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R. Habib and F. Lescourret
INRA Ecophysiologie et HorticulturEM>INRA Ecophysiologie et Horticulture - Domaine St Paul
Site Agroparc 84914 Avignon cedex 9 (France)
I. INTRODUCTION
Extending the definition of Dickler and Schäfermeyer (1991), integrated production system (IPS) in Horticulture is defined here as the production of high quality horticultural products, giving priority to ecologically safer methods, minimising undesirable side-effects and use of agrochemicals, to enhance the safeguards to the environment and human health.
In Europe, social, political, and economical pressure is making IPS a cornerstone of agricultural policy and market competitiveness (Sansavini, 1997). The same evolution is discernible throughout the world. Intensive management of horticultural crops is now an irreversible trend in Europe and other parts of the world (e.g., in China). And IPS is the scientific answer to combine intensification and low environmental impact practices. Therefore, IPS, seeking to encourage high technology grounded on scientific advances, has been the necessary innovation of European agriculture in the 1980s (Sansavini, 1990). Its premises have now been adopted world-wide (e.g., the Korean report), and research will be even more focused on in the future (e.g., the Norwegian report). The needs for IPS research in Horticulture is now recognised world-wide since all over the world, and particularly in DCs, horticultural crr the world, and particularly in DCs, horticultural crops are often considered to be actually or potentially a better source of rural income than other crops. In Taiwan the production value for fruit, vegetable and ornamental crops was 2.06, 1.29 and 0.3 billion US dollars, accounting for respectively 33.5, 20.9 and 4.9% of the total crop production value. In Sri Lanka, a total of 0.2 M hectares of horticultural crops produces almost the same rupee value as the total rice crop cultivated on about 0.8 M hectares, illustrating furthermore the high income generating potential of these crops. Another important task in horticultural area in DCs is to defend the supply base of essential items in the peoples' diet (such as hot pepper, garlic and onion in Korea, leafy vegetables in Sri Lanka, vegetables in China), and that of major income sources of horticultural farmers (such as mandarin oranges and apple in Korea, green chilli, rambutan and avocado in Sri Lanka). Another objective is to develop new items for export market. For example, the total export value of fresh fruits and vegetables has increased in Sri Lanka from Rs. 48 million in 1983 to Rs. 377 million in 1996.
However, Horticulture is a high-risk business that demands professional expertise based on the synthesis and results of well-grounded research (Sansavini, 1997), to meet market requirements along with economic objectives. Therefore, current research programmes essentially aim t current research programmes essentially aim to save production cost and labor and to enhance the crop quality, and growers' income.
Although the multidisciplinary research field open by this extending interest in IPS obviously requires system analysis methodology (Rossing and Heong, 1997) combining biotechnical, pest, disease and economical modeling, advisory services for agriculture have traditionally based their tests of technological innovations upon factorial experiments repeated in space and time. Considering the number of factors to take into account, this allows only slow and incomplete responses. Furthermore, nowadays, farm advisors and farmers have to adapt their consulting and choices to outstanding and rapid changes in the socio-economic context. They require decision support systems based on explanatory models (Rossing et al., 1997) offering the means of simulating the consequences of technical changes or innovations rather than experimental references that would be obviously incomplete. Such systems have been attempted for farmers in several countries like USA (Hall & Lemon, 1990) or New Zealand (Laurenson et al., 1994). However, their relevance concerning the complexity of the decision-making process depends on a careful integration of socio-economic and biological models (Dent et al., 1994).
Relevance of current models for their application to the field
of IPS in Horticulture will be cono the field
of IPS in Horticulture will be considered in §2, and proposals
to make them more effective will be presented in §3. Although
IPS includes all aspects of crop management, we will consider
separately hereafter those concerning cultural methods, and those
concerning crop protection, to emphasize clearly the needs for
new research efforts.
II. IPS RESEARCH IN THE WORLD
2.1. Cultural methods
Actual research in the field of IPS/cultural methods in Horticulture most often consists of traditional scientific approach considering individual factors or objectives, rather than a systemic approach at the field/farm level. A rapid survey of the international literature shows that, except for greenhouse crops where integrated research is far more advanced (e.g., Papadopoulos et al., 1997), the bulk of scientific reports concerns the independent effects of various factors on crop performance, such as irrigation, fertilization, soil management, growth regulators, planting density, and so on. Few research efforts concern farm management packages for the integration of several cultural practices, such as irrigation, fertilization and pest control (e.g., in Korea) on production and quality. Such efforts when attempted remain far beyond the actual needs for systemic research. As noted in the Introduction, factorial experiments repeated in time and space are time-consuming and costrepeated in time and space are time-consuming and costly. And it is also probably time to recognize the more basic limitations of the experimental approach, even the so-called 'prototyping' methodology developed in Europe during the last decade, as done for arable farming by Rossing et al. (1997): (1) whenever possible, only a limited number of systems can be evaluated, (2) the knowledge produced is expert-based, which narrows the range of available options and obscures understanding of systems behavior. In their paper, these authors claim the need for explorative studies based on transparent models to overcome these shortcomings.
Numerous models have been produced in horticultural research, that generally concern only one part of the plant production cycle. Some of them, which deal especially with organ growth, relate explicitly or implicitly biological processes to cultural practices (e.g. perennial fruit crops: Buwalda, 1991; Grossman & DeJong, 1994; Génard and Huguet, 1996; greenhouse cucumber: Chamont, 1993; greenhouse lettuce: Liebieg and Alscher, 1993; processing tomato: Bussières, 1994). Other models try to cover the whole cycle (e.g. kiwifruit: Doyle et al., 1989; Testolin & Costa, 1990; cut-flower roses: Lieth and Pasian, 1991; onion: De Visser, 1994) but they are not always mechanistic enough, according to Dent et al. (1994), to allow a sufficient portability. Moreover, except a few attempts (e.g. kiwifruit: T, except a few attempts (e.g. kiwifruit: Testolin & Costa, 1992; Brussels sprout: Hamer, 1993), models rarely incorporate the variation of fruit quality criterion, even one as simple as the variability in fruit size at harvest.
In conclusion, models of cultural methods designed for their use
in IPS still lie in the future. Consequences for IPS/cultural
methods research will be considered in §3.1 hereafter.
2.2. Crop protection
The concept of integrated crop protection has gained currency in the 70s from the definition of integrated pest management, that was first employed in 1953 in Germany (Sansavini, 1990), and in the USA soon after, under the term of 'integrated control (Stern et al., 1959 cited by Jacobsen, 1997). By now, it has spread throughout the world, leading to an impressive research effort.
We are mainly concerned here by warning systems (WS) devoted to forecasting and management of spraying strategies. The aim of WS is to help decision making, which in turn will result in better disease/pest control by fungicide/pesticide usage. It has led to numerous research attempts throughout the world (e.g. Switzerland: Blaise and Gessler, 1990; USA: Steiner, 1990; Norway: Hesjedal and Edland, 1992; France: Maurin and Fricot, 1993; Israel: Safran and Levy, 1995; UK: Xu, 1996). Expert systems were also proposed to help diagnosis by imitating problem-solving mechanisms used by experts (emitating problem-solving mechanisms used by experts (e.g. Roach et al., 1987; Kemp et al., 1988). However, the current use of WS is far from the anticipated potential, and only a limited amount of available knowledge is used by growers, probably because of a 'mis-match' between the actual needs of farmers and the area of application of models (Xu, 1994; Doyle, 1997). According to Xu (1996), the ideal WS for a particular crop should include models of all important disease/pest expected on that crop and their interactions, should be driven by weather data recorded locally (e.g. Juhel et al., 1993), and significant non-weather factors affecting disease development (i.e., management practice, irrigation, weed control, pruning, landscape features). With regard to the future of IPS, Doyle further identified four main reasons for this gap: (1) non-chemical control has received scant attention, (2) minimizing environmental damage has been largely ignored, (3) more mechanistic models are needed for exploring alternative methods of control, (4) multi-species simulation tools are required to match actual management decisions. In Europe, these preoccupations are echoed in the most recent literature (France: Bockstaller et al., 1997; Germany: Gutsche and Rossberg, 1997; UK: Pickett et al., 1997; The Nederlands: Wijnands, 1997).
Tools and decision aids integrating crop protection with the agroecosystem,
crop growth, and loss models for the most part still th, and loss models for the most part still lie in the
future (Jacobsen, 1997). This will be addressed in §3.2 hereafter.
III. DEVELOPMENT OF NEW MODELING TOOLS
3.1 Cultural methods
Modeling the effect of cultural methods on crop quality is perhaps the major need for the future of IPS/cultural methods research (Sansavini, 1997), for determining which factors in field management can be manipulated during a season to enhance quality, and proposing simulation tools to help better determione the integration of several cultural practices to successfully reach a given management objective. This supposes to address explicitly complex system modeling, including the technical choices and specific management constraints the producers are facing.
From a historical point of view, complex systems came to imply complicated models under the assumption that a reductionist approach could insure scientific rigor. On the other hand, the need for simplicity in crop models is often underlined for management purposes. Biotechnical models must then both be comprehensive enough and manipulate variables that can easily be measured in the field for on-farm application. Therefore, we prefer to define here a complex system as a system that will lose essential characteristics when simplified (Legay, 1997). For instance, in fruit tree cropping system, the technical choices and specific management g system, the technical choices and specific management constraints of the producer are essential part of the system, and cannot be avoided, or considered as individual non-interrelated practices, and the between-fruit variability is essential to model as it determines largely the producers' income. Building a comprehensive mechanistic model including these constraints would put the level of complexity far beyond the one usually achieved in classical mechanistic fruit crop models (e.g. Buwalda, 1991; Grossman and DeJong, 1994). Therefore, we propose to break the modelers' dilemma and consider the model as a tool to study the system under a given point-of-view (Legay, 1997). This point-of-view primarily includes the choice of characteristics that are essential to be maintained in the system. This implies to use the top-down method to build the model, starting from the system analysis in terms of targets assigned to the model, ending by the incorporation to the model of process-based knowledge (or descriptive relationships when knowledge is lacking, emphasizing then the pressing needs for new research efforts). Our bet is that early identification and incorporation of the main limiting factors of the crop, and early incorporation of technical operations in the crop model forces simplicity. Doing so, the researcher is facing reality, and tries to answer the increasing demands for advisory systems for producers, and scenario studies for policy makers. , and scenario studies for policy makers.
Lescourret et al. (1998a, b) exemplified the proposed approach for adult kiwifruit vines without limitation of nutrients. They considered kiwifruit vine as a good case study, since it is simpler than other fruit crops. The main biological output of the model was the fruit population of an orchard with a size at harvest for each fruit. This criterion is especially important for determining orchard profitability.
The model was built from the consideration of the different levels where fruit variability is structured, and phenology inspired the architecture of the model, which was planned to be composed of three submodels describing female and male flowering (kiwifruit being dioecious), pollination, and fruit growth, respectively. Concerning management practices, the authors focused on planting scheme, considered in the pollination model, on cane pruning considered in the flowering model, and on fruit thinning and water management, considered to occur during fruit development, in the growth model. The structure of the model allows easy consideration of other management variables (such as nutrient supply, sensitivity to pests, effects of growth regulators, ), as well as outputs other than fruit size (such as refractometric index). Although not yet used for on-farm application, this model has proved to be efficient in simulating the variability in kiwifruit production in a way that riability in kiwifruit production in a way that exacerbates the role of management practices, and seems to offer real opportunities to better reason the combination of cultural practices to reach a given production goal.
Furthermore, the methodology of model construction presented here
should be only part of a systemic analysis of orchard management.
For instance, the kiwifruit model should also be linked to an
existing economic model (e.g. in a framework described in Attonaty
et al., 1996), in order to investigate the financial consequences
of various strategies. Linking such sub-systems is likely to promote
the evolution of the basic models.
3.2 Interaction between crop production and protection
The necessity to link crop protection and crop damage has early been recognized (Stern et al., 1959 cited by Jacobsen, 1997) under the concepts of "economic injury level" and "economic threshold". Pests and diseases are yield-reducing factors that affect plant physiological processes in a way depending on the pathogen (e.g. table 2 of Rossing and Heong, 1997). Therefore, recognizing (Blaise et al., 1996) that optimization of spraying strategies is only possible if one can quantify the consequences of a crop protection decision, the problem comes to quantify the risks of yield/quality losses through disease/pest occurrences. The study of these interactions is of major importance as far as crop yieractions is of major importance as far as crop yield and quality, along with economic profit are concerned. Some models have been proposed in Horticulture which describe such interactions (apple: Elfving et al., 1983; potato: Johnson and Teng, 1990; grapevine: Blaise et al., 1996).
A good example is given in Blaise et al. (1996). In current spraying
strategies against downy mildew of grape vine, many of the treatments
are applied as an "insurance" against a non-quantifiable
risk of loosing a significant part of the yield. An obvious solution
for reducing fungicide inputs would be to limit the treatments
to the number of sprays required to maintain the yield potential.
The authors have therefore built a model that simulates the interaction
between the host and the pathogen in terms of carbon acquisition
by the vine (i.e. the disease area on leaves reduces proportionally
the photosynthetically-active leaf area), and of pathogen development
(i.e. the reduction in leaf area reduces the available colonizable
area). In such coupled models, the simulation of fruit mass is
essential to evaluate the risk of different spraying strategies.
Although this work is still in progress and not yet in use for
on-farm decision support, it opens the way for the future of IPS,
integrating crop and disease/pest management.
IV. CONCLUSION
In the horticultural context, biotechnical models describing thecultural context, biotechnical models describing the interrelated biological and technical processes leading to fruit/vegetable/ornamental production and quality, and able to simulate the effects of changes in cultural practices on biological outputs, are then of special concern. Another essential problem is the coupling of pest/disease and crop modeling, emphasizing the environmental effects of cultural methods and spraying strategies. Alternative strategies should also be addressed specifically. All will be essential for future development of decision support tools for horticultural crop management, taking into account the impact on yield and on environment of management decisions.
This general evolution sounds credible, after the last decade's
exponential increase in power of both the hardware and software
capabilities of personal computers. During this time, the field
of modeling in Horticulture has experienced a similar explosion.
It offers exciting new opportunities for advancing the use of
mathematical and simulation modeling in both science and technology
transfer, from the modeling of biological processes at the organ
level, to the simulation of horticultural crop management systems
devoted to decision support.
Literature cited
Attonaty, J-M., Chatelin, M.H. & Garcia, F. 1996. In Information and communication technology applications in agriculture: state of the art and fu applications in agriculture: state of the art and future prospectives, Dutch Soc. for Informatics in Agriculture, Ed, Wageningen, V.10, pp. 667-678.
Blaise, P., Dietrich, R. & Jermini, M. 1996. Acta Hort., 416, 285-291.
Blaise, Ph. & Gessler, C. 1990. Acta Hort., 276, 63-70.
Bussières, P. 1994. Ann. Bot., 73, 75-82.
Buwalda, J. 1991. Ecological Modeling, 57, 43-64.
Chamont, S. 1993. Acta Hort., 328, 195-203.
Dent, J. B., McGregor, M. J. & Edward-Jones, G. 1994. In The study of livestock farming systems in a research and development framework, EAAP Publication N 63, eds. A. Gibon & J. C. Flamant, pp. 25-36.
De Visser, C.L.M. 1994. J. Hort. Sci., 69 (3), 501-518.
Dickler, E. & Schäfermeyer, S. 1991. In International Organisation for Biological control. West Paleartic Region section, Bulletin 1991/XIV/3, 67 pp.
Doyle, C.J. 1997. Agriculture, Ecosystems and Environment, 64, 165-172.
Doyle, C.J., Moore, W.B. & Henzell, R.F. 1989. Agricultural Systems, 31, 321-347.
Génard, M. & Huguet, J-G. 1996. Tree Physiol., 16, 407-415.
Grossman, Y. L. & DeJong, T. M. 1994. Tree Physiology, 14, 329-345.
Hall, F. R. & Lemon, J. R. 1990. Acta Hort., 276, 323-346.
Hamer, P.J.C. 1993. J. Hort. Sci., 68 (6), 871-882.
Hesjedal, K. & Edland, T. 1992. Acta Phytopatho. et Entomo. 871-882.
Hesjedal, K. & Edland, T. 1992. Acta Phytopatho. et Entomo. Hungarica, 27, 277-280.
Jacobson, B.J. 1997. Annu. Rev. Phytopathol., 35, 373-391.
Johnson, K.B. & Teng, P.S. 1990. Phytopathology, 80, 416-425.
Juhel, A., Froidefond, G., Clerjeau, M. & Virolleaud, J. 1993. EPPO Bulletin, 23, 673-680.
Kemp, R.H., Stewart, T.M. & Boorman, A. 1988. N.Z.J. Crop and Hort. Sci., 17, 89-96.
Laurenson, M. R., Buwalda, J. G. & Walker, T. S. 1994. N.Z.J. Crop and Hort. Sci., 22, 239-250.
Legay, J-M., 1997. Lexpérience et le modèle. Un discours sur la méthode. INRA Ed., Coll. Sciences en question, Paris, 111 pp.
Lescourret, F., Habib, R., Génard, M., Agostini, D. & Chadoeuf, J. 1998a. Agric. Syst., 56 (1), 67-89.
Lescourret, F., Génard, M., Habib, R. & Pailly, O. 1998b. Agric. Syst., 56 (1), 91-123.
Liebig, H-P. & Alscher, G. 1993. Acta Hort., 328, 155-159
Lieth, J.H. & Pasian, C.C. 1991. Scientia Hortic., 46, 109-128.
Maurin, G. & Fricot, L. 1993. EPPO Bulletin, 23, 615-618.
Papadopoulos, A.P., Pararajasingham,S., Shipp, J.L., Jarvis, W.R., Jewett, T.J. & Clarke, D.D. 1997. Hort. Rev., 21, 1-39.
Roach, J., Virkar, R., Drake, C. & Weaver, M. 1987. Computers and Electronics in Agriculture, 2, 97-108.
Rossing, W.A.H. & Heong, K.L. 1997. Field Crop Res., 51, 83-100.
Rossing, W.AA.H. & Heong, K.L. 1997. Field Crop Res., 51, 83-100.
Rossing, W.A.H., Meynard, J.M. & van Ittersum, M.K. 1997. European Journal of Agronomy, 7, 271-283.
Sansavini, S. 1990. HortScience, 25, 842-846.
Sansavini, S. 1997. Sci. Hort. 68: 25-36.
Safran, E. & Levy, Y. 1995. agronomie, 15, 49-57.
Steiner, P.W. 1990. Acta Hort., 273, 139-148.
Testolin, R. & Costa, G. 1990. Acta Hort., 276, 239-246.
Testolin, R. & Costa, G. 1992. Acta Hort., 313, 99-105.
Xu, X.-M. 1994. Norwegian J. Agric. Sci., 17, 267-280.
Xu, X.-M. 1996. Acta Hort., 416, 279-283.