Performing Department
(N/A)
Non Technical Summary
Intercropping is the practice of growing two or more species in the same place and/or time. In best-case scenarios, intercrops yield more per acre than monocrops, with fewer inputs, and offer long-term yield stability. Breeding for intercropping (B4I) is an underexploited strategy to improve intercropping and our proposal's focus. Purpose-bred intercrop-adapted cultivars would complement agronomic optimization and help overcome barriers to adoption.Breeding better intercrops means testing lots of diversity in intercropping situations. For best results, breeders need to do this early on the selection process, when 100's to 1000's of potential varieties need to be tested for each species. In this situation, it isn't practical to test all combinations of both crops. This limits selection for intercrop performance until the end of the breeding process when most candidates are already removed.The purpose of our Coordinated Innovation Network is to demonstrate solutions to these problems, advancing B4I by example. We focus on oat-pea (Avena sativa, Pisum sativum) as a model of cereal-legume grain intercrops.We will demonstrate an approach wherein only a tractable sample of oat-pea combinations needs to be grown in the field, but all oats and all peas are observed with at least one partner of the other species. Though we limit field testing to only a subset of oat-pea combinations, we will obtain DNA fingerprints on all oats and all peas. Our multi-year, multi-location pilot trial will evaluate thousands of intercrop plots while only sparsely testing the possible oat-pea combinations. Based on the genomic and plant trait data, we will train a machine learning model to predict which peas, oats, and pea-oat combinations yield best together. If successful, our approach will enable selection for intercrop-ability to happen even in the early stages of breeding, when most candidate varieties haven't yet been field tested.Our results will enable the design of selection strategies that will accelerate the improvement of oat-pea and other intercropping systems through breeding.We would like to see our network grow into a B4I community, addressing many species and intercrops.
Animal Health Component
0%
Research Effort Categories
Basic
75%
Applied
25%
Developmental
(N/A)
Goals / Objectives
Our overall goal is to provide proof-of-concept that genomic selection and phenomics can overcome the otherwise intractable combinatorial problem that B4I represents.Obj. 1: Pilot genomic prediction as a decision-support tool for assessing the intercrop-ability of oats with peas, peas with oats, and their specific combinations.Obj. 2: Develop non-destructive, remote, and proximal-sensing-based predictions of intercropped grain yield both overall and partitioned by species.Obj. 3: Develop and validate a crop growth model suitable for cereal-legume intercrops like our focal oat-pea system.Obj. 4: Design selection strategies for accelerated improvement of oat-pea intercrops using stochastic simulation and multi-objective optimization.
Project Methods
Field Trials: Conduct a multi-year, multi-location field trial evaluating the performance of 4,000 total oat-pea intercrop plots (400 plots each over 10 location-years). Sparse co-testing using a p-rep p-loc experimental design of 480 oat and 440 pea genotypes.Phenotyping: Remote and proximal sensing with UAVs and ground-based camera systems. Biomass sampling, grain yields, and more.Genome-wide genotyping of all oats and all peas.Genomic prediction coupling genotype and phenotype data. Novelty: first attempt at genomic prediction of intercrop performance and the estimation of "general and specific mixing ability" parameters.Training ML models to predict intercropped grain yield both overall and partitioned by species based on non-destructive, remote and proximal-sensing.Adapting crop growth models to model intercrops.Multi-objective optimization and stochastic simulation to design future selection strategies for intercrops, especially for oat-pea.