A Co-ordinated Demand-and-Supply-Led Root-to-Shoot Partitioning Model
3.2 Nitrogen restriction
All three models are able to achieve non-restricted growth before the end of the reproductive growth phase. The fixed partitioning model achieves this first, followed by the demand-led partitioning model and then the dual demand-and-supply-led partitioning model respectively.
Immediately post the start of spring regrowth, both roots and shoots grow for a few days under all three partitioning strategies (Figure 6.4). This indicates that the release of carbohydrate from stored sources ensures that there is no imbalance in the resources at this time. This is because the carbohydrate storage function releases up to 67 % of its resources into AVC in a single day. Therefore the available carbohydrate for growth at the start of spring exceeds daily demands. This increased concentration is probably not realistic, and the net effect is that some carbohydrate is allocated to roots by the imbalance function that could potentially be better utilised in shoots if the amount of carbohydrate released from storage was slower.
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Dtural Nonstrui
0.018 0.016 0.014 0.012 0.01 0.008 0.006 0.004 0.002 0
101 201 301 401 501 Iteration (days)
Figure 6.7 The effect of the seasonal daily nitrogen absorption function and the initial N used to initiate the simulation (StartN = 0.005 g) and the growth pattern of the single-tiller ramet on the non-structural nitrogen content ( AVN; Nstorage) of the ramet. Daily soil nitrogen availability as described in Section 3 of Chapter 4. (Environmental conditions: Plant activity during the growing and non-growing season as defined in Section 4.3 of Chapter 4.
Growing season proceeds from Yearday 1 to Yearday 242 of each year.)
1 0 . 8 0 . 6 0 . 4 0 . 2 0 - 0 . 2 - - 0 . 4 - 0 . 6 - 0 . 8 -1
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0.8 0.7 0 . 6 0.5 0.4 0 . 3 0 . 2 0.1 O
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Figure 6.8 The effect of combined N limitation and carbohydrate limitation on: a. The imbalance function, b. Ideal and actual root to shoot ratios ( £JalrlshY>5>t, and alrlshYiSt , respectively), c. Actual live root mass ( ) and live shoot mass ( ). Daily soil nitrogen availability as described in Section 3 of Chapter 4. (Environmental conditions: Plant activity during the growing and non-growing season as defined in Section 4.3 of Chapter 4.
Growing season proceeds from Yearday 1 to Yearday 242 of each year.)
this period. This reflected a reduction in stored carbohydrate because the size of the tillers was smaller over winter due to the restriction of growth during winter. This reduction of carbohydrate availability reduced the amount of early spring blade growth, which in turn reduced carbohydrate production later in the second spring. The imbalance function induced substantial dieback of roots during spring in order to allow more resources to be allocated to shoots. However this did not overcome the main problem, which was that there was too little carbohydrate stored during winter to overcome the demand deficit that developed during the early part of spring regrowth. Clearly carbohydrate limitation during spring regrowth is a critical hurdle for grass plants to overcome. This problem is dealt with in the next chapter.
4 DISCUSSION
The dual demand-and-supply-led partitioning model presented here uses two signals of control, relative growth demand from shoots and roots and daily relative resource/demand imbalance (supply). The relative growth demand of shoots and roots is a medium-term response to shifts in relative nitrogen and carbon resource availability. It mimics the effect of signals from roots to shoots and from shoots to roots that adjust the relative growth demand in response to relative nutrient availability (Van der Werf & Nagel 1996; Bangerth et al. 2000; Farrar & Jones 2000). The daily allocation in response to actual imbalance reflects more immediate shifts in resource allocation that increases growth of the organs that source the more limiting nutrient (McDonald et al. 1986; Van der Werf & Nagel
1996).
The model assumes that growth activity is co-ordinated both by shoot phenology (Cheng, Coleman & Box 1990) and internal resource availability (Andrews et al. 2001).
This assumption is not unreasonable, as shoot structures serve multiple purposes that change in time (photosynthesis, sexual reproduction, and vegetative reproduction in clonal plants) and consequently change growth demands, while roots serve two unchanging purposes, nutrient resource capture and plant anchorage to the substrate.
A key advantage of this method is that it upgrades or downgrades relative root and shoot growth activity proactively in response to resource availability. This allows the plant to adapt its relative resource demands. This is a fundamental change from previous models where daily demands placed by roots and shoots are determined by a fixed relative growth
rate, and allocation is based on the imbalance in internal resource availability only (Thornley & Johnson 1990; Reynolds & Chen 1996). As pointed out in the introduction supply-led partitioning alone can potentially lead to unrealistic root-to-shoot ratios when availability of one of the two resources is extremely restricted. This problem is important in grassland environments where soil nitrogen availability is both seasonal (Blair et al. 1998) and temporally-limited by soil water availability (Birch 1958). During low soil nitrogen periods, supply-led partitioning would cause plants to tend to unrealistically large root-to- shoot ratios, because supply-led co-ordination models respond to internal resource deficits by increasing resource allocation to the organ that supplies the more limiting nutrient. The increased absolute root growth during nitrogen-poor periods under supply-led partitioning increases resource wastage because there is a small probability of increasing nitrogen supply rate as the availability of nitrogen to bunchgrasses is more dependent on temporal environmental cues than spatial distribution, as these species have limited capacity to forage spatially for soil nutrients (Briske & Derner 1998). The dual demand-and-supply-led method employed in this model is proactive because it reduces resource wastage by downgrading growth during resource-poor periods and upgrading growth during resource- rich periods. It also ensures that some resources are always allocated to shoots, which is essential because individual leaves have a set life-expectancy so bunchgrass plants must continually grow new leaves in order to maintain photosynthetic machinery.
A further criticisim of the previous co-ordinated root-to-shoot partitioning models referred to in this chapter is that they are very simple differential equation models (Thornley
& Johnson 1990; Reynolds & Chen 1996). As such they consider roots and shoots as two biomass pools that source environmental resources and compete for internal ramet groups.
Due to this very simplified approach, these models ignore the feedback delays on resource supply introduced by the growth and death of individual organs and they ignore spatial resource availability effects. It is precisely because the feedback delay is ignored that these models produce such convincingly stable growth dynamics. Consider bunchgrasses growing in a closed sward. Allocation of most resources to roots during nitrogen-poor periods would reduce the size of leaves growing during that period. Photosynthesis would initially be maintained at sufficient levels by the existing leaves which have blade surface in the top portions of the canopy, but would subsequently decline when these leaves begin to senesce as the new reduced leaves would have much lower photosynthetic productivity.
Consequently carbohydrate supply would be compromised by the lack of sufficient photosynthetic surface on the reduced leaves. If this delay effect was included in the
previous differential equation models their behaviour would be far less stable, emphasising the need for an additional feedback signal to dampen the delay effect. The structure of the TILLERTREE model considers growth of individual organs directly, which necessitated seeking an approach that would dampen the delay effect of organ growth on root-to-shoot allocation.
The dual demand-and-supply-led partitioning model presented here does not claim to be the final solution to the complex problem of root-to-shoot allocation, but it provides a basis for further research into modelling root-to-shoot allocation. A weakness of the approach adopted here is that it does not consider structural root growth explicitly, but rather treats it as an amorphous biomass that is maintained in some functional balance with shoot growth. This approach is appropriate for the objectives of this thesis, which focuses on growth abilities of clonal bunchgrasses in terms of their shoot structural architecture and how this is modified by resource allocation. However, for a more detailed investigation into root-to-shoot partitioning, rules for root growth need to be made more explicit. Spatial foraging of roots leads to quite rapid shifts in root distribution and dieback (Arredondo &
Johnson 1999), so biomass turnover and resource demand may be more substantial than accounted for in the present model. Presumably root growth could be represented structurally in the TILLERTREE model using root segment objects like tiller that link consecutively together to form a root 'tree'. Careful attention woud need to be given to rules governing recruitment of side roots, and rules of allocation during resource competition between root structures. Such an extension would necessarily require a 3-dimensional version of the model since root foraging is explicitly a 3-dimensional process (Hutchings &
deKroon 1994). The problem of explicit structural root growth will be addressed in subsequent research. For the remainder of this thesis, the emphasis shifts to the consequences of shoot architecture for growth and resource capture in bunchgrasses and how this is modified by disproportionate resource allocation.