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Performance of a hybrid cannot be determined with absolute precision. Even though the tests are conducted in a uniform manner, uncontrollable variability exists among experimental plots due to environmental differences in soil, fertility, moisture, insects, diseases, and other sources of variation. Because this variability exists, statistics are used as a tool to examine differences among hybrids. A statistical method of spatial analysis has been used to allow for similarities between neighboring plots based on their location in the field in order to adjust for the unknown environmental variation (Brownie et al., 1993). The particular spatial model allows for correlations that decrease exponentially as distance between plots increases in both row and column directions.
Coefficient of variation (CV) is a relative assessment of trial variability. It measures experimental error caused by variation in management practices and immeasurable factors in the environment as a percent of mean yield for the trial. To summarize values of CV for multiple environments, the average of the values for individual sites (avg CV) is reported. Lower values generally indicate trials with less unexplained variation, hence, more reliable trials (though high mean yields also tend to produce lower CVs).
Standard error of the mean (SEM) is listed as a general indicator of precision since it measures how well a true hybrid mean was estimated. For individual trials, SEM varies across hybrids (due to accounting for spatial variation within the site) and is summarized by reporting the average SEM (avg SEM). On average, this indicates how well hybrid means were measured across all replications within the trial. Where multiple trials were combined for regional and statewide data, SEM is reported. For combined datasets, the hybrid mean is an average over environments and replications within environments, weighted by precision associated with each environment. The SEM for averages over environments is the same for each hybrid and mainly reflects differences in performance of hybrids across environments. Thus, lower values of SEM tend to indicate greater consistency in hybrid rankings across environments.
All reported trials meet an established criterion for precision by having an average value of the standard error of a difference between hybrid means (avg SEDiff) below a threshold value. Avg SEDiff is calculated as the square root of the average variance of a difference between two hybrid means. Threshold SEDiff values are based on OVT data from 1990 – 2013, and are calculated as the value twice as large as that predicted from the historical data following Bowman and Rawlings (1995).
In assessing hybrid performance, the largest yield difference between two hybrids which can reasonably be attributed to chance variation, is listed at the bottom of each table as the least significant difference (LSD). Where multiple trials were combined for regional and statewide data, LSD accounts for variation across all environments. However, for individual trials, this is reported as the average LSD (avg LSD), and represents the difference of hybrids within a trial. Hybrids whose yields differ by less than the average LSD are not statistically different. Those hybrids that are not different from the highest observed yield are denoted in the tables with an asterisk (*); the highest yielding hybrid is denoted by a double asterisk (**). The LSD for comparisons among hybrid means is applied at the 10% level, which indicates 90% confidence that yield differences are not due to chance variation. The degrees of freedom associated with the LSD (df LSD) are also reported in the tables.
Hybrid performance may appear inconsistent among environments within an area or among years at a particular location. Year-to-year variation in weather and pest pressure is sufficiently large enough to make predictions of hybrid performance based on single-year data less reliable than predictions using multiple-year data. Research has shown that multiple-year means across environments provide the best prediction of hybrid performance. Thus it is important to examine results from more than one location and more than one year to obtain a more accurate picture of relative hybrid performance. When available, growers should examine 2- and 3-year multiple environment data provided in the EVEN numbered tables in this report. If these data are not available, growers can use the single year, multiple location data provided.
New hybrids are being introduced each year and these hybrids are potentially higher yielding or pest resistant than the current hybrids. It is suggested that growers plant new hybrids on a small number of acres to determine if it is adapted to their farm. Other agronomic characteristics may be as equally important as yield. Yield information presented in this report should be used in junction with other available information and personal experience when selecting hybrids.