Progress 11/01/15 to 10/31/20
Target Audience:Two primary group of scientists are the primary target audience for work resulting from this project. Those include plant breeders interested in understanding plant-environment interactions to decide efficiency breeding strategies and resource allocation and those interested in understanding how selection (artificial or natural) can alter the genetic control and modulation of the effect of environment on phenotypes. The target audience has had access to the work generated by this project through presentations of research results (in oral and poster format) at scientific meetings such as the Maize Genetics Conference and the Plant and Animal Genome Conference. In addition, our program has provided relevant opportunities for the training of undergraduate research assistant to gain experience on both field and laboratory activities. This project has also provided direct support for a graduate student in the field of plant breeding and plant genetics who has recently graduated and is currently starting a position as Agriculturist with the USDA-APHIS. This student received training in the area of plant science, primarily focused on plant breeding, field phenotyping and experimentation and large-scale quantitative data analysis. Also, because of the integration of this project within the G2F Maize G X E project, this student played a pivotal role interacting with a vast network of researchers that continue to be part of this collaborative project. Changes/Problems:
What opportunities for training and professional development has the project provided?This project provided the opportunity to train several undergraduate hourly research assistants that supported different aspects of the project over the years including germplasm generation and increase and field and controlled environment phenotyping. The experience gained through this involvement has motivated at least two of those undergraduate research assistants (Sydney Graham and Kyle Bavery) to pursue advanced degrees in different aspects of plant science. This grant also supported a graduate student (Bridget McFarland) who had major responsibility on the design, development and implementation of all aspects of the experimental work of this project as well as the analysis and interpretation of the results generated. Because of the close connection of this project and the Maize GXE project within G2F, in addition to her contributions on supporting all the research activities, Bridget played a pivotal role coordinating communication and data organization, quality control and release for the G2F Maize GXE project as a whole. Bridget also presented about the initiative, the Maize GXE project and the role that the University of Wisconsin has played on the coordination of the initiative at numerous research conference and cooperators' meetings over the years. Bridget defended her PhD thesis on January 8th , 2021 and is currently starting a position as Agriculturist with the USDA-APHIS. The work related to the evaluation of the effect of selection on the modulation of GXE is in the process of been submitted for publication to G3. The planting density work will follow with Crop Science as a potential target journal. How have the results been disseminated to communities of interest?In addition to numerous oral and poster presentations, a manuscript entitled "The role of selection on plant performance and stability in Maize" is in the process of being submitted to the journal G3 describing some of the results from the evaluation of the effect of selection on modulation of stability. Another manuscript entitled "The effect of increased planting density on yield component traits in maize" is also in preparation describing the density effect of yield component traits. A publication describing the data collected by the G2F Maize GXE project was published in 2020 in BMC Research Notes with Bridget McFarland as the primary author. What do you plan to do during the next reporting period to accomplish the goals?
What was accomplished under these goals?
One of the goals of this research was to assess how selection has influenced trait stability and to determine which environmental variables were most influential in hybrid maize performance. To accomplish such goal, a total of 102 unique inbreds were crossed to the non-Stiff Stalk expired-plant variety protection (exPVP) inbred DK3IIH6 and planted in 31 environments ranging from 34.73 to 43.32 latitude and -75.43 to -94.72 longitude across 11 states in the United States that were part of the Maize G X E project within the Genomes to Fields (G2F) initiative using a randomized complete block design with two field replications per location in 2016 and 2017. A total of 3,042 plots were harvested in 2016 and 2,991 plots in 2017. Hybrids were developed by crossing the inbreds founded in, or derived from, the Iowa Stiff Stalk Synthetic (BSSS) population with varying levels of selection. The 102 inbreds used reflect varying levels of selection: 10 of the 16 original BSSS founder lines, 19 lines randomly derived from BSSS cycle 0, 18 lines selected directly from a BSSS inbred, 35 lines with one or more BSSS-derived inbred(s) as a generational relative, and 20 double haploid (DH) inbred lines derived from a synthetic population that combined six core BSSS lines (B73, B84, LH145, PHB47, NKH8431 and PHJ40). Together, all 102 of these inbreds comprised four main groupings that were evaluated in this study: Unselected, Selected, Advanced, and Synthetic. Finlay-Wilkinson linear regression (FWR) was used to assess trait stability for productivity and related agronomic and phenological traits. Partial least squares regression (PLSR) was used to identify the environmental covariates (EC) related to day length, field management, precipitation, soil, temperature, and wind, that were most influential on each of the phenotypic traits and across the groupings of lines representing different selection levels. PLSR was run using phenotypic trait models composed of environment (E) + genotype (G) + (genotype by environment) G×E and G + G×E variance separately. We observed increased stability and improved performance in newer, highly selected inbreds relative to unselected inbreds for all traits, except stalk lodging. While soil classifications, such as phosphorous and organic matter, were of chief importance across all hybrids, we did not find that groupings having undergone more selection responded significantly more to agronomic inputs than other groupings. When comparing ECs across PLSR models, the G + G×E model generated environmental rankings based on predicted hybrid performance that were significantly correlated to the FWR environmental rankings, suggesting that environmental variance (E + G + G×E) is not a good indicator of environment ranking, while G+ G×E better explains hybrid performance. Our results illustrate that selection in maize has improved performance and stability across environments and that different ECs were relevant depending on the trait considered. A second goal of this project was to assess the effect of planting density on grain yield and its component traits to elucidate which component traits have been primarily modified while breeding for higher productivity in the context of increasing planting density. To achieve that goal, we used three biparental recombinant inbred lines families with inbred line PHW65 as the common parent, which reflect older, minimally selected and commercially relevant germplasm, respectively, across a range of planting densities in connection with production-level yield data from the Maize GXE project within G2F; and to genetically dissect the architecture of kernel yield component responses across the varying planting densities. To assess the effect of different planting densities in plant development and components of yield, an "Ever-Increasing Density" (EID) plot format was used. The EID scheme deployed altered plant spacing within each plot to systematically simulate planting densities from 17,205 to 258,148 plants ha-1. The evaluation was conducted at a Madison, Wisconsin location in 2018 and 2019. Ear, cob, and kernel traits (yield component traits) were evaluated from the uppermost ear on one representative plant per plot using an image-based phenotyping tool. The components of ear height, ear length and weight, cob length and weight, cob width, kernel depth, width and area, kernel row number, and kernel weight were significantly influenced by the density treatment and they varied across families; however, ear width did not significantly change across treatments or families. The less selected family (PHW65 x MoG) produced the heaviest cobs and kernels, and largest kernel size, while the commercially relevant and highly selected family (PHW65 x PHN11) produced the lightest cobs and smallest kernels. The less selected family consistently had the widest range of values for numerous traits (ear height, maximum ear width, total cob weight, and kernel depth), while the highly selected family displayed less variability and had the smallest range of values for kernel weight and kernel width. Across all families, increased planting density resulted in decreases in ear, cob, and kernel weight, as well as kernel size (width, depth, and area). Joint linkage mapping identified seven genomic regions on chromosomes 6, 7, 8 and 9 associated with kernel size traits and ear height at the three highest planting densities of 51,604, 85,925, and 258,385 plants ha-1. When connecting EID data with production-level G2F data at comparable planting densities, the yield component traits that were most positively correlated with grain yield were number of ears, while kernel size was negatively correlated. The significant correlation between ear height in the production-level environments with ear height at two of the EID treatments, the same phenotypic trait with an expected correlation to one another, supports the use of the EID design to evaluate varying planting density effects. Our findings demonstrate the utility of alternative planting density schemes to understand the effects of variable planting density on yield component traits and genetically dissect grain yield.
Bridget McFarland (2021) The effects of artificial selection and planting density on performance stability across environments and yield component traits in maize (Zea mays L.). University of Wisconsin, Madison - PhD dissertation
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