Plant Nitrogen Content Research Articles

Hyperspectral inversion of nitrogen content in maize leaves based on different dimensionality reduction algorithms

Fast, accurate, and non-destructive detection of the nitrogen (N) content in corn leaves is of great significance for the precise dynamic management of nitrogen fertilizer application for corn. Hyperspectral data can provide an important means for detecting the nitrogen content in plants. Existing research has mainly focused on using various vegetation indices or 3–5 band combinations to estimate leaf nitrogen content, ignoring the different in spectral characteristics of hyperspectral data and failing to characterize most of the spectral information. Some scholars have used principal component analysis and wavelet analysis dimensionality reduction algorithms, but used different bands for these models. Therefore, more and different inversion models need to be introduced to improve the use of spectral data and increase the universality of the model. The present study selected three different methods to reduce data dimensionality, including the Successful Projections Algorithm (SPA) and the Least Absolute Shrinkage and Selection Operator (LASSO) and the Elastic Net (EN) algorithms. Then the processed spectral reflectance information and observational data for synchronous leaf nitrogen content were used to construct an inversion model used to predict leaf nitrogen content. Nine inversion models were constructed based on different dimensionality reduction and regression methods. Based on the coefficient of determination (R2) and root mean square error (RMSE), the accuracy of each model was tested. The main results follow: (1) Dimensionality reduction processing of hyperspectral data can effectively prevent data from overfitting, limit the correlation between adjacent frequency bands, and reduce data redundancy. An EN dimensionality reduction algorithm (EN-Partial Least Squares Regression (PLSR)) model R2 = 0.96, RMSE = 0.19) was better than a SPA (SPA-PLSR model R2 = 0.90, RMSE = 0.26) and LASSO (LASSO-PLSR model R2 = 0.89, RMSE = 0.37) dimensionality reduction algorithm. (2) For the same dimensionality reduction method, the accuracy of the regression model based on PLSR was higher than that of other models. Among the nine inversion models in this paper, the EN-PLSR inversion model has the best fitting effect (R2 = 0.96, RMSE = 0.19). (3) Obvious changes in nitrogen content have little effect on the overall hyperspectral reflectance curve. This study provides a reference for high-efficiency and non-destructive testing of corn nitrogen content using hyperspectral technology.

Compensation of high nitrogen toxicity and nitrogen deficiency with biochar amendment through enhancement of soil fertility and nitrogen use efficiency promoted rice growth and yield

AbstractThe quality and nutritional status of agricultural soils are depleting gradually, and biochar is widely used in soil quality improvement. A sustainable approach of biochar application needs a better understanding of its interaction with nitrogen application and the final effect on crop growth. In our study, the effect of different biochar application levels (0, 15, 30, and 60 tons ha−1) in combination with nitrogen fertilizer levels (0, 150, 300, and 450 kg ha−1) on soil properties, rice growth, and nitrogen use efficiency were investigated. The results showed that sole application of biochar (B60) did not promote the rice growth except the soil organic carbon (SOC), total nitrogen, total phosphorous, and available potassium compared with no biochar addition, while a single application of 450 kg N ha−1 (N450) adversely affected the rice physiological and yield traits by destroying rice leaf ultrastructure and reducing the stomatal length by 13%, stomatal width 3% and density 12%, plant height 5%, dry biomass plant−1 4%, spike weight plant−1 8%, and grain weight plant−1 9% compared to N300 unless it was treated in combination with biochar. Biochar can significantly compensate the toxicity of excessive nitrogen and deficiency of low nitrogen fertilizer on rice growth. Compared to a single application of B0+N450 (450 kg N ha−1), combined application of biochar and nitrogen B30+N450 could elevate rice plant nitrogen content by 13%, plant height 30%, aboveground dry biomass plant−1 136%, spike weight plant−1 34%, and grain weight plant−1 36% while increments in total nitrogen accumulation (TNA 79%%) and nitrogen use efficiency (NUE 35%) were noted in B30+N300 treatment. These results suggest that biochar amendment combined with a proper amount of nitrogen fertilizer is an effective approach to promote soil conditions, manage nitrogen utilization, improve plant growth and increase crop yield on a sustainable basis.

Effects of humic acid on the nitrogen utilization efficiency and microbial communities in aquaponic systems

Aquaponic systems consisting of recirculating aquaculture systems and hydroponic beds. Raft-floating aquaponics integrated with a moving-bed biofilm reactor (MBBR) was established. Humic acid (HA) was added to the aquaponic systems to promote nutrient recovery. An elementary analyzer was used to determine the nitrogen content of fish, plants, and sediments to calculate the NUE. High-throughput sequencing and quantitative polymerase chain reaction (qPCR) were conducted to reveal the influence of HA on microbial communities and nitrification genes. During an 80 day experimental period, the effects of water quality parameters, nitrogen utilization efficiency (NUE), and microbial communities in variable concentrations of HA-based aquaponic systems were compared. Among the HA-based systems, the one with an optimal concentration of 15 ppm indicated the best performance. In HA-based aquaponic systems with 15 ppm concentration, the maximum concentrations of total ammonia nitrogen and NO2−–N were 6.02 mg/L and 12.693 mg/L, which were significantly lower than those in aquaponic systems with 0 ppm and 30 ppm. However, the accumulation of nitrate in the HA-based aquaponic systems with 15 ppm was greater than that in the others. The NUE of the 15 ppm HA-based aquaponic system was 58.34%, which was significantly higher than that of the 0 ppm (50.83%) and the 30 ppm (47.25%) systems. Consequently, the threshold concentration of HA was established as 15 ppm. The microbial communities of different HA-based systems revealed the presence of denitrifying and nitrifying microbial populations for nitrogen recovery in moving bed biofilm reactors (MBBRs); higher levels of these microbial populations thrived in the 15 ppm HA system than in the other systems. However, there was no significant difference in microbial communities from root samples. The highest copies of amoA and nxrB were found at 15 ppm for both MBBRs and roots, indicating that the addition of HA to the aquaponic systems yielded more products and improved water quality and NUE by changing the composition of microbial communities based on the threshold for HA input concentration. This study provided that HA show enormous potential for an interesting research and the development of green technologies for biological wastewater treatment and bioremediation.

Effects of nitrogen regulation and strip intercropping on faba bean biomass, nitrogen accumulation and distribution, and interspecific interactions

AbstractEffects of nitrogen fertilization on the accumulation and distribution of biomass and plant nitrogen content in intercropped faba beans, together with its impact on the interspecific interactions between faba bean and wheat has not been well studied. Here, we have used a logistic model to analyze the cropping systems (i.e., monocropped faba bean, monocropped wheat, and faba bean intercropped with wheat) by evaluating the effects of different levels of nitrogen application on the dynamic changes in biomass and nitrogen accumulation, along with the interspecific interaction. Using monocropped and intercropped faba beans in field experiments spanning two years, we found that nitrogen application increased both monocropped and intercropped faba bean yields. The yield of intercropped faba beans was greatest at the lower nitrogen input for two consecutive years. Nitrogen application increased the biomass and nitrogen accumulation in both monocropped and intercropped faba beans. The simulation parameters that increased were maximum nitrogen accumulation (A') and maximum accumulation rate (R'max) while the initial cumulative rate (r') decreased. Compared with monocropping, intercropped faba bean–wheat showed increased yield and biomass accumulation, along with a significantly increased initial growth rate (r) and a maximum instantaneous growth rate (Rmax). Intercropping reduced stem and leaf biomass and the nitrogen distribution ratio while increasing the pod content. In the early intercropped faba bean growth stage, there was no competition for nutrients or any kind of yield advantage, while in the later stages, the nutrient competitiveness of intercropping was stronger than the intraspecific competition, showing the advantages of intercropping on biomass and yield (i.e., relative crowding coefficient [K] >1, relative interspecific competitiveness [RC] >0, and relative competition intensity [RCI] <0). In summary, nitrogen application of 45 kg ha–1 could be the desired amount that maximizes the nutrient competition and interspecific advantages in the middle and late stages of faba bean growth when intercropped, thus contributing to the yield.

Variable responses of carbon and nitrogen contents in vegetation and soil to herbivory and warming in high‐Arctic tundra

AbstractChemical responses of tundra vegetation and tundra soil to environmental changes are likely to differ, with implications for ecosystem functioning, yet they are rarely compared. Here, we aimed at comparing sensitivity and magnitude of short‐term carbon and nitrogen responses of three main tundra ecosystem compartments: vascular plants, mosses, and soil, to two environmental perturbations: herbivore disturbance and warming. In a full‐factorial field experiment in the high‐Arctic Svalbard, we simulated herbivore disturbance as spring grubbing activity by pink‐footed geese (Anser brachyrhynchus) and passively increased summer temperatures using open‐top chambers. Manipulations were set up within three habitats that differ in soil moisture and carried out for two consecutive growing seasons. Overall, we found small and few significant responses to herbivore disturbance and warming, suggesting that carbon and nitrogen contents of high‐Arctic ecosystems are relatively resistant to these perturbations, at least in the short term. However, the three ecosystem compartments still differed in their sensitivity to perturbations (vascular plants > soil > mosses), and this was exacerbated by their differential sensitivity across habitats (mesic > moist > wet). Also, while vascular plants responded to herbivore disturbance in mesic and wet habitats and to warming in mesic and moist habitats, soil and mosses only responded to herbivore disturbance in mesic and wet habitats, respectively. Responses to treatments were generally consistent across the two growing seasons, despite great differences in temperature conditions and large between‐year variations in the chemical composition of the three ecosystem compartments. These findings highlight the potential for environmental perturbations to have small, yet differential short‐term impacts on the carbon and nitrogen contents of vascular plants, mosses, and soil, both within and between tundra habitats. Our results imply that assessments of a single ecosystem compartment in a given context cannot be extrapolated to the whole ecosystem, thus stressing the importance of considering both vegetation and soil carbon and nitrogen responses, and how they display across habitats, in order to better understand how environmental changes might affect biogeochemical processes in the tundra. Longer‐term studies should dig deeper into the relative role of (simulated) global change drivers vs. natural inter‐annual climatic fluctuations for tundra ecosystem carbon and nitrogen dynamics.

Detection and Dynamic Variation Characteristics of Rice Nitrogen Status after Anthesis Based on the RGB Color Index

We aimed to elucidate the color changes of rice leaves after anthesis and create an algorithm for monitoring the nitrogen contents of rice leaves and of the whole plant. Hence, we aimed to provide a theoretical basis for the precise management of rice nitrogen fertilizer and the research and development of digital image nutrition monitoring equipment and reference. We selected the leaf colors of the main stems of four major rice varieties promoted in production, including Huaidao 5 (late-maturing medium japonica rice), Yangjing 4227 (early maturing late japonica rice), Changyou 5 (late japonica hybrid rice), and Yongyou 8 (late japonica hybrid rice). Under different nitrogen levels, the leaf R, G, and B values of the four rice varieties at different stages after anthesis, the dynamic changes in RGB normalized values, the correlations between RGB normalized values and leaf SPAD values, the leaf nitrogen content and whole plant nitrogen content, and the nitrogen prediction model were studied. The research results demonstrate the following: (1) regardless of nitrogen levels, the leaf of R, G, B, NRI, NGI and NBI of different rice varieties after anthesis followed the order, G > R > B. R, G, NRI, NGI, and days after heading could be fitted according to a logarithmic equation, y = aebx (0.726 ≤ R2 ≤ 0.992); B, NBI, and days after heading could be fitted using a linear equation, y = a + bx (0.863 ≤ R2 ≤ 0.992). Both fitting effects were significant (except NGI). (2) A quadratic function (Y = −1296.192x2 + 539.419x − 10.914; Y = −1173.104x2 + 527.073x − 12.993) was adopted to construct a monitoring model for the NBI and SPAD values of japonica rice and hybrid japonica rice leaves after anthesis and the R2 values were 0.902 and 0.838, respectively. Exponential functions (Y = 5.698e7.261x; Y = 3.371e9.326x) were employed to construct monitoring models of leaf nitrogen content, and the R2 values were 0.833 and 0.706, respectively. Exponential functions (Y = 5.145e4.9143x; Y = 3.966e5.364x) were also used to construct a monitoring model for the nitrogen content of the whole plant, and the R2 values were 0.737 and 0.511, respectively. The results obtained from prediction tests by using Determination Coefficient (R2), Relative Percent Deviation (RPD), and Root Mean Square Error (RMSE) showed that it was feasible, accurate, and efficient to use a scanner for measuring the nitrogen content of rice.

Bradyrhizobium brasilense as an efficient soybean microsymbiont in two contrasting soils of the southwestern region of Piauí (Cerrado biome)

The goal of this study was to evaluate the efficiency of Bradyrhizobium brasilense strains native to the soil of the semiarid region in northeastern Brazil in symbiosis with soybeans in two contrasting soils in southwestern Piauí. A pot experiment was conducted using a randomized block design in a 10 × 2 factorial scheme. There were 10 nitrogen (N) sources: six native Bradyrhizobium strains [UFLA06-13, UFLA06-15, UFLA06-19, UFLA06-21, UFLA06-22 (B. brasilense), and UFLA06-24 (Bradyrhizobium sp.)], two controls with strain SEMIA 5019 (B. elkanii) and a commercial inoculant [SEMIA 5079 (B. japonicum) + SEMIA 5080 (B. diazoefficiens)] recommended for soybeans, and two controls without the application of an inoculant (one with and the other without the application of mineral N). The second experimental factor corresponded to the use of two soils (Oxisol and Quartzarenic Neosol). All strains showed increased nodulation and shoot nitrogen content in soybean plants in both soils. Most strains promoted higher nitrogen fixation when inoculated in the Oxisol. UFLA06-19, UFLA06-22, and UFLA06-24 were efficient in nitrogen accumulation in the shoots of soybeans in the Oxisol. This is the first report regarding the efficiency of B. brasilense strains in symbiosis with soybeans under different soil conditions.

Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions

In recent years, farmers have often mistakenly resorted to overuse of chemical fertilizers to increase crop yield. However, excessive consumption of fertilizers might lead to severe food poisoning. If nutritional deficiencies are detected early, it can help farmers to design better fertigation practices before the problem becomes unsolvable. The aim of this study is to predict the amount of nitrogen (N) content (mg ​l−1) in cucumber (Cucumis sativus L., var. Super Arshiya-F1) plant leaves using hyperspectral imaging (HSI) techniques and three different regression methods: a hybrid artificial neural networks-particle swarm optimization (ANN-PSO); partial least squares regression (PLSR); and unidimensional deep learning convolutional neural networks (CNN). Cucumber plant seeds were planted in 20 different pots. After growing the plants, pots were categorized and three levels of nitrogen overdose were applied to each category: 30%, 60% and 90% excesses, called N30%, N60%, N90%, respectively. HSI images of plant leaves were captured before and after the application of nitrogen excess. A prediction regression model was developed for each individual category. Results showed that mean regression coefficients (R) for ANN-PSO were inside 0.937–0.965, PLSR 0.975–0.997, and CNN 0.965–0.985 ranges, test set. We conclude that regression models have a remarkable ability to accurately predict the amount of nitrogen content in cucumber plants from hyperspectral leaf images in a non-destructive way, being PLSR slightly ahead of CNN and ANN-PSO methods.

Prediction of Plant Nutrition State of Rice under Water-Saving Cultivation and Panicle Fertilization Application Decision Making

Rice is a staple food crop in Asia. The rice farming industry has been influenced by global urbanization, rapid industrialization, and climate change. A combination of precise agricultural and smart water management systems to investigate the nutrition state in rice is important. Results indicated that plant nitrogen and chlorophyll content at the maximum tillering stage were significantly influenced by the interaction between water and fertilizer. The normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE), obtained from the multispectral images captured by a UAV, exhibited the highest positive correlations (0.83 and 0.82) with plant nitrogen content at the maximum tillering stage. The leave-one-out cross-validation method was used for validation, and a final plant nitrogen content prediction model was obtained. A regression function constructed using a nitrogen nutrition index and the difference in field cumulative nitrogen had favorable variation explanatory power, and its adjusted coefficient of determination was 0.91. We provided a flow chart showing how the nutrition state of rice can be predicted with the vegetation indices obtained from UAV image analysis. Differences in field cumulative nitrogen can be further used to diagnose the demand of nitrogen topdressing during the panicle initiation stage. Thus, farmers can be provided with precise panicle fertilization strategies for rice fields.

Chitin combined with selenium reduced nitrogen loss in soil and improved nitrogen uptake efficiency in Guanxi pomelo orchard

Nitrogen cycling in soil, which associated with microbes, plays an important role in plant growth. Irrational application of nitrogen fertilizer could disrupt the structure of soil microbial community, thus inhibiting the uptake of nitrogen by plants and increasing nitrogen leaching in soil. Field and pot leaching experiments with the combined application of chitin fertilizer and selenium (Se) were carried out in order to develop an approach to improve the efficiency of nitrogen fertilizer utilization and reduce runoff by nitrogen loss in orchards of Guanxi pomelo in Fujian Province. Our results showed that application of chitin fertilizer combined with Se to the soil with reduced nitrogen and phosphate fertilizer (nitrogen fertilizer decreased by 25% and phosphate fertilizer decreased by 50%) could significantly increase the fruit yield, vitamin C and solid-acid ratio in the fruit. The application of chitin fertilizer and Se can not only lead to the increase of total nitrogen content in plant leaves but also the alkali-hydrolyzable nitrogen content in soil which enhancing soil nitrogen supplying capacity. It has been found that the adding the chitin fertilizer and Se into soil can significantly affect the structure and functional categories microbial communities and its activities. This is directly evidenced by the findings that the expression level of several groups of N metabolism and transporting related genes (i.e. amoAB and nxrA in nitrification, narG, nirK, norBC, and nosZ in denitrification, nirD, narH in dissimilatory nitrogen reduction, and ureC in ammoniation) has been drastically up-regulated. Our results indicate this strategy for reducing N and P input while maintaining and improving plant performance by supplementing with micronutrient Se and chitin fertilizer can increase the fruit yield and improve the quality of Guanxi pomelo through improving fertilizer use efficiency.

Intraspecific trait variation drives grassland species richness and productivity under changing precipitation

AbstractIt has been increasingly recognized that plant traits and their intraspecific variation play pivotal roles in determining how ecosystem structure and function respond to a changing environment. Yet, it remains unclear how and which plant traits regulate the assembly process and thus affect ecosystem structure and function under climate changes. Here, we selected six steppe sites with snow fences along a precipitation gradient in inner Mongolia. We measured traits including plant height and leaf carbon, nitrogen, and phosphorus concentrations. We incorporated intraspecific trait variation to quantify the strength of external filters (assembly processes outside the community at a larger spatial scale), the strength of internal filters (assembly processes within the local community), and functional redundancy (the number of species having a similar function to an ecosystem). We applied these assembly rules to explain the changes in biodiversity and productivity in response to changing precipitation. We found that increased rainfall reduced plant carbon (C), nitrogen (N), and phosphorous (P) concentrations, but did not affect plant height. High snowfall increased phosphorous concentration, but did not affect other traits. Latent variable models identified climate (rainfall) and soil nutrient conditions (soil total N and P content) were more important than external filtering processes in predicting species richness. However, external filtering processes were the most important predictor of productivity. More specifically, we found with the increase in steppe productivity, the strength of the external filtering on leaf N increased and on plant height decreased, leading to leaf N converging into a small range but broadening the range of plant height. The internal filters had no significant effects on species richness or productivity. Additionally, we found that low precipitation reduced functional redundancy. We emphasize that intraspecific trait‐variation drives plant productivity and richness responses to precipitation changes by regulating community assemblage. Our finding also underlines the importance to separate the contributions of different functional traits in regulating the responses of ecosystem structure and function to climate changes.

Estimation of Paddy Rice Nitrogen Content and Accumulation Both at Leaf and Plant Levels from UAV Hyperspectral Imagery

Remote sensing-based mapping of crop nitrogen (N) status is beneficial for precision N management over large geographic regions. Both leaf/canopy level nitrogen content and accumulation are valuable for crop nutrient diagnosis. However, previous studies mainly focused on leaf nitrogen content (LNC) estimation. The effects of growth stages on the modeling accuracy have not been widely discussed. This study aimed to estimate different paddy rice N traits—LNC, plant nitrogen content (PNC), leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA)—from unmanned aerial vehicle (UAV)-based hyperspectral images. Additionally, the effects of the growth stage were evaluated. Univariate regression models on vegetation indices (VIs), the traditional multivariate calibration method, partial least squares regression (PLSR) and modern machine learning (ML) methods, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM), were evaluated both over the whole growing season and in each single growth stage (including the tillering, jointing, booting and heading growth stages). The results indicate that the correlation between the four nitrogen traits and the other three biochemical traits—leaf chlorophyll content, canopy chlorophyll content and aboveground biomass—are affected by the growth stage. Within a single growth stage, the performance of selected VIs is relatively constant. For the full-growth-stage models, the performance of the VI-based models is more diverse. For the full-growth-stage models, the transformed chlorophyll absorption in the reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI) performs best for LNC, PNC and PNA estimation, while the three band vegetation index (TBVITian) performs best for LNA estimation. There are no obvious patterns regarding which method performs the best of the PLSR, ANN, RF and SVM in either the growth-stage-specific or full-growth-stage models. For the growth-stage-specific models, a lower mean relative error (MRE) and higher R2 can be acquired at the tillering and jointing growth stages. The PLSR and ML methods yield obviously better estimation accuracy for the full-growth-stage models than the VI-based models. For the growth-stage-specific models, the performance of VI-based models seems optimal and cannot be obviously surpassed. These results suggest that building linear regression models on VIs for paddy rice nitrogen traits estimation is still a reasonable choice when only a single growth stage is involved. However, when multiple growth stages are involved or missing the phenology information, using PLSR or ML methods is a better option.

The diversity and co-occurrence network of soil bacterial and fungal communities and their implications for a new indicator of grassland degradation

Grassland degradation is a retrogressive successionofgrasslandvegetation, which leads to the loss of biodiversity and the degradation of ecosystem functions. Soil microbiomes play critical roles in the functioning and services of grassland ecosystems, yet little is known about how their diversity, structure and co-occurrence network characteristics respond to grassland degradation. Here, we used lllumina Miseq technique to evaluate soil bacterial and fungal communities in a meadow steppe with different degrees of degradation in Northeastern China. Our results showed that Actinobacteria, Proteobacteria, and Chloroflexi and Acidobacteria were the dominant bacterial phyla, while Ascomycota, Basidiomycota, and Zygomycota were the predominant fungal phyla. The relative abundance of taxa assigned to Actinobacteria, Gemmatimonadetes, Firmicutes, and Deinococcus-Thermus increased with increasing degradation degrees, whereas those affiliated with Acidobacteria and Nitrospirae showed a decreasing pattern. Compared to bacteria, the relative abundance of most fungal phyla decreased gradually along the degradation gradient. Bacterial Shannon diversity index possessed a similar hump pattern, while fungal diversity decreased with increasing degree of grassland degradation. Bacterial and fungal communities have different responses to grassland degradation, indicating that fungi are more sensitive to grassland degradation than bacteria. Both bacterial and fungal community structures were significantly different among the three sites. Changes in soil bacterial and fungal community structures were best explained by soil salinity and pH. Plant diversity and nitrogen concentration in aboveground plant tissues were also important factors for regulating fungal communities. Co-occurrence network analysis revealed that microbial taxa increased positive interactions and average degree to strengthen the adaptability of microorganisms to grassland degradation. These findings could enhance our understanding of the formation and maintenance of microbial community diversity in degraded grasslands and the development of a new indicator for grassland ecosystem management.

Response of forage plants to alteration of temperature and spring thaw date: implications for geese in a warming Arctic

AbstractChanges in summer temperatures in Arctic Alaska have led to longer and warmer growing seasons over the last three decades. Corresponding with these changes in climate, the abundance and distributions of geese have increased and expanded over the same period. We used an experimental approach to assess the response of goose forage plants to simulated environmental change. We subjected Carex subspathacea, a preferred goose forage growing on the Arctic Coastal Plain (ACP) of Alaska, to manipulations of temperature and timing of spring thaw to measure potential effects in terms of plant nitrogen concentration, aboveground biomass, and total nitrogen availability. Carex subspathacea responded to warming in a dynamic fashion. Increases in temperature led to decreases in leaf nitrogen concentration but increases in aboveground biomass. The increase in biomass was stronger than the decline in nitrogen concentration such that total nitrogen availability was increased with temperature for the first 35–40 d of the season. Grazing removal accounted for only minimal offtake of biomass, and we found no indication that grazing maintained elevated levels of nitrogen concentration longer in the season as reported in other studies. Based on demonstrated relationships in the literature between forage nitrogen concentrations and gosling growth rates, we conclude that there is currently abundant high‐quality forage available across the ACP. This finding fits with recent evidence of high gosling growth rates and increasing trends in goose abundance on the ACP. Our results suggest that with climate warming of a few degrees, nitrogen concentration of forage may decrease, but forage biomass and total nitrogen availability will increase. Our data suggest that nitrogen concentration will not fall below the minimum threshold required by geese in the near future. As such, we suggest that there is currently no bottom‐up limitation to goose numbers on the ACP.

Estimation of spikelet number per area by UAV-acquired vegetation index in rice (Oryza sativa L.)

ABSTRACT The objectives of this study were to find the best vegetation index (VI) associated with plant nitrogen content at the reproductive stage in rice, to associate the number of spikelets with this VI and solar radiation, and to estimate the number of spikelets. Rice cultivars Ishikawa 65 and Koshihikari were grown in the field in 2019 and 2020 at various nitrogen application rates and transplanting densities. From 30 days before heading to just after heading, the field was imaged with a multispectral camera. The images were processed with predefined ground control point data to create VI maps. From the maps, VI data were retrieved from the canopy area where plants were harvested for the determination of plant nitrogen content at the reproductive stage and of the number of spikelets at maturity. Among 6 VIs tested, the chlorophyll index green (CIgreen) had the highest coefficient of determination (R 2) with plant nitrogen content at the reproductive stage and was the only VI with a linear relation with plant nitrogen content. The number of spikelets per unit area was well explained by multiple regression with CIgreen at 15 days before heading (CIG15) and cumulative solar radiation in the 15 days before heading (CSR15) as independent variables. A higher CIG15 would increase the number of spikelets differentiated and a higher CSR15 would reduce the rate of degeneration by increasing dry matter production.

The evolution of bacterial community structure and function in microalgal-bacterial consortia with inorganic nitrogen fluctuations in piggery digestate

The inorganic nitrogen concentrations in full-scale anaerobic digestion plants always vary seasonally, challenging the removal stability of microalgal-bacterial consortia in treating piggery digestate. Using a continuous photobioreactor, this study revealed the evolution of bacterial community under inorganic nitrogen fluctuations with influent nitrate loading ranged from 11 to 90 mg-N L−1 and influent ammonium loading ranged from 130 to 550 mg-N L−1. The loading of gradient nitrogen concentrations demonstrated that the optimal removal rate of total inorganic nitrogen was 45.62 mg-N L−1 d−1 along with the total chlorophyll concertation of 19.27 mg L−1, when introducing 240 mg-N L−1 of ammonium and 11 mg-N L−1 of nitrate. High throughput sequencing indicated that the dominant community members for nitrogen removal were shifted from prokaryotic algae to nitrifying and denitrifying bacteria, with nitrate being changed to ammonium. With the increasing influent ammonium loading, the suspended microalgal bacterial consortia gradually changed to microalgal bacterial biofilm, which was attributed to the biofilm-producing prokaryotes Thauera Comamona and Arenimonas. Co-occurrence network analyses illustrated that more functional bacterial modules were formed under ammonium fluctuation. Further correlation test identified influent ammonium loading as the important functional parameter shaping the bacterial community. Overall, this study expands our understanding of the homeostasis in microalgal-bacterial system in the cleaner production of piggery digestate.

Transcriptomic and photosynthetic responses to grafting of the Nod1 gene in nodulated and non-nodulated soybeans

Legume plants form symbiotic relationships with rhizobia to convert N2 into ammonia, and the nodulation status can affect plant development including photosynthesis. However, the relationship between nitrogen fixation and photosynthesis during carbon and nitrogen metabolism remains unclear. This study was undertaken to unravel regulation of nodulation and photosynthesis using a spontaneous nonnodulated soybean mutant by grafting. The results of inheritance and gene mapping showed that the nonnodulated mutant was controlled by a recessive gene overlapped with the reported rj1 locus, and might be a new rj1 allele with 1 bp deletion in the fourth exon in comparison to the sequence of normal nodulation plants. According to grafting results, soybean nodulation is obviously determined by the roots, not the seedlings. Moreover, nitrogen content along with related metabolic enzyme activity, and photosynthetic capacity were enhanced by nonnodulated scions grafted with nodulated roots. Contrary results were obtained for nodulated scions grafted with nonnodulated roots. A total of 853 differentially expressed genes (DEGs) in the leaves and 1874 in the roots were identified by transcriptome analyses of the grafting treatments. We identified 285 differential gene ontology (GO) terms and 57 differential pathway terms identified in the leaves, while 856 differential GO terms and 207 differential pathway terms in the roots. Twenty DEGs interacting at translation level were selected, and the results of transcriptome analyses were verified by q-PCR. These findings indicated that the nodulation-related Nod allelic gene increases the nitrogen content of nonnodulated plants, which affects the enzymes involved in nitrogen metabolism, leading to changes in hormone levels and further regulation of photosynthesis and carbon metabolism.

The relative contribution of indigenous and introduced arbuscular mycorrhizal fungi and rhizobia to plant nutrient acquisition in soybean/maize intercropping in unsterilized soils

It has been demonstrated that intercropped crops may benefit from inoculation with rhizobia or arbuscular mycorrhizal (AM) fungi in soybean/maize intercropping systems under sterilized conditions. Little is known about the relative contribution of indigenous and introduced AM fungi and rhizobia to nutrient acquisition of intercropped soybean and maize in unsterilized soils. In this study, we evaluated the effectiveness of indigenous and introduced AM fungi and rhizobia in a soybean/maize intercropping system under unsterilized soil conditions. The results show that introduced rhizobial inoculation promoted soybean nodulation and growth. Single AM fungal inoculation promoted indigenous rhizobial nodulation and increased maize AM root colonization, but had little effect on maize growth. Co-inoculation strongly increased plant dry weight, and nitrogen (N) and phosphorus (P) content of the intercropped soybean compared to those with only indigenous rhizobial and AM fungal communities, but had no effect on those of maize in the same intercropped system. These results indicate that AM fungal inoculation promoted nodulation by indigenous rhizobia. Effective rhizobial inoculation combined with introduced AM fungi may not only promote symbiotic N2 fixation, but also enhance rhizosphere processes, which has the potential to improve nutrient acquisition in the field. Soybean is a weak competitor compared with maize in intercropping systems. Inoculation with introduced AM and rhizobia ameliorated the growth suppression of intercropped soybean, but inhibited growth of intercropped maize, indicating that inoculation of introduced AM fungi and/or rhizobia partly shifted the competitive advantage from maize to soybean. In conclusion, introduced AM fungi and rhizobia contributed more than indigenous populations to promoting growth and nutrient acquisition of the intercropped soybean in unsterilized soils.

Plant nitrogen concentration is more sensitive in response to degradation than phosphorus concentration in alpine meadow

Exploring the early warning signals of plant and soil characteristics in response to degradation is of great theoretical significance to illuminate the underlying driving mechanism of alpine meadow degradation. Here, in alpine meadow of Damxung County on the Tibetan Plateau, a field survey along degradation gradients was conducted to measure plant and soil properties: from non-degradation to severely degradation, simultaneous impairments in plant total biomass, plant coverage, plant N concentration, soil moisture, soil total C and N were detected, which significantly (P<0.05) decreased by 85.36%, 54.30%, 25.23%, 26.95%, 28.94% and 12.85% in sequence, while no significant change in plant P concentration was observed. As grassland degradation increased, a consistent decrease of plant N: P ratios (<14) not only demonstrated that N was a strong limiting factor for plants growth, but also supported our hypothesis that plant N concentration was more sensitive in response to degradation than plant P concentration. Path analyses further indicated that soil moisture and soil total nitrogen were the most critical factors that mediated the plant N: P in response to degradation. Although our findings suggested that N resorption efficiency was improved after grassland degradation, the increase in grassland degradation would enhance the N concentration decoupling from P concentration, which in turn might further exacerbate the N limitation and the degradation in alpine ecosystems. The early-warning signals of grassland degradation obtained here not only can improve the understanding of grassland degradation, but also can provide guidance for the restoration and management of degraded alpine meadow on the Tibetan Plateau.

Intelligent optimal tracking with asymmetric constraints of a nonlinear wastewater treatment system

AbstractThe wastewater treatment is an effective method for alleviating the shortage of water resources. In this article, a data‐driven iterative adaptive tracking control approach is developed to improve the control performance of the dissolved oxygen concentration and the nitrate nitrogen concentration in the nonlinear wastewater treatment plant. First, the model network is established to obtain the steady control and evaluate the new system state. Then, a nonquadratic performance functional is provided to handle asymmetric control constraints. Moreover, the new costate function and the tracking control policy are derived by using the dual heuristic dynamic programming algorithm. In the present control scheme, two neural networks are constructed to approximate the costate function and the tracking control law. Finally, the feasibility of the proposed algorithm is confirmed by applying the designed strategy to the wastewater treatment plant.