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1.
Front Plant Sci ; 9: 1971, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30687366

RESUMO

Breeding wheat with enhanced levels of grain zinc (Zn) and iron (Fe) is a cost-effective, sustainable solution to malnutrition problems. Modern wheat varieties have limited variation in grain Zn and Fe, but large-scale screening has identified high levels of Zn and Fe in wild relatives and progenitors of cultivated wheat. The most promising sources of high Zn and Fe are einkorn (Triticum monococcum), wild emmer (T. dicoccoides), diploid progenitors of hexaploid wheat (such as Aegilops tauschii), T. spelta, T. polonicum, and landraces of T. aestivum. This study evaluate the effects of translocations from rye and different Aegilops species in a "Pavon-76" wheat genetic background and utilized in the wheat biofortification breeding program at CIMMYT that uses diverse genetic resources, including landraces, recreated synthetic hexaploids, T. spelta and pre-breeding lines. Four translocations were identified that resulted significantly higher Zn content in "Pavon 76" genetic background than the check varieties, and they had increased levels of grain Fe as well-compared to "Pavon 76." These lines were also included in the breeding program aimed to develop advanced high Zn breeding lines. Advanced lines derived from diverse crosses were screened under Zn-enriched soil conditions in Mexico during the 2017 and 2018 seasons. The Zn content of the grain was ranging from 35 to 69 mg/kg during 2017 and 38 to 72 mg/kg during 2018. Meanwhile grain Fe ranged from 30 to 43 mg/kg during 2017 and 32 to 52 mg/kg during 2018. A highly significant positive correlation was found between Zn and Fe (r = 0.54; P < 0.001) content of the breeding lines, therefore it was possible to breed for both properties in parallel. Yield testing of the advanced lines showed that 15% (2017) and 24% (2018) of the lines achieved 95-110% yield potential of the commercial checks and also had 12 mg/kg advantage in the Zn content suggesting that greater genetic gains and farmer-preferred wheat varieties were developed and deployed. A decade of research and breeding efforts led to the selection of "best-bet" breeding lines and the release of eight biofortified wheat varieties in target regions of South Asia and in Mexico.

2.
Field Crops Res ; 214: 373-377, 2017 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-29200604

RESUMO

Wheat is a major staple food crop providing about 20% of dietary energy and proteins, and food products made of whole grain wheat are a major source of micronutrients like Zinc (Zn), Iron (Fe), Manganese (Mn), Magnesium (Mg), Vitamin B and E. Wheat provides about 40% intake of essential micronutrients by humans in the developing countries relying on wheat based diets. Varieties with genetically enhanced levels of grain micronutrient concentrations can provide a cost-effective and sustainable option to resource poor wheat consumers. To determine the effects of commonly deployed dwarfing genes on wheat grain Zn, Fe, Mn and Mg concentrations, nine bread wheat (Triticum aestivum) and six durum wheat (T. turgidum) isoline pairs differing for Rht1 (= Rht-B1b) and one bread wheat pair for Rht2 (= Rht-D1b) dwarfing genes were evaluated for three crop seasons at N.E. Borlaug Research Station, Cd. Obregon, Sonora, Mexico. Presence of dwarfing genes have significantly reduced grain Zn concentration by 3.9 ppm (range 1.9-10.0 ppm), and Fe by 3.2 ppm (range 1.0-14.4 ppm). On the average, about 94 ppm Mg and 6 ppm Mn reductions occurred in semidwarf varieties compared to tall varieties. The thousand kernel weight (TKW) of semidwarf isolines was 2.6 g (range 0.7-5.6 g) lower than the tall counterparts whereas the plant height decreased by 25 cm (range 16-37 cm). Reductions for all traits in semidwarfs were genotype dependent and the magnitude of height reductions did not correlate with reductions in micronutrient concentrations in wheat grain. We conclude that increased grain yield potential of semidwarf wheat varieties is associated with reduced grain micronutrient concentrations; however, the magnitude of reductions in micronutrients varied depending on genetic background and their associated pleiotropic effect on yield components.

3.
Plant Methods ; 13: 62, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28769997

RESUMO

BACKGROUND: Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra. With the bands, vegetation indices are constructed for predicting agronomically important traits such as grain yield and biomass. However, since vegetation indices only use some wavelengths (referred to as bands), we propose using all bands simultaneously as predictor variables for the primary trait grain yield; results of several multi-environment maize (Aguate et al. in Crop Sci 57(5):1-8, 2017) and wheat (Montesinos-López et al. in Plant Methods 13(4):1-23, 2017) breeding trials indicated that using all bands produced better prediction accuracy than vegetation indices. However, until now, these prediction models have not accounted for the effects of genotype × environment (G × E) and band × environment (B × E) interactions incorporating genomic or pedigree information. RESULTS: In this study, we propose Bayesian functional regression models that take into account all available bands, genomic or pedigree information, the main effects of lines and environments, as well as G × E and B × E interaction effects. The data set used is comprised of 976 wheat lines evaluated for grain yield in three environments (Drought, Irrigated and Reduced Irrigation). The reflectance data were measured in 250 discrete narrow bands ranging from 392 to 851 nm (nm). The proposed Bayesian functional regression models were implemented using two types of basis: B-splines and Fourier. Results of the proposed Bayesian functional regression models, including all the wavelengths for predicting grain yield, were compared with results from conventional models with and without bands. CONCLUSIONS: We observed that the models with B × E interaction terms were the most accurate models, whereas the functional regression models (with B-splines and Fourier basis) and the conventional models performed similarly in terms of prediction accuracy. However, the functional regression models are more parsimonious and computationally more efficient because the number of beta coefficients to be estimated is 21 (number of basis), rather than estimating the 250 regression coefficients for all bands. In this study adding pedigree or genomic information did not increase prediction accuracy.

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