Genetic and Environmental Predictors for Determining Optimal Seeding Rates of Diverse Wheat Cultivars

Agronomy

Jochum J. Wiersma¹  Jordan D. Stanley²  Joel K. Ransom²  Grant H. Mehring³ 

[1] Department of Agronomy and Plant Genetics, Univ. of Minnesota, Crookston, MN 56716, USA;Department of Plant Sciences, North Dakota State Univ., Fargo, ND 58102, USA;WestBred, Bayer Crop Science, Fargo, ND 58102, USA;

关键词: wheat cultivars;    seeding rate;    predictive model;    response curve;    tillering;    yield;   

DOI  :  10.3390/agronomy10030332

来源: DOAJ

【 摘 要 】

Seeding rate for maximum grain yield can differ for diverse hard red spring wheat (HRSW) (Triticum aestivum L.) cultivars and is derived from a yield response curve to seeding rates. Six groups of HRSW cultivars with combinations of Rht-B, Rht-D, and Ppd-D genes were planted at five seeding rates in 21 environments during 2013−2015 throughout Minnesota and eastern North Dakota, USA. Seeding rates ranged from 1.59 to 5.55 million seeds ha⁻¹ and planting timings were optimal and delayed dates. An analysis of covariance predictive model with 13 predetermined training environments was built for yield and tillering, and validated with eight predetermined environments. Optimal seeding rates from the yield model were not predictive for yield, with latitude of the environment negatively skewing the predictions from observed values. A second yield model fit to only the six lowest-yielding environments (<4.8 Mg ha⁻¹) was more predictive (R² = 0.44), and revealed yield response to seeding rate was influenced by cultivar traits for photoperiod response (Ppd-D gene) and plant stature (semi-dwarfing gene Rht-D). The tillering model was also predictive for the validation environments, with a R² of 0.71. Using regression predictions for yield and tillering from training and validation datasets with HRSW genetic and geographic predictors shows promise to help recommend seeding rates for future environments.

【 授权许可】

Unknown