Optimisation of CO2 and temperature control in greenhouse crops by means of growth models at different abstraction levels - I. Control strategies, growth models and input data

Efficient greenhouse climate control has to consider the main factors of th e complex ecological, technical and economic system. This can be performed only on the base of models. Since all models exhibit specific deficiencies, it was investigated, if the deficiencies of each of the growth models can be reduced by combining a model at high abstraction level (REGRESSION MODEL ) with a model at low abstraction level (SUCROS 87). After a review of literature knowledge of CO2 and temperature control appro aches for greenhouse climatisation, a concept of a combined on-line model h as been elaborated. This model consists of the main modules PLANNING, GROWT H PREDICTION, GREENHOUSE CLIMATE and OPTIMISATION. Input data are classified into the modules holding (crop parameters, potent ial climate regimes, parameters of greenhouse and site, prices for producti on factors, opportunity costs of greenhouse area), market (prices of the pr oducts) and outside climate. For predicting outside climate (weather) on da ily basis, historical data (long term normals of Hannover-Herrenhausen) are implied. For climate data on a hourly basis a procedure was developed base d on measured data. The PLANNING MODULE comprises the PLANNING MODEL, LINEAR PROGRAMMING and em pirical data. The module for GROWTH PREDICTION encloses submodels for daily growth and for hourly growth. The data of the PREDICTION MODULE used for O PTIMISATION aiming at maximisation of profit and the outputs of the GREENHO USE CLIMATE MODULE feed the CONTROLLER of greenhouse climate, resulting in REAL GROWTH. Real growth values are fed back to the PREDICTION MODULE and i n weekly intervals to the PLANNING MODULE. The generation of the input data is reported and their uncertainties are di scussed.