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‘ND Henson’ soybean [Glycine max(L.) Merr.] (Reg. No. CV‐517, PI 675334) was first tested as ND09‐5604 and was released in January 2015 by the North Dakota Agricultural Experiment Station, North Dakota State University (NDSU), to provide producers with Maturity Group 0 conventional cultivar (relative maturity 0.0), generally adapted as a full‐season cultivar from 48 to 49° N latitude. ND Henson was evaluated by the NDSU soybean breeding program in advanced yield trials for a total of 27 location‐years that included 2011 to 2014. Testing for iron‐deficiency chlorosis (IDC) was conducted from 2012 to 2014 at a total of 10 sites located in eastern North Dakota, based on four replicates per site. Screening for resistance to races 3 and 4 of Phytophthora sojae(M.J. Kaufmann and J.W. Gerdemann) was conducted using the hypocotyl‐injection method. ND Henson was released because it has (i) resistance to races 3 and 4 of P. sojae, (ii) high yield in North Dakota environments, (iii) lodging resistance, and (iv) tolerance to IDC.

Cultivar selection is one of the best ways to manage iron‐deficiency chlorosis (IDC) problems in soybean [Glycine max(L.) Merr.]. The objective was to determine if precision farming techniques of planting IDC‐tolerant cultivars in calcareous soil areas and high‐yielding cultivars in non‐IDC areas would increase soybean yield. We used paired sites within the same field. The sites were located in areas of a field where IDC was present and absent. The same commercial soybean cultivars were planted on the paired sites. Results showed that visual scores for IDC could not identify the highest‐yielding cultivar in IDC‐affected areas. If the only information available to growers is yield on non‐IDC sites and visual IDC ratings, then the yield of the whole field could be increased by planting two different cultivars. If yield data from replicated performance testing of numerous different cultiars was available for IDC sites and also for non‐IDC sites, then growers may be able to identify a single cultivar that has high yield across the entire field.

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.

We developed educational software to show graduate students how to plan molecular marker experiments. These computer simulations give the students feedback on the precision of their experiments. The objective of the software was to show students using a hands-on approach how: (1) environmental variation influences the range of the estimates of the magnitude of a quantitative trait loci (QTL) and; (2) how the number of F2plants used to map the QTL influences the range of estimates of the magnitude of the QTL. The histograms generated by the software show the range in the magnitude of the QTL genetic effect as the magnitude of the residual variance and number of F2plants are varied. Students will learn the influence of environmental variation on the magnitude of the QTL and why it is important to report confidence intervals for the QTL magnitude. The number of F2plants used in the experiment can also be varied to simulate the influence of segregating background genes that are not linked to the QTL. This software will enhance student understanding of the results of molecular marker experiments that they have either read about or conducted from their own research effort.

This educational software was developed to aid teachers and students in their understanding of how the process of identifying the most likely quantitative trait loci (QTL) position is determined between two flanking DNA markers. The objective of the software that we developed was to: (1) show how a QTL is mapped to a position on a chromosome using a hands-on approach; (2) show how varying the guessed position of the QTL changes the mixture of QTL genotypes within each marker class. A least squares approach was used to determine the Fstatistic associated with each guessed QTL position between the flanking DNA markers. When the Fstatistic reaches a maximum value, the correct position of the QTL has been identified. The instructor identifies the true position of the QTL, while the students enter a guess as to the QTL position between flanking markers. The histograms generated by the software show the discrepancy between the true and guessed mixture of QTL genotypes within each of the nine marker classes. The output also shows how the design matrix and test statistic change as the guessed QTL position is altered. This software will aid the instructor in explaining how a QTL is mapped. Students will learn to associate the degree of recombination between markers and the QTL with resulting magnitude of the Fstatistic.

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.

‘ND1406HP’ soybean [Glycine max(L.) Merr.] (Reg. No. CV‐516, PI 673929) was first tested as ND06‐4642 and was released in May 2014 by the North Dakota Agricultural Experiment Station, North Dakota State University (NDSU), to provide producers with a Maturity Group 0 conventional cultivar (0.6), generally adapted as a full‐season cultivar from 45 to 48° N latitude. ND1406HP was evaluated by the NDSU soybean breeding program in advanced yield trials for a total of 32 location‐years that included 2008 to 2013. Testing for iron‐deficiency chlorosis (IDC) was conducted from 2011 to 2013 at a total of 11 sites located in eastern North Dakota, based on four replicates per site. ND1406HP was released because it has (i) high yield in North Dakota environments, (ii) high protein and can be used to produce high‐quality tofu or soymilk, (iii) lodging resistance, and (iv) tolerance to IDC.

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.

‘Ashtabula’ soybean [Glycine max(L.) Merr.] (Reg. No. CV‐500, PI 655938) was first tested as ND02‐2367 and was released in January 2009 by the North Dakota Agricultural Experiment Station, North Dakota State University to provide producers with a Maturity Group 0 conventional cultivar (0.4), generally adapted as a full‐season cultivar from 46 to 48° N latitude. Ashtabula was evaluated by the North Dakota State University soybean breeding program in advanced yield trials for a total of 24 location‐years that included 2005 to 2008. Testing for iron deficiency chlorosis (IDC) was conducted in 2005 at four sites located in eastern North Dakota, based on three replicates per site. Screening for resistance to race 4 of Phytophthora sojae(M.J. Kaufmann & J.W. Gerdemann) was conducted using the hypocotyl‐injection method. Ashtabula was released because it has resistance to race 4 of Phytophthora sojae, high yield in North Dakota environments, lodging resistance, and tolerance to iron deficiency.

‘Cavalier’ soybean [Glycine max(L.) Merr.] (Reg. No. CV‐499, PI 654358) was first tested as ND02‐2019, and was developed by the North Dakota Agricultural Experiment Station, North Dakota State University. Cavalier was released in January 2008 and is a Maturity Group 00 conventional cultivar (00.7), generally adapted as a full‐season cultivar from 48 to 49° N latitude. Cavalier was released because it has (i) resistance to race 4 of Phytophthora sojae(M.J. Kaufmann and J.W. Gerdemann), (ii) high yield in North Dakota environments, (iii) lodging resistance, and (iv) tolerance to iron‐deficiency chlorosis.

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.