Showing papers in "Crop Science in 2016"

What should students in plant breeding know about the statistical aspects of genotype × Environment interactions?

Abstract: A good statistical analysis of genotype × environment interactions (G × E) is a key requirement for progress in any breeding program. Data for G × E analyses traditionally come from multi-environment trials. In recent years, increasingly data are generated from managed stress trials, phenotyping platforms, and high throughput phenotyping techniques in the field. Simultaneously, and complementary to the phenotyping, more elaborate genotyping and envirotyping occur. All of these developments further increase the importance of a sound statistical framework for analyzing G × E. This paper presents considerations on such a framework from the point of view of the choices that need to be made with respect to the content of short academic courses on statistical methods for G × E. Based on our experiences in teaching statistical methods to plant breeders, for specialized G × E courses between three and 5 d are reserved. The audience in such courses includes MSc students, PhD students, postdocs, and researchers at breeding companies. For such specialized courses, we propose a collection of topics to be covered. Our outlook on G × E analyses is two-fold. On the one hand, we see the G × E problem as the building of predictive models for genotype-specific reaction norms. On the other hand, the G × E problem consists in the identification of suitable variance-covariance models to describe heterogeneity of genetic variance and correlations across environments. Our preferred class of statistical models is the class of mixed linear-bilinear models. These statistical models allow us to answer breeding questions on adaptation, adaptability, stability, and the identification and subdivision of the target population of environments. By a citation analysis of the literature on G × E, we show that our preference for mixed linear-bilinear models for analyzing G × E is supported by recent trends in the types of methods for G × E analysis that are most frequently cited.

Reinventing potato as a diploid inbred line-based crop

Abstract: The third most important food crop worldwide, potato (Solanum tuberosum L.) is a tetraploid outcrossing species propagated from tubers. Breeders have long been challenged by polyploidy, heterozygosity, and asexual reproduction. It has been assumed that tetraploidy is essential for high yield, that the creation of inbred potato is not feasible, and that propagation by seed tubers is ideal. In this paper, we question those assumptions and propose to convert potato into a diploid inbred line–based crop propagated by true seed. Although a conversion of this magnitude is unprecedented, the possible genetic gains from a breeding system based on inbred lines and the seed production benefits from a sexual propagation system are too large to ignore. We call on leaders of public and private organizations to come together to explore the feasibility of this radical and exciting new strategy in potato breeding. S.H. Jansky, USDA–ARS Vegetable Crops Research Unit, Dep. of Horticulture, Univ. of Wisconsin, 1575 Linden Dr., Madison, WI; A.O. Charkowski, Dep. of Plant Pathology, Univ. of Wisconsin, Madison, WI; D.S. Douches, Dep. of Plant, Soil, and Microbial Sciences, Mich. State Univ., East Lansing, MI; G. Gusmini, Pepsico, St. Paul, MN; C. Richael, Simplot Plant Sciences, Boise, ID; P.C. Bethke and D.M. Spooner, USDA–ARS Vegetable Crops Research Unit, Dep. of Horticulture, Univ. of Wisconsin, Madison, WI; R.G. Novy, USDA– ARS Small Grains and Potato Germplasm Research Unit, Aberdeen, ID; H. De Jong, Agriculture and Agri-Food Canada, Fredericton, New Brunswick, Canada (retired); W.S. De Jong, School of Integrative Plant Sciences, Cornell Univ., Ithaca, NY; J.B. Bamberg USDA–ARS, Dep. of Horticulture, Univ. of Wisconsin, Madison, WI, and US Potato Genebank, Sturgeon Bay, WI; A.L. Thompson, Dep. of Plant Sciences, North Dakota State Univ.; B. Bizimungu, Agriculture and Agri-Food Canada, Fredericton, New Brunswick, Canada; D.G. Holm, Dep. of Horticulture and Landscape Architecture, Colorado State Univ., San Luis Valley Research Center, Center, CO; C.R. Brown, USDA– ARS, Prosser, WA; K.G. Haynes, USDA–ARS, Beltsville, MD; V.R. Sathuvalli, Dep. of Crop and Soil Science, Oregon State Univ., Hermiston Agricultural Research and Extension Center, Hermiston, OR; R.E. Veilleux, Dep. of Horticulture, Virginia Tech, Blacksburg, VA; J.C. Miller, Jr., Dep. of Horticultural Sciences, Texas AM J.M. Bradeen, Dep. of Plant Pathology, Univ. of Minnesota, St. Paul, MN; J. Jiang, Dep. of Horticulture, Univ. of Wisconsin, Madison, WI. G. Gusmini is an employee of PepsiCo, Inc.; the views expressed in this presentation are those of the author and do not necessarily reflect the position or policy of PepsiCo Inc. C. Richael is an employee of Simplot Plant Sciences; the views expressed in this presentation are those of the author and do not necessarily reflect the position or policy of Simplot Plant Sciences. Received 3 Dec. 2015. Accepted 25 Jan. 2016. *Corresponding author (shelley.jansky@ars. usda.gov; shjansky@wisc.edu). Abbreviations: CIP, International Potato Center; ILs, introgression lines; RILs, recombinant inbred lines; TPS, true potato seed. Published in Crop Sci. 56:1412–1422 (2016). doi: 10.2135/cropsci2015.12.0740 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Published July 7, 2016

Yield Responses to Planting Density for US Modern Corn Hybrids: A Synthesis-Analysis

Abstract: Identifying an optimal plant density is a critical management decision for corn (Zea mays L.) production. The main objectives of this study were to: (i) investigate the grain yield responses to plant density (yield–density relationship), (ii) identify best fitted yield–density response curves, and (iii) explore genotype (G) ́ environment (E) interaction effect on yield–density response models. Analysis was conducted on meta-data (124,374 observations) gathered from 22 US states and 2 Canadian provinces, diverse sites (E), for years from 2000–2014 on multiple hybrids (G). Yield data were further grouped into four yield environments (low [LY], <7 Mg ha-1; medium [MY], 7–10 Mg ha-1; high [HY], 10–13 Mg ha-1; and very high [VHY], >13 Mg ha-1 yielding groups). Primary outcomes from this analysis were: (1) strong G ́ E interaction; (2) a quadratic model best fitted yield–density relationship; (3) four contrasting yield–density responses identified as dominant in each yield productivity environment, i.e., a declining, a constant, an increasing, and ever-increasing type; (4) the yield productivity environment varied for the different corn comparative relative maturity (CRM) groups, i.e., the LY environment for long-maturing hybrids matched with a MY or HY environment for short maturing hybrids; and (5) maximum yielding plant density (MYPD) was lower but maximum yield was greater for longversus short-maturing hybrids. In summary, optimal plant density should be decided based on detailed G ́ E analysis of production conditions that include factors such as CRM, yield productivity environment (weather–soil ́ management practices), and site information. Y. Assefa, P.V.V. Prasad, and I.A. Ciampitti, Dep. of Agronomy, Kansas State Univ., 2004 Throckmorton Plant Science Center, Manhattan, KS 66506; P. Carter, M. Hinds, G. Bhalla, R. Schon, M. Jeschke, and S. Paszkiewicz, DuPont Pioneer, 7100 NW 62nd Ave., Johnston, IA 50131. Assigned to Associate Editor Jeff Melkonian. Received 8 Apr. 2016. Accepted 5 June 2016. *Corresponding author (ciampitti@ksu.edu). Abbreviations: CRM, corn relative maturity; E, environment; G, genotype; HY, high yielding; LY, low yielding; M, management; MY, medium yielding; VHY, very high yielding; MYPD, maximum yielding planting density; RMSE, root mean square error. Published in Crop Sci. 56:2802–2817 (2016). doi: 10.2135/cropsci2016.04.0215 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Published July 28, 2016

Use of Crop Growth Models with Whole-Genome Prediction: Application to a Maize Multienvironment Trial

Abstract: High throughput genotyping, phenotyping, and envirotyping applied within plant breeding multienvironment trials (METs) provide the data foundations for selection and tackling genotype x environment interactions (GEIs) through whole-genome prediction (WGP). Crop growth models (CGM) can be used to enable predictions for yield and other traits for different genotypes and environments within a MET if genetic variation for the influential traits and their responses to environmental variation can be incorporated into the CGM framework. Furthermore, such CGMs can be integrated with WGP to enable wholegenome prediction with crop growth models (CGM-WGP) through use of computational methods such as approximate Bayesian computation. We previously used simulated data sets to demonstrate proof of concept for application of the CGM-WGP methodology to plant breeding METs. Here the CGM-WGP methodology is applied to an empirical maize (Zea mays L.) drought MET data set to evaluate the steps involved in reduction to practice. Positive prediction accuracy was achieved for hybrid grain yield in two drought environments for a sample of doubled haploids (DHs) from a cross. This was achieved by including genetic variation for five component traits into the CGM to enable the CGM-WGP methodology. The five component traits were a priori considered to be important for yield variation among the maize hybrids in the two target drought environments included in the MET. Here, we discuss lessons learned while applying the CGM-WGP methodology to the empirical data set. We also identify areas for further research to improve prediction accuracy and to advance the CGM-WGP for a broader range of situations relevant to plant breeding.

The 2NS Translocation from Aegilops ventricosa Confers Resistance to the Triticum Pathotype of Magnaporthe oryzae.

Abstract: Wheat blast is a serious disease caused by the fungus Magnaporthe oryzae (Triticum pathotype) (MoT). The objective of this study was to determine the effect of the 2NS translocation from Aegilops ventricosa (Zhuk.) Chennav on wheat head and leaf blast resistance. Disease phenotyping experiments were conducted in growth chamber, greenhouse, and field environments. Among 418 cultivars of wheat (Triticum aestivum L.), those with 2NS had 50.4 to 72.3% less head blast than those without 2NS when inoculated with an older MoT isolate under growth chamber conditions. When inoculated with recently collected isolates, cultivars with 2NS had 64.0 to 80.5% less head blast. Under greenhouse conditions when lines were inoculated with an older MoT isolate, those with 2NS had a significant head blast reduction. With newer isolates, not all lines with 2NS showed a significant reduction in head blast, suggesting that the genetic background and/or environment may influence the expression of any resistance conferred by 2NS. However, when near-isogenic lines (NILs) with and without 2NS were planted in the field, there was strong evidence that 2NS conferred resistance to head blast. Results from foliar inoculations suggest that the resistance to head infection that is imparted by the 2NS translocation does not confer resistance to foliar disease. In conclusion, the 2NS translocation was associated with significant reductions in head blast in both spring and winter wheat.

Extending the Marker × Environment Interaction Model for Genomic-Enabled Prediction and Genome-Wide Association Analysis in Durum Wheat

Abstract: The marker × environment interaction (M×E) genomic model can be used to generate predictions for untested individuals and identify genomic regions whose effects are stable across environments and others that show environmental specificity. The objectives of this study were: (1) to extend the M×E interaction model using priors that produce shrinkage and variable selection such as Bayesian Ridge Regression (BRR) and BayesB (BB), respectively, and (2) to evaluate the genomic prediction accuracy of M×E interaction, single-environment, and across-environment models using a multi-parental durum wheat population characterized for grain yield, grain volume weight, thousand-kernel weight and heading date in four environments. Breeding value predictions were generated for two prediction problems: cross-validation problem 1 (CV1) and cross-validation problem 2 (CV2). In general, results showed that the M×E interaction model performed better than the single-environment and across-environment models, in terms of minimization of the model residual variance, for both CV1 and CV2. The improved data-fitting gain over the other models was more evident for thousand-kernel weight and heading date (up to two-fold differences) as compared to grain yield and grain volume weight, which showed more complex genetic bases and smaller single-marker effects. Considering the Bayesian models used, BB showed better overall prediction accuracy than BRR. As proof-of-concept for the M×E interaction model, the major controllers of heading date, Ppd and FT on chromosomes 2A, 2B, and 7A, showed stable effects across environments as well as environment-specific effects. For GY, besides the regions on chromosomes 2B and 7A, additional chromosome regions with large marker effects were detected in all chromosome groups

Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

Abstract: Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype ́ environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location–year combinations genotyped with genotyping-bysequencing (GBS). Mixed models were used to obtain either overall or by-environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega-environments (MEs) for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context. B. Lado, P. González Barrios, and L. Gutiérrez, Dep. of Statistics, College of Agriculture, Universidad de la República, Garzón 780, Montevideo, Uruguay; L. Gutierrez, Dep. of Agronomy, Univ. of Wisconsin–Madison, 1575 Linden Dr., Madison, WI 53706; M. Quincke and P. Silva, Programa Nacional de Investigación Cultivos de Secano, Instituto Nacional de Investigación Agropecuaria, Est. Exp. La Estanzuela, Colonia 70000, Uruguay. Received 2 Apr. 2015. Accepted 14 Sept. 2015. *Corresponding author (gutierrezcha@wisc.edu). Abbreviations: ALL, all location–year combinations; AYT, advanced yield trial; BLUE, best linear unbiased estimation; CV, cross-validation; DOL, location Dolores; DZ, location Durazno; GBLUPg ́e, by-environment genomic predictions; GBLUPM, overall genomic predictions for the mean of the environments; GBS, genotyping-by-sequencing; GEI, genotype ́ environment interaction; GGE, genotypic main effect and genotype ́ environment interaction matrix; GS, genomic selection; INIA, National Agricultural Research Institute; LE, location La Estanzuela; ME, mega-environment; MET, multienvironment trial; PYT, preliminary yield trial; R2, location Ruta2; WBP, Wheat Breeding Program; YET, elite yield trial; YOU, location Young. Published in Crop Sci. 56:2165–2179 (2016). doi: 10.2135/cropsci2015.04.0207 © Crop Science Society of America 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Published January 29, 2016

A Pipeline Strategy for Grain Crop Domestication

Abstract: In the interest of diversifying the global food system, improving human nutrition, and making agriculture more sustainable, there have been many proposals to domesticate wild plants or complete the domestication of semidomesticated orphan crops. However, very few new crops have recently been fully domesticated. Many wild plants have traits limiting their production or consumption that could be costly and slow to change. Others may have fortuitous preadaptations that make them easier to develop or feasible as high-value, albeit lowyielding, crops. To increase success in contemporary domestication of new crops, we propose a pipeline approach, with attrition expected as species advance through the pipeline. We list criteria for ranking domestication candidates to help enrich the starting pool with more preadapted, promising species. We also discuss strategies for prioritizing initial research efforts once the candidates have been selected: developing higher value products and services from the crop, increasing yield potential, and focusing on overcoming undesirable traits. Finally, we present new-crop case studies that demonstrate that wild species’ limitations and potential (in agronomic culture, shattering, seed size, harvest, cleaning, hybridization, etc.) are often only revealed during the early phases of domestication. When nearly insurmountable barriers were reached in some species, they have been (at least temporarily) eliminated from the pipeline. Conversely, a few species have moved quickly through the pipeline as hurdles, such as low seed weight or low seed number per head, were rapidly overcome, leading to increased confidence, farmer collaboration, and program expansion.

Introduction to a Special Issue on Genotype by Environment Interaction

Abstract: Expression of a phenotype is a function of the genotype, the environment, and the differen- tial sensitivity of certain genotypes to different environments, also known as genotype by envi- ronment (GE) interaction. This special issue of Crop Science includes a collection of man- uscripts that reviews the long history of GE research, describes new and innovative ideas, and outlines future challenges. Improving our understanding of these complex interactions is expected to accelerate plant breeding progress, minimize risk through improved cultivar deploy- ment, and improve the efficiency of crop pro - duction through informed agriculture. Achieving these goals requires the integration of broad and diverse science and technology disciplines.

Half a Century of Studying Genotype × Environment Interactions in Plant Breeding Experiments

Abstract: Variation in crop performance is directly affected by the environment in which the plant grows. Analyses and estimation of genotype × environment interactions (G×E) have the potential to provide information about the characteristics of genotypes, identify elite genotypes and suitable environmental conditions, establish breeding objectives, and make recommendations for crop management practices. For the past half century, a variety of statistical models have been used for estimating G×E in plant breeding field experiments to facilitate the allocation of superior genotypes to the target population of environments. The most commonly used models are described in this review starting with linear regression and ANOVA models. We then describe modifications in the form of multiplicative models, models that can accommodate external variables, and mixed effect models. Quantification of differential effects of segments of a genome across environments is shown by exploiting marker × environment (M×E) interactions. We close with a brief overview of some nonparametric concepts that aim to understand genotypic stability.

Predicting responses in multiple environments: Issues in relation to genotype × Environment interactions

Abstract: Prediction of the phenotypes for a set of genotypes across multiple environments is a fundamental task in any plant breeding program. Genomic prediction (GP) can assist selection decisions by combining incomplete phenotypic information over multiple environments (MEs) with dense sets of markers. We compared a range of ME-GP models differing in the way environment-specific genetic effects were modeled. Information among environments was shared either implicitly via the response variable, or by the introduction of explicit environmental covariables. We discuss the models not only in the light of their accuracy, but also in their ability to predict the different parts of the incomplete genotype × environment interaction (G × E) table: (G t ; E t ), (G u ; E t ), (G t ; E u ), and (G u ; E u ), where G is genotype, E is environment, both tested (t; in one or more instances) and untested (u). Using the ‘Steptoe’ × ‘Morex’ barley (Hordeum vulgare L.) population as an example, we show the advantage of ME-GP models that account for G × E. In addition, for our example data set, we show that for prediction in the most challenging scenario of untested environments (E u ), the use of explicit environmental information is preferable over the simpler approach of predicting from a main effects model. Besides producing the most general ME-GP model, the use of environmental covariables naturally links with ecophysiological and crop-growth models (CGMs) for G × E. We conclude with a list of future research topics in ME-GP, where we see CGMs playing a central role.

Genome-wide Association for Plant Height and Flowering Time across 15 Tropical Maize Populations under Managed Drought Stress and Well-Watered Conditions in Sub-Saharan Africa

Abstract: Genotyping breeding materials is now relatively inexpensive but phenotyping costs have remained the same. One method to increase gene mapping power is to use genome-wide genetic markers to combine existing phenotype data for multiple populations into a unified analysis. We combined data from 15 biparental populations of maize (Zea mays L.) (>2500 individual lines) developed under the Water-Efficient Maize for Africa project to perform genome-wide association analysis. Each population was phenotyped in multilocation trials under water-stressed and well-watered environments and genotyped via genotyping-by-sequencing. We focused on flowering time and plant height and identified clear associations between known genomic regions and the traits of interest. Out of ~380,000 single-nucleotide polymorphisms (SNPs), we found 115 and 108 that were robustly associated with flowering time under well-watered and drought stress conditions, respectively, and 143 and 120 SNPs, respectively, associated with plant height. These SNPs explained 36 to 80% of the genetic variance, with higher accuracy under wellwatered conditions. The same set of SNPs had phenotypic prediction accuracies equivalent to genome-wide SNPs and were significantly better than an equivalent number of random SNPs, indicating that they captured most of the genetic variation for these phenotypes. These methods could potentially aid breeding efforts for maize in Sub-Saharan Africa and elsewhere. The methods will also help in mapping drought tolerance and related traits in this germplasm. We expect that analyses combining data across multiple populations will become more common and we call for the development of algorithms and software to enable routine analyses of this nature. J.G. Wallace, Dep. of Crop and Soil Sciences, The Univ. of Georgia, Athens, GA 30602-6810; J.G. Wallace and E.S. Buckler, Inst. for Genomic Diversity, Cornell Univ., Ithaca, NY 14853; E.S. Buckler, USDA – Agricultural Research Service, Ithaca, NY 14853; X. Zhang, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico, DF, Mexico; Y. Beyene, M. Olsen, and B.M. Prasanna, CIMMYT, P.O. Box 1041, Village Market 00621, Nairobi, Kenya; K. Semagn, Dep. of Agricultural, Food and Nutritional Science, Univ. of Alberta, Edmonton, Canada. Received 16 Oct. 2015. Accepted 06 June 2016. *Corresponding authors (b.m.prasanna@cgiar. org; jason.wallace@uga.edu). Assigned to Associate Editor Seth Murray. Abbreviations: BLUP, best linear unbiased predictors; FT, FLOWERING LOCUS T; G × E, genotype × environment; GBS, genotyping-by-sequencing; GWAS, genome-wide association; H2, broad-sense heritability; h2, narrow-sense heritability; MITE, miniature inverted-repeat transposable element; NAM, nested association mapping; QTL, quantitative trait locus; RMIP, resample model inclusion probability; SNP, single-nucleotide polymorphism; WEMA, Water-Efficient Maize for Africa Published in Crop Sci. 56:2365–2378 (2016). doi: 10.2135/cropsci2015.10.0632 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Published July 28, 2016

Improving Maize Grain Yield under Drought Stress and Non-stress Environments in Sub-Saharan Africa using Marker-Assisted Recurrent Selection

Abstract: In marker-assisted recurrent selection (MARS), a subset of molecular markers significantly associated with target traits of interest are used to predict the breeding value of individual plants, followed by rapid recombination and selfing. This study estimated genetic gains in grain yield (GY) using MARS in 10 biparental tropical maize (Zea may L.) populations. In each population, 148 to 184 F2:3 (defined as C0) progenies were derived, crossed with a single-cross tester, and evaluated under water-stressed (WS) and well-watered (WW) environments in subSaharan Africa (SSA). The C0 populations were genotyped with 190 to 225 single-nucleotide polymorphism (SNP) markers. A selection index based on marker data and phenotypic data was used for selecting the best C0 families for recombination. Individual plants from selected families were genotyped using 55 to 87 SNPs tagging specific quantitative trait loci (QTL), and the best individuals from each cycle were either intercrossed (to form C1) or selfed (to form C1S1 and C1S2). A genetic gain study was conducted using test crosses of lines from the different cycles F1 and founder parents. Test crosses, along with five commercial hybrid checks were evaluated under four WS and four WW environments. The overall gain for GY using MARS across the 10 populations was 105 kg ha−1 yr−1 under WW and 51 kg ha−1 yr−1 under WS. Across WW environments, GY of C1S2–derived hybrids were 8.7, 5.9, and 16.2% significantly greater than those of C0, founder parents, and commercial checks, respectively. Results demonstrate the potential of MARS for increasing genetic gain under both drought and optimum environments in SSA. Y. Beyene, K. Semagn, S. Mugo, L. Machida, M. Olsen, and B.M. Prasanna, International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Village Market 00621, Nairobi, Kenya; J. Crossa, G. Alvarado, M. Bänziger; CIMMYT, Apdo. Postal 6-641, 06600, Mexico DF, Mexico; G.N. Atlin, Bill and Melinda Gates Foundation, Seattle, WA, USA; A. Tarekegne, CIMMYT, 12.5 Km peg Mazowe Road, P.O. Box MP163, Mount Pleasant, Harare, Zimbabwe; B. Meisel, Monsanto South Africa (Pty) Ltd, P. O. Box 69933, Bryanston, 2021, South Africa; P. Sehabiague, Monsanto SAS, Croix de Pardies, BP21, 40305 Peyrehorade, France; B.S. Vivek, CIMMYTIndia, c/o ICRISAT, Patancheru 502324, India; S. Oikeh, African Agricultural Technology Foundation (AATF), P.O. Box 30709-00100, Nairobi, Kenya. Received 28 Feb. 2015. Accepted 26 Aug. 2015. *Corre sponding author (y.beyene@cgiar.org). Abbreviations: AD, anthesis date; ASI, anthesis–silking interval; GS, genomic selection; GY, grain yield; MABC, marker-assisted backcrossing; MARS, marker-assisted recurrent selection; MAS, marker-assisted selection; mQTL, meta-QTL; P-MTI, multiple-trait phenotypic index; PH, plant height; QC, quality control; QTL, quantitative trait loci; SD, silking date; SNP, single-nucleotide polymorphism; SSA, sub-Saharan Africa; WS, water-stressed; WW, well-watered. Published in Crop Sci. 56:344–353 (2016). doi: 10.2135/cropsci2015.02.0135 Freely available online through the author-supported open-access option. © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved. Published November 23, 2015

Analysis and Handling of G × E in a Practical Breeding Program

Abstract: Genotype by environment interaction (GE) is a reality in plant breeding and crop production, and has to be dealt with. There are but two viable options to deal with GE: to utilize it or to avoid it, depending on whether it is repeatable. Repeatable GE can be selected for (utilized) whereas unrepeatable GE has to be selected against (avoided). To utilize GE involves identifying repeatable GE, dividing the target region into subregions or megaenvironments (ME) based on the repeatable GE pattern, and selecting within ME. By definition, GE within ME is unrepeatable and has to be avoided. To avoid unrepeatable GE is to test in a sufficient number of environments (locations and years) representing the target ME and to select both high mean performance and high stability. My preferred analytic tool for identifying repeatable GE, ME analysis, representative test locations, and superior genotypes is GGE (genotypic main effect plus GE) biplots, which was demonstrated using oat (Avena sativa L.) yield data from multilocation multiyear trials. Some important issues on GE study, in relation to genotype evaluation, were discussed. These included the framework of multiyear multilocation trials, the distinction between repeatable and nonrepeatable components of GE, the need to consider both genotypic main effect (G) and GE, and the relative importance of mean performance (G) vs. stability (GE) in genotype evaluation.

Maize stalk lodging: Flexural stiffness predicts strength

Abstract: Late-season stalk lodging in maize (Zea mays L.) is a major agronomic problem that has farreaching economic ramifications. More rapid advances in lodging resistance could be achieved through development of selective breeding tools that are not confounded by environmental factors. It was hypothesized that measurements of stalk flexural stiffness (a mechanical measurement inspired by engineering beam theory) would be a stronger predictor of stalk strength than current technologies. Stalk flexural stiffness, rind penetration resistance and stalk bending strength measurements were acquired for five commercial varieties of dent corn grown at five planting densities and two locations. Correlation analyses revealed that stalk flexural stiffness predicted 81% of the variation in stalk strength, whereas rind penetration resistance only accounted for 18% of the variation in stalk strength. Strength predictions based on measurements of stalk flexural stiffness were not confounded by hybrid type, planting density, or planting location. Strength predictions based on rind penetration resistance were moderately to severely confounded by such factors. Results indicate that stalk flexural stiffness is a good predictor of stalk strength and that it may outperform rind penetration resistance as a selective breeding tool to improve lodging resistance of future varieties of maize. D.J. Robertson, S.Y. Lee, M. Julias, and D.D. Cook, Division of Engineering, New York Univ. Abu Dhabi, Abu Dhabi, United Arab Emirates. Received 1 Nov. 2015. Accepted 20 Jan. 2016. *Corresponding author (douglascook@nyu.edu). Published in Crop Sci. 56:1711–1718 (2016). doi: 10.2135/cropsci2015.11.0665 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Published May 27, 2016

Soybean Genetic Gain in Maturity Groups III to V in Argentina from 1980 to 2015

Abstract: Fil: de Felipe, Matias. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina

Gene Action and Heterotic Groups of Early White Quality Protein Maize Inbreds under Multiple Stress Environments

Abstract: Food insecurity and malnutrition are major challenges facing rural populations in sub-Saharan Africa. A total of 150 quality protein maize (Zea mays L.) (QPM) hybrids generated from 30 earlymaturing QPM inbreds plus six checks were evaluated under drought, low soil N, and Striga [Striga hermonthica (Delile) Benth.]-infested environments in Nigeria for 2 yr. The objectives were to (i) examine the gene action conditioning the traits in the inbreds, (ii) classify them into heterotic groups using two methods, (iii) identify the best QPM inbred testers across environments, and (iv) identify stable and high-yielding hybrids. General and specific combining ability (GCA and SCA, respectively) mean squares were significant (P < 0.01) for grain yield and other traits across environments, indicating that additive and nonadditive gene actions were important in the inheritance of most traits of the inbreds. Preponderance of SCA sum of squares over GCA for most measured traits across environments indicated that nonadditive gene action largely modulated inbred trait inheritance. The GCA effects of multiple traits (HGCAMT) method classified the inbreds into three heterotic groups each under drought and across environments and four groups under low N and Striga-infested environments. Single nucleotide polymorphism (SNP)-based method placed the inbreds into three groups across environments and was more efficient. TZEQI 6 and TZEQI 55 were identified as testers across environments. TZEQI 44  TZEQI 4, TZEQI 35  TZEQI 39, TZEQI 35  TZEQI 59, TZEQI 6  TZEQI 35, and TZEQI 45  TZEQI 33 were the most stable and highest-yielding hybrids across environments and should be commercialized for improved nutrition and food security in sub-Saharan Africa. B. Badu-Apraku, A.O. Talabi, I.C Akaogu, B. Annor, G. Melaku, Y. Fasanmade and M. Aderounmu, International Institute of Tropical Agriculture (UK) Limited, 7th Floor, Grosvenor House, 125 High Street, Croydon CR) 9XP, UK; M.A.B. Fakorede, and R.O. Akinwale, Dep. of Crop Production and Protection, Obafemi Awolowo Univ., Ile-Ile. Nigeria; M. Oyekunle, 3. IAR, Amadu Bello Univ., Samaru, Nigeria. Received 3 May 2015. Accepted 1 Sept. 2015. *Corresponding author (b.badu-apraku@cgiar.org). Abbreviations: AMMI, additive main effects and multiplicative interactions; ASI, anthesis–silking interval; EPP, ears per plant; G  E, genotype  environment interaction; GCA, general combining ability; GD, genetic distance; HGCAMT, general combining ability effects of multiple traits; IITA, International Institute of Tropical Agriculture; IPCA, interaction principal component axes; MI, multiple-trait-base index; NCD II, North Carolina Design II; OPV, open-pollinated variety; PCA, principal component analysis; PIC, polymorphic information content; QPM, quality protein maize; SCA, specific combining ability; SNP, single nucleotide polymorphism; WAP, weeks after planting; WCA, West and Central Africa. Published in Crop Sci. 56:183–199 (2016). doi: 10.2135/cropsci2015.05.0276 Freely available online through the author-supported open-access option. © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved. Published December 3, 2015

Genotype × Environment Interaction and Stability Analysis for Watermelon Fruit Yield in the United States

Abstract: One of the major breeding objectives for watermelon (Citrullus lanatus [Thumb.] Matsum & Nakai) is improved fruit yield. High yielding genotypes have been identified, so we measured their stability for fruit yield and yield components over diverse environments. The objectives of this study were to (i) evaluate the yield of watermelon genotypes over years and locations, (ii) identify genotypes with high stability for yield, and (iii) measure the correlations among univariate and multivariate stability statistics. A diverse set of 40 genotypes was evaluated over 3 yr (2009, 2010, and 2011) and eight locations across the southern United States in replicated trials. Yield traits were evaluated over multiple harvests, and measured as marketable yield, fruit count, percentage cull fruit, percentage early fruit, and fruit size. There were strong effects of environment as well as genotype ́ environment interaction (G ́E) on watermelon yield traits. Based on multiple stability measures, genotypes were classified as stable or unstable for yield. There was an advantage of hybrids over inbreds for yield components in both performance and responsiveness to favorable environments. Cultivars Big Crimson and Legacy are inbred lines with high yield and stability. A significant (P < 0.001) and positive correlation was measured for Shukla’s stability variance (si 2), Shukla’s squared hat (ŝi 2), Wricke’s ecovalence (Wi), and deviation from regression (Sd) for all the traits evaluated in this study. M. Dia and T.C. Wehner, Dep. of Horticultural Science, North Carolina State Univ., Campus Box 7609, Raleigh, NC 27695-7609; R. Hassell, Clemson University, Coastal Research and Education Center, 2700 Savannah Hwy, Charleston, SC 29414; D.S. Price, Georgia County Extension, SW District, 110 West 13th Ave, Suite C, Cordele, GA 31015; G.E. Boyhan, University of Georgia, Dep. of Hort., 1111 Miller Plant Science Bldg., Athens, GA 30602; S. Olson, North Florida REC, Univ. of Florida, 155 Research Road, Quincy, FL 32351-5677; S. King, Texas A&M University, Dep. of Hort. Sci., 1500 Research Pkwy, Ste A120, College Station, TX 77845; A.R. Davis, USDA-ARS, 911 East Highway 3, Lane, OK 74555; G.E. Tolla, Monsanto/Seminis Veg Seeds, 37437 State Hwy 16, Woodland, CA 95695. Received 12 Oct. 2015. Accepted 2 Feb. 2016. *Corresponding author (tcwehner@gmail.com). Abbreviations: AEC, average environment coordinate; AMMI, additive main effects and multiplicative interaction; bi, linear regression coefficient; CI, Clinton, NC; FL, Quincy, FL; GA, Cordele, GA; GGE, genotype main effects plus genotypic ́ environment interaction effect; GGL, genotype main effects plus genotypic ́ location interaction effect; G ́E, Genotype ́ environment interaction; G01 or 1, AU-Jubilant; G02 or 2, Allsweet; G03 or 3, Big Crimson; G04 or 4, Black Diamond; G05 or 5, Calhoun Gray; G06 or 6, Calsweet; G07 or 7, Carolina Cross#183; G08 or 8, Charleston Gray; G09 or 9, Congo; G10 or 10, Crimson Sweet; G11 or 11, Desert King; G12 or 12, Early Arizona; G13 or 13, Early Canada; G14 or 14, Fiesta F1; G15 or 15, Georgia Rattlesnake; G16 or 16, Golden Midget; G17 or 17, Graybelle; G18 or 18, Hopi Red Flesh; G19 or 19, Jubilee; G20 or 20, King & Queen; G21 or 21, Legacy; G22 or 22, Mickylee; G23 or 23, Minilee; G24 or 24, Mountain Hoosier; G25 or 25, NC Giant; G26 or 26, Navajo Sweet; G27 or 27, Peacock WR-60; G28 or 28, Quetzali; G29 or 29, Regency F1; G30 or 30, Royal Flush F1; G31 or 31, Sangria F1; G32 or 32, Starbrite F1; G33 or 33, Stars-N-Stripes F1; G34 or 34, Stone Mountain; G35 or 35, Sugar Baby; G36 or 36, Sugarlee; G37 or 37, Sweet Princess; G38 or 38, Tendersweet OF; G39 or 39, Tom Watson; G40 or 40, Yellow Crimson; KN, Kinston, NC; M, trait mean; OK, Lane, OK; PC, principal component; SC, Charleston, SC; SVP, singular value partitioning; TX, College Station, TX; CA, Woodland, CA; si 2, Shukla’s stability variance; Ŝi 2, Shukla’s squared hat; Sd, deviation from regression; Wi, Wricke’s ecovalence; YSi, Kang’s stability statistic Published in Crop Sci. 56:1645–1661 (2016). doi: 10.2135/cropsci2015.10.0625 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved. Published June 15, 2016

Development and Validation of Red Root Marker-Based Haploid Inducers in Maize

Abstract: One of the critical limitations for the in vivo production of doubled haploid (DH) lines in maize (Zea mays L.) is the inability to effectively identify haploids in a significant proportion of induction crosses due to the possibility of complete or partial inhibition of the currently used R1-nj (Navajo) color marker. In this study, we demonstrate that the R1-nj marker could result in a high proportion of false positives among the haploids identified, besides being ineffective in germplasm with natural anthocyanin expression in pericarp tissue. To address these limitations, we developed haploid inducer lines with triple anthocyanin color markers, including the expression of anthocyanin coloration in the seedling roots and leaf sheaths, in addition to the Navajo marker on the seed. Although these inducers show acceptable haploid induction rates ranging from 8.6 to 10.2%, they exhibited relatively poor agronomic performance compared with tropicalized haploid inducers within tropical environments. The addition of the red root marker more accurately identified haploids among the germinating seedlings, including four tropical inbred lines and eight breeding populations that showed complete inhibition of R1-nj. We also demonstrate that the red root marker can be used for haploid identification in germplasm with natural anthocyanin expression in the pericarp. A survey of 546 tropical inbreds and 244 landraces showed that anthocyanin accumulation in the roots of germinating seedlings is very rare compared with anthocyanin accumulation in the seed and leaf sheath tissues. As a result, the red root marker can serve as a highly complementary marker to R1-nj to enable effective identification of haploids within a wide range of tropical maize germplasm. V. Chaikam, and L. Martinez, CIMMYT, Apdo. Postal 6-641 06600, Mexico D.F, Mexico; A.E. Melchinger, and W. Schipprack, Institute of Plant Breeding, Seed Science and Population Genetics, Univ. of Hohenheim, D-70593 Stuttgart, Germany; P.M. Boddupalli, CIMMYT, ICRAF campus, UN Avenue, Gigiri, Nairobi, Kenya. Received 26 Oct. 2015. Accepted 24 Dec. 2015. *Corresponding author (b.m.prasanna@cgiar.org). Abbreviations: DH, doubled haploid; FDR, false discovery rate; FNR, false negative rate; HIR, haploid induction rate; MCC, Matthews correlation coefficient. Published in Crop Sci. 56:1678–1688 (2016). doi: 10.2135/cropsci2015.10.0653 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Published April 8, 2016

Predicting Soybean Relative Maturity and Seed Yield Using Canopy Reflectance

Abstract: Optimized phenotyping, the observable characteristics attributed to the interaction between genotype and the environment, using canopy reflectance measurements may increase the efficiency of cultivar development. The objectives of this study were to: (i) assess canopy reflectance as a tool for predicting soybean maturity and seed yield; (ii) identify specific development stages that contribute to maturity and yield estimation; and (iii) test the stability and utility of maturity and yield estimation models across environments. Canopy reflectance, maturity, and seed yield were measured on 20 maturity group (MG) 3 and 20 MG 4 soybean cultivars released from 1923 to 2010. Measurements were conducted on six irrigated and water-stressed environments in 2011 and 2012. Cultivar, environment, and cultivar by environment sources of variation were all significant for maturity, yield, and reflectance. Maturity estimation models were created using the visible, red edge, and near-infrared spectrum as well as normalized difference vegetation index (NDVI) and water index ratios. Yield estimation models using the red edge, near-infrared, and visible NDVI indices explained much of the variation in yield among genotypes. No significant trends were found for canopy reflectance data collected at specific development stages or in different water regimes contributing to more accurate yield estimation; however, later development stages (R5-R6) were more accurate for maturity estimation due to spectral data identifying senescing vegetation. Performance of canopy reflectance models for maturity and yield accounted for a significant portion of variability among genotypes for maturity in some environments and for seed yield in most environments. B.S. Christenson, W.T. Schapaugh Jr., N. An, V. Prasad, and A.K. Fritz, Dep. of Agronomy, Kansas State Univ., Manhattan, KS 66506; K.P. Price, AgPixel, LLC, 5530 West Parkway, Suite 300, Johnston, IA 50131. Received 10 Apr. 2015. Accepted 15 Oct. 2015. *Corresponding author (wts@ksu.edu). Published in Crop Sci. 56:625–643 (2016). doi: 10.2135/cropsci2015.04.0237 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Published January 22, 2016

Regional and Temporal Variation in Soybean Seed Protein and Oil across the United States

Abstract: Fil: Rotundo, Jose Luis. Consejo Nacional de Investigaciones Cientificas y Tecnicas; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias; Argentina

Variation Among Maize Hybrids in Response to High Vapor Pressure Deficit at High Temperatures

Abstract: Temperature and vapor pressure deficit (VPD) are two important environmental factors influencing stomatal conductance and transpiration. A limited transpiration rate (TRlim) trait expressed under high VPD has been shown to offer an approach to increase crop yield in water-limited areas. The benefit of the TRlim trait is that it lowers the effective VPD under which plants lose water and so conserves soil water to support crop growth for use during drought periods later in the growing season. Previous studies at moderate temperatures (32 degrees C and lower) identified 12 maize (Zea mays L.) hybrids that express the TRlim trait. A critical question is whether the TRlim trait is also expressed by these hybrids under temperatures up to 38 degrees C, which are relevant in environments where maize may be grown. Five hybrids failed to express the TRlim trait at 38 degrees C but seven hybrids had sustained expression of the trait at 38 degrees C. The loss of expression of the TRlim response in the five hybrids was found to occur in the very narrow range of temperature increase from 36 to 38 degrees C. The genetic differences in water use among these maize hybrids could be useful in selecting hybrids that are especially well adapted for temperature conditions in a targeted production area.