maxplots (
Analysis of the Maxplot and SprayStrip structures.
Source  DF 

MaxRow  2 
MaxCol  2 
MaxLine  2 
Error(MaxPlot)  2 
SprayStrip  2 
Error(SprayStrip) = SprayStrip*MaxCol  4 
SprayStrip*MaxLine  4 
Error(MaxPlot*SprayStrip)  8 
Analysis of the Maxplot, SprayStrip and Miniplot structures.
Source  DF 

MaxRow  2 
MaxCol  2 
MaxLine  2 
Error(MaxPlot)  2 
Spray  2 
Error(Spray) = Spray*MaxCol  4 
Spray*MaxLine  4 
Error(MaxPlot*Spray)  8 
MiniLine  2 
MiniLine*MaxLine  4 
Error(MaxPlot*MiniPlot)  89 
The Error(MaxPlot*MiniPlot) consists of the variation of the replications of the mini and maxtreatments within each maxplot. There are three observations from the sprayed subsection providing 2 degrees of freedom (DF), three observations from the unsprayed maxline section providing 2 degrees of freedom, 4 observations from the MaxLine in the miniplots subsection providing 3 degrees of freedom and 2 observations each from the MiniLines separated from the MaxLine to provide 1 degree of freedom each, to total 2 + 2 + 3 + 1 + 1 = 9 degrees of freedom from each maxplot. The additional source of variability comes from the interaction of the MiniLines with the MaxRows and MaxCols providing 9 degrees of freedom. Thus, there are 89 degrees of freedom for Error(MaxPlot intersecting with MiniPlot). This effect can be represented in Proc Glimmix code as “yrow(MaxRow MaxCol MiniLine)” where “yrow” is a code denoting the number of crop rows within each of the MaxPlots for each of the above conditions; that is, sprayed MaxLine, unsprayed MaxLine, and unsprayed MiniLine. Under the unsprayed condition, one of the MiniLines is the same as the MaxLine while the other two of the MiniLines are not identical to the MaxLine.
Cotton rows in the mini and maxplots (including sprayed rows) were measured for damaged white flower counts (up to 10 white flowers per plot type) daily between 62 and 89 DAP. So, DAP is a repeated measure on each of the plots and there is just one more error term plus all of the interactions of the treatment effects with DAP. The resulting analysis of variance is presented in
Analysis of the Maxplot, SprayStrip, Miniplot and Days after Planting structures.
Source  DF 

MaxRow  2 
MaxCol  2 
MaxLine  2 
Error(MaxPlot)  2 
SprayStrip  2 
Error(SprayStrip) = SprayStrip *MaxCol  4 
SprayStrip *MaxLine  4 
Error(MaxPlot* SprayStrip)  8 
MiniLine  2 
MiniLine*MaxLine  4 
Error(MaxPlot*MiniPlot)  89 
DAP  16 
MaxLine*DAP  32 
SprayStrip *DAP  32 
SprayStrip *MaxLine*DAP  64 
MiniLine*DAP  32 
MiniLine*MaxLine*DAP  64 
Error(Day)  1696 
The Error(Day) term comes from the interaction of DAP with each of the rows within a MaxPlot across the MaxPlots and can be computed as DAP*yrow(MaxRow MaxCol MiniLine). This error term expression enables the evaluation of the possibility of correlation among the repeated measures. The Proc Glimmix statement used to estimate the autocorrelation among these equally spaced time points is:
The response variable is binomial (number of damaged flowers out of 10 examined at random), so the binomial distribution is needed to describe this data. The process for analyzing categorical data is to reduce the model to just those effects that are significant, thus the above model (as described in
Type 1 analysis of variance listing degrees of freedom and describing expected mean squares (EMS) for the 2008 experiment.
Source 

Expected Mean Squares (EMS) † 

MaxLine  2  
Spray  2  
MaxLine*Spray  4  
MiniLine  2  
MaxLine*MiniLine  4  
DAP  16  
MaxLine*DAP  32  
Spray*DAP  32  
MaxLine*Spray*DAP  64  
MiniLine*DAP  32  
MaxLine*MiniLine*DAP  64  
MaxRow  2  
MaxCol  2  
MaxRow*MaxLine  2  
MaxCol*Spray  4  
MaxRow*MaxCol*Spray  8  
yrow(MaxR*MaxL*MiniL)  89 
Analysis of variance table for sums of squares and expected mean squares to accompany
Source  DF  Sum of Squares  Mean Square 

MaxLine  2  1.224409  0.612205 
Spray  2  1.941194  0.970597 
MaxLine*Spray  4  0.804896  0.201224 
MiniLine  2  5.199635  2.599817 
MaxLine*MiniLine  4  0.058314  0.014578 
DAP  16  1.601946  0.100122 
MaxLine*DAP  32  0.558275  0.017446 
Spray*DAP  32  0.793727  0.024804 
MaxLine*Spray*DAP  64  0.795100  0.012423 
MiniLine*DAP  32  1.635895  0.051122 
MaxLine*MiniLine *DAP  64  1.185991  0.018531 
MaxRow  2  0.045929  0.022964 
MaxCol  2  0.013550  0.006775 
MaxRow*MaxLine  2  0.045241  0.022621 
MaxCol*Spray  4  0.039992  0.009998 
MaxRow*MaxCol*Spray  8  0.150683  0.018835 
yrow(MaxRow*MaxLine*MiniLine)  89  1.751922  0.018838 
Residual  1696  21.966128  0.012952 
Despite the uncontrollable effects of weather on plant growth and the marked difference in numbers of TPB each year, the statistical analyses indicate that effects due to plot sizes and cultivar interspersion had no effect on TPB preference for these three cotton cultivars. The design of the experiment used to obtain this result is unique because of the different analysis tiers required, which arose due to the different sizes of plots and arrangement of cultivars among them. The questions of this study are frequently asked by experimenters (e.g., [
It is necessary to verify assumptions used to decide results in experiments. This research (1) verified that use of WFAI in HPR research is not influenced by plot size and interspersion of cultivar lines among plots and (2) demonstrated the potential expansion of frego cotton as a baseline tool to evaluate the efficacy of other TPB pest management tactics. Consequently, the susceptible frego line is a useful standard for HPR evaluations for TPB resistance. The WFAI as an assay tool in HPR evaluations is useful because these counts can be rapidly collected in small plot, large plot or commercial field trials.
Frego cotton could also have utility as a tool in understanding dispersal of plant bugs throughout the production season in commercial fields. Thus, other research is underway to evaluate the use of frego cotton as a trap crop. Expansion of some components of the modified Latin square design described in this paper may prove useful in these larger scale efforts. A newly available, glyphosate tolerant frego line should also be helpful for implementation of research projects in commercial fields, similar to several projects motivated by a decision support system described by Oosterhuis and Bourland [
The authors thank Ronald E. Britton for assistance in the preparation of the manuscript and the comments of two anonymous reviewers that improved the paper. Funding support for this research from Cotton, Inc. is appreciated.
The authors declare no conflict of interest.
proc glimmix data=max1 method=mspl pconv=0.1001;
title2 "Final model with nonsignificant terms deleted.";
class dap week miniline spraystrip maxcol maxrow maxline yrow;
model clean/nplant = spraystrip miniline dap dap*spraystrip dap*miniLine / ddfm=kr;
order model terms were deleted;
maxline*spraystrip dap*miniline*maxline dap*maxline miniline*maxline maxline;
random maxcol maxrow yrow(maxrow maxline miniline) spraystrip *maxcol spraystrip *maxcol*maxline maxline*maxrow;
random dap/subject = yrow(maxrow maxline miniline) type=ar(1) rside;
lsmeans dap*miniline dap*spraystrip /diff ilink;
parms /
lowerb=1e8,1e8,1e8,1e8,1e8,1e8,1e8,1e8;
ods output lsmeans=lsm diffs=dif;
ods listing exclude diffs;
ods rtf exclude lsmeans diffs;
run;