Reconciling root plasticity and architectural ground rules in tree root growth models with voxel automata

Mulia, Rachmat; Dupraz, Christian; Noordwijk, Meine van

doi:10.1007/s11104-010-0502-3

Cited by 30 publications

(27 citation statements)

References 45 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This simplification could partly explain the model overestimation of SOC stocks in the 0.0-0.1 m layer of the alleys compared to observed data. This result suggests that it could be useful to couple the CAR-BOSAF model with a model describing root architecture and root growth (Dunbabin et al, 2013;Dupuy et al, 2010), for instance using voxel automata (Mulia et al, 2010). Moreover, the model described a slight increase in SOC stocks in the middle of the alleys rather than close to the trees in the alleys.…”

Section: Representation Of Soc Spatial Heterogeneity In Agroforestry mentioning

confidence: 95%

High organic inputs explain shallow and deep SOC storage in a long-term agroforestry system – combining experimental and modeling approaches

et al. 2018

Self Cite

View full text Add to dashboard Cite

Abstract. Agroforestry is an increasingly popular farming system enabling agricultural diversification and providing several ecosystem services. In agroforestry systems, soil organic carbon (SOC) stocks are generally increased, but it is difficult to disentangle the different factors responsible for this storage. Organic carbon (OC) inputs to the soil may be larger, but SOC decomposition rates may be modified owing to microclimate, physical protection, or priming effect from roots, especially at depth. We used an 18-year-old silvoarable system associating hybrid walnut trees (Juglans regia × nigra) and durum wheat (Triticum turgidum L. subsp. durum) and an adjacent agricultural control plot to quantify all OC inputs to the soil -leaf litter, tree fine root senescence, crop residues, and tree row herbaceous vegetation -and measured SOC stocks down to 2 m of depth at varying distances from the trees. We then proposed a model that simulates SOC dynamics in agroforestry accounting for both the whole soil profile and the lateral spatial heterogeneity. The model was calibrated to the control plot only.Measured OC inputs to soil were increased by about 40 % (+ 1.11 t C ha −1 yr −1 ) down to 2 m of depth in the agroforestry plot compared to the control, resulting in an additional SOC stock of 6.3 t C ha −1 down to 1 m of depth. However, most of the SOC storage occurred in the first 30 cm of soil and in the tree rows. The model was strongly validated, properly describing the measured SOC stocks and distribution with depth in agroforestry tree rows and alleys. It showed that the increased inputs of fresh biomass to soil explained the observed additional SOC storage in the agroforestry plot. Moreover, only a priming effect variant of the model was able to capture the depth distribution of SOC stocks, suggesting the priming effect as a possible mechanism driving deep SOC dynamics. This result questions the potential of soils to store large amounts of carbon, especially at depth. Deep-rooted trees modify OC inputs to soil, a process that deserves further study given its potential effects on SOC dynamics.

show abstract

Section: Representation Of Soc Spatial Heterogeneity In Agroforestry mentioning

confidence: 95%

High organic inputs explain shallow and deep SOC storage in a long-term agroforestry system – combining experimental and modeling approaches

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Still, to a considerable extent, secondary thickening of transport roots occurs in response to, and in coordination with fine branch root development, so the branching pattern present at any time is likely to contain more regularity and predictability than one would expect from the way it is formed. Mulia et al (2010) showed that a dynamic fine root response to local uptake conditions can be reconciled in model algorithms with an emerging fractal branching architecture for woody roots.…”

Section: B Test Of Fractal Branching Assumptions (Also Seementioning

confidence: 99%

Root distribution of trees and crops: competition and/or complementarity.

Noordwijk¹,

Lawson²,

Soumaré³

et al. 2015

Tree-Crop Interactions: Agroforestry in a Changing Climate

113

View full text Add to dashboard Cite

show abstract

“…Alternative approaches to explicitly accounting for root system architecture, e.g. continuous (Dupuy et al 2010) or voxel automata (Mulia et al 2010) approaches, also bear promises, especially when aiming at coupling such models with models of root functioning, integrating root-soil interactions occurring in the rhizosphere. The surface of uptake of a root is not only depending on root system architecture, whatever the level of accuracy of its representation in plant nutrition models.…”

Section: Why and Which Plant Nutrition Models What For?mentioning

confidence: 99%

“…In addition, root system architecture is characterized by a tremendous plasticity (e.g. Hodge 2009;Hodge et al 2009;Mulia et al 2010;Pagès 2011). At a biophysical point of view, excess water (actually limited aeration) and mechanical constraints such as hard pans, rock fragments, etc.…”

Section: Root Segment Modelmentioning

confidence: 99%

Acquisition of phosphorus and other poorly mobile nutrients by roots. Where do plant nutrition models fail?

et al. 2011

View full text Add to dashboard Cite

Background In the context of increasing global food demand, ecological intensification of agroecosystems is required to increase nutrient use efficiency in plants while decreasing fertilizer inputs. Better exploration and exploitation of soil resources is a major issue for phosphorus, given that rock phosphate ores are finite resources, which are going to be exhausted in decades from now on. Scope We review the processes governing the acquisition by plants of poorly mobile nutrients in soils, with a particular focus on processes at the root-soil interface. Rhizosphere processes are poorly accounted for in most plant nutrition models. This lack largely explains why present-day models fail at predicting the actual uptake of poorly mobile nutrients such as phosphorus under low input conditions. A first section is dedicated to biophysical processes and the spatial/temporal development of the rhizosphere. A second section concentrates on biochemical/biogeochemical processes, while a third section addresses biological/ecological processes operating in the rhizosphere. Conclusions New routes for improving soil nutrient efficiency are addressed, with a particular focus on breeding and ecological engineering options. Better mimicking natural ecosystems and exploiting plant diversity appears as an appealing way forward, on this long and winding road towards ecological intensification of agroecosystems. (Résumé d'auteur

show abstract

Reconciling root plasticity and architectural ground rules in tree root growth models with voxel automata

Cited by 30 publications

References 45 publications

High organic inputs explain shallow and deep SOC storage in a long-term agroforestry system – combining experimental and modeling approaches

High organic inputs explain shallow and deep SOC storage in a long-term agroforestry system – combining experimental and modeling approaches

Root distribution of trees and crops: competition and/or complementarity.

Acquisition of phosphorus and other poorly mobile nutrients by roots. Where do plant nutrition models fail?

Contact Info

Product

Resources

About