Random mating has been used successfully to break linkages in cross-pollinated crops. In self-pollinated crops, however, the requirements for cross-pollination can markedly influence the feasibility of random mating. The objective of this study was to evaluate Miravalle's bulked-pollen method for its ability to simulate random mating in cotton (Gossypium hirsutum L.). Pollen from a selection from Auburn G1-213 glandless (gl(2)gl(2), gl(3)gl(3)) and `TM-1' glanded (GL(2)GL(2), GL(3)GL(3)) cottons were mixed at five different proportions of blooms (1: 13, 1: 26, 1: 39, 1: 52, and 1: 65) from the two genotypes to serve as a model for Miravalle's bulked-pollen method in upland cotton. Observed progeny genotypic ratios were nearly as expected except for the 1: 26 treatment, which had an excess of glandless progeny. Crosses containing `Stoneville 825' nectariless (ne(1)ne(1), ne(2)ne(2)) developed with this methodology were also assayed for the nectariless trait, and the trait segregated as expected. Combining the glanded data resulted in the expected segregation. Cotton pollen mixed well and the final boll set was excellent. The results of this study indicate that the bulked-pollen methodology can be used to develop a random-mated population.

The southern root-knot nematode (RKN) [Meloidogyne incognita (Kofoid & White)] is a serious pest of cotton (Gossypium hirsutum L.) with detrimental effects being most pronounced on sandy soils that are also infested with the Fusarium wilt pathogen. Varietal resistance is an effective method of managing the RKN/Fusarium wilt complex. In 1970, a high level of RKN resistance was developed in the germplasm line Auburn 623 RNR, but no commercial cultivar has been developed with this near-immunity level of resistance. The objective of this study was to evaluate the mode of inheritance of RKN resistance in M-315 RNR (M-315), a germplasm line with the Auburn 623 RNR source of resistance, and in M78-RNR, a day-neutral version of the race stock line T78. These lines were crossed with M8, an RKN-susceptible cotton line, and with each other. The parental, F1, F2, and backcross generations of these crosses were evaluated in the greenhouse for RKN reproduction 40 d after planting in a Wickham sandy loam soil that had been infested with either 5,000 or 10,000 RKN eggs per pot. The minimum number of genes conditioning resistance in M-315 and M78-RNR was estimated at two and one, respectively. Mendelian analyses indicated that a two gene, one dominant (Mi1) and one additive (Mi2), model fit the data for M-315. The data from crosses with M78-RNR indicated that it had only the dominant Mi1 gene. These data indicate that the Auburn 623 RNR source of RKN resistance should be easily transferable to commercial cultivars. T he southern root-knot nematode (RKN) is a serious pest of cotton, primarily on lighter textured soils. Root-knot nematode infection impairs cotton root function, which severely limits plant growth (O’Bannon and Reynolds, 1965), and predisposes the plant to Fusarium wilt infection (Martin et al., 1956). The RKN/Fusarium wilt

Genes for improved yield and fiber quality are available in Australian cultivars and wild ac- cessions of cotton (Gossypium hirsutum L.); how- ever, their combining ability with U.S. cultivars is unknown. We evaluated combining ability and inheritance of yield and fiber traits among nine diverse cotton lines: two cultivars developed in Australia, two experimental lines from wild ac- cessions, and five U.S. cultivars. Parents and F 2 's from a half-diallel cross were grown in Leeper silty clay loam and Marietta sandy clay loam in 1999 and 2000. F 2 hybrids had higher lint yield, heavier bolls and longer fibers than parents. Vari- ance components and genetic effects were calcu- lated utilizing an extended additive dominance model with genotype by environment interaction effects using a mixed norm quadratic unbiased estimation analysis. Parents varied in genetic combining ability (GCA). 'Fibermax 832', devel- oped in Australia, was the best in GCA for yield and fiber quality. 'Stoneville 474' was the best in GCA for yield. Experimental line, B 1388, was good in GCA for fiber strength, although other properties suffered. 'Paymaster 1560' exhibited good GCA ability for yield and fiber length. 'Fibermax 975' exhibited good GCA for fiber length. Lint yield, boll size, and fiber elongation had approximately equal additive and dominance genetic effects. Lint percentage and fiber strength exhibited primarily additive genetic effects. Micronaire and length exhibited primarily domi- nance genetic effects. A significant residual com- ponent of the phenotypic variance was present for each trait except lint percentage. The Austra- lian cultivars and wild accessions can combine with cultivars from U.S. breeding programs to provide genes for fiber and/or yield improvement.

The coefficient of variation (CV) has been used for many years by researchers to determine the validity of performance trials. The premise behind the CV, that the standard deviation is proportional to the mean, compromises one of the assumptions of a normal distribution (i.e., that the sample mean and sample variance are independent), If there is a nonnormal distribution, then the data may need to be transformed, which in turn may invalidate the use of the CV. A constant CV across trials implies a relationship between the error variance and the mean such that the slope = 2.0 in the regression of In error variance on In mean. The purpose of this paper is to stress the relationship between the error variance and mean in the CV, review this relationship in actual yield data, and examine the effects of various transformations on CVs and coefficient of determination (R-2), The In error variance regressed on In mean for several agronomic crops ranged from -0.11 for full-season corn (Zea mays L.) to 1.31 for oat (A vena sativa L.). Although some crops had a nonzero regression coefficient for this relationship, none approached 2.0, the level that supports the hypothesis that the CV is a viable tool for comparing the relative variation of different trials. Data transformations (e.g., square root, logarithmic, angular, inverse, reverse, and addition of a constant) tend to lower CV values in most cases, but can cause dramatic increases, depending on the nature of the variance and the specific transformation. On the other hand, R-2, which is a measure of the amount of variability accounted for in the model, remains relatively unaffected by most transformations. Reasonable R-2 values for determining validity of performance trials may vary by location and crop species, Examination of North Carolina data revealed that discarded trials generally had R-2 values less than 50%. The R-2 may be affected by the size of the dataset, and so adjusted R-2 maybe more useful for comparing trials of varying sizes; however, adjusted R-2 will tend to be larger where there are larger differences among entries. There is no one perfect measure of the validity of trial data, but the R-2 and the adjusted R-2 are reasonable alternatives to the CV and should be examined along with other statistical measures when evaluating crop performance data.