<p>Dissecting the soybean grain yield (GY) to approach it as a sum of its associated processes seems a viable approach to explore this trait considering its complex multigenic nature. Monteith (1972, 1977) first defined potential yield as the result of three physiological efficiencies: light interception (Ei), radiation use efficiency (RUE) and harvest index (HI). Though this rationality is not recent, few works assessing these three efficiencies as strategies to improve crops have been carried out. This thesis approaches yield from the perspective of Ei, RUE, and HI to better understand yield as the result of genetic and physiological processes. This study reveals the phenotypic variation, heritability, genetic architecture, and genetic relationships for Ei, RUE, and HI and their relationships with GY and other physiological and phenological variables. Similarly, genomic prediction is presented as a viable strategy to partially overcome the tedious phenotyping of these traits. A large panel of 383 soybean recombinant inbred lines (RIL) with significant yield variation but shrinkage maturity was evaluated in three field environments. Ground measurements of dry matter, photosynthesis (A), transpiration (E), water use efficiency (WUE), stomatal conductance (gs), leaf area index (LAI) and phenology (R1, R5, R8) were measured. Likewise, RGB imagery from an unmanned aircraft system (UAS) were collected with high frequency (~12 days) to estimate the canopy dynamic through the canopy coverage (CC). Light interception was modeled through a logistic curve using CC as a proxy and later compared with the seasonal cumulative solar radiation collected from weather stations to calculate Ei. The total above ground biomass collected during the growing season and its respective cumulative light intercepted were used to derive RUE through linear models fitting, while apparent HI was calculated through the ratio seeds dry matter vs total above-ground dry matter. Additive-genetic correlations, genome wide association (GWA) and whole genome regressions (WGR) were performed to determine the relationship between traits, their association with genomic regions, and the feasibility of predicting these efficiencies through genomic information. Our results revealed moderate to high phenotypic variation for Ei, RUE, and HI. Additive-genetic correlation showed a strong relationship of GY with HI and moderate with RUE and Ei when the whole data set was considered, but negligible contribution of HI on GY when just the top 100 yielding RILs were analyzed. High genetic correlation to grain yield (GY) was also observed for A (0.87) and E (0.67), suggesting increase in GY can be achieved through the improvement of A or E. The GWA analyses showed that Ei is associated with three SNPs; two of them located on chromosome 7 and one on chromosome 11 with no previous quantitative trait loci (QTLs) reported for these regions. RUE is associated with four SNPs on chromosomes 1, 7, 11, and 18. Some of these QTLs are novel, while others are previously documented for plant architecture and chlorophyll content. Two SNPs positioned on chromosome 13 and 15 with previous QTLs reported for plant height and seed set, weight and abortion were associated with HI. WGR showed high predictive ability for Ei, RUE, and HI with maximum correlation ranging between 0.75 to 0.80. Both directed and undirected multivariate explanatory models indicate that HI has a strong relationship with A, average growth rate of canopy coverage for the first 40 days after planting (AGR40), seed-filling (SFL), and reproductive length (RL). According to the path analysis, increase in one standard unit of HI promotes changes in 0.5 standard units of GY, while changes in the same standard unit of RUE, and Ei produce increases on GY of 0.20 and 0.19 standard units. This study presents novel genetic knowledge for Ei, RUE, HI and GY along with a set of tools that may contribute to the development of new cultivars with enhanced light interception, light conversion and optimized dry matter partitioning in soybean. This work not only complements the physiological knowledge already available with the genetic control of traits directly associated with yield, but also represents a pioneer attempt to integrate traditional physiological traits into the breeding process in the context of physiological breeding<br></p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/9805328 |
| Date | 16 October 2019 |
| Creators | Miguel A Lopez (7371827) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Developing_the_Yield_Equation_for_Plant_Breeding_Purposes_in_Soybean_i_Glycine_max_i_L_Merr_/9805328 |
Page generated in 0.0021 seconds