Walnuts are the second most produced and consumed tree nut, with over 2.6 million metric tons produced in the 2022-23 harvest cycle alone. The United States is the second largest producer, accounting for 25% of the total global supply. Nonetheless, producers face an ever-growing demand in a more uncertain climate landscape, which requires effective and efficient walnut selection and breeding of new cultivars with increased kernel content and easy-to-open shells. Past and current efforts select for these traits using hand-held calipers and eye-based evaluations. Yet there is plenty of morphology that meets the eye but goes unmeasured, such as the volume of inner air or the convexity of the kernel. Here, we study the shape of walnut fruits based on X-ray CT (Computed Tomography) 3D reconstructions. We compute 49 different morphological phenotypes for 1264 individuals comprising 149 accessions. These phenotypes are complemented by traits of breeding interest such as ease of kernel removal and kernel weight. Through allometric relationships —relative growth of one tissue to another—, we identify possible biophysical constraints at play during development. We explore multiple correlations between all morphological and commercial traits, and identify which morphological traits can explain the most variability of commercial traits. We show that using only volume and thickness-based traits, especially inner air content, we can successfully encode several of the commercial traits.

Sequencing-based genotyping of heterozygous diploids requires sufficient depth to accurately call heterozygous genotypes. In interspecific hybrids, alignment of reads to both parental genomes simultaneously can generate haploid data, potentially eliminating the problem of heterozygosity. Two populations of interspecific hybrid rootstocks of walnut (Juglans) and pistachio (Pistacia) were genotyped using alignment to the maternal genome, paternal genome, and dual alignment to both genomes simultaneously. Downsampling was used to examine concordance of imputed genotype calls as a function of sequencing depth. Dual alignment resulted in datasets essentially free of heterozygous genotypes, simplifying the identification and removal of cross-contaminated samples. Concordance between full and downsampled genotype calls was always highest after dual alignment. Nearly all SNPs in dual alignment datasets were shared with the corresponding single-parent datasets, but 60-90% of single-parent SNPs were private to that dataset. Private SNPs in single-parent datasets had higher rates of heterozygosity, lower levels of concordance, and were enriched in fixed differences between parental genomes (“homeo-SNPs”) compared to shared SNPs in the same dataset. In multi-parental walnut hybrids, the paternal-aligned dataset was ineffective at resolving population structure in the maternal parent. Overall, the dual alignment strategy effectively produced phased, haploid data, increasing data quality and reducing cost.

Traditionally, walnut kernel color is scored by a human on a four-point color scale ranging from extra light to Amber, developed by the California DFA. Two important reasons for using a high throughput machine vision system for kernel phenotyping is to obtain better quantitative resolution to use in molecular breeding and to avoid errors in human vision phenotyping. RGB and LAB values give much more depth to qualitative calculations, and machine data is far more consistent than human scoring. Previous studies on walnut kernel imaging utilized a thresholding-based approach to segment walnut kernels from their background, however, detection accuracy can be improved. In this study, we make changes to detection methods, primarily using PyTorch computer vision processing and an improved thresholding method to better segment walnut kernels. The PyTorch CNN pipeline allows us to use specific photos to train a model, and the model can segment photos without the need to figure out image thresholds. Our proposed thresholding method uses the magick package in R instead of the ImageJ macros that were previously used. After taking photos with a CVS, we used the CNN model as well as an R script to identify and segment kernels from the background. Our preliminary data shows that with enough training, the CNN model is more robust in edge cases where we have overlapping kernels or shifted images. This paper will focus on exploring the differences between the three thresholding methods, and use the best method for future breeding projects.

Similar to other cropping systems, few walnut cultivars are used as scion in commercial production. Germplasm collections can be used to diversify cultivar options and hold potential for improving crop productivity, disease resistance and stress tolerance. In this study we explored the anatomical and biochemical bases of photosynthetic capacity in 11 J. regia accessions in the USDA-ARS National Clonal Germplasm Repository. Net assimilation rate (An) differed significantly among accessions and was greater in those from lower latitudes coincident with increases in stomatal and mesophyll conductance, leaf thickness, mesophyll porosity and gas-phase diffusion, and leaf nitrogen, and lower leaf mass and stomatal density. High CO2-saturated assimilation rates led to increases in An under limiting conditions. Greater An was found in lower latitude accessions native to climates with more frost-free days, greater precipitation seasonality, and lower temperature seasonality. As expected, water stress consistently impaired photosynthesis with the highest % reductions in three lower latitude accessions (A3, A5, and A9), which had the highest An under well-watered conditions. However, An for A3 and A5 remained amongst the highest under dehydration. J. regia accessions, which have leaf structural traits and biochemistry that enhance photosynthesis, could be used as commercial scions or breeding parents to enhance productivity.