Summary (Open access available pdf )
Maize (Zea mays L.) is among the most important grains contributing to global food security. Eighty years of genetic gain for yield of maize under both favorable and unfavorable stress-prone drought conditions have been documented for the US Corn Belt, yet maize remains vulnerable to drought conditions, especially at the critical developmental stage of flowering. Optimum AQUAmax (Dupont Pioneer) maize hybrids were developed for increased grain yield under drought and favorable conditions in the US Corn Belt. Following the initial commercial launch in 2011, a large on-farm data set has been accumulated (10,731 locations) comparing a large sample of the AQUAmax hybrids (78 hybrids) to a large sample of industry-leading hybrids (4287 hybrids) used by growers throughout the US Corn Belt. Following 3 yr (2011–2013) of on-farm industry-scale testing, the AQUAmax hybrids were on average 6.5% higher yielding under water-limited conditions (2006 locations) and 1.9% higher yielding under favorable growing conditions (8725 locations). In a complementary study, 3 yr (2010–2012) of hybrid-by-management-by-environment evaluation under water-limited conditions (14 locations) indicated that the AQUAmax hybrids had greater yield at higher plant populations when compared to non-AQUAmax hybrids. The combined results from research (2008–2010) and on-farm (2011–2013) testing throughout the US Corn Belt over the 6-yr period from 2008 to 2013 indicate that the AQUAmax hybrids offer farmers greater yield stability under water-limited conditions with no yield penalty when the water limitations are relieved and growing conditions are favorable.
Citing among others:
- Cooper, M., C. Gho, R. Leafgren, T. Tang, and C. Messina. 2014a. Breeding drought-tolerant maize hybrids for the US Corn-belt: Discovery to product. J. Exp. Bot. 65:6191–6204. doi:10.1093/jxb/eru064.
Germplasm, genetics, phenotyping, and selection, combined with a clear definition of product targets, are the foundation of successful hybrid maize breeding. Breeding maize hybrids with superior yield for the drought-prone regions of the US corn-belt involves integration of multiple drought-specific technologies together with all of the other technology components that comprise a successful maize hybrid breeding programme. Managed-environment technologies are used to enable scaling of precision phenotyping in appropriate drought environmental conditions to breeding programme level. Genomics and other molecular technologies are used to study trait genetic architecture. Genetic prediction methodology was used to breed for improved yield performance for drought-prone environments. This was enabled by combining precision phenotyping for drought performance with genetic understanding of the traits contributing to successful hybrids in the target drought-prone environments and the availability of molecular markers distributed across the maize genome. Advances in crop growth modelling methodology are being used to evaluate the integrated effects of multiple traits for their combined effects and evaluate drought hybrid product concepts and guide their development and evaluation. Results to date, lessons learned, and future opportunities for further improving the drought tolerance of maize for the US corn-belt are discussed.
AQUAmax®, drought, maize, managed environments, phenotyping, stress, tolerance, yield.
- Cooper, M., C.D. Messina, D. Podlich, L.R. Totir, A. Baumgarten, N.J. Hausmann, D. Wright, and G. Graham. 2014b. Predicting the future of plant breeding: Complementing empirical evaluation with genetic prediction. Crop Pasture Sci. 65:311–336. doi:10.1071/CP14007
For the foreseeable future, plant breeding methodology will continue to unfold as a practical application of the scaling of quantitative biology. These efforts to increase the effective scale of breeding programs will focus on the immediate and long-term needs of society. The foundations of the quantitative dimension will be integration of quantitative genetics, statistics, gene-to-phenotype knowledge of traits embedded within crop growth and development models. The integration will be enabled by advances in quantitative genetics methodology and computer simulation. The foundations of the biology dimension will be integrated experimental and functional gene-to-phenotype modelling approaches that advance our understanding of functional germplasm diversity, and gene-to-phenotype trait relationships for the native and transgenic variation utilised in agricultural crops. The trait genetic knowledge created will span scales of biology, extending from molecular genetics to multi-trait phenotypes embedded within evolving genotype–environment systems. The outcomes sought and successes achieved by plant breeding will be measured in terms of sustainable improvements in agricultural production of food, feed, fibre, biofuels and other desirable plant products that meet the needs of society. In this review, examples will be drawn primarily from our experience gained through commercial maize breeding. Implications for other crops, in both the private and public sectors, will be discussed.Note about first author:
Mark Cooper completed his BAgrSc and PhD degrees and a Graduate Certificate in Education at the University of Queensland. From 1990 to 2000 Mark taught courses in genetics and plant breeding at the University of Queensland and supervised graduate students pursuing MSc and PhD degrees in quantitative genetics, plant breeding and related topics. In 1997, Mark was recognized by the Australian Institute of Agricultural Science and Technology as the Young Professional in Agriculture. In 2000, Mark joined Pioneer Hi-Bred and now has technology development and leadership responsibilities as a DuPont Fellow. His research has focused on seeking working answers to questions of broad relevance to plant breeding, including: (1) What can we learn about the genetic architecture of quantitative traits? (2) How do plant breeding strategies achieve sustainable long-term genetic improvement for quantitative traits? (3) What technologies can improve the effectiveness of plant breeding methodology? These and other related questions are pursued to improve our understanding of the limits of predictability that can be achieved when applying plant breeding strategies to long-term sustainable improvement of agricultural systems. Results from these research efforts have been implemented and have resulted in the release of commercial maize products. His research efforts have been recognized with a number of industry awards, including two Pioneer Achievement in Research Awards and in 2013 the DuPont Bolton/Carothers Innovative Science Award.
- Crossa J, Perez P, Hickey J, Burgueño J, Ornella L, Cerón-Rojas J, Zhang X, Dreisigacker S, Babu R, Li Y, Bonnett D, Mathews K (2014) Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity 112, 48–60. doi:10.1038/hdy.2013.16 (Open Access)
Summary
Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center’s (CIMMYT’s) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT’s maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.
- Sebastian SA, Streit LG, Stephens PA, Thompson JA, Hedges BR, Fabrizius MA, Soper JF, Schmidt DH, Kallem RL, Hinds MA, Feng L, Hoeck JA (2010) Context-specific marker-assisted selection for improved grain yield in elite soybean populations. Crop Science 50, 1196–1206. doi:10.2135/cropsci2009.02.0078 (Open access)
Summary
Despite the importance of grain yield potential to plant breeders and society in general, it has been difficult to identify grain yield quantitative trait loci (QTL) effective for marker-assisted selection (MAS) across a wide range of genetic and/or environmental contexts. However, as genotyping becomes more cost effective, it might be feasible to use preliminary yield trials to model a target genotype within each context and immediately select the progeny that approach that target genotype in real time. In the present study, elite soybean cultivars with residual heterogeneity were leveraged as populations (the genetic context) to detect yield QTL within a limited set of environments (the environmental context), to model a target genotype, and to select subline haplotypes that comprised the target genotype. The yield potential of the selected subline haplotypes were then compared to their respective mother lines in highly replicated yield trials across multiple environments and years. Statistically significant yield gains of up to 5.8% were confirmed in some of the selected sublines, and two of the improved sublines were released as improved cultivars. This context-specific MAS (CSM) approach might also be applicable to the more typical biparental and backcross populations commonly used in plant breeding programs. Factors that can affect the efficiency and applicability of CSM are discussed.
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