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Prediction of the Effects of CO2 Enrichment and Artificial Lighting on the Performance of a Greenhouse Production System
Copyright 0 IFAC Control Applications and Ergonomics in Agriculture, Athens, Greece, 1998
PREDICTION OF THE EFFECTS OF CO2 ENRICHMENT AND ARTIFICIAL LIGHTING ON THE PERFORMANCE OF A GREENHOUSE PRODUCTION SYSTEM. Application to Begonia x hiemalis Pot Plant Production
P. Giaglaras1, A. Baille2, C.Kittas 1
(1) University of Thessaly, Department of Agriculture, Animal and Crop Production Laboratory of Agricultural Structures and Climate Regulation, Pedion Areos 38344, Volos, Greece. (2) INRA, Unite de Bioclimatologie, Domaine St. Paul, Site Agroparc,
84914 Avignon Cedex 9, France
Abstract : In this study, we tried to predict the influence of artificial lighting and/or CO2 enrichment on the production system of flowered Begonia x hiemalis pot plants. At this aim, a global simulation model was developed including the main four components of the production system: the external climate (stochastic model), the crop (growth and development model), the greenhouse and its equipment (physical model) and the grower (decisional model) .. Taking the gross margin of production as performance criterion of the system, the sirmulator was used to analyse the behaviour and the performance of the system with four configurations : a) only heating, b) heating and CO2 enrichment, c) heating and artificial lighting and d) heating, CO2 enrichment and artificial lighting. The simulation outputs gave useful information on the performances of these production strategies. Copyright © 1998 IFAC. Keywords: Environmental Engineering, Agriculture, Simulators, Optimization, Iterative methods.
1. INTRODUCTION
light, etc.) that determine the yield and the quality of the plants. Commercial brochures accompanying those equipment do not leave any doubt about their profitability. However, due to the complexity of the greenhouse production system and of the response of the plants to climatic factors, it is not so easy to evaluate the profitability of those facilities (Challa, 1989). Case specific studies and experiments are needed and this is not always sufficient because it is well known that the results of agricultural experiments are valid only for the conditions in which they are obtained.
Greenhouse producers are constantly faced with decisions regarding various investment opportunities and management strategies to improve the results of their activity. An important criterion for farmers to select is the profitability of an investment or of a strategy (Verstegen, et al., 1995). Greenhouse and equipment providers continuously propose updated technological facilities (heating, cooling, CO 2 enrichment, artificial lighting) that allow the grower to fix at a given set-point the environmental variables (e.g., temperature, humidity, CO 2 concentration,
213
In what regards carbon dioxide and artificial lighting facilities, there is no doubt that enriching the air inside the greenhouse with carbon dioxide and supplying the plants with additional lighting increases their net assimilation at least instantly. This doesn 't mean that net assimilation will continue to be elevated under continuous enrichment and lighting and it is not said that an increase of net assimilation will lead in a proportional increase of the quantity and the quality of the product (Mortensen, 1987). Experiments with such facilities report contradictory results depending mainly on the local climate conditions, on the biological material and cultural practice used and on the way CO2 enrichment and lighting were performed (set-points, stage of growth, duration, etc.) (Bierhuizen, et aI. , 1984).
of the outputs and it influences the decisions taken by the grower. For the duration of a cultivation (about 4 months) we can consider that the market is stable what discards us from the need to simulate its evolution. The components of the system are not independent but they interact strongly.
3. THE SIMULATOR The simulator reproduces the functioning of the system iteratively, using the following models and sub-models (Fig. 1):
The aim of this study is to predict the profitability of carbon dioxide enrichment and artificial lighting facilities for the production of ornamental pot plants in the greenhouse. For that purpose we selected the system of production of Begonia x hiemalis flowered pot plants and we analysed it's functioning. An overall simulator was then developed that calculates the gross margin per pot and per unit of covered area using as inputs the greenhouse location, the variety, the planting date, the greenhouse and equipment configuration, the strategy of climate control, the cultivation method and the objectives of the grower.
•
a stochastic model that produces instant external climate variables from mean climate data (CLIMATE GENERATOR, Supit, 1986),
•
a physical model estimating the evolution of the internal climate under the influence of external climate, the greenhouse and the crop, before any regulation action (SPONTANEOUS CLIMATE),
• sub-models simulating the operation of the climate regulation equipment and estimating the modifications they produce to the internal climate and the related energy and material consummations (CLIMATE REGULATION EQUIPMENT) • a biological model that estimates the growth of a group of plants from their previous state variables and from the present internal climate, (CROP GROWTH)
In this paper we present the analysis of the system, a description of the main components of the simulator and the results obtained by running the simulator for two locations in France (North and South), for two cultivation periods (winter and summer) using four greenhouse production systems: a) only heating, b) heating and CO2 enrichment, c) heating and artificial lighting and d) heating, CO 2 enrichment and artificial lighting.
• sub-models (tables, logical links, etc.) simulating the decisions of the grower related to the operation of the regulation equipment (set-points, period and duration of operation, etc.), to the choice of cultural actions to perform to the crop and to the ending of the cultivation (regulation strategy, indirect actions),
2. THE «POT PLANT PRODUCTION» SYSTEM
• sub-models simulating the effects of cultural actions on the plants and the cost of the inputs they use (DIRECT ACTIONS),
The hole system of Begonia x hiemalis production consists of a series of four sub-systems : A) «Mother plant production», B) «Nursery», C) «Flowered pot plant production» and D) «Commercialisation». In this study we focus on the «Flowered pot plant production» system that concerns the production of flowered pot plants (output) in greenhouses, starting from the transplantation of rooted cuttings (input).
• a model estimating the commercial value of the plants (EV ALUATION). The physical models used were taken from the literature (Bot, 1983; Papadakis, 1989; Boulard and Baille, 1993; Draoui, 1994; Baille, et al., 1994; Giaglaras, et aI. , 1995). They were adjusted in order to offer the possibility to change their parameters and adjust to any given real greenhouse configuration. The operation of climate regulation equipment is simulated by simple models using the ON-OFF mode of operation, without accounting for inertia.
Ohe main components of the «Flowered pot plant production» system are the crop, the external climate, the greenhouse and it's equipment, and the grower. The market although it belongs to the environment of our system it plays an important role on its operation since it determines the cost of the inputs and the price
214
/ ---PHYSICAL . . . . ! \
ENVIRONMENT '\ \
/
/
/' ........ INTRANT'S MARKET
(
INTRANTS
/
Plantlets
--INSTALLATION
PERFORMANCE CRITERION gross margin
/'1
/
---
-"
-
........
r
"- ........
--
PRODUCT MARKET
----
-
\
)
/'
---
Fig. 1. Structure of the simulator the cv. Heidi and 4 cultivation of the cv. Line were used for the validation of the model with different climate conditions than those used for parameter estimation ("extrapolative" validation). The behaviour of the model to the validation testings was very satisfactory, demonstrating its capacity to produce liable extrapolative predictions (Data not shown).
An entirely new carbohydrate concentration driven model was developed for the purpose of this study relating the climate variables, temperature, light and CO 2 concentration, with morphogenesis via photosynthesis and carbohydrate concentration inside the plant (Giaglaras, 1996). Carbohydrate concentration is estimated as the ratio of glucose quantity to the volume of the plant. Glucose budget of the plant is simulated dynamically at the plant level (Fig. 2). The volume is given from the growth of the dimensions of the leaves (length and width) and of the shoots (length and diameter). In the estimation of the growth of the dimensions of the plant organs, an inhibitory effect of light on elongation was introduced.
-
I
TemperaJure f Pion. Dry Mallet L _ _!1
ughl 1 TentptraJuTl L....
co,
I
__
A stochastic model was used for the simulation of the appearance of shoots and inflorescence (branching rate). For the development of this biological model, climate ~tmodt 1
1producrion
and growth data from 16 greenhouse cultivation of Begonia x hiemalis were used. Data collected at Montfavet (South France) from three cultivation realised with the cultivar Heidi (leaf cuttings) and two cultivation with the cultivar Line (stem cuttings) were used to estimate the parameters and validate the model ("interpolative" validation). Data collected at Angers (North Quest France) from 7 cultivation of
_ra~
-
I
-I
Fig. 2. Glucose budget at the plant level. continuous lines: glucose flux , dashed lines: infonnation flux .
215
4. SYSTEM PERFORMANCE TESTS USING THE SIMULATOR.
and November ended when the plants reached their maximum value (Table 1). The plants planted on September received very high light values at the beginning of the cultivation and even if their concentration of carbohydrates was not limiting for the production of leaves arid flowers, expansion of the leaves and internodes was inhibited. This result is better understood adding that the first 5leaves of the plants issued from leaf cuttings represent about 40% of the total final leaf area of the plant and their internodes are responsible for more than 50 % of total final plant height. Light is limiting for the plantation of November in the North. Carbohydrate concentrations are very low and this inhibited flower production.
4.1. Systems tested The simulator was used to analyse the behaviour and the performance of four greenhouse production systems: a) only heating, b) heating and CO2 enrichment, c) heating and artificial lighting and d) heating, CO2 enrichment and artificial lighting, using leaf cuttings. The model was run first to test only heating for two locations in France (North and South) and for three planting dates (September, November and January). Then the addition of CO 2 enrichment (3 set-points, 400, 650 and 900 ppm) or of artificial lighting (10 W m,2 and 20 W m'2) facilities to the heated greenhouse were tested with the plantation of November in North.
In the South we observed that the appearance of the three open flowers comes at the same moment for all three plantations but the final results give a higher gross margin to the plantation of November. This is due to the fact that the higher light levels received by the plantations of September and January inhibit the growth of the volume of the plants because of the negative effect of light on leaf area expansion.
Finally both CO 2 enrichment and a 10 W m,2 lighting were added to the heated greenhouse.
4.2. The results of the performance tests Only heating .- In the North the plantations of January Table 1. Final results of the tests with only heating (PARLQ, T!!J~ mean daily PAR and temperature; SPARj : sum of PAR in the growing period) Cultivation
Duration
Ending Criterion
(days) NORTH "North", September "North", November "North" , January SOUTH "South", September "South", November "South", January
PARi,d
Ta,I
SPARi
MJm·2 d,1
°C
MJm'2
Gross margin FFprl
85 110 100
marge ~ maximum maximum
1.6 0.9 1.9
19.3 18.7 18.9
210 100 192
8.2 7.0 9.6
90 77 87
marge ~ maximum marge ~
2.2 1.5 2.2
19.7 18.8 18.9
201 112 198
8.0 12.0 7.0
Heating and CO 2 enrichment in the North ; the installation of a CO 2 enrichment facility in greenhouse situated in the North does not improve significantly the final result of the plantation of November (Table 2). CO2 enrichment from 400 to 900 ppm does not affect significantly the mean carbohydrate concentration of the plants. Its effect on the production cost is much more important and this results to lower gross margins as CO 2 concentration increases.
and greater biomass, shoot and flower production. Heating and artificial lighting in the North; Artificial lighting can reduce the duration of growth by about 12 days (Table 3). The plants make 3 open flowers faster than without lighting and that happens because they have greater carbohydrate concentrations. However, shorting the duration of the cultivation with lighting will not increase the gross margin of the system. The gross margin will even decrease with the 20 W m,2 system.
This kind of result could not be predicted using classic mass driven biological models, since with those models adding CO 2 means greater assimilation
Heating, CO 2 enrichment and artificial lighting in the North; The installation of both CO2 enrichment and artificial lighting will increase the mean
216
carbohydrate content of the plants compared to the only heated and to the heated and lighted greenhouse. The duration of cultivation is about 10 days shorter than with only heating, but it is similar to the duration obtained with heating and lighting (Table 4).
not reduce further the duration of the cultivation. Increasing CO 2 concentration will decrease the gross margin because this will affect the cost of production without having any significant influence on crop value.
CO 2 addition in a heated and lighted greenhouse will
Table 2. Final results of the tests with heating and CO2 enrichment at 400 or 650 or 900 ppm for the plantation of November in the North. Comparison with the heated only greenhouse (So). Duration
Mean CO2 concentration
Mean relative glucose concentration
days 110 104 107 106
ppm 418 473 560 655
%
System
heated only (SO) heated + 400 ppm, without lighting) heated + 650 ppm, without lighting) heated + 900 ppm, without lighting)
56.8 59.5 61.7 62.6
Gross margin FFpr l 7.0 6.8 4.9 3.6
Table 3. Final results of the tests with heating and lighting with 10 W m· 2 or 20 W m-2 for the plantation of November in the North. Comparison with the heated only greenhouse (SO). Duration
Daily PAR
Mean relative glucose concentration
days
MJm-2 d- 1
%
mean 0.91 0.92 1.00
56.8 64.9 70.7
System
heated only (SO) heated + IOW m- 2 lighting heated + 20 W m- 2 lighting
110 98 98
min 0.17 0.17 0.16
max 3.43 2.44 2.36
Gross margin FFpr l 7.0 7.1 5.7
Table 4. Final results of the tests with heating. lighting with 10 W m- 2 and COl enrichement at 400 or 650 or 900 ppm for the plantation of November in the North. Comparison with the heated only greenhouse (SO) and with the heated and lighted with 10 W m-2_without CO2, Duration
Mean CO 2 concentration
Mean Daily PAR
Mean relative glucose concentration
Gross margin
days 110 98 101 98 101
ppm 418 397 456 603 767
MJ m- 2 d- 1
%
0.91 0.92 0.94 0.92 0.94
56.8 64.9 69.2 69.3 70.8
FFprl 7.0 7.1 5.6 4.3 1.5
System
heat. heat. heat. heat. heat.
only (SO) + 10 W m- 2 lighting (S2) +400 ppm + IOW m-2) +650 ppm + IOW m- 2 ) +900 ppm + IOW m- 2 )
model need further examination. The hypothesis that the appearance of leaves and flowers depends on carbohydrate concentration and the way carbohydrate concentration is calculated (carbohydrate mass/shoot and leaf volume) is very innovative in biological modelling and must be investigated and validated. If the simulator is to be used under high light conditions the negative effect of light on leaf area expansion should be tested experimentally.
5. GENERAL DISCUSSION AND CONCLUSIONS The simulator reproduces quite well the behaviour of the «pot plant production» system and confirms situations that even if they were observed in practice they could not be explained using simple biological models. However some aspects of the biological
217
According to the results of the testings :
Draoui, B. (1994). Caracterisation et analyse du comportement thermo-hydrique d'une serre horticole. Identification in-situ des parametres d'un mode le dynamique. These de doctorat, Univeriste de Nice-Sophia Antipolis. Giaglaras, P. (1996). Modelisation du fonctionnement de cultures omementales sous serre. Application a l 'evaluation de strategies climatiques (enrichissement en CO 2 et eclairage artificiel) pour Begonia x hiemalis. These de Doctorat, Univ. de Paris Sud, U.F.R. Scientifique d'Orsay. Giaglaras, P., M. BailIe, and A. Bailie (1995). Net photosynthesis response to light and air CO 2 concentration of Begonia x hiemalis: whole plant measurements and modelling. Scientia Horticulturae, 63: pp 83-100. Mortensen, L.M. (1987). Review: CO 2 enrichment in greenhouses. Crop responses. Scientia Horticulturae, 33: 1-25. Papadakis, G. (1989). Experimental analysis and dynamic simulation of the greenhouse Phd thesis, Agricultural microclimate. University of Athens, Greece. (in Greek) Sup it, I. (1986). Manual for generation of weather data. Simulation Report CABO-TT No. 7. Verstegen, J.A.A.M., R.B.M. Huirne, A.A. Dijkhuizen and J.P.c. Kleijnen (1995). Economic value of management information systems in agriCUlture: a review of evaluation approaches. Computers and Electronics in Agriculture, 13: 273-288.
• carbohydrate level of Begonia x hiemalis is not limiting in the South nor in the North for the plantations of September and January. So, CO2 enrichment and artificial lighting are less important than shading facilities. • artificial lighting is much more interesting than CO2 enrichment, for the plantation of November of Begonia x hiemalis in the North. This is not in accordance with what is done in practice (C02 is considered prioritary) and it is worthwhile to examine further this result. The simulator that was developed during this study can be used to analyse long term effects on thecrop of various greenhouse engineering applications. His main disadvantage is that he uses a new and very detailed biological model and this limits his extension to other horticultural crops. But, simplify an explanatory model is possible when it is impossible to make explanatory a simple model. The testings performed during this study give some indications about the key factors, parameters and functions of the simulator that should be examined further and about the points that can be neglected or simplified. However simplifying the model may limit its extrapolative power, his main advantage for optimisation studies.
Acknowledgements. This work was realised at the Bioclimatology Unit of the National Agricultural Research Institut of Avignon (INRA, France) with the financial support of the Agency of Environment and Energy Management of France (A.D.E.M.E).
REFERENCES Bailie, M., A. Bailie, J-c. Laury (1994). A simplified model for predicting evapotranspiration rate of nine ornamental species vs. climate factors and leaf area. Scientia Horticulturae, 59: 217-232. Bierhuizen, I.F. , J.M. Bierhuizen and G.F.P. Martakis (1984). The effect of light and CO2 on photosynthesis of various pot plants. Gartenbauwissenschaft, 49 (5/6): 251-257. Bot, G.P.A. (1983). Greenhouse climate: from physical process to a dynamic model. Phd. thesis, Agricultural University of Wageningen, The Netherlands. Boulard, T. and A. Bailie (1993). A simple greenhouse climate control model incorporating effects of ventiliation and evaporative cooling. Agricultural and Forest Meteorology, 65: 14515. Chalia, H. (1989). Modelling for crop growth control. Acta Horticulturae, 248: 209-216.
218
PREDICTION OF THE EFFECTS OF CO2 ENRICHMENT AND ARTIFICIAL LIGHTING ON THE PERFORMANCE OF A GREENHOUSE PRODUCTION SYSTEM. Application to Begonia x hiemalis Pot Plant Production
P. Giaglaras1, A. Baille2, C.Kittas 1
(1) University of Thessaly, Department of Agriculture, Animal and Crop Production Laboratory of Agricultural Structures and Climate Regulation, Pedion Areos 38344, Volos, Greece. (2) INRA, Unite de Bioclimatologie, Domaine St. Paul, Site Agroparc,
84914 Avignon Cedex 9, France
Abstract : In this study, we tried to predict the influence of artificial lighting and/or CO2 enrichment on the production system of flowered Begonia x hiemalis pot plants. At this aim, a global simulation model was developed including the main four components of the production system: the external climate (stochastic model), the crop (growth and development model), the greenhouse and its equipment (physical model) and the grower (decisional model) .. Taking the gross margin of production as performance criterion of the system, the sirmulator was used to analyse the behaviour and the performance of the system with four configurations : a) only heating, b) heating and CO2 enrichment, c) heating and artificial lighting and d) heating, CO2 enrichment and artificial lighting. The simulation outputs gave useful information on the performances of these production strategies. Copyright © 1998 IFAC. Keywords: Environmental Engineering, Agriculture, Simulators, Optimization, Iterative methods.
1. INTRODUCTION
light, etc.) that determine the yield and the quality of the plants. Commercial brochures accompanying those equipment do not leave any doubt about their profitability. However, due to the complexity of the greenhouse production system and of the response of the plants to climatic factors, it is not so easy to evaluate the profitability of those facilities (Challa, 1989). Case specific studies and experiments are needed and this is not always sufficient because it is well known that the results of agricultural experiments are valid only for the conditions in which they are obtained.
Greenhouse producers are constantly faced with decisions regarding various investment opportunities and management strategies to improve the results of their activity. An important criterion for farmers to select is the profitability of an investment or of a strategy (Verstegen, et al., 1995). Greenhouse and equipment providers continuously propose updated technological facilities (heating, cooling, CO 2 enrichment, artificial lighting) that allow the grower to fix at a given set-point the environmental variables (e.g., temperature, humidity, CO 2 concentration,
213
In what regards carbon dioxide and artificial lighting facilities, there is no doubt that enriching the air inside the greenhouse with carbon dioxide and supplying the plants with additional lighting increases their net assimilation at least instantly. This doesn 't mean that net assimilation will continue to be elevated under continuous enrichment and lighting and it is not said that an increase of net assimilation will lead in a proportional increase of the quantity and the quality of the product (Mortensen, 1987). Experiments with such facilities report contradictory results depending mainly on the local climate conditions, on the biological material and cultural practice used and on the way CO2 enrichment and lighting were performed (set-points, stage of growth, duration, etc.) (Bierhuizen, et aI. , 1984).
of the outputs and it influences the decisions taken by the grower. For the duration of a cultivation (about 4 months) we can consider that the market is stable what discards us from the need to simulate its evolution. The components of the system are not independent but they interact strongly.
3. THE SIMULATOR The simulator reproduces the functioning of the system iteratively, using the following models and sub-models (Fig. 1):
The aim of this study is to predict the profitability of carbon dioxide enrichment and artificial lighting facilities for the production of ornamental pot plants in the greenhouse. For that purpose we selected the system of production of Begonia x hiemalis flowered pot plants and we analysed it's functioning. An overall simulator was then developed that calculates the gross margin per pot and per unit of covered area using as inputs the greenhouse location, the variety, the planting date, the greenhouse and equipment configuration, the strategy of climate control, the cultivation method and the objectives of the grower.
•
a stochastic model that produces instant external climate variables from mean climate data (CLIMATE GENERATOR, Supit, 1986),
•
a physical model estimating the evolution of the internal climate under the influence of external climate, the greenhouse and the crop, before any regulation action (SPONTANEOUS CLIMATE),
• sub-models simulating the operation of the climate regulation equipment and estimating the modifications they produce to the internal climate and the related energy and material consummations (CLIMATE REGULATION EQUIPMENT) • a biological model that estimates the growth of a group of plants from their previous state variables and from the present internal climate, (CROP GROWTH)
In this paper we present the analysis of the system, a description of the main components of the simulator and the results obtained by running the simulator for two locations in France (North and South), for two cultivation periods (winter and summer) using four greenhouse production systems: a) only heating, b) heating and CO2 enrichment, c) heating and artificial lighting and d) heating, CO 2 enrichment and artificial lighting.
• sub-models (tables, logical links, etc.) simulating the decisions of the grower related to the operation of the regulation equipment (set-points, period and duration of operation, etc.), to the choice of cultural actions to perform to the crop and to the ending of the cultivation (regulation strategy, indirect actions),
2. THE «POT PLANT PRODUCTION» SYSTEM
• sub-models simulating the effects of cultural actions on the plants and the cost of the inputs they use (DIRECT ACTIONS),
The hole system of Begonia x hiemalis production consists of a series of four sub-systems : A) «Mother plant production», B) «Nursery», C) «Flowered pot plant production» and D) «Commercialisation». In this study we focus on the «Flowered pot plant production» system that concerns the production of flowered pot plants (output) in greenhouses, starting from the transplantation of rooted cuttings (input).
• a model estimating the commercial value of the plants (EV ALUATION). The physical models used were taken from the literature (Bot, 1983; Papadakis, 1989; Boulard and Baille, 1993; Draoui, 1994; Baille, et al., 1994; Giaglaras, et aI. , 1995). They were adjusted in order to offer the possibility to change their parameters and adjust to any given real greenhouse configuration. The operation of climate regulation equipment is simulated by simple models using the ON-OFF mode of operation, without accounting for inertia.
Ohe main components of the «Flowered pot plant production» system are the crop, the external climate, the greenhouse and it's equipment, and the grower. The market although it belongs to the environment of our system it plays an important role on its operation since it determines the cost of the inputs and the price
214
/ ---PHYSICAL . . . . ! \
ENVIRONMENT '\ \
/
/
/' ........ INTRANT'S MARKET
(
INTRANTS
/
Plantlets
--INSTALLATION
PERFORMANCE CRITERION gross margin
/'1
/
---
-"
-
........
r
"- ........
--
PRODUCT MARKET
----
-
\
)
/'
---
Fig. 1. Structure of the simulator the cv. Heidi and 4 cultivation of the cv. Line were used for the validation of the model with different climate conditions than those used for parameter estimation ("extrapolative" validation). The behaviour of the model to the validation testings was very satisfactory, demonstrating its capacity to produce liable extrapolative predictions (Data not shown).
An entirely new carbohydrate concentration driven model was developed for the purpose of this study relating the climate variables, temperature, light and CO 2 concentration, with morphogenesis via photosynthesis and carbohydrate concentration inside the plant (Giaglaras, 1996). Carbohydrate concentration is estimated as the ratio of glucose quantity to the volume of the plant. Glucose budget of the plant is simulated dynamically at the plant level (Fig. 2). The volume is given from the growth of the dimensions of the leaves (length and width) and of the shoots (length and diameter). In the estimation of the growth of the dimensions of the plant organs, an inhibitory effect of light on elongation was introduced.
-
I
TemperaJure f Pion. Dry Mallet L _ _!1
ughl 1 TentptraJuTl L....
co,
I
__
A stochastic model was used for the simulation of the appearance of shoots and inflorescence (branching rate). For the development of this biological model, climate ~tmodt 1
1producrion
and growth data from 16 greenhouse cultivation of Begonia x hiemalis were used. Data collected at Montfavet (South France) from three cultivation realised with the cultivar Heidi (leaf cuttings) and two cultivation with the cultivar Line (stem cuttings) were used to estimate the parameters and validate the model ("interpolative" validation). Data collected at Angers (North Quest France) from 7 cultivation of
_ra~
-
I
-I
Fig. 2. Glucose budget at the plant level. continuous lines: glucose flux , dashed lines: infonnation flux .
215
4. SYSTEM PERFORMANCE TESTS USING THE SIMULATOR.
and November ended when the plants reached their maximum value (Table 1). The plants planted on September received very high light values at the beginning of the cultivation and even if their concentration of carbohydrates was not limiting for the production of leaves arid flowers, expansion of the leaves and internodes was inhibited. This result is better understood adding that the first 5leaves of the plants issued from leaf cuttings represent about 40% of the total final leaf area of the plant and their internodes are responsible for more than 50 % of total final plant height. Light is limiting for the plantation of November in the North. Carbohydrate concentrations are very low and this inhibited flower production.
4.1. Systems tested The simulator was used to analyse the behaviour and the performance of four greenhouse production systems: a) only heating, b) heating and CO2 enrichment, c) heating and artificial lighting and d) heating, CO2 enrichment and artificial lighting, using leaf cuttings. The model was run first to test only heating for two locations in France (North and South) and for three planting dates (September, November and January). Then the addition of CO 2 enrichment (3 set-points, 400, 650 and 900 ppm) or of artificial lighting (10 W m,2 and 20 W m'2) facilities to the heated greenhouse were tested with the plantation of November in North.
In the South we observed that the appearance of the three open flowers comes at the same moment for all three plantations but the final results give a higher gross margin to the plantation of November. This is due to the fact that the higher light levels received by the plantations of September and January inhibit the growth of the volume of the plants because of the negative effect of light on leaf area expansion.
Finally both CO 2 enrichment and a 10 W m,2 lighting were added to the heated greenhouse.
4.2. The results of the performance tests Only heating .- In the North the plantations of January Table 1. Final results of the tests with only heating (PARLQ, T!!J~ mean daily PAR and temperature; SPARj : sum of PAR in the growing period) Cultivation
Duration
Ending Criterion
(days) NORTH "North", September "North", November "North" , January SOUTH "South", September "South", November "South", January
PARi,d
Ta,I
SPARi
MJm·2 d,1
°C
MJm'2
Gross margin FFprl
85 110 100
marge ~ maximum maximum
1.6 0.9 1.9
19.3 18.7 18.9
210 100 192
8.2 7.0 9.6
90 77 87
marge ~ maximum marge ~
2.2 1.5 2.2
19.7 18.8 18.9
201 112 198
8.0 12.0 7.0
Heating and CO 2 enrichment in the North ; the installation of a CO 2 enrichment facility in greenhouse situated in the North does not improve significantly the final result of the plantation of November (Table 2). CO2 enrichment from 400 to 900 ppm does not affect significantly the mean carbohydrate concentration of the plants. Its effect on the production cost is much more important and this results to lower gross margins as CO 2 concentration increases.
and greater biomass, shoot and flower production. Heating and artificial lighting in the North; Artificial lighting can reduce the duration of growth by about 12 days (Table 3). The plants make 3 open flowers faster than without lighting and that happens because they have greater carbohydrate concentrations. However, shorting the duration of the cultivation with lighting will not increase the gross margin of the system. The gross margin will even decrease with the 20 W m,2 system.
This kind of result could not be predicted using classic mass driven biological models, since with those models adding CO 2 means greater assimilation
Heating, CO 2 enrichment and artificial lighting in the North; The installation of both CO2 enrichment and artificial lighting will increase the mean
216
carbohydrate content of the plants compared to the only heated and to the heated and lighted greenhouse. The duration of cultivation is about 10 days shorter than with only heating, but it is similar to the duration obtained with heating and lighting (Table 4).
not reduce further the duration of the cultivation. Increasing CO 2 concentration will decrease the gross margin because this will affect the cost of production without having any significant influence on crop value.
CO 2 addition in a heated and lighted greenhouse will
Table 2. Final results of the tests with heating and CO2 enrichment at 400 or 650 or 900 ppm for the plantation of November in the North. Comparison with the heated only greenhouse (So). Duration
Mean CO2 concentration
Mean relative glucose concentration
days 110 104 107 106
ppm 418 473 560 655
%
System
heated only (SO) heated + 400 ppm, without lighting) heated + 650 ppm, without lighting) heated + 900 ppm, without lighting)
56.8 59.5 61.7 62.6
Gross margin FFpr l 7.0 6.8 4.9 3.6
Table 3. Final results of the tests with heating and lighting with 10 W m· 2 or 20 W m-2 for the plantation of November in the North. Comparison with the heated only greenhouse (SO). Duration
Daily PAR
Mean relative glucose concentration
days
MJm-2 d- 1
%
mean 0.91 0.92 1.00
56.8 64.9 70.7
System
heated only (SO) heated + IOW m- 2 lighting heated + 20 W m- 2 lighting
110 98 98
min 0.17 0.17 0.16
max 3.43 2.44 2.36
Gross margin FFpr l 7.0 7.1 5.7
Table 4. Final results of the tests with heating. lighting with 10 W m- 2 and COl enrichement at 400 or 650 or 900 ppm for the plantation of November in the North. Comparison with the heated only greenhouse (SO) and with the heated and lighted with 10 W m-2_without CO2, Duration
Mean CO 2 concentration
Mean Daily PAR
Mean relative glucose concentration
Gross margin
days 110 98 101 98 101
ppm 418 397 456 603 767
MJ m- 2 d- 1
%
0.91 0.92 0.94 0.92 0.94
56.8 64.9 69.2 69.3 70.8
FFprl 7.0 7.1 5.6 4.3 1.5
System
heat. heat. heat. heat. heat.
only (SO) + 10 W m- 2 lighting (S2) +400 ppm + IOW m-2) +650 ppm + IOW m- 2 ) +900 ppm + IOW m- 2 )
model need further examination. The hypothesis that the appearance of leaves and flowers depends on carbohydrate concentration and the way carbohydrate concentration is calculated (carbohydrate mass/shoot and leaf volume) is very innovative in biological modelling and must be investigated and validated. If the simulator is to be used under high light conditions the negative effect of light on leaf area expansion should be tested experimentally.
5. GENERAL DISCUSSION AND CONCLUSIONS The simulator reproduces quite well the behaviour of the «pot plant production» system and confirms situations that even if they were observed in practice they could not be explained using simple biological models. However some aspects of the biological
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According to the results of the testings :
Draoui, B. (1994). Caracterisation et analyse du comportement thermo-hydrique d'une serre horticole. Identification in-situ des parametres d'un mode le dynamique. These de doctorat, Univeriste de Nice-Sophia Antipolis. Giaglaras, P. (1996). Modelisation du fonctionnement de cultures omementales sous serre. Application a l 'evaluation de strategies climatiques (enrichissement en CO 2 et eclairage artificiel) pour Begonia x hiemalis. These de Doctorat, Univ. de Paris Sud, U.F.R. Scientifique d'Orsay. Giaglaras, P., M. BailIe, and A. Bailie (1995). Net photosynthesis response to light and air CO 2 concentration of Begonia x hiemalis: whole plant measurements and modelling. Scientia Horticulturae, 63: pp 83-100. Mortensen, L.M. (1987). Review: CO 2 enrichment in greenhouses. Crop responses. Scientia Horticulturae, 33: 1-25. Papadakis, G. (1989). Experimental analysis and dynamic simulation of the greenhouse Phd thesis, Agricultural microclimate. University of Athens, Greece. (in Greek) Sup it, I. (1986). Manual for generation of weather data. Simulation Report CABO-TT No. 7. Verstegen, J.A.A.M., R.B.M. Huirne, A.A. Dijkhuizen and J.P.c. Kleijnen (1995). Economic value of management information systems in agriCUlture: a review of evaluation approaches. Computers and Electronics in Agriculture, 13: 273-288.
• carbohydrate level of Begonia x hiemalis is not limiting in the South nor in the North for the plantations of September and January. So, CO2 enrichment and artificial lighting are less important than shading facilities. • artificial lighting is much more interesting than CO2 enrichment, for the plantation of November of Begonia x hiemalis in the North. This is not in accordance with what is done in practice (C02 is considered prioritary) and it is worthwhile to examine further this result. The simulator that was developed during this study can be used to analyse long term effects on thecrop of various greenhouse engineering applications. His main disadvantage is that he uses a new and very detailed biological model and this limits his extension to other horticultural crops. But, simplify an explanatory model is possible when it is impossible to make explanatory a simple model. The testings performed during this study give some indications about the key factors, parameters and functions of the simulator that should be examined further and about the points that can be neglected or simplified. However simplifying the model may limit its extrapolative power, his main advantage for optimisation studies.
Acknowledgements. This work was realised at the Bioclimatology Unit of the National Agricultural Research Institut of Avignon (INRA, France) with the financial support of the Agency of Environment and Energy Management of France (A.D.E.M.E).
REFERENCES Bailie, M., A. Bailie, J-c. Laury (1994). A simplified model for predicting evapotranspiration rate of nine ornamental species vs. climate factors and leaf area. Scientia Horticulturae, 59: 217-232. Bierhuizen, I.F. , J.M. Bierhuizen and G.F.P. Martakis (1984). The effect of light and CO2 on photosynthesis of various pot plants. Gartenbauwissenschaft, 49 (5/6): 251-257. Bot, G.P.A. (1983). Greenhouse climate: from physical process to a dynamic model. Phd. thesis, Agricultural University of Wageningen, The Netherlands. Boulard, T. and A. Bailie (1993). A simple greenhouse climate control model incorporating effects of ventiliation and evaporative cooling. Agricultural and Forest Meteorology, 65: 14515. Chalia, H. (1989). Modelling for crop growth control. Acta Horticulturae, 248: 209-216.
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