Evaluating large numbers of genotypes and phenotypes in
multi-environment trials is key to crop improvement for biomass performance in
sorghum. In this dissertation, we
developed an approach that integrates crop growth models with remote-sensing
data and genetic information for modeling and predicting sorghum biomass yield.
The goal of studies described in Chapter 2 was to parameterize the Agricultural
Production Systems sIMulator (APSIM) crop growth models with remote-sensing and
ground-reference data to predict variation in phenology and yield-related
traits for 18 commercial grain and biomass sorghum hybrids. These studies
showed that (i) biomass sorghum hybrids tended to have higher maximum plant
height, final dry biomass and radiation use efficiency (RUE) than grain
sorghum, (ii) photoperiod-sensitive sorghum hybrids exhibited greater biomass
potential in longer growing environments and (iii) the parameterized APSIM
models performed well in above-ground biomass simulations across years and
locations. Crop growth models that integrate remote-sensing data offer an
efficient approach to parameterize models for larger plant breeding
populations. Understanding the genetic architecture of biomass productivity and
bioenergy-related traits is another key aspect of bioenergy sorghum breeding
programs. In Chapter 3, 619 sorghum genotypes from the sorghum diversity panel
were individually crossed to ATx623 to create a half-sib population that was
planted and evaluated in field trials in three consecutive years.
Single-nucleotide polymorphisms (SNPs) were used in a genome-wide association
study (GWAS) to identify genetic loci associated with variation in plant
architecture and biomass productivity. A few SNPs associated with these traits
were located in previously described genes including the sorghum dwarfing genes
<i>Dw1</i> and <i>Dw3</i> and stay-green QTLs <i>Stg1</i> and <i>Stg4</i>. Of
particular interest were seven genetic loci that were discovered for biomass
yield. For three of these loci, the minor or uncommon allele exhibited a
favorable effect on productivity suggesting opportunities to further improve
the crop for biomass accumulation through plant breeding. Marker-assisted and genomic selection strategies
may provide tools to introgress and exploit these genes for bioenergy sorghum
development. Since parameterizing biophysical crop models requires extensive
time and manual effort, a simple model was developed in Chapter 4 that used
time-dependent measurements of RGB canopy cover and daily radiation coupled
with end-of-season biomass for estimating seasonal radiation use efficiency
(SRUE) in 619 sorghum hybrids. SRUE was shown to be a stable and heritable trait that has a positive
relationship with aboveground dry biomass (ADB) over seasons. GWAS identified 11 SNPs associated with SRUE with the
favorable effect represented by the minor allele for seven of these SNPs. Increasing the frequency
of these favorable alleles may improve the breeding population. These results
demonstrated that the simple model for calculating SRUE can be used in genetic
studies and for parameterizing biophysical crop models. The studies integrating crop growth models with
remote sensing technologies provide an opportunity to evaluate a large number
of phenotypes for the target population to understand the underlying genetic
variation of bioenergy sorghum.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/17263916 |
| Date | 18 December 2021 |
| Creators | Kai-Wei Yang (11851139) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/INTEGRATING_CROP_GROWTH_MODELS_AND_REMOTE_SENSING_FOR_PREDICTING_PERFORMANCE_IN_SORGHUM/17263916 |
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