Multi-trait multi-environment Bayesian model reveals G x E interaction for nitrogen use efficiency components in tropical maize

Abstract

Identifying maize inbred lines that are more efficient in nitrogen (N) use is an important strategy and a necessity in the context of environmental and economic impacts attributed to the excessive N fertilization. N-uptake efficiency (NUpE) and N-utilization efficiency (NUtE) are components of N-use efficiency (NUE). Despite the most maize breeding data have a multi-trait structure, they are often analyzed under a single-trait framework. We aimed to estimate the genetic parameters for NUpE and NUtE in contrasting N levels, in order to identify superior maize inbred lines, and to propose a Bayesian multi-trait multi-environment (MTME) model. Sixty-four tropical maize inbred lines were evaluated in two experiments: at high (HN) and low N (LN) levels. The MTME model was compared to single-trait multi-environment (STME) models. Based on deviance information criteria (DIC), both multi- and single-trait models revealed genotypes x environments (G x E) interaction. In the MTME model, NUpE was found to be weakly heritable with posterior modes of heritability of 0.016 and 0.023 under HN and LN, respectively. NUtE at HN was found to be highly heritable (0.490), whereas under LN condition it was moderately heritable (0.215). We adopted the MTME model, since combined analysis often presents more accurate breeding values than single models. Superior inbred lines for NUpE and NUtE were identified and this information can be used to plan crosses to obtain maize hybrids that have superior nitrogen use efficiency.

Luxury Vegetative Nitrogen Uptake in Maize Buffers Grain Yield Under Post-silking Water and Nitrogen Stress: A Mechanistic Understanding

“…It is also notable that despite differences in grain yield (Table 4), post-silking N uptake was similar between N veg and n veg under reproductive stress (Table 3). Several studies have found that grain yield is a major driver of post-silking N uptake (Akintoye et al, 1999; Worku et al, 2007; Liu et al, 2014), yet data from this experiment suggests that other factors, such as post-silking N availability or pre-silking N uptake, are important regulators of post-silking N uptake. We found that at all three nodal positions on both post-silking sampling dates, leaf CER was consistently greater in NW rep and Nw rep compared to nW rep and nw rep (Figure 3), indicating that post-silking N supply may increase assimilate supply, although this difference in leaf CER was numeric only.…”

Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques

“…Considering that, implementing different approaches to estimate height and N with UAV-based remote systems is essential to optimize the monitoring of areas with multiple varieties. Currently, one of the main objectives of maize breeding programs is to identify genotypes with high efficiency in N usage [38,39]. Obtaining rapid predictions with an alternative approach like machine learning and UAV-based image may enable programs, technicians, and farmers to evaluate multiple genotypes each year, allowing them to optimize the selection of the most promising plants concerning N use efficiency.…”

Multi-trait multi-environment models in the genetic selection of segregating soybean progeny

“…Even though data collected in plant breeding studies often present a multi-trait multi-environment structure, as they take into consideration the genetic correlations and the G × E interaction, more complex models are required, rendering the computation process more laborious. Some studies have demonstrated the potential of the Bayesian approach for genetic evaluation in plant breeding considering multi-trait or multi-environment models [22–25]. In this approach, the parameters are interpreted as random variables, following the law of probability, which assumes a priori knowledge [25,26].…”