Nitrogen Nutrition Diagnosis Research Articles

Heat Stress Recovery of Chlorophyll Fluorescence in Tomato (Lycopersicon esculentum Mill.) Leaves through Nitrogen Levels

To study the impact of nitrogen application on the photosynthetic structure and photosystem activity of tomato (Lycopersicon esculentum Mill.) leaves during the recovery stage after heat stress, the OJIP curve and JIP parameters were determined through a control experiment in an artificial climate chamber. The tomato variety was “Jinfen No. 1”. Four day/night temperature levels (25 °C/15 °C as control CKT; 30 °C/20 °C, lightly high-temperature LHT; 35 °C/25 °C, moderate high-temperature MHT; 40 °C/30 °C, severe high-temperature SHT) were set for a duration of 7 days. Five nitrogen supply levels (N1–N5: 0, 1.3, 1.95, 2.6 and 3.75 g/plant, respectively; 2.6 g/plant is the recommended nitrogen application rate, CKTN4) were applied. The results showed that the O, K, J, I and P phases on the chlorophyll a fluorescence curve were significantly affected by different nitrogen treatments in heat stress recovery. Compared with CKT, with the increase in nitrogen supply, the fluorescence intensity of SHTN2-SHTN5 treatment increased significantly at P, I and J phases, while that of MHTN1-MHTN4 treatment decreased. The fluorescence intensity of SHTN5 and SHTN3 increased by 13.27% and 10.10% in the P phase, 13.52% and 12.1% in the I phase and 20.16% and 26.18% in the J phase, respectively. There were highly significant differences (p < 0.01) in the impact of high temperatures and nitrogen levels on the fluorescence parameters. On the 1st day after short-term heat stress, N had no significant effect on Fv/FM, Fv, Fo and FM; however, their interaction was significant (p < 0.05). On the 8th day, there were no significant interaction effects between HT and N for Fv/Fo, ABS/RC and DIo/RC. Fv/Fo proved to be sensitive to the application of both high temperatures and nitrogen. Under all five nitrogen applications, temperature played a significant role in increasing DIo/RC, especially for N2 and N3. The results indicated that decreasing the nitrogen application under SHT resulted in a higher number of active RCs and an increased value of specific energy flux (ABS/RC, TRo/RC and DIo/RC), indicating the enhanced ability of RC to reduce plastoquinone. The study provides a reference for the diagnosis of nitrogen nutrition under high-temperature stress using chlorophyll fluorescence methods.

Nutrient Diagnostics and Biochemical Indicators of Oat Grain Quality Predicted by Leaf Juice Amino Acid Concentration

In order to find out the relationship between the level of nitrogen nutrition of oat plants of the Yakov variety and the content of amino acids in leaf juice, as well as the possibility of using this indicator to assess the content and composition of proteins, the activity of amylases, catalases and peroxidases in ripe grain and the activity of theseenzymes in germinating grain, a field experiment was carried out at the Field Experimental Station of Russian State Agrarian University – Moscow Timiryazev Agricultural Academy, on sod-podzolic medium loamy soil. In the field experiment, variants with different levels of nitrogen nutrition of oat plants that had passed post-harvest ripening were tudied. Ammonium nitrate, double superphosphate, and potassium chloride were applied in advance. As a result, it was found that as the level of nitrogen nutrition of plants increased, the concentration of amino acids in the juice of oat leaves at the stage of formation of the first stem node decreased, which was closely correlated with the nitrogen doses. These data indicate the possibility of using this indicator for the diagnosis of nitrogen nutrition in oat plants. The studies also revealed a reliable correlation between the content of amino acids in leaf juice and the concentration of proteins soluble in water and solutions of 10% KCl, 0.2% NaOH, as well as the activity of acid (pH 5.5), neutral (pH 7) and alkaline (pH 8) isoenzymes of α-amylase and acid isoforms of β-amylase in ripe grain. In addition, the amino acid content of leaf juice was found to be significantly correlated with the activity of acidic α-amylase, acidic and neutral β-amylase in germinated grain. The results obtained demonstrate the possibility of predicting the level of protein accumulation in oat grains. It is also possible to predict the protein composition, activity of α- and β-amylase, catalase, and peroxidase isoenzymes from the content of amino acids in leaf juice in the phase of the first stem node formation. A reliable correlation was also found for the activity of acid and neutral isoenzymes of α-amylase, catalase, and peroxidase in ripe and germinated oat grain.

Nitrogen nutrition diagnosis and estimation of seed cotton yield based on critical nitrogen concentration for drip irrigation cotton in Southern Xinjiang, China.

The stability and adaptability of the critical nitrogen concentration in drip-irrigated cotton fields in Southern Xinjiang were investigated by measuring the nitrogen uptake (Nuptake), nitrogen nutrition index (NNI), and accumulated nitrogen deficit (Nand). A two-year field study was conducted using five nitrogen levels (0, 75, 150, 300, and 450 kg·hm-2), with cotton cultivars 'Xinluzhong55' and 'Xinluzhong78' as the test materials. We analyzed the effects of nitrogen addition on plant dry matter (PDM) and critical nitrogen concentration dilution curves (PNCc). The Nuptake, NNI, and Nand models as well as a model of the relationships between NNI, Nand, and the relative seed cotton yield (RY) were established based on the PNCc for both cultivars. The results showed that the PNCc, maximum nitrogen concentration (PNCmax), and minimum nitrogen concentration (PNCmin) dilution models in drip-irrigated cotton showed power function relationships with plant dry matter (PDM), with determination coefficients (R2) of 0.905 and 0.960, 0.919 and 0.950, 0.934 and 0.969 for Xinluzhong55 and Xinluzhong78, respectively. Model validation with independent experimental data showed that the critical nitrogen concentrations in cotton grown under drip-irrigated conditions were simulated satisfactorily. The Xinluzhong55 and Xinluzhong78 1:1 line R2, root mean square error (RMSE) and relative estimation error (RE) of the simulated and observed critical nitrogen concentrations were 0.987, 0.051 g·kg-1, 1.95% and 0.984, 0.044 g·kg-1, 1.91%, respectively, showing good stability and adaptability between the two years. According to the Nuptake, NNI, and Nand model, we concluded that 300 kg·hm-2 was the optimum level of nitrogen application in drip-irrigated cotton in Southern Xinjiang. Based on the fitting results of nitrogen level and seed cotton yield, the theoretical appropriate nitrogen applications for Xinluzhong55 and Xinluzhong78 were 322.7 and 336.4 kg·hm-2, respectively. The R2 of NNI, Nand and RY in different growth stages were greater than 0.900, the NNI, Nand and RY estimation model of seed cotton yield based on 1:1 lines of R2, RMSE, and RE were 0.899-0.989, 0.05-0.13, and 4.1%-9.9% and 0.902-0.981, 0.04-0.12, and 5.1%-9.5%, all of which were highly significant. These results could provide new directions for the evaluation and diagnosis of optimum nitrogen application levels and nitrogen status, as well as the estimation of seed cotton yield in drip-irrigated cotton in Southern Xinjiang.

Nitrogen nutrition diagnosis for cotton under mulched drip irrigation using unmanned aerial vehicle multispectral images

Remote sensing has been increasingly used for precision nitrogen management to assess the plant nitrogen status in a spatial and real-time manner. The nitrogen nutrition index (NNI) can quantitatively describe the nitrogen status of crops. Nevertheless, the NNI diagnosis for cotton with unmanned aerial vehicle (UAV) multispectral images has not been evaluated yet. This study aimed to evaluate the performance of three machine learning models, i.e., support vector machine (SVM), back propagation neural network (BPNN), and extreme gradient boosting (XGB) for predicting canopy nitrogen weight and NNI of cotton over the whole growing season from UAV images. The results indicated that the models performed better when the top 15 vegetation indices were used as input variables based on their correlation ranking with nitrogen weight and NNI. The XGB model performed the best among the three models in predicting nitrogen weight. The prediction accuracy of nitrogen weight at the upper half-leaf level (R2=0.89, RMSE=0.68 g m–2, RE=14.62% for calibration and R2=0.83, RMSE=1.08 g m–2, RE=19.71% for validation) was much better than that at the all-leaf level (R2=0.73, RMSE=2.20 g m–2, RE=26.70% for calibration and R2=0.70, RMSE=2.48 g m–2, RE=31.49% for validation) and at the plant level (R2=0.66, RMSE=4.46 g m–2, RE=30.96% for calibration and R2=0.63, RMSE=3.69 g m–2, RE=24.81% for validation). Similarly, the XGB model (R2=0.65, RMSE=0.09, RE=8.59% for calibration and R2=0.63, RMSE=0.09, RE=8.87% for validation) also outperformed the SVM model (R2=0.62, RMSE=0.10, RE=7.92% for calibration and R2=0.60, RMSE=0.09, RE=8.03% for validation) and BPNN model (R2=0.64, RMSE=0.09, RE=9.24% for calibration and R2=0.62, RMSE=0.09, RE=8.38% for validation) in predicting NNI. The NNI predictive map generated from the optimal XGB model can intuitively diagnose the spatial distribution and dynamics of nitrogen nutrition in cotton fields, which can help farmers implement precise cotton nitrogen management in a timely and accurate manner.

Monitoring of Nitrogen Transport in Pear Trees Based on Ground Hyperspectral Remote Sensing and Digital Image Information

To study nitrogen transport monitoring in pear trees based on ground-based hyperspectral remote sensing and digital image information. First, based on ground hyperspectral remote sensing and digital image information, combined with the characteristics of visible and near-infrared spectral data and digital image data, a beet nitrogen nutrition diagnosis model based on visible and near-infrared spectral and digital image information was established. The results showed that under water and nitrogen management conditions, the marked nitrogen use efficiency at soil profiles 15, 45, and 75 cm was 28.2%, 22.3%, and 16.3%, respectively. From the analysis of soil and plant nitrogen measured in this experiment, it can be seen that nitrogen nitrate fertilizer should be properly applied to pear trees in the future.

A model of plant and ear nitrogen for the diagnosis of nitrogen nutrition of malting barley to coordinate the relationship between grain yield and quality

Scientific and reasonable application of nitrogen fertiliser is an important agronomic measure to achieve both a high yield and a high quality of malting barley, and determining the appropriate nitrogen concentration is important for optimising the two. To this end, we constructed critical concentration curves for plant and ear nitrogen of malting barley as a tool for diagnosing nitrogen nutrition. Two varieties of barley, Zhepi 33 and Yangnongpi 8, were used as experimental materials, and five nitrogen application levels (ranging from 0 to 300 kg·ha–1) were established in three field experiments. Plant dry matter first increased and then stabilised and the nitrogen concentration increased continuously with an increase in nitrogen application rate. Based on analyses of the relationship between dry matter and nitrogen concentration, dilution models of critical nitrogen concentration in the plant (Np) and ear (Ne) of malting barley were established, and were expressed as power function equations: Np = 3.879PDM–0.444 (R2 = 0.9705) and Ne = 2.198EDM–0.152 (R2 = 0.7968). Next, nitrogen nutrition indexes were constructed to identify nitrogen-limiting and non-limiting treatments. To ensure an optimal grain protein concentration of 10–12%, the Ne index and the Np index were required to be 0.897–1.061 and 0.699–0.992 at the heading stage, respectively. The results provide a theoretical foundation for the diagnosis and regulation of nitrogen nutrition in malting barley and have great significance for the production of high-quality and high-yielding malting barley.

内蒙古地区甜菜临界氮浓度稀释模型的构建及应用

<p id="C3">Sugar beet is one of the two major sugar crops in China. However, both nitrogen sufficiency and deficiency will affect the yield and sugar content in sugar beet, and excessive nitrogen will cause environmental risks. Judging nitrogen nutrition of sugar beet in a reasonable range is of great significance for ensuring the yield, quality and reducing environmental risks of sugar beet. The critical nitrogen concentration dilution curve is the basis of crop nitrogen nutrition diagnosis. The objective of this present study was establishing the dilution curve model of the critical nitrogen concentration of sugar beet in China and to diagnose the nitrogen nutrition of sugar beet by using the corresponding nitrogen nutrient index. Four experiments with different N rates for two cultivars of sugar beet were conducted in Inner Mongolia from 2017 to 2018. The above-ground biomass and under-ground tubers were sampled and their nitrogen concentration was determined at seedling, leaf cluster growth, tuber bulking, sugar accumulation, and harvest stages. The total plant biomass and nitrogen concentration were calculated. Based on the calculation, the critical nitrogen concentration dilution model and the corresponding nitrogen nutrient index were established. The results indicated that the negative power relationship between the above-ground biomass and nitrogen concentration was observed. The determination coefficient (<italic>R</italic> ²) for the relationship was 0.45 on average. The <italic>R</italic> ² for the negative power relationship between total plant biomass and nitrogen concentration was 0.94 on average, which was higher than the relationship between the above-ground biomass and nitrogen concentration. It was more reasonable to establish a critical nitrogen concentration dilution model based on the total plant biomass and nitrogen concentration, which was less affected by the cultivars. The <italic>R</italic> ² for the integrated critical nitrogen concentration dilution curve of sugar beet varieties KWS9167 and KWS1676 reached 0.94. The constructed nitrogen nutrition model can be used to estimate the plant nitrogen status in sugar beet. The critical nitrogen concentration dilution model for the main sugar beet varieties in Northern China was <italic>N</italic>_c = 4.23<italic>W</italic> ^-0.49. The optimized nitrogen rate of KWS9167 and KWS1676 based on the model was about 160 kg hm ^-2 and 180 kg hm ^-2, respectively.

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

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.

Simulation of the critical nitrogen dilution curve in Jiangxi double-cropped rice region based on leaf dry matter weight.

In order to investigate the feasibility of using rice critical nitrogen concentration as a nitrogen nutrition diagnosis index, a two-year positioning field gradient experiment using four rice varieties and four nitrogen levels (0, 75, 150, 225 kg·ha-1 for early rice; 0, 90, 180, 270 kg·ha-1 for late rice) was conducted for early and late rice. The critical dilution curves (Nc%) of the double-cropped rice based on leaf dry matter (LDM) were constructed and verified using the field data. Two critical nitrogen dilution curves and nitrogen nutrition indexes (NNI) of rice LDM were constructed for early rice [Nc% = 2.66LDM-0.79, R2 = 0.88, NNI ranged between 0.29-1.74, and the average normalized root mean square error (n-RMSE = 19.35%)] and late rice [Nc% = 7.46LDM-1.42, R2 = 0.91, NNI was between 0.55-1.53, and the average (n-RMSE = 15.14%)]. The relationship between NNI and relative yield was a quadratic polynomial equation and suggested that the optimum nitrogen application rate for early rice was sightly smaller than 150 kg·ha-1, and that for late rice was about 180 kg·ha-1. The developed critical nitrogen concentration dilution curves, based on leaf dry matter, were able to diagnose nitrogen nutrition in the double-cropped rice region.

Construction of a critical nitrogen dilution curve for maize in Southwest China

There is an urgent need for suitable nitrogen nutrition models for Southwest China, which take into account nutritional differences at the cultivar level, to provide scientific guidance for cultivar-specific fertilizer applications during maize production. In this study, the nitrogen-efficient maize cultivar Zhenghong 311 and the nitrogen-inefficient maize cultivar Xianyu 508 were used in a three-year field experiment and a 2-year field pot experiment with nitrogen application rates ranging from 0 to 450 kg·hm−2 to construct a critical nitrogen dilution curve model for each maize cultivar. The usefulness of this model to diagnose nitrogen status and evaluate maize fertilization needs was subsequently analyzed. We found that the critical nitrogen concentration in maize aboveground tissues was a power function of the biomass, described by the equations Nc = 26.126 W−0.292 and Nc = 25.826 W−0.302 for ZH 311 and XY 508 cultivars, respectively. The fitting degree of these equations was significant or highly significant, demonstrating the suitability of these models to diagnose N deficiency and fertilization needs in maize plants grown in the hilly areas of central Sichuan. A very significant linear positive correlation between the nitrogen nutrient index (NNI) and nitrogen concentration in the aboveground tissues was detected. Based on this, we calculated the nitrogen concentration (Nt) for an NNI equal to 1 at different maize growth stages in both cultivars and observed that the Nt value can be used as a reference index for nitrogen nutrition diagnosis. Additionally, we found a highly significant quadratic convex function relationship between the NNI (y) and the nitrogen fertilizer level (x). The following regression equations were derived for these maize cultivars with the data obtained from each growth period along five consecutive years (2011–2015): yZH 311 = − 0.000005x2 + 0.003074x + 0.553206 (R2 = 0.5432**) and yXY 508 = − 0.000004x2 + 0.002914x + 0.512555 (R2 = 0.6279**). For an NNI value equal to 1, the nitrogen application level required was 224.07 kg·hm−2 for ZH 311 and 283.01 kg·hm−2 for XY 508, indicating that the suitable application rate for the nitrogen-efficient cultivar is lower than that for the nitrogen-inefficient cultivar. Our experimental data reinforce the concept that selecting nitrogen-efficient maize cultivars is an effective technical measure to reduce nitrogen input needs and increase nitrogen use efficiency during maize production.

Diagnosis of Nitrogen Nutrition in Sugar Beet Based on the Characteristics of Scanned Leaf Images

Sugar beet is an important economic crop in Northwest China. In this area, efficient use of nitrogen (N) fertilizer has become crucial due to decreased profits associated with both under- and oversupply relative to sugar beet requirements. Thus, fast and non-destruction diagnostic tools for estimating plant N status have an important role in reducing N inputs while maintaining sugar beet yield and qualify. The objective of our study was to quantify leaf color characterization of sugar beet with an inexpensive scanner and establish the relationship with yield, leaf nitrogen content (LNC), plant total nitrogen content (PTNC), chlorophyll content (CC), soil nitrate nitrogen content (SNNC) and soil plant analysis development (SPAD) readings in sugar beet. In 2017 and 2018, field experiments were conducted with five N treatments ranging from 0 to 180 kg N ha−1. The main results showed the following: The SPAD readings (SPR) and CC exhibited a significant or highly significant correlation (maximum = 0.70, P < 0.01), both of which reflected well the N nutrient status of the entire plant. Furtherly, a detailed association analysis revealed that there was a close relationship (maximum = − 0.63, P < 0.01) of LNC, SPR, PTNC, CC and yield with leaf color parameter Red/Blue (R/B), which was recommended as leaf color parameters for N diagnosis in sugar beet. In addition, based on the distribution of R/B value under different N rate, the yield was low with greater R/B value than 1.36 indicating an insufficient N supply, and with the R/B value was lower than 1.36, the theoretical yield reached its peak indicating an adequate supply of N fertilizer. To summarize, compared to the complicated and expensive of hyperspectral and other remote sensing technologies, scanned leaf image (SLI) processing technique was a simple, inexpensive and reliable method of determining sugar beet N status that has potential as a diagnostic tool for determining crop N requirement.

Critical nitrogen dilution curves and nitrogen nutrition diagnosis of spring maize under different precipitation patterns in Weibei dryland

<p indent=0mm>The excessive nitrogen (N) applications, large rainfall variations and poor water-nitrogen couplings are main problems to efficient nitrogen fertilizer uses in spring maize production of Weibei dryland. Critical nitrogen dilution curves under different rainfall scenarios in Weibei dryland were constructed in this study to analyze the feasibilities of diagnosing and evaluating nitrogen nutritional conditions in terms of nitrogen nutrition index (NNI), which would provide a theoretical basis for reasonable nitrogen fertilizations application of dryland maize in response to different rainfalls. The experiment design using Zhengdan 958 (ZD958) and Shaandan 8806 (SD8806) as tested materials was five treatments level, N applied at 0(N0), 75(N1), 150(N2), 270(N3), and 360(N4) kg hm^–2 in 2016 and 2017, and at 0(N0), 90(N0), 180(N2), 270(N3), and 360(N4) kg hm^–2 in 2018 and 2019, respectively. It was rainy at the ear stage and dry at the grain stage in 2016 and 2018, whereas dry at the ear stage and rainy at the grain stage in 2017 and 2019. Critical nitrogen dilution curve models for spring maize with two precipitation patterns were constructed and verified using the data collected in the four-year position nitrogen fertilization experiment. The results showed that: (1) increased nitrogen fertilizer application significantly increased aboveground biomass and plant N concentrations, and there were significantly different among different treatments. Both critical nitrogen concentrations (<italic>N</italic>_c) and aboveground biomass conformed the exponential relations with the two precipitation patterns, but there were differences between the parameters of the models for these relations (a. Rainy at the ear stage: <italic>N</italic>_c = 35.98DM^–0.35; b. Dry at the ear stage: <italic>N</italic>_c = 35.04DM^–0.23). The relatively stable model had a linear correlation between the fitted and actual plant N concentrations, which shown that the RMSE and <italic>n</italic>–RMSE were 1.03 and 5.75% at the ear stage over the rainy years and 1.53 and 6.78% at the ear stage in the dry years, respectively. (2) at the different growth stages, NNI were increased with the increased application, and there were differences in the optimal nitrogen application under different precipitation conditions. The optimum N rate in the form of basal fertilizers was <sc>150–180 kg</sc> hm^–2, and in the form of top dressing fertilizers was <sc>45–75 kg</sc> hm^–2 at the ear stage in the rainy years. The nitrogen nutrition index (NNI) was significantly correlated with relative nitrogen uptake (RN_upt), as were relative aboveground biomass (RDW) and relative yield (RY). When the NNI was 1.02 at the ear stage in the rainy years, the maximum RY was 0.95; and when the NNI was 1.08 at the ear stage in the dry years, the maximum RY was 0.92. The critical nitrogen dilution curve model and nitrogen nutrition index model constructed in this study were able to accurately predict nitrogen nutrition conditions from jointing stage to maturity stage under the two precipitation patterns of spring maize. They would provide an important guidance for nitrogen diagnosis and fertilization application in maize growing stage.

Establishment and Application of Critical Nitrogen Dilution Curve for Rice Based on Leaf Dry Matter

In order to investigate the feasibility of using rice leaf critical nitrogen concentration as a nitrogen nutrition diagnosis index, a three-year positioning experiment with large-spike rice cultivar (Wuyoudao4) and multiple-spike rice cultivar (Songjing9) under five nitrogen levels (0, 60, 120, 180, and 240 kg·ha−1) was conducted. A critical nitrogen dilution curve and a nitrogen nutrition index (NNI) of rice leaf dry matter were constructed for Wuyoudao4 (Nc = 1.96LDM−0.56, R2 = 0.87, NNI was between 0.6–1.26, and Normalized Root Mean Square Error (n-RMSE) = 13.07%) and Songjing9 (Nc = 1.99LDM−0.44, R2 = 0.94, NNI was between 0.64–1.29, and n-RMSE = 15.89%). The relationship between dry matter and nitrogen concentration of rice leaves was a negative power function, and the model had good stability over the three years. The developed critical nitrogen concentration dilution curve, based on leaf dry matter, was able to diagnose nitrogen nutrition in rice efficiently. The model established in this study could be used to directly regulate and control the nitrogen nutrition of rice leaves.

Diagnosis of Nitrogen Nutrition in Rice Leaves Influenced by Potassium Levels.

Evaluation of nitrogen (N) status by leaf color is a kind of classic nutritional diagnostic method. However, the color of leaves is influenced not only by N, but also by other nutrients such as potassium (K). Two-year field trials with a factorial combination of N and K were conducted to investigate the effects of different N and K rates on soil plant analysis development (SPAD) readings and leaf N, K, magnesium (Mg), and chlorophyll concentrations. Visual inspections in leaf greenness revealed darker green leaves with increasing N rates, while paler green leaves with increasing K rates. Data showed that SPAD readings, chlorophyll, N and Mg concentrations, and the chloroplast area increased significantly with raising N rates, while declined sharply with the increase in K rates due to the antagonistic relationships between K+ and NH4 + as well as Mg2+. It was also probable that the increase in K promoted the growth of leaves and diluted their N and Mg concentrations. The paler leaf appearance resulting from the application of K may overestimate the actual demand for N in the diagnosis of rice N status. The strong antagonistic relationships between K+, NH4 +, and Mg2+ should be considered in rice production and fertilization.

Critical nitrogen dilution curve and nitrogen nutrition diagnosis of maize with drip irrigation

Critical nitrogen dilution curve and nitrogen nutrition diagnosis of maize with drip irrigation

Nitrogen nutrition diagnosis for olive trees grown in super-intensive cropping systems

The results of a 4-year experiment combining irrigation, N, and K showed that to maintain productivity and tree growth it is necessary to attain a higher leaf N status than current diagnosis standards. To sustain high oil yield and quality and to avoid excessive vigor, a critical N concentration was established for olive trees of 1.94% for leaves sampled in July. A negative relationship was obtained between N leaf concentration and some quality parameters. Above 1.94% N, total phenolic content and oxidative stability fell below 220 mg kg−1 and 10 hr, respectively. In addition, above this N leaf concentration other factors including the alternate bearing index (ABI) and vegetative growth distorted productive and qualitative responses. Moreover, the differential productive response of trees related to alternate fruit bearing status implies that the interpretation of leaf nitrogen content can be more accurate when ABI suggests an ON tree status.

Advances in plant nutrition diagnosis based on remote sensing and computer application

Hyperspectral remote sensing, visible light remote sensing and canopy color analysis have been widely concerned for rapid diagnosis of crop growth and nutrition. They are expected to develop into potential nondestructive diagnostic techniques for crop nitrogen nutrition in the new era on account of the advantages of stable, rapid, convenient and nondestructive results, together with the good correlation between canopy color parameter NRI and plant nitrogen nutrition index and yield satisfying the demand for nondestructive diagnosis of nitrogen nutrition, and their feasibility to monitor plant growth status and nitrogen nutrition level in real time and quickly. At present, with the rapid development of remote sensing satellite, unmanned aerial vehicles remote sensing and Internet of things, remote sensing will be more and more widely used in plant nutrition diagnosis.

Remote Diagnostics Efficiency in Case of Plants Nitrogen Nutrition

The article summarizes foreign and own research on the development of photometric methods for the diagnosis of nitrogen nutrition of plants. Previous own research has shown that ground and aerospace surveys can be successfully used to diagnose nitrogen nutrition of plants. However, despite significant advantages over traditional chemical diagnostics, both terrestrial and space indications of the availability of agricultural crops with nitrogen have certain limitations: terrestrial - by the scale of coverage, aerospace - by time parameters. At the same time, it was shown that the use of low-flying aircraft, in particular helicopters, is most suitable for photometric inspection of crops, although its application has a significant limitation - at the cost of diagnostic work In this regard, the most promising for the rapid diagnosis of nitrogen nutrition of plants is the use of unmanned aerial vehicles - UAV, equipped with appropriate photometric equipment. Testing the effectiveness of remote, using unmanned aerial vehicles, diagnosis of nitrogen nutrition test culture - winter wheat in the field experiment with increasing doses of nitrogen fertilizers showed a close dependence of the UAV instrument readings, namely the vegetative index (NDVI), with the supply of plants with nitrogen. Efficiency and technological simplicity, as well as the economic component of the use of unmanned aerial vehicles for monitoring crops of crops, essentially open a new page in solving the problem of nitrogen nutrient nutrition in plants.

Abstracts of Nippon Dojo-Hiryogaku Zasshi

Abstracts of Nippon Dojo-Hiryogaku Zasshi

Effect of nitrogen on vertical distribution of canopy nitrogen and chlorophyll relative value (SPAD value) of summer maize in sub-humid areas

以半湿润地区土垫旱耕人为土为供试土壤, 采用田间试验, 研究了不同施氮水平下夏玉米(Zea mays L.)拔节期、灌浆期和成熟期3个生育期冠层叶片氮素、叶绿素相对值(SPAD值)的垂直分布规律及其差异; 同时对各层叶片含氮量、SPAD值与施氮量进行相关分析。结果表明, 在各生育期不同叶层叶片含氮量按上、中、下层顺序呈明显递减规律, 从全生育期不同施氮处理看, 上层比中层增加6.64%, 中层比下层增加5.18%。随施氮量增加, 中上层叶片含氮量差异增大, 中下层叶片含氮量差异减小。冠层内叶片SPAD值垂直分布规律与叶片含氮量分布规律相类似。相关分析表明, 全生育期各层叶片SPAD值与叶片含氮量呈极显著线性正相关关系(R=0.503^**)。进一步分析发现, 各层叶片SPAD值、叶片含氮量与施氮量的相关性以上层叶关系最为密切, 揭示了夏玉米氮素营养诊断的较好叶片是上层叶位。