A dynamic root simulation model in response to soil moisture heterogeneity

Li, Songyang; Gao, Jixun; Zhu, Quanyin; Zeng, Lingqiu; Ji, Liu

doi:10.1016/j.matcom.2014.11.030

Cited by 9 publications

(6 citation statements)

References 33 publications

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“…28 Besides, the root architecture is important for plants to access soil resources, and morphological and physiological adaptation of the root system under stress conditions may result in the continuation of nutrient absorption and utilization. 29 Most studies on root uptake have assumed that the entire root is equally active. 30 It is recognized that the deleterious effects of salt stress were not limited to some physiological traits, but they can affect other biochemical processes namely primary and secondary metabolisms.…”

Section: Discussionmentioning

confidence: 99%

Identification of the NaCl-responsive metabolites in Citrus roots: A lipidomic and volatomic signature

Lamine

Gargouri

Mliki

2020

Plant Signaling & Behavior

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It is known that the first osmotic phase affects the growth rates of roots immediately upon addition of salt; thus, dissecting metabolites profiling provides an opportunity to throw light into the basis of plant tolerance by searching for altered signatures that may be associated with tolerance at this organ. This study examined the influence of salt treatment on fatty acid composition and chemical composition of the essential oil of C. aurantium roots. Results proved that, under salt treatment, an increase of double bond index and linoleic desaturation ratio was pointed out. On the other hand, the reduction of saturated fatty acids was spotted. Such treatment also induced quantitative changes in the chemical composition of the essential oils from C. aurantium roots and increased markedly the rates of monoterpenes, while the sesquiterpenes decreased significantly. Both primary and secondary metabolites were found to be significantly salt responsive, including one fatty acid (palmitoleic acid) and six volatiles (E-2-dodecenal, tetradecanal, γ-Elemene, trans-caryophyllene, α-Terpinene and germacrene D). Plasticity at the metabolic level may allow Citrus plants to acclimatize their metabolic ranges in response to changing environmental conditions.

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Section: Discussionmentioning

confidence: 99%

Identification of the NaCl-responsive metabolites in Citrus roots: A lipidomic and volatomic signature

Lamine

Gargouri

Mliki

2020

Plant Signaling & Behavior

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“…Jerusalem artichoke has strong salt tolerance that is largely related to the ecological and biological characteristics of the root system 30 . The root architecture is important for plants to access soil resources, and morphological and physiological adaptation of the root system under stress conditions may result in continuation of nutrient absorption and utilization 31 . Roots of Jerusalem artichoke (NY-1) were mainly distributed in the 0–15 cm depth layer ( Fig.…”

Section: Discussionmentioning

confidence: 99%

Salinity altered root distribution and increased diversity of bacterial communities in the rhizosphere soil of Jerusalem artichoke

Yang

Long

et al. 2016

Sci Rep

111

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The interaction between roots and bacterial communities in halophytic species is poorly understood. Here, we used Jerusalem artichoke cultivar Nanyu 1 (NY-1) to characterise root distribution patterns and determine diversity and abundance of bacteria in the rhizosphere soil under variable salinity. Root growth was not inhibited within the salinity range 1.2 to 1.9 g salt/kg, but roots were mainly confined to 0–20 cm soil layer vertically and 0–30 cm horizontally from the plant centre. Root concentrations of K+, Na+, Mg2+ and particularly Ca2+ were relatively high under salinity stress. High salinity stress decreased soil invertase and catalase activity. Using a next-generation, Illumina-based sequencing approach, we determined higher diversity of bacteria in the rhizosphere soil at high than low salinity. More than 15,500 valid reads were obtained, and Proteobacteria, Acidobacteria, Bacteroidetes and Actinobacteria predominated in all samples, accounting for >80% of the reads. On a genus level, 636 genera were common to the low and high salinity treatments at 0–5 cm and 5–10 cm depth. The abundance of Steroidobacter and Sphingomonas was significantly decreased by increasing salinity. Higher Shannon and Chao 1 indices with increasing severity of salt stress indicated that high salt stress increased diversity in the bacterial communities.

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“…According to Li et al [20], the nodes should be as small as possible to enhance connection and coverage. e proposed method is compared to other existing methods to perform the similar task.…”

Section: Review Of Existing Irrigation Systemsmentioning

confidence: 99%

Intrusion Detection Using Machine Learning for Risk Mitigation in IoT-Enabled Smart Irrigation in Smart Farming

Raghuvanshi¹,

Singh

Sajja

et al. 2022

Journal of Food Quality

107

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The majority of countries rely largely on agriculture for employment. Irrigation accounts for a sizable amount of water use. Crop irrigation is an important step in crop yield prediction. Field harvesting is very reliant on human supervision and experience. It is critical to safeguard the field’s water supply. The shortage of fresh water is a major challenge for the world, and the situation will deteriorate further in the next years. As a result of the aforementioned challenges, smart irrigation and precision farming are the only viable solutions. Only with the emergence of the Internet of Things and machine learning have smart irrigation and precision agriculture become economically viable. Increased efficiency, expense optimization, energy maximization, forecasting, and general public convenience are all benefits of the Internet of Things (IoT). As systems and data processing become more diversified, security issues arise. Security and privacy concerns are impeding the growth of the Internet of Things. This article establishes a framework for detecting and classifying intrusions into IoT networks used in agriculture. Security and privacy are major concerns not only in agriculture-related IoT networks but in all applications of the Internet of Things as well. In this framework, the NSL KDD data set is used as an input data set. In the preprocessing of the NSL-KDD data set, first all symbolic features are converted to numeric features. Feature extraction is performed using principal component analysis. Then, machine learning algorithms such as support vector machine, linear regression, and random forest are used to classify preprocessed data set. Performance comparisons of machine learning algorithms are evaluated on the basis of accuracy, precision, and recall parameters.

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A dynamic root simulation model in response to soil moisture heterogeneity

Cited by 9 publications

References 33 publications

Identification of the NaCl-responsive metabolites in Citrus roots: A lipidomic and volatomic signature

Identification of the NaCl-responsive metabolites in Citrus roots: A lipidomic and volatomic signature

Salinity altered root distribution and increased diversity of bacterial communities in the rhizosphere soil of Jerusalem artichoke

Intrusion Detection Using Machine Learning for Risk Mitigation in IoT-Enabled Smart Irrigation in Smart Farming

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