Determining Optimum Seeding Rates for Diverse Hard Red Spring Wheat (Triticum Aestivum L.) Cultivars
Date of Award
2016
Publisher
North Dakota State University
Document Type
Dissertation
Degree Awarded
Doctor of Philosophy (PhD)
Program
Plant Sciences
Department
Plant Sciences
College
Agriculture, Food Systems and Natural Resources
Faculty Advisor
Ransom, Joel K.
Description
Seeding rate for maximum grain yield can differ for diverse hard red spring wheat (HRSW) (Triticum aestivum L.) cultivars and can be derived from a seeding rate response curve. Six groups of HRSW cultivars with combinations of Rht-B, Rht-D, and Ppd-D with two cultivars per group were planted in 2013-2015 at five seeding rates in 23 trials throughout Minnesota (MN) and eastern North Dakota (ND), USA. Seeding rates ranged from 1.59 – 5.55 million seeds ha-1. Planting dates represented optimum and delayed seeding dates. Agronomic measurements for plant height, lodging, stems per plant, protein, and yield were obtained. Stand loss measurements, defined as the amount of viable seeds that did not become established plants, ranged from 11-19% across seeding rates most commonly planted in the region. There was a seeding rate by cultivar interaction for plant height, protein, lodging, stems plant-1, and yield. As seeding rate increased stems per plant consistently decreased and there were large differences in tillering capacity between cultivars. Increased seeding rate caused increased lodging for those cultivars with a capacity to lodge. Seeding rate for maximum yield of the cultivars differed. Combined over all cultivars, the seeding rate for maximum yield increased as the average yield of an environment decreased. An analysis of covariance (ANCOVA) predictive model was built for yield and tillering. The model for yield across all environments was not predictive with a validation R2 of 0.01. However, when only the bottom six yielding environments out of the total 21 environments were used to build a yield model the predictions were more accurate with a validation R2 of 0.44. The model built and validated for tillering was predictive for the validation environments with an R2 of 0.71 for validation environments. Seeding rate trials continue to be useful for producers making seeding rate decisions for a range of agronomic reasons. Additionally, using regression predictions and separate training and validation datasets to predict yield and tillering with HRSW, genetic and geographic predictors show promise for recommending seeding rates for future environments.
Recommended Citation
Mehring, Grant Harry, "Determining Optimum Seeding Rates for Diverse Hard Red Spring Wheat (Triticum Aestivum L.) Cultivars" (2016). Plant Sciences. 259.
https://digitalcommons.ndsu.edu/plant-sci/259
Rights
NDSU policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Handle Identifier
https://hdl.handle.net/10365/28029