Search

An optimisation algorithm, as an essential part of a model-based method to design greenhouses for a broad range of climatic and economic conditions, was described. This algorithm – a modified controlled random search using parallel computing – maximised the annual Net Financial Return (NFR) for a tomato grower by selecting the best alternative to fulfil eight design elements: type of greenhouse structure, material of the cover, outdoor shade screen, whitewash properties, thermal screen, heating system, cooling system and CO2 enrichment system. As an example, the algorithm was applied to two locations with different climatic and economic conditions, Almeria and The Netherlands. Due to the warm climate with high radiation levels in Almeria, a greenhouse with a relatively large specific ventilation area (20% compared to 14% for Dutch conditions), seasonal whitewash and a low-capacity direct air heater (50 W m−2 compared to 200 W m−2 for Dutch conditions) was selected. In contrast, for the relatively cold climate with low radiation levels of the Netherlands, a 100% aluminium thermal screen and no whitewash would give the best result. The design method produced realistic greenhouses and related annual NFR, indicating that the method performs well. An analysis of the close-to-best greenhouses showed that, for both locations, a structure with high light transmissivity considerably enhanced the greenhouse performance whereas an outdoor shade screen, geothermal heating and mechanical cooling would be not economical. These results demonstrate the feasibility of a model-based design approach that produces suitable greenhouse designs for given climatic and economic conditions. [ABSTRACT FROM AUTHOR]

An economic model was developed as a key component of a model-based method to design greenhouses for a broad range of climatic and economic conditions. This economic model was linked to an existing greenhouse climate-crop yield model to calculate the annual Net Financial Result (NFR) of a greenhouse. The aim of this study was to identify – among ten predefined design alternatives – the greenhouse with the highest annual NFR for tomato production under southern Spanish conditions. The basic designs were either the parral greenhouse, or a multi-tunnel, possibly fitted with any combination of heating, fogging and CO2 enrichment. Results demonstrated that the multi-tunnel, fitted with only a fogging system was most profitable, followed by the multi-tunnel with heating, CO2 enrichment and fogging. However, the difference in NFR between such a design and a simple parral was small with respect to the difference in investment. A sensitivity analysis of the NFR of the two technology extremes shows that tomato price, the fraction of marketable yield and the Photosynthetically Active Radiation (PAR) transmission of the cover had the largest bearing on NFR. With increasing technology level, the NFR depended less on outdoor climate and more on tomato price. This indicates that a low-tech greenhouse diminishes the risk of variations among price paths in different years, whereas a high-tech greenhouse covers better the “weather risk”. The best design was also affected by climate management and the joint impact of climate modification techniques. These results demonstrated that a model-based design approach can cope with multi-factorial design aspects. [ABSTRACT FROM AUTHOR]