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Production Practices and Quality Assessment of Food Crops. Vol. 1

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MODELLING FRUIT QUALITY: ECOPHYSIOLOGICAL,<br />

AGRONOMICAL AND ECOLOGICAL PERSPECTIVES<br />

MICHEL GÉNARD AND FRANÇOISE LESCOURRET<br />

Plantes et Systèmes de culture Horticoles, Institut National de la Recherche Agronomique, Domaine<br />

St. Paul, site Agroparc, 84914 Avignon Cedex 9, France; E-mail: Michel.Genard@avignon.inra.fr,<br />

Francoise.Lescourret@avignon.inra.fr<br />

1. INTRODUCTION<br />

Orchard management is facing new challenges, instigating new research <strong>and</strong> fruit<br />

production strategies. The new market organisation requires to improve the quality<br />

<strong>of</strong> processes, produces <strong>and</strong> the environment, especially in Europe under current<br />

EU regulations. To be more competitive in this new market organisation, growers<br />

have to take into account more constraints than earlier. Indeed, they have to adapt<br />

their technical choices to the present concerns about environment <strong>and</strong> fruit quality.<br />

On what basis can this adaptation be made? The knowledge reported in the scientific<br />

literature is poorly used by orchard managers. In fact the extension services<br />

<strong>of</strong>fering advices to growers are <strong>of</strong>ten conducting their own experiments to advance<br />

empirical knowledge useful to growers (e.g. Giauque et al., 1997). This empirical<br />

knowledge is very efficient but takes time to be established. In this new context,<br />

growers will have to rapidly adapt their technical management to ensure sustainable<br />

horticulture. Scientists must bear responsibility concerning the development<br />

<strong>of</strong> decision support systems, the role <strong>of</strong> which is thought to increase in the coming<br />

years (Knight, 1997). They may improve ‘the decision processes that generate<br />

cropping systems to incorporate more information, to take into account a wider range<br />

<strong>of</strong> criteria, <strong>and</strong> to allow for faster adaptation’ (Boiffin et al., 2001).<br />

Decision support systems are usually constituted <strong>of</strong> biological, technical <strong>and</strong><br />

economic models. They all have to be well adapted to the new objectives <strong>and</strong><br />

constraints <strong>of</strong> fruit production. For instance, the economic model has to take into<br />

account environmental costs (Doyle, 1997), the technical model has to consider how<br />

to create <strong>and</strong> manage orchards, <strong>and</strong> the biological model has to consider the effects<br />

<strong>of</strong> orchard management on crop quality. In terms <strong>of</strong> biological models applied to<br />

fruit production, there is a lot to be done as almost none <strong>of</strong> the current models<br />

deal with important aspects such as the response <strong>of</strong> the plant to orchard management<br />

practices, pest attacks <strong>and</strong> diseases, <strong>and</strong> consequences on fruit production<br />

<strong>and</strong> quality. Such biological models are now needed to innovate in an integrated<br />

view through evaluation, exploration <strong>and</strong> learning (Boiffin et al., 2001).<br />

To be able to help growers to face unusual situations, the biological model must<br />

be, in our sense, process-based. Indeed, process-based models now lead to better<br />

predictions in unusual situations than empirical models which can only be used under<br />

the range <strong>of</strong> environmental <strong>and</strong> agronomical conditions used to develop them.<br />

Moreover, only process-based models will be able in the future to easily integrate<br />

R. Dris <strong>and</strong> S. M. Jain (eds.), <strong>Production</strong> <strong>Practices</strong> <strong>and</strong> <strong>Quality</strong> <strong>Assessment</strong> <strong>of</strong> <strong>Food</strong> <strong>Crops</strong>,<br />

<strong>Vol</strong>. 1, “Preharvest Practice”, pp. 47–82.<br />

© 2004 Kluwer Academic Publishers. Printed in the Netherl<strong>and</strong>s.<br />

47

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