In crop species, adaptation to different agroclimatic regions creates useful variation but also leads to unwanted genetic correlations. Bouchet.......

Cereal Chem. 84(2):130-136The goal of this research is to understand the key factors affecting ethanol production from grain sorghum. Seventy genotypes and elite hybrids with a range of chemical compositions and physical properties selected from ≈1,200 sorghum lines were evaluated for ethanol production and were used to study the relationships of composition, grain structure, and physical features that affect ethanol yield and fermentation efficiency. Variations of 22% in ethanol yield and 9% in fermentation efficiency were observed among the 70 sorghum samples. Genotypes with high and low conversion efficiencies were associated with attributes that may be manipulated to improve fermentation efficiency. Major characteristics of the elite sorghum genotypes for ethanol production by the drygrind method include high starch content, rapid liquefaction, low viscosity during liquefaction, high fermentation speed, and high fermentation efficiency. Major factors adversely affecting the bioconversion process are tannin content, low protein digestibility, high mash viscosity, and an elevated concentration of amylose-lipid complex in the mash.

High-dimensional and high throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

Drought tolerance is an important agronomic trait but the genetic and physiological mechanisms that condition its expression are poorly understood. Molecular genetics and quantitative trait loci analysis provide a new and powerful approach to understand better the inheritance and expression of this trait. The purpose of this study was to use molecular markers to identify genetic loci associated with the expression of pre‐flowering drought tolerance in sorghum [Sorghum bicolor (L.) Moench]. Two genotypes with contrasting drought reactions, TX7078 (pre‐flowering tolerant, post‐flowering susceptible) and B35 (pre‐tiowering susceptible, post‐tlowering tolerant), were selected as parents for a sample of recombinant inbred (RI) lines. Ninety‐eight RI lines were evaluated in two different years under conditions of pre‐tlowering drought and full irrigation. This information was used to quantify the drought tolerance of each line. The population was also genotyped with 150 RAPD and 20 RFLP markers that mapped to 17 linkage groups. By means of these markers, six regions of the genome were found to be specifically associated with pre‐flowering drought tolerance. Eight additional regions were more generally associated with yield or yield components under fully irrigated conditions. Several loci were associated with the expression of drought tolerance under both mild and severe drought stress conditions.

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.