Doubled haploid technology is a feasible, fast, and cost-efficient way of producing completely homozygous lines in maize. Many factors contribute to the success of this system including the haploid induction rate (HIR) of inducer lines, the inducibility of donor background, and environmental conditions. Sixteen inducer lines were tested on eight different genetic backgrounds of five categories in different environments for the HIR to determine possible interaction specificity. The HIR was assessed using the R1-nj phenotype and corrected using the red root marker or using a gold-standard test that uses plant traits. RWS and Mo-17-derived inducers showed higher average induction rates and the commercial dent hybrid background showed higher inducibility. In contrast, sweet corn and flint backgrounds had a relatively lower inducibility, while non-stiff stalk and stiff stalk backgrounds showed intermediate inducibility. For the poor-performing donors (sweet corn and flint), there was no difference in the HIR among the inducers. Anthocyanin inhibitor genes in such donors were assumed to have increased the misclassification rate in the F₁ fraction and, hence, result in a lower HIR.

The symbiotic relationship between soybean [Glycine max L. (Merr.)] roots and bacteria (Bradyrhizobium japonicum) lead to the development of nodules, important legume root structures where atmospheric nitrogen (N₂) is fixed into bio‐available ammonia (NH₃) for plant growth and development. With the recent development of the Soybean Nodule Acquisition Pipeline (SNAP), nodules can more easily be quantified and evaluated for genetic diversity and growth patterns across unique soybean root system architectures. We explored six diverse soybean genotypes across three field year combinations in three early vegetative stages of development and report the unique relationships between soybean nodules in the taproot and non‐taproot growth zones of diverse root system architectures of these genotypes. We found unique growth patterns in the nodules of taproots showing genotypic differences in how nodules grew in count, size, and total nodule area per genotype compared to non‐taproot nodules. We propose that nodulation should be defined as a function of both nodule count and individual nodule area resulting in a total nodule area per root or growth regions of the root. We also report on the relationships between the nodules and total nitrogen in the seed at maturity, finding a strong correlation between the taproot nodules and final seed nitrogen at maturity. The applications of these findings could lead to an enhanced understanding of the plant‐Bradyrhizobium relationship and exploring these relationships could lead to leveraging greater nitrogen use efficiency and nodulation carbon to nitrogen production efficiency across the soybean germplasm.

We develop a Bayesian variable selection method, called SVEN, based on a hierarchical Gaussian linear model with priors placed on the regression coefficients as well as on the model space. Sparsity is achieved by using degenerate spike priors on inactive variables, whereas Gaussian slab priors are placed on the coefficients for the important predictors making the posterior probability of a model available in explicit form (up to a normalizing constant). Embedding a unique model based screening and using fast Cholesky updates, SVEN produces a highly scalable computational framework to explore gigantic model spaces, rapidly identify the regions of high posterior probabilities and make fast inference and prediction. A temperature schedule is used to further mitigate multimodal posterior distributions. The temperature value is guided by our model selection consistency results which hold even when the norm of mean effects solely due to the unimportant variables diverges. An appealing byproduct of SVEN is the construction of novel model weight adjusted prediction intervals. The performance of SVEN is demonstrated through a number of simulation experiments and a real data example from a genome wide association study with over half a million markers. Supplementary materials for this article are available online.

Most farrowing facilities in the United States use stalls and heat lamps to improve sow and piglet productivity. This study investigated these factors by comparing production outcomes for three different farrowing stall layouts (traditional, expanded creep area, expanded sow area) and use of one or two heat lamps. Data were collected on 427 sows and their litters over one year. Results showed no statistical differences due to experimental treatment for any of the production metrics recorded, excluding percent stillborn. Parity one sows had fewer piglets born alive (p < 0.001), lower percent mortality (p = 0.001) and over-lay (p = 0.003), and a greater number of piglets weaned (p < 0.001) with lower average daily weight gain (ADG) (p < 0.001) and more uniform litters (p = 0.001) as compared to higher parity sows. Farrowing turn, associated with group/seasonal changes, had a significant impact on most of the production metrics measured. Number of piglets born influenced the percent stillborn (p < 0.001). Adjusted litter size had a significant impact on percent mortality (p < 0.001), percent over-lay (p < 0.001), and number of piglets weaned (p < 0.001). As the number of piglets weaned per litter increased, both piglet ADG and litter uniformity decreased (p < 0.001). This information can be used to guide producers in farrowing facility design.