The Optimal Lateral Root Branching Density for Maize Depends on Nitrogen and Phosphorus Availability

Abstract

Observed phenotypic variation in the lateral root branching density (LRBD) in maize (Zea mays) is large (1-41 cm 21 major axis [i.e. brace, crown, seminal, and primary roots]), suggesting that LRBD has varying utility and tradeoffs in specific environments. Using the functional-structural plant model SimRoot, we simulated the three-dimensional development of maize root architectures with varying LRBD and quantified nitrate and phosphorus uptake, root competition, and whole-plant carbon balances in soils varying in the availability of these nutrients. Sparsely spaced (less than 7 branches cm 21 ), long laterals were optimal for nitrate acquisition, while densely spaced (more than 9 branches cm 21 ), short laterals were optimal for phosphorus acquisition. The nitrate results are mostly explained by the strong competition between lateral roots for nitrate, which causes increasing LRBD to decrease the uptake per unit root length, while the carbon budgets of the plant do not permit greater total root length (i.e. individual roots in the high-LRBD plants stay shorter). Competition and carbon limitations for growth play less of a role for phosphorus uptake, and consequently increasing LRBD results in greater root length and uptake. We conclude that the optimal LRBD depends on the relative availability of nitrate (a mobile soil resource) and phosphorus (an immobile soil resource) and is greater in environments with greater carbon fixation. The median LRBD reported in several field screens was 6 branches cm 21 , suggesting that most genotypes have an LRBD that balances the acquisition of both nutrients. LRBD merits additional investigation as a potential breeding target for greater nutrient acquisition.

Functional implications of root cortical senescence for soil resource capture

“…greater carbon and nutrient availability due to reallocation after cell senescence), and allows the plant to grow more root length and creates the potential for a greater lateral branching density and/or length. Dense lateral branching has a greater utility for the capture of immobile nutrients, including potassium and phosphorus, compared to nitrogen (Postma et al 2014). In contrast, decreased lateral branching density in suboptimal nitrogen availability decreased intra-and inter-root competition and had greater utility in plants with RCS.…”

“…Cortical phene states that reduce living cortical tissue reduce root respiration and nutrient content, thereby permitting greater resource allocation to other plant functions including growth and reproduction Lynch 2015). For example, the development of root cortical aerenchyma (RCA) in maize improves plant performance in environments with suboptimal nutrient and water availability (Zhu et al 2010;Postma and Lynch 2011a, b;Jaramillo et al 2013;Saengwilai et al 2014;Chimungu et al 2015), an effect which modeling studies show can be attributed to a reduction in root metabolic costs (Postma et al 2014;Dathe et al 2016). Fewer cell files or greater cell size in the root cortex of maize substantially reduces root respiration and improves soil exploration and water acquisition in conditions of suboptimal water availability (Chimungu et al 2014a(Chimungu et al , 2014b.…”

OpenSimRoot: widening the scope and application of root architectural models

“…By simulating these processes, Postma et al . (2014a) estimated that the optimal branching density (assuming that parent roots have the same root branching density) for maize was lower when N availability decreased. The benefit of fewer but longer laterals in low‐N soils was confirmed in a genotypic contrast study (Zhan et al ., 2015).…”

“…Postma et al . (2014a) showed how the optimal branching density in maize depends on the relative availability of P and N. The module involves three parts: (1) the simulation of plant nutrient requirements; (2) the simulation of nutrient acquisition; and (3) stressors which define how suboptimal plant nutrient concentrations affect physiology or growth (Fig. 4).…”

“…A post‐simulation analysis of root geometry, nutrient uptake and carbon (C) costs enabled the comparison of different RSAs with respect to their efficiency in taking up phosphorus (P) relative to C costs (Nielsen et al ., 1994, 1997; Lynch & Beebe, 1995; Lynch et al ., 1997; Ge et al ., 2000; Rubio et al ., 2001; Walk et al ., 2004, 2006). Later versions coupled physiological mechanisms, such as root respiration, nutrient uptake, canopy photosynthesis and RSA, to simulate how the root phenotype dynamically interacts with the soil environment, and how this interaction influences the acquisition of soil resources and, consequently, plant growth (Postma & Lynch, 2011a,b, 2012; Dathe et al ., 2013, 2016; Postma et al ., 2014a; York et al ., 2016). The initial focus was on P capture (Lynch & Beebe, 1995; Ge et al ., 2000; Ma et al ., 2001; Postma & Lynch, 2011b), which was later expanded to include C (photosynthesis), nitrogen (N), potassium (K) and water (Postma et al ., 2008; Postma & Lynch, 2011a; Dathe et al ., 2013).…”