Perennial grains hold promise, especially for marginal landscapes or with limited resources where annual versions struggle.

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1—the summer 2015 and winter 2016 growing seasons–of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project’s goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.

Heat stress adversely affects wheat production in many regions of the world and is particularly detrimental during reproductive development and grainfilling. The objective of this study was to identify quantitative trait loci (QTL) associated with heat susceptibility index (HSI) of yield components in response to a short-term heat shock during early grainfilling in wheat. The HSI was used as an indicator of yield stability and a proxy for heat tolerance. A recombinant inbred line (RIL) population derived from the heat tolerant cultivar 'Halberd' and heat sensitive cultivar 'Cutter' was evaluated for heat tolerance over 2 years in a controlled environment. The RILs and parental lines were grown in the greenhouse and at 10 days after pollination (DAP) half the plants for each RIL received a three-day heat stress treatment at 38°C/ 18°C day/night, while half were kept at control conditions of 20°C/18°C day/night. At maturity, the main spike was harvested and used to determine yield components. A significant treatment effect was observed for most yield components and a HSI was calculated for individual components and used for QTL mapping. QTL analysis identified 15 and 12 QTL associated with HSI in 2005 and 2006, respectively. Five QTL regions were detected in both years, including QTL on chromosomes 1A, 2A, 2B, and 3B. These same regions were commonly associated with QTL for flag leaf length, width, and visual wax content, but not with days to flowering. Pleiotropic trade-offs between the maintenance of kernel number versus increasing single kernel weight under heat stress were present at some QTL regions. The results of this study validate the use of the main spike for detection of QTL for heat tolerance and identify genomic regions associated with improved heat tolerance that can be targeted for future studies.

A physically anchored consensus map is foundational to modern genomics research; however, construction of such a map in oat (Avena sativa L., 2n = 6x = 42) has been hindered by the size and complexity of the genome, the scarcity of robust molecular markers, and the lack of aneuploid stocks. Resources developed in this study include a modified SNP discovery method for complex genomes, a diverse set of oat SNP markers, and a novel chromosome-deficient SNP anchoring strategy. These resources were applied to build the first complete, physically-anchored consensus map of hexaploid oat. Approximately 11,000 high-confidence in silico SNPs were discovered based on nine million inter-varietal sequence reads of genomic and cDNA origin. GoldenGate genotyping of 3,072 SNP assays yielded 1,311 robust markers, of which 985 were mapped in 390 recombinant-inbred lines from six bi-parental mapping populations ranging in size from 49 to 97 progeny. The consensus map included 985 SNPs and 68 previously-published markers, resolving 21 linkage groups with a total map distance of 1,838.8 cM. Consensus linkage groups were assigned to 21 chromosomes using SNP deletion analysis of chromosome-deficient monosomic hybrid stocks. Alignments with sequenced genomes of rice and Brachypodium provide evidence for extensive conservation of genomic regions, and renewed encouragement for orthology-based genomic discovery in this important hexaploid species. These results also provide a framework for high-resolution genetic analysis in oat, and a model for marker development and map construction in other species with complex genomes and limited resources.

Six hundred thirty five oat (Avena sativa L.) lines and 4561 single-nucleotide polymorphism (SNP) loci were used to evaluate population structure, linkage disequilibrium (LD), and genotypephenotype association with heading date. The first five principal components (PCs) accounted for 25.3% of genetic variation. Neither the eigenvalues of the first 25 PCs nor the cross-validation errors from K = 1 to 20 model-based analyses suggested a structured population. However, the PC and K = 2 model-based analyses supported clustering of lines on spring oat vs. southern United States origin, accounting for 16% of genetic variation (p < 0.0001). Single-locus F-statistic (F ST ) in the highest 1% of the distribution suggested linkage groups that may be differentiated between the two population subgroups. Population structure and kinship-corrected LD of r 2 = 0.10 was observed at an average pairwise distance of 0.44 cM (0.71 and 2.64 cM within spring and southern oat, respectively). On most linkage groups LD decay was slower within southern lines than within the spring lines. A notable exception was found on linkage group Mrg28, where LD decay was substantially slower in the spring subpopulation. It is speculated that this may be caused by a heterogeneous translocation event on this chromosome. Association with heading date was most consistent across location-years on linkage groups Mrg02, Mrg12, Mrg13, and Mrg24. Core Ideas• An oat association-mapping panel contributed by active breeding programs worldwide.• Characterized population structure and found subdivisions related to adaptation• Characterized genome-wide and chromosomespecific linkage disequilibrium• Performed association-mapping and post hoc modeling of heading date• Found several consistently associated QTL