Drought is an important factor limiting corn (Zea mays L.) yields in the Texas High Plains, and adoption of drought‐tolerant (DT) hybrids could be a management tool under water shortage. We conducted a 3‐yr field study to investigate yield, evapotranspiration (ET), and water use efficiency (WUE) in DT hybrids. One conventional (33D49) and 4 DT hybrids (P1151HR, P1324HR, P1498HR, and P1564HR) were grown at three water regimes (I100, I75, and I50, referring to 100, 75, and 50% ET requirement) and three planting densities (PD) (5.9, 7.4, and 8.4 plants m−2). Yield (13.56 Mg ha−1) and ET (719 mm) were the greatest at I100 but WUE (2.1 kg m−3) was the greatest at I75. Although DT hybrids did not always have greater yield and WUE than 33D49 at I100, hybrids P1151HR and P1564HR consistently had greater yield and WUE than 33D49 at I75 and I50. Compared to 33D49, P1151HR and P1564HR had 8.6 to 12.1% and 19.1% greater yield at I75 and I50, respectively. Correspondingly, WUE was 9.8 to 11.7% and 20.0% greater at I75 and I50, respectively. Greater PD resulted in greater yield and WUE at I100 and I75 but PD did not affect yield and WUE at I50. Yield and WUE in greater PD (8.4 plants m−2) were 6.3 to 8.3% greater than those in smaller PD (5.9 plants m−2). The results of this study demonstrated that proper selection of DT hybrids can increase corn yield and WUE under water‐limited conditions.

No abstract

No abstract

Stable quantitative trait loci (QTL) are important for deployment in marker assisted selection in wheat (Triticum aestivum L.) and other crops. We reported QTL discovery in wheat using a population of 217 recombinant inbred lines and multiple statistical approach including multi-environment, multi-trait and epistatic interactions analysis. We detected nine consistent QTL linked to different traits on chromosomes 1A, 2A, 2B, 5A, 5B, 6A, 6B and 7A. Grain yield QTL were detected on chromosomes 2B.1 and 5B across three or four models of GenStat, MapQTL, and QTLNetwork while the QTL on chromosomes 5A.1, 6A.2, and 7A.1 were only significant with yield from one or two models. The phenotypic variation explained (PVE) by the QTL on 2B.1 ranged from 3.3–25.1% based on single and multi-environment models in GenStat and was pleiotropic or co-located with maturity (days to heading) and yield related traits (test weight, thousand kernel weight, harvest index). The QTL on 5B at 211 cM had PVE range of 1.8–9.3% and had no significant pleiotropic effects. Other consistent QTL detected in this study were linked to yield related traits and agronomic traits. The QTL on 1A was consistent for the number of spikes m-2 across environments and all the four analysis models with a PVE range of 5.8–8.6%. QTL for kernels spike-1 were found in chromosomes 1A, 2A.1, 2B.1, 6A.2, and 7A.1 with PVE ranged from 5.6–12.8% while QTL for thousand kernel weight were located on chromosomes 1A, 2B.1, 5A.1, 6A.2, 6B.1 and 7A.1 with PVEranged from 2.7–19.5%. Among the consistent QTL, five QTL had significant epistatic interactions (additive × additive) at least for one trait and none revealed significant additive × additive × environment interactions. Comparative analysis revealed that the region within the confidence interval of the QTL on 5B from 211.4–244.2 cM is also linked to genes for aspartate-semialdehyde dehydrogenase, splicing regulatory glutamine/lysine-rich protein 1 isoform X1, and UDP-glucose 6-dehydrogenase 1-like isoform X1. The stable QTL could be important for further validation, high throughput SNP development, and marker-assisted selection (MAS) in wheat.

No abstract