INTEGRATING CROP GROWTH MODELS AND REMOTE SENSING FOR PREDICTING PERFORMANCE IN SORGHUM

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 Dw1 and Dw3 and stay-green QTLs Stg1 and Stg4. 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.