Effects of Salinity and Relative Humidity on Two Melon Cultivars Differing in Salt Tolerance

An, Ping; Inanaga, Shinobu; Lee, Alexander; Li, X.J.; Ali, Muharam; Matsui, Takaaki; Sugimoto, Yukihiro

doi:10.1023/a:1016273818726

Cited by 19 publications

(3 citation statements)

References 37 publications

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“…Negative effects of salinity on crop growth and/or fruit yield were alleviated in melon (An et al, 2002) and tomato (Li et al, 2001) when the plants were grown under high relative humidity, which reduced E compared to standard conditions.…”

Section: Crop Growth and Productionmentioning

confidence: 99%

Modelling transpiration of greenhouse gerbera (Gerbera jamesonii H. Bolus) grown in substrate with saline water in a Mediterranean climate

Carmassi

Bacci

Bronzini

et al. 2013

Scientia Horticulturae

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Gerbera plants were grown in semi-closed rockwool culture under greenhouse conditions in different seasons in a Mediterranean climate. The plants were irrigated using either fresh (FW; 1.0 mol m−3NaCl)or moderately saline (SW; 9.0 mol m−3NaCl) water. In autumn, NaCl concentration did not influence significantly plant growth, flower production and transpiration (E), which instead were reduced in springin the plants irrigated with SW. In both seasons, water salinity did not affect leaf stomatal resistance (rl),which was determined by the inversion of the Penman–Monteith (PM) equation or measured with a diffusion porometer. The PM formula and two regression equations were calibrated and validated for estimating the hourly rate of daytime transpiration (Ed); a regression model was also fit to nocturnal transpiration (En). Regression models predicted Edas a function of vapour pressure deficit (VPD) and/or the radiation intercepted by the canopy. Leaf area index (LAI), which is required by all the equations, was modelled as function of crop thermal time (i.e. growing degree days). The PM model predicted Ed using a constant value of rl. Model calibration and validation were performed using independent data sets. The irrigation with FW or SW did not require a different calibration of transpiration models. Both PM formula and regression equations provided accurate estimates of Ed; fitted equations explained between 80% and96% of the variance in measured Ed. A linear regression of En against (LAI·VPD) accounted for 92% of measured En

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Section: Crop Growth and Productionmentioning

confidence: 99%

Modelling transpiration of greenhouse gerbera (Gerbera jamesonii H. Bolus) grown in substrate with saline water in a Mediterranean climate

Carmassi

Bacci

Bronzini

et al. 2013

Scientia Horticulturae

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“…In addition, among fungal taxa at genus level, the major population at the harvesting stage was Fusarium. Yang et al [46] reported that the muskmelon root exudate functions to stimulate Fusarium oxysporum spore germination and mycelial growth. Plant age as well as plant cultivars, and environmental factors are important factors that affect the composition and quantity of root exudates [47,48].…”

Section: Discussionmentioning

confidence: 99%

Temporal Variability in the Rhizosphere Bacterial and Fungal Community Structure in the Melon Crop Grown in a Closed Hydroponic System

Lin

Mukhtar

et al. 2021

Agronomy

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Microbes can establish a pathogenetic or symbiotic relationship with plants in soil and aquatic ecosystems. Although change in bacterial and fungal community in soil and their interaction with plants have been widely studied, little is known about their community structure in hydroponic systems across plant growth stages under different nutrient treatments. This study used next-generation sequencing analysis to assess the temporal changes in melon rhizosphere bacterial and fungal community structure across six different nutrient treatments. We found significant changes in the microbial community composition (especially for bacteria) between growth stages (R = 0.25–0.63, p < 0.01) than nutrient treatments. Proteobacteria dominated the bacterial community at the phylum level across melon growth stages (59.8% ± 16.1%). The genera Chryseobacterium, Pseudomonas, and Massilia dominated the rhizosphere in the flowering and pollination stage, while Brevibacillius showed the highest relative abundance in the harvesting stage. However, the rhizosphere was dominated by uncultured fungal taxa, likely due to the application of fungicides (Ridomil MZ). Further, linear regression analysis revealed a weak influence of bacterial community structure on melon yield and quality, while fruit weight and quality moderately responded to Mg and K deficiency. Nevertheless, the relative abundance of bacterial genus Chryseobacterium in the vegetative stage showed a strong correlation with fruit weight (R2 = 0.75, p < 0.05), while genera Brevibacillus, Lysobacter, and Bosea in late growth stages strongly correlated with fruit sweetness. Overall, temporal variability in the microbial (especially bacterial) community structure exceeds the variability between nutrient treatments for the given range of nutrient gradient while having little influence on melon yield.

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“…Higher levels can disrupt transpiration, hindering water and nutrient transport, reducing fruit size and sweetness, and increasing the risk of fungal diseases and pests [36]. Lower humidity levels can cause excessive evaporation, stressing plants and negatively impacting growth and fruit quality [40]. Carbon dioxide, essential for photosynthesis and particularly crucial for fruit development, should be above 1200 ppm to increase fruit size, number, and sugar content [35].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Powered Forecasting of Climate Conditions in Smart Greenhouse Containing Netted Melons

Jeon,

Kim,

Hwang

et al. 2024

Agronomy

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The greenhouse environment plays a crucial role in providing favorable conditions for crop growth, significantly improving their quality and yield. Accurate prediction of greenhouse environmental factors is essential for their effective control. Although artificial intelligence technologies for predicting greenhouse environments have been researched recently, there are limitations in applying these to general greenhouse environments due to computing resources or issues with interpretability. Moreover, research on environmental prediction models specifically for melon greenhouses is also lacking. In this study, machine learning models based on MLR (Multiple Linear Regression), SVM (Support Vector Machine), ANN (Artificial Neural Network), and XGBoost were developed to predict the internal temperature, relative humidity, and CO2 conditions of melon greenhouses 30 min in advance. The XGBoost model demonstrated high accuracy and stability, with an R2 value of up to 0.9929 and an RPD (Residual Predictive Deviation) of 11.8464. Furthermore, the analysis of the XGBoost model’s feature importance and decision trees revealed that the model learned the complex relationships and impacts among greenhouse environmental factors. In conclusion, this study successfully developed a predictive model for a greenhouse environment for melon cultivation. The model developed in this study can facilitate an understanding and efficient management of the greenhouse environment, contributing to improvements in crop yield and quality.

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Effects of Salinity and Relative Humidity on Two Melon Cultivars Differing in Salt Tolerance

Cited by 19 publications

References 37 publications

Modelling transpiration of greenhouse gerbera (Gerbera jamesonii H. Bolus) grown in substrate with saline water in a Mediterranean climate

Modelling transpiration of greenhouse gerbera (Gerbera jamesonii H. Bolus) grown in substrate with saline water in a Mediterranean climate

Temporal Variability in the Rhizosphere Bacterial and Fungal Community Structure in the Melon Crop Grown in a Closed Hydroponic System

Machine Learning-Powered Forecasting of Climate Conditions in Smart Greenhouse Containing Netted Melons

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