|GUTIÉRREZ, LUCÍA - University Of Wisconsin|
|SMITH, KEVIN - University Of Minnesota|
Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/7/2021
Publication Date: 12/13/2021
Citation: Neyhart, J.L., Gutiérrez, L., Smith, K.P. 2021. Optimizing the choice of test locations for multi-trait genotypic evaluation. Crop Science. 62(1):192-202. https://doi.org/10.1002/csc2.20657.
Interpretive Summary: To develop improved crop varieties, plant breeders often submit promising selections to regional trials with the goal of evaluating variety performance for multiple important traits across different environmental conditions. The selection of varieties as entries for these trials is often based on many seasons of previous observations; yet the locations of these trials, important contributors to non-genetic variation in plant performance, are usually selected in a less data-driven manner. These trials could be made more efficient - both in resources and breeding progress - by choosing locations that yield higher data utility for variety selection. Using historical agronomic and malting quality trait data from two long-term regional barley trials, we assessed the utility of data collected at individual locations by the precision and repeatability of the data, as well as by the representativeness of locations to broader regional mega-environments. We developed a flexible procedure to optimize the selection of locations based on this information, which led to a 58-75% reduction in the number of locations – and therefore plant evaluation costs – with little loss in the utility of data for making variety selections. This approach may be useful for individual plant breeding programs or collaborations wishing to increase the resource efficiency of these important regional evaluation trials.
Technical Abstract: Plant breeding programs expend significant resources on multi-location testing to evaluate genotypes for advancement or potential cultivar release. The selection of genotype entries for these trials is typically based on previous phenotypic data or predictions; yet locations, important contributors to non-genetic variation, are often chosen in a less data-driven manner. Using agronomic and quality trait data from two long-term regional barley (Hordeum vulgare L.) nurseries, our objectives were to i) measure the precision, repeatability, and representativeness of test locations based on multi-trait data, and ii) optimize the selection of test locations for use in future trials. When considering traits individually, ideal locations could be identified simply, but a combined analysis of 11 traits indicated that very few locations were broadly favorable, and considerable tradeoffs are necessary. We developed a flexible optimization procedure to select the locations based on their precision, repeatability, and representativeness for multiple traits, while simultaneously constraining the total number of locations. Optimization led to a 58-75% reduction in the number of locations – and therefore phenotyping costs – with little loss in data utility. Importantly, our approach allowed locations to be selected for phenotyping different sets of traits (e.g. either agronomic or both agronomic and malting quality), mimicking the often nested structure of trait data collection. This approach may be useful for individual plant breeding programs or collaborations wishing to increase the resource efficiency of these important regional evaluation trials.