Determining the Efficacy of a Hybridizing Agent in Wheat (Triticum aestivum L.).

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.

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery.

BACKGROUND: Automated 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. RESULTS: Both 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. CONCLUSIONS: With 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.