Nitrogen nutrition of Myriophyllum spicatum: variation of plant tissue nitrogen concentration with season and site in Lake Wingra

1976 ◽  
Vol 6 (2) ◽  
pp. 137-144 ◽  
Author(s):  
DALE S. NICHOLS ◽  
DENNIS R. KEENEY
Keyword(s):  
Plant Tissue ◽  
Nitrogen Nutrition ◽  
Nitrogen Concentration ◽  
Myriophyllum Spicatum ◽  
Tissue Nitrogen

scholarly journals Nitrogen Nutrition of Greenhouse Pepper. II. Effects of Nitrogen Concentration and NO3: NH4 Ratio on Growth, Transpiration, and Nutrient Uptake

HortScience ◽  
2001 ◽  
Vol 36 (7) ◽  
pp. 1252-1259 ◽  
Author(s):  
A. Bar-Tal ◽  
B. Aloni ◽  
L. Karni ◽  
R. Rosenberg
Keyword(s):  
Nutrient Uptake ◽  
Nitrogen Nutrition ◽  
Nitrogen Concentration ◽  
N Uptake ◽  
Total N ◽  
Hydroponic System ◽  
Leaf Chlorophyll ◽  
N Source ◽  
N Concentration ◽  
Canopy Nitrogen

The objective of this research was to study the effects of N concentration and N-NO3: N-NH4 ratio in the nutrient solution on growth, transpiration, and nutrient uptake of greenhouse-grown pepper in a Mediterranean climate. The experiment included five total N levels (0.25 to 14 mmol·L-1, with a constant N-NO3: N-NH4 ratio of 4) and five treatments of different N-NO3: N-NH4 ratios (0.25 to 4, with a constant N concentration of 7 mmol·L-1). Plants were grown in an aero-hydroponic system in a climate-controlled greenhouse. The optimum N concentrations for maximum stem and leaf dry matter (DM) production were in the range of 8.0 to 9.2 mmol·L-1. The optimum N-NO3: N-NH4 ratio for maximal stem DM production was 3.5. The optimum value of N concentration for total fruit DM production was 9.4 mmol·L-1. Fruit DM production increased linearly with increasing N-NO3: N-NH4 ratio in the range studied. The N concentration, but not N source, affected leaf chlorophyll content. Shorter plants with more compacted canopies were obtained as the N-NO3: N-NH4 ratio decreased. The effect of N concentration on transpiration was related to its effect on leaf weight and area, whereas the effect of a decreasing N-NO3: N-NH4 ratio in reducing transpiration probably resulted from the compacted canopy. Nitrogen uptake increased as the N concentration in the solution increased. Decreasing the N-NO3: N-NH4 ratio increased the N uptake, but sharply decreased the uptake of cations, especially Ca.

scholarly journals Nitrogen Nutrition of Greenhouse Pepper. I. Effects of Nitrogen Concentration and NO3: NH4 Ratio on Yield, Fruit Shape, and the Incidence of Blossom-end Rot in Relation to Plant Mineral Composition

HortScience ◽  
2001 ◽  
Vol 36 (7) ◽  
pp. 1244-1251 ◽  
Author(s):  
A. Bar-Tal ◽  
B. Aloni ◽  
L. Karni ◽  
J. Oserovitz ◽  
A. Hazan ◽  
...  
Keyword(s):  
Fruit Quality ◽  
Nitrogen Nutrition ◽  
Nitrogen Concentration ◽  
Fruit Yield ◽  
Bell Pepper ◽  
High Quality ◽  
Total N ◽  
Molar Ratios ◽  
N Concentration ◽  
Blossom End Rot

Blossom-end rot (BER) is one of the major physiological disorders of green-house bell pepper (Capsicum annuum L.). The objective of the present work was to study the effects of the solution N concentration and N-NO3: N-NH4 ratio on fruit yield and the incidence of BER and other fruit-quality traits of greenhouse-grown bell pepper in a Mediterranean climate. Three experiments were conducted: Expt. 1 included five total N concentrations (0.25 to 14 mmol·L-1, with a constant N-NO3: N-NH4 ratio of 4); Expt. 2 included five treatments of different NO3: NH4 molar ratios (0.25 to 4, with a constant N concentration of 7 mmol·L-1); and Expt. 3 included three treatments of different NO3: NH4 molar ratios (1.0, 3.0 and 9.0, with a constant N concentration of 7 mmol·L-1). Plants were grown in an aero-hydroponics system in Expts. 1 and 2 and in tuff medium in Expt. 3, in greenhouses in Israel. The optimal values of N concentration for total fruit yield and for high fruit quality (marketable) were 9.3 and 8.3 mmol·L-1, respectively. The total and high-quality fruit yields both increased with increasing N-NO3: N-NH4 ratio in the range studied. The total and high-quality fruit yields both decreased sharply as the NH4 concentration in the solution increased above 2 mmol·L-1. The increase in the NH4 concentration in the solution is the main cause of the suppression of Ca concentration in the leaves and fruits and the increased incidence of BER. The occurrence of flat fruits also increased with increasing NH4 concentration in the solution.

scholarly journals Leek hybrid yield potentional and relation on adaptation to light interception under nitrogen supply

Genetika ◽  
2012 ◽  
Vol 44 (3) ◽  
pp. 549-559
Author(s):  
Dubravka Savic
Keyword(s):  
Nitrogen Uptake ◽  
Dry Matter ◽  
Nitrogen Nutrition ◽  
Nitrogen Concentration ◽  
Crop Productivity ◽  
Light Interception ◽  
Nitrogen Supply ◽  
Allium Porrum ◽  
Total Nitrogen Concentration ◽  
Nitrogen Demand

We have studied the effect of nitrogen supply on growth as well as relation on adaptation to light interception of leek (Allium porrum L.,) hybrid Alita, genotype of known genetic background. During the vegetative and generative plant growth phases, besides genetic potential many factors affect their productivity. The aim was to investigate genome expression dependent on nitrogen nutrition and light interception. Nitrogen in correlation with light availability has important effect on the growth of plants and the formation of leaf area, what it is necessary for yield of dry matter. Investigation has been done in open field grown leek commercial hybrid Alita (Allium porrum L.,) to consider the way of its genotype response to correlation of light interception and nitrogen nutrition. Investigated traits are leek crop productivity, light interception and chemical analyses of plants. Leek crop productivity was determined through the dry matter production, leaf area development and light interception. Analyses of leek plants comprehended chemical determination and calculation of total nitrogen concentration, nitrogen critical concentration in dry matter, nitrogen demand and, nitrogen uptake in leek crop. Correlation among investigated parameters was assigned to comprehensive hypothetical model of growth and productivity of leek crop grown at open field.It was shown that for nitrogen uptake (Nu), nitrogen demand (ND) and total nitrogen concentration (Nt) parameters variants of mineral nutrition plays significant role (pNu=0.002; pND=0.045; pNt=0.011). Obtained results indicated that correlation of nitrogen and light interception could be used as criteria in plant breeding.

Commercial wheat fertilization based on nitrogen nutrition index and yield forecast

2020 ◽  
Author(s):  
Carmen Plaza ◽  
María Calera ◽  
Jaime Campoy ◽  
Anna Osann ◽  
Alfonso Calera ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Nitrogen Nutrition ◽  
Water Productivity ◽  
Nitrogen Concentration ◽  
Balance Model ◽  
Practical Application ◽  
Spatially Distributed ◽  
Yield Forecast ◽  
Nitrogen Nutrition Index ◽  
The Relationship

<p>This work describes the practical application on commercial wheat plots of the methodology developed and evaluated in Albacete, Spain, in the framework of the project FATIMA (http://fatima-h2020.eu/). The application considers two different methodologies for the prescription of nitrogen management prior to the flowering season, based on the diagnosis of crop nitrogen status based on nitrogen nutrition index (NNI) maps and the yield forecast spatially distributed. The NNI is the ratio between the actual nitrogen concentration (Na) over the critical nitrogen concentration (Nc) for the crop analysed (Justes et al 1997). The nitrogen uptake was determined from relationship between Nc and biomass, where biomass was estimated by a crop growth model based on the water productivity. The Na was derived from the relationship between the amount of nitrogen in the canopy, estimated from spectral vegetation index based on the Red-edge and the biomass. The knowledge about the NNI allows fertilizing at critical moments throughout the wheat campaign. The NNI maps for the analysed plots, were obtained throughout wheat development to flowering, of eight dates in the study campaign. The yield forecast is calculated through the relationship between biomass and the harvest index. The spatially distributed yield relies in the use of management zone maps (MZM) based on temporal series of remote sensing data. The MZMs were calculated for pre-flowering state to estimate yield, and capture the within-field variability of wheat production. Thus, the classical N balance model is used to calculate the N requirements at pixel scale, varying the target yield according to the MZM. The practical application was made in wheat commercial plots in the study area, analysing the performance of the proposed nitrogen fertilization strategies. The results indicated the possible optimization of the N application, maintaining or increasing the wheat productivity and reaching the higher levels of protein content in the area.</p><p>Keywords: Remote sensing, wheat, biomass, nitrogen nutrition index (NNI), fertilization.</p>

scholarly journals Nitrogen concentration in dry matter of the fifth leaf during growth of greenhouse tomato plants

2002 ◽  
Vol 20 (4) ◽  
pp. 626-629 ◽  
Author(s):  
Jorge E. Rattin ◽  
Jerônimo L. Andriolo ◽  
Márcio Witter
Keyword(s):  
Nutrient Solution ◽  
Dry Matter ◽  
Plant Density ◽  
Nitrogen Nutrition ◽  
Nitrogen Concentration ◽  
Dry Matter Accumulation ◽  
Iron Chelate ◽  
Greenhouse Tomato ◽  
N Concentration ◽  
Commercial Mixture

The nitrogen concentration in dry matter of the fifth leaf during growth of a greenhouse tomato crop was determined. Plants of hybrid Monte Carlo were grown in 4.5 L bags, using a commercial substrate, in a plant density of 3.3 plants m-2. A nutrient solution containing, in mmol L-1: KNO3, 4.0; K2SO4, 0.9; Ca(NO3)2, 3.75; KH2PO4, 1.5; MgSO4, 1.0; iron chelate 19. 10³, was used as reference. Microelements were added by a commercial mixture. The T3 treatment was equal to the reference nutrient solution, whereas in treatments T1, T2, T4 and T5 quantities of all nutrients from T3 were multiplied by 0.25, 0.50, 1.25 and 1.50, respectively. In each treatment, the volume of 1 L of nutrient solution was supplied to each plant once a week by fertigation. Periodically destructive measurements were made from anthesis to ripening of the first truss, to determine dry matter and N concentration in shoot and in fifth leaf tissues, counted from the apex to the bottom of the plant. Five dilution curves were fitted from data of N concentration in the fifth leaf and shoot dry matter accumulation during growth of plants. A general relationship was adjusted between actual N concentration in shoot (Nt) and in the fifth leaf (Nf): Nt = 1.287 Nf (R² = 0.80). This relationship could be used to estimate the N status of plants by means of a nitrogen nutrition index (NNI), from analysis of the fifth leaf sap.

Tissue nutrient content of Gracilaria spp. (Rhodophyta) and water quality along an estuarine gradient

1995 ◽  
Vol 46 (6) ◽  
pp. 975 ◽  
Author(s):  
JL Horrocks ◽  
GR Stewart ◽  
WC Dennison
Keyword(s):  
Water Quality ◽  
Water Column ◽  
Plant Tissue ◽  
Time Course ◽  
Inorganic Nitrogen ◽  
River Mouth ◽  
Nutrient Content ◽  
Moreton Bay ◽  
Tissue Nutrient Content ◽  
Tissue Nitrogen

Tissue nutrient content of Gracilaria spp. (Rhodophyta) was tested as a bioindicator of water column nutrient availability in the Logan River and southern Moreton Bay, south-eastem Queensland. Macroalgae were incubated for one to two weeks within flow-through incubation chambers suspended in the water column. Tissue nutrient content of Gracilaria spp, and water column nutrients were measured at five sites over a five-month period. Tissue nitrogen content (%N) was correlated with dissolved inorganic nitrogen (DIN) at a site 15 km upstream from the Logan River mouth (r² = 0.81), at the Logan River mouth (r² = 0.50), and at a Moreton Bay site 8 km from the Logan River mouth (r² = 0.71). Time-course analyses of water column nutrients and plant tissue content showed more significant correlations with nitrogen (N) than with phosphorus (P). Plant tissue nitrogen-to-phosphorus (N:P) molar ratios ranged between 19 and 23 whereas water column N:P ratios were between 2 and 6, suggesting low nitrogen availability relative to plant requirements and possible N limitation. In the laboratory, Gracilaria verrucosa was subjected to treatments of N, P or N + P nutrient additions. Deepening of the thallus colouration was observed after additions of N. Chlorophyll and phycoerythrin concentrations increased in treatments with N addition; however, owing to wide variability between phycoerythrin replicates, only chlorophyll increases were significant. The amino acid citrulline also increased with the addition of N and accounted for up to 16% of the total tissue N. Macroalgae may be more useful than traditional water quality sampling for integrating biologically available pulses of nutrients, especially for a limiting nutrient such as N in coastal marine ecosystems.

scholarly journals Using a Hybrid Neural Network Model DCNN–LSTM for Image-Based Nitrogen Nutrition Diagnosis in Muskmelon

Horticulturae ◽  
2021 ◽  
Vol 7 (11) ◽  
pp. 489
Author(s):  
Liying Chang ◽  
Daren Li ◽  
Muhammad Khalid Hameed ◽  
Yilu Yin ◽  
Danfeng Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Nitrogen Fertilizer ◽  
Precision Agriculture ◽  
Short Term Memory ◽  
Nitrogen Nutrition ◽  
Nitrogen Concentration ◽  
Convolution Neural Network ◽  
Deep Convolution Neural Network ◽  
Nutrition Diagnosis

In precision agriculture, the nitrogen level is significantly important for establishing phenotype, quality and yield of crops. It cannot be achieved in the future without appropriate nitrogen fertilizer application. Moreover, a convenient and real-time advance technology for nitrogen nutrition diagnosis of crops is a prerequisite for an efficient and reasonable nitrogen-fertilizer management system. With the development of research on plant phenotype and artificial intelligence technology in agriculture, deep learning has demonstrated a great potential in agriculture for recognizing nondestructive nitrogen nutrition diagnosis in plants by automation and high throughput at a low cost. To build a nitrogen nutrient-diagnosis model, muskmelons were cultivated under different nitrogen levels in a greenhouse. The digital images of canopy leaves and the environmental factors (light and temperature) during the growth period of muskmelons were tracked and analyzed. The nitrogen concentrations of the plants were measured, we successfully constructed and trained machine-learning- and deep-learning models based on the traditional backpropagation neural network (BPNN), the emerging convolution neural network (CNN), the deep convolution neural network (DCNN) and the long short-term memory (LSTM) for the nitrogen nutrition diagnosis of muskmelon. The adjusted determination coefficient (R2) and mean square error (MSE) between the predicted values and measured values of nitrogen concentration were adopted to evaluate the models’ accuracy. The values were R2 = 0.567 and MSE = 0.429 for BPNN model; R2 = 0.376 and MSE = 0.628 for CNN model; R2 = 0.686 and MSE = 0.355 for deep convolution neural network (DCNN) model; and R2 = 0.904 and MSE = 0.123 for the hybrid model DCNN–LSTM. Therefore, DCNN–LSTM shows the highest accuracy in predicting the nitrogen content of muskmelon. Our findings highlight a base for achieving a convenient, precise and intelligent diagnosis of nitrogen nutrition in muskmelon.

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