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Root‐knot nematodes (Meloidogynespp.) are important root parasites that limit lima bean (Phaseolus lunatusL.) production, although genetic resistance is available. In accession L‐136, we investigated the apparent independence of genes controlling nematode reproduction and root galling. Segregation of nematode egg production and root‐galling phenotypes in F1, F2, F2:3, and F2:7‐9populations from L‐136 × susceptible ‘Henderson Bush’ was determined in growth pouch, greenhouse pot, and field screenings. Three nuclear genes designated mir‐1(recessive), Mig‐1(dominant), and Mjg‐1(dominant) were found to control M. incognitareproduction, M. incognitagalling, and M. javanicagalling, respectively. Contrast analysis indicated that gene combinations affected nematode egg production and gene combinations provided comprehensive resistance. This independent resistance to root‐knot galling and reproduction, reported for other leguminous crops and cotton (Gossypiumspp.), has important implications for breeding broad‐based nematode resistance.

There are differences among soybean [Glycine max(L.) Merr.] genotypes for tolerance to water‐saturated soil conditions. Phytophthora root rot caused by Phytophthora sojae(M.J. Kaufmann and J.W. Gerdemann) is problematic for soybean when the soil is excessively wet. Our objectives were (i) to determine if genotype‐by‐environment (G × E) interactions existed for yield between water‐saturated (irrigated) vs. control (nonirrigated) environments using 37 soybean genotypes; and (ii) to determine whether resistance and/or partial resistance to P. sojaewas associated with tolerance to water‐saturated soil conditions. The G × E was significant between the water‐saturated treatment and control. Some genotypes that yielded well under control conditions did not yield well under saturated conditions. Based on yield, we identified eight genotypes that were tolerant and eight genotypes that lacked tolerance to water‐saturated soil conditions. These 16 genotypes were evaluated for resistance and partial resistance to Phytophthora using three virulence phenotypes. Those genotypes that were tolerant to water‐saturated soil conditions possessed either a major gene or good partial resistance to P. sojae. We concluded that resistance to P. sojaewas an important factor that increased tolerance to water‐saturated soil, but that unknown other genetic factors were also important.

The genetic gain formula has been used to determine the best allocation of resources. The assumptions of this formula are that genotypes are selected based on the mean of data averaged across several locations within years. When plant breeders select genotypes after the first year of replicated yield tests, the assumptions of the genetic gain formula are violated. For this reason, information provided by the genetic gain formula is not useful for determining the optimum allocation of replicates and environments. Our objectives were to (i) develop a heritability formula that included covariances between genotype‐by‐environment (G × E) interaction effects for the same genotype evaluated in different environments; and (ii) show how these G × E covariances influence resource allocation strategies to maximize genetic gain. Our operational genetic gain formula considers genetic gain to be a correlated response between the test and target environments and allows for G × E covariances between the same genotype evaluated at two selection sites. When selection was conducted at two sites that had a small G × E component of variance and also conducted at two sites with a large G × E component of variance, the realized gain was equal in the target environments. Optimal resource allocation cannot be decided, based on the genetic gain formula.

Breeders need information on the best way to allocate resources to develop higher‐yielding soybean [Glycine max(L.) Merr.] lines using a fixed amount of resources. Our objectives were to determine the best resource allocation for first‐year yield tests by varying the number of replicates, number of lines per population, and number of populations. Thirty lines developed from each of 10 populations were tested in two selection environments. All 300 lines were evaluated in each of the seven environments. Five environments were used as validation environments to compare the mean of the selected lines to the overall mean. Increasing the number of replicates did not increase the effectiveness of selection. When 30 lines were tested from five populations, the realized selection differential varied from 170 to 310 kg ha−1, depending on which of the five populations were evaluated. We concluded that no more than 15 lines should be sampled from as many different populations as possible. The best allocation of resources to maximize the response to selection for first‐year yield evaluation was the use of a single replicate at one location.

Soybean breeders need to evaluate alternative strategies for increasing yield, protein, and oil content. The correlated response for yield when selection is conducted for increased protein among unreplicated plant rows is unknown. Our objective was to evaluate selection for increased protein content at an intensity of 50% using unreplicated plots to determine the correlated response for yield, gross value per hectare, oil content, and the direct response for protein content. Ten populations that included 30 lines each were evaluated for yield, protein, and oil content at each of seven environments. Lines were selected solely for high protein content with one replicate at each of three selection environments and based on the average of two replicate in three selection environments. Additionally, selection was conducted on the basis of the population mean in the selection environment(s). Selected and unselected lines were evaluated in four validation environments that were not the same as the selection environments. Selection among individual lines solely for increased protein content decreased yield 70 kg ha−1and increased protein content 7 g kg−1, when lines were selected on the basis of the mean of two replicates and three environments. Selection among populations for increased protein content, decreased yield 110 kg ha−1and increased protein content 5 g kg when selection was based on the population mean averaged across two replicates and three environments. On average, selection for increased protein content resulted in decreased yield.

If recurrent selection methods are effective, the frequency of favorable alleles is expected to increase in the population. Random changes in gene frequency will also occur, if effective population size is small, due to the effect of sampling. In maize (Zea maysL.), where dominance gene action is positive in direction for traits such as grain yield, genetic drift results in inbreeding depression. Genetic drift is the change in gene frequency due to sampling in a finite population. Two major processes are expected to be operating to change the mean over cycles of recurrent selection in a population of finite size, selection acting to increase the mean and inbreeding depression due to genetic drift acting to decrease the mean. Estimation of the effects of genetic drift may be useful because the choice of what constitutes adequate effective population size may be altered, on the basis of empirical results. The objectives of the current study were to compare progress between reciprocal recurrent selection (RRS) and intrapopulation selection that included half‐sib (HT) and S2(S) selection in ‘Iowa Stiff Salk Synthetic’ (BSSSC0), and to separate the effects of selection from those of genetic drift. Effects due to selection for grain yield improvement of populations on a per‐cycle basis were not significantly different between methods of recurrent selection. When advanced cycles were evaluated for grain yield in crosses with an unrelated inbred line (Mo17), improvements paralleled improvement in the populations. The improved populations crossed to an unrelated population [BSCB1(R)Cn], which was used reciprocally as the tester for BSSS(R)Cn, suggested that response to RRS in improving the population cross mean was similar to the populations improved by either HT or S methods of selection. Effects of genetic drift were significant and should be considered in long‐term selection programs. Differences among selection methods for genetic drift were not significant.

Progress from selection is dependent on the additive variance within the population. Selection and genetic drift are two biological processes that can change gene frequencies and thus change the magnitude of the genetic variance of a breeding population over cycles of a recurrent selection program. Our objective was to determine if changes in genetic variance had occurred after recurrent selection primarily for grain yield in two maize (Zea maysL.) selection programs originating from ‘lowa Stiff Stalk Synthetic’ (BSSSCO). One program consisted of reciprocal recurrent selection and the second program consisted of seven cycles of half‐sib followed sequentially by three cycles of S2per se recurrent selection. Nonselected S2progenies from each of five populations [BSSSCO, BSSS(R)CS, BSSS(R)C9, BSI3(S)C0=BSSS(HT)C7, and BSI3(S)C3] were evaluated in a replications‐within‐sets experiment conducted in six environments. Average grain yield of the nonselected S2progenies relative to the original BSSSCO population increased 62% after nine cycles of reciprocal recurrent selection and 81% after seven cycles of half‐sib followed by three cycles of S2recurrent selection. Evidence suggests that genetic variance decreased in the population improved using reciprocal recurrent selection but not in the population improved by using the sequential half‐sib and S2recurrent selection methods. Estimates of genetic variance among random S2progenies decreased in the BSSS(R)C9 population, when compared to the BSSSCO, for stalk lodging; dropped ears; grain yield; and grain moisture at harvest, and increased for root lodging. Genetic variance estimates among random S2progenies increased for root and stalk lodging, and grain yield, but decreased for dropped ears and moisture at harvest for BSI3(S)C3 relative to BSSSCO.

The value of the soybean [Glycine max(L.) Merr.] to processors is primarily determined by the protein and oil content of the seed, while the value to the producer is determined by the yield. Breeders need to consider how selection for yield can be combined with selection for protein and oil to maximize gross value per hectare (GVH). Previous research has not compared selection criteria within segregating populations to identify lines with the highest GVH. The objective of this study was to select lines from segregating populations for yield, protein, oil, the sum of protein and oil, total kg of protein and oil per hectare, and GVH to determine which criterion identified lines with the highest GVH. Experimental lines selected from three populations were evaluated for GVH, based on three protein to oil price ratios. GVH of lines selected for yield was equal to that of lines selected for a combination of yield, protein, and oil content. Based on this study it appears that soybean breeders who have a goal of maximizing GVH can select solely for yield and avoid expensive protein and oil testing. The lines that were high in protein, oil, or sum of protein and oil content, were low yielding and low in GVH at all three protein to oil price ratios. The higher value per kilogram seed of lines selected for protein, oil, or the sum of protein and oil content, did not compensate for the lower yield.

Methods to simultaneously select for yield and stability need to be compared to determine which index is the best choice to identify high‐yielding, stable genotypes. A comparison of the repeatability of different measures of stability would be useful because stability statistics with high repeatability should be superior to identify stable genotypes. Stability measures were evaluated for 116 oat (Avena sativaL.) lines in 36 Iowa environments. The objectives were (i) to evaluate the mean selection differential when selection was for yield, stability statistics, or simultaneous selection for both yield and stability, using different indices; (ii) to compare repeatability of the stability regression (b1) stability variance component (σ21), and deviation from regression mean square (sd21) across random subsets of 18 environments; and (iii) estimate rank correlations between stability statistics and mean grain yield, in all environments. Simultaneous selection for yield and stability, based on three indices, identified high‐yielding, stable genotypes. Selection solely for yield identified genotypes with high mean yield that on average were not stable. Selection solely for each stability measure identified genotypes with low yield that were stable. The repeatability of b1was 0.61, and the index that used this measure of stability combined with selection for yield identified 12 genotypes that yielded 3% less than the average of the highest yielding 12 genotypes. Repeatability of σ21was 0.28 and repeatability of σ21was 0.17. The correlation between yield and b1was 0.73, between yield and σ21was 0.21, and between yield and σ21was 0.28.

The pathogenicity of haploid progeny from the cross of cultures CR3 and 63.5 of Erysiphe graminis[DC.] Merat hordeiEm. Marchal was determined on 11 barley (Hordeum vulgareL.) varieties, each known to have a resistance gene at the Ml‐alocus. Different genes were shown to condition the pathogenicity of cultures CR3 and 63.5 on the ll varieties, indicating the corresponding resistance genes in those varieties are different. Genes in seven of the varieties — ‘Algerian,’ ‘Black Russian,’ ‘Ricardo,’ ‘No. 22,’ ‘Gopal,’ ‘Iso 20,’ and ‘Line 4831’ — had previously been designated Ml‐athrough Ml‐a7. We propose the genes in varieties ‘Heil's Hanna,’ ‘Monte Cristo,’ ‘Iso 12,’ and ‘A222’ be designated Ml‐a8, Ml‐a9, Ml‐al0, and Ml‐a11, respectively.

Conservation of existing germplasm is essential because of the constant erosion of gene pools and native germplasm throughout the world. Conservation of rice (Oryza sativaL.) germplasm, especially accessions in the rice portion of the USDA‐ARSSm all Grains Collection, is essential because rice is not native to the United States. This study was conducted to determine if rice accessions that did not emerge when seeded in the field could be rejuvenated in the laboratory. Six‐thousand rice accessions were seeded at the Rice Research and Extension Center, University of Arkansas, Stuttgart, AR, in 1991. Two‐hundred‐and‐eleven of the accessions, originally introduced from 21 countries, failed to emerge in the field. The viability of these accessions was tested in the growth chamber, on agar medium, and by an embryo culture method. Germination in the growth chamber recovered 27 lines that had been stored for an average of 10.6 yr at Beltsville, MD, and Aberdeen, ID. Fifty‐eight additional lines with an average storage age of 15.2 yr were rejuvenated by culturing the grain on agar medium. Embryo culture was used to test the remaining 126 lines, and 76 lines with an average storage time of 19.1 yr survived and produced seeds. All three laboratory techniques recovered rice germplasm that did not emerge in the field. However, embryo culture was superior to grain culture and both of these methods were more effective in rejuvenating rice germplasm than the growth chamber method.

Measurements of ear height, ear weight, and a potential torque index were made on individual plants jointly chosen from within 972 hills of maize (Zea maysL.). A stalk lodged and unbroken plant in each hill were measured. Plants were classified as stalk lodged or unbroken plants whether or not they were root lodged. The measurements on each were then compared. A paired Student's t test was used to see if stalk lodged and unbroken plants differed for the traits measured. Potential torque was calculated by multiplying ear height by ear mass. No relationships were found between ear height, ear weight, or potential torque and stalk lodging. The results are encouraging to those using stalk lodging in selection indices.