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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

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.