grass alley crop

AGRICULTUP~L AND FOREST METEOROLOGY ELSEVIER

Agriculturaland Forest Meteorology 81 (1996) 255-272

Modelling evapotranspiration partitioning in a shrub/grass alley crop R~gis Tournebize, Herv~ Sinoquet 1, Francois Bussi~re Station Agropddoclimatique, INRA des Antilles-- Guyane BP 515, 97165 Pointe gz Pitre C~dex, Guadeloupe

Received 27 February 1995; accepted 26 October 1995

Abstract

Improving intercropping requires a thorough understanding of resource, particularly water, between the plant species. Evaporative demand was modelled for the soil and the components of a shrub/grass intemrop planted in rows, by determining the energy balance of each component and by distinguishing between sunlit and shaded foliage. Modelling was based on a light partitioning model and micrometeorological data from the canopy. Stomatal conductance, necessary for estimating transpiration, was modelled as a function of photosynthetically active radiation (PAR). This enabled the validation of the model with evapotranspiration measurements in the field, during 10 days. There was a good agreement between the measured and estimated fluxes, for both the soil/grass layer and shrubs (r 2 = 0.95). The difference, less than 10% at a daily scale, between the measurements and the model was due to the difficulty in estimating the flux transfer resistances, ie stomatal and aerodynamic. The model enables the analysis of the influence of microclimate changes due to shrub development on grass transpiration.

1.

Introduction

Intercropping consists in growing several plant species simultaneously in the same field. Advantages currently attributed to intercropping are the increase of land productivity and sustainability (Willey, 1979; Govinden et al., 1984; Francis, 1986), but the scientific basis of these advantages has been seriously questioned (Ong, 1994; Azam-Ali, 1995). Simulation models based on plant-environment interactions may be helpful in understanding the functioning of plant canopies. Special features of intercrop modelling are the resource partitioning between the components (Caldwell and Hansen, 1993) and

1 Present address: Bioclimatologie- - PIAF, INRA, Domaine de Crouelle, 63039 ClermontFerrand C6dex 02, France.

0168-1923/96/$15.00 Copyright © 1996 Elsevier Science B.V. All rights reserved. SSDI 01 68- 192 3(95)023 18-6

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R. Tournebize et a l . / Agricultural and Forest Meteorology 81 (1996) 255-272

the responses of plants to environmental conditions modified by intercropping. In particular, light and water competition are closely connected (Caldwell, 1987; Cannell and Grace, 1993) and strongly involved in intercrop behaviour, e.g. the balance between the components species (McMurtrie and Wolf, 1983). Light partitioning between intercrop components has been modelled in several ways (see the review by Sinoquet and Caldwell, 1995). Most light models for intercropping are based on the turbid medium analogy, i.e. Beer's law that has been extended to account for light partitioning (Rimmington, 1984; Sinoquet and Bonhomme, 1991). Some of them deal with row intercropping (Marshall and Willey, 1983; Ryel et al., 1990, Sinoquet and Bonhomme, 1992). Analysing water partitioning is complex because it involves simultaneously aboveground and below-ground spaces. Below-ground, it depends on the availability of water and the root system development and its ability to extract water (Ozier-Lafontaine et al., 1995). Above-ground, meteorological conditions and spatial distribution of foliage determine the water demand of each intercrop component. Water consumption results from interactions between soil water availability and climatic demand as well as biological responses such as stomatal closure or leaf rolling. Water use of the intercrop is also influenced by rainfall interception that leads to evaporation of free water on the foliage and heterogeneous water distribution on the soil surface (Bussibre, 1995). Above-ground, measurements of transpiration partitioning between each species are rare and mainly concern and tree-grass canopies (Kelliher et al., 1990; Berbigier et al., 1991). Some models for estimating the transpiration of intercrop components exist (McMurtrie and Wolf, 1983; Wallace, 1995). The first is a long term model and resource competition is therefore only considered very briefly. The second is based on the approach of Shuttleworth and Wallace (1985), where the energy balance of each component is parameterised with a set of resistance terms at an hourly scale. This approach was already used and validated e.g.; Dolman and Wallace, 1991, Wallace et al., 1993, on a large and horizontal site of millet. In the Caribean countries, intercrops are mainly developed for food crops, on small plots where using of machines is impossible, mostly due to the slope. These two characteristics combined with the spatial heterogeneity of multi-species canopy prevent from using classical approach based on K-theory. Firstly because of the size of the field (typically less than 1 ha), there is an important effect of advection. Secondly due to the slope, the horizontal flux must be taken into account, and thirdly due to the arrangement of the species. In the tree-grass intercrops, the relationships between the environmental variables at each level and outside are poorly known. The use of meteorological data measured inside the canopy as input values for the model seems to be more appropriate to take account of all the interactions of the crops on microclimate. The aim of this paper was to validate a model of evapotranspiration partitioning in a shrub/grass alley crop. This kind of stand is frequent in the Caribbean (Ford, 1987). Moreover the shrub is used as an hedgerow and its foliage coming from pruning could be distributed to animals during periods of drought. For the validation, this model was connected with a conductance model in order to relate evapotranspiration to climatic demand.

R. Tournebize et al. / Agricultural and Forest Meteorology 81 (1996) 255-272 2. Materials

and

257

methods

2.1. Model description The model was based on the determination of an energy balance of the foliage of each species and of the soil surface. The model uses the light model of Sinoquet and Bonhomme (1992), which can estimate light partitioning between species as affected by the spatial distribution of foliage. The energy balances are computed from the meteorological data measured within the canopy. 2.1.1. Energy balance of the foliages and the soil surface The simplified energy balance of foliage i within a multiple-foliage canopy, disregarding the energy gain produced by the biochemical reactions of the system and the variations of stored energy, may be written (1)

Rni - H i - AE i = 0

where the energy fluxes (Win -2) are the net radiation absorbed by the foliage Rni, the latent heat flux AEi, and the sensible heat flux H i. A similar budget may be written for the soil surface (subscript s) by adding the ground heat conduction flux G. Rns

-

H s -

AE s -

G ~- 0

(2)

The model deals with the sunlit and shaded foliage of each species. Differences in surface temperature can be expected between sunlit and shaded leaves because of the energetic effect on the radiative balance and the irradiance effect on stomatal control (Caldweil et al., 1986). From this point, the term 'component' will refer to the soil surface, the sunlit or the shaded foliage of either species in the intercrop. 2.1.2. Net radiation Net radiation absorbed by a component i was fluxes coming from the radiation sources: incident and Rd0, atmospheric radiation, R a, radiation soil-canopy system, proportional to o-. Ti4, where

expressed as a linear combination of solar direct and diffuse radiation, Rb0 emitted by the components of the T is the temperature.

2M R n i = Rb0" Cb,i -]- Rd0" Cd,i "~- R a " Ca,i -~- E Cj,i" o'. Tin -~ Cs, i . or" Ti4

j=l

(3)

where the Cx. i are coefficients of radiation exchanges between radiation sources X and component i. Subscript j refers to the vegetation components of the canopy. Coefficients Cx, i are computed by using the model of Sinoquet and Bonhomme (1992), i.e. a model for solar radiation transfer in row intercropping based on the turbid medium analogy. The model allows to compute light partitioning in a multiple-component canopy with two-dimensional variations in leaf area distribution. The shortwave radiation balance takes into account interception of incident direct and diffuse radiation, and multiple scattering. Fractions of sunlit and shaded leaf area are computed from the interception probability of direct radiation (e.g. see Sinoquet et al., 1993). Absorbed radiation coming from scattering and incident diffuse light is shared

258

R. Tournebize et al./ Agricultural and Forest Meteorology 81 (1996) 255-272

according to the fractions of shaded and sunlit leaf area. Due to the non-linear effect of the optical properties of soil and vegetation, the model separately computes the shortwave radiation balance of photosynthetically active radiation (PAR) and near infrared (NIR) wavebands. Optical properties of soil and leaf surfaces are assumed to be constant in each waveband. According to Varlet-Grancher (1975), 50% of the incident radiation is attributed to each waveband. Thermal radiation transfer is computed by extending the shortwave radiation model. Leaf elements and soil surface are assumed to behave as black bodies. Interception of atmospheric radiation R~, i.e. computation of coefficients C~.,, is made in a way similar to that of diffuse sky radiation. Interception of radiation emitted by the foliage, i.e. computation of coefficients Cj, i, is made in a way similar to that of scattered solar radiation. In that case, coefficient Ci. i includes radiation lost by the own emission of component i. Net radiation absorbed by the soil surface R,s and above the soil-canopy system Rn0 are also expressed with an equation similar to Eq. (3), i.e. by considering the contribution of the radiation sources to Rn~ and Rn0. Because Rno is measured rather than atmospheric radiation Ra, this allows Ra to be expressed as a function of R~0, surface temperatures of emitting components and the incident and reflected solar radiation. 2.1.3. S e n s i b l e h e a t f l u x

Sensible heat flux H i lost by a plant component i may be written: H i = 2.L i.p.

Cp. (T~- Tai)/ra i

(4)

where p is the air density (kg m - 3), Cp is the heat capacity of the air (J k g - 1 K - l ), 7]i is the surface temperature of component i and Ta i is the air temperature (K) and L i is the leaf area index. Multiplying by 2 and by L i allows one to express the heat flux per unit of soil surface area. Aerodynamic resistance to heat transfer ra i (s m - 1) was calculated from wind speed and temperature difference between the leaf surfaces and surrounding air, using relationships between Nusselt, Reynolds and Grasshof numbers (Monteith and Unsworth, 1990, p. 101 and 123). Similar considerations were used to estimate the sensible heat flux H S lost by the soil surface. 2.1.4. L a t e n t h e a t f l u x

Latent heat flux A E i lost by a plant component i may be written (Monteith and Unsworth, 1990)

, ei = L,. ( p . Cpl

) . ( esi-

(5)

where y is the psychrometric constant (Pa K - ~) and ea i is the water vapour pressure in air. The saturated water vapour pressure es i (Pa) at surface temperature 7] is calculated using the formula of Tetens (1930). The equivalent resistance rw i (sm -1) to vapour transfer combines the aerodynamic resistance to water transport--assumed to be equal to ra i (Jarvis, 1976)--and, if needed, the stomatal resistance of both sides of the leaves. Resistance rw i may be written I / r w i = 1 / ( r a i + r~i ) + 1 / ( r a i + r;i )

(6)

R. Tournebize et al./Agricultural and Forest Meteorology 81 (1996) 255-272

259

where r~i and r~i and are the stomatai resistances of the upper (adaxial) and lower (abaxial) sides of the leaf. To calculate the evaporative climatic demand, stomatal resistance must be neglected. When stomatal control has to be taken into account, which is the case for validating the model with transpiration measurements, stomatal resistances are computed from the empirical model of Jarvis (1976): rsx = ( a , + P A R ) / ( a z × PAR)

(7)

where x refers to either leaf side, P A R is the PAR irradiance on the leaf derived from the light model. Parameters a I and a 2 depend on the plant species and the soil water availability. Latent heat flux from the soil surface A E s is computed from similar considerations. The soil resistance rw s to water transport is the sum of the aerodynamic resistance--assumed to be the same as the resistance to heat transfer--and a resistance rss due to the non-saturated soil surface. The latter is related to the water potential energy at the soil surface, which is related to the physical properties of the soil and the water content. Bussi~re (1985) derived an empirical model for rs~ in the vertisols of Guadeloupe (French Antilles) rs s = 200(0/0sat) - 1 . 6

(8)

where 0 and 0sa t a r e , respectively, the actual and maximal water content of the 0.05 m surface soil layer. Because the model is not linked with a soil water transfer model, changes in soil water content during the day were not taken into consideration and may led to discrepancies in soil fluxes estimation (Massman, 1992). However in all the measurement periods the daily variation of 0 does not exceed 1%. 2.1.5. H e a t conduction f l u x to the soil

The heat conduction flux G is calculated from the heat storage method (Brunet, 1984), i.e. from measured soil temperatures at different depths and estimated thermal properties. Heat capacity of the soil layer is estimated from its gravimetric water content by using the equation of De Vries (1963). Thermal conductivity at the bottom of the soil layer was set to 1 W m-1 K - l , according to measurements made on soils with high clay content (Bussi~re and Cellier, 1994). 2.1.6. Solving the energy b a l a n c e s - - p r a c t i c a l p r o c e s s

The energy balances of the soil-canopy system form a set of Eqs. (1) and (2) where the unknowns are the surface temperatures T~ and T~. Fluxes associated with a component i mainly depend on surface temperature T~. Because some longwave radiation emitted by the other elements may reach component i, Rni also depends on the surface temperature of the other components. However, the amounts of long wave energy coming from the other components are small with regard to the other fluxes so that the energy balance is most sensitive to T~. The light model presented in Tournebize and Sinoquet (1995) was ran for layers of thickness 0.1 m, i.e. 9 to 14 layers according to the period. All the parameters used in the model were either directly measured or taken from literature. Hence, no fitting of parameter values was undertaken.

260

R. Tournebizeet al. /Agricultural and Forest Meteorology 81 (1996) 255-272 Canopy description

|DRIVING VARIABLES

L e a ~ Is01ar R a d ~ Orientation distribution /Incident PAR ,IDIGI /Sun Location

I

Stomatalparameters I/Rn0

~-Air (each/eveOI Soil ! Ta , Ts |l ea u 1o [Yj initialisation

,~Rad!atiQn model Sunlit and Shaded area Exchange coefficients (Shortwave and ~ Leaf PAR Irradiance [ l ,, L~rodynamic resistance,

II s o - i ~

Radiation

Rnj =f (Rnj,T0) I-I!._ f ( Ta, U, Tj ) - G -- f (Ta, Ts ,O )

~

I.,

I Energy budget = Rnj+ Hj +XEj(+ G) ]

new Tj value I

File Output Fluxes Surfaces Temperatures I

I next time step I I

Fig. 1. Simplified flowchart of the model. Subscript j represents both species in each condition, and soil.

Fig. 1 shows the organisation of the model. After the computation of the radiation exchange coefficients, energy balances o f the soil-canopy components are computed by setting surface temperatures at the air temperature. As long as the energy balances do not equal zero, surface temperatures are updated according to N e w t o n - R a p h s o n ' s minimization method (Nougier, 1985). Thereafter final values of surface temperatures and energy fluxes are stored in a file. 2.2. Experimental layout 2.2.1. Site description The experiment was carried out on an alley cropping system at the I N R A Centre in Godet, Guadeloupe, French Antilles (16020 ' N, 61o30 ' W), on a chromic vertisol. The canopy consisted of north-south rows of Gliricidia sepium, a perennial C 3 legume shrub, on a natural pasture of a C a grass, the angleton grass, Dichanthium aristatum. The Gliricidia plant population was approximately 16600 plants ha - I , with a 2 m row spacing and 0.3 m within the row. The intercrop was maintained for 3 years with grass moving about once a month and shrub clipping every two months.

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R. Tournebize et al. / A N icultural and Forest Meteorology 81 (1996) 255-272

Table 1 Canopy structure during the four study period in 1992 DOY a Dichanthium aristatum

Gliricidia sepium

Ageb LAI 137 143 177 185

Ageb H-WC

Leaf area d (m-2 m-I)

(d)

Pure.+std

Mix. und. row_+std Mix. mid. row.+ std (d)

(m)

27 37 21 29

0.6/+__0.2 1.2/_+0.1 0.4/.+0.2 0.3/.+0.1

1.5/__+0.1 1.9/_+0.1 0.8/.+0.1 0.9/_+0.1

1.07-1.06 4.2 1.40-1.22 5.1 0.87-0.87 3.5 1.07-1.08 5.1

0.7/_+0.2 1.1/+0.2 0.7/-+0.2 0.8/.+0.2

27 37 28 37

a Central day of year of the periods. b Number of days since last clipping. c Height and Width. J Per meter of row. The experiment took place between May and July 1992. Measurements for the validation o f the model were made during four periods o f 3 days each. Measurement periods overlapped two regrowth cycles (shrubs were clipped between periods 2 and 3, see Table 1) which were distinguished by their canopy structure and soil water content. 2.2.2. C a n o p y c h a r a c t e r i s a t i o n

Canopy structure measurements included the spatial distribution of leaf area of both components (see Tournebize and Sinoquet, 1995 for the details). Leaf area indexes (L~) of both species and row dimensions of the shrub are shown in Table 1. Pure grass L i ranged between 0.3 and 1.2 and was lower than that of intercropped grass (between 0.8 and 1.5). The higher values of L i obtained for the shaded angleton grass may be attributed to the higher nitrogen nutrition often found in shaded grass (Cruz et al., 1993). However, the higher Li of the intercropped stand may also be a consequence of higher specific leaf area of leaves growing under low irradiance conditions (Cruz et al., 1993). L~ of both pure and associated grass were lower in periods 3 and 4 because of the increased water stress. Shrub leaf area per meter of row varied between 3.5 to 5.1 m 2 m - l (equivalent to 1.7 and 2.6 L~ per total ground area, respectively) while its cover r a t e - - d e f i n e d as the ratio of row width to row s p a c i n g - - r a n g e d from 44% to 61%. Finally the L i of both the shrubs and the grass varied from 2.5 (period 3) to 4.1 (period 2) (see Tournebize and Sinoquet, 1995). 2.2.3. M e a s u r e m e n t o f the m i c r o c l i m a t e a n d soil d r i v i n g v a r i a b l e s

Two solarimeters and a differential pyrradiometer (Kipp and Zonen, Delft, Netherlands) located above the canopy measured incident global, incident diffuse and net radiation R n respectively. As described in a previous paper (Tournebize and Sinoquet, 1995), stationary amorphous silicon cells (Solems, Palaiseau, France) measured P A R transmitted below the shrub canopy, that is the P A R incident on the grass layer. In order to characterize the micrometeorology of each component, we placed in situ ventilated air intakes (Fig. 2). Each intake sampled air from a horizontal 0.01 m wide layer where the temperature was recorded. The air is conducted throught a heated tube to

262

R. Tournebize et al. / Agricultural and Forest Meteorology 81 (1996) 255-272

P(

2m

)t

Fig. 2. Diagram of the shrub/grass intercrop showing the location of microclimate measurements.

avoid condensation until it came to a capacitive sensor (HMP 35A, Vaisala, Helsinki, Finland) and a thermocouple type T (copper-constantan). The vapour pressure was then calculated according to the true air temperature. In order to avoid systematic measurement errors due to the sensors, the sampled air at each level was sent alternately (every 3 min) to the same sensors. At the air intakes level, a cup anemometer (Cimel, Paris, France) measured wind speed. Temperature, humidity and wind speed were measured in the intercrop in the middle of the row 0.05 m above the grass canopy and half way up the shrub canopy (Fig. 2), and at a height of 2 m in the pure grass plot. Two series of 6 thermocouples regularly placed between the rows of Gliricidia measured the soil temperature at depths of 0.5, 0.1 and 0.05 m. At the beginning and at the end of each period, gravimetric soil moisture was measured from 4 samples across the row width, in 0.1 m layers down to 0.6 m, at the beginning and end of each series. For the pure grass, two samples were taken every 0.1 m down to a depth of 0.6 m. Except for soil water contents, a datalogger and multiplexer (21X, AM32, Campbell Scientific, Shepshed, UK) recorded the data every 10 s and averaged them every 30 rain. Soil water contents ( 0 ) were measured gravimetrically. Soil water content at saturation was assumed to be equal to the whole soil porosity (0sa t -= 0.61 k g k g - l ) .

2.2.4. Measurements of water fluxes 2.2.4.1. Chamber measurements. The evapotranspiration rate (AET) of the s o i l / g r a s s layer was estimated from the increase of air humidity in a closed chamber (Reicosky and Peters, 1977). This method was adapted to carry out measurements in different locations under the shrub canopy. The chamber (0.3 x 0.5 X 0.5 m) was made from an altuglass frame and polyethylene plastic film. Air was mixed by four fans installed in pairs on opposite sides of the chamber wall. They mixed about 10 times the chamber volume per minute. The relative humidity was recorded with a H M P 35A sensor, and the temperature with a thermocouple. Sensors were connected to a 21 X datalogger scanning them each second during measurements, which take no more than 1 min. Chamber measurements were made rapidly to minimize possible effects of temperature increases and other s o i l / p l a n t environment changes (Reicosky, 1985). Measurements were taken regularly about 10 times a day on areas evenly spaced in the inter-row (4 sites) and in 2 locations

R. Tournebize et al. / Agricultural and Forest Meteorology 81 (1996) 255-272

263

for the pure stand. AET was calculated from the linear part of the rising water content, excluding data recorded during cloudy periods. Daily evapotranspiration was estimated assuming a linear evolution between each measurement. 2.2.4.2. Sap flow measurements. G. sepium transpiration was measured using the sap

flow method on 5 sample shrubs. The selection of sample shrub was based on two criteria: 1. the length of the trunk must be at least 0.2 m, to support sap flow system; 2. the diameter of the trunk must fall within 1 standard error of the population mean (34.6 mm + 2.6). The method (e.g. Sakuratani, 1981; Valancogne and Nasr, 1989) consists in heating a part of the stem and measuring the different ways in which this energy is transported, or stored, including transpiration flux. This method has been previously tested on potted Gliricidia with satisfactory results (Ozier-Lafontaine and Tournebize, 1993). Data were recorded with a 21X datalogger scanning sensors every 10 s and averaging values every 30 min. 2.2.5. Measurement o f stomatal resistance

Stomatal resistance was measured with a steady state porometer, Li-Cor 1600 (Li-Cor, Lincoln, NE, USA) used as recommended by McDermit (1990). Resistance of each side from five to eight leaves was measured every 90 min simultaneously to PAR leaf irradiance, air humidity and leaf surface temperature. Data were averaged on 90 min time intervals. Measurements were made two days before and two days after each model validation period, except for period 4 where stomatal measurements were made only a day before. Stomatal resistance measurements were used to establish an empirical model of stomatal closure as a function of PAR irradiance according to Eq. (7) (Jarvis, 1976). Parameters of the stomatal model were estimated by non-linear fitting (SAS, 1987).

3. Results 3.1. Microclimate

Light microclimate in this intercrop has been previously described (Tournebize and Sinoquet, 1995). Mean PAR transmission below the shrubs, i.e. incident PAR on the grass layer, ranged from 33% to 52% depending on the shrub canopy structure. Wind speed was largely reduced within the canopy in comparison with wind at the reference level (2 m), about 5 to 10 times depending on the size of the shrub (periods 2 and 3). Sometimes wind speed was greater at the grass level than at the shrub level. This indicates that momentum transfer was not conservative within the canopy. Conversely, this may have been due to the row structure of the shrub canopy involving consistent turbulence structures between rows when the foliage density was large enough. All along the experiment air temperature and vapour pressure were much higher within the canopy than at the reference level, about 3°C and 400 Pa at midday, respectively.

R. Tournebize et a l . / Agricultural and Forest Meteorology 81 (1996) 255-272

264

GLIRICIDIA O) I

E

75o.

C:~

500-

]~~l

ANGLETON GRASS

OSeries 1 & 2 @ Series 3 & 4 !

Asori..

b)

• Series

IE

2500O <~

O3 < 0

3 0[ 0

6 0I 0

9 0I 0

1 2100

1 5100

1800

30O

P.A.R. (/zmole m - 2 s - 1 )

600

900

P.A.R. (/zmole m

12O0

150O

1800

2 s-l)

Fig. 3. Model of the stomatal resistance of the lower sides of (a) Gliricidia and (b) grass leaves as a function of P A R .

3.2. S o i l

water

content

During periods 1 and 2, the gravimetric soil moisture was 42%, which is close to the field capacity. Periods 3 and 4 showed drier soils with a water content of 32% and 30%, respectively. For the sampled soil depth (0.6 m), there was no significant difference between the pure and the mixed plots. 3.3. S t o m a t a l r e s i s t a n c e

The stomatal resistance of the lower side of leaves was related only to the PAR irradiance (Fig. 3). For both species, model parameters were estimated separately for periods 1 and 2, and periods 3 and 4 (Table 2). The difference in parameters expressed the effect of water stress on stomatal closure, due to the lower soil water content during the last measurement periods. There were no significant differences in stomatal behaviour between the pure and the mixed grass. This may be due to: 1. the similar soil water content in both treatments, and

Table 2 Models of stomatal resistance of the two species during the different series studied Side

Upper

Model retained

r~i = k

Grass series I and 2

Lower (s m

Gliricidia Gliricidia

series 1 and 2

series 3 and 4

I

+PARa/(a2.PAR)(sm

k = 854.7

a 1 = 3 2 . 9 a 2 = 4 3 10 - 4 + 1 9 -+5.2 10 - 4 n = 2 8

k = 854.7 + 134 n = 16

a 1 = 4 0 0 a 2 = 4 0 10 4 4- 143 -+4.4 10 - 4 n = 36

k = 3125

a t=1909a

-+274 n = 16

+164

k = 3125

a~ = 4 6 4 a 2 = 72 10 4

_+274 n = 16 a PAR (~molm-2).

r2i = a

± s t d a I + s t d a 2 nbr

4- 134 n = 16

Grass series 3 and 4

i)

_+std a~ nbr

+302

2=402

10 4

-+107n=12

4-1910

4n=28

I)

R. Tournebize et al. / Agricultural and Forest Meteorology 81 (1996) 255-272 4OOO

4000-

a)

I

o o

0

3000

I

o

3000-

© o 0 shrub grass

2000 --l

0

~,

Oshrub &grass

2000

.< I000

<

265

o

I000-

I 600

3 0

I 900

12400

1500

o

PAR (/zmole m - 2 s - 1 )

P 300

I 600

I 900

1200

ABAXIAL Rs ( s m - 1 )

Fig. 4. Resistance of the upper sides, of both species, as a function of PAR (a) and on the conductance of the lower sides (b).

2. the absence of morphogenetic response to environmental factors of the stomatal density in leaves of this graminaceous species as described by Haegelin (1992). For both species, the stomatal resistance of the upper side was virtually independent of any environmental factor, such as PAR irradiance, even of the stomatal closure of the lower side (Fig. 4). Thus a constant stomatal resistance has been assumed for the adaxial side (Table 2). For the angleton grass, it was important to take into account the resistance of both leaf sides which are at the same level (from 1 to 3) (Fig. 4(a)). For Gliricidia, this ratio was greater and varied from 4 to 10 (Fig. 4(b)).

3.4. Water consumption The AET rates recorded during the two days for each period are presented in Fig. 5. The AET of the combined intercrop varied according to the daily climatic conditions and with plant development, between 3.3 and 5.3 mm d-J, which is equivalent to 10-13 MJm -2 d -~. The ratio of AET to global radiation ranged from 0.45 to 0.50.

I

zg-

c'4 24I 2o-

g

N 1g03 12-

~

4 2

3

4

PERIODS inc R G

[SS~GUricidia T V-~Mixed grass AET ~ P u r e

grass AET

Fig. 5. Measurements of the daily evapotranspiration (AET) of the s o i l / p u r e grass and intercropped grass and the transpiration of Gliricidia for two days in each period as a function o f incident global radiation (including

RG).

R. Tournebize et al. /Agricultural and Forest Meteorology 81 (1996) 255-272

266

PURE

GRASS

O M e a s u r e d AET - - Simulated bET

[

MIXED

a)

300

I O M e a s u r e d AET

GRASS

AET

--Simulated

b)

o

2o0

E o

oo

[-~

i00

tOO

i fl

i

112

[

i

i

Bi

i

16

TIME (h)

i 12

E

i

0

16

8

12

16

8

12

16

TIME (h)

Fig. 6. Comparison between the measured and estimated values of evapotranspiration of (a) soil/pure grass and (b) mixed grass for two contrasted days, DOY 132 and 176. The contribution of the s o i l / g r a s s layer always represented between 30% to 50% of the total flux and fluctuated little whatever the leaf index and global radiation. This could be due to the role of a screen played by the shrub canopy. However, when there were variations in radiation, it was the transpiration of the shrub which evolved, for example during the third period (Fig. 5). The s o i l / p u r e grass layer showed an AET similar to that of the intercrop for the period studied (Table 1). 3.5. M o d e l v a l i d a t i o n 3.5.1. E v a p o t r a n s p i r a t i o n o f g r a s s

For the AET of the pure grass, Fig. 6(a) and Fig. 7(a) illustrate the good agreement of the model with the measurements independently of the period. The correlation coefficient was 0.84 and the slope 0.92, with a confidence interval of 0.02. The mean residual was - 7 W m -2, with a standard deviation of 19 W m -2. The distribution of the residuals according to the time was bell-shaped, with an underestimation of the model around midday and an overestimation at the beginning and end of the day for the low fluxes. This was due, partially, to the fact that the estimation of soil water resistances, invariable during the day, was poorly adapted to include the effects of the water transfers of the soil. For the intercropped grass crop (Fig. 6(b) and Fig. 7(b)), we obtained a correlation coefficient of 0.88 and a slope of 1.06, with a confidence interval of 0.02. The mean P U R E GRASS

300

MIXED GRASS

300

1 • Series 2 ~ Series 3

i:

0 Series

[

200

•

~-

200

• Series

0

4

•

E-, £r3

A •

too -

I 0 MEASURED

200

AET

(Wm

300

-2)

r~

too

N

o

!

0

o i o

MEASURED

AET

200 (W m -~)

300

Fig. 7. Comparison between the measured and estimated values of evapotranspiration of (a) soil/pure grass and (b) mixed grass for all the observations.

R. Tournebize et al. / Agricultural and Forest Meteorology 81 (1996) 255-272

267

GLIRICIDIA

300 O Measured

200

A E T - - Simulated A E T

o

o

I00

.<

0

i 8

i

lIE i

i 18

g 8q

i

i 12

i

i 18

TIME (h) Fig. 8. C o m p a r i s o n

between

contrasted days, DOY

the m e a s u r e d

and e s t i m a t e d

values

o f the transpiration

of

Gliricidia for t w o

132 and 176.

residual was + 7 . 5 W m -2, with a standard deviation of 18.5 W m -2. The distribution was homogeneous, with a tendency to a larger deviation for the low fluxes, illustrating the limits of chamber method, e.g.: • comparison between separate measurements coming from 1 min of measurements, with simulated data using half-hour time steps • modification of microclimate, especially wind.

3.5. 2. Transpiration of Gliricidia Fig. 8 shows the diurnal variation in the simulated and measured transpiration for the two days selected. The model correctly estimated this variable and its diurnal evolution according to the climatic conditions. Linear regression analysis (Fig. 9) with all observations corresponding to ten days of comparison shows a good agreement between the model and measurements for all the series. The slope of the regression line was 1.04 and the r 2 coefficient 0.94. The residuals, with a mean of + 12 W m -2 (standard deviation 19 W m -2) showed a slight systematic overestimation of the modelled Gliricidia transpiration.

GLIRICIDIA 3OO

o Series i t

,&.~'~,

• Series 2

A~,.~E/_~__

,',Series S

4 ~ , ~ ~ ~'-

• Series 4

O *"

A 100-

~

0,0

,

100

MEASURED

200

300

T. (W m -2)

Fig. 9. C o m p a r i s o n b e t w e e n the m e a s u r e d and e s t i m a t e d v a l u e s o f the transpiration o f Gliricidia for all the o b s e r v a t i o n s ( n = 240).

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4. Discussion

The approach used here combines a detailed modelling of radiative transfer (Sinoquet and Bonhomme, 1992), and a simplified modelling of heat and mass transfers ('big leaf' type approach). This is for two reasons. First because no satisfactory method exists for simulating the convective transfers inside heterogeneous canopies, and second, because the intercepted radiation is highly variable within the canopy (Goudriaan, 1989) and it must be known as precisely as possible for determining heat exchanges. It is now widely admitted that the K-theory fails in predicting fluxes within two layer canopies (e.g. Stigter, 1994). The important rule of wind gusts that drive turbulent mixing and transport within the canopy (Finnigan and Raupach, 1987) may be increased by the heterogeneity or the periodic structure of the canopy. These aerodynamic effects were confirmed by our experiment where non conservative momentum fluxes were observed under some conditions. It is therefore particularly difficult to assess exchanges within the canopy from meteorological data collected above the canopy. Moreover, the heterogeneity of the canopy and the important risk of advection due to the small size of intercropping fields lead to additional difficulties in parameterising fluxes. In a separate two layer canopy with a relatively porous shrub layer, like the one used in this experiment, results show that variables collected at one level for each species is appropriate. For an another type of structure the choice of input variable must be reconsidered, see e.g. Dolman and Wallace (1991). The model enabled us to make the distinction between soil evaporation and grass transpiration. The part of soil evaporation in the total flux was 65% and 52% for DOY 132 and 76% and 64% for DOY 176, for pure and intercropped angleton grass, respectively. The lower proportion of soil evaporation in the intercrop was due to the low amount of radiation transmitted to the soil because of the large development of both the shrub and grass. The model also made it possible to determine the contribution of sunlit and shaded foliage separately. For Gliricidia, the surface of shaded foliage, partly depending o n L i, varied from 61% to 67% and was responsible tbr approximately 50% of the transpiration. For the grass component of the intercrop, the proportion of shaded foliage varied from 66% to 84% and was responsible for only about 30% of transpiration. On the contrary, the pure grass had a reasonably stable percentage of shaded foliage, around 33%, which contributed to 25% of the transpiration. These results are consistent with those of Petersen et al. (1992) in cotton which indicate that shaded foliage, which made up 66% of the surface area, contributed to 2 1 - 3 3 % of transpiration.

4.1. Understorey et~apotranspiration The small differences between simulated and measured evaporation may be due to various reasons: first, only limited experimental data were available because the closed chamber allowed only a few measurements, and second, the soil and canopy resistances were difficult to parameterise. Soil resistance was calculated from the water content of the first 5 centimeters of soil, while soil evaporation was driven by the water content of the first millimeters. This is particularly the case in soils with a very low hydraulic

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conductivity like the vertisol in the study site. The soil model may be improved by using a soil water transport model with appropriate hydrodynamic properties near the surface. The whole model could be better evaluated with separate experimental measurements of soil evaporation and plant transpiration like in an intercrop in which sap flow could be used on both species. Moreover, as other authors have stressed for crops grown in rows (Ham and Kluitenberg, 1993), our calculation of soil evaporation can only be estimated poorly by using mean temperatures compared to simulated evaporation with surface temperatures measured on strips of soil parallel to the rows.

4.2. Shrub transpiration A small overestimation was observed in the simulation data. As the radiative model has been validated elsewhere and showed no biases (Tournebize and Sinoquet, 1995), this is mainly due to the rough estimate of the stomatal and aerodynamic resistances. In a heterogeneous and open canopy, the micrometeorological conditions which drive stomatal aperture varied on a large scale, a better modelling of conductance for the whole canopy is difficult. The sensitivity of the model to conductance is not negligible since a 50% variation of the stomatal conductance leads to a 15% to 20% modification in transpiration (Tournebize, 1994). Improving the model would require a better modelling of the stomatal conductance based on its effective biological regulation. Moreover, for more developed canopies, where the microclimatic gradient is larger, it would be desirable to develop a model to calculate the induced microclimatic conditions from reference level. On the opposite, for canopies where both species form together a horizontally homogeneous canopy, a more traditional approach based on the exponential decrease of the wind could be applied.

5. Conclusion The model enabled the analysis of evaporative demand partitioning between components of an intercrop. In this study it was used for estimating the transpiration of each species by empirically modelling their stomatal conductance. Even if the stomatal modelling could be improved the model is adapted to estimate the intercrop evapotranspiration with less than 10% of error, at a daily scale. The most important disadvantage of this approach is the difficulty of a priori modelling due to the measurement input variables inside canopy. The improvement of the model requires more precise estimation of the stomatal conductance of both species. For example, the contraints due to soil water availability could be taken into account through a root extraction model (Lafolie et al., 1991, Ozier-Lafontaine et al., 1995) which optimize water flux from soil potentiality and climatic demand. In this form, coupling it with a leaf photosynthesis model would make it possible to calculate assimilate production and to estimate water use efficiency of each species as affected by their spatial arrangement in the intercrop. Further knowledge of the exchanges within heterogeneous crops and their simulation are still necessary for introducing the effect of plant structure on these exchanges and

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h o w the i n t e r c r o p m o d i f i e s the local m i c r o c l i m a t e . S e v e r a l p r o m i s i n g w a y s h a v e b e e n e x p l o r e d but t h e y still p r e s e n t m a j o r l i m i t a t i o n s w h i c h p r e v e n t their routine use. L a g r a n g i a n m o d e l s r e q u i r e k n o w l e d g e o f m o m e n t u m , heat and w a t e r v a p o u r source and sink distribution in the c a n o p y ( R a u p a c h , 1989) w h i c h leads to p r o b l e m s o f m e a s u r e m e n t and m o d e l l i n g o f a c c u r a t e c r o p structure. H i g h e r o r d e r c l o s u r e s c h e m e s ( M e y e r s and P a w U, 1987) are n e c e s s a r y to take into a c c o u n t such i n t e r a c t i o n s in Eulerian models.

Acknowledgements This s t u d y w a s s u p p o r t e d by a grant f r o m the M i n i s t ~ r e de la R e c h e r c h e et de la t e c h n o l o g i e (91 L 0567).

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