Identification of Spring Wheat with Superior Agronomic Performance under Contrasting Nitrogen Managements Using Linear Phenotypic Selection Indices

Abstract

Both the Linear Phenotypic Selection Index (LPSI) and the Restrictive Linear Phenotypic Selection Index (RLPSI) have been widely used to select parents and progenies, but the effect of economic weights on the selection parameters (the expected genetic gain, response to selection, and the correlation between the indices and genetic merits) have not been investigated in detail. Here, we (i) assessed combinations of 2304 economic weights using four traits (maturity, plant height, grain yield and grain protein content) recorded under four organically (low nitrogen) and five conventionally (high nitrogen) managed environments, (ii) compared single-trait and multi-trait selection indices (LPSI vs. RLPSI by imposing restrictions to the expected genetic gain of either yield or grain protein content), and (iii) selected a subset of about 10% spring wheat cultivars that performed very well under organic and/or conventional management systems. The multi-trait selection indices, with and without imposing restrictions, were superior to single trait selection. However, the selection parameters differed quite a lot depending on the economic weights, which suggests the need for optimizing the weights. Twenty-two of the 196 cultivars that showed superior performance under organic and/or conventional management systems were consistently selected using all five of the selected economic weights, and at least two of the selection scenarios. The selected cultivars belonged to the Canada Western Red Spring (16 cultivars), the Canada Northern Hard Red (3), and the Canada Prairie Spring Red (3), and required 83-93 days to maturity, were 72-100 cm tall, and produced from 4.0 to 6.2 t ha^-1 grain yield with 14.6-17.7% GPC. The selected cultivars would be highly useful, not only as potential trait donors for breeding under an organic management system, but also for other studies, including nitrogen use efficiency.

Keywords: LPSI; Prairie provinces; RLPS; Smith index; breeding; multi-trait selection; organic agriculture; selection index.

Figure 1

Scatter plots of the best linear unbiased estimators (BLUEs) of four agronomic traits computed from five conventionally and four organically managed environments. All correlations were significant at p < 0.01. The units of measurement were as follows: grain yield (t ha⁻¹), plant height (cm), maturity (d), and grain protein content (%).

Figure 2

Linear regression plots of the best linear unbiased estimators (BLUEs) computed for each environment (a–d) and all environments within the conventional (Con) or organically (Org) management systems (e,f). The units of measurement were as follows: grain yield (t ha⁻¹), plant height (cm), maturity (d), and grain protein content (%).

Figure 3

Bar graph of expected genetic gain (EGG) for grain yield (Yield), plant height (Pht), maturity (Mat), and grain protein content (GPC) plus response to selection and the correlations between each index and the genetic merit based on the Smith Linear Phenotypic Selection Index (LPSI) and Kempthorne and Nordskog Restrictive Linear Phenotypic Selection Index (RLPSI). The RLPSI analyses were done by restricting the expected genetic gain either for yield or GPC to zero. The best linear unbiased estimators computed from the conventionally or organically managed environments and five economic weights (w) were used in the analysis. The economic weights, such as w(−1, −1, 120, 70) refer to an economic weight of −1 for maturity, −1 for plant height, 120 for grain yield, and 70 for GPC, respectively. See Table S6 for details.

Figure 4

Comparison of the phenotypic performance of 32 cultivars consistently selected for their superior performance under organic management system using all five economic weights and the Smith Linear Phenotypic Selection Index (LPSI) and Kempthorne and Nordskog Restrictive Linear Phenotypic Selection Index (RLPSI). The RLPSI analyses were done by restricting the expected genetic gain either for yield (Yield = 0) or grain protein content (GPC = 0). The y-axes represent the best linear unbiased estimators computed from the conventionally or organically managed environments. See Table S1 for details.