Introduction
Understanding how climate variability and change will impact upon crop
production systems is a research challenge of utmost importance to society.
To date, studies of climate change impacts on the terrestrial biosphere have
been completed without recognition of the integrated nature of the biosphere.
Crop simulation models are widely utilised as they incorporate many known
effects of how changes in atmospheric conditions can impact upon crop growth,
development and yield. However, they do not simulate the wider interactions
of crops and the environment. For example, climate change will impact upon
water resources which will in turn impact upon the water available for
irrigation of crops. used the Hadley Centre Earth
System Model (HadGEM2-ES) to evaluate climate impacts on the terrestrial
biosphere under a range of emission scenarios. By doing so they were able to
assess several elements of the terrestrial system in a way that was fully
integrated and consistent with the climate projections. However, they were
only able to include natural systems as crops are not yet included in the
model. Including a representation of crops within land surface models will
facilitate a more comprehensive, integrated and internally consistent
simulation of the impacts of climate change and variability on the full land
system, accounting for interactions between different components and
processes. This will ultimately enable improved projections of the impacts of
climate change on food and water security, including interactions between the
two. There is increasing evidence that the cultivation of crops affects
weather and climate on local scales. Croplands now occupy 12 % of
Earth's ice-free land surface and in several regions of the world are the
dominant vegetation type on the land surface (e.g. midwest USA,
Indo-Gangetic Plain). This extensification of agriculture has altered the
biophysical characteristics of the land surface potentially altering regional
climate. Therefore, there is reasoning to consider crops and climate as a
truly coupled system and hence motivation to develop models which can fully
represent the coupled feedbacks between them.
Efforts to simulate the environmental impacts on crop production are commonly
thought to have begun in the 1960s at Wageningen . Since then
crop modelling has grown and there are now many models available in the
research and agronomic domains. Such models have been deployed both as
decision support tools and to research the impacts of climate change on
future crop production. Recent advances in crop modelling include the
application of crop models, traditionally developed at the field level, to
cover the globe on a gridded basis and inter-comparison
of many crop models in simulating the same crop and the same set of
conditions .
The investigation of how croplands affect weather and climate is much less
mature. The initial expansion of cropland area came at the expense of forests
and the impact of this deforestation has received considerable research
attention. However, croplands have also replaced more similar native
grasslands. For example, showed that the near-surface climate
over the now intensively cultivated winter wheat belt in Oklahoma, USA, is
significantly different to that over adjacent grasslands.
identify the differences in phenology between managed croplands and natural
grasslands as the determinant of the differences.
The increase in understanding of how croplands might differentially impact
the climate compared to natural vegetation has led to a recent surge in model
development whereby land surface or global vegetation models have been
extended to include explicit parametrisations of crops, in place of the use
of grasslands as a surrogate (see review of ). Some developments
have been motivated by improving the carbon and water budget of land surface
modelling , others to include croplands in global or regional
climate models to better represent their impact on the atmosphere
, while others have been motivated to consistently
simulate both yield and environmental impacts .
The aim of this model development was to develop a combined land surface and
crop model capable of simulating both the impacts of climate variability on
crop productivity, as well as the impact of croplands on the climate. To
achieve this we have added a crop-specific parametrisation to the Joint UK
Land Environment Land Surface (JULES) land surface model. JULES is the land
surface scheme of the UK Met Office Unified Model and the next generation UK
Earth System Model (UKESM) and, therefore, can be in time coupled to a
state-of-the-art climate model. A full description of JULES can be found in
and . JULES does not currently include an explicit
parametrisation of crops; instead, over cropped regions, the
C3 or C4 grass plant functional
types are used. Previous work has included crops in the model.
included a crop parametrisation in MOSES (i.e. in the fully
coupled land surface–climate model) based on the groundnut version of the
crop model GLAM. More recently, extended JULES to include a
parametrisation of wheat based on the crop model SUCROS. Neither
nor developed a generic representation of crops
suitable for the examination of different crops throughout the globe,
something that is important from an Earth system modelling perspective.
Therefore, the objective of this study was to develop a generic
parametrisation of crops applicable to many crop types and at the global
scale. However, the model has been designed to be flexible, meaning users can
reparametrise the model depending on requirements (e.g. to represent
different crop cultivars).
The following section describes the model development,
Sects. and present an evaluation of
the new model when applied at global and site levels, respectively, followed
by a Discussion (Sect. ).
Crop model parameters used in JULES-crop.
Parameter
Unit
Equation
Description
Tb
∘C
Eq. ()
base temperature
To
∘C
Eq. ()
optimum temperature
Tm
∘C
Eq. ()
maximum temperature
TTemr
∘C d
Eq. ()
thermal time between sowing and emergence
TTveg
∘C d
Eq. ()
thermal time between emergence and flowering
TTrep
∘C d
Eq. ()
thermal time between flowering and maturity/harvest
Pcrit
h
Eq. ()
critical photoperiod
Psens
h-1
Eq. ()
sensitivity of development rate to photoperiod
rdir
–
Eq. ()
coefficient for determining relative growth of roots vertically and horizontally
αroot
–
Eq. ()
coefficient for determining partitioning
αstem
–
Eq. ()
as above
αleaf
–
Eq. ()
as above
βroot
–
Eq. ()
as above
βstem
–
Eq. ()
as above
βleaf
–
Eq. ()
as above
γ
m2kg-1
Eq. ()
coefficient for determining specific leaf area
δ
–
Eq. ()
as above
τ
–
Eq. ()
fraction of stem growth partitioned to Cresv
fC
–
Eqs. (), (), ()
carbon fraction of dry matter
κ
–
Eq. ()
allometric coefficient which relates Cstem to h
λ
–
Eq. ()
as above
Model description
The essence of JULES-crop is illustrated in Fig. . The
additional model equations required to simulate crops essentially partition
the carbon uptake of vegetation already simulated by JULES in to several crop
organs and the size of the crop, important for land surface–atmosphere
feedbacks, is derived from the organ biomass using allometric equations. The
pattern of partitioning of assimilated carbon to the crop organs is affected
by the crop development rate which itself is influenced by temperature. In
addition to the new equations describing crop growth and development, changes
to the model structure were also required to accommodate the additional plant
functional types. New equations describing crop growth and development were
added to the model. Each crop is considered as an additional plant functional
type and a distinction is made between natural and crop plant functional
types within the model, with the crop plant functional types requiring extra
parameters to be specified. The detailed description of the crop
parametrisation is split in to three parts. Firstly, the equations that
determine the start and duration of the crop growing season are
described. Secondly, the equations determining the rate of crop
growth are described. Lastly, the changes to model structure are
outlined. A full listing of new model parameters and variables can be found
in Tables and , respectively.
Schematic of JULES-crop.
Growing season and development
The crop growing season begins when the crop is sown. This date can either be
prescribed (i.e. if it is known) or calculated dynamically based on
environmental criteria. In the latter case, sowing only occurs when the soil
is wet enough (θ2>θc,2, where
θ2 is the soil moisture content in the second layer and
θc,2 is the critical soil moisture content in the second
layer), which is warm enough (Tsoil,3>Tb+2K, where Tsoil,3 is the temperature in the third soil
layer and Tb is the base temperature), and when days are not rapidly
shortening (dP/dt>-0.02 h d-1, where P is the
day length). We wish to make users aware of this sowing option; however, we
feel it needs further optimising and so results using the dynamic sowing date
will not be included here. The use of subsurface soil moisture and
temperature variables prevents sowing occurring too early in response to
short-term fluctuations in weather. The rate of day length criterium ensures
that crops are not sown too late in the year when conditions for growth are
deteriorating.
Crop model variables in JULES-crop.
Variable
Unit
Equation
Description
New variables
Teff
∘C
Eqs. (), ()
effective temperature
DVI
–
Eqs. (), (), (), ()
development Index
Cleaf
kg C m-2
Eqs. (), (), (), ()
leaf carbon pool
Cstem
kg C m-2
Eqs. (), (), ()
stem carbon pool
Croot
kg C m-2
Eqs. (), (), ()
root carbon pool
Charv
kg C m-2
Eqs. (), (), ()
harvested organ carbon pool
Cresv
kg C m-2
Eqs. (), ()
stem reserve carbon pool
pleaf
–
Eqs. (), ()
fraction of NPP partitioned to Cleaf
pstem
–
Eqs. (), (), ()
fraction of NPP partitioned to Cstem
proot
–
Eqs. (), ()
fraction of NPP partitioned to Croot
pharv
–
Eqs. (), ()
fraction of NPP partitioned to Charv
P
h
Eq. ()
photoperiod (day length)
RPE
–
Eqs. (), ()
Relative Photoperiod Effect
Existing variables
T
∘C
Eq. ()
1.5 m temperature on each tile
L
m2m-1
Eq. ()
leaf area index
SLA
m2kg-1
Eqs. (), ()
Specific Leaf Area
h
m
Eq. ()
canopy height
Π
kg C m-2
Eqs. (), ()
net primary productivity
Ac
kg C m-2
Eq. ()
net carbon assimilation
Rdc
kg C m-2
Eq. ()
canopy dark respiration
Once sown, the crop develops through three stages: sowing to emergence,
emergence to flowering, and flowering to maturity. Harvest is assumed to
occur at crop maturity. The rate of crop development is related to thermal
time. Given the 1.5 m tile temperature (T), an effective
temperature (Teff) is calculated based upon the crop-specific
cardinal temperatures (Tb,To,Tm – see
Table for description).
Teff=0forT<TbT-TbforTb≤T≤To(To-Tb)1-T-ToTm-ToforTo<T<Tm0forT≥Tm.
Teff is greatest and hence development is fastest at
T=To. As temperature falls below or rises above
To the rate of development linearly decreases until no
development occurs when either T≤Tb or T≥Tm. For the sowing to emergence phase, Teff is not
affected by Tm or To (i.e. Teff=T-Tb). This equation is a “standard” way of calculating
effective temperature . An important difference to other
available models is that JULES-crop simulates a decline of Teff above
the maximum temperature, whereas others keep Teff at the maximum value
no matter how high temperatures get.
For some crops, progress towards flowering is slowed if the day length (P)
is less than (greater than) a crop-specific critical photoperiod
(Pcrit) for long-day (short-day) crop types. The degree of
sensitivity to the photoperiod is represented by the parameter Psens
which is positive for short-day plants and negative for long-day plants. This
conceptual approach was motivated by . Therefore, to slow
development Teff is multiplied by the relative photoperiod effect
(RPE), which is defined as follows:
RPE=1-(P-Pcrit)Psens.
The status of crop development is represented by the development index (DVI)
which takes the value of -1 upon sowing, increasing to 0 on emergence, 1 at
the end of vegetative stage and 2 at crop maturity. The rate of increase of
DVI is calculated as follows, where TTemr is the thermal time between sowing
and emergence, TTveg is the thermal time between emergence and flowering and
TTrep is the thermal time between flowering and harvest:
dDVIdt=TeffTTemrfor-1≤DVI<0TeffTTvegRPEfor0≤DVI<1TeffTTrepfor1≤DVI<2.
The growing season ends when DVI = 2 at which time the prognostic
variables related to crop growth (L,h,Croot,Charv,Cresv) are reset to minimal values close to 0. To prevent
growing seasons continuing indefinitely when conditions are no longer
suitable, the crop is also harvested if the soil temperature in the second
soil layer falls below Tb at any time after DVI = 1 or if
LAI > 15 (leaf area index). Vernalisation, a cold temperature requirement for development
in some crops, is not included in this model version.
Growth
To simulate crop growth, net primary productivity (Π) is accumulated over
a day and then partitioned between five carbon pools: root
(Croot), structural stem (Cstem), stem reserves
(Cresv), leaves (Cleaf), and harvested organs
(Charv). The original formulation for Π in JULES includes
assumptions about the sizes of the leaf, stem and root carbon pools in order
to estimate respiration loses. Stem carbon is a function of leaf area index
(Eq. 42 of ) and root carbon is set to equal leaf carbon.
Because these carbon pools are now explicitly simulated, Π is
recalculated for the crop types with the following equation based on an
algebraic reduction of the set of equations used in JULES:
Π=0.0121-rgAc-RdcCroot+CstemCleaf,
where rg is the fraction of gross primary productivity less
maintenance respiration that is assigned to growth respiration,
Ac is the net canopy photosynthesis, and Rdc is the
rate of non-moisture-stressed canopy dark respiration. Cleaf,
Cstem and Croot are the carbon content of leaf, stem
and root, respectively.
The carbon in Π is accumulated over a day and then divided into five crop
components according to “partition coefficients”, one for each of the four
root, stem, leaf and harvest pools defined above and a reserve pool. These
components are added to the (state variable) pools of carbon describing the
crop.
dCrootdt=prootΠ,dCleafdt=pleafΠ,dCstemdt=pstemΠ(1-τ),dCharvdt=pharvΠ,dCresvdt=pstemΠ,τ
where τ is the fraction of stem carbon that is partitioned in to the
reserve pool. proot+pleaf+pstem+pharv=1.0.
Partition coefficients for a given crop are typically predefined in
process-based crop models according to either the length of time since
emergence or to crop development stage (DVI; i.e. a function of thermal time
since emergence). They are represented by fixed values for a given period of
time (or thermal time) since emergence, and these values are listed in a
look-up table and referenced for each iteration of the model (e.g. WOFOST,
).
Here we define the partition coefficients as a function of thermal time using
six parameters to describe continuously varying partition coefficients over the
duration of the crop cycle. We use a multinomial logistic to define this
function:
proot=eαroot+(βrootDVI)eαroot+(βrootDVI)+eαstem+(βstemDVI)+eαleaf+(βleafDVI)+1,pstem=eαstem+(βstemDVI)eαroot+(βrootDVI)+eαstem+(βstemDVI)+eαleaf+(βleafDVI)+1,pleaf=eαleaf+(βleafDVI)eαroot+(βrootDVI)+eαstem+(βstemDVI)+eαleaf+(βleafDVI)+1,pharv=1eαroot+(βrootDVI)+eαstem+(βstemDVI)+eαleaf+(βleafDVI)+1,
where α and β are empirically derived parameters describing the
shape of the thermal time-varying partition coefficient for leaves, roots and
stems, and DVI is the development index. Thus, for only six parameters (which
is also the absolute minimum number of parameters needed to define partition
coefficients for four carbon pools) we can define a much wider range of
shapes of thermal time varying partition coefficients. Furthermore, these six
parameters can be more feasibly calibrated than a larger number of “look-up”
partition coefficients. This parametrisation is illustrated in Fig. overlaid with example observed partitioning fractions
from .
Following the formulation of , once carbon is no longer
partitioned to stems, carbon from the stem reserve pool is mobilised to the
harvest pool at a rate of 10 % a day:
Charv=Charv+(0.1Cresv)Cresv=0.9Cresv}forpstem<0.01.
Leaf senescence is treated simplistically by mobilising carbon from the leaf
to the harvest pool at a rate of 0.05 d-1 once DVI has reached 1.5. This
equation was inspired by Eq. (7), but based the period for which senescence
starts on a specific DVI value (1.5) rather than waiting for partitioning of
leaves to cease since for some crop types this does not happen.
Charv=Charv+(0.05Cleaf)Cleaf=0.95Cleaf}forDVI>1.5.
At the end of each growth time step (24 h), the amount of carbon in
the leaves is related to leaf area index (L) by
L=CleaffCSLA,
where
SLA=γDVI+0.06δ.
The values of γ and δ were determined by fitting the
relationship to the paired values of DVI and SLA (specific leaf area) reported in .
The amount of carbon in the stem is related to the crop height by
h=κCstemfCλ.
The values of κ and λ were determined by fitting the relationship to the
paired values of h and Cstem at the Mead FLUXNET site .
Equations () and () are rearranged to derive the
carbon content of leaves and stems, respectively, before each growth time
step.
Fraction of daily accumulated net primary productivity partitioned
to roots (purple), stems (blue), leaves (yellow) and harvested parts (red) of
the crop as a function of development index (DVI; 0 = emergence,
1 = flowering, 2 = maturity) for wheat, rice, soybean and maize.
Because root biomass increases during the crop growing season the fraction of
roots in each JULES soil layer varies according to the equation of
which defines the fraction of roots at depth z as
f=1-e-za,
where
a=drCrootfCrdir,
where dr is 0.5 for all crop types, and rdir is a crop-specific parameter.
To ensure crop establishment, the growing season is curtailed if the sum of
root, leaf, stem and reserve carbon falls below the initial seed carbon
content (or zero carbon content) if the sowing date is determined dynamically.
Changes to JULES code structure
The standard version of JULES represents the land surface as a combination of
up to nine surface types including five plant functional types: broadleaf
trees, needleleaf trees, C3 grass, C4
grass, shrubs, bare soil, inland lakes, snow and ice. Surface fluxes of heat,
moisture and momentum are determined independently for each tile before being
combined to a single set of fluxes according to the relative fractions of
each tile. Each crop type is considered as a different tile. Therefore, it is
possible to simulate many crops or crop varieties at a site or grid box in a
single integration of JULES, in addition to the standard five plant
functional types. The parameters required to represent vegetation within
JULES were extended to the crop tile(s). The values were copied across from
the JULES default parameters for C3 and
C4 grass, depending on the crop photosynthetic capacity
(see Table ).
The values of the parameters required in Eqs. ()–() determine which crops are being simulated
and can be varied according to different user requirements, e.g. crop species
(e.g. maize or wheat), generic crop type (e.g. C3 cereals)
or cultivar (e.g. soybean PS123121 or soybean 21h321). Each parameter is
described in Table . Values for each parameter can be
determined by calibration against relevant observational data such as leaf
area index, biomass, and yield from agricultural field stations. For this study
such an exercise was not performed. Instead, suitable values were determined
from either the literature or by tuning to fit site-level data in order to
establish a model version that could be evaluated at site and global scales.
Global simulation
Model set-up
To evaluate the potential of JULES-crop as a global gridded crop model,
simulations for the period 1960–2010 were performed over the global
domain. Four crop types were simulated: wheat, soybean, maize and rice.
Parameter values are in Table and were either taken
from the crop science literature or calibrated as described below.
Specifically, the values for the partition parameters
αroot, stem, leaf and βroot, stem, leaf and
the specific leaf area coefficients γ and δ were calibrated
against data in . The allometric coefficients κ and
λ were determined by calibration against paired crop height and stem
biomass data from FLUXNET sites. The cardinal temperatures (Tb,
To, and Tm) were specified values in line with
the range of values reported in the literature (see , and
). The effect of photoperiod was not included (by setting
Pcrit to 24) due to our method of determining thermal time
between emerging and flowering (TTveg) and thermal time
between flowering and harvest (TTrep) (see below).
JULES plant functional type parameters extended to represent crop
types wheat, soybean, maize and rice.
Crop type
Wheat
Soybean
Maize
Rice
c3
1
1
0
1
dr
0.5
0.5
0.5
0.5
dqcrit
0.1
0.1
0.075
0.1
fd
0.015
0.015
0.025
0.015
f0
0.9
0.9
0.8
0.9
neff
8.00×10-4
8.00×10-4
4.00×10-4
8.00×10-4
nl(0)
0.073
0.073
0.06
0.073
σl
0.032
0.032
0.025
0.032
Tlow
0
0
13
0
Tupp
36
36
45
36
Parameter values used to represent crop types wheat, soybean, maize
and rice. See Table for parameter definitions.
Crop type
Wheat
Soybean
Maize
Rice
Tb
0
5
8
8
To
20
27
30
30
Tm
30
40
42
42
TTemr
35
35
80
60
TTveg
See Fig.
TTrep
See Fig.
Pcrit
24
24
24
24
Psens
0.00
0.00
0.00
0.00
rdir
0.0
0.0
0.0
0.0
αroot
18.5
20.0
13.5
18.5
αstem
16.0
18.5
12.5
19.0
αleaf
18.0
19.5
13.0
19.5
βroot
-20.0
-16.5
-15.5
-19.0
βstem
-15.0
-14.5
-12.5
-17.0
βleaf
-18.5
-15.0
-14.0
-18.5
γ
27.3
25.9
22.5
20.9
δ
-0.0507
-0.1451
-0.2587
-0.2724
τ
0.40
0.18
0.35
0.25
fC
0.5
0.5
0.5
0.5
κ
1.4
1.6
3.5
1.4
λ
0.4
0.4
0.4
0.4
The parameter rdir was set to 0 for all crop types, which
effectively removes the effect of increasing root carbon on the vertical
distribution. Early tests of the model revealed that including an effect of
increasing root carbon led to high levels of water stress at the start of the
crop growing season leading to poor crop growth. Therefore, the effect was
“turned off”. The parametrisation was left in the model to allow other
model users to experiment further with dynamic root growth.
The global model runs were driven by the CRU-NCEP v4 climate data extended to
include 2012 (N. Viovy, personal communication, 2013) as used by the Global
Carbon Project . This was regridded to a N96 grid
(1.875∘ longitude × 1.25∘ latitude) and used with
ancillaries from HadGEM2-ES to
evaluate the performance of the model in a Earth system model set-up. A
multi-layer canopy radiation scheme was used, accounting for direct/diffuse
radiation components including sunflecks (can_ran_mod = 5).
The main run was from 1960 to 2010 and the spin-up consisted of repeating the
first 10 years 5 times. The sowing dates were taken from ,
and a value for each land grid box was obtained using nearest-neighbour
extrapolation. The values of TTveg and
TTrep were allowed to vary spatially and determined such
that, when used with the CRU-NCEP temperature climatology 1990–2000 and the
sowing date, the crop reached DVI = 2.0 at the
harvesting dates, with x=TTveg(TTveg+TTrep)=0.5,0.45,0.6,0.6 for
soybean, maize, wheat, and rice, respectively. Photoperiod sensitivity was not
considered.This is because including it would have made calculating
TTveg and TTrep almost impossible,
because three variables would need calibrating at each grid cell (total TT,
critical photoperiod, and sensitivity to photoperiod) from one observation
(growing season duration). For comparison a control run was completed using
the same model set-up but with the crop code switched off. This run is used
to assess performance against the standard land surface scheme in the Met
Office Hadley Centre Earth System Model – HadGEM2-ES.
Figure shows the planting date of and the
derived maps of TTveg and TTrep.
derived gridded planting dates from national- or district-level-reported planting dates which are given in months rather than days.
Therefore, there is little spatial or temporal variation in the sowing date
which might well be expected due to variations in local climate and
management practices. However, the data serves a purpose in global modelling
studies by providing an approximate start point for the growing season at the
right time of year. Our method of calculating the crop thermal time
requirements produces considerable spatial variability which is determined in
reality by variation in the choice of crop cultivar chosen. Other global crop
modelling studies have approached the issue of specifying these requirements
at the global scale in different ways. chose three sets of
thermal time requirements and applied them over the globe allowing for
assessment of which were most suitable after the simulations, whereas
related thermal time requirements (calculated from
in a similar manner to this study) to the annual accumulated
thermal time and then used that relationship to determine thermal time
requirements under future climate. The approach in this study was chosen as
the simplest and most likely to achieve growing seasons of lengths close to
observed. Due to the absence of a vernalisation parametrisation in the model
only spring wheat was considered. The crop fractions were taken from
and regridded to the N96 HadGEM2-ES resolution.
provide observations in the year 2000 which were used to describe the crop
coverages for the whole integration period due to a lack of available data
sets covering this time period.
Global distribution planting date from , interpolated
to NCEP grid, and the thermal time from emergence to flowering (TT_veg) and
from flowering to harvest (TT_rep) for each crop type. See text for details
of calculation.
Evaluation
The simulated grid box annual yield for each crop averaged over the
50 years is shown in Fig. alongside global
gridded observations for circa 2000 . Figure
shows that in general the model is underestimating yields in arid, irrigated
regions and overestimating them in tropical regions. In particular, simulated
maize yields are significantly larger than observations in tropical regions.
Given that the model does not include any information on the yield gap (the
difference between actual farm-level yield and potential yield) or important
land management such as irrigation the spatial variability of model output
should not be too closely compared to that of observed yield. Instead, a
greater appreciation of model performance can be gained from examining the
year to year fluctuations in yield, given that the effects of changes in
management and technology materialise over several years.
Global distribution of average wheat, soybean, maize and rice yield
(Mg ha-1) in (a) observations regridded to N96
resolution and (b) JULES-crop global simulations (assuming a
moisture content of 16 % and a carbon fraction of 0.5).
Simulated (red) and observed (black) global yield of wheat, soybean,
maize and rice between 1961 and 2008. Values in the top right are results of a
correlation between observations and JULES-crop simulations.
Simulated (red), observed (black dashed) and detrended observed
(black) country-level yields of (a) maize, (b) soybean,
(c) rice and (d) wheat between 1961 and 2008. Values in the
top right are results of a correlation between detrended observations and
JULES-crop simulations.
Figures and
show the simulated global- and country-level yields for wheat, soybean, maize
and rice between 1960 and 2008 compared to the reported yields of .
Simulated global yield was determined by multiplying the simulated annual
maximum yield at each grid cell by the observed harvested area from
regridded to the HadGEM2-ES spatial resolution. This grid cell
estimate of production was summed over all grid cells to produce an estimate
of global production which was then divided by the total harvested area to
provide an estimate of global yield. Grid cell yields were determined from
the annual maximum value of Charv which was multiplied by 2 to
convert from carbon mass to total biomass, by 1.16 to account for grain
moisture content, and by 10 to convert from kilograms per squared metre to
megagrams per hectare. Not all grid cells were included in the analysis. Cells
were excluded if the annual maximum DVI was less than 1.5 which was possible
if the growing season was curtailed if LAI > 15 or tsoil,2<Tbse. A similar analysis was conducted to determine country-level
yields with averages taken over all grid cells within a particular country. Country yield
observations were de-trended for comparison with model output. This is because the increasing
trend in yield observations over the last 50 years is due to improvements in agricultural
technology and management and increasing atmospheric carbon dioxide. This trend is not reproduced
by the model as these processes were not represented in our set-up.
Country crop area weighted annual cycle of crop type (top) and
grid-box mean (middle) leaf area index (LAI) and grid-box mean (bottom) net
primary production (NPP). Area averages weighted by crop area in top panel,
and total plant functional type area in middle and bottom panels. Vertical
bars indicate standard deviation of monthly values.
The average simulated yield for maize is overestimated; however, the model
does a reasonable job of reproducing the inter-annual variability at the
global (r=0.48) and country scales (Fig. a),
although there is a tendency to simulate larger variability than observed.
For soybean, average yield is again much greater than observed but year on
year variability is correlated with observations (r=0.37) providing some
confidence in the model's ability to simulate the observed response of
soybean yield to climate. Regionally, in countries such as the USA (r=0.39) and
India (r=0.52) JULES-crop is able to reasonably capture inter-annual
variability of yields (Fig. b). For rice, yield
levels are higher than reported, variability is overestimated and not
correlated with observations (r=0.24). At the country level, model
simulations in India (r=0.57) correlate with observations (Fig. c). The average simulated yield level for wheat
is similar to the most recent observations; however, when comparing the year to
year fluctuations in yield, the correlation between simulated and observed yields is
low (r=0.019). Because JULES-crop only simulates spring wheat then the
comparison to reported wheat yields is slightly unfair given that the
majority of wheat produced globally is from winter varieties. It is
encouraging that the best agreement between simulated and observed yield
fluctuations at the national level is for Turkey (r=0.46) and Australia
(r=0.53), in which spring wheat varieties dominate.
For all crops there is a tendency for JULES-crop to simulate larger
variability than observed. This may in part be explained by the lack of
certain processes in the model (particularly those to do with land
management). For example, not including a representation of irrigation in the
model may explain why the model predicts lower yields than observations as
irrigation would act to reduce the extent of crop failure in drought years.
The model also does not include the impacts of pests and disease which may
reduce overall yields in some years. Importantly, the model does not as yet
include a nitrogen cycle which may reduce overall GPP (gross primary production),
bringing the simulations in line with observations. This may also explain why there
are strong deviations between the magnitude of observed and modelled yield in tropical
countries where climatic conditions for growth are good in the absence of the limitations described above.
For some countries simulations of yield capture the magnitude and variability of observations.
In other countries the model reproduces the variability in yield but over-predicts the magnitude.
There are also countries where the model performs poorly in simulating both variability and magnitude.
This variety of results is due in part to the use of generic parametrizations for global model runs which is a
limitation of this type of Earth system model set-up. By using parameters that do not vary spatially we can
not fully represent the range of crop varieties that are found globally.
Country crop area weighted average mean annual cycle of surface
moisture flux (E), sensible heat flux (H), net short-wave radiation
(SWnet) and upward long-wave radiation (LWup) from
JULES-crop simulation (red) and standard JULES simulation (black) forced with
CRU-NCEP meteorological driving data. Vertical bars indicate standard
deviation of monthly values.
To evaluate the impact of including the crop parametrisation on JULES, output
from the simulation with crops included is compared to a control simulation
of the standard JULES configuration with grass plant functional types taking
the land fraction of crops. Impacts on the land surface will be mostly
mediated via direct changes to the vegetation structure and also via indirect
effects on state variables, most obviously the soil moisture content. To
begin to examine the potential for impact, the changes to a key vegetation
variable LAI values are shown in Fig. for
four major crop producing countries. To produce the country averages, grid
cell LAI are combined by weighting by the grid cell contribution to total
country crop area. In the USA and China each crop growing season occupies the
similar set of summer months, whereas for India and Brazil the wheat cropping
season is distinct from the other three crops. Peak LAI is greatest in Brazil
and lowest in China, which is most likely a reflection of the absence of
irrigation in the model and the relative abundance of rainfall in each
country. In comparison to the standard JULES configuration the addition of
crops adds a seasonality to LAI as there is no default seasonality to
vegetation characteristics in JULES. The annual variation of crop LAI is
dampened when aggregated with the other plant functional types, which explains
the non-zero LAI in the non-growing season in the JULES-crop simulation.
Figure shows that the inclusion of crops alters the
grid box net primary production (NPP) in terms of the timing of peak fluxes.
There are also lower fluxes in winter due to the more realistic treatment of
LAI at this time. Therefore, including a representation of crops in JULES may
help improve the seasonality of LAI, which affects carbon fluxes.
Figure shows that the impact of these differences in
vegetation size during the year is greatest for the surface moisture flux and
sensible heat flux rather than the components of the radiation balance. The
largest impacts are on the sensible heat flux towards the end of the crop
growing season which is higher with the inclusion of crops. For India, there
is a concomitant decrease in the surface moisture flux, implying that the
total available energy at the surface is unaltered but is partitioned
differently between sensible and latent heat fluxes. The impact of JULES-crop
on the energy balance is however minimal. In this configuration the model is
forced by prescribed meteorology at screen height. This has the tendency to
dampen the model in comparison to a full atmospheric simulation in which the
boundary layer state is able to evolve. It may, therefore, be expected that a
GCM (global climate model) may be more sensitive to changes in the surface state.
Simulated (solid lines) and observed (dots) leaf area index (LAI),
canopy height (CANHT), gross primary production (GPP) and harvest carbon
(HARVC) at a range of FLUXNET sites and years. Simulations performed with
JULES-crop crop type soybean (red), standard JULES C3
grass plant functional type with phenology (green), and standard JULES
C3 grass plant functional type without phenology (blue).
Site simulation
Model set-up
To further understand the impact of adding crops to JULES, site-level
simulations were also performed. Sites were selected by the vegetation cover (only croplands were considered)
and by the availability of meteorological and biological data required to force the model and evaluate model results. The sites
selected were are all in the USA: Mead in Nebraska and
Bondville and Fermi in Illinois. For each site, three simulations were
performed: the standard configuration of JULES, standard JULES with the
existing phenology parametrisation turned on, and the full JULES-crop
parametrisation. For the JULES-crop simulation, the fractional coverage of the
relevant crop type was set to 1 with all other functional types set to 0. For
the JULES (non-crop) simulations, the fractional coverage of the relevant
grass functional type (i.e. C3 grass for soybean,
C4 for maize) was set to 1. All crop parameters were
prescribed the same value as in the global simulations. The sowing date and
thermal time requirements were taken from the relevant grid cell for each
site.
Simulated (solid lines) and observed (dots) latent (LE) and sensible
(H) heat fluxes at a range of FLUXNET sites and years. Simulations
performed with JULES-crop crop type soybean (red), standard JULES
C3 grass plant functional type with phenology (green), and
standard JULES C3 grass plant functional type without
phenology (blue).
Evaluation
Figures and compare
JULES-crop simulations for the soybean crop type with standard JULES
C3 grass plant functional type with and without phenology,
and with observations where available. The crop parametrisation captures the
evolution of LAI and canopy height across the season,
although the model underestimates these growth variables. The model also
simulates lower GPP fluxes compared to
observations; however, crop yields are comparable. The standard
C3 grass with phenology configuration of JULES also
simulates growth and decay of vegetation cover but over a longer period of
time than the observed growing season. Without the phenology routine the LAI
is set to the default for C3 grass of 2.0 all year.
Interestingly, the more realistic simulation of vegetation cover does not
lead to improved simulation of surface fluxes. At all sites similar
characteristics of the simulations are evident. During winter all three
configurations simulate similar latent and sensible heat fluxes in line with
observations (Fig. ). Towards the start of the
growing season the standard configuration of JULES with constant
LAI = 2.0 overestimates latent heat flux due to an unrealistically large
vegetation coverage. The simulations with phenology and crops have lower
vegetation cover and simulate lower latent heat flux but are still noticeably
greater than observations. At around the peak of crop cover, all simulations
underestimate the latent heat flux and overestimate the sensible heat flux
due to lower simulated LAI compared to observations.
Site-level simulations for the maize crop type are shown in Figs. and . The crop
parametrisation is reasonably successful in capturing the LAI and canopy height
of maize at all evaluation sites. Similarly, GPP and yields are lower than observed although the seasonal
pattern of GPP is close to observations. Overall, model simulations broadly
capture the patterns of latent and sensible heat fluxes although, again, there
are no major improvements in model performance with the explicit inclusion of
crops. At Fermi, in 2006, the crop-specific simulation captures the observed
evolution of LAI reasonably well with peak LAI slightly closer to
observations than the standard JULES simulations. However, this,
again, does not improve the simulation of heat fluxes.
All model configurations overestimate the partitioning of energy in to latent
heat before the growing season begins and underestimate it during the crop
growing season, despite widely varying LAI values. This could be due to the
relatively weak LAI-surface conductance relationship found in JULES
. This is reflected in the low sensitivity to LAI between
fixed phenology and seasonal phenology. In these simulations we would therefore not expect
a large response to an alternative representation of crop LAI phenology. This
comparison serves as a reminder that improving the realism of a model may not
guarantee improved performance in the model in other aspects. The results
also show that JULES (crop and standard configurations) is not able to
capture the magnitude of observed GPP fluxes. This suggests that using the
standard physiological parameters for C3 and
C4 grasses is not appropriate when representing crops
particularly as JULES does not include nitrogen fertilisation explicitly.
Tuning of parameters that describe leaf nitrogen, for example, may improve
fluxes of GPP and hence overall yields. It is also worth noting that the
parameters used for the crop model in the site simulations are from the
global set-up and hence probably not optimal for site simulations.
Simulated (solid lines) and observed (dots) LAI,
CANHT, GPP and
HARVC at a range of FLUXNET sites and years. Simulations performed with
JULES-crop crop type maize (red), standard JULES C3 grass
plant functional type with phenology (green), and standard JULES
C3 grass plant functional type without phenology (blue).
Discussion and conclusions
When designing JULES-crop we took a flexible approach in acknowledgement of
the different requirements of the science community. This means the model can
be used to address a range of science questions, for example, (a) to assess
global climate impacts on crop functional types over long integrations with
climate model output, (b) to represent a number of crop cultivars of the same
crop type at the site scale forced with weather observations and (c) to
assess how crops may impact on biogeophysical feedbacks to climate including
albedo, partitioning of turbulent fluxes and seasonality of LAI. In this
paper we present results from a generic, crop functional type parametrisation
implemented at both global and site scales to show how this model performs in
an Earth system model context. Having the aim of generality necessarily means
that the model loses out in terms of specificity. However, with further
effort it should be possible to tailor the model set-up for more specific
applications but with the requirement that attention is given to the choice
of parameter values. Default values are provided here as a starting point for
model development and initial evaluation.
These results demonstrate the importance of evaluating the performance of
JULES-crop in a holistic sense, assessing both its ability to simulate land
surface fluxes in addition to crop growth and development dynamics and to
recognise that identified biases in performance are the result of the
combined JULES-crop model, not just the added crop component. Adding a crop
parametrisation has increased the complexity of JULES. However, this has not
led to an immediate improvement in the model's simulation of surface fluxes,
at least at the measurement sites examined. More effort needs to go into
developing the parameter sets for crops within JULES, particularly for the
existing set of plant functional type parameters which control productivity.
Simulated (solid lines) and observed (dots) LE and
H fluxes at a range of FLUXNET sites and years. Simulations
performed with JULES-crop crop type maize (red), standard JULES
C3 grass plant functional type with phenology (green), and
standard JULES C3 grass plant functional type without
phenology (blue).
Comparing the regional patterns of yield to observations gives useful
insight into the existing limits of the model. It is clear that some important
processes are missing, particularly irrigation (although this model
development will shortly be submitted for release). Developing a nitrogen
cycle for JULES (model development also in progress) should also improve the
model simulations, as introducing nitrogen limitation has been shown to reduce
overall productivity in Earth system models . JULES-crop will
still exclude many management factors which affect regional yields.
estimated global yield gaps and showed they were greatest in
tropical regions. Although not directly comparable with our simulations, this
study shows us that JULES-crop simulations are likely to overestimate yields
in tropical regions compared with observations. However, we have
deliberately not introduced a yield gap adjustment as it would not be
physically based and as such would be difficult to apply to future
simulations. It is, however, important to capture regional differences due to
management as they will effect patterns in productivity and hence feedbacks
to the climate. In an Earth system model context it is better to represent
these management processes explicitly where possible, as they effect not only
crop growth but also may well influence the local climate directly (e.g.
irrigation; ).
As a yield simulation model, there are encouraging signs that JULES-crop can
simulate variability in yield associated with climate fluctuations. However,
it is clear that JULES-crop overestimates the magnitude of this variability.
Whilst the absence of irrigation is most likely a contributing factor to the
overestimation of yield variability, the implication that the model is too
sensitive to changes in environmental conditions should also be investigated
further.
Including crops in JULES gives a more realistic seasonal cycle of leaf area
index, which affects the seasonality of carbon fluxes (timing of peak flux and
lower winter fluxes). This was seen at both the global and site levels. The
impact of crops on the energy balance was to alter the partitioning of latent
and sensible heat fluxes particularly in winter, which led to small impacts on
temperature in some countries. These impacts were marginal at the country and
site scales despite quite large differences in LAI. It is possible that the
relationship between LAI and evaporation is too weak in JULES
, which may explain why a more realistic representation of LAI did not improve
the energy fluxes. We may expect a higher sensitivity in fully coupled
atmosphere models.
Crop production systems are by their very nature heavily influenced by
humans. This represents a challenge to the JULES model which, to date,
assumed vegetation to be static and, within each vegetation tile, homogeneous
by the use of global constants for parameter values. The level to which this
approach can be extended to crops is limited. Whilst some processes might be
considered fundamental (i.e. photosynthesis) others can vary from place to
place for the same crop (e.g. sensitivity of development rate to day length).
Furthermore, human interference can alter the fundamental process, for
example the application of fertiliser to increase leaf nitrogen contents
impacting on photosynthesis. For applications of JULES-crop that rely on
accurate yield simulations, the inclusion of either a yield gap variable or
the factors that determine it such as fertiliser applications, pest control,
and soil fertility should be a priority for future model development. Inclusion
of winter wheat is also of high priority for JULES-crop. This is important for
use of JULES-crop as a yield simulation model but also an Earth system model,
as the additional presence of vegetation cover from autumn to spring would
impact on surface characteristics (albedo, heat capacity, etc.).