Genetic and agronomic assessment of cob traits in corn under low and normal nitrogen management conditions

Abstract

Exploring and understanding the genetic basis of cob biomass in relation to grain yield under varying nitrogen management regimes will help breeders to develop dual-purpose maize. With rising energy demands and costs for fossil fuels, alternative energy from renewable sources such as maize cobs will become competitive. Maize cobs have beneficial characteristics for utilization as feedstock including compact tissue, high cellulose content, and low ash and nitrogen content. Nitrogen is quantitatively the most important nutrient for plant growth. However, the influence of nitrogen fertilization on maize cob production is unclear. In this study, quantitative trait loci (QTL) have been analyzed for cob morphological traits such as cob weight, volume, length, diameter and cob tissue density, and grain yield under normal and low nitrogen regimes. 213 doubled-haploid lines of the intermated B73 × Mo17 (IBM) Syn10 population have been resequenced for 8575 bins, based on SNP markers. A total of 138 QTL were found for six traits across six trials using composite interval mapping with ten cofactors and empirical comparison-wise thresholds (P = 0.001). Despite moderate to high repeatabilities across trials, few QTL were consistent across trials and overall levels of explained phenotypic variance were lower than expected some of the cob trait × trial combinations (R (2) = 7.3-43.1 %). Variation for cob traits was less affected by nitrogen conditions than by grain yield. Thus, the economics of cob usage under low nitrogen regimes is promising.

QTL mapping of three ear traits using a doubled haploid population of maize

“…Although QTL discrepancies have been observed between our data and previous results due to diverse mapping populations, various marker types and different environments (Liu, Cai et al., ; Sabadin et al., ; Xiang et al., ; Zhang et al., ), there are some common QTLs detected, such as qEL2 and qEL8 (Huo et al., ; Jansen et al., ). The cob diameter in the present study was not obtained directly by measuring the cob, but by subtracting the kernel length determined by image‐based software (Shi et al., ) from the ear diameter, which eliminates the possibility of extra errors caused by manual measurement because of threshing leftover on cobs.…”

“…The cob diameter in the present study was not obtained directly by measuring the cob, but by subtracting the kernel length determined by image‐based software (Shi et al., ) from the ear diameter, which eliminates the possibility of extra errors caused by manual measurement because of threshing leftover on cobs. In reality, the major QTL for CD with PVE of 22.6%, qCD1 , identified in our study overlaps with the QTL mapped based on genotyping by sequencing (Su et al., ) and in a IBM DH population for certain trial conditions (Jansen et al., ). These results indicate that these QTLs are stable across selected lines and conditions, so they may provide robust candidates for the breeding of maize with desired ear traits to improve the overall grain yield.…”

Identification of quantitative trait loci for leaf‐related traits in an IBM Syn10 DH maize population across three environments

“…B73 × Mo17 is a hybridization advantage model in maize; B73 belongs to the BSSS population (Zuber, ) and Mo17 to the Lancaster population (Russell, ). This population was introduced by Iowa State University (Jansen et al., ).…”

“…B73 9 Mo17 is a hybridization advantage model in maize; B73 belongs to the BSSS population (Zuber, 1973) and Mo17 to the Lancaster population (Russell, 1972). This population was introduced by Iowa State University (Jansen et al, 2015). After pollination, the leaf-related traits were investigated as follows: ten normal plants in a row were randomly selected for measurement of LL (cm), LW (cm), LAr (cm 2 ) and LA (degree).…”

“…Compared with the IBM Syn4 DH, IBM Syn10 DH has a higher genetic resolution for fine QTL mapping studies (Liu et al., ). Moreover, the increases in genetic resolution and marker density will improve the efficiency of marker‐assisted selection and the precision of QTL mapping (Jansen et al., ).…”