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

Grant H Mehring,Joel K Ransom,Jordan D Stanley,Jochum J Wiersma

doi:10.3390/agronomy10030332

Grant H Mehring, Joel K Ransom + Show 2 more

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https://doi.org/10.3390/agronomy10030332

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Abstract

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.

Highlights

Plant density in cereals is important as grain yield is impacted by final plant stand [1,2,3,4,5]
Predicting the yield of plots in eight validation environments with a model built from 13 training environments proved difficult
The model was moderately predictive of yield for the training dataset (R2 = 0.46), the validation dataset was poorly fit, with a R2 of only 0.01

Summary

Introduction

Plant density in cereals is important as grain yield is impacted by final plant stand [1,2,3,4,5]. A seeding rate by cultivar interaction is often found in cereal crop research [1,4,5,6,7,8]. Cultivars of hard red spring wheat (HRSW) (Triticum aestivum L.) have genetic differences affecting plant height [10,11,12,13], tillering [6,14], photoperiod sensitivity [13,15], and yield [12], among many other genetically controlled traits. Spikes per plant in cereal grains is a quantitative trait affected by environment conditions, soil fertility, planting date, and other agronomic practices [16]. Li et al [17]

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