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Istituto di Biometeorologia, Consiglio Nazionale delle Ricerche, Firenze (Italy) Dipartimento di Biologia delle Piante Agrarie, Università degli Studi di Pisa, Pisa (Italy)

The actual development of greenhouse cultural systems is characterized by an increase in economic engagement, with a more high demand of advances techniques to optimize all productivity factors. Social and environmental constraints force growers to use lower impact systems and to adopt specific technologies for the reduction of resource exploitation: energy, water, etc. Electronics and computer science support this process, mainly by means of innovative sensors and efficient modeling that have become irreplaceable tools for the integration of knowledge and innovative technologies. The analysis of current experiences reveals a dual approach for controlling plant nutrition, called by Le Bot et al. (1998) "inductive or deductive regulations". The first approach is applied to the actual practice of fertigation in open or closed types of soilless cultures and it is a posteriori regulation. In this case, nutrient solution with pre-set values of both pH and electrical conductivity (EC) is delivered to the crop with frequent or continuous EC measurements in the drain water. According to EC values of the drainage a modification of nutrient solution can be estimated, determining the intervention of the grower. In recirculating-water (closed-loop) growing systems, in order to reduce the use of water and nutrients, the nutrient solution has to be re-used as long as possible and this requires frequent chemical analyses that may be conducted on-line by an expensive chemicalsensor-based control system or off-line by means of conventional laboratory analysis or easyto-use test-kits. In principle, the deductive (a priori) Greenhouse data (dimension, coverage type, etc.) Meteorological data (Ta, Rs, RH) Irrigation system data (recirculating solution volume) regulation can optimize the control of Crop data (cultivar, density, potential yield, LAI max, growth rate) Water quality data (pH, EC) greenhouse crop mineral nutrition, since it Fertirrigation data (original water, nutritive solution, pH, EC, Na , MC , NO ) is based on the knowledge of plant responses to the change of its Nodes, LAI, tomato potential yield Plant growth model environment. Models may be considered Mineral element concentration, EC EC model nutrient solution as surrogates for sensors (Lieth, 1999) and Water model ETr, water consumption this approach should allow the reduction Evaluation module of growers' dependence on off-line and/or on-line analysis of the recirculation water. High values of nutrient solution salinity can reduce plant growth and final yield of tomato until 3-4 % for each dS/m but the cost of irrigation water increases with its quality (Stanghellini et al., 2005). It means that a continue balance between Fig.1 ­ Main modules of SGx system these factors is required to obtain maximal economical advantage. In order to satisfy these exigencies, taking into account recent progress in irrigation and fertigation and with particular attention to the reduction of environmental impact of polluted water, a software program for fertigation management of soilless tomato in closed system was realized. As shown in Fig.1 the system, called SGx and running with hourly temporal scale, integrates different models and procedures to evaluate the effects of environmental parameters on tomato growth, fruit yield and transpiration and the feedback effects on nutrient solution composition.

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ON LINE Scheduling of water management Composition of nutrient solution Monitoring of recirculating solution Limiting factors for plant growth Yield reduction due to EC values Plant water demand Water consumption Trend for the next five days

OFF LINE Effect of temperature and radiation on plant growth Evaluation of the effects of cultivation system on production Water consumption Quantity of nutrients discharged into the environment Evaluation of potential yield of different tomato cultivars on the basis of water quality and climatic factors; Percentage of yield reduction due to EC variation Economic balance of different strategic choices

The main models and modules are: 1) a model to simulate plant growth (derived from TOMGRO, Jones et al., 1991); 2) a model to estimate water requirements according to microclimatic conditions. It integrates different ETR functions on the basis of plant development; 3) a model to evaluate mineral element concentration and EC changes in the nutrient solution according to initial solution composition and plant water consumption (Carmassi et al., 2003); 4) an evaluation module to support growers about the interventions to be carried out in real time to replenish the nutrient solution or to give cultural suggestions (e.g., the number of periodic flushing of nutrient solution and the most suitable tomato cultivar to be cultivated taking into account different irrigation water quality) (Fig.2). For the support in real time the system runs on-line using air temperature, solar radiation, relative humidity and water-consumption data to simulate actual and 5-days forecasted mineral element concentration and electrical conductivity (EC) of the recirculating nutrient solution. For the support about "strategic" decisions, the system can run off-line using, like inputs, meteorological data set provided by the user. A A B



FIG. 2 ­ Example of SGx outputs: A) Estimated EC trend of nutrient solution; B) Estimated Tomato Yield; C) Nutrient solution computation; D) Environmental index computed on the basis of mineral elements discharged.

. Bibliography Le Bot, J., Adamowicz, S., Robin, P., 1998. Modelling plant nutrition of horticultural crops: a review. Scientia Horticulturae, 74: 47-82. Carmassi G., Incrocci L., Malorgio M., Tognoni F., Pardossi A. 2003. A simple model for salt accumulation in closed-loop hydroponics. Acta Horticulturae 614: 149-154. Jones J.W., Dayan E., Allen L.H., Van Keulen H., Challa H. 1991. A dynamic tomato growth and yield model. Transaction of the ASAE 34(2): 663-672. Lieth, J.H. 1999. Crop management models for decision support and automated optimization. Acta Horticulturae 507:271-277. Stanghellini C., Pardossi A., Kempkes F., Incrocci L. 2005. Closed water loop in greenhouses: effect of water quality and value of produce. Acta Horticulturae (in press).



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