Plant Nitrogen Content Research Articles

Development of machine learning models for estimating wheat biophysical variables using satellite-based vegetation indices

Monitoring wheat biophysical variables during the growing season is essential for achieving precision agriculture's important goals. This study estimated plant height, leaf development stage, leaf area index, dry matter, and plant nitrogen content in wheat from Landsat-7 images in North Iran using machine learning algorithms. Forty points were selected from ten wheat fields (270 ha), and the agronomic parameters were recorded during the 2011–2012 growing season. The machine learning models applied in this study were Artificial Neural Network (ANN), Support Vector Machine (SVM), and Deep Neural Network (DNN). Results suggest that the DNN model was the best for predicting all the parameters, except for the nitrogen percentage, in which SVM and DNN have the same accuracy. The DNN model R2 was 0.82, 0.95, 0.95, and 0.90 RMSE (Root Mean Square Error) 9.61, 0.46, 0.47 and 1.2 and MAE (Mean Absolute Error) 26.4, 0.34, 0.28, and 0.84 for estimating plant height, leaf area index, leaf development stage and dry matter biomass, respectively. The DNN and SVM algorithms can effectively predict plant nitrogen percentage with identical R2, RMSE, and MAE values of 0.83–0.84, 0.44–0.46, and 0.33–0.36, respectively. These results show that machine learning models have great potential to provide timely and accurate data for better wheat management under field conditions from satellite imagery.

Pixel-class prediction for nitrogen content of tea plants based on unmanned aerial vehicle images using machine learning and deep learning

Pixel-class prediction for nitrogen content of tea plants based on unmanned aerial vehicle images using machine learning and deep learning

Nanoparticles and biochar with adsorbed plant growth-promoting rhizobacteria alleviate Fusarium wilt damage on tomato and watermelon

The addition of biochars and nanoparticles with adsorbed Azotobacter vinelandii and Bacillus megaterium alleviated damage from Fusarium infection in both tomato (Solanum lycopersicum) and watermelon (Citrullus lanatus) plants. Tomato and watermelon plants were grown in greenhouse for 28 and 30 days (respectively) and were treated with either nanoparticles (chitosan-coated mesoporous silica or nanoclay) or varying biochars (biochar produced by pyrolysis, gasification and pyrogasification). Treatments with nanoparticles and biochars were applied in two variants – with or without adsorbed plant-growth promoting bacteria (PGPR). Chitosan-coated mesoporous silica nanoparticles with adsorbed bacteria increased chlorophyll content in infected tomato and watermelon plants (1.12 times and 1.63 times, respectively) to a greater extent than nanoclay with adsorbed bacteria (1.10 times and 1.38 times, respectively). However, the impact on other endpoints (viability of plant cells, phosphorus and nitrogen content, as well antioxidative status) was species-specific. In all cases, plants treated with adsorbed bacteria responded better than plants without bacteria. For example, the content of antioxidative compounds in diseased watermelon plants increased nearly 46% upon addition of Aries biochar and by approximately 52% upon addition of Aries biochar with adsorbed bacteria. The overall effect on disease suppression was due to combination of the antifungal effects of both nanoparticles (and biochars) and plant-growth promoting bacteria. These findings suggest that nanoparticles or biochars with adsorbed PGPR could be viewed as a novel and sustainable solution for management of Fusarium wilt.

Mitigating the adverse effect of warming on rice canopy and rhizosphere microbial community by nitrogen application: An approach to counteract future climate change for rice

The adverse impact of climate change on crop production continues to increase, necessitating the development of suitable strategies to mitigate these effects and improve food security. Several studies have revealed how global warming negatively impacts the grain-filling stage of rice and that this effect could be mitigated by nitrogen; however, the impact of nitrogen application on rice canopy and rhizosphere microbial communities remains unclear. We conducted a study using an open-field warming system. Results showed that warming influenced rice canopy by decreasing aboveground biomass and harvest index, whereas nitrogen application had positive effect on rice production under warming conditions by increasing the plant nitrogen content, biomass, harvest index and soil fertilities. Moreover, soil ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3−-N) contents were significantly decreased under warming, which were higher after nitrogen application. Notably, warming and nitrogen fertilizer caused 19 % (P < 0.01) and 7 % (P < 0.05) variations, respectively, in the β diversity of the microbial community, respectively. The impact of warming was significant on NH4+-N-related microorganisms; however, this impact was weakened by nitrogen application for microbes in the rhizosphere. This study demonstrated that enhanced nitrogen fertilizer can alleviate the adverse impact of warming by weakening its effects on rhizosphere microbes, improving soil fertility, promoting rice nitrogen uptake, and increasing the aboveground biomass and harvest index. These findings provide an important theoretical basis for developing practical, responsive cultivation strategies.

Effect of Sowing Date and Nitrogen on Quality of Beet Leaf (Beta vulgaris var. bengalensis)

The experiment entitled “Effect of sowing date and nitrogen on quality of beet leaf (Beta vulgaris var. bengalensis)” was conducted during summer, 2020 at Vegetable Research Farm, RHRS, ASPEE College of Horticulture and Forestry, Navsari Agricultural University, Navsari, Gujarat, India. The experiment was laid out in Split plot design with five replications. The experiment was arranged with three levels of sowing date (D1:1st Fortnight of January D2:2nd Fortnight of January and D3:1st Fortnight of February) and four doses of nitrogen (N1:0 kg N ha-1, N2:50 kg N ha-1, N3: 75 kg N ha-1 and D4: 100 N kg ha-1). Total chlorophyll content (1.91 mg g-1), moisture content (88.51 %), ash content (7.37 %) and iron content (4.56 ppm) in plant from the 2nd cutting were recorded higher in 1st fortnight of January sowing (D1).&#x0D; Among the nitrogen doses, ascorbic acid (71.07 mg 100 g-1), total chlorophyll content (2.18 mg g-1), moisture content (89.22 %) and ash content (8.65 %) were obtained higher with application of nitrogen @ 100 kg ha-1 (N4). The iron, nitrogen and potassium content (4.71 ppm, 1.83% and 2.67 %, respectively) in plant and nitrogen (231.18 kg ha-1) in soil were higher under nitrogen at 100 kg ha-1 (N4).&#x0D; Beet leaf sown at 1st fortnight of January with 100 kg N ha-1 (D1N4) had given the best performance in the quality parameter i.e. total chlorophyll content (2.30 mg g-1).

Impact of conservation tillage on wheat performance and its microbiome.

Winter wheat is an important cereal consumed worldwide. However, current management practices involving chemical fertilizers, irrigation, and intensive tillage may have negative impacts on the environment. Conservation agriculture is often presented as a sustainable alternative to maintain wheat production, favoring the beneficial microbiome. Here, we evaluated the impact of different water regimes (rainfed and irrigated), fertilization levels (half and full fertilization), and tillage practices (occasional tillage and no-tillage) on wheat performance, microbial activity, and rhizosphere- and root-associated microbial communities of four winter wheat genotypes (Antequera, Allez-y, Apache, and Cellule) grown in a field experiment. Wheat performance (i.e., yield, plant nitrogen concentrations, and total nitrogen uptake) was mainly affected by irrigation, fertilization, and genotype, whereas microbial activity (i.e., protease and alkaline phosphatase activities) was affected by irrigation. Amplicon sequencing data revealed that habitat (rhizosphere vs. root) was the main factor shaping microbial communities and confirmed that the selection of endophytic microbial communities takes place thanks to specific plant-microbiome interactions. Among the experimental factors applied, the interaction of irrigation and tillage influenced rhizosphere- and root-associated microbiomes. The findings presented in this work make it possible to link agricultural practices to microbial communities, paving the way for better monitoring of these microorganisms in the context of agroecosystem sustainability.

Using an optimized texture index to monitor the nitrogen content of potato plants over multiple growth stages

Plant nitrogen content (PNC) is vital for evaluating crop nitrogen nutrient status and for net primary productivity. Therefore, rapid and accurate acquisition of crop PNC information can help reduce fertilizer and increase efficiency in modern agriculture. This study investigated whether an optimized texture index constructed from hyperspectral characteristic wavelength images acquired from a unmanned aerial vehicle (UAV) may be used to estimate PNC in potatoes over multiple growth stages. A potato field trial conducted in 2019 in Beijing, China, included different planting densities, nitrogen fertilizer gradients, and potato varieties with three replicates. A UAV remote sensing platform served to acquire hyperspectral images during three critical N demand periods for potatoes. In addition, we simultaneously conducted field sampling to obtain ground-truth PNC measurements. Following the classical form of vegetation indices, 12 texture indices were constructed based on hyperspectral texture features, and an arbitrary variable combination optimization algorithm was used to optimize these indices. Finally, the texture index, which had the highest correlation with potato PNC, was used as the best indicator for estimating the PNC status of potato at multiple growth stages and whether this indicator, in combination with spectral information, could further improve the accuracy of potato PNC estimation was subsequently explored. The results showed that (i) the optimal texture index TTI3 (R494-Cor, R578-Hom, R514-Sem) maintained a good linear relationship with PNC at the late stage of potato growth, and the accuracy and stability of the PNC estimation models constructed based on it was significantly better than that of a single texture metric. (ii) Compared with spectral information alone, the texture index combined with spectral features improved the accuracy of potato PNC estimation. More specifically, TTI3 (R494-Cor, R578-Hom, R514-Sem) combined with the three-band spectral index TBI 5 (530, 734, 514) achieved the best estimation accuracy with calibrated R2, root mean square error (RMSE), and normalized RMSE of 0.77%, 0.28%, and 9.88%, respectively. The results of this study showed that the texture index constructed by combining multiple texture metrics enhanced the association between texture features and potato PNC over multiple growth stages, thus improving the monitoring accuracy of potato nitrogen nutrition status. This study can provide a reference for accurately managing crop nitrogen nutrition.

Effect of Application Different Combinations of Phosphogypsum and Humic Acids Mixed With Saline Irrigation Water on Broccoli Yield and NPK Concentration

Long-term irrigation with saline water causes detrimental effects on the soil-crop system. The study aimed to determine the best combination of Phosphogypsum and humic acid can mitigate the negative effects of saline water irrigation on broccoli growth. Therefore, two-factorial field experiment was conducted according to a randomized complete block design with three replications during autumn season of 2021 in Fallujah district /Anbar governorate in sandy loam soil. The first factor included three levels of saline water irrigation, namely 2.5, 5.0 and 7.0 dS.m-1. while the second factor involved three levels of humic acids i.e., 0.0, 0.25 and 0.50 g/l mixed with three levels of phosphogypsum that is 0.0, 0.25 and 0.50 g/l. Fruits weight, height, yield, plant nitrogen content, plant phosphorus, and plant potassium content were measured. The results showed that the combinations under study ( humic acids and phosphogypsum) had a crucial role in reducing the negative effects of irrigation salinity. Moreover, the macronutrients availability increased with increasing humic acids concentration in irrigation water. The observed results show a significant increase in the weight of broccoli fruit and yield at T8 by giving 325 g. plant-1and 8.66 t.h-1 respectively under effect of the combination under study. Also, the studied combinations led to increase the N,P,and K concentration in plant tissues. Where the highest observed averages of nitrogen and potassium were 3.96% and 2.56% at T8 treatment. While The highest concentration of phosphorus was observed at treatment T9 reached 0.47%.

Maize Nitrogen Grading Estimation Method Based on UAV Images and an Improved Shufflenet Network

Maize is a vital crop in China for both food and industry. The nitrogen content plays a crucial role in its growth and yield. Previous researchers have conducted numerous studies on the issue of the nitrogen content in single maize plants from a regression perspective; however, partition management techniques of precision agriculture require plants to be divided by zones and classes. Therefore, in this study, the focus is shifted to the problems of plot classification and graded nitrogen estimation in maize plots performed based on various machine learning and deep learning methods. Firstly, the panoramic unmanned aerial vehicle (UAV) images of maize farmland are collected by UAV and preprocessed to obtain UAV images of each maize plot to construct the required datasets. The dataset includes three classes—low nitrogen, medium nitrogen, and high nitrogen, with 154, 94, and 46 sets of UAV images, respectively, in each class. The training set accounts for eighty percent of the entire dataset and the test set accounts for the other twenty percent. Then, the dataset is used to train models based on machine learning and convolutional neural network algorithms and subsequently the models are evaluated. Comparisons are made between five machine learning classifiers and four convolutional neural networks to assess their respective performances, followed by a separate assessment of the most optimal machine learning classifier and convolutional neural networks. Finally, the ShuffleNet network is enhanced by incorporating SENet and improving the kernel size of the Depthwise separable convolution. The findings demonstrate that the enhanced ShuffleNet network has the highest performance; its classification accuracy, precision, recall, and F1 scores were 96.8%, 97.0%, 97.1%, and 97.0%, respectively. The RegNet, the optimal model among deep learning models, achieved accuracy, precision, recall, and F1 scores of 96.4%, 96.9%, 96.5%, and 96.6%, respectively. In comparison, logistic regression, the optimal model among the machine learning classifiers, attained accuracy of 77.6%, precision of 79.5%, recall of 77.6%, and an F1 score of 72.6%. Notably, the logistic regression exhibited significant enhancements of 19.2% in accuracy, 17.5% in precision, 19.5% in recall, and 24.4% in the F1 score. In contrast, RegNet demonstrated modest improvements of 0.4% in accuracy, 0.1% in precision, 0.6% in recall, and 0.4% in the F1 score. Moreover, ShuffleNet-improvement boasted a substantially lower loss rate of 0.117, which was 0.039 lower than that of RegNet (0.156). The results indicated the significance of ShuffleNet-improvement in the nitrogen classification of maize plots, providing strong support for agricultural zoning management and precise fertilization.

A comprehensive analysis of CO2 exchanges in agro-ecosystems based on a generic soil-crop model-derived methodology

Carbon emissions in agriculture play a major role in climate change. Modelling studies enable to investigate the impacts of climate change in crops, accounting for soil organic carbon feedbacks and CO2 concentrations. But it is primordial that crop models properly consider the CO2 exchanges at the level of crop rotations beyond the cycle of a single crop. With this goal in mind, we used the outputs of the soil-crop model STICS in its standard pre-parameterized version to model (i) the Gross Primary Productivity (GPP), derived from the autotrophic respiration and the Net Primary Productivity, which is computed through the daily change in plant carbon (C) pools; (ii) the Ecosystem Respiration (RECO), with the autotrophic component being derived from the plant biomass, plant nitrogen concentration and GPP, and the heterotrophic component from the mineralization of residues and organic matter; and (iii) the Net Ecosystem Exchange, equal to the sum of GPP and RECO. The comparison of simulations with field observations indicates that the model is able to simulate accurately daily CO2 fluxes originating from a long-term and diversified crop rotation (efficiency EF equal to 0.79 for GPP, 0.59 for RECO and 0.67 for NEE). Concerning the evaluation of the cumulated fluxes over the 16-year rotation, the model is able to evaluate it accurately for RECO, with a slight underestimation (normalized deviation ND = 15.7%), and very accurately for GPP (ND = 5.12%). But for NEE, the relative overestimation is higher (ND = 62.2%), indicating that a more precise estimation of HR is required to obtain reliable net C budgets. The model also succeeds to capture the trends in the influence of several environmental drivers on CO2 fluxes. It globally proves to be a valuable tool in the investigation of CO2 exchanges of crop rotations in historical and future climatic conditions.

Innovative fertilizers and soil amendments based on hydrochar from brewery waste

Hydrothermal carbonization is one of the new ways to produce valuable products from waste materials. The aim of this study was to develop a novel method for the production of hydrochars from brewery residues for agricultural applications. Waste materials treated at 140 °C, 170 °C, and 200 °C were used as a source of renewable nitrogen and as delivery agents of fertilizer microelements (Cu, Mn, and Zn). HTC materials were enriched with nutrients using an innovative spray sorption method. The surface texture was characterized by the nitrogen adsorption technique, functional groups were identified (FT-IR), and thermogravimetric analysis of the materials was performed. Preliminary germination tests have shown phytotoxic effects at doses greater than 20% (relative to plant nitrogen requirements) for unenriched hydrochars. To minimize inhibition of plant growth, air aging tests were conducted for 30 days, followed by successive drying at 110 °C for 72 h. It was observed that the higher the temperature of the carbonization process, the faster the desorption of components in a solution simulating soil conditions (NaNO3) occurs. The pot tests on cucumber (Cornichon de Paris) showed that the biometric parameters of the plant (stem length and root ball parameters) for the hydrochar obtained at 170 °C were higher than in the group fertilized with mineral fertilizer. Twice the fresh plant mass was noted for this group compared to the reference. In addition, the study recorded a particular increase in nitrogen content in plants fertilized with a 150% dose of enriched hydrochar, where the element content increased by about 100% compared to the group with mineral fertilizer applied (100% dose). The study shows that properly prepared hydrochars from brewery waste offer great potential as a nitrogen source and micronutrient delivery agents for the agrochemical sector.Graphical Innovative fertilizers and soil amendments based on hydrochar from brewery waste

Over 50 Years of Overgrowth of the Ash Dump, The Content of Nitrogen and Phosphorus Changed in Young Soils but it Did Not Change in Plants

Changes in the nitrogen (N) and phosphorus (P) content in the ash substrate and plant leaves during the primary succession of overgrowing ash dumps of different ages were studied. The work was carried out on the young (overgrowth duration 5–8 years) and old (overgrowth duration 53–56 years; two sites – with meadow and forest vegetation) ash dumps of a thermal power plant in the Middle Urals. In the emerging soil and leaves of model plants, the content of N and P was determined on each dump. In young soils, a predictable and explainable successional dynamics of N and P was established: over 53–56 years, the N content increased 2.4–7.1 times, while the P content decreased 1.1–2.1 times. In plant leaves, the content of N and P at different stages of overgrowth was actually constant: 1.6–2.1% of N and 2.2–2.9 mg/g of P. In general, it has been found that in successionally young habitats, and in more advanced ones with developing forest vegetation, against the background of a multiple increase in the N content in the soil, the N content in plants remains low. With a high probability, on both dumps, the availability of nitrogen is a factor limiting the development of plants. This is evidenced by the results of the analysis of N/P ratio values in leaves and comparison of our array of N values in leaves with global averages of N content in the same species. Thus, the results with respect to the successional dynamics of the content of nitrogen and phosphorus in soils and plants of dumps of different ages turned out to be surprisingly little consistent with each other.

Effects of Inorganic and Organic Fertilizers on the Growth of Chickpea Plant and Soil Organic Carbon and Nitrogen Contents

Rapid economic and population growth has led to rising atmospheric greenhouse gas emissions, necessitating adequate soil and crop management for enhanced CO2 absorption. Plant and soil microorganisms have various effects on atmospheric CO2 capturing. Chickpea (Cicer arietinum L.), a leguminous plant, establishes a symbiotic association with rhizobium bacteria and mycorrhizal fungi, enabling it to fix atmospheric nitrogen (N2) and sequester more CO2 into the biomass and then to the soil. However, the application type of fertilizers influences this plant’s growth and soil’s capacity to retain carbon (C). In this background, the current research aimed to explore the impact of various organic and inorganic fertilization methods on the growth and nutrient content of chickpea plants and nitrogen content and soil organic carbon. A long-term field trial was started in 1996 at Çukurova University Research Center, with the five treatments such as Control (without fertilizer), Animal manure (25 t ha-1), Mineral fertilizer (NPK), Compost (25 t ha-1), and Mycorrhiza + Compost (10 t ha-1). Chickpeas were planted and harvested in 2020. During harvesting, plant samples at 1 m2 area, and soil samples at 0 to 15 cm and 15 to 30 cm depths were taken. The shoot, root and pod fresh biomass and tissue C, P, K, N, and Zn concentrations were determined. Similarly, the soil P, organic C and total N concentrations were determined. The results of the study indicate that mineral fertilizer resulted in a notable enhancement in the shoot, root and seed biomass of chickpea plants. Furthermore, mineral fertilizer resulted in a higher concentration of carbon in the roots, while the treatments involving animal manure, compost +AMF, and compost showed higher concentrations of K in both shoots and seeds. Seed P concentration was higher when animal manure was applied and exhibited similarity to the compost treatment. In terms of soil properties, the application of animal manure led to increased levels of soil organic carbon and P. Moreover, the compost treatment showed an increase in organic carbon in deeper soil depth. At 15-30 cm soil depth, both animal manure and compost treatments contributed to improved levels of total nitrogen. The good effects of organic fertilizers on soil fertility and nutrient levels in sustainable farming practices are highlighted by these findings. When compared to mineral fertilizer, animal dung specifically showed considerable improvements in soil organic carbon, nitrogen, and phosphorus.

Analysis of Soil Chemical Properties, Production, and Nitrogen Content of Pakcoy in Various Growth Media and Trichoderma Dosages

The use of compost added by Trichoderma is one of improving the soil's chemical properties and can be used to increase the production of pakcoy plants. The purpose of this study was to determine the effectiveness of compost derived from various types of palm oil waste and the application of Trichoderma on soil chemical properties as well as production and nitrogen content in pakcoy plants. This study used an experimental method with parameters observing plant fresh weight. The research used two factors. The first factor was three types of palm oil waste, midrib leaf, oil palm empty bunches, and solid decanter. The second factor was the dose of Trichoderma, 50 ml, 100 ml, and 150 ml per plant. Planting media derived from solid decanter was able to intensification the production of pakcoy compared to midrib leaf and oil palm empty bunches.

Accuracy Comparison of Estimation on Cotton Leaf and Plant Nitrogen Content Based on UAV Digital Image under Different Nutrition Treatments

The rapid, accurate estimation of leaf nitrogen content (LNC) and plant nitrogen content (PNC) in cotton in a non-destructive way is of great significance to the nutrient management of cotton fields. The RGB images of cotton fields in Shihezi (China) were obtained by using a low-cost unmanned aerial vehicle (UAV) with a visible-light digital camera. Combined with the data of LNC and PNC in different growth stages, the correlation between N content and visible light vegetation indices (VIs) was analyzed, and then the Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BP), and stepwise multiple linear regression (SMLR) were used to develop N content estimation models at different growth stages. The accuracy of the estimation model was assessed by coefficient of determination (R2), root mean squared error (RMSE), and relative root mean square error (rRMSE), so as to determine the optimal estimated growth stage and the best model. The results showed that the correlation between VIs and LNC was stronger than that between PNC, and the estimation accuracy of different models decreased continuously with the development of growth stages, with higher estimation accuracy in the peak squaring stage. Among the four algorithms, the best accuracy (R2 = 0.9001, RMSE = 1.2309, rRMSE = 2.46% for model establishment, and R2 = 0.8782, RMSE = 1.3877, rRMSE = 2.82% for model validation) was obtained when applying RF at the peak squaring stage. The LNC model for whole growth stages could be used in the later growth stage due to its higher accuracy. The results of this study showed that there is a potential for using an affordable and non-destructive UAV-based digital system to produce predicted LNC content maps that are representative of the current field nitrogen status.

Combination of Graphene Oxide and Rhizobium Improved Soybean Tolerance in Saline-Alkali Stress

Soybean (Glycine max L.) is the most important crop plant in the world. Soil saline-alkali seriously inhibits soybean yield and quality. This study aims to investigate the impact of graphene oxide (GO) and Rhizobium (Rh) on the expression of soybean-related genes as well as the growth and yield under saline-alkali stress. The results show that GO + Rh-treated increased the number of root nodules by 5.43 times compared with the control (Ctrl), the total nitrogen content and root system parameters of plants were also significantly improved. GO + Rh-treated reduced the Na+/K+ ratio and the osmotic substances, while the activities of antioxidant enzymes SOD, POD, CAT and APX in GO + Rh-treated soybean plants increased significantly by 69.18%, 69%, 75.64% and 48.38% compared with the control plants. The REC, MDA and H2O2 content decreased significantly by 46.73%, 42.80% and 43.53%. In addition, GA3 content, among all related saline-alkali hormones, was increased by 100.20% compared with the Ctrl. The expression level of GmGBP1, a key gene for GA3 synthesis, at most increased 6.42 times compared to the Ctrl. The results further reveal that GO + Rh-treated obviously improves the yield traits of soybean plants, which confirms that GO + Rh-treated could be effective in enhancing soybean tolerance to saline-alkali stress. Our findings provide a new strategy for improving the saline-alkali tolerance of soybean, as well as a new perspective for exploiting and utilizing large-area saline-alkali soil.

Stoichiometric characteristics and homeostasis of leaf nitrogen and phosphorus responding to different water surface elevations in hydro-fluctuation zone of the Three Gorges Reservoir

As a type of wetland ecosystem with off-season 30 m water level fluctuation, the huge changes in the ecological environment, plant species, and vegetation dynamics in the hydro-fluctuation zone of the Three Gorges Reservoir (TGR) area have attracted a wide range of attention. In this present study, six typical locations in the water level fluctuating zone were used as the research objects, and the effects of different water surface elevations on the stoichiometric characteristics and homeostasis of leaf nitrogen and phosphorus were studied through a sample survey method. Results revealed that leaf nitrogen content was linearly correlated with leaf phosphorus content along water surface elevation. And water surface elevation significantly affected the nitrogen and phosphorus content of dominant plants. Four dominant species [Cynodon dactylon (Linn.) Pers, Xanthium sibiricum Partin ex Wider, Abutilon theophrasti Medik, and Bidens pilosa Linn] exhibited specific differences in the phosphorus steady state index (Hp) and nitrogen steady state index (HN). Although belonging to different categories, both HP and HN of four dominant species were in the same order: X. sibiricum > A. theophrasti > C. dactylon > B. Pilosa. The interspecific differences in HN and HP indicated that there were differences in the characteristics of nutrient utilization of dominant species and their adaption to water surface elevation. Furthermore, as the elevation increases, the community coverage increased and the community stability index also increased. This might indicate that in the fluctuating zone habitat, the plant’s nitrogen and phosphorus utilization strategy affects the distribution and composition of plant community along water surface elevation, and ultimately affects the stoichiometric homeostasis on the community levels.

Estimation of Winter Wheat Plant Nitrogen Concentration from UAV Hyperspectral Remote Sensing Combined with Machine Learning Methods

Nitrogen is one of the most important macronutrients and plays an essential role in the growth and development of winter wheat. It is very crucial to diagnose the nitrogen status timely and accurately for applying a precision nitrogen management (PNM) strategy to the guidance of nitrogen fertilizer in the field. The main purpose of this study was to use three different prediction methods to evaluate winter wheat plant nitrogen concentration (PNC) at booting, heading, flowering, filling, and the whole growth stage in the Guanzhong area from unmanned aerial vehicle (UAV) hyperspectral imagery. These methods include (1) the parametric regression method; (2) linear nonparametric regression methods (stepwise multiple linear regression (SMLR) and partial least squares regression (PLSR)); and (3) machine learning methods (random forest regression (RFR), support vector machine regression (SVMR), and extreme learning machine regression (ELMR)). The purpose of this study was also to pay attention to the impact of different growth stages on the accuracy of the model. The results showed that compared with parametric regression and linear nonparametric regression, the machine learning regression method could evidently improve the estimation accuracy of winter wheat PNC, especially using SVMR and RFR, the training set of the model at flowering and filling stage explained 93% and 92% of the PNC variability respectively. The testing set of the model at flowering and filling stages explained 88% and 91% of the PNC variability, the root mean square error of the validation set (RMSEtesting) was 0.82 and 1.23, and the relative prediction deviation (RPD) was 2.58 and 2.40, respectively. Therefore, a conclusion was drawn that it was the best choice to estimate winter wheat PNC at the flowering and filling stage from UAV hyperspectral imagery. Using machine learning methods, SVMR and RFR, respectively, could achieve the most outstanding estimation performance, which could provide a theoretical basis for putting forward the PNM strategy.

Nodule Synthetic Bacterial Community as Legume Biofertilizer under Abiotic Stress in Estuarine Soils.

Estuaries are ecologically important ecosystems particularly affected by climate change and human activities. Our interest is focused on the use of legumes to fight against the degradation of estuarine soils and loss of fertility under adverse conditions. This work was aimed to determine the potential of a nodule synthetic bacterial community (SynCom), including two Ensifer sp. and two Pseudomonas sp. strains isolated from Medicago spp. nodules, to promote M. sativa growth and nodulation in degraded estuarine soils under several abiotic stresses, including high metal contamination, salinity, drought and high temperature. These plant growth promoting (PGP) endophytes were able to maintain and even increase their PGP properties in the presence of metals. Inoculation with the SynCom in pots containing soil enhanced plant growth parameters (from 3- to 12-fold increase in dry weight), nodulation (from 1.5- to 3-fold increase in nodules number), photosynthesis and nitrogen content (up to 4-fold under metal stress) under all the controlled conditions tested. The increase in plant antioxidant enzymatic activities seems to be a common and important mechanism of plant protection induced by the SynCom under abiotic stress conditions. The SynCom increased M. sativa metals accumulation in roots, with low levels of metals translocation to shoots. Results indicated that the SynCom used in this work is an appropriate ecological and safe tool to improve Medicago growth and adaptation to degraded estuarine soils under climate change conditions.

Effect of inter-tillage weeding on rice yield, rice growth, and nutrient dynamics without agrochemical and fertilizer application: Results of a three-year study

Traditionally, frequent inter-tillage is believed to increase rice yield without the application of chemicals. However, research on rice growth and soil nutrient dynamics under such cultivation management is lacking. In this study, we performed rice cultivation without applying agrochemicals and fertilizers to elucidate the influence of inter-tillage, especially, three years after switching to no chemical application. The rice growth, rice yield, and nutrient (N, P, and K) dynamics over three years (2018–2020) were assessed, including three different treatments: 0-, 2-, and 5-times inter-tillage (0-T, 2-T, and 5-T, respectively) and conventional cultivation as rough reference. In the first year, the brown rice yield in inter-tillage fields (531–566 g/m2) was higher than the regional average yield for conventional rice cultivation (497 g/m2) after several years of fallow with herbicide. In the second year, following nutrient consumption in the first year, the yield in inter-tillage fields (220–272 g/m2) was less than the regional average yield (547 g/m2). There was no significant variation (P < 0.05) in yields in the three inter-tillage fields in the first and second years. However, in the third year, the order of yield achieved in experimental fields was 5-T (354 g/m2) > 2-T (209 g/m2) > 0-T (173 g/m2), although these yields were lower than the regional average yield (561 g/m2). The exchangeable ammonium concentration in the soil and the ammonium concentration in the soil solution from inter-tillage fields decreased in the third year compared to the second year's results. However, the rice yield, growth, and nitrogen concentration in rice plants in 5-T increased in the third year. Our findings indicate that several years of inter-tillage make the soil environment effectively suitable for fertilizer- and chemical-free rice cultivation.