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A mixed model framework was defined for QTL analysis of multiple traits across multiple environments for a RIL population in pepper. Detection power for QTLs increased considerably and detailed study of QTL by environment interactions and pleiotropy was facilitated. For many agronomic crops, yield is measured simultaneously with other traits across multiple environments. The study of yield can benefit from joint analysis with other traits and relations between yield and other traits can be exploited to develop indirect selection strategies. We compare the performance of three multi-response QTL approaches based on mixed models: a multi-trait approach (MT), a multi-environment approach (ME), and a multi-trait multi-environment approach (MTME). The data come from a multi-environment experiment in pepper, for which 15 traits were measured in four environments. The approaches were compared in terms of number of QTLs detected for each trait, the explained variance, and the accuracy of prediction for the final QTL model. For the four environments together, the superior MTME approach delivered a total of 47 regions containing putative QTLs. Many of these QTLs were pleiotropic and showed quantitative QTL by environment interaction. MTME was superior to ME and MT in the number of QTLs, the explained variance and accuracy of predictions. The large number of model parameters in the MTME approach was challenging and we propose several guidelines to help obtain a stable final QTL model. The results confirmed the feasibility and strengths of novel mixed model QTL methodology to study the architecture of complex traits.

The closed greenhouse is a recent innovation in the horticulture industry. Cooling by ventilation is replaced partly (in semi-closed greenhouses) or completely (in closed greenhouses) by mechanical cooling. Excess solar energy is collected and stored to be reused to heat the greenhouse. In temperate climates, this concept combines improved crop production with energy savings. This paper presents an overview of climate, crop growth and development, and crop yield in closed and semi-closed greenhouses. The technical principles of a closed greenhouse are described and the macroclimate and microclimate arising from this are studied. The consequences of the typical growth conditions found in closed greenhouses for crop physiology and crop yield are examined. Finally, the experiences of commercial growers are presented. In temperate climates, closed greenhouses can reduce the use of fossil fuel-derived energy by 25 -35%, compared with open greenhouses. With high global radiation, the climate in closed greenhouses is characterised by high CO 2 concentrations, high air humidity, improved temperature control, and a vertical temperature gradient. An annual increase in production of 10 -20% is realistic, with reduced amounts of supplied CO 2 . The yield increase is primarily obtained through increased rates of photosynthesis due to the higher CO 2 concentrations in closed greenhouses. To introduce this innovation into practice, knowledge transfer was a key factor for its implementation and the realisation of increased production levels. Future trends will require minimising the use of fossil fuels and increasing the level of control of the production process. Closed and semi-closed greenhouses fit seamlessly into this trend as they allow for a more controlled climate and higher levels of production, combined with savings in fossil fuel use.

Models predicting growth and yield have been developed for a large number of crops. This paper describes a dynamic, mechanistic model for sweet pepper, addressing issues such as leaf area expansion, dry matter partitioning and validation.Leaf area formation and organ initiation are simulated as a function of temperature sum. Light absorption and photosynthesis are calculated for a multilayered uniform canopy. Leaf photosynthesis is calculated for the various leaf layers according to the biochemical model of Farquhar, and integrated to canopy photosynthesis. Net assimilate production is calculated as the difference between canopy gross photosynthesis and maintenance respiration. The net assimilate production is used for growth of the different plant organs and for growth respiration. INTRODUCTIONModels are powerful tools to test hypotheses, to synthesize knowledge, to describe and understand complex systems and to compare different scenarios. Models may be used in decision support systems, greenhouse climate control and prediction and planning of production.Often descriptive and explanatory models are distinguished. Descriptive models, also called statistical, regression, empirical or black-box models, reflect little or none of the mechanisms that are the cause of the behaviour of a system, whereas explanatory models consist of a quantitative description of these mechanisms and processes (Penning de Vries et al., 1989). Explanatory models contain sub-models at least one hierarchical level deeper than the response to be described, e.g., crop photosynthesis and leaf area expansion are processes one hierarchical level below crop growth. Although the explanatory crop growth models in horticulture do, to some extent, reflect physiological processes, they do not incorporate all knowledge on biochemical mechanisms at the cellular level. On the other hand, if they did, the models would be impossible to manage and use for predictions and for analysis at the crop level.Models predicting growth and yield have been developed for a large number of crops, including a few models for sweet pepper (e.g., Marcelis et al., 1998; Buwalda et