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There is a need for precise irrigation strategies to reduce leaching and water use, whilst avoiding drought exposure. A model-based prediction and visualization method is proposed to help estimate the precise required irrigation dosage and frequency to achieve desired leaching fraction and drought exposure as a multi-objective optimization problem. The method produces a contour plot that shows the estimated leaching and drought exposure under uncertain evapotranspiration. Three sandy soils and three clay soils were examined for a total of 6 different soils, which included a clay soil case with protected Chrysanthemum cultivation. The results illustrate the usefulness of this type of visualization in decision support. Predictions indicate that compared to a conventional irrigation strategy, leaching could be reduced by 40% and water use by 9% whilst maintaining a low drought exposure (below 1%). Furthermore, predictions indicate that reducing evapotranspiration uncertainty would require an irrigation frequency increase (∼20%) or a total irrigation dosage increase (∼10%). Compared to clay soils, sandy soils require a higher frequency irrigation (more than once per 2 days) to prevent drought exposure. The proposed model-based framework and visualization method provides a useful understanding of low-risk irrigation strategies that account for evapotranspiration uncertainty, while reducing water use and minimizing leaching, giving valuable insights to agricultural professionals and policymakers alike. Additionally, the developed framework has the potential to be used as a core for decision and control tools developed for sustainable agriculture and water resource management.

High-tech greenhouses are major consumers of energy. This energy consumption is responsible for considerable carbon emissions and contributes to climate change and global warming. Light-emitting diodes (LEDs) have been promoted as a solution to the energy problem of illuminated greenhouses, due to their high efficacy in converting electricity to light. Current LEDs consume around 40% less electricity than the commonly used high-pressure sodium (HPS) lamps, while emitting equivalent light intensities.Despite their high efficacy, adoption of LEDs in greenhouses has been limited. High investment costs, lack of knowledge regarding the influence of LEDs on the greenhouse climate and crop, and low trust towards their claimed benefits, have all been cited as obstacles for adoption. Greenhouses with LEDs also require more heating than greenhouses with HPS lamps: due to their higher efficacy, LEDs contribute less heat to the greenhouse, which must be compensated by the heating system. Therefore, it has so far been unclear precisely how much energy can be saved by a transition to LEDs in greenhouses, and what factors contribute to this potential saving.This thesis explores how LEDs influence the energy consumption of greenhouses. It investigates the consequence of replacing HPS lamps by LEDs in terms of lighting demand, heating demand, and total energy use; analyzes how LEDs influence the greenhouse climate and energy balance; and examines further possibilities for energy saving by LEDs in future scenarios.Chapter 1 provides an overview of the greenhouse energy problem, using the Netherlands as an example. The chapter shows that the advent of LEDs in greenhouses has been accompanied by great expectations which have so far failed to materialize. The chapter suggests that a transparent and quantitative assessment of the potential benefits of LEDs will help adjust expectations towards this new lighting technology and promote the trust of growers. Process-based mathematical model

There is a necessity to increase the performance of food production in agriculture, this means, that precise management support in farming systems is required to reduce water use and drainage while avoiding crop stress. Management support based on model predictions is used to increase the performance of food production. However, sources of uncertainty affect the model predictions. Uncertainty in soil properties and uncertain evapotranspiration translate into uncertain predictions, and consequently in risk of performance loss. This paper presents the code and method to analyze performance uncertainty (and risk of performance loss) due to uncertain circumstances. The method is based on using the De Graaf evapotranspiration model and the EMMAN3G model, a Richards equation-based soil water model, as modules to conduct a performance uncertainty study.

Two thirds of human water use is linked to agricultural practices including crop irrigation. Furthermore, excess irrigation leads to drainage problems. For this reason, reduced irrigation strategies need to be implemented to protect water resources. However, low irrigation may lead to crop water stress. A fast and inexpensive way to predict the necessary amount of irrigation required is by a model-based approach. With this approach, it is possible to explore the relation between irrigation, crop water stress and drainage. However, parameter uncertainty can reduce prediction accuracy. Therefore, the aims of this research were: (1) to develop and test a methodology that allows the analysis of uncertainty sources in irrigation strategies (2) to identify how much irrigation can be reduced while maintaining a low risk of crop stress, and (3) to explore the influence of uncertainty in soil parameters and evapotranspiration on model predictions. Results from a realistic case considered in this study indicated that, while maintaining a low risk of crop stress (<1 %), it is possible to reduce drainage (by 88 %) and water use (22 %) for a conventional irrigation strategy. This reduction is dependent on the type of risk aversion strategy and is specific for a case scenario where variations are certain.

Background: Size of fruit is an important issue in determining yield at harvest. Even under controlled conditions, variation between fruit and trusses can be considerable. As an easy to measure indication of size, the diameter of tomatoes growing in trusses was assessed in three experiments with different number of tomatoes per truss, as well as cultivars, and also by varying the level of ions in the recirculated drain water.Results: By applying the von Bertalanffy growth model, more than 99% of the variation present could be explained by the time of fruit set for all tomatoes growing anywhere in the trusses. A linear relationship between time of fruit set and the biological shift factor, an indication of developmental age, was observed. Integrating this linear relationship in the analysis of the diameter data removed one stochastic variable (biological shift factor), effectively halving the number of parameters to be estimated.Conclusion: The results of the present study indicate that the major part of the variation present in the diameter of tomatoes growing in trusses is the result of variation in the time of fruit set of individual fruits. The position within the greenhouse (i.e. local differences in assimilates supply) exerted only a minor effect on diameter development. Accordingly, the time of fruit set largely determines fruit size. Likely, growing conditions before fruit set are crucial for final fruit size. The time of fruit set of each tomato in the truss and the local growing conditions within the greenhouse that affect assimilate supply need to be assessed accurately for a reliable size prediction.

The transition period is a critical phase in the life of dairy cows. Metabolic andinfectious disorders occur mostly in the first weeks after calving. These disorderscan be considered as critical transitions for which early-warning indicators mightbe available following the theory of resilience of biological systems. Sensor datamight be useful to notice early-warning signals like slower recovery fromperturbations, increased autocorrelations and increased variance. Sensor data(measuring activity and behaviour) and extensive reference data were collectedfor a group of 22 dairy cows during a period from 2 weeks prior to expectedparturition until 6 weeks after parturition. During this period the cows werescored daily for health status. The number of days of diminished health (DDH)were used a health measure of a cow. The correlations of the log-transformedDDH with several sensor quantities were determined. Correlations with averagevalues were significant (*) for inactive time and eating time. Correlations withvariances were significant (*) for ear temperature and number of steps.Correlations with autocorrelations were not significant. Correlations withnonperiodicity were significant for eating time (*), number of steps (**), motionindex (**) and lying time (***); where nonperiodicity was defined as the meansquared error of the correlogram with a sinusoid with a 24h cycle and anamplitude of 0.25. The high correlations before parturition of some sensor datawith nonperiodicity might be used as indicator for critical transitions afterparturition. Further research is needed to validate whether a regular life mayprevent disorders in dairy cows.

Changes in transcription factor levels, epigenetic status, splicing kinetics and mRNA degradation can each contribute to changes in the mRNA dynamics of a gene. We present a novel method to identify which of these processes is changed in cells in response to external signals or as a result of a diseased state. The method employs a mathematical model, for which the kinetics of gene regulation, splicing, elongation and mRNA degradation were estimated from experimental data of transcriptional dynamics. The time-dependent dynamics of several species of adipose differentiation-related protein (ADRP) mRNA were measured in response to ligand activation of the transcription factor peroxisome proliferator-activated receptor d (PPARd). We validated the method by monitoring the mRNA dynamics upon gene activation in the presence of a splicing inhibitor. Our mathematical model correctly identifies splicing as the inhibitor target, despite the noise in the data.

Various environmental signals integrate into a network of floral regulatory genes leading to the final decision on when to flower. Although a wealth of qualitative knowledge is available on how flowering time genes regulate each other, only a few studies incorporated this knowledge into predictive models. Such models are invaluable as they enable to investigate how various types of inputs are combined to give a quantitative readout. To investigate the effect of gene expression disturbances on flowering time, we developed a dynamic model for the regulation of flowering time in Arabidopsis thaliana. Model parameters were estimated based on expression time-courses for relevant genes, and a consistent set of flowering times for plants of various genetic backgrounds. Validation was performed by predicting changes in expression level in mutant backgrounds and comparing these predictions with independent expression data, and by comparison of predicted and experimental flowering times for several double mutants. Remarkably, the model predicts that a disturbance in a particular gene has not necessarily the largest impact on directly connected genes. For example, the model predicts that SUPPRESSOR OF OVEREXPRESSION OF CONSTANS (SOC1) mutation has a larger impact on APETALA1 (AP1), which is not directly regulated by SOC1, compared to its effect on LEAFY (LFY) which is under direct control of SOC1. This was confirmed by expression data. Another model prediction involves the importance of cooperativity in the regulation of APETALA1 (AP1) by LFY, a prediction supported by experimental evidence. Concluding, our model for flowering time gene regulation enables to address how different quantitative inputs are combined into one quantitative output, flowering time.