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Greenhouse models are important tools for the analysis and design of greenhouse systems and for offering decision support to growers. While many models are available, relatively few include the influence of supplementary lighting on the greenhouse climate and crop. This study presents GreenLight, a model for greenhouses with supplemental lighting. GreenLight extends state of the art models by describing the qualitative difference between the common lighting system of high-pressure sodium (HPS) lamps, and the newest technology for horticultural lighting - the light-emitting diodes (LEDs). LEDs differ from HPS lamps in that they operate at lower temperatures, emit mostly convective heat and relatively little radiative heat, and can be more efficient in converting electricity to photosynthetically active radiation (PAR). These differences can have major implications on the greenhouse climate and operation, and on the amount of heat that must be supplied from the greenhouse heating system. Model predictions have been evaluated against data collected in greenhouse compartments equipped with HPS and LED lamps. The model predicted the greenhouse's heating needs with an error of 8–51 W m−2, representing 1–12% of the measured values; the RMSE for indoor temperature was 1.74–2.04 °C; and the RMSE for relative humidity was 5.52–8.5%. The model is freely available as open source MATLAB software at https://github.com/davkat1/GreenLight. It is hoped that it may be further evaluated and used by researchers worldwide to analyse the influence of the most recent lighting technologies on greenhouse climate control. Image 1 • Model for lighting in greenhouses, including HPS and LED lamps, was developed. • Model was evaluated using data from a greenhouse with HPS and LED top-lights. • Model evaluates energy requirements of the greenhouse with an accuracy of 1–12%. • Model predicts indoor temperature and humidity with an RRMSE of 6–11%. • Model is publicly available in free open source format. [ABSTRACT FROM AUTHOR]

Within the modern greenhouse horticultural sector energy usage is planned using mathematical models that simulate the greenhouse's future performance. These models contain parameters whose values can be inaccurate which create errors in model predictions. This reduces the effectiveness of energy management and planning done using these models. This study proposes and demonstrates an algorithm to quantify the impact of parameter errors on greenhouse gas and electric power prediction uncertainty. The proposed algorithm introduces a Polynomial Chaos Expansion as a method for the sensitivity analysis in the domain of greenhouse horticulture. Contrary to commonly used sensitivity analyses, this approach introduces the analysis of higher order interactions into the domain of greenhouse horticultural research. It was found that for both electric power and gas production the HPS lamp power rating was the most influential individual parameter. Moreover, this study found that for power demand the uncertainty in parameters relating to the lamp system were far more impactful than those related to the crop or greenhouse structure, with a respective coefficient of variation of 24 %, 5 % and 5 %. This study makes a notable and novel conclusion that for parameters related to the greenhouse structure, larger groups of parameters were responsible for prediction uncertainty through higher order interactions of second to sixth order. These results reinforce the importance of future greenhouse research considering the impact of higher order parameter interactions on prediction uncertainty using the algorithm proposed in this study. • An electrical power and gas demand uncertainty analysis was performed. • A polynomial chaos expansion was used as part of an uncertainty analysis. • The proposed algorithm shows groups of parameters impacting prediction uncertainty. • Lamp intensity is the key factor for power and gas demand prediction uncertainty. [ABSTRACT FROM AUTHOR]

The possibility of improving the accuracy of climate monitoring in greenhouse cultivation by way of model based filtering was explored. The focus was on estimating the average climate inside a greenhouse compartment. Starting point was employing an extended Kalman filter (EKF), combined with a greenhouse climate differential equation model. In two different greenhouses (A and B), temperature and humidity were monitored with a 5-min sampling resolution with a sensor grid. The available data sets spanned 1 and 0.5 years. With the average over all sensors as reference signal, the root mean squared errors (RMSEs) of the unfiltered signals (coming from single sensors) were 0.43 °C and 0.48 g m−3 for greenhouse A, and 0.80 °C and 0.64 g m−3 for greenhouse B. The filter was compared with a moving average (MA) filter, and an unscented Kalman filter (UKF). Overall, monitoring accuracy was not improved by any of the filters, and in most cases it deteriorated. Performance was strongly linked to the choice of filter, where the EKF outperformed the other filters by a considerable difference. The violations on the assumptions of whiteness and normality of the noise were severe but had a moderate effect on the RMSEs (0.11 °C and 0.10 g m−3 for greenhouse A). A clear link was found between model accuracy and monitoring accuracy. A 10–15 fold decrease of state errors was associated with an RMSE reduction down to 0.1 °C and 0.1 g m−3, the expected equivalent of increasing the number of climate sensors from 1 to 25. • A novel methodology for assessing data filtering methods was developed. • Three data filters were tested in two different greenhouses for in total 1.5 years. • None of the filters improved climate monitoring accuracy. • Bias, non-Gaussianity, and auto-correlation had a moderate effect on performance. • Increasing model accuracy has the same effect as increasing the number of sensors. [ABSTRACT FROM AUTHOR]

Brassinosteroid (BR) signaling is essential for plant growth and development. In Arabidopsis (Arabidopsis thaliana), BRs are perceived by the BRASSINOSTEROID INSENSITIVE1 (BRI1) receptor. Root growth and hypocotyl elongation are convenient downstream physiological outputs of BR signaling. A computational approach was employed to predict root growth solely on the basis of BRI1 receptor activity. The developed mathematical model predicts that during normal root growth, few receptors are occupied with ligand. The model faithfully predicts root growth, as observed in bri1 loss-of-function mutants. For roots, it incorporates one stimulatory and two inhibitory modules, while for hypocotyls, a single inhibitory module is sufficient. Root growth as observed when BRI1 is overexpressed can only be predicted assuming that a decrease occurred in the BRI1 half-maximum response values. Root growth appears highly sensitive to variation in BR concentration and much less to reduction in BRI1 receptor level, suggesting that regulation occurs primarily by ligand availability and biochemical activity. [ABSTRACT FROM AUTHOR]

Parameter estimation plays an important role in physical modelling, but can be problematic due to the complexity of spatiotemporal models that are used for analysis, control and design in industry. In this paper we aim to circumvent these problems by using a methodology that approximates a model, or a part of a model, by a first-order plus dead time (FOPDT) approximation, explicit in the physical parameters. The FOPDT model with its physical parameters can be calibrated and validated to experimental data via an Output Error identification. The methodology is illustrated by a model of a temperature-controlled food storage room using experimental data. The complex part of the model is reduced to an accurate first-order model that has predictive power with respect to physical parameter variations. Moreover, this methodology allows one to test model adjustments for phenomena that were not considered in the physical model, in a relatively easy way. [Copyright &y& Elsevier]

Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor - target gene interactions) but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive). In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence. [ABSTRACT FROM AUTHOR]

An intriguing phenomenon in plant development is the timing and positioning of lateral organ initiation, which is a fundamental aspect of plant architecture. Although important progress has been made in elucidating the role of auxin transport in the vegetative shoot to explain the phyllotaxis of leaf formation in a spiral fashion, a model study of the role of auxin transport in whorled organ patterning in the expanding floral meristem is not available yet. We present an initial simulation approach to study the mechanisms that are expected to play an important role. Starting point is a confocal imaging study of Arabidopsis floral meristems at consecutive time points during flower development. These images reveal auxin accumulation patterns at the positions of the organs, which strongly suggests that the role of auxin in the floral meristem is similar to the role it plays in the shoot apical meristem. This is the basis for a simulation study of auxin transport through a growing floral meristem, which may answer the question whether auxin transport can in itself be responsible for the typical whorled floral pattern. We combined a cellular growth model for the meristem with a polar auxin transport model. The model predicts that sepals are initiated by auxin maxima arising early during meristem outgrowth. These form a pre-pattern relative to which a series of smaller auxin maxima are positioned, which partially overlap with the anlagen of petals, stamens, and carpels. We adjusted the model parameters corresponding to properties of floral mutants and found that the model predictions agree with the observed mutant patterns. The predicted timing of the primordia outgrowth and the timing and positioning of the sepal primordia show remarkable similarities with a developing flower in nature. [ABSTRACT FROM AUTHOR]

Background: The genetic control of floral organ specification is currently being investigated by various approaches, both experimentally and through modeling. Models and simulations have mostly involved boolean or related methods, and so far a quantitative, continuous-time approach has not been explored. Results: We propose an ordinary differential equation (ODE) model that describes the gene expression dynamics of a gene regulatory network that controls floral organ formation in the model plant Arabidopsis thaliana. In this model, the dimerization of MADS-box transcription factors is incorporated explicitly. The unknown parameters are estimated from (known) experimental expression data. The model is validated by simulation studies of known mutant plants. Conclusions: The proposed model gives realistic predictions with respect to independent mutation data. A simulation study is carried out to predict the effects of a new type of mutation that has so far not been made in Arabidopsis, but that could be used as a severe test of the validity of the model. According to our predictions, the role of dimers is surprisingly important. Moreover, the functional loss of any dimer leads to one or more phenotypic alterations. [ABSTRACT FROM AUTHOR]

In the published simulation studies on greenhouse climate control that employ optimal control, often non-realistic weather forecasts are employed, e.g. the realisation of the weather or artificially created forecasts are used. This research aims to quantify the effect of weather forecast errors on the performance of the controlled greenhouse system measured in terms of operational return. The operational return is defined as the difference between the cost of resources (r e s o u r c e u s e × c o s t) and the income through yield (y i e l d × p r o d u c t p r i c e). A stochastic model of the weather forecast error was identified based on historical weather observations and forecasts from a weather forecasting service. An uncertainty analysis using the stochastic model showed that a considerable number of control inputs are sensitive to the forecast errors. A simulation study involving three 7 d a y -intervals throughout the growing season showed, however, that the performance of the controlled greenhouse system is not significantly affected by the forecast error, a performance decrease of 0.03 e u r o. m − 2 (2 %) was observed with respect to the case in which perfect forecasts were used. The results suggest that an optimal control algorithm which (a) is updated every 15 min with the full state information, (b) uses forecasts published every 6 h and (c) uses published forecasts with a weather forecast error similar to the weather forecasting service used here, is able to mitigate the effect of the weather forecast error on the performance of the greenhouse system. • A stochastic forecast error model is identified using historical Dutch climate data. • Quantification of controlled greenhouse system performance loss for forecast errors. • Performance comparison with controller taking into account forecast uncertainty. [ABSTRACT FROM AUTHOR]

LED lighting is appointed as the successor of HPS lighting in greenhouses since it can lead to a more sustainable cultivation, i.e. it converts electrical energy into photosynthetically active radiation more efficiently. To quantify the effect of this more efficient conversion within the operation of the greenhouse system, an optimal controller is proposed to generate optimal control trajectories for the controllable inputs of the greenhouse. The optimal controller makes use of an economic objective function, i.e. the difference between income (y i e l d × p r o d u c t p r i c e) and cost of resources (r e s o u r c e u s e × c o s t). The performance of this optimally controlled greenhouse system is compared with respect to the state-of-the-practice. Simulation experiments suggest optimal control can increase the economic objective by 10 % to 65.14 €. m − 2 compared to 58.96 €. m − 2 for the state-of-the-practice, for tomatoes cultivated in a Dutch weather conditions. The model of the optimally controlled greenhouse is used to compare the performance of different lighting systems, i.e. no lighting, HPS lighting and LED lighting. An increase of 9 % in the operational return is observed for LED lighting compared to HPS lighting. The electricity that is saved due to the more energy-efficient conversion in the LED lighting results in a 30 % decrease in carbon footprint when comparing a greenhouse with LED lighting to a greenhouse with HPS lighting. • A model suitable for the control of the greenhouse climate is developed. • The model allows for the optimization of a purely economic cost function. • Optimal control increases the return by 10% compared to the state-of-practice. • Simulations suggest a 9 % increase in return when using LED lights instead of HPS. • Carbon footprint decreases by 30 % when using LED lights instead of HPS. [ABSTRACT FROM AUTHOR]

Crop modelling is an essential part of biosystems engineering; selecting or developing a crop model for a specific application, having its requirements and desires, is difficult if not impossible without the required domain knowledge. This paper presents a fundamentally different model selection approach based on biological functionalities. This is enabled by a common structure, which allows for a combining of components, yielding new models. This increased design space allows the development of models which are better suited to the application than the original models. The use of a common structure, and its potential, are demonstrated by a use-case involving the selection of a tomato crop model for a model-based control application, but the rationales and methodologies can apply to other crops and applications as well. In this paper, 27 valid model combinations have been created from 4 models. In the use-case presented, the models are compared to data originating from a real system. The predictive performance of a model is quantified by the Root-Mean-Squared-Error (RSME) between the predictions and data. One trade-off is model accuracy versus computational speed. With the model set used, a 13% decrease in RSME was obtained by allowing a 7.5% increase in model computation time compared to one of the original models. • A fundamentally different model selection approach using biological functionalities. • 27 valid model combinations have been created from 4 models. • A model combination has been found which predicts better than the original models. [ABSTRACT FROM AUTHOR]

High-tech greenhouses are characterised by high yields and high energy consumption. Current trends towards expanded use of supplemental lighting further increase the intensity of crop production and energy use, and it is expected that the availability of light-emitting diodes (LEDs) will accelerate this trend. At the same time, an increase in greenhouse lighting reduces the heating energy needed from the heating system. This study presents a novel concept for greenhouses, where both lighting and heating are derived exclusively from lamps. Such greenhouses can be highly efficient, as light is used both for crop growth and for heating. If the electric grid is based on renewable sources, such greenhouses can also be carbon-neutral and fossil-free. By using model simulations for a tomato greenhouse in the Netherlands, it was found that such greenhouses could be realised by employing a heat storage system with a heat storage capacity of 2 MJ m−2 and LEDs with a power capacity of 150 W m−2 and a photosynthetic photon flux density (PPFD) of 450 μmol m−2 s−1. The greenhouse heated by light was predicted to have 44% higher yields and 60% higher energy inputs than a reference greenhouse, equipped with a boiler and LEDs with a PPFD of 200 μmol m−2 s−1. This result was part of a general trade-off that was found between yield and energy efficiency. This exploration helped identify avenues for further improvement of the energy efficiency of greenhouses heated by lamps, highlighting their promise as a potential new direction in greenhouse intensification. • A novel approach for greenhouse climate control, using only lamps and no boiler. • Model simulations uncover the attributes required to make such systems feasible. • Increasing lamp intensity alone increases yields but reduces energy efficiency. • Avenues for improving the energy efficiency of heating by light are proposed. [ABSTRACT FROM AUTHOR]

Currently the Dutch greenhouse horticultural sector has a high energy demand. The present use of weather forecasts can exacerbate this high energy consumption by contributing to suboptimal prediction and trading of the greenhouse's power demand. This study investigates the role of weather forecast errors on energy prediction power and trading uncertainty in greenhouse horticulture. This was done using an uncertainty analysis and computer model of a tomato producing Venlo style greenhouse in Bleiswijk, The Netherlands. This greenhouse model was used to predict the greenhouse's gas and electrical power demand. The study concluded that errors in the weather forecast of outdoor radiation, temperature and wind speed caused an overestimation of greenhouse energy demand. A sensitivity analysis showed that the radiation forecast error had the greatest impact on predicted greenhouse power demand errors with a mean relative error of 6.1 %. Predicted gas demand errors were most dependent on the outside wind speed forecast mean relative error (18.0 %) and temperature forecast error (17.2 %). A power trading uncertainty analysis was done to investigate the impact of predicted energy demand errors on the cost of buying power on the Dutch imbalance and Amsterdam Power Exchange day-ahead market. This cost analysis found that the volume of initial power trading was greater than corrective trading. Additionally, the higher volatility in short term power prices resulted in higher corrective power costs per unit of power than if the power demand had been initially predicted with more accuracy. • The study shows that weather forecasts create energy demand uncertainty. • A data-based sensitivity analysis was done for input data weather variables. • The greenhouse power costs are calculated using imbalance and day-ahead markets. • As the weather forecasts lengthen, the energy prediction error and power costs increase. • The radiation forecast has the greatest impact on the predicted power error. [ABSTRACT FROM AUTHOR]

Biochemical systems involving a high number of components with intricate interactions often lead to complex models containing a large number of parameters. Although a large model could describe in detail the mechanisms that underlie the system, its very large size may hinder us in understanding the key elements of the system. Also in terms of parameter identification, large models are often problematic. Therefore, a reduced model may be preferred to represent the system. Yet, in order to efficaciously replace the large model, the reduced model should have the same ability as the large model to produce reliable predictions for a broad set of testable experimental conditions. We present a novel method to extract an “optimal” reduced model from a large model to represent biochemical systems by combining a reduction method and a model discrimination method. The former assures that the reduced model contains only those components that are important to produce the dynamics observed in given experiments, whereas the latter ensures that the reduced model gives a good prediction for any feasible experimental conditions that are relevant to answer questions at hand. These two techniques are applied iteratively. The method reveals the biological core of a model mathematically, indicating the processes that are likely to be responsible for certain behavior. We demonstrate the algorithm on two realistic model examples. We show that in both cases the core is substantially smaller than the full model. [ABSTRACT FROM AUTHOR]

• A novel analysis of the effect of LEDs on the greenhouse energy budget is presented. • The total energy system, including heating and lighting demand, was examined. • Energy savings in multiple climate scenarios from around the world were examined. • Energy for light was reduced by 40% while energy for heating increased by 9–49% • A transition to LEDs was predicted to save 10–25% of total greenhouse energy demand. Greenhouses in high latitudes consume vast amounts of energy for heating and supplemental lighting. Light emitting diodes (LEDs) have been suggested as having great potential for reducing greenhouse energy use, as they are extremely efficient at converting electricity to light. However, LEDs emit very little heat, which must be compensated by the greenhouse heating system. Thus, it is unclear how much energy can be saved by LEDs when the need for extra heating is taken into account. This study presents a first analysis of the energy demands for greenhouses transitioning from high-pressure sodium (HPS) to LED lighting, providing a quantification of the total energy savings achieved by LEDs. Model simulations using GreenLight , an open source greenhouse model, were used to examine a wide range of climates, from subtropical China to arctic Sweden, and multiple settings for indoor temperature, lamp intensity, lighting duration, and insulation. In most cases, the total energy saving by transition to LEDs was 10–25%. This value was linearly correlated with the fraction of energy used for lighting before the transition, which was 40–80%. In all scenarios, LEDs reduced the energy demand for lighting but increased the demand for heating. Since energy for lighting and heating is often derived from different origins, the benefits of a transition to LEDs depend on the environmental and financial costs of the available energy sources. The framework provided here can be used to select lighting installations that make optimal use of available energy resources in the most efficient and sustainable manner. [ABSTRACT FROM AUTHOR]

The poultry red mite, Dermanyssus gallinae , is the most significant pest of egg laying hens in many parts of the world. Control of D. gallinae could be greatly improved with advanced Integrated Pest Management (IPM) for D. gallinae in laying hen facilities. The development of a model forecasting the pests’ population dynamics in laying hen facilities without and post-treatment will contribute to this advanced IPM and could consequently improve implementation of IPM by farmers. The current work describes the development and demonstration of a model which can follow and forecast the population dynamics of D. gallinae in laying hen facilities given the variation of the population growth of D. gallinae within and between flocks. This high variation could partly be explained by house temperature, flock age, treatment, and hen house. The total population growth variation within and between flocks, however, was in part explained by temporal variation. For a substantial part this variation was unexplained. A dynamic adaptive model (DAP) was consequently developed, as models of this type are able to handle such temporal variations. The developed DAP model can forecast the population dynamics of D. gallinae , requiring only current flock population monitoring data, temperature data and information of the dates of any D. gallinae treatment. Importantly, the DAP model forecasted treatment effects, while compensating for location and time specific interactions, handling the variability of these parameters. The characteristics of this DAP model, and its compatibility with different mite monitoring methods, represent progression from existing approaches for forecasting D. gallinae that could contribute to advancing improved Integrated Pest Management (IPM) for D. gallinae in laying hen facilities. [ABSTRACT FROM AUTHOR]

To explore the possibility of automating the process of detecting oestrus cows via electronic olfaction, we designed and built an integrated measurement system consisting of a breath sample device, electronic nose, and a diagnosis model. The diagnosis performance, using a data set of 71 measurements on 52 cows in total, was a receiver operating characteristic curve with an area under the curve of 0.86, which had 83% sensitivity and 86% specificity for a specifically chosen threshold value. These results indicate a potential for automated detection based on breath sampling. [ABSTRACT FROM AUTHOR]