Simulation of competition between barley and wild oats under different managements and climates

Ecological Modelling, 71 (1994) 269-287

269

Elsevier Science B.V., Amsterdam

Simulation of competition between barley and wild oats under different managements and climates Robert Grant Department of Soil Science, Universityof Alberta, Edmonton, Alberta T6G 2E3, Canada (Received 12 May 1992; accepted 16 March 1993)

ABSTRACT Grant, R., 1994. Simulation of competition between barley and wild oats under different managements and climates. EcoL Modelling, 71: 269-287. The simulation of competition among different plant populations growing within a common ecosystem should be based upon the explicit simulation of the processes whereby these populations compete for irradiance, water and nutrients. In the mathematical ecosystem model presented here, each plant population is simulated independently within a common soil-atmosphere ecosystem. Exposure to irradiance is calculated from the vertical distribution of leaf area, calculated in turn from the elongation of each internode, sheath (if monocot) or petiole (if dicot), and leaf on each tiller or branch of each population within a common canopy. Access to water and nutrients is determined by the vertical distribution of root length and surface area, calculated from the elongation of primary and secondary root axes of each population through a common soil profile. The model reproduced losses of grain yield by barley (Hordeum vulgare L.) from 0 to 70% caused by different densities and emergence dates of wild oats (Avena fatua L.) that were recorded from several field trials in central Alberta. The sensitivity of simulated yield losses to wild oat competition under different climate and management was then compared to that reported in the literature. Examination of model results led to the hypothesis that sensitivity to competition among plant populations is determined by the availability of water, nutrients and other ecological resources at the site of study. Contrasting results of such competition in the literature may perhaps be explained by this hypothesis, but the absence of detailed data for soil and climate from experimental sites prevents rigorous testing.

1. I N T R O D U C T I O N

The m a n a g e m e n t of competition among plant populations for irradiance, nutrients and water under site-specific conditions of soil, climate and m a n a g e m e n t is an important part of food production. Because this competition is complex, mathematical modelling may become a useful means of 0304-3800/94/$07.00 © 1994 - Elsevier Science B.V. All rights reserved

SSDI 0 3 0 4 - 3 8 0 0 ( 9 3 ) E 0 0 2 8 - 2

270

R. G R A N T

predicting the outcome of hypothesized management strategies. The outcome of competition among plant populations has been modelled as functions of plant density and relative times of emergence using linear regressions (O'Donovan et al., 1985) or rectangular hyperbolae (Cousens et al., 1987) fitted to results from field experiments. However, such models cannot account for the effects of soil, climate and management on competition, such that their parameters are highly specific to the site and year in which field experiments are carried out. The predictive value of these models is therefore limited. Competition among plant populations for irradiance and water has also been modelled through dynamic simulation of plant growth. Competition for irradiance has been simulated from the vertical distributions of leaf area for each competing species (Spitters and Aerts, 1983; Kropff, 1988), based on the assumption that the horizontal distributions of leaf area are uniform within each layer. At low plant densities, however, horizontal distributions may deviate from uniformity, such that the competitive ability of taller species may be overestimated (Spitters and Aerts, 1983). Under these conditions competition for irradiance has been simulated from the 'zone of influence' (Fischer and Miles, 1973) around individual plants (Wilkerson et al., 1990). However, in this approach the vertical distributions of leaf area are ignored, and an empirical competition factor is used to calculate the relative efficiency of irradiance interception for each population. This competition factor changes during the growing season, and must therefore be fitted from field data. At high plant densities, overlapping zones of influence complicate this simulation. The simulation of competition must explicitly account for changes in the density of each plant population, in the timing and duration of its growth, and for changes in resource availability. Simulation under diverse soils, climates and managements will require a fundamental approach to the reproduction of irradiance interception and of water and nutrient uptake by the competing populations. In earlier work, algorithms were presented for calculating gas and energy exchange between the atmosphere and the canopies of single plant populations (Grant et al., 1989b, 1993; Grant and Baldocchi, 1992), and for the uptake of water and nutrients by the root systems of single plant populations (Grant, 1991; Grant et al., 1993). These algorithms function within a larger ecosystem model, where they have been used to simulate interspecific plant competition (Grant and Huck, 1989). Simulated competition for irradiance is based on hypotheses of concurrent interception by leaf surfaces of each population defined according to area, height, and orientation within a common canopy. Simulated competition for water and nutrients is based on hypotheses of concurrent uptake by root surfaces of each population defined according to area, depth and

SIMULATION

OF COMPETITION

BETWEEN

BARLEY

AND WILD OATS

271

spatial density within a common soil profile. In this study, these hypotheses are further developed and model results are tested against growth of wild oats (Arena fatua) and barley (Hordeum vulgare) reported by O'Donovan et al. (1985) from several field trials in central Alberta. 2. METHODS

2.1. Simulation of competition for irradiance Simulated competition for irradiance is based on the vertical positions of the leaf surfaces of each plant population within a common canopy. These positions are calculated hourly from the areas of each leaf L, and the lengths of each sheath S and internode N on each node K of each tiller T of each plant species S. The area or length of each organ is calculated as the time integral of its hourly extension rate (Grant and Hesketh, 1992):

6AL(s,T,K)/6t = f(q,t) M -°'33

M

(1)

6Ls(s,T,K)/6 t = /3ML(s,T,K -0.67 )Ms(s,T,K)S -0.50 Ms( s,r,K )/St ,

(2)

8LN(s,T,K)/8 t = y e AZ,MN(S,T,K)6My(s,T,K)/St, 0.67

(3)

where: AL~S,W,K)= leaf a r e a ( m 2 plant-I), LS(S,T,K)= sheath length (m), L N(S,T,K) = internode length (m), M LCS,T,K)= leaf dry mass (DM) (g plant 1), MS(S,T,K) = sheath DM (g plant-I), MN(S.T,K)=internode DM (g plant ~), 0t = canopy turgor potential (MPa). Leaf length (L L in m) is calculated from A L assuming a constant length:width ratio. Values for the parameters o~ in Eq. 1,/3 in Eq. 2 and y and A in Eq. 3 have been developed and tested for different densities of maize (Grant and Hesketh, 1992). Values of these parameters were selected for barley such that AL~S,T,K), LS(S,T,K), and LN(S,T,K) integrated over time from Eqs. 1-3 reproduced the nodal distributions of leaf area, leaf length, sheath length and internode length observed in wheat by Percival (1921) and by Kirby and Appleyard (1987). The calculation of 8Mc~s,r,t:)/St , 8Ms(s,T,K)/St , 8MN(s,T,K)/St, and their time integrals ML~S,T,K), MSCS,T,K), and MN~S,r,K), follows the phytomer concept of Hesketh et al. (1988), as described in Grant and Hesketh (1992). This concept was adapted for barley and wild oats by using rates of ontogenetic development reported by Cao and Moss (1989), Cudney et al. (1989), and Rooney et al. (1989) within a generalized submodel of monocot phenology (Grant, 1989b). Each leaf surface ALCS,T,K) is partitioned into horizontal canopy layers L according to its inclination class M:

A

L(S,T,K,L,M)

=

AL(S,T,K,L)FM'

(4)

272

R.GRANT

where:

AL(S,T,K,L) ~--AL(S,T,K)FL,

(5)

and where: F M =fraction of AL(S,T,K,L) in inclination class M, F L = fraction of AL(S,T,K) in canopy layer L. The canopy layer L to which each AL(S,T,K,L,M) is allocated is calculated

as: L =

)

~ LN(S,1,K) d- E LN($,T,K) d- LS(S,T,K) + LLv(S,T,K,L,M ) /XL,

k=l

k=l

(6)

where: LLv(S,T,K,L,M )) = LL(S,T,K,L,M) in the vertical direction (m), X L = depth of canopy layers (m). The first term in Eq. 6 represents the height to the proximal end of the tiller S, T ( = 0 for the main stem), the second represents the height of the stalk of the tiller S, T to the proximal end of the sheath S, T,K, the third represents the height of the sheath S, T,K, and the fourth represents the vertical height of the leaf S, T,K,L,M above the distal end of the sheath. The value of X L in Eq. 6 was selected to be 1/35 of the maximum height of the combined canopies, with a minimum value of 0.01 m, such that the combined canopy was resolved into 35 horizontal layers. The value of LLv(S,T,K,L,M ) in Eq. 6 is calculated from the median angle of inclination class M (O'M):

1 LLv(S,T,K,L,M) = Z (sin O'MLL(s,T,K,L,M) ).

(7)

m=M

The value of o-i declines with increasing M, such t h a t LLv(S,T,K,L,M ) is incremented with LL(S,T,K,L,M) at successively lower o-u. This increment order is used to simulate leaf curvature from more vertical inclination at the proximal end to more horizontal at the distal end. Each AL(S,T,K,L,M) is resolved into azimuth classes N on the assumption of uniform axial orientation. Each AL(S,T,K,L,M,N) is resolved into classes defined by exposure to photosynthetically active irradiance I, consisting of direct B and indirect D exposures. The value of AL(S,T,K,L,M,N,B) is calculated for each layer L from the interception of direct irradiance I(m in layer L + 1 above:

AL(S,T,K,L,M,N,B) AL(S,T,K,L,M,N) {FB(L+I) X ICOS ~(M,N) lsin fl},

~

)-~ ~

~

~ AL(S,r,K,L+ 1,M,N,B)

s=l t=l k=l m=l n=l

(8)

SIMULATION OF COMPETITION BETWEEN BARLEY AND WILD OATS

273

where: FB(L + 1)

and

A L(S,T,K,L + 1,M,N,B)/A L(S,T,K,L + 1,M,N)'

=

(9)

where:

~b(M,U) = incident angle from leaf normal of Itm on AL(S,T,K,L,M,N,B) and /3 = solar inclination. T h e integral in Eq. 8 represents the total interception of I(m in the layer above L. T h e value of AL(S,T,K,L,M,N,D) is calculated as: AL(S,T,K,L,M,N,D)

= AL(S,T,K,L,M,N)

T h e intensity of I(B ) at each

I(M,N,B ) = (1

-- A L ( S , T , K , L , M , N , B ) .

(10)

At~S,r,I
- a i ) ( 1 - T/)I(B ) ]COS tD(M,N) I,

where: I(M,N,B ) = I(B ) absorbed by AL(S,T,K,L,M,N,B) (/zmol m -2 s 1), ai = albedo of I, r i = transmission coefficient of I. T h e intensity of I(D ) a t each AL(S,T,K,L,M,N) is calculated for each of a n u m b e r of sky zones Y as: Y

I{L,M,N,O) = ~] (1-- Cei)(1--Wi)I,L,V,D)ICOS

A(M,Ny) I,

(12)

y=l

where: I(C,M,N,O) = I(o ) absorbed by At~S,T,K,L,M,N) (/xmol m -2 s-1), i(L,r,O) = I(o ) in layer L from sky zone Y, A(M,Ny ) = incident angle from leaf normal of I(L,Y,D ) o n AL(S,T,K,L,M,N). T h e intensity of I(Ly, O) in layer L is calculated from the interception of I(L,V,O) in the layer L + 1 above:

I(L,Y,D) =I(L+l,r,D)

× l COS S

T

s=l

t=l

1--(1--ai)(1-ri)E

K

M

N

E Y'. Y'. E AL{S,T,K,L+1,M,N) k=l

m=l

n=l

A(M,N,y ) I / s i n 7(r)} T

K

+ E E E s=l

S

t=l

k=l

M

E m=l

N

E {ri/(1--ai)I{M,N,mAL(s,r,I(,L+,,M,N,m},

(13)

n=l

where: Y(r) = inclination of sky zone Y. T h e first integral in Eq. 13 represents the total absorption of I(L,r,D } in the layer above L, and the second integral represents downward scattering of I(M,N,O) from the layer above L. T h e total interception of I(B ) and I(D ) is calculated by aggregating At~S,r,I
274

R. GRANT

Eqs. 8-13. All L within the aggregated layers share common values for FB(L+a) (Eq. 9) and I(L,M,N,D) (Eq. 12). In this way, assumptions about self-shading within layers are made for layers with a common total leaf area. The calculated interception of I(B) in Eq. 8 and I(L,V,m in Eq. 13 is based on assumptions of uniform horizontal distribution and no self-shading of AL(S,T,K,L,M,N) within each L. In fact, distribution is more random than uniform, and some self-shading will occur. Consequently, AI_(S,T,K,L,M,N) is multiplied in each L by a random coefficient between 0.1 (representing extreme non-uniform distribution and self-shading) and 1.0 (representing completely uniform distribution and no self shading) prior to its use in Eqs. 8-13. Total rates of CO e fixation are calculated for each tiller of each species (Q(s,T) in /zmol m -2 s - l ) : K

L

M

N

2

Q(S,T) = E E E E E k=l 1=1 m=l n=l r=l

{Q(s,T,K,L,M,N,R)AL(s,T,K,L,M,N,R)},

(14)

where: Q(S,T,K,L,M,N,m = photosynthetic rate of AL(S,T,K,L,M,N,m (/zmo1 m -2 s-l).

The value of Q(S,T,K,L,M,N,m is calculated from I(M,N,B) (Eq. 11) and I(L,M,N,D) (Eq. 12), and from canopy temperature and water potential (Grant and Baldocchi, 1992; Grant et al., 1993) as described in Grant (1992). Assuming constant I during each hour, Q(S,T~ (g C H 2 0 m-Z h - l ) is accumulated in a soluble pool in each tiller (CT(s,T) in g C H 2 0 m -2 ground area). A temperature-dependent fraction of this pool (F T) is mobilized hourly (Grant, 1989a), from which the requirements for maintenance and growth respiration of each organ (McCree, 1988) are removed before calculating organ DM growth:

(15) where: O = shoot organs (leaf, sheath, stalk, reserves, chaff and grain), MO(S,T,K) = organ DM (g m-e), PO(S,T,K)----phenology-dependent partitioning coefficient (Grant, 1989b), RMo(s,7-,K) = organ maintenance respiration (g m -2 h - l ) , RGo(s,T,K) ----organ growth respiration (g m -2 h - I ) . Values of 6Mo(s,T,K)/6t from Eq. 15 for leaf, sheath and stalks are used to calculate organ extension in Eqs. 1-3 and spatial distribution of leaf area in Eqs. 4-7.

2.2. Competition for water and nutrients Simulated competition for water and nutrients is based on concurrent uptake by the root systems of each population from horizontal layers in a

SIMULATION

OF COMPETITION

BETWEEN

BARLEY

275

AND WILD OATS

common soil profile. The simulated uptake of water and nutrients requires estimates of root length density and surface area in each layer (Grant, 1991, 1993; Grant et al., 1993). The vertical distribution of root length density is controlled by the vertical extension of primary axes arising from a common plant node K and the horizontal extension of higher order axes associated with each primary axis (Porter et al., 1986). Extension of axes from each node K in each layer L of each order X is driven by DM growth through mobilization of C H 2 0 from a soluble pool in each root system (Cn(s) in g C H 2 0 m - 2 ground area) to which each CT(S,T) contributes according to phenology-dependent partitioning coefficients:

~MR(s,K,L,X)/~t = F R ( L ) C R ( s ) - R M R ( s , K , L , X ) -

RGR(s,K,L,X)

(16)

where: Fa(L) = fraction of CR(S~ mobilized for growth of each X from each K in each L, RMR(s,K,L,X)= root maintenance respiration (g m - 2 h ~), RGR(s,K,L,X) = root growth respiration (g m -2 h-1). The presence of the subscript L in FR, RMR, and R G R reflects the influence of soil temperature (Grant et al., 1990) on root growth processes at different depths. Eq. 16 is first executed for the higher order axes ( X = 2) from the soil surface downward in all L more than 0.05 m above the root tip of each primary axis K (Klepper, 1987). Eq. 16 is then executed for the primary axes ( X = 1) only in the layer L in which the root tip of each axis K is located. The maximum extension of higher-order axes enabled by 6MR(s,K,L,X)/6t from primary axis K in layer L is:

6LRc(S,K,L,2)/6t = 6MR(s,K,L,2)/6t DR(1 -- PR)/(Trr2(s,2))

(17)

(van Noordwijk, 1987)where: 6LRc(S,K,L,2)/6t = carbon-limited extension of higher-order axes (m m -2 h - l ) , D R = root specific mass (m 3 g DM-L), PR = root porosity (m 3 m-3), rr~(s,2) = radius of higher-order roots (m). Extension in each layer L may be limited by the water status of the root and the resistance of the soil:

t~LRs(S,K,L,2)/t~t = NCS,K,L,2)C~RLRe(S,K,L,2)( ~T(S,L ) -- ~b0 -- 09(S,K,L,2))

(18)

(Greacen and Oh, 1972; Klepper, 1990), where: 6LRs(S,K,L,2)/6t = soillimited extension of higher-order axes (m m - 2 h - 1 ), N~s,K,L,2) = number of root axes (m-2), ~R = root elastic modulus (MPa 1 h - 1 ), LRe~S,K,L,2) = length of the elongating zones of higher-order axes (m), $TtS,L)= root turgor (MPa), ~O0 = root turgor below which 6LRc(S,K,L,2)/6t = O, W(S,K,L,2)) = soil resistance (MPa). The value of N(S,K,L,2 ) w a s estimated from LR(S,K,L,2) and the branching density of first-and second-order laterals (Porter et al., 1986). The value of 4'R was calculated from that used by Rickman et al. (1992). The value of

276

R. GRANT

LRe(S,K,L,2) was 0.01

m. The value of OT(S,L) was estimated from ~R(S,L), the root water potential, calculated as in Grant et al. (1989a, 1993). A value of 0.2 MPa was used for 00. The value of tO(s,K,L,2) was estimated from the soil elastic modulus (Grant, 1993), calculated in turn from soil bulk density and water potential according to data from Shierlaw and Alston (1984) as presented by Klepper (1990). The higher-order root extension 6LRts, K,L,2)/6t is taken to be the lesser of the carbon-limited rate 6LRc(S,K,I.,Z)/St (Eq. 17) and the soil-limited rate t~LRs(S,K,L,2)/t~t (Eq. 18). If t~LRs(S,K,L,2)/t~t < t~LRc(S,K,L,2)/6t , then 6MR(s,K,r,Z)/6t is calculated from 6LRs(S,K,L,Z)/6t according to Eq. 17. Similarly, primary root extension 6LR(s,K,L,1)/6t and DM growth 3MR(s,K,L,I)/6t is calculated as the lesser of carbon- and soil-limited rates for each axis K in the layer L in which the root tip is present:

6LRc(S,K,I~,I)/(3t =6MR(s,K,L,j,/3t DR(1-- PR)/(('rrr2(s,1))Z(s)),

(19)

where: 3LRc(S,K,L,1)/6t = carbon-limited extension of primary axes (m h-1), rR(S,1) = radius of primary roots (m), Z(s ~ = plant density (m-Z), and

6LRs(S,K,L,1)/6t = N(S,K,L,1)C~RLRe(S,K,L,1)( OT(S,L) -- ~0 -- O)(S,K,L,1)), where: 6LRc(S,K,I.,1)/6t = soil-limited extension of primary axes (m The value of CR(S~ is recalculated from 6MR(s,K,C,X~/6t after

(20)

h-~). each L from the soil surface downward. Thus soil limitations to higher-order root extension in upper layers (Eq. 18), as when low water content causes low OT(S,L~and high W(S,K,L,X),cause comparatively higher CR(S) to be available for higher-order root extension in lower layers, and for primary root extension in the lowest layer of each axis. Conversely, the absence of soil limitations in the upper layers will allow CR(S~ to be comparatively more depleted by higher-order extension, reducing extension in lower layers. Negative root growth, or dieback, occurs when FR(L)CR(s)< RMR(s,K,L,X) (gq. 16). Total root lengths LR(S,K,r,X~ are calculated as the time integrals of ~LR(s,K,L,X)/6t. These values are used to calculate water and nutrient uptake from root length density and root surface area as described elsewhere (Grant, 1991; Grant and Baldocchi, 1992; Grant et al., 1993).

2.3. Experiment O'Donovan et al. (1985) reported results of competition between populations of wild oats and of barley or wheat from several field trials between 1972 and 1983 at Lacombe and Vegreville in central Alberta. In these trials, wild oats were planted to emerge at intervals between 8 days before and 8 days after the cereal crop. Growth of barley and wheat was expressed

SIMULATION

OF COMPETITION

BETWEEN

BARLEY

AND WILD OATS

277

in terms of grain yield as functions of wild oat density and dates of emergence relative to that of the cereal crop. The ecosystem simulation model including the equations described above was run using hourly data for irradiance, air temperature, humidity, wind speed and precipitation reported from Vegreville during 1991, and data for the physical and chemical characteristics of a Malmo silt loam (Typic Cryoboroll) (Grant et al., 1990), a common soil in central Alberta. The model run was initialized on 1 May 1991 at which time the water content of the simulated soil profile was assumed to be at field capacity. A simulated barley population was planted on 21 May 1991 at a density of 175 plants m -2, the average density reported for the field trials of O'Donovan et al. (1985), following a simulated broadcast fertilization of 4 g m 2 of both NH4-N and NO3-N, a common fertilization practice in the experimental area. The simulated barley population (S = 1) was grown concurrently with a wild oat population (S = 2) at densities of 135, 175 or 260 plants m 2 planted at 3-day intervals between 12 day before, and 12 days after, the barley population. These three densities represented the lowest, medium, and highest of those for which data were reported by O'Donovan et al. (1985). Because of the similarity in ontogeny and growth between wild oats and cereals (Morishita and Thill, 1988; Cudney et al., 1989), the simulated barley and wild oat populations were assumed to be physiologically identical. The sensitivity of simulated barley grain yield to wild oat density and time of emergence was compared to that reported by O'Donovan et al. (1985). In order to test model sensitivity to different climate and management, the loss of simulated barley grain yield for different dates of wild oat emergence was then contrasted with that when rainfall or N fertilization was reduced by 50%, or when barley density was increased by 50%. No model parameters were changed during any of the simulations. 3. R E S U L T S

3.1. Barley yield loss vs. wild oat density and emergence date Data from O'Donovan et al. (1985) for three site-years are shown in Fig. 1 in which the percent loss of barley grain yield is expressed in relation to the relative time of wild oat emergence. Average barley yields recorded at these sites in the absence of wild oats was 434 g DM m - 2 while that simulated from the model run was 427 g DM m-2. The similarity of these yields indicated that the availability of resources to the barley population in the absence of competition was accurately reproduced by the model. The simulated effect of wild oat density on percent yield loss of barley increased with earliness of wild oat emergence. This interaction is also apparent in the model of Kropff (1988) although not in those of O'Donovan (1985) or

278

R. G R A N T

80-

[] - . . . .

~ ' ~

70"

"- • A ".,

60"

0

~

.

~

............

175

- -

'~-

260

....

.

50"

\\

40"

~

135

0

3o. 20"

-.

lo

8 ~

0

[] []

-10

• 2

-

•

, -8

•

•

.

. -4

•

•

•

, 0

•

•

•

. 4

"

"

"

' 8

"

"

"

' 12

Relative Emergence Date (d) Fig. 1. Simulated (lines) and recorded (symbols) percent yield loss of barley at a density of 175 plants m -2 as a function of the relative emergence date of wild oats at densities of 135, 175 and 260 plants m -2. Recorded data from O'Donovan et al. (1985).

Cousens et al. (1987). No further effect on simulated barley yield was apparent w h e n wild oat e m e r g e n c e was m o r e than 6 days before barley emergence. The absence of an effect was attributed in the model to slower early-season growth in the earlier wild oat plantings caused by cooler soil t e m p e r a t u r e s in early to mid May. A similar absence is apparent in the field data of O ' D o n o v a n et al. (1985). Grain yield, grain n u m b e r and kernel mass of the tiller (T = 2) of the simulated barley population were relatively m o r e affected by earliness of wild oat e m e r g e n c e than were those of the main stem (T = 1) (Table 1). The simulated tiller contributed 40% of barley grain yield for the latest wild oat planting vs. 32% for the earliest. Morishita and Thill (1988) also reported relatively greater reductions in grain yield and kernel mass from barley tillers vs. main stems caused by competition with wild oats in field plots. In the model, this effect was partly attributed to the later growth and development of the tillers than of the main stems, and hence greater exposure to competition. The effect of wild oat e m e r g e n c e date on barley yield loss in the model was caused by changes over time in the vertical distribution of leaf area and root length of each plant population, and hence in its access to irradiance, water and nutrients. These changes are shown on 21 July, two months after barley planting, for wild oat e m e r g e n c e 6 days before and after barley e m e r g e n c e w h e n both wild oat and barley densities were 175 plants m -2 (Figs. 2 to 4). To show vertical distributions of leaf area, the 35 canopy layers were aggregated into groups of five, and the total leaf density of the tillers in each group was plotted against the height of its mid-point. To

279

S I M U L A T I O N O F C O M P E T I T I O N B E T W E E N B A R L E Y A N D WILD OATS

TABLE 1 Dry mass of grain and dry mass per kernel for the main stem and tiller of a simulated barley population infested with a wild oat population emerging at different dates. Density of both populations is 175 plants m -2 Date of emerg,

a

llB 8B 6B 3B 0 3A 6A 8A llA control

Grain dry mass (g m-2)

Kernel dry mass (g)

Kernel number (m-2)

main stem

tiller

main stem

tiller

main stem

tiller

125.9 130.3 120.1 145.9 200.0 208.4 224.2 229.1 233.6 256.5

59.3 66.2 56.9 76.4 107.1 120.1 149.6 157.2 155.6 170.2

0.029 0.029 0.028 0.029 0.030 0.030 0.032 0.032 0.032 0.033

0.020 0.021 0.020 0.022 0.022 0.022 0.024 0.025 0.025 0.027

4419 4569 4352 5067 6665 6996 7081 7086 7230 7685

2976 3144 2819 3459 4868 5597 6117 6263 6260 6332

a B = before, A = after barley emergence. show vertical distributions of root length, the total length of all higher-order axes associated with all primary axes of each population in each soil layer was plotted against the depth of its mid-point. Most leaf area in the simulated wild oat-free barley was estimated from Eqs. 1-3 to be 0.5-0.75 m above the soil surface (Fig. 2a), and root length density was estimated from Eqs. 17-20 to decline exponentially with depth to 1.0 m below the soil surface (Fig. 2b). Simulated profiles of root length density are similar to those recorded from the same soil type near E d m o n t o n (Xu and Juma, 1993). For the earlier date of wild oat emergence, most wild oat and barley leaf area was estimated to be 0.6-0.7 and 0.4-0.5 m respectively above the ground surface (Fig. 3a). A competitive advantage in simulated irradiance interception was thus held by the wild oat over the barley (Eqs. 12-13). Simulated root length density of the wild oat was higher at all depths through the soil profile, proportionately more so with depth (Fig. 3b). A competitive advantage in simulated uptake of water and nutrients was thus also held by the wild oat over the barley. For the later date of wild oat emergence, these advantages were reversed (Fig. 4a and 4b).

3.2. Barley yield loss vs. rainfall, N fertilization and barley density R e d u c t i o n of each rainfall event by 50% caused a reduction in the simulated grain yield of barley in the absence of wild oats from 427 to 219 g D M m -2. The simulated yield of barley in competition with wild oats was

280

R. GRANT

(a) 1.0'

0

E

v

.

8

'

~

0.6'

¢. m

0.4"

O

0.2" ........

O.C

-

, 1

--,

.... 2

- --, 3

barlev main badey tiller

.... 4

• --, 5

6

Leaf Area Density (m = m'3 )

(b) 0.0-

-0.2 " A

E

v

-0.4 ' -0.6

O

-0.8 -1.0

barley

-1.2 .01

.1

1

10

Root Length Density (m m'S x 1 0 4

)

Fig. 2. Vertical distributions of (a) leaf area density and (b) root length density simulated on 21 July for a barley population of 175 plants m -2 planted on 21 May. reduced proportionately more, so that the percent yield loss of barley from its wild oat-free value was increased, more so for later wild oat emergence dates (Fig. 5). In the model, the increased percent yield loss under reduced rainfall was caused by water stress that was aggravated by increased competition from wild oats for limited soil water. Aggravation of water stress from wild oat competition would likely explain the findings of Torner et al. (1991), who reported barley yield losses from 300 plants m -2 of wild oats of 13 and 30% in two wetter seasons vs. 43 and 66% in two drier seasons. Reducing pre-plant fertilization of N by 50% to 2 g m -2 of both NH4-N and NO3-N did not change the simulated grain yield of barley in the absence of wild oats (427 vs. 443 g DM m-2). However, the simulated percent yield loss of barley for each wild oat emergence date was 10-15% greater with lower fertilization (Fig. 5) because the wild oats accelerated the removal of mineral N from the soil. Reports differ in the literature concerning the effects of N on cereal-wild oat competition. Carlson and Hill (1986) reported reduced relative yield of spring wheat with increased N fertilizer at wild oat densities lower ( < 0.25 of the wheat) than those in

S I M U L A T I O N O F C O M P E T I T I O N B E T W E E N B A R L E Y AND WILD OATS

281

(a)

0.8

E r04 -r" o.2 0.0

-. - , . . . , 1

2

..

,

3

-

.........

wild oats m a i n

. . . . .

wild

-

,"

4

-

,"

oats •

5

tiller

''

6

Leaf Area Density (m 2 m 3

)

(b)

0.0[ -0.2 vE J=:

-o4 : -0.6 0.8"

'" *iarlev

1.0" -1 2 .01

.........

.1

1

Root Length Density (m m 3

wild

oats

10

x 10-4 )

Fig. 3. Vertical distributions of (a) leaf area density and (b) root length density simulated on 21 July for barley and wild oat populations of 175 plants m -2 planted on 21 and 15 May, respectively.

this study. Satorre and Snaydon (1992) reported that N fertilizer reduced the severity of competition from cereals experienced by wild oat, but found no significant interaction between N and wild oats on cereal growth. Bell and Nalewaja (1968) reported lower percent yield losses from wild oat competition in fertilized vs. unfertilized barley, but higher in wheat, indicating that physiological differences among these species may influence competitive effects. In the simulated experiment reported here, the grain yield of barley plus wild oats over that of barley alone increased less with lower N, indicating greater competition for N with lower fertilization. For the 21 May planting with 8.0 g m -2 of fertilizer N, as an example, the yield of barley + wild oats vs. that of barley alone was 614 vs. 427 g m -2, while with 4.0 g m - 2 they were 464 vs. 443 g m - 2 . Because barley and wild oats were assumed to be physiologically identical, the relative yield of barley was thus lower when N was reduced. Increasing barley density by 50% to 262.5 plants m - 2 increased the simulated grain yield of barley in the absence of wild oats from 427 to 535 g m - 2 . However, barley yield simulated in competiton with wild oats in-

282

R. GRANT

(a) 1.0"

0.8" ........

~ ..................... :7"

o4: -/---'~-I-

'~ 0.2 £

.............. .

.

.

.

.

.

.

.

.

.

barlev barley tiller wild oats m a i n wild oats tiller

.

..... O.O

--

-,1

--,-2

-, - - ",3 4

- -,5

" ",

6

Leaf Area Density (m 2 m" 3 )

(b) 0.0-

-0.2 -0.4 " -0.6 " O

-0.8 ."'"'"

-1.0

/

.........

-1.2 .001

.01

.1

badev wild oats

1

10

Root Length Density (m m "3 x 10 .4 )

Fig. 4. Vertical distributions of (a) leaf area density and (b) root length density simulated on 21 July for barley and wild oat populations of 175 plants m -2 planted on 21 and 27 May, respectively.

r

f

...... ......

218 109 218

8.0 8.0 4.0

175 175 175

............

218

8.0

262.5

80' 70. 60 -

O .-I

50-

"-,

, _. . . .

-,

p

..

4O ~"

30

~"

20

°-~

10-

oI -10 - - - , • • • , - - " , . . . . . . . -12 -8 -4 0 4

Relative Emergence

' 8

" " " ' 12

Date (d)

Fig. 5. Simulated percent yield loss of barley as a function of the relative emergence date of wild oats at a density of 175 plants m -2 under different rainfall (r in mm), fertilization (f in g m -2) and barley densities (p in plants m-2).

SIMULATION

OF

COMPETITION

BETWEEN

BARLEY

AND

WILD

283

OATS

TABLE 2 Grain yields and evapotranspiration (ET) from different densities of barley and wild oat populations simulated at Vegreville between 1 May and 31 August 1991. Both populations were planted on 21 May 1991. Total precipitation recorded between 1 May and 31 August 1991 was 218 mm Density (m -2) barley

Grain Yield (g m -2)

wild oats 0 135 175 175

175 175 175 262.5

total

barley

175 310 350 437.5

427 319 307 327

wild oats 0 246 307 218

ET (mm) total 427 565 614 545

477 500 506 520

creased relatively less than did that in their absence, so that percent yield loss for each w e e d emergence date was 5 - 1 0 % higher (Fig. 5). R e p o r t s differ in the literature concerning the effects of barley density on percent yield loss caused by wild oats. Evans et al. (1991) r e p o r t e d that the relative yield of barley in competition with wild oats increased with barley density, although Torner et al. (1991) reported that barley yield was little influenced by barley density in competition with wild oats. In the simulated experiment reported here, the effect of barley density was c o n f o u n d e d with that of soil water. Total recorded precipitation was less than total simulated evapotranspiration at Vegreville during the 1991 growing season,

0.0"

ii"

-o.21 E ..C G)

b

w

175+

0

175+135 175 + 175 262 + 175

-0.4 ' -0.6 "

-o.6.

- I .0 '

-1.2

-

o.16

•

,

•

.

.

.

.

,

.

.

.

.

.

.

"

•

,

o.21 0 . 2 4 0 . 2 7 6 . 3 0 o . 3 3 0.36

Water Content (m 3 m 3

)

Fig. 6. Vertical distributions of water content simulated on 21 July for different barley (b) and wild oat (w) densities (plants m - 2 ) planted on 21 May. Bold lines indicate water contents at - 0 . 0 3 and - 1 . 5 MPa.

284

R. G R A N T

more so with higher total plant density (Table 2). More rapid water removal by higher plant densities caused lower soil water contents during the growing season (Fig. 6), which in turn reduced gas exchange between the canopies and the atmosphere (Grant et al., 1993). Reduced gas exchange caused simulated grain yields to decline, such that percent yield loss increased with barley density when total barley + wild oat density exceeded 350 plants m -2 (Table 2). 4. DISCUSSION The outcome of competition between barley and wild oats varies widely with site, year, management and climate (Morishita and Thill, 1988), such that unique relationships between outcome and the densities and emergence dates of the competing populations are unlikely to exist. Parameters for such relationships may vary widely among different experiments (e.g. Cousens et al., 1987; Torner et al., 1991). Examination of model responses to changing rainfall, fertilizer and population density leads to the hypothesis that the competitive effects on barley of a given density of wild oats will be determined by the availability of resources such as water and nutrients for which competition occurs at the site of study. As these resources become more limiting, the relative effects should increase. The fundamental process-based hypotheses of which the simulation model is composed allow sensitivity to changing resource availability caused by management and climate. This sensitivity is achieved without the use of site-specific parameters as required by less fundamental regression-based hypotheses. Such sensitivity will eventually allow the application of models such as this to the study of competition among plant populations across a wider range of conditions than is possible with regression models. However, the application of process-based simulation models to the study of competition requires a more fundamental understanding of the dynamics of plant growth than currently exists for many plant species. Lack of such understanding for wild oats forced the assumption in this simulation study that barley and wild oats are identical in growth habits. While there is some evidence for this assumption (Morishita and Thill, 1988; Cudney et al., 1989), there is also evidence that the competitive abilities of these species may differ among varieties. Much of this difference may be associated with relative morphological development (Cousens et al., 1991), such that results from different experiments may not be comparable. The generalized phenology submodel used in the simulation model allows varietal differences in morphological development to be represented (Grant, 1989b). However, these differences are rarely reported from experimental sites, limiting the rigor with which competition hypotheses may be tested.

S I M U L A T I O N O F C O M P E T I T I O N B E T W E E N B A R L E Y AND WILD OATS

285

O t h e r site data, including those for soil and climate characteristics, are also necessary for testing, but are also rarely reported from experimental sites. The reporting of more comprehensive site data in future studies will allow the testing of improved competition hypotheses, and the eventual development of predictive capabilities for the behaviour of complex plant communities. The algorithms used here to simulate competition for irradiance, water and nutrients offer advantages over those used in regression or 'zone of influence' models. As part of the larger ecosystem model, they may be used to represent several species S, each of which may emerge over several different dates within a c o m m o n ecosystem, allowing a more realistic simulation of complex plant communities than is otherwise possible. In the ecosystem model, these algorithms are used for each cell within a matrix of soil profiles under a c o m m o n climate. Cell dimensions appropriate to the size of the simulated plant species may be selected to maintain the validity of the assumption that leaf and root surfaces of each species are randomly distributed within each layer of the cell. Plant densities may be distributed non-uniformly over the matrix, so that the effects of isolated plants or groups of plants may be represented. In this way, assumptions about interplant competition necessary in 'zone of influence' models may be avoided. ACKNOWLEDGEMENTS This research was partially supported by a grant from the National Science F o u n d a t i o n and utilized the C R A Y - 2 facility of the National Center for Supercomputing Applications at the University of Illinois in Urbana-Champaign. REFERENCES Bell, A.R. and Nalewaja, J.D., 1968. Competition of wild oat in wheat and barley. Weed Sci., 16: 505-508. Cao, W. and Moss, D.N., 1989. Temperature effect on leaf emergence and phyllochron in wheat and barley. Crop Sci., 29: 1018-1021. Carlson, H.L. and Hill, J.E., 1986. Wild oat (Arena fatua) competition with spring wheat: Effect of nitrogen fertilization. Weed Sci., 34: 29-33. Cousens, R., Brain, P., O'Donovan, J.T. and O'Sullivan, P.A., 1987. The use of biologically realistic equations to describe the effects of weed density and relative time of emergence on crop yield. Weed Sci., 35: 720-725. Cousens, R.D., Weaver, S.E., Martin, T.D., Blair, E.M. and Wilson, J., 1991. Dynamics of competition between wild oats (Arena fatua L.) and winter cereals. Weed Res., 31: 203-210.

286

R. GRANT

Cudney, D.W., Jordan, L.S., Corbett, C.J. and Bendixen, W.D., 1989. Developmental rates of wild oats (Arena fatua) and wheat (Triticum aestivum). Weed Sci., 37: 521-524. Evans, R.M., Thill, D.C., Tapia, L., Shafii, B. and Lish, J.M., 1991. Wild oat (Arena fatua) and spring barley (Hordeum vulgare) density affect spring barley grain yield. Weed Tech., 5: 33-39. Fischer, R.A. and Miles, R.E., 1973. The role of spatial pattern in the competition between crop plants and weeds. Math. Biosci., 18: 335-350. Grant, R.F., 1989a. Simulation of carbon accumulation and partitioning in maize, Agron. J., 81: 563-571. Grant, R.F., 1989b. Simulation of maize phenology. Agron. J., 81: 451-458. Grant, R.F., 1991. The distribution of water and nitrogen in the soil-crop system: A simulation study with validation from a winter wheat field trial. Fert. Res., 27: 199-214. Grant, R.F., 1992. Simulation of carbon dioxide and water deficit effects upon photosynthesis of soybean leaves with testing from growth chamber studies. Crop Sci., 32: 1313-1321. Grant, R.F., 1993. Simulation model of soil compaction and root growth. I. Model structure. Plant Soil, 150: 1-14. Grant, R.F. and Baldocchi, D.D., 1992. Energy transfer over crop canopies: simulation and experimental verification. Agric. For. Meteorol., 61: 129-149. Grant, R.F. and Hesketh, J.D., 1992. Canopy structure of maize (Zea mays L.) at different populations: simulation and experimental verification. Biotronics, 21: 11-24. Grant, R.F. and Huck, M.G., 1989. Inter-specific crop competition: parallel processing on the CRAY-2. In: Workshop of the Biological Systems Simulation Group. Illinois State Water Survey, Urbana, IL, p. 33. Grant, R.F., Frederick, J.R., Hesketh, J.D. and Huck, M.G., 1989a. Simulation of growth and morphological development of maize under contrasting water regimes. Can. J. Plant Sci., 69: 401-418. Grant, R.F., Peters, D.B., Larson, E.M. and Huck, M.G., 1989b. Simulation of canopy photosynthesis in maize and soybean. Agric. For. Meteorol., 48: 75-92. Grant, R.F., Izaurralde, R.C. and Chanasyk, D.S., 1990. Soil temperature under conventional and minimum tillage: simulation and experimental verification. Can. J. Soil Sci., 70: 289-304. Grant, R.F., Rochette, P. and Desjardins, R.L., 1993. Energy exchange and water use efficiency of crops in the field: Validation of a simulation model. Agron. J., 85: 916-928. Greacen, E.L. and Oh, J.S., 1972. Physics of root growth. Nature New Biol., 235: 24-25. Hesketh, J.D., Warrington, I.J., Reid, J.F. and Zur, B., 1988. The dynamics of corn canopy development: Phytomer ontogeny. Biotronics, 17: 69-77. Kirby, E.J.M. and Appleyard, M., 1987. Development and structure of the wheat plant. In: F.G.H. Lupton (Editor), Wheat Breeding. Chapman and Hall, London, pp. 287-311. Klepper, B., 1987. Origin, branching and distribution of root systems. In: J.V. Lake, D.A. Rose and P.J. Gregory (Editors), Root Development and Function. Effects of the Physical Environment. Cambridge University Press, UK, pp. 103-124. Klepper, B., 1990. Root growth and water uptake. In: Irrigation of Agricultural Crops. Agronomy Monograph no. 30, Agron. Soc. Am., Madison, WI, pp. 281-322. Kropff, M.J., 1988. Modelling the effects of weeds on crop production. Weed Res., 28: 465-471. McCree, K.J., 1988. Sensitivity of sorghum grain yield to ontogenetic changes in respiration coefficients. Crop Sci., 28: 114-120. Morishita, D.W. and Thill, D.C., 1988. Wild oat (Arena fatua) and spring barley (Hordeum vulgare) growth and development in monoculture and mixed culture. Weed Sci., 36: 43-48.

S I M U L A T I O N O F C O M P E T I T I O N B E T W E E N B A R L E Y A N D WILD OATS

287

O'Donovan, J.T., de St. Remy, E.A., O'Sullivan, P.A., Dew, D.A. and Sharma, A.K., 1985. Influence of the relative time of emergence of wild oat (Arena fatua) on yield loss of barley (Hordeum vulgare) and wheat (Triticum aestivum). Weed Sci., 33: 498-503. Percival, J., 1921. The Wheat Plant. Duckworth, London. Porter, J.R., Klepper, B. and Belford, R.K., 1986. A model (WHTROOT) which synchronizes root growth and development with shoot development for winter wheat. Plant Soil, 92: 133-145. Rickman, R.W., Waldman, S.E. and Klepper, E., 1992. Calculating daily root length density profiles by applying elastic theory to agricultural soils. J. Plant Nutr., 15: 661-675. Rooney, J.M., Brain, P. and Loh, S.Y., 1989. The influence of temperature on leaf production and vegetative growth of Arena fatua. Ann. Bot., 64: 469-479. Satorre, E.H. and Snaydon, R.W., 1992. A comparison of root and shoot competition between spring cereals and Arena fatua L. Weed Res., 32: 45-55. Shierlaw, J. and Alston, A.M., 1984. Effect of soil compaction on root growth and uptake of phosphorus. Plant Soil, 77: 15-28. Spitters, C.J.T. and Aerts, R., 1983. Simulation of competition for light and water in crop-weed associations. Aspects Appl. Biol., 4: 467-483. Torner, C., Gonzalez Andujar, J.L. and Fernandez-Quintanilla, C., 1991. Wild oat (Arena sterilis L.) competition with winter barley: plant density effects. Weed. Res., 31: 301-307. van Noordwijk, M., 1987. Methods for quantification of root distribution pattern and root dynamics in the field. In: Proceedings 20th Colloq. Int. Potash Institute, Berne, Switzerland, pp. 263-281. Wilkerson, G.G., Jones, J.W., Coble, H.D. and Gunsolus, J.L., 1990. SOYWEED: A simulation model of soybean and common cocklebur growth and competition. Agron. J., 82: 1003-1010. Xu, J.G. and Juma, N.G., 1993. Above- and below-ground net primary production of four barley cultivars in western Canada. Can. J. Plant Sci., 72: 1131-1140.