Leaf Nitrogen Status Research Articles

Identifying optimal ground feature classification and assessing leaf nitrogen status based on UAV multispectral images in an apple orchard

Identifying optimal ground feature classification and assessing leaf nitrogen status based on UAV multispectral images in an apple orchard

Application of hyperspectral technology for leaf function monitoring and nitrogen nutrient diagnosis in soybean (Glycine max L.) production systems on the Loess Plateau of China

Farmland mulching and nitrogen application are the main agricultural management measures to improve soybean (Glycine max L.) production in dryland agriculture on the Loess Plateau. However, the interaction effects of various farmland mulching materials and nitrogen application rates on soybean leaf functionality and nitrogen nutrition status remains contradictory and inconclusive, and effective and rapid diagnostic of the leaf functionality and nitrogen status are still not well-understood. Therefore, the aim of this study is twofold: 1) to investigate the interactive effects of different mulching methods and nitrogen application levels on soybean leaf growth, physiological traits, and final grain yield throughout the entire growth period; 2) to establish a chlorophyll content monitoring model based on spectral parameters and a diagnostic model for soybean nitrogen status, and to assess the effectiveness of the latter in accurately diagnosing soybean nitrogen requirements. A two-year (2021 and 2022) field experiment, which consisted of four nitrogen levels (0, 60, 120 and 180 kg N ha−1) and four mulching strategies (NM: no mulching, SM: straw mulching, FM: film mulching, SFM: straw and film mulching), was conducted in the soybean growing seasons. Subsequently, nitrogen dilution curves were established based on chlorophyll content and the optimal spectral index to compute corresponding nitrogen nutrition index (NNI) values. The results indicated that: 1) FM, in comparison to SM and SFM, had the ability to delay leaf senescence, increase chlorophyll content, and improve physiological growth parameters, thereby increasing seed yield; 2) the application of 120 kg N ha−1 stood out, boosting soybean photosynthetic productivity and providing a solid foundation for high soybean yields; 3) the Random forest (RF) model had the highest accuracy in chlorophyll content estimation (R2 =0.854, RMSE=2.765 and MRE=5.758 % in validation set); and 4) both the nitrogen dilution curves constructed based on chlorophyll content and the optimal spectral index proved to be reasonably accurate in quantitatively diagnosing the nitrogen status of soybeans. Specifically, N0 and N180 indicated sustained nitrogen deficiency and excess, respectively, while N60 and N120 effectively met soybean's nitrogen requirements. In summary, the FMN120 effectively stabilized seed yield by regulating key functional traits in soybean leaves. This study provides a theoretical basis for the application of precision agriculture and remote sensing technology in monitoring and diagnosing plant nitrogen nutrition in soybean production.

Genome-wide association studies for phenological and agronomic traits in mungbean (Vigna radiata L. Wilczek).

Mungbean (Vigna radiata L. Wilczek) is one of the important warm-season food legumes, contributing substantially to nutritional security and environmental sustainability. The genetic complexity of yield-associated agronomic traits in mungbean is not well understood. To dissect the genetic basis of phenological and agronomic traits, we evaluated 153 diverse mungbean genotypes for two phenological (days to heading and days to maturity) and eight agronomic traits (leaf nitrogen status using SPAD, plant height, number of primary branches, pod length, number of pods per plant, seeds per pod, 100-seed weight, and yield per plant) under two environmental conditions. A wide array of phenotypic variability was apparent among the studied genotypes for all the studied traits. The broad sense of heritability of traits ranged from 0.31 to 0.95 and 0.21 to 0.94 at the Delhi and Ludhiana locations, respectively. A total of 55,634 genome-wide single nucleotide polymorphisms (SNPs) were obtained by the genotyping-by-sequencing method, of which 15,926 SNPs were retained for genome-wide association studies (GWAS). GWAS with Bayesian information and linkage-disequilibrium iteratively nested keyway (BLINK) model identified 50 SNPs significantly associated with phenological and agronomic traits. In total, 12 SNPs were found to be significantly associated with phenological traits across environments, explaining 7%-18.5% of phenotypic variability, and 38 SNPs were significantly associated with agronomic traits, explaining 4.7%-27.6% of the phenotypic variability. The maximum number of SNPs (15) were located on chromosome 1, followed by seven SNPs each on chromosomes 2 and 8. The BLAST search identified 19 putative candidate genes that were involved in light signaling, nitrogen responses, phosphorus (P) transport and remobilization, photosynthesis, respiration, metabolic pathways, and regulating growth and development. Digital expression analysis of 19 genes revealed significantly higher expression of 12 genes, viz. VRADI01G08170, VRADI11G09170, VRADI02G00450, VRADI01G00700, VRADI07G14240, VRADI03G06030, VRADI02G14230, VRADI08G01540, VRADI09G02590, VRADI08G00110, VRADI02G14240, and VRADI02G00430 in the roots, cotyledons, seeds, leaves, shoot apical meristems, and flowers. The identified SNPs and putative candidate genes provide valuable genetic information for fostering genomic studies and marker-assisted breeding programs that improve yield and agronomic traits in mungbean.

A spectral decomposition method for estimating the leaf nitrogen status of maize by UAV-based hyperspectral imaging

Leaf nitrogen status plays a crucial role in characterizing maize nutrient activity, which ultimately affects both the photosynthetic efficiency and yield formation of maize. For this reason, hyperspectral imaging technology based on unmanned aerial vehicle (UAV) has emerged as a popular tool for estimating crop phenotypic traits. Canopy structure and leaf nutrition together determine the crop canopy spectrum. Efficient separation of the spectral information sensitive to the leaf nitrogen status from the canopy spectrum is considered of utmost importance for improving the estimation accuracy of maize leaf nitrogen status. Along these lines, the main goal of this work was to develop a canopy spectral decomposition method to reduce the interference of other traits on leaf nitrogen concentration (LNC) and leaf nitrogen density (LND) estimation using UAV-based hyperspectral images. First, the weights of the leaf nitrogen status, aboveground biomass (AGB), and leaf area index (LAI) contributing to the canopy spectrum were calculated by using the entropy weight method. Then, the sensitive bands of LNC and LND before and after spectral decomposition were selected by implementing the successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithm. Finally, the reflectance of all bands and sensitive bands was compared to estimate maize LNC and LND using partial least squares regression (PLSR), Gaussian process regression (GPR), support vector regression (SVR), and random forest regression (RFR). These models were systematically tested using independent samples. From the acquired results, it was demonstrated that the correlation coefficient (r) between LNC and each band increased after spectral decomposition compared to the correlation before spectral decomposition. The r between the band reflectance in the near infrared region and LNC or LND after spectral decomposition was significantly higher than that before spectral decomposition. In addition, the sensitive bands of maize leaf nitrogen status after spectral decomposition were around 470 nm, 538 nm, 638 nm, 682 nm, 710 nm, 734 nm, and 830 nm. Regardless of using the reflectance of all bands or sensitive bands, the four estimation models of LNC and LND after spectral decomposition performed better than those before spectral decomposition. The estimation models of LNC and LND constructed by CARS-SVR can successfully reproduce the estimation accuracies of the models constructed by using all-bands-SVR, with R2 on the testing set of 0.68. Theresultshighlight that spectral decomposition is an effective method to significantly improve the estimation accuracy of maize leaf nitrogen status using UAV hyperspectral images, thus effectively reducing the interference of canopy structure traits (AGB and LAI).

Characteristics of Picea neoveitchii tree growth in mountain areas of central China: insights from isotopic compositions and satellite-derived indices

ABSTRACT Leaf nitrogen (N) status and stable isotope ratios of carbon (δ 13C) and nitrogen (δ 15N) were used to study environmental factors that control mountain individuals of Picea neoveitchii trees, a coniferous species endemic and endangered in China. From May to September 2016, we carried out observations at four different altitude locations extending southeast of Daba Mountain in western Hubei Province. Needle-shaped leaf δ 13C was positively correlated with needle N and C content calculated from the needle area (Narea and Carea content), needle δ 15N, needle mass, and leaf mass per area (LMA), respectively. Needle δ 15N was also positively correlated with monthly temperature and precipitation for the current month and last month. The seasonal normalised difference vegetation index (NDVI) was highest in June at the lowest altitude and August at the highest altitude. We found that N availability as an important driving factor for tree growth is controlled by surface soil temperature, while in summer, air temperatures above 23 °C exceed the physiological threshold of trees and limit the growth of trees. We concluded that the negative effect of higher temperature on tree growth is greater than the positive effect of higher nitrogen.

Projected Elevated [CO2] and Warming Result in Overestimation of SPAD-Based Rice Leaf Nitrogen Status for Nitrogen Management

Nitrogen (N) has a unique place in agricultural systems with large requirements. To achieve optimal nitrogen management that meets the needs of agricultural systems without causing potential environmental risks, it is of great significance to increase N use efficiency (NUE) in agricultural systems. A chlorophyll meter, for example, the SPAD-502, can provide a simple, nondestructive, and quick method for monitoring leaf N status and NUE. However, the SPAD-based crop leaf’s N status varies greatly due to environmental factors such as CO2 concentration ([CO2]) or temperature variations. In this study, we conducted [CO2] (ambient and enriched up to 500 μmol moL1) and temperature (ambient and increased by 1.5~2.0 °C) controlled experiments from 2015 to 2017 and in 2020 in two Free-Air CO2 Enrichment (FACE) sites. Leaf characters (SPAD readings, chlorophyll a + b, N content, etc.) of seven rice cultivars were measured in this four year experiment. Here, we provide evidence that SPAD readings are significantly linearly correlated with rice leaf chlorophyll a + b content (chl a + b) and N content, while the relationships are profoundly affected by elevated [CO2] and warming. Under elevated [CO2] treatment (E), the relationship between chl a + b content and N content remains unchanged, but SPAD readings and chl a + b content show a significant difference to those under ambient (A) treatment, which distorts the SPAD-based N monitoring. Under warming (T), and combined elevated [CO2] and warming (ET) treatments, both of the relationships between SPAD and leaf a + b content and between leaf a + b content and N content show a significant difference to those under A treatment. To deal with this issue under the background of global climate change dominated by warming and elevated [CO2] in the future, we need to increase the SPAD reading’s threshold value by at least 5% to adjust for applying N fertilizer within the rice cropping system by mid-century.

Leaf-nitrogen status affects grain yield formation through modification of spike differentiation in maize

• A moderate nitrogen rate ensures appropriate nitrogen content in leaves. • An appropriate nitrogen content in leaves affects grain yield formation by adjusting spike differentiation. • The leaf-N status influences spike differentiation through N accumulation and transport. Spike differentiation plays a pivotal role in maize ( Zea mays L.) yield formation. However, it is unclear if leaf-N status affects yield formation by modifying spike differentiation. To understand how the spike differentiation is affected by leaf N content, as affected by the N rate, a field experiment was conducted. The experiment consisted of four N rates: N0 (0 kg N ha –1 ), N1 (129 kg N ha 1 ), N2 (184.5 kg N ha −1 ), and N3 (300 kg N ha −1 ). At zero (N0) or low (N1) N rate, N stress significantly reduced the leaf N content of summer maize, which in turn reduced the N accumulation and N transfer from leaves to spike during spike differentiation, compared to the moderate N rate (N2), especially at the tassel-floret and ear-spikelet differentiation stage (FT-SE). So the reproductive growth was delayed by 2–4 days under N0 and N1, abortive florets of the ear were increased by 28.2–166.2 %, and the anthesis-silking interval was lengthened by 2–4 days, compared with N2, which reduced the kernel set and subsequently the silk and grain number per ear. Appropriate N rate (N2) could increase N metabolism-related enzymatic activities and increase the content of glutamine (Gln) and asparagine (Asn) in leaves, which in turn increased N transfer to spike to promote spike differentiation and decrease abortive florets. Finally, the silk and kernel set under N2 were increased, which resulted in an increased in grain number per ear and yield by 3.5–27.4 % and 13.1–41.8 %, respectively, compared with N0 or N1. However, the excessive N rate (N3) demonstrated a threshold, which no longer promoted spike differentiation. Moreover, leaves had an N-absorption peak at the FT-SE, and the N content positively correlated with the total numbers of silks and florets per ear. The efficient absorption of N at the FT-SE was beneficial to increase floret fertility. Collectively, a moderate N fertilizer rate (around 185 kg N ha −1 ) assured appropriate N accumulation in leaves and N transfer to spike, improved spike development and increased floret fertility, grain number and yield.

Chlorophyll meter‐based leaf nitrogen status to manage nitrogen in tropical potato production

AbstractPotato (Solanum tuberosum L.) is a N intensive crop, and meeting its requirements with N fertilization is the primary practice to improve N recovery and achieve suitable tuber yield. A 3‐site‐year (SY) study was conducted to assess soil plant analysis development (SPAD)‐502 chlorophyll meter efficacy for providing potato leaf real‐time N status to adjust N timing and rate using nitrogen sufficiency index (NSI) thresholds of 90 or 95%. We evaluated effects of in‐season SPAD‐based N managements, as well as a reference with non‐limiting N application, a fixed‐timing (planting and hilling) conventional N fertilization, and a zero‐N control on crop N uptake, tuber yield, and N‐use efficiency of potato cultivar Agata grown in tropical clay soils. Tuber yields were similar in both SPAD‐based managements. Under no intensive rainfall events after N applications, SPAD‐based managements reduced N applications by 38–63% and resulted in comparable tuber set, bulking, and yield relative to conventional N fertilization. Additionally, SPAD‐based management at a NSI threshold of 90% resulted in greater potato N‐uptake efficiency and tuber yield per unit of N applied. SPAD‐502 sensor was efficient for detecting plant N status when environmental conditions were more conducive for potato production and optimized N management by reducing application rates. However, with less favorable temperature and solar radiation for potato cultivation, and with intensive rainfall events following N application, SPAD readings did not guide to a proper N fertilization and resulted in reduced tuber yield. Therefore, under such conditions, a more accurate method for detecting plant N status should be used.

Smart paddy field monitoring system using deep learning and IoT

Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.

Tomato Domestication Attenuated Responsiveness to a Beneficial Soil Microbe for Plant Growth Promotion and Induction of Systemic Resistance to Foliar Pathogens.

Crop domestication events followed by targeted breeding practices have been pivotal for improvement of desirable traits and to adapt cultivars to local environments. Domestication also resulted in a strong reduction in genetic diversity among modern cultivars compared to their wild relatives, though the effect this could have on tripartite relationships between plants, belowground beneficial microbes and aboveground pathogens remains undetermined. We quantified plant growth performance, basal resistance and induced systemic resistance (ISR) by Trichoderma harzianum, a beneficial soil microbe against Botrytis cinerea, a necrotrophic fungus and Phytophthora infestans, a hemi-biotrophic oomycete, in 25 diverse tomato genotypes. Wild tomato related species, tomato landraces and modern commercial cultivars that were conventionally or organically bred, together, representing a domestication gradient were evaluated. Relationships between basal and ISR, plant physiological status and phenolic compounds were quantified to identify potential mechanisms. Trichoderma enhanced shoot and root biomass and ISR to both pathogens in a genotype specific manner. Moreover, improvements in plant performance in response to Trichoderma gradually decreased along the domestication gradient. Wild relatives and landraces were more responsive to Trichoderma, resulting in greater suppression of foliar pathogens than modern cultivars. Photosynthetic rate and stomatal conductance of some tomato genotypes were improved by Trichoderma treatment whereas leaf nitrogen status of the majority of tomato genotypes were not altered. There was a negative relationship between basal resistance and induced resistance for both diseases, and a positive correlation between Trichoderma-ISR to B. cinerea and enhanced total flavonoid contents. These findings suggest that domestication and breeding practices have altered plant responsiveness to beneficial soil microbes. Further studies are needed to decipher the molecular mechanisms underlying the differential promotion of plant growth and resistance among genotypes, and identify molecular markers to integrate selection for responsiveness into future breeding programs.

Low-Cost Multispectral Sensor Array for Determining Leaf Nitrogen Status

A crop’s health can be determined by its leaf nutrient status; more precisely, leaf nitrogen (N) level, is a critical indicator that carries a lot of worthwhile nutrient information for classifying the plant’s health. However, the existing non-invasive techniques are expensive and bulky. The aim of this study is to develop a low-cost, quick-read multi-spectral sensor array to predict N level in leaves non-invasively. The proposed sensor module has been developed using two reflectance-based multi-spectral sensors (visible and near-infrared (NIR)). In addition, the proposed device can capture the reflectance data at 12 different wavelengths (six for each sensor). We conducted the experiment on canola leaves in a controlled greenhouse environment as well as in the field. In the greenhouse experiment, spectral data were collected from 87 leaves of 24 canola plants, subjected to varying levels of N fertilization. Later, 42 canola cultivars were subjected to low and high nitrogen levels in the field experiment. The k-nearest neighbors (KNN) algorithm was employed to model the reflectance data. The trained model shows an average accuracy of 88.4% on the test set for the greenhouse experiment and 79.2% for the field experiment. Overall, the result concludes that the proposed cost-effective sensing system can be viable in determining leaf nitrogen status.

Changes in yield components, morphological, physiological and fruit quality traits in processing tomato cultivated in Italy since the 1930’s

Yield of processing tomato (Solanum lycopersicum L.) increased (∼ 50%) in Italy since the 1930’s. The aim of this work was to assess the changes in yield components associated with morphological, physiological and fruit quality traits in processing tomato cultivars cropped in Italy from the mid-1930s until nowadays, introduced by breeding in six representative cultivars. Marketable yield showed an increase of 0.6% per year of release since the 1930’s. The highest marketable yield was obtained in modern cultivars due to a higher harvest index, fruit number and the ratio between ripe fruit and total fruit in comparison with the old ones. However, no single trait drove the highest marketable yield in modern cultivars. In fact, both morphological (smaller plant height and leaf area index) and physiological (accelerated plant senescence, higher leaf nitrogen status, and lower potential plant water and chlorophyll contents) traits contributed to increase marketable yield in modern cultivars. Moreover, total plant dry weight (shoot + fruit) of a single plant decreased, whereas its total fruit fresh weight and fruit dry matter content were stable and not correlated with the year of release, thus suggesting that a higher sink strength and homogeneity of fruit ripening were also involved in the highest marketable yield showed by modern cultivars. A great effort of breeders was done in the improvement of important fruit quality traits required by Italian canning industries. Fruit colour and Brix yield were positively correlated with the year of release, while viscosity and total carotenoids were negatively correlated with the year of release. However, no improvement was achieved for important traits such as soluble solids content, fruit dry weight and total fruit yield, which instead should be considered in the future breeding programmes, to improve both yield and quality of processing tomato.

Intraspecific variation in photosynthetic nitrogen-use efficiency is positively related to photosynthetic rate in rice (Oryza sativa L.) plants

To explore the natural variation in leaf photosynthesis and investigate its relations with leaf nitrogen (N) status, 121 rice cultivars were pot-grown and their leaf photosynthetic rate (PN) and N content were measured on the flag leaves at heading stage before anthesis. The results showed that there were substantial natural variations in PN, leaf N content based on leaf area and leaf mass, and photosynthetic N-use efficiency (PNUE). PN ranged between 15.4 and 32.6 μmol m-2 s-1; PN and PNUE of four widely grown cultivars in China were significantly lower than PN and PNUE of the cultivar Nancay PA, which possessed the highest values for PN and PNUE. Moreover, PNUE was positively related to PN across cultivars, the high PNUE was related to high stomatal conductance, high transpiration, and low leaf mass per area. We concluded that intraspecific variation in PNUE is positively related to PN in rice plants.

Potential of UAV-Based Active Sensing for Monitoring Rice Leaf Nitrogen Status.

Unmanned aerial vehicle (UAV) based active canopy sensors can serve as a promising sensing solution for the estimation of crop nitrogen (N) status with great applicability and flexibility. This study was endeavored to determine the feasibility of UAV-based active sensing to monitor the leaf N status of rice (Oryza sativa L.) and to examine the transferability of handheld-based predictive models to UAV-based active sensing. In this 3-year multi-locational study, varied N-rates (0–405 kg N ha−1) field experiments were conducted using five rice varieties. Plant samples and sensing data were collected at critical growth stages for growth analysis and monitoring. The portable active canopy sensor RapidSCAN CS-45 with red, red edge, and near infrared wavebands was used in handheld mode and aerial mode on a gimbal under a multi-rotor UAV. The results showed the great potential of UAV-based active sensing for monitoring rice leaf N status. The vegetation index-based regression models were built and evaluated based on Akaike information criterion and independent validation to predict rice leaf dry matter, leaf area index, and leaf N accumulation. Vegetation indices composed of near-infrared and red edge bands (NDRE or RERVI) acquired at a 1.5 m aviation height had a good performance for the practical application. Future studies are needed on the proper operation mode and means for precision N management with this system.

Effects of Light and Nutrition Manipulations on Thermal Respiratory Acclimation and Nocturnal Dynamics of Leaf Dark Respiration

The ability of a plant to acclimate metabolically to thermal changes is necessary to maintain a positive carbon balance. It is likely that a plant’s acclimatory potential is a function of leaf nitrogen and/or leaf carbohydrate status. Two important issues assessed concerning leaf dark respiration (RD) were the effects of growth temperature, light, and fertilization on thermal respiratory acclimation and changes in respiratory parameters (indicative of acclimation) throughout the dark period. Soybean (Glycine max (L.) Merr.) plants were grown in greenhouses under a full factorial treatment arrangement of temperature, light, and nutrition. RD was measured at three temperatures to estimate respiratory parameters (cool respiration R13, warm respiration R25, and the temperature response of respiration EO) three times throughout the night (6 pm, 11 pm, and 4 am). Respiratory parameters did not differ throughout the night. Thermal acclimation was observed in warm grown plants under optimal growing conditions (i.e., high light and high fertilization); however, acclimation did not occur when limitations were imposed (i.e., shade or no fertilization). These findings suggest thermal acclimation will occur so long as plants do not undergo limitations. This may have major implications for natural ecosystems and may play a role in assessing an ecosystems resiliency to climate change.

Remote Sensing of Leaf and Canopy Nitrogen Status in Winter Wheat (Triticum aestivum L.) Based on N-PROSAIL Model

Plant nitrogen (N) information has widely been estimated through empirical techniques using hyperspectral data. However, the physical model inversion approach on N spectral response has seldom developed and remains a challenge. In this study, an N-PROSAIL model based on the N-based PROSPECT model and the SAIL model canopy model was constructed and used for retrieving crop N status both at leaf and canopy scales. The results show that the third parameter (3rd-par) retrieving strategy (leaf area index (LAI) and leaf N density (LND) optimized where other parameters in the N-PROSAIL model are set at different values at each growth stage) exhibited the highest accuracy for LAI and LND estimation, which resulted in R2 and RMSE values of 0.80 and 0.69, and 0.46 and 21.18 µg·cm−2, respectively. It also showed good results with R2 and RMSE values of 0.75 and 0.38% for leaf N concentration (LNC) and 0.82 and 0.95 g·m−2 for canopy N density (CND), respectively. The N-PROSAIL model retrieving method performed better than the vegetation index regression model (LNC: RMSE = 0.48 − 0.64%; CND: RMSE = 1.26 − 1.78 g·m−2). This study indicates the potential of using the N-PROSAIL model for crop N diagnosis on leaf and canopy scales in wheat.

Correlation of Leaf and Root Senescence During Ripening in Dry Seeded and Transplanted Rice

Dry seeding is a resource-saving rice establishment method. With an equivalent yield, dry seeded flooded rice (DSR) has been considered as a replacement for traditional transplanted flooded rice (TFR). However, the differences in leaf and root senescence during grain filling between DSR and TFR were seldom identified. In this study, the root length, root tip number and leaf senescence of rice varieties Huanghuazhan and Yangliangyou 6 during ripening were compared between DSR and TFR. Results showed that top three leaves in DSR had the characteristics of relatively lower SPAD value, lower N content and premature leaf senescence. In addition, both the total root length and total root tip number of DSR were significantly lower than those of TFR. In conclusion, premature and quick leaf senescence was related with inadequate root length and root tip number during ripening, which might result from the deficiency of nitrogen supply in DSR. Techniques on improving leaf nitrogen status and delaying the leaf senescence during grain-filling in DSR should be developed in future researches.

Using a chlorophyll meter (SPAD-502) to estimate the total chlorophyll and nitrogen contents in leaves of ‘Manee-Esan’ pummelo

The chlorophyll meter (SPAD-502) is a simple tool which can expediently estimate the total chlorophyll and nitrogen contents in many species. However, the relationship between the total chlorophyll and leave nitrogen contents, with the SPAD readings in 'Manee-Esan' pummelo (Citrus grandis (L.) Osbeck), have not been fully established and may be ideal to predict the nitrogen requirements. The objective of this study was to establish a correlation between the total chlorophyll contents and the possibility to estimate the leaf nitrogen status with SPAD-502 of 'Manee-Esan' pummelo. Leaves of the pummelo were sampled in May to December, 2015 and analyzed with a SPAD-502 meter under field conditions and in the laboratory to determine the degree of leaf greenness, extracted chlorophyll contents and total nitrogen contents. The results showed that the leaf greenness values, total chlorophyll contents and total nitrogen contents of the leaf samples ranged from 69.71 to 90.92 SPAD-unit, 0.57 to 1.39 mg cm-2 and 1.40 to 2.00% N, respectively. In addition, SPAD values had a positive correlation with total chlorophyll contents and total nitrogen contents (y = 0.0311x - 1.7427, R2=0.3473 for total chlorophyll and y = 0.0124x + 0.6809, R2=0.052 for total nitrogen, respectively). The SPAD-502 meter readings could estimate the chlorophyll content of pummelo leaves regardless of the temporal and spatial considerations. It is suggested that the SPAD value, determined by a portable chlorophyll meter, can be used to obtain a quick estimation of the total chlorophyll and nitrogen content of the pummelo leaves.

Changes in foliar nitrogen resorption of Phyllostachys edulis with culm development

Leaf nitrogen resorption is very important to Phyllostachys edulis development because the withdrawn nitrogen can help newly emerging and growing culms. However, few studies have focused on the ontogenetic changes in leaf nitrogen resorption of P. edulis. Here, we examined the variability in mature leaf nitrogen concentrations (Nm), nitrogen resorption efficiency (NRE) and proficiency (NRP or Ns) and leaf-level nitrogen use efficiency (NUE) of the current-, 3rd- and 5th-year culms in P. edulis stands under extensive management. Analyses of variance and correlation indicated that patterns of Nm, NRP, NRE and NUE were markedly affected by culm age and leaf nitrogen status. Nm, Ns and NRE were significant higher in younger (current-year) culms with 1-year lifespan leaves, while NUE was markedly higher in older (3rd- or 5th-year) culms with 2-year lifespan leaves. Significant linear correlations between Nm and NRP, NRE and NUE, Nm and NUE, Ns and NRE were found for each culm age, and Nm was significantly positively correlated to NRE for all culms pooled. Higher proficiency in older culms led to higher NUE and lower NRE, these relationships can be modulated by Nm, which in turn, is restrained by leaf N availability and acquisition. Our results revealed that at the intraspecific level, P. edulis can adjust its leaf NRE, NRP, and leaf-level NUE in concert with culm development. Understanding nitrogen resorption characteristics and NUE of P. edulis can help decision-makers design appropriate deforestation strategies and achieve precise N fertilization for sustainable bamboo forest management.

Seasonal variations of leaf and canopy properties tracked by ground-based NDVI imagery in a temperate forest

Changes in plant phenology affect the carbon flux of terrestrial forest ecosystems due to the link between the growing season length and vegetation productivity. Digital camera imagery, which can be acquired frequently, has been used to monitor seasonal and annual changes in forest canopy phenology and track critical phenological events. However, quantitative assessment of the structural and biochemical controls of the phenological patterns in camera images has rarely been done. In this study, we used an NDVI (Normalized Difference Vegetation Index) camera to monitor daily variations of vegetation reflectance at visible and near-infrared (NIR) bands with high spatial and temporal resolutions, and found that the infrared camera based NDVI (camera-NDVI) agreed well with the leaf expansion process that was measured by independent manual observations at Harvard Forest, Massachusetts, USA. We also measured the seasonality of canopy structural (leaf area index, LAI) and biochemical properties (leaf chlorophyll and nitrogen content). We found significant linear relationships between camera-NDVI and leaf chlorophyll concentration, and between camera-NDVI and leaf nitrogen content, though weaker relationships between camera-NDVI and LAI. Therefore, we recommend ground-based camera-NDVI as a powerful tool for long-term, near surface observations to monitor canopy development and to estimate leaf chlorophyll, nitrogen status, and LAI.