Simulating form and function of root systems: efficiency of nitrate uptake is dependent on root system architecture and the spatial and temporal variability of nitrate supply
By Vm Dunbabin, Z. Rengel, A. Diggle. 2004
100 citations recorded by the source corpus.
Summary 1Root-system architecture, and plastic variation in architecture and physiological function, influence the capacity of plants to acquire nutrients from non-uniform soil. Previous theoretical analyses of the relationship between root architecture and nutrient acquisition have largely assumed uniform soils and unresponsive root systems. We extend these studies by considering non-uniform nutrient supply and plasticity in root growth and uptake physiology. 2Using modelling, we investigated the growth and nitrate uptake of two extreme theoretical architectural types: dichotomous (highly branched) and herringbone (sparsely branched). Root systems with plastic or non-plastic root-growth and nitrate-uptake responses, supplied with non-uniform distributions of soil nitrate, were simulated. 3The simulated herringbone root system had a higher nitrate-uptake efficiency (NUE, nitrate-N m−3 soil) when supply varied spatially and temporally (compared with variation in space alone), and NUE was affected only by the capacity to elevate uptake kinetics locally. In contrast, the efficiency of the dichotomous root system decreased under spatially and temporally dynamic nitrate supply (compared with static supply), and was influenced only by the capacity to proliferate roots in nitrate patches. 4These results suggest that root-system NUE is not solely a function of the ion-transport characteristics of soil, but is also influenced by the transitory nature of the nutrient source and the structure of the root system.
Subfields: Plant Science, Soil Science
View Paper: https://www.semanticscholar.org/paper/57d6e31881a9b8f39b946f359d1a3c92622de5bc
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Plant Science
In Soil Science
- Peter B. Reich
- S. Lavorel
- William K. Cornwell
- Joseph M. Craine
- I. Wright
- L. Sack
- Eric Garnier
- J. Lynch
Citations in the corpus
- Maize root growth angles become steeper under low N conditions (2013, background): “Exploration of arger soil volumes at greater depth has been predicted to be beter enhanced by a more sparsely branched herringbone like root ystem (Dunbabin et al., 2004).”
- Acquisition of phosphorus and other poorly mobile nutrients by roots. Where do plant nutrition models fail? (2011, background): “…accounting for root system architecture, a notable exception being the work of Dunbabin and coworkers, which applied mostly to nitrate, and P too (Dunbabin et al. 2003, 2004, 2006), and the recent work of Leitner et al. (2010a) dealing with spatial heterogeneity of P distribution and root system…”
- Modelling the rhizosphere: a review of methods for ‘upscaling’ to the whole‐plant scale (2006, background): “The model included plasticity by linking the signal representing plant demand to a signal representing the capacity of a root segment to supply NO3 – via a positive feedback: thus when demand for NO3 – was large, more of the root growth was allocated to segments in local patches of NO3 – (Dunbabin e”
- A dynamic root system growth model based on L-Systems (2010, methodology): “Advances with regards to additional rhizosphere traits have been made by Dunbabin et al. (2006) by simulating the effects of phospholipid surfactants on water and nutrient uptake.”
- Root Development and Nutrient Uptake (2006, background): “Besides the above two static models (fractal geometry and topology), dynamic models of root system architecture are also often used to simulate growing root systems on the basis of simple production rules (Lynch and Nielsen, 1996; Dunbabin et al., 2002, 2004; Pagès, 2002; Pagès et al., 2004).”
- New handbook for standardised measurement of plant functional traits worldwide (2013, background): “References on theory, significance and large datasets: Eissenstat and Yanai (1997); Wahl and Ryser (2000); Steudle (2001); Pregitzer et al.”