A methodology for model-based greenhouse design: Part 1, a greenhouse climate model for a broad range of designs and climates

A methodology for model-based greenhouse design: Part 2, description and validation of a tomato yield model

A methodology for model-based greenhouse design: Part 3, sensitivity analysis of a combined greenhouse climate-crop yield model

Greenhouses are complex systems whose size, shape, construction material, and equipment for climate control, lighting and heating can vary largely. The greenhouse design can, together with the outdoor weather conditions, have a large impact on the economic performance and the environmental consequences of the production. The aim of this study was to identify a greenhouse design out of several feasible designs that generated the highest net financial return (NFR) and lowest energy use for seasonal tomato production across Norway. A model-based greenhouse design method, which includes a module for greenhouse indoor climate, a crop growth module for yield prediction, and an economic module, was applied to predict the NFR and energy use. Observed indoor climate and tomato yield were predicted using the climate and growth modules in a commercial greenhouse in southwestern Norway (SW) with rail and grow heating pipes, glass cover, energy screens, and CO 2 -enrichment. Subsequently, the NFR and fossil fuel use of five combinations of these elements relevant to Norwegian conditions were determined for four locations: Kise in eastern Norway (E), Mære in midwestern Norway (MW), Orre in southwestern Norway (SW) and Tromsø in northern Norway (N). Across designs and locations, the highest NFR was 47.6 NOK m −2 for the greenhouse design with a night energy screen. The greenhouse design with day and night energy screens, fogging and mechanical cooling and heating having the lowest fossil energy used per m 2 in all locations had an NFR of −94.8 NOK m −2 . The model can be adapted for different climatic conditions using a variation in the design elements. The study is useful at the practical and policy level since it combines the economic module with the environmental impact to measure CO 2 emissions. • A simulation model was applied to evaluate greenhouse design elements in Norway. • The economic and environmental performance of tomato production was determined. • Observed temperature, CO 2 -concentration and yield were predicted fairly accurately. • The greenhouse with a night energy screen had the highest Net Financial Return. • Investing in temperature regulation equipment reduced the fossil fuel use.

ISHS International Symposium on New Technologies for Environment Control, Energy-Saving and Crop Production in Greenhouse and Plant Factory - Greensys 2013 GREENHOUSE DESIGN FOR VEGETABLE PRODUCTION IN SUBTROPICAL CLIMATE IN TAIWAN

Relevance of research. At both global and regional levels, climate change has become an indisputable fact, the presence of which has posed to humanity the challenge of solving a number of extremely important and complex tasks related to the development and implementation of a strategy for their practical continued existence. Data base on evaporation and water needs for agricultural crops in the different periods of their growing, depending on the climatic conditions, are the basis for the development of design and formation of operational regimes of water regulation carried out by justifying the necessary methods of water regulation, types, structures and modes of operation of hydro-reclamation systems and calculation of their parameters. Aim of the study is to estimate the changes in water needs during crop cultivation on the drained lands of the Western Polissya in Ukraine in the variable climatic, agricultural and ameliorative conditions for the substantiation of appropriate adaptive decisions to it. To achieve this goal, the authors evaluated the weather and climate conditions in the Western Polissya in Ukraine and calculated the evaporation in the studied conditions, planned and carried out a large-scale computer experiment, based on a complex of predictive-simulation models concerning the basic regimes and technological variables of the hydro-reclamation system parameters, climate conditions, water regime, water regulation technologies and the productivity of drained lands for the schematized natural, agricultural and ameliorative conditions. Research methods. The research methods were based on the application of system theory along with the systematic approach, system analysis and modeling oriented on widespread use of computers and related software in developing modern approaches to the substantiation of technical and technological solutions for water regulation on the drained lands in the conditions of climate change. The object of the study is the drainage system “Birky” in Rivne region, typical for the region in relation to the natural land reclamation conditions. Results of the study and the main conclusions. It was established the needs for additional irrigation of cultivated crops on the drained lands of the Western Polissya in Ukraine in the current weather and climatic conditions. Based on the long-term forecast the vegetative values of the total evaporation and the formation of water needs for the drained lands in the variable climatic, agricultural land reclamation conditions were determined. The technological efficiency of different technologies of the irrigation on the drained lands was evaluated. This approach to the estimation of changes in water needs during crop cultivation in the variable climatic, agricultural and ameliorative conditions allows determining the best technology of water regulation for growing a particular crop under the studied conditions in terms of the most rational use of water resources and the efficiency of system functioning. Prospects. The obtained results can be effectively used for justification of regime and technological decisions in the projects of construction and reconstruction of hydro-reclamation systems of the Western Polissya in Ukraine in the variable climate conditions and developing hydro-technical adaptive measures to the predicted climate change in the region.

A methodology for model-based greenhouse design: Part 5, greenhouse design optimisation for southern-Spanish and Dutch conditions

Implementation of greenhouse climate control simulator based on dynamic model and vapor pressure deficit controller

In Turkey, protected cultivation includes the production in greenhouses and under low plastic tunnels. The total area reached 64 912 ha in 2014, of which 76% is greenhouses. As in the other Mediterranean countries, protected cultivation is mainly located where the climatic conditions are favourable for production without any additional heating. However, in the last decade high tech greenhouses have been introduced particularly in order to use renewable alternative energy for greenhouse heating. In this research a greenhouse climate and crop yield model was used in order to evaluate possible improvements under simple structures and high tech greenhouses, applying different climate control strategies in different locations of Turkey. The results showed that significant yield increase could be provided by increasing the heating set-point to 13°C in unheated low tech greenhouses. Also evaporative cooling systems could be used for early planting dates for high tech greenhouses. It was concluded that greenhouse climate and crop yield model could be used as a tool to estimate the improvements of climate control strategies on yield.

The impact of different types of insect-proof screens on greenhouse climate in humid tropics needs to be quantified in order to develop technically and economically optimal greenhouse constructions. Thus, physical properties of various insect-proof screens were determined in the laboratory at the Institute of Horticultural and Agricultural Engineering, University of Hannover, Germany. Specially adapted experimental greenhouses were built at AIT campus in Bangkok, Thailand. A dynamic energy and mass balance model of the greenhouse system was developed to predict the greenhouse climate from external weather data and properties of insect screens. Scenario simulations were carried out to predict the effect of screen properties on greenhouse climate. External and greenhouse climate measurements were made in Bangkok. The internal climate measurements were carried out concurrently in two similar, naturally-ventilated greenhouses covered with different insect-proof screens on ventilation openings. Tomato (Lycopersicon esculentum ‘King Kong II’) plants were grown in the greenhouse during the experimental period. Model predictions of greenhouse air temperature were compared to measurements from the two greenhouses and good agreement was achieved. The results form a good basis for decisions on screened greenhouse design improvements and climate control interventions in screened greenhouses in the humid tropical climates. INTRODUCTION Although temperatures and global radiation are very suitable for vegetable cultivation throughout the year in the humid tropics, open air cultivation is hindered by heavy rainfall and high humidity which cause crop damage and infestation by diseases. High incidence of pests such as thrips and aphids is also a big problem. Therefore, greenhouse systems which can provide the optimal plant growth environment while offering maximum protection against pests need to be developed. According to von Zabeltitz (1993), air temperature and air humidity should not be higher in tropical greenhouses than in the open air. Cooling-related expense is one of the most important greenhouse operation costs in the humid tropics. But the efficiency of fan and pad cooling systems in greenhouses in the humid tropics is limited due to high humidity of ambient air (H-J. Tantau, pers. commun., 2004). It is also expensive. Naturally-ventilated greenhouses can result in saving of cooling-related costs. Physical exclusion of pests through the use of insect-proof screens on greenhouse ventilation openings is an increasingly popular way of reducing pesticide applications. A consequence of the use of insect-proof screens is that they restrict air-flow and thus affect the climate in the greenhouse. The effect of insect-proof screens on greenhouse climate depends on the physical properties of the screens (i.e. discharge coefficient and spectral transmissivity) and the ratio of the screen area to floor area. Different authors have reported different values of discharge coefficients of greenhouse openings (Sase and Christianson, 1990; Montero et. al. 1997; Munoz et. al. Proc. IC on Greensys Eds.: G. van Straten et al. Acta Hort. 691, ISHS 2005 45

CFD based heat transfer parameter identification of greenhouse and greenhouse climate prediction method

By generating high quality data without the big time investment and economic cost of real experiments, dynamic greenhouse climate and crop simulation models can support decisions on greenhouse climate control, crop management and greenhouse design. The reliability of simulation-based decisions depends on both the prediction accuracy and interpretability of simulation models. The prediction accuracy of these simulation models can be increased by: 1) improving mechanisms in process-based models; 2) calibrating process-based model parameters; 3) deriving black-box relationships from data. Considering the descending interpretability from (1) to (3), this study presents a knowledge-based data-driven modelling approach where firstly a process-based model is selected and modified based on domain knowledge, then data-driven improvement is applied including two steps: parameter value estimation by particle filter (PF) and further black-box improvement by deep neural networks (DNN). The approach was tested with an example of greenhouse climate-tomato production system modelling. Modules from GreenLight (Katzin et al., 2020) and TOMSIM (Heuvelink, 1995, 1996) were selected, modified and integrated into a process-based greenhouse climate-tomato model. Validation showed that PF-calibration of five greenhouse parameters decreased the seasonal relative root mean squared error (RRMSE) of indoor air vapor pressure predictions from 40.7% of that before PF-calibration to 16.4%, while it did not decrease the RRMSE of indoor air temperature predictions. Combining the PF-calibrated model with a DNN trained on a season of data decreased the RRMSE of indoor air temperature from 15.0% without DNN to 6.7%, and decreased the RRMSE of indoor air vapor pressure to 12.6%. The knowledge-based data-driven greenhouse climate-tomato model had a relative error of 0.9% for seasonal total fresh yield, and an RRMSE of 6.6% for the cumulative yield throughout the season. If process-based model parameters were not calibrated before combining the model with DNNs, the required amount and diversity of DNN training data increased because more information needed to be learnt from data by the DNNs. Without PF-calibration, combining a DNN trained on 50 days of data with the process-based model resulted in RRMSEs of 44.8% and 31.8% for indoor air temperature and vapor pressure prediction, respectively; with PF-calibration, the RRMSEs were decreased to 13.1% and 17.9%. The proposed three-step knowledge-based data-driven approach can not only improve the model prediction accuracy, but can also help to track and interpret the improvements.

A method to improve the efficiency of greenhouse climate control based on the framework of optimal control theory is described. By exploiting a dynamic model of the greenhouse crop production process, information on auction price, operating costs of the climate conditioning equipment and outdoor climate conditions, the optimal greenhouse climate control scheme balances basic costs against revenues for operating the equipment. In a greenhouse experiment (using lettuces) the behaviour of conventional greenhouse climate control by the grower was measured. Then, in simulation experiments, optimal control strategies were calculated for the same conditions (outdoor climate, auction price, energy price). The results support the conclusion that a considerable improvement in the efficiency of greenhouse climate management is possible. This improvement may well exceed 15%.

Intelligent control and energy optimization in controlled environment agriculture via nonlinear model predictive control of semi-closed greenhouse

A methodology for model-based greenhouse design: Part 4, economic evaluation of different greenhouse designs: A Spanish case

Towards discrete time model for greenhouse climate control