Performing Department
(N/A)
Non Technical Summary
Farmers in the Midwestern and Eastern U.S. are adopting perennial crops as a method for sequestering carbon, diversifying farm incomes, and reducing the effects of soil erosion and fertilizer run-off. Specifically, chestnuts are increasingly being planted to accomplish these goals. Industry stakeholders recognize that continued expansion of chestnut plantings will require the development of varieties with improved yield density. However, breeding for this trait has historically been constrained by technical challenges associated with calculating yields, as well as chestnuts' lengthy time to maturity and their substantial acreage requirements.This post-doctoral fellowship proposal overcomes these challenges by leveraging existing on-farm genetic resources. In Aim 1, I will implement machine-learning methods for estimating yield density on an individual tree-basis, using drone-acquired aerial imagery of mature chestnut orchards. Aim 2 of this project will combine this phenotypic data with molecular markers to produce genomic predictions of trees' breeding values, allowing for the selection of parental trees with optimum yield densities to use in subsequent crosses.This research proposal is aligned with the first and second aims of the AFRI-EWD post-doctoral fellowship program - sustainable agricultural intensification, and agricultural climate adaptation - as well as addressing the AFRI-FAS Program Area "PHPPP", with a particular focus within the Priority Area "1e. Plant Breeding for Agricultural Production" (Program Code: A1141). By providing critical mentorship and professional development for the post-doctoral fellow, Dr. Scott Brainard, this proposal also achieves the fourth goal of the AFRI EWD program: to advance science by supporting postgraduate education in the agricultural disciplines.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
A key crop in U.S. agroforestry systems is Chinese chestnut (Castanea mollissima), and its interspecific hybrids with Japanese (C. mollissimaxcrenata) and European chestnut (C. mollissimaxsativa). Chestnuts are a unique temperate nut crop with a starchy, rather than oily, texture (Li et al. 2022a). They can serve as a staple food and as a replacement for maize in processed food and industrial applications (Paciulli et al. 2018; Raczyk et al. 2021). Modern chestnut cultivars are precocious annual bearers (Hunt et al. 2009) and produce nuts with an amino acid balance similar to milk and eggs (Sacchetti et al. 2004). The global market for chestnuts is currently $7.5 billion USD and has been growing at an average annual rate of 3.3% since 2017 (FAOSTAT 2022). Like all woody perennial crops, cultivation of chestnuts also provides a number of ecological benefits. Their perennial root system can help capture excess nutrients and reduce eutrophication of surface waters, and as a permanent landscape cover, they provide habitat for birds, beneficial insects, and other wildlife (Roces-Diaz et al. 2018). And perhaps most significantly, chestnuts store large amounts of carbon: they sequester >0.75 t carbon/acre in woody biomass over their first five years, scaling to more than 8 t carbon/acre sequestered by maturity (Wolz et al. 2018).Aim 1: Development of remote-sensing methods to unlock on-farm genetic resources using two mature chestnuts orchards;Aim 2: Utilization of high-density molecular markers to calculate genomic-estimated breeding values of seedling trees currently growing in mature chestnut orchards.Aim 1. Development of remote-sensing methods to unlock on-farm genetic resources using two mature chestnuts orchards?Background: A limitation with evaluating chestnuts within a traditional breeding program is the substantial acreage required to grow large seedling populations, due to their size at maturity, and the decades required for trees to reach their mature yield-potential (Hunt et al. 2009). However, in contrast to most tree crops, which rely on the cultivation of clonal germplasm, farmers are currently growing large seedling populations, due to the risk of graft failure in the Midwest climate (Jaynes 1975). These seedling trees primarily represent half-sib families descended from high-performing maternal parents (Rutter et al. 1991), and as such, they comprise the types of progeny families that would be evaluated within many breeding program designs. Such existing on-farm genetic diversity therefore offers a unique opportunity to implement a participatory breeding program, wherein phenotypic data is collected from existing mature orchards (Revord et al. 2022). This performance data, combined with genotypic information, can then be used to make genomic predictions of complementary parents for future crosses, as well as cheaply screen future seedling populations to improve the overall performance of seed lots grown by farmers. A key trait in this regard is yield density, but the substantial labor requirements associated with manually collecting nuts from individual trees - which often drop over a multi-week period - and normalizing these measurements for total canopy size, has historically prohibited farmers and researchers from measuring this trait quantitatively.Maximally utilizing this on-farm genetic diversity will require the development of protocols that can be applied to thousands of trees across multiple locations, in order to exploit the substantial extant genetic diversity (Li et al. 2022b), minimize the contribution of spatial heterogeneity to environmental variance components (Heslot et al. 2014; Oakey et al. 2016; Mao et al. 2020), and ultimately optimize training population composition (Isidro et al. 2015; Akdemir and Isidro-Sánchez 2019; Berro et al. 2019). This project addresses the current lack of scalable methods to rapidly and accurately perform such phenotyping for key agronomic traits such as canopy burr density, burr size, and total canopy volume. Specifically, this approach will combine manual, ground-truthing evaluation of trees with remote sensing using drones equipped with high-resolution cameras (see example images in Fig. 1). Recent advances in drone imagery have made application of these methods both economically efficient (Weinstein et al. 2019), and scalable to the evaluation of large numbers of individual trees in horticultural environments (Johansen et al. 2018; Tu et al. 2019; Dong et al. 2020). Specific to this project, previous research suggests phenotyping of key agronomic traits in chestnuts is now feasible using such remote sensing methods (Di Gennaro et al. 2020; Pádua et al. 2020). In addition, deep learning methods have been successfully used to identify chestnut burrs using RGB images taken from the ground (Adão et al. 2019). Aim 2. Utilization of high-density molecular markers to calculate genomic-estimated breeding values of seedling trees currently growing in mature chestnut orchardsBackground: The immediate manner in which these seedling chestnut orchards can be utilized within a breeding program is through the identification of superior parents. These would then be crossed to produce improved progeny families for cultivation by farmers. Given this ultimate objective, it is critical to develop methods to not simply select trees exhibiting high yield density, but rather to select on the basis of maximal breeding value for yield density. Particularly for a trait such as yield, where non-additive variance components are known to be large (Hardwick and Andrews 1980), it is essential to partition variances, and select specifically on the basis of additive genetic value (Lynch and Walsh 1998). An ideal method for doing so in a diverse set of half-sib families, such as represented in these two orchards, is to use dense molecular markers to calculate genomic-estimated breeding values (Bernardo 2002). Such an approach will be preferred to alternative methods aimed at detecting QTL (such as genome-wide association, or multi-parent linkage mapping) that could then be selected for using marker-assisted selection. For traits such as yield density, which have multiple underlying components, and are therefore likely to be under highly polygenic control, the phenotypic variance explained by detectable QTL is expected to be very low, despite their potentially high heritabilities (Brachi et al. 2011).
Project Methods
Aim 1. Development of remote-sensing methods to unlock on-farm genetic resources using two mature chestnuts orchards?Experimental approach: Dr. Brainard will primarily work in two large, mature chestnut orchards in Eastern Ohio: Wintergreen Chestnut Orchard in Mantua, Ohio and Empire Chestnut Company in Carrolton, Ohio (both members of the Route 9 Chestnut cooperative - see attached letter of support). At each site, Dr. Brainard will phenotype fields roughly 20 acres in size, composed of trees planted on 40' centers, such that their canopies are clearly distinguishable from one another. Dr. Brainard will phenotype roughly 1,000 trees at each location, comprising 10 half-sib families descended from high-performing maternal parents from the grafted seed orchard located at the Horticulture and Agroforestry Research Center of the University of Missouri located in New Franklin, Missouri. Multiple grafted copies of these maternal parents also exist at each location. For many of these seedlings and grafted trees there is also historical performance data on nut quality and qualitative yield data, with which validation of digital phenotyping methods can be performed. This will facilitate a broadening of the ground-truthing of image analysis pipelines beyond the manual measurements Dr. Brainard makes during the fellowship. Dr. Brainard will GPS map each orchard in order to facilitate automated pre-programed drone flights using commercially available software (e.g., DroneDeploy), and will conduct multiple drone flights immediately prior to, during and following nut drop.Aim 2. Utilization of high-density molecular markers to calculate genomic-estimated breeding values of seedling trees currently growing in mature chestnut orchardsExperimental approach:Phenotypic data will be combined with genomic information in order to make genomic-estimated predictions of breeding values of all phenotyped trees. The seedlings grown on the two experimental orchards used in this study are either pure Chinese (C. mollisima) trees, or interspecific hybrids with Japanese chestnut (C. crenata) and European chestnut (C. sativa). The recent development of high-quality, chromosome-scale reference genomes for both Chinese (Staton et al. 2019; Xing et al. 2019) and Japanese (Shirasawa et al. 2021) chestnut will therefore make possible the application of cost-effective "genotyping-by-sequencing" methods in the identification of polymorphic molecular markers (Deschamps et al. 2012). In brief, this project will utilize genotypes obtained from paired-end Illumina sequencing of genomic DNA that is extracted from lyophilized leaf tissue and digested with the restriction enzymeApeKI(Elshire et al. 2011; Westbrook et al. 2020). These sequence reads will then be aligned to either theC. mollisimaorC. crenatareference assemblies. In chestnuts, variant calling from Illumina sequence reads has traditionally been performed using GATK (DePristo et al. 2011), however Dr. Brainard will also assess the relative performance (both in terms of total number of markers, and their quality) of Stacks (Rochette et al. 2019) and TASSEL (Bradbury et al. 2007), both of which are commonly used to identify single-nucleotide polymorphisms in agricultural crops. This is a highly cost-effective method of accurately and rapidly identifying the thousands of such molecular markers that are necessary for constructing genomic prediction models.The lab run by Collaborating Mentor Dr. Holliday is a national leader in developing efficient methods for both the sequencing of chestnut, and the bioinformatic pipelines necessary to develop such molecular markers. He recently successfully applied similar workflows to predict chestnut blight resistance for thousands of American chestnut trees in a backcross breeding program, which led to selection of elite families for restoration of this species (Westbrook et al. 2020). His support and mentorship will therefore be crucial to the success of applying these methods to otherCastaneaspecies.Once molecular markers are obtained, Dr. Brainard will calculate genomic-estimated breeding values using best linear unbiased predictors (Henderson 1963). Molecular markers will be used to estimate the realized relationship matrix (Am) (Kang et al. 2008). Yield densities will then be regressed onto a mixed-effects linear model which includes a random term for additive genetic value, following a distribution with a variance-covariance structure determined fromAm. For this project, the software package StageWise (https://github.com/jendelman/StageWise/ ) will be utilized to perform this analysis. StageWise facilitates the inclusion of multi-environment data in genomic selection by implementing a "two-stage" approach, whereby best linear estimators of phenotypes are first calculated as fixed effects in an initial regression that models only the experimental design, and the standard error of these estimates is then included in a second stage where BLUPs are then calculated (Piepho et al. 2012; Damesa et al. 2017). The presence of replication of maternal trees across the two orchards Dr. Brainard will study will enable utilizing multi-environment information in this manner. Dr. Brainard will assess the predictive ability of these BLUPs using a cross-validation approach, calculating the correlation of predicted values with observed phenotypes, using the latter as an estimate of true genotypic values.