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the most decisive parameters in consumer satisfaction ( Hilaire et al., 2000 ; Vaysse et al., 2000 ). To take quality into consideration in crop models, the refractometric index is preferable to sugar content to which it is well correlated ( Hilaire

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objectives of this work are to: 1) use recent literature to update temperature-based crop model parameters of the CROPGRO-Tomato model; and 2) recalibrate and evaluate the model for more accurate simulation of temperature effects on crop development, daily DM

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yield, which permits integration of the knowledge in different disciplines on the potato crop yield levels using the measured physiological, ecological, agrometeorological, and agronomical parameters of the plant. The input variables of the model can be

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periods was reported for peaches ( Marra et al., 2002 ). Recent testing of multiple models on multiple tree fruit crops concluded using thermal time to predict plant growth is more precise than calendar days ( Darbyshire et al., 2014 ). Fruit, or nut

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, Food, and Fisheries 2020). These models will help cherry growers better understand the susceptibility of their crops to cold damage throughout the fall to spring season and can be used to assist with cold damage management decision-making. Furthermore

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further parameters required to run the crop model. Hence, they were not suitable for the initial development or evaluation of the quality model and considered an independent dataset for a preliminary evaluation of the seasonal analysis only

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.1% when the α value was 1.26. This parameter was often applied in the field crops ( Szilagyi, 2014 ; Tongwane et al., 2017 ). Although the Priestley-Taylor model slightly overestimated the measured ET o , considering its robustness and simplicity, this

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independent variables considered by the model were global radiation (Rg) and air temperature in the greenhouse, crop age and water uptake, as well as a guide ion. Brun and Chazelle (1996) described the (NO 3 – -N) uptake kinetic on roses with a multiple

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can improve the productivity of greenhouse crops, but capital costs for climate control systems and operating costs for these systems to control heaters, lights, vents, fans, and/or supplemental CO 2 systems to maintain environmental parameters at

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accepting a new variable in the model was the increase of R 2 and the lowest correlation coefficient between variables ( Canavos, 1988 ). Results and Discussion Climatic and growth parameters during crop growth. Average values of T, Rg

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