BackgroundAutomated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm.ResultsBoth selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds.ConclusionsWith the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing.
For hybrid wheat (Triticum aestivum L.) to be successful in the Great Plains, goodquality hybrid seed production must be reliable, and hybrid yield must exceed best commercial inbred cultivars (commercial heterosis). This research evaluates hybrid wheat cultivars developed from a full diallel of 26 parents that were planted in an augmented design at three locations in Nebraska in each of two years. The effects of using chemical hybridization were evaluated by testing the parents against hybrids that were created as full-sib crosses and showed no detrimental effects of the chemical hybridization method on the hybrid performance. Maternal effects were tested by comparing the reciprocals for each combination of parents, where it was shown that reciprocal effects were of minor importance. General and specific combining abilities and narrow-sense heritability were obtained and are being used to select parents for future hybrid combinations.
Hybrid wheat (Triticum spp.) has the potential to boost yields and enhance production under changing climates to feed the growing global population. Production of hybrid wheat seed relies on male sterility, the blocking of pollen production, to prevent self-pollination. One method of preventing self-pollination in the female plants is to apply a chemical hybridizing agent (CHA). However, some combinations of CHA and genotypes have lower levels of sterility, resulting in decreased hybrid purity. Differences in CHA efficacy are a challenge in producing hybrid wheat lines for commercial and experimental use. Our primary research questions were to estimate the levels of sterility for wheat genotypes treated with a CHA and determine the best way to analyze differences. We applied the CHA sintofen (1-(4-chlorphyl)-1,4-dihydro-5-(2-methoxyethoxy)-4-oxocinnoline-3-carboxylic acid; Croisor 100) to 27 genotypes in replicate. After spraying, we counted seed in bagged female heads to evaluate CHA efficacy and CHA-by-genotype interaction. Using logit and probit models with a threshold of 7 seeds, we found differences among genotypes in 2015. Sterility was higher in 2016 and fewer genotypic differences were found. When CHA-induced sterilization is less uniform as in 2015, zero-inflated and hurdle count models were superior to standard mixed models. These models calculate mean seed number and fit data with limit-bounded scales collected by agronomists and plant breeders to compare genotypic differences. These analyses can assist in selecting parents and identifying where additional optimization of CHA application needs to occur. There is little work in the literature examining the relationship between CHAs and genotypes, making this work fundamental to the future of hybrid wheat breeding.
Hybrid wheat (Triticum aestivum L.) offers potential yield advantages over conventional inbred cultivars. For hybrid wheat to be a commercial success, the cost to produce the hybrid seed needs to be minimized. Although wheat is naturally self‐pollinated, hybrid wheat seed production can be improved by increasing the amount and availability of pollen for cross‐pollination. This research examined 19 pollination traits using the Hard Winter Wheat Association Mapping Panel for 3 years. Anther extrusion, pollen 50 date (date at which a genotype has 50% of spikes pollinating), plant height, and pollination duration (last spike pollen 50 date minus first spike pollen 50 date) were identified as the most important traits for hybrid seed production. Anther extrusion, plant height, and pollen 50 date varied widely among genotypes, while pollination duration had significant genotypic differences in one year of testing. These traits also had significant genotype × year interactions, but better and poorer performers were consistent among years. Anther extrusion was weakly, negatively correlated with plant height, and high anther extrusion semi‐dwarf genotypes were identified. Pollination duration was reduced in a high temperature (>30°C) environment, and genotypic differences in pollination duration were identified only in a milder temperature (24°C) environment. Hierarchical clustering suggested that excellent pollinator genotypes with high anther extrusion and longer pollination duration tended to pollinate early and were of short to moderate stature. Pollination traits were higher when temperatures were mild, which benefited early genotypes because they pollinated before higher temperatures limited their pollination duration.
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