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Historically, xenia effects were hypothesized to be unique genetic contributions of pollen to seed phenotype, but most examples represent standard complementation of Mendelian traits. We identified the imprinteddosage-effect defective1(ded1) locus in maize (Zea mays) as a paternal regulator of seed size and development. Hypomorphic alleles show a 5–10% seed weight reduction whended1is transmitted through the male, while homozygous mutants are defective with a 70–90% seed weight reduction.Ded1encodes an R2R3-MYB transcription factor expressed specifically during early endosperm development with paternal allele bias. DED1 directly activates early endosperm genes and endosperm adjacent to scutellum cell layer genes, while directly repressing late grain-fill genes. These results demonstrate xenia as originally defined: Imprinting ofDed1causes the paternal allele to set the pace of endosperm development thereby influencing grain set and size.

Crop improvement programs focus on characteristics that are important for plant productivity. Typically genes underlying these traits are identified and stacked to create improved cultivars. Hence, identification of valuable traits for plant productivity is critical for plant improvement. Here we describe an important characteristic for maize productivity. Despite the fact mature maize ears are typically covered with kernels, we find that only a fraction of ovaries give rise to mature kernels. Non-developed ovaries degenerate while neighboring fertilized ovaries produce kernels that fill the ear. Abortion occurs throughout the ear, not just at the tip. We show that the fraction of aborted ovaries/kernels is genetically controlled and varies widely among maize lines, and low abortion genotypes are rare. Reducing or eliminating ovary abortion could substantially increase yield, making this characteristic a new target for selection in maize improvement programs.

Doubled haploids (DHs) are an important breeding tool for creating maize inbred lines. One bottleneck in the DH process is the manual separation of haploids from among the much larger pool of hybrid siblings in a haploid induction cross. Here, we demonstrate the ability of single‐kernel near‐infrared reflectance spectroscopy (skNIR) to identify haploid kernels. The skNIR is a high‐throughput device that acquires an NIR spectrum to predict individual kernel traits. We collected skNIR data from haploid and hybrid kernels in 15 haploid induction crosses and found significant differences in multiple traits such as percent oil, seed weight, or volume, within each cross. The two kernel classes were separated by their NIR profile using Partial Least Squares Linear Discriminant Analysis (PLS‐LDA). A general classification model, in which all induction crosses were used in the discrimination model, and a specific model, in which only kernels within a specific induction cross, were compared. Specific models outperformed the general model and were able to enrich a haploid selection pool to above 50% haploids. Applications for the instrument are discussed.