Derivation of a Dynamic Model of the Kinetics of Nitrogen Uptake Throughout the Growth of Lettuce: Calibration and Validation

Zhang, Kefeng; Burns, Ian G.; Turner, Mary K.

doi:10.1080/01904160802208345

Cited by 13 publications

(6 citation statements)

References 39 publications

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“…Nitrogen uptake rate is influenced by many factors such as nutrient concentrations, light intensity, humidity, temperature and ambient carbon dioxide concentration (Tiaz and Zeiger, 2002;Zhang et al, 2008). NO 3 uptake rate by plant is higher than other nutrient uptake rates, such as Ca 2+ , Mg 2+ and K + (Seawright et al, 1998).…”

Section: Nitrogen Uptake By Plantsmentioning

confidence: 99%

“…NO 3 uptake rate by plant is higher than other nutrient uptake rates, such as Ca 2+ , Mg 2+ and K + (Seawright et al, 1998). NO 3 uptake kinetic in plant was studied using stable nitrogen isotope ( 15 N) and the results showed that increase in NH 4 + concentration resulted in higher uptake rate by the plant and Michaelis-Menten kinetic was the best-fit kinetic model (Inselsbacher et al, 2013;Zhang et al, 2008). However, the preference for NH 4 + and NO 3 depends on their concentrations, growing stages and plant's genetic factors (Fink and Feller, 1998); thus making it difficult to model nitrogen uptake by plant.…”

Section: Nitrogen Uptake By Plantsmentioning

confidence: 99%

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Nitrogen transformations in aquaponic systems: A review

Wongkiew

Chandran

et al. 2017

Aquacultural Engineering

234

139

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In recent years, aquaponic systems have gained significant popularity as soilless agriculture systems for organic fruits and vegetables production with concomitant remediation of aquaculture effluent. Aquaponics is a potential sustainable food production system that integrates aquaculture with hydroponics in which nitrogen-rich effluent from the fish production is utilized for plant growth. Because nitrogen is one of the most important inputs in an aquaponic system, it is critical to investigate the nitrogen transformations in the system for enhanced recovery of resources. Since studies on nitrogen transformations and nitrogen utilization efficiency (NUE) in aquaponic systems have been very limited, this review critically examines the important fates of nitrogen from input to outputs (e.g., ammonia nitrogen generation, nitrification, nitrate assimilation and nitrogen loss) to improve NUE in aquaponic systems. Various factors affecting the nitrogen transformations are also discussed. Furthermore, an example of nitrogen imbalance between nitrate uptake and nitrate generation rates in an aquaponic system was demonstrated. This review aims to advance our current understanding of nitrogen transformations and outlines future research needs in aquaponic systems, a sustainable model for efficient water and nutrient managements, and food production.

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Section: Nitrogen Uptake By Plantsmentioning

confidence: 99%

Section: Nitrogen Uptake By Plantsmentioning

confidence: 99%

Nitrogen transformations in aquaponic systems: A review

Wongkiew

Chandran

et al. 2017

Aquacultural Engineering

234

139

View full text Add to dashboard Cite

show abstract

“…Moreover, dynamic monitoring of uptake can further mitigate risk, but models alone tend to be insufficient. Nitrate accumulation in leaves is another cause for control in CEA which not only impacts nutrient use efficiency, but also has adverse effects to human health if consumed. , Nitrate accumulation has been shown to be affected both by substrate and atmospheric conditions illustrating the need for more comprehensive models in DCEA. Like energy dynamics, nutrient and toxin dynamics may benefit from ML due to the abundance of unobservable variables.…”

Section: Resultsmentioning

confidence: 99%

Dynamically Controlled Environment Agriculture: Integrating Machine Learning and Mechanistic and Physiological Models for Sustainable Food Cultivation

et al. 2021

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Inefficiencies and imprecise input control in agriculture have caused devastating consequences to ecosystems. Urban controlled environment agriculture (CEA) is a proposed approach to mitigate the impacts of cultivation, but precise control of inputs (i.e., nutrient, water, etc.) is limited by the ability to monitor dynamic conditions. Current mechanistic and physiological plant growth models (MPMs) have not yet been unified and have uncovered knowledge gaps of the complex interplay among control variables. Moreover, because of their specificity, MPMs are of limited utility when extended to additional plant species or environmental conditions. Simultaneously, although machine learning (ML) can uncover latent interactions across conditions, phenotyping bottlenecks have hindered successful application. To bridge these gaps, we propose an integrative approach whereby MPMs are used to construct the foundations of ML algorithms, reducing data requirements and costs, and ML is used to elucidate parameters and causal inference in MPM. This review highlights research about control and automation in CEA, synthesizing literature into a framework whereby ML, MPM, and biofeedback inform what we call dynamically controlled environment agriculture (DCEA). We highlight synergistic characteristics of MPM and ML to illustrate that a DCEA framework could contribute to urban resilience, human health, and optimized productivity and nutritional content.

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“…As shoot growth recovered, both plant demand for N (Clement et al 1978;Clarkson et al 1992;Fitter 1997;Jeuffroy et al 2002) and the supply of carbohydrates to the root (Clement et al 1978;Mengel & Viro 1978;Lestienne et al 2006) would have increased and could have contributed to increased N uptake. As uptake is dependent upon respiratory energy (van der Werf et al 1988;Lambers et al 2008;Zhang et al 2008), cold acclimation could help to maintain N uptake through up-regulation of respiration within the plant (Atkin & Tjoelker 2003). The response may, however, vary between species due to differing respiratory costs of ion uptake (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…; Zhang et al . ), cold acclimation could help to maintain N uptake through up‐regulation of respiration within the plant (Atkin & Tjoelker ). The response may, however, vary between species due to differing respiratory costs of ion uptake (e.g.…”

Section: Discussionmentioning

confidence: 99%

Source of nitrogen associated with recovery of relative growth rate in Arabidopsis thaliana acclimated to sustained cold treatment

Atkinson

Sherlock

Atkin

2014

Plant Cell & Environment

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To determine (1) whether acclimation of carbon metabolism to low temperatures results in recovery of the relative growth rate (RGR) of plants in the cold and (2) the source of N underpinning cold acclimation in Arabidopsis thaliana, we supplied plants with a nutrient solution labelled with (15) N and subjected them to a temperature shift (from 23 to 5 °C). Whole-plant RGR of cold-treated plants was initially less than 30% of that of warm-maintained control plants. After 14 d, new leaves with a cold-acclimated phenotype emerged, with the RGR of cold-treated plants increasing by 50%; there was an associated recovery of root RGR and doubling of the net assimilation rate (NAR). The development of new tissues in the cold was supported initially by re-allocation of internal sources of N. In the longer term, the majority (80%) of N in the new leaves was derived from the external solution. Hence, both the nutrient status of the plant and the current availability of N from external sources are important in determining recovery of growth at low temperature. Collectively, our results reveal that both increased N use efficiency and increases in nitrogen content per se play a role in the recovery of carbon metabolism in the cold.

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Derivation of a Dynamic Model of the Kinetics of Nitrogen Uptake Throughout the Growth of Lettuce: Calibration and Validation

Cited by 13 publications

References 39 publications

Nitrogen transformations in aquaponic systems: A review

Nitrogen transformations in aquaponic systems: A review

Dynamically Controlled Environment Agriculture: Integrating Machine Learning and Mechanistic and Physiological Models for Sustainable Food Cultivation

Source of nitrogen associated with recovery of relative growth rate in Arabidopsis thaliana acclimated to sustained cold treatment

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