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residual maximum likelihood (REML) estimates of the variance components and BLUPs of the random effects in the mixed model. To investigate the genetic effects influencing resistance to fusarium wilt, the disease severity scores for the screenings were

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potential of such a model to predict compactness accurately. Elucidating the nature of compactness and compactness-influencing traits will be helpful because moving toward fruit with optimal compactness will naturally aid in reducing disease and insect

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Nondestructive estimates of fruit volume are used for yield prediction. They are also used to study the relationship between fruit expansion rate and susceptibility to diseases or physiological disorders such as fruit cracking. A model relating bell pepper (Capsicum annuum) fruit diameter and length to its volume was derived using the equation of the volume of a sphere as the starting point. The model has the following formula: VF = KD2 Lπ/6, where VF is fruit volume, K is the shape factor that varies with fruit type, D is fruit diameter, and L is fruit length. The model is simple, easy to use in the field, and may account for variations in fruit shape. Regression analyses using actual fruit volume of bell pepper measured with the water displacement method and the volume estimated using different equations showed that accuracy of the new model is comparable to that of one of the best models previously proposed. However, because the model is less complex than previous models, it is easier to use in the field.

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Overhead fogging or misting is an essential technique applied in modern greenhouses for cooling and humidifying. This technique can be used to promote yield and quality of greenhouse crops either by providing favorable environment for the plant growth or by increasing the efficiency of greenhouse pest and disease control. In this study, the effect of high-pressure overhead misting on greenhouse climate and leaf surface microclimate conditions for cucumber crops in a glass greenhouse was investigated. It was found that the temperature of the greenhouse air was lowered by 5-6 °C and relative humidity was increased by 20% to 30% during misting. The temperature of sunlit leaves was slightly reduced in the morning (2-3 °C), and leaf wetness duration was significantly extended by misting. Leaf wetness duration under misting was predominately influenced by light intensity at the leaf level and was modelled as a function of misting period and average radiation intensity. Results of this study can be used to improve the predictions of pest and disease breakout and the efficiency of their control measures. The empirical model developed in this study can be integrated with leaf surface microclimate models to correctly predict surface moisture conditions and evaporative cooling from water films at the leaf surface.

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explanatory variables.” There are three main purposes for developing and using statistical models: 1) prediction, 2) explanation, and 3) description ( Heinze et al., 2018 ; Shmueli, 2010 ). Descriptive models are meant to capture the association between

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added to the model, further enhancing the value for harvest prediction. In many plants disease identification and decision systems, the pictures, descriptions, and links between diseases and characteristics are defined and inserted for web inquiries

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). This model mainly uses functions that include maximum and minimum temperatures and solar radiation during the months of July and August. A daily risk index is calculated starting from the phenological stage of fruit set, providing prediction in real

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temperature or crop temperature control (ATC or LTC) and standard deviation. z Discussion Microclimate prediction models applicable for model-based greenhouse climate control are scarce. We therefore aimed at creating a simple microclimate

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; predicting external fruit color as affected by drought stress; and optimizing and evaluating the model efficiency by comparing the model predictions with the observations collected from two experiments conducted in two different growing environments (i

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infection centers. Hall (1984) developed a dollar spot prediction model based on air temperature and precipitation in Ontario, Canada. He referred to dollar spot as occurring in a series of “steps.” He defined a step as a point at which a decline in the

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