Showing papers in "Crop Science in 2018"

Major Management Factors Determining Spring and Winter Canola Yield in North America

Abstract: Canola (Brassica napus L. cv. ‘Canola’) production has both economic and agronomic advantages. The objectives of this review were to summarize the key management factors determining crop productivity and to propose plausible pathways to narrow the gap between actual and potential yield. A synthesis study was conducted on data available from performance trials and by reviewing >100 reports in peerreviewed journals, extension publications, and websites. The main outcomes obtained from this synthesis suggested that canola attainable yield could be 4 Mg ha−1 with a potential maximum yield of 7 Mg ha−1. However, actual average yields in North America region were ~1.7 Mg ha−1 for the period 2000 to 2014. Available inseason water, water distribution at critical stages, and nutrient supply (soil plus fertilizer) all contribute to a significant portion of canola yield. Other management factors such as seeding rate, rotation, and cultivar selection substantially affect plant performance. Tillage might have an economic and environmental effect, but overall, the outcome of the meta-analysis did not show significant effect on yield. The review suggests that water supply, balanced nutrition, early planting (for both winter and spring types) in shallow depth (10–19 mm), high seeding rate (6 kg ha−1), and diverse rotation (canola every 3 or 4 yr) are among the best management practices to increase yields. Future lines of research should focus on improving planting operations that diminish early-season heterogeneity, finetuning optimal seeding rates based on modern varieties at varying yield environments, and searching for compatible hybrids to replant without heterogeneity at harvest. Y. Assefa, P.V.V. Prasad, M. Stamm, and I.A. Ciampitti, Dep. of Agronomy, Kansas State Univ., 2004 Throckmorton Plant Science Center, Manhattan, Kansas 66506; C. Foster, Y. Wright, S. Young, P. Bradley, John Deere, 7100 NW 62nd 18 Ave., Johnston, IA 50131. Received 6 Feb. 2017. Accepted 6 Sept. 2017. *Corresponding author (ciampitti@ksu.edu). Assigned to Associate Editor Roxana Savin. Abbreviations: BMP, best management practice. Published in Crop Sci. 58:1–16 (2018). doi: 10.2135/cropsci2017.02.0079 © 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 15, 2018

Sustaining the Future of Plant Breeding: The Critical Role of the USDA-ARS National Plant Germplasm System

Abstract: Plant breeders require genetic diversity to develop cultivars that are productive, nutritious, tolerant of biotic and abiotic stresses, and make efficient use of water and fertilizer. The USDA-ARS National Plant Germplasm System (NPGS) is a major source for global plant genetic resources (PGR), with accessions representing improved cultivars, breeding lines, landraces, and crop wild relatives (CWR), coupled with passport and trait evaluation data. The goal of this article is to facilitate use of PGR in plant breeding programs. Our specific objectives are (i) to summarize the structure and operation of the NPGS and its consultative and support committees, (ii) to review current use of the system by plant breeders, (iii) to describe constraints to improving the utility of PGR, and (iv) to discuss ways in which the NPGS might evolve to better meet the challenges facing agriculture and society in coming decades. The NPGS will enhance its relevance to plant breeding provided there is (i) ongoing attention to filling the gaps in NPGS collections, especially for CWR; (ii) a major increase in efforts to phenotype and genotype accessions using standardized methods; (iii) enhanced information content of the Genetic Resources Information Network (GRIN)-Global system and improved interoperability with other databases; (iv) increased attention to prebreeding activities; (v) improved training opportunities in practices for incorporating PGR in breeding programs; and (vi) expanded outreach efforts to strengthen public support for the NPGS. We believe these steps will be implemented most effectively through coordinated efforts among USDA-ARS, universities, the private sector, and international partners. P.F. Byrne, Dep. of Soil and Crop Sciences, Colorado State Univ., Fort Collins, CO 80523; G.M. Volk, USDA-ARS National Laboratory for Genetic Resources Preservation, 1111 S. Mason St., Fort Collins, CO 80521; C. Gardner, USDA-ARS, Plant Introduction Research Unit, and Dep. of Agronomy, Iowa State Univ., Ames, IA 50011; M.A. Gore, Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY 14853; P.W. Simon, USDA-ARS, Vegetable Crops Research Unit, Dep. of Horticulture, Univ. of Wisconsin, Madison, WI 53706; S. Smith, Dep. of Agronomy, Iowa State Univ., Ames, IA 50011. P.F. Byrne and G.M. Volk contributed equally to this work. Received 19 May 2017. Accepted 21 Nov. 2017. *Corresponding authors (Patrick.Byrne@colostate.edu, Gayle.Volk@ars.usda.gov). Assigned to Associate Editor Jorge da Silva. Abbreviations: CGC, Crop Germplasm Committee; CWR, crop wild relatives; GBIF, Global Biodiversity Information Facility; GEM, Germplasm Enhancement of Maize; GRIN, Genetic Resources Information Network; GWAS, genome-wide association study(ies); NGRAC, National Germplasm Resources Advisory Council; NGRP, National Genetic Resources Program; NIFA, National Institute for Food and Agriculture; NLGRP, National Laboratory for Genetic Resources Preservation; NPGCC, National Plant Germplasm Coordinating Committee; NPGS, National Plant Germplasm System; PBCC, Plant Breeding Coordinating Committee; PCR, polymerase chain reaction; PGR, plant genetic resources; RTAC, Regional Technical Advisory Committee; RWA, Russian wheat aphid; SAES, State Agricultural Experiment Stations; SNP, single-nucleotide polymorphism. Published in Crop Sci. 58:1–18 (2018). doi: 10.2135/cropsci2017.05.0303 © 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 online January 12, 2018

Can Cover or Forage Crops Replace Fallow in the Semiarid Central Great Plains

Abstract: Growing a crop in place of fallow may improve soil properties but result in reduced soil water and crop yields in semiarid regions. This study assessed the effect of replacing fallow in no-till winter wheat (Triticum aestivum L.)–fallow with cover, forage, or grain crops on plant available water (PAW), wheat yield, grain quality, and profitability over 5 yr, from 2007 to 2012. Plant available water at wheat planting was reduced the most when the fallow period was the shortest (i.e., following grain crops) or when biomass production was the greatest. Winter and spring lentil (Lens culinaris Medik.) produced the least biomass, used the least soil water, and had the least negative effect on yield. For every 125 kg ha−1 of cover or forage biomass grown, PAW was reduced by 1 mm, and for every millimeter of PAW, wheat yield was increased by 5.5 kg ha−1. There was no difference in wheat yield whether the preceding crop was harvested for forage or left as standing cover. In years with above-average precipitation, wheat yield was reduced 0 to 34% by growing a crop in place of fallow. However, in years with below-average precipitation, wheat yield was reduced 40 to 70% without fallow. There was minimal negative impact on wheat yield growing a cover or forage crop in place of fallow if wheat yield potential was 3500 kg ha−1 or greater. Net returns were reduced 50 to 100% by growing a cover crop. However, net returns were increased 26 to 240% by growing a forage crop. Integrating annual forages into the fallow period in semiarid regions has the greatest potential for adoption. J.D. Holman, K. Arnet, S Maxwell, and T. Roberts, Dep. of Agronomy, Kansas State Univ., 4500 E. Mary St., Garden City, KS 67846; J. Dille, Dep. of Agronomy, Kansas State Univ., 3701 Throckmorton Plant Sciences Center, Manhattan, KS 66506; A. Obour, Dep. of Agronomy, Kansas State Univ., Agricultural Research Center-Hays 1232 240th Ave., Hays, KS 67601; K. Roozeboom, Dep. of Agronomy, Kansas State Univ., 2004 Throckmorton Plant Sciences Center, Manhattan, KS 66506; A. Schlegel, Kansas State Univ., Southwest Research and Extension Center, 1474 State Highway 96, Tribune, KS 67879. Received 29 May 2017. Accepted 30 Aug. 2017. *Corresponding author ( jholman@ksu.edu). Assigned to Associate Editor Jamie Foster Malone. Abbreviations: ET, evapotranspiration; PAW, plant available water; W-F, winter wheat–fallow rotation; W-GP, winter wheat–field pea rotation; W-SC-F, winter wheat–summer crop–fallow rotation; W-W, continuous winter wheat; WUE, water use efficiency. Published in Crop Sci. 58:932–944 (2018). doi: 10.2135/cropsci2017.05.0324 © 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 4, 2018

Establishment and Function of Cover Crops Interseeded into Corn

Abstract: Cover crops can provide ecological services and improve the resilience of annual cropping systems; however, cover crop use is low in corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotations in the upper Midwest due to challenges with establishment. Our objective was to compare three planting methods to establish cover crops (winter rye [Secale cereale L. ‘Rymin’], red clover [Trifolium pretense L. ‘Medium’], hairy vetch [Vicia villosa Roth], field pennycress [Thlaspi arvense L. ‘MN-106’], and a mixture of oat [Avena sativa L.], pea [Pisum sativum L.], and tillage radish [Raphanus sativus L.]) (MIX) in corn at the seven-leaf collar stage. Planting methods included directed broadcast into the inter-row (DBC), directed broadcast with light incorporation (DBC+INC), and a high-clearance drill (DRILL). The DRILL method achieved greater fall biomass than DBC for all species except pennycress, and DRILL and DBC+INC increased red clover and hairy vetch spring biomass compared with DBC. Cover crops did not affect corn grain or silage yield and reduced yield of the subsequent soybean crop by 0.4 Mg ha−1 (10%) only when poor termination of hairy vetch occurred at one site. Cover crops with >390 kg ha−1 of spring biomass reduced soil nitrateN compared with the no-cover control. These results support that cover crops can be interseeded into corn at the seven-leaf collar stage in the upper Midwest to reduce soil nitrate-N while maintaining corn and subsequent soybean yields; however, effective cover crop termination is critical to avoid competition with the subsequent soybean crop. R.L. Noland, M.S. Wells, C.C. Sheaffer, and J.A. Coulter, Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 411 Borlaug Hall, 1991 Upper Buford Circle, St. Paul, MN 55108; J.M. Baker, USDAARS, St. Paul, MN; K.L. Martinson, Dep. of Animal Science, Univ. of Minnesota, St. Paul, MN. Received 20 June 2017. Accepted 3 Oct. 2017. *Corresponding author (mswells@umn.edu). Assigned to Associate Editor Jeff Melkonian. Abbreviations: a.e., acid equivalent; CHK, control treatment with no cover crop; DBC, directed broadcast of cover crop seed into the interrow; DBC+INC, directed broadcast into the inter-row with light soil incorporation; DM, dry matter; DRILL, high-clearance no-till drill; GDU, growing degree unit; MIX, mixture of oat (48%), pea (48%), and tillage radish (4%); PAR, photosynthetically active radiation. Published in Crop Sci. 58:863–873 (2018). doi: 10.2135/cropsci2017.06.0375 © 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 4, 2018

Genetic Gains for Grain Yield in CIMMYT's Semi-Arid Wheat Yield Trials Grown in Suboptimal Environments

Abstract: Wheat (Triticum aestivum L.) is a major staple food crop grown worldwide on >220 million ha. Climate change is regarded to have severe effect on wheat yields, and unpredictable drought stress is one of the most important factors. Breeding can significantly contribute to the mitigation of climate change effects on production by developing drought-tolerant wheat germplasm. The objective of our study was to determine the annual genetic gain for grain yield (GY) of the internationally distributed Semi-Arid Wheat Yield Trials, grown during 2002-2003 to 2013-2014 and developed by the Bread Wheat Breeding program at the CIMMYT. We analyzed data from 740 locations across 66 countries, which were classified in low-yielding (LYE) and medium-yielding (MYE) environments according to a cluster analysis. The rate of GY increase (GYC) was estimated relative to four drought-tolerant wheat lines used as constant checks. Our results estimate that the rate of GYC in LYE was 1.8% (38.13 kg ha-1 yr-1), whereas in MYE, it was 1.41% (57.71 kg ha-1 yr-1). The increase in GYC across environments was 1.6% (48.06 kg ha-1 yr-1). The pedigrees of the highest yielding lines through the coefficient of parentage analysis indicated the utilization of three primary sources-'Pastor', 'Baviacora 92', and synthetic hexaploid derivatives-to develop drought-tolerant, high and stably performing wheat lines. We conclude that CIMMYT's wheat breeding program continues to deliver adapted germplasm for suboptimal conditions of diverse wheat growing regions worldwide.

Root System Architecture and Its Association with Yield under Different Water Regimes in Durum Wheat

Abstract: Durum wheat (Triticum durum Desf) is a major cereal crop grown globally, but its production is often hindered by droughts Breeding for adapted root system architecture should provide a strategic solution for better capturing moisture The aim of this research was to adapt low-cost and high-throughput methods for phenotyping root architecture and exploring the genetic variability among 25 durum genotypes Two protocols were used: the “clear pot” for seminal root and the “pasta strainer” to evaluate mature roots Analysis of variance revealed significant segregation for all measured traits with strong genetic control Shallow and deep root classes were determined with different methods and then tested in yield trials at five locations with different water regimes Simple trait measurements did not correlate to any of the traits consistently across field sites Multitrait classification instead identified significant superiority of deep-rooted genotypes with 16 to 35% larger grains in environments with limited moisture, but 9 to 24% inferior in the drip irrigated site Combined multitrait classification identified a 28 to 42% advantage in grain yield for the class with deeper roots at two environments where moisture was limited Further discrimination revealed that yield advantage of 37 to 38% under low moisture could be achieved by the deepest root types, but that it also caused a 20 to 40% yield penalty in moisture-rich environments compared with the shallowest root types In conclusion, the proposed methodologies enable low-cost and quick characterization of root behavior in durum wheat with significant distinction of agronomic performance

Shifts in Soybean Yield, Nutrient Uptake, and Nutrient Stoichiometry: A Historical Synthesis-Analysis

Abstract: Few studies have investigated changes over time in nutrient uptake and yield, in addition to the study of nutrient stoichiometry as a metric of nutrient limitations in soybean [Glycine max (L.) Merr.]. A comprehensive synthesisanalysis was performed by compiling a global historical soybean database of yield, total biomass, and nutrient (N, P, and K) content and concentration in studies published from 1922 to 2015. This period was divided in three eras based on genetically modified soybean events: Era I (1922–1996), Era II (1997–2006), and Era III (2007–2015). The main findings of this review are: (i) seed yield improved from 1.3 Mg ha−1 in the 1930s to 3.2 Mg ha−1 in the 2010s; (ii) yield increase was primarily driven by increase in biomass rather than harvest index (HI); (iii) both N and P HIs increased over time; (iv) seed nutrient concentration remained stable for N and declined for both P (18%) and K (13%); (v) stover nutrient concentration remained stable for N, diminished for P, and increased for K; (vi) nutrient ratios portray different trends for N/P (Era I and III > II), N/K (Era I > II and III), and K/P (Era II and III > I); (vii) yield per unit of nutrient uptake (internal efficiency) increased for N (33%) and P (44%) and decreased for K (11%); and (viii) variations in nutrient internal efficiency were primarily explained by increase in nutrient HI for N and K, but equally explained by both HI for P and seed P concentration. These findings have implications for soybean production and integrated nutrient management to improve yield, nutrient use efficiency, and seed nutrient composition. G.R Balboa, Dep. of Agronomy, Kansas State Univ., Manhattan, KS 66506, and Rio Cuarto National Univ., Argentina; V.O Sadras, South Australian Research and Development Institute, Adelaide, SA; I.A Ciampitti, Kansas State Univ., Manhattan, KS 66506. Received 9 June 2017. Accepted 1 Oct. 2017. *Corresponding author (ciampitti@ksu. edu). Assigned to Associate Editor Jeffrey Coulter. Abbreviations: HI, harvest index; IE, internal efficiency; KHI, potassium harvest index; KIE, potassium internal efficiency; Kseed, seed potassium concentration; Kstover, stover potassium concentration; NHI, nitrogen harvest index; NIE, nitrogen internal efficiency; Nseed, seed nitrogen concentration; Nstover, stover nitrogen concentration; PHI, phosphorus harvest index; PIE, phosphorus internal efficiency; Pseed, seed phosphorus concentration; Pstover, stover phosphorus concentration. Published in Crop Sci. 58:43–54 (2018). doi: 10.2135/cropsci2017.06.0349 © 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 15, 2018

Identification of QTLs and Meta-QTLs for Seven Agronomic Traits in Multiple Maize Populations under Well-Watered and Water-Stressed Conditions

Abstract: Drought is one of the most important factors contributing to crop yield loss. To develop drought-tolerant maize (Zea mays L.) varieties, it is important to explore the genetic mechanism and genes involved. In this study, we identified 69 quantitative trait loci (QTLs) for plant height, ear height, anthesis-silking interval, ear weight, cob weight, 100-kernel weight, and ear length in two F₂:₃ populations in both drought-stressed and unstressed conditions. These QTLs explained 4.0 to 17.2% of phenotypic variation in a single watering condition. Approximately 52 of the 69 QTLs were identified under water-stressed conditions. Moreover, 21 stable QTLs were validated in one or two F₂:₃ populations under multiple water-stressed conditions. Remarkably, bin 4.09 (umc2287–umc2011) had two stable QTLs for ear height and anthesis-silking interval; bin 1.07_1.08 (bnlg1025–mmc0041) identified three stable QTLs for ear, cob, and 100-kernel weights; bin 4.08_4.09 (umc2041–umc2287) validated four stable QTLs for ear weight, cob weight, 100-kernel weight, and ear length; and bin 9.04_9.06 (umc1120–umc2134) mapped three stable QTLs for ear weight, cob weight, and ear length that were consistent with phenotypic correlations among traits, supporting pleiotropy of QTLs and playing important roles in conferring growth and yield advantages under drought stress. Additionally, we identified 36 meta-QTLs across 26 populations under 52 well-watered and 38 water-stressed conditions using a meta-analysis, and we predicted 39 candidate genes in the corresponding meta-QTL intervals. These results provide valuable information for further mapping quantitative traits and revealing the genetic basis of drought tolerance.

Cross the Best with the Best, and Select the Best: HELP in Breeding Selfing Crops

Abstract: Hybrid-enabled line profiling (HELP) is a new integrated breeding strategy for self-fertilizing crops that combines existing and recently identified elements, resulting in a strategy that synergistically exceeds existing breeding concepts. Heterosis in selfing crops is often driven by additive and additive X additive gene action, the molecular basis of which is increasingly being revealed. Unlike nonadditive heterosis, additive forms can be relatively easily fixed in homozygous lines, meaning that their seed can simply be resown to express the same “heterosis.” Crossing diverse, complementary “selfing” parents to create the desired trait or allele line profile requires strict male sterility of the female; this can now be achieved relatively easily through present and emerging chemical, environmental, or genetic techniques. Fairly small amounts of hybrid seed are needed, with no need to scale up seed production, as it is not the hybrid that will be commercialized. After multilocation testing, homozygous lines from only the most superior hybrids, driven mainly by additive effects and additive X additive gene action, are rapidly derived using techniques such as doubled haploids. Multilocation testing and molecular confirmation of target line profiles then identify superior lines for release to farmers. The HELP strategy integrates modern high-throughput versions of existing and new concepts and methodologies into a breeding system strategy that focuses on the most superior crosses, <10% of all crosses. This focus results in significant increases in efficiency and can reverse the edible yield plateauing seen or feared in some of our major selfing food crops.

DeltaGen: A Comprehensive Decision Support Tool for Plant Breeders

Abstract: In this paper, we introduce a unique new plant breeding decision support software tool DeltaGen, implemented in r and its package Shiny. DeltaGen provides plant breeders with a single integrated solution for experimental design generation, data quality control, statistical and quantitative genetic analyses, breeding strategy evaluation, simulation, and cost analysis, pattern analysis, index selection, and underlying basic theory on quantitative genetics. Key analysis procedures in DeltaGen were demonstrated using three datasets generated from forage breeding trials in Australia, New Zealand, and the United States. Analyses of the perennial ryegrass seasonal growth data in Case Study 1 was based on residual maximum likelihood analysis and pattern analysis. A graphical summary of the performance of entries across locations was generated, and entries with specific and broad adaptation were identified. The quantitative genetic analysis and breeding method simulation procedures applied to the perennial ryegrass half-sib (HS) family data in Case Study 2 enabled estimation of quantitative genetic parameters, prediction of genetic gain, and calculation of costs per selection cycle. These results enabled comparison of three breeding methods, which also included genomic selection, and their simulation. Data from Case Study 3 were analyzed to investigate a multivariate approach to identify HS families of switchgrass with breeding values that would enable an increase in biomass dry matter yield (DMY) and cell wall ethanol (CWE) and a decrease in Klason lignin (KL). The Smith– Hazel index developed enabled identification of HS families with genetic worth for increasing DMY and CWE and reducing KL, in contrast with individual trait selection. Analysis of the datasets in all three case studies provides a snapshot of the key analyses available within DeltaGen. This software tool could also be used as a teaching resource in plant breeding courses. DeltaGen is available as freeware at http://agrubuntu.cloudapp.net/plantBreedingTool/ M.Z.Z. Jahufer and D. Luo, AgResearch, Grasslands Research Centre, Private Bag 11008, Palmerston North, New Zealand. Received 28 July 2017. Accepted 26 Jan. 2018. *Corresponding author (zulfi.jahufer@ agresearch.co.nz). Assigned to Associate Editor Jeffrey Endelman. Abbreviations: ApWFgsy–HS, among and within half-sib family; BLUP, best linear unbiased predictor; CWE, cell wall ethanol; DMY, dry matter yield; FS, full-sib; DG, genetic gain; GEBV, genomic estimated breeding value; GS, genomic selection; HS, half-sib; HSPT, half-sib family with progeny testing; KL, Klason lignin; MANOVA, multitrait analysis of variance; REML, residual maximum likelihood; SH, Smith– Hazel; TAFE, Technical and Further Education. Published in Crop Sci. 58:1118–1131 (2018). doi: 10.2135/cropsci2017.07.0456 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY license (https:// creativecommons.org/licenses/by/4.0/). Published March 15, 2018

30 Years of Progress toward Increased Biomass Yield of Switchgrass and Big Bluestem

Abstract: Breeding to improve biomass production of switchgrass (Panicum virgatum L.) and big bluestem (Andropogon gerardii Vitman) for conversion to bioenergy began in 1992. The purpose of this study was (i) to develop a platform for uniform regional testing of cultivars and experimental populations for these species, and (ii) to estimate the gains made by breeding during 1992 to 2012. A total of 25 switchgrass populations and 16 big bluestem populations were planted in uniform regional trials at 13 locations in 2012 and 2014. The reference region was USDA Hardiness Zones 3 through 6 in the humid temperate United States. Significant progress toward increased biomass yield was made in big bluestem and within upland-ecotype populations, lowland-ecotype populations, and hybrid-derived populations of switchgrass. Four mechanisms of increasing biomass yield were documented: (i) increased biomass yield per se, (ii) later flowering to extend the growing season, (iii) combined later flowering from the lowland ecotype with survivorship of the upland ecotype in hybrid-derived populations, and (iv) increased survivorship of late-flowering lowland populations in hardiness zones that represent an expansion of their natural adaption zone. Switchgrass exhibited all four mechanisms in one or more improved populations, whereas improved populations of big bluestem were likely influenced by two of the four mechanisms. The uniform testing program was successful at documenting increases in biomass yield, identifying the mechanisms for increased yield, and determining adaptation characteristics and limitations of improved populations.

Plant Population and Maize Grain Yield: A Global Systematic Review of Rainfed Trials

Abstract: Maize (Zea mays L.) productivity has increased globally as a result of improved genetics and agronomic practices. Plant population and row spacing are two key agronomic factors known to have a strong influence on maize grain yield. A systematic review was conducted to investigate the effects of plant population on maize grain yield, differentiating between rainfall regions, N input, and soil tillage system (conventional tillage [CT] and no-tillage [NT]). Data were extracted from 64 peer-reviewed articles reporting on rainfed field trials, representing 13 countries and 127 trial locations. In arid environments, maize grain yield was low (mean maize grain yield = 2448 kg ha−1) across all plant populations with no clear response to plant population. Variation in maize grain yield was high in semiarid environments where the polynomial regression (p < 0.001, n = 951) had a maximum point at ?140,000 plants ha−1, which reflected a maize grain yield of 9000 kg ha−1. In subhumid environments, maize grain yield had a positive response to plant population (p < 0.001). Maize grain yield increased for both CT and NT systems as plant population increased. In high-N-input (r2 = 0.19, p < 0.001, n = 2 018) production systems, the response of plant population to applied N was weaker than in medium-N-input (r2 = 0.49, p < 0.001, n = 680) systems. There exists a need for more metadata to be analyzed to provide improved recommendations for optimizing plant populations across different climatic conditions and rainfed maize production systems. Overall, the importance of optimizing plant population to local environmental conditions and farming systems is illustrated. Dep. of Agronomy, Stellenbosch Univ., Private Bag X1, Matieland 7602, Stellenbosch, South Africa. Received 2 Jan. 2018. Accepted 22 May 2018. *Corresponding author (pieterswanepoel@sun.ac.za). Assigned to Associate Editor Jeff Melkonian. Abbreviations: CA, conservation agriculture; CT, conventional tillage; GRM, general regression model; NT, no-tillage. Published in Crop Sci. 58:1819–1829 (2018). doi: 10.2135/cropsci2018.01.0003 © 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 12, 2018

Yield Stability Differs in Commercial Maize Hybrids in Response to Changes in Plant Density, Nitrogen Fertility, and Environment

Abstract: Continued yield increases in modern commercial maize (Zea mays L.) hybrids will require increased plant density, improved nitrogenuse efficiency, and breeding for a hybrid’s potential yield response to this management. The objective of this study was to determine the genetic variation of commercial hybrids in response to plant density and nitrogen (N) fertilizer levels to assist breeding programs to select hybrids with high yield stability or adaptability to crop management. From 2011 to 2014, 101 hybrids were grown in eight different environments at two planting densities (79,000 and 110,000 plants ha-1) and three N rates (0, 67, and 252 kg N ha-1). Broad-sense heritability increased with increased N rate and plant density. Increased plant density altered yield from -0.60 Mg ha-1 to +0.58 Mg ha-1 under high N conditions, whereas the yield response to increased N ranged from +4.47 to +5.64 Mg ha-1. Hybrids that combined above-average yield under unfertilized and low-N conditions exhibited greater-than-average yield stability across environments under high-N conditions. Hybrid yield stability variance was larger under high-N than under low-N conditions because of greater genotype × environment interaction. Hybrids that were adaptable to high plant density and N conditions exhibited greater-than-average yield potential and yield variation across environments. Selecting hybrids with both high yield and yield stability may be difficult, as yield under lower N levels and yield increases with high N fertilization were negatively correlated. Affiliations: A.T. Mastrodomenico, J.W. Haegele, J.R. Seebauer, and F.E. Below, Dep. of Crop Sciences, Univ. of Illinois, Urbana, IL 618014730; A.T. Mastrodomenico, current address: PR-445 Road, km 56.5, Limagrain, Londrina, Brazil PR 86115-000; J.W. Haegele, current address: WinField United, Mahomet, IL 61853. Received 6 June 2017. Accepted 14 Oct. 2017. *Corresponding author (fbelow@illinois.edu). Assigned to Associate Editor Manjit Kang. Abbreviations: BLUP, best linear unbiased predictor; CH, Champaign, IL; CRM, corn relative maturity; DK, DeKalb, IL; HB, Harrisburg, IL; KN, kernel number; KW, kernel weight; NUE, nitrogen-use efficiency; V2-V4, between the twoand four-leaf growth stages. Published in Crop Sci. 58:230–241 (2018). doi: 10.2135/cropsci2017.06.0340 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY license (https:// creativecommons.org/licenses/by/4.0/). Published January 15, 2018

Multienvironment Models Increase Prediction Accuracy of Complex Traits in Advanced Breeding Lines of Rice

Abstract: Genotype ́ environment interaction (G ́ E) is the differential response of genotypes in different environments and represents a major challenge for breeders. Genotype ́ year-interaction (G ́ Y) is a relevant component of G ́ E, and accounting for it is an important strategy for identifying lines with stable and superior performance across years. In this study, we compared the prediction accuracy of modeling G ́ Y using covariance structures that differ in their ability to accommodate correlation among environments. We present the use of these approaches in two different rice (Oryza sativa L.) breeding populations (indica and tropical japonica) for predicting grain yield, plant height, and three milling quality traits—milling yield, head rice percentage, and grain chalkiness—under different cross-validation (CV) scenarios. We also compared model performance in the context of global predictions (i.e., predictions across years). Most of the benefits of multienvironment models come from modeling genetic correlations between environments when predicting performance of lines that have been tested in some environments but not others (CV2). For predicting the performance of newly developed lines (CV1), modeling between environment correlations has no effect compared with considering environments independently. Response to selection of multienvironment models when modeling covariance structures that accommodate covariances between environments was always beneficial when predicting the performance of lines across years. We also show that, for some traits, high prediction accuracies can be obtained in untested years, which is important for resource allocation in small breeding programs. E. Monteverde and S. McCouch, Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca NY 14853; J.E. Rosas, Dep. of Statistics, College of Agriculture, Univ. de la República, Garzón 780, Montevideo, Uruguay; J.E. Rosas, P. Blanco, and F. Pérez de Vida, National Rice Research Program, National Institute of Agricultural Research (INIA), INIA Treinta y Tres, Villa Sara 33000, Uruguay; V. Bonnecarrère, Biotechnology Unit, National Institute of Agricultural Research (INIA), Estación Experimental Wilson Ferreira Aldunate, Rincón del Colorado 90200, Uruguay; G. Quero, Dep. of Plant Biology, College of Agriculture, Univ. de la República, Garzón 809, Montevideo, Uruguay; L. Gutiérrez, Dep. of Agronomy, Univ. of Wisconsin–Madison, 1575 Linden Dr., Madison, WI 53706. Received 19 Sept. 2017. Accepted 8 May 2018. *Corresponding author (srm4@cornell.edu). Assigned to Associate Editor Aaron Lorenz. Abbreviations: BLUE, best linear unbiased estimator; BLUP, best linear unbiased prediction; CV, cross-validation; GBLUP, genomic best linear unbiased prediction; GBS, genotyping by sequencing; GC, grain chalkiness; GK, Gaussian kernel; GS, genomic selection; GY, grain yield; G ́ E, genotype ́ environment interaction; G ́ Y, genotype ́ year interaction; MTM, multiple-trait model; MY, milling yield; PH, plant height; PHR, head rice percentage; SNP, Single-nucleotide polymorphism. Published in Crop Sci. 58:1519–1530 (2018). doi: 10.2135/cropsci2017.09.0564 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY license (https:// creativecommons.org/licenses/by/4.0/). Published June 21, 2018

Development of Genomic Prediction in Sorghum

Abstract: Genomic selection can increase the rate of genetic gain in plant breeding programs by shortening the breeding cycle. Gain can also be increased through higher selection intensities, as the size of the population available for selection can be increased by predicting performance of nonphenotyped, but genotyped, lines. This paper demonstrates the application of genomic prediction in a sorghum [Sorghum bicolor (L.) Moench] breeding program and compares different genomic prediction models incorporating relationship information derived from molecular markers and pedigree information. In cross-validation, the models using marker-based relationships had higher selection accuracy than the selection accuracy for models that used pedigree-based relationships. It was demonstrated that genotypes that have not been included in the trials could be predicted quite accurately using marker information alone. The accuracy of prediction declined as the genomic relationship of the predicted individual to the training population declined. We also demonstrate that the accuracy of genomic breeding values from the prediction error variance derived from the mixed model equations is a useful indicator of the accuracy of prediction. This will be useful to plant breeders, as the accuracy of the genomic predictions can be assessed with confidence before phenotypes are available. Four distinct environments were studied and shown to perform very differently with respect to the accuracy of predictions and the composition of estimated breeding values. This paper shows that there is considerable potential for sorghum breeding programs to benefit from the implementation of genomic selection.

Genetic Analysis of Sugar Composition and Its Relationship with Protein, Oil, and Fiber in Soybean

Abstract: Soybean [Glycine max (L.) Merr.] is one of the most important crops in the world. It is a major source of vegetable oil for consumption and protein meal for animal feeds and has also been widely used in human food industries because of its nutritive and health benefits. To provide useful information for soybean quality improvement, seed individual sugars, total sugar, protein, oil, and dietary fiber were genetically analyzed in replicated trials with 323 germplasm lines grown in South Dakota and 137 cultivars and breeding lines grown in Virginia. The results indicated significant differences among the genotypes for all traits investigated. Environment effect and genotype ́ environment interaction were also significant in most cases. Heritability estimates were high (94.45–97.79%) for all traits in the germplasm population, and higher in the population of breeding lines for most traits. High genotypic correlation existed between sucrose and total sugar, which helps improvement of digestible sugars and sweetness in soybean food. However, attention should be paid to the lines with higher sucrose but lower oligosaccharides, since stachyose was positively associated with total sugar. Genotypic correlations between seed sugars and protein were insignificant or very low in most cases, implying that alteration of seed sugars might not necessarily affect protein. In some cases, however, there might be negative correlations between seed sugars and oil or dietary fiber in soybean. This study also identified some unique germplasm lines with a desired level of a specific seed composition: one with high sucrose, five with low raffinose, 15 with high total sugar, seven with high protein, and four high in both sucrose and total sugar. G.-L. Jiang, R.A. Bowen, A. Miller, and H. Berry, Agricultural Research Station, Virginia State Univ., PO Box 9061, Petersburg, VA 23806; P. Chen, Dep. of Crop, Soil and Environmental Sciences, Univ. of Arkansas, Fayetteville, AR 72701, current address, Univ. of Missouri, Fisher Delta Research Center, Portville, MO 63873; J. Zhang, Plant Science Dep., South Dakota State Univ., Brookings, SD 57007, current address, Dep. of Agronomy, Iowa State Univ., Ames, IA; L. Florez-Palacios and A. Zeng, Dep. of Crop, Soil and Environmental Sciences, Univ. of Arkansas, Fayetteville, AR 72701; X. Wang, Plant Science Dep., South Dakota State Univ., Brookings, SD 57007, current address, College of Agriculture, Yunnan Univ., Kunming, China. Received 11 Mar. 2018. Accepted 10 July 2018. *Corresponding author (gjiang@vsu.edu, gljiang99@yahoo. com). Assigned to Associate Editor Owen Hoekenga. Abbreviations: HPAEC-PAD, high-performance anion-exchange chromatography coupled with pulsed amperometric detection; HPLC, high-performance liquid chromatography; NIFA, National Institute of Food and Agriculture; NIR, near-infrared; PI, plant introduction; RIL, recombinant inbred line. Published in Crop Sci. 58:2413–2421 (2018). doi: 10.2135/cropsci2018.03.0173 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY license (https:// creativecommons.org/licenses/by/4.0/). Published August 30, 2018

Genome-wide Association Study of Agronomic Traits in a Spring-Planted North American Elite Hard Red Spring Wheat Panel

Abstract: Author(s): Godoy, J; Gizaw, S; Chao, S; Blake, N; Carter, A; Cuthbert, R; Dubcovsky, J; Hucl, P; Kephart, K; Pozniak, C; Prasad, PVV; Pumphrey, M; Talbert, L | Abstract: Inbred cultivars and advanced breeding lines have been subjected to numerous recombination cycles, have strong allelic selection for desired traits, and share important attributes for adaptation and agronomic performance Genetic variation in elite gene pools captured using molecular markers is immediately useful for cultivar development The primary goal of this study was to implement a genome-wide association study for 17 agronomic traits using elite inbred lines A panel consisting of 237 elite hard red spring wheat (Triticum aestivum L) lines from different wheat breeding institutions in North America were evaluated in 11 locations over 2 yr A total of 19,192 polymorphic single-nucleotide polymorphism (SNP) markers from the Illumina 90K SNP array and markers linked to major genes controlling plant height, photoperiod sensitivity, and vernalization were used to assay the population Linkage disequilibrium was observed to decay within a map distance of ~3 cM in the A and B genomes and 7 cM in the D genome A total of 226 marker-trait associations were identified Potentially novel associations were detected for grain yield on chromosome 2B and kernels per spike on 1B and 7D, whereas others colocalized with well-known adaptation loci for photoperiod response, vernalization, and plant height The frequency of positive alleles for specific marker-trait associations differed among the programs, suggesting targets for introgression by the respective breeding programs

Agronomic Performance and Yield Stability of the RNA Interference-Based Bean golden mosaic virus-Resistant Common Bean

Abstract: Bean golden mosaic virus (BGMV) causes the main common bean (Phaseolus vulgaris L.) viral disease in Brazil, causing yield losses of 40 to 100%. Effective resistance to BGMV has not been identified in common bean lines tested in Brazil. Therefore, Embrapa used a transgenic approach to develop effective resistance to BGMV (event Embrapa 5.1), using RNA interference and plant transformation through the biolistic method. In the present work, we evaluate the agronomic performance and yield stability of 10 transgenic BGMV-resistant common bean elite lines from the carioca market class in 31 field trials conducted in Brazil from 2012 to 2014 to identify superior line suitable for release as a new cultivar. The results showed that the presence of the transgene did not cause any loss in yield and conferred greater yield stability in the transgenic elite lines because of the resistance to BGMV. The first commercial product developed with the BGMV resistance was selected, the line CNFCT 16205 (cv. BRS FC401 RMD), which is also the first genetically modified common bean cultivar developed in the world. The line exhibited high yield potential and stability, standard commercial seeds, a normal growing cycle (85–94 d), and effective resistance to BGMV. In addition, it has moderate resistance to anthracnose. However, it is susceptible to Cowpea mild mottle virus (CPMMV). BRS FC401 RMD can contribute to the sustainability of the common bean crop in Brazilian agriculture sector, and to the stability in the supply and price of common bean in the domestic market.