layers used for water uptake (TLAYPG in the tree.100 file; see Table 5.3); for SRC willow, this should equal 7, since rooting depth can be up to 130cm (Crow and Houston, 2004) and soil water simulation layer 7 represents the 120-150 cm depth. This value is in fact only used to calculate nutrient and water access for tree survival; growth is calculated according to availability in the top 30cm of soil, since the majority of roots are found at this depth; this is true of SRC willow (Crow and Houston, 2004) as well as crops more commonly simulated with DayCent.
Following this initial calculation of maximum production, production is then reduced if N demand cannot be met. Demand is calculated according to the C:N ratio, and availability is based on soil N availability plus any N fixation: uptake of both fixed and soil available N is adjusted for total root biomass (adjustment is set according to a factor calculated in the RTIMP.F module). Respiration is calculated as a fraction of the maximum rate according to temperature, size of C pool and plant component C as a fraction of that plant component C at optimum LAI.
Dates of first and last growth and senescence can be set in the schedule file, according to available information on growing cycle, and coppicing dates should be set according to management practices for the site modelled, if known, or a date selected during the recommended harvest time period if not.
5.2.2 Partitioning
The tree.100 input file (as introduced in Table 3.3 of Chapter 3) allows the user to set Juvenile and mature values for C partitioning (using FCFRAC(1-5,1-2); see Table 5.3), enabling the model to simulate the difference between establishing and mature trees. Organs simulated are: leaves; fine branches; coarse wood; fine roots; coarse roots. Maximum, minimum and initial C:N ratios of the partitions are also pre-set (according to CERFOR(1-3,1-5,1); see Table 5.3), C:N will lie somewhere between maximum and minimum depending on availability relative to demand. Maximum and minimum values for juvenile roots are set separately to allow for variation with availability of water (according to TFRTCW(1-2) or N TFRTCN(1-2) see Table 5.3); maximum values will be applied in the event of nutrient or water shortages, resulting in an increase in the proportion of below ground biomass.
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Coppicing reintroduces the juvenile tendency towards rapid growth in woody plants, whilst the presence of established roots enables even faster growth by supplying water and nutrients to leaves, and energy stores in below ground and remaining above ground plant parts provide additional reserves for growth (Philippot, 1996). Spatial distribution and numbers of new stems are important in determining “free growth” through internode elongation, and the resultant leaf numbers. Therefore it has been suggested that explicit simulation of individual stem sprouting and as well as resultant leaf numbers and canopy architecture, using an individual plant level demographic approach, could give improved representation of the rate of post coppice growth (Ceulemans, 1996; Philippot, 1996). However explicit, individual tree-level simulation would require significantly greater complexity than the stand-level of simulation currently applied in DayCent, and is not synonymous with a useful simulation of coppice growth. For example the ECOPHYS model applies an individual plant level demographic approach to coppice modelling, and incorporates complex 3D simulation of individual leaf positions to calculate LAI, but cannot
simulate multiple stems or self-thinning (Philippot, 1996).
Conversely, complex demographic approaches can be incorporated into stand-level simulations; for example ForestGrowth explicitly simulates average stem numbers according to user input, and can simulate stem death to represent self-thinning over the course of the coppice cycle.
ForestGrowth applies a simplified approach to leaf architecture simulation, by assuming horizontal uniformity over the stand, but is still able to simulate variation in photosynthetic capacity without individual tree-level simulation, by discretising the canopy by height to simulate changes in average leaf angles and LAI with height in canopy (Tallis et al., 2013).
Amichev et al. (2011) adapted the multi-stem approach to represent a single stem (with total mass of all separate stems) using the 3PG model to remove the need for highly specific input data on bud numbers. To simulate post coppice growth without multi-stem simulation, the 3PG model resets the age of the trees at the start of each coppice cycle, to reinstate juvenile growth patterns, but retains the root biomass from the end of the last coppice cycle (Amichev et al., 2011). This enables the model to simulate fast canopy closure following coppicing, and the increase in above ground biomass growth with the first three successive coppice cycles, since the established root system creates a high root:leaf ratio enabling good supply of water to leaves (Amichev et al., 2011; Philippot, 1996).
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Alternatively, the PALM model, which is similar to DayCent in terms of drivers and partitioning, simulates post coppicing translocation of nutrients from root to shoot explicitly as an immediate C transfer of 20% (Shibu et al., 2012). DayCent has the option to simulate root death to coincide with a tree removal event; hence a similar transfer could be built in relatively easily, with some minor changes to the model code, to transfer the root C and N to the forest C and N stores respectively.Whilst Miscanthus translocates nutrients from above ground to below ground organs at the start of the dormant winter period, this would be disadvantageous for woody tree species where the perennial organs are also above ground (Brereton et al., 2013). Numerous studies suggest that translocation of nutrients into roots prior to coppicing does not occur for SRC willow, hence there is no need to build such transfers into the model (Bollmark, 1999; Deckmyn et al., 2004; Shibu et al., 2012).
Simulation of post coppicing growth with the PALM model is initially partitioned to above ground biomass, due to application of a parameter to prevent root biomass exceeding a set proportion of above ground biomass (Shibu et al., 2012). Given the evidence for variation in root: shoot over lifecycle (Amichev et al., 2011; Pacaldo et al., 2012), setting a fixed ratio to limit below ground growth is not ideal. An alternative approach to ensure that the transferred C and N is applied to above ground biomass growth, and thereby account for the impact of coppicing on carbon metabolism and N translocation, would be to set different C partitioning to be applied during this period. Since DayCent already applies two sets of C partitioning for juvenile and established trees, it is relatively easy to add a third set, favouring above ground growth, to be applied during a user defined post coppicing period.
Due to increased photosynthesis post coppicing, resulting from multiple shoots and limited self- shading (Ceulemans, 1996; Philippot, 1996), simulation may be further improved by applying an increased photosynthetic conversion factor to account for increased activity and to allow the model to be parameterised to fit observed levels of post coppice growth. Again, the model code can be altered to apply a different photosynthetic conversion factor for the same user-defined post-coppicing period.
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5.2.3 Senescence and decomposition
Unlike some of the more complex tree growth simulation models, e.g.ECOPHYS (Philippot, 1996) DayCent incorporates full simulation of senescence, organ death and decomposition. The
tree.100 input file includes user defined values for monthly death rate for all partitions, and the
proportion of leaves to senesce in the scheduled month. Following simulation of (partial) organ death, decomposition is simulated at a proportion of the maximum rate (which can be set in the
tree.100 file) adjusted for substrate lignin content, C:N ratio and the impact of temperature and
relative moisture content of soil.
5.3 Improvement – changes to model code
The model code is made up of over 200 subcomponents- some are in C, some are in FORTRAN, hence both a FORTRAN and a C compiler must be used. It was only necessary to alter the FORTRAN code (and ancillary files) in this instance, since this includes most of the crop and tree growth components.
Unlike the code changes implemented in Chapter 4, there are no complex algorithms to be built in, instead alterations simply allow for transfer of C and N on coppicing, and enable a choice of
parameters to be used in the existing model algorithms, depending on whether a coppice event has recently taken place. Code changes are given in full in Appendix Section 3 but are described in brief here; Figure 5.2 outlines the key code changes and introduction of new parameters to event files, whilst Figure 5.3 can be compared directly to Figure 5.1 to illustrate how this model