Munoz, J. R. | Search bioRxiv

bioRxiv2026

Sixteen thousand grafts reveal limited scion genetic control of grapevine bench-grafting success

Nurseries produce tens of millions of bench-grafted grapevines (Vitis vinifera L.) each year, yet the genetics underlying grafting success rate remain poorly understood. Here, we dissected the genetic architecture of this trait in a scion mapping population. We grafted 138 progeny from a Riesling x Cabernet Sauvignon cross, along with both parents, onto 1103P, with 40 grafts per genotype in each of three separate field blocks and grafted on separate days (16,645 grafts). Success averaged 91.1 %. Scion genotype explained 19 % of the variance among blocks (entry-mean heritability 0.42); genotype x block variance exceeded genotype variance, and a few genotypes failed almost completely in a single block. Success was uncorrelated with yield of the mother vines. QTL mapping across six phenotype definitions and four haplotype-resolved parental genome assemblies detected one modest, reproducible QTL on chromosome 7 (Cabernet Sauvignon alleles; +3.9 percentage points; 11 to 15 % of variance) and suggestive regions on chromosomes 5 and 9. Most variation in grafting success within this elite V. vinifera cross was non-genetic and attributable to events affecting entire bundles. These results set expectations for effect sizes, replication, and process control in genetic studies of grafting success.

genetics↗

bioRxiv2026

Low-cost rhizotron imaging and zero-shot deep-learning resolve temporal, spatial, and genetic variation in grapevine rootstock root systems

Root system architecture shapes how grapevine rootstocks take up water and nutrients, yet roots remain the least phenotyped grapevine organ because they are hidden and hard to image. We present a low-cost phenotyping pipeline that pairs custom acrylic rhizotrons (about US$30 each) with a consumer flatbed scanner and BiRefNet, a general-purpose deep-learning model used without training on root images, followed by automated mask cleaning, skeleton-based trait extraction, and soil moisture mapping. We tested it on nine commercial rootstocks scanned 16 times over 42 days after transplanting (DAT), with half under a ten-day water deficit. From 1,108 images we extracted 21 whole-root, depth-resolved, and topological traits. Genotypes differed in nearly every trait and in how they changed over time. Heritability of size and branching traits peaked at 0.92-0.93 between 21 and 31 DAT and fell for width, depth, and convex hull once roots reached the rhizotron walls, defining the best measurement window. The image-derived soil moisture map accurately tracked the deficit and its recovery. Deficit plants shifted new root growth to deeper soil without growing less overall, and the substrate dried fastest around older and denser roots. Root brightness decreased with root age and local moisture, and transport segments (axes serving several tips) were brighter than terminal laterals in every genotype. Root system size was associated with stomatal conductance in well-watered plants, and stomatal recovery after re-watering correlated with new root growth. The pipeline turns simple hardware into a quantitative, time-resolved root phenotyping platform suitable for breeding.

plant biology↗