A review of methods for sensing the nitrogen status in plants: advantages, disadvantages and recent advances

Temporal and spatial drivers of the structure of macroinvertebrate assemblages associated with Laminaria hyperborea detritus in the northeast Atlantic

Kelp forests occur on more than a quarter of the world's coastlines, serving as foundation species supporting high levels of biodiversity. They are also a major source of organic matter in coastal ecosystems, with the majority of primary production released and exported as detritus. Kelp detritus also provides food and shelter for macroinvertebrates, which comprise important components of inshore food-webs. Hitherto, research on kelp detritus-associated macroinvertebrate assemblages remains relatively limited. We quantified spatiotemporal variability in the structure of detritus-associated macroinvertebrate assemblages within Laminaria hyperborea forests and evaluated the influence of putative drivers of the observed variability in assemblages across eight study sites within four regions of the United Kingdom in May and September 2015. We documented 5167 individuals from 106 taxa with Malacostraca, Gastropoda, Isopoda and Bivalvia the most abundant groups sampled. Assemblage structure varied across months, sites, and regions, with highest richness in September compared to May. Many taxa were unique to individual regions, with few documented in all regions. Finally, key drivers of assemblage structure included detritus tissue nitrogen content, depth, sea surface temperature, light intensity, as well as L. hyperborea canopy density and canopy biomass. Despite their dynamic composition and transient existence, accumulations of L. hyperborea detritus represent valuable repositories of biodiversity and represent an additional kelp forest component which influences secondary productivity, and potentially kelp forest food-web dynamics.

Genome-wide association studies identifies genetic loci related to fatty acid and branched-chain amino acid metabolism and histone modifications under varying nitrogen treatments in safflower (Carthamus tinctorius)

Effective identification and usage of genetic variation are prerequisites for developing nutrient-efficient cultivars. A collection of 94 safflower (Carthamus tinctorius ) genotypes (G) was investigated for important morphological and photosynthetic traits at four nitrogen (N) treatments. We found significant variation for all the studied traits except chlorophyll b (chl b ) among safflower genotypes, nitrogen treatments and G×N interaction. The examined traits showed a 2.82-50.00% increase in response to N application. Biological yield (BY) reflected a significantly positive correlation with fresh shoot weight (FSW), root length (RL), fresh root weight (FRW) and number of leaves (NOL), while a significantly positive correlation was also observed among carotenoids (C), chlorophyll a (chl a ), chl b and total chlorophyll content (CT) under all treatments. Superior genotypes with respect to plant height (PH), FSW, NOL, RL, FRW and BY were clustered into Group 3, while genotypes with better mean performance regarding chl a , chl b C and CT were clustered into Group 2 as observed in principal component analysis. The identified eight best-performing genotypes could be useful to develop improved nitrogen efficient cultivars. Genome-wide association analysis resulted in 32 marker-trait associations (MTAs) under four treatments. Markers namely DArT-45481731 , DArT-17812864 , DArT-15670279 and DArT-45482737 were found consistent. Protein-protein interaction networks of loci associated with MTAs were related to fatty acid and branched-chain amino acid metabolism and histone modifications.

Enhancement of soybean tolerance to water stress through regulation of nitrogen and antioxidant defence mechanisms mediated by the synergistic role of salicylic acid and thiourea

Water stress (WS) poses a significant threat to global food and energy security by adversely affecting soybean growth and nitrogen metabolism. This study explores the synergistic effects of exogenous salicylic acid (SA, 0.5 mM) and thiourea (TU, 400 mg L-1), potent plant growth regulators, on soybean responses under WS conditions. The treatments involved foliar spraying for 3 days before inducing WS by reducing soil moisture to 50% of field capacity, followed by 2 weeks of cultivation under normal or WS conditions. WS significantly reduced plant biomass, chlorophyll content, photosynthetic efficiency, water status, protein content, and total nitrogen content in roots and leaves. Concurrently, it elevated levels of leaf malondialdehyde, H2O2, proline, nitrate, and ammonium. WS also triggered an increase in antioxidant enzyme activity and osmolyte accumulation in soybean plants. Application of SA and TU enhanced the activities of key enzymes crucial for nitrogen assimilation and amino acid synthesis. Moreover, SA and TU improved plant growth, water status, chlorophyll content, photosynthetic efficiency, protein content, and total nitrogen content, while reducing oxidative stress and leaf proline levels. Indeed, the simultaneous application of SA and TU demonstrated a heightened impact compared to their separate use, suggesting a synergistic interaction. This study underscores the potential of SA and TU to enhance WS tolerance in soybean plants by modulating nitrogen metabolism and mitigating oxidative damage. These findings hold significant promise for improving crop productivity and quality in the face of escalating water limitations due to climate change.

Fast and low-cost method for direct and simultaneous determination of nitrogen and carbon in soybean leaves using benchtop and portable near-infrared devices

BACKGROUND

The current techniques for determining carbon and nitrogen content to provide information about the nutritional status of plants are time-consuming and expensive. For this reason, the objective of this study was to develop an analytical method for the direct and simultaneous determination of nitrogen and carbon elemental content in soybean leaves using near-infrared spectroscopy and compare the performance of conventional (1100-2500 nm spectral range) and portable equipment (1100-1700 nm spectral range). Partial least-squares regression models were developed using 27 soybean leaf samples collected during the 2021 harvest and applied for the simultaneous determination of carbon and nitrogen in 13 samples collected during the 2022 harvest.

RESULTS

The root-mean-square error of prediction values for nitrogen and carbon were low (2.42 g kg-1 and 4.37 g kg-1 respectively) for the benchtop method yielded low but higher for the portable method (3.82 g kg-1 and 10.7 g kg-1 respectively). The benchtop method did not show significant differences when compared with the reference method for determining nitrogen and carbon. In contrast, the portable methodology showed potential as a screening method for determining nitrogen levels, particularly in fieldwork.

CONCLUSION

The methodologies evaluated in this study were implemented and evaluated under real crop monitoring conditions, using independent sets of calibration and prediction samples. Their utilization enables the acquisition of cost-effective, safe analytical data aligning with the principles of green analytical chemistry. © 2023 Society of Chemical Industry.

Gamma radiation vs high pressure pretreatment on physicochemical characteristics of rice bran hydrolysate

This study investigated the effect of using gamma radiation and high-pressure processing as pretreatment, to consider the structural and amino acid composition changes in rice bran hydrolysate (RBH). The extraction yield and degree of hydrolysis of the irradiated sample were greater than those of the pressurized and control samples, which radiation at 10 kGy gave 31 % yield. Protein content of the control was the highest at 36.1 %, with 32.4 % in pressurized sample at 500 MPa. Control had the highest concentration of total and branched-chain amino acids, with a value of 25,834 mg/100g. Before and after extraction, the microstructure changed visibly and protein agglomeration can be significantly induced by applying a high-pressure. Therefore, this study showed the potential of using both pretreatment methods prior to enzymolysis extraction, with radiation producing more extract. High-pressure produced more protein content, but neither method produced any difference in amino acid content.

Comprehensive Evaluation of Tomato Growth Status under Aerated Drip Irrigation Based on Critical Nitrogen Concentration and Nitrogen Nutrient Diagnosis

In order to provide a theoretical basis for the rational application of nitrogen fertilizer for tomatoes under aerated drip irrigation, a model of the critical nitrogen dilution curve was established in this study, and the feasibility of the nitrogen nutrition index (NNI) for the real-time diagnosis and evaluation of the nitrogen nutrient status was explored. The tomato variety "FENOUYA" was used as the test crop, and aerated drip irrigation was adopted by setting three levels of aeration rates, namely, A1 (dissolved oxygen concentration of irrigation water is 5 mg L-1), A2 (dissolved oxygen concentration of irrigation water is 15 mg L-1), and A3 (dissolved oxygen concentration of irrigation water is 40 mg L-1), and three levels of nitrogen rates, namely, N1 (120 kg ha-1), N2 (180 kg ha-1) and N3 (240 kg ha-1). The model of the critical nitrogen concentration dilution of tomatoes under different aerated treatments was established. The results showed that (1) the dry matter accumulation of tomatoes increased with the increase in the nitrogen application rate in a certain range and it showed a trend of first increase and then decrease with the increase in aeration rate. (2) As the reproductive period progressed, the nitrogen concentration in tomato plants showed a decreasing trend. (3) There was a power exponential relationship between the critical nitrogen concentration of tomato plant growth and above-ground biomass under different levels of aeration and nitrogen application rate, but the power exponential curves were characterized by A1 (Nc = 15.674DM-0.658), A2 (Nc = 101.116DM-0.455), A3 (Nc = 119.527DM-0.535), N1 (Nc = 33.819DM-0.153), N2 (Nc = 127.759DM-0.555) and N3 (Nc = 209.696DM-0.683). The standardized root mean square error (n-RMSE) values were 0.08%, 3.68%, 3.79% 0.50%, 1.08%, and 0.55%, which were less than 10%, and the model has good stability. (4) The effect of an increased nitrogen application rate on the critical nitrogen concentration dilution curve was more significant than that of the increase in aeration rate. (5) A nitrogen nutrition index model was built based on the critical nitrogen concentration model to evaluate the nitrogen nutritional status of tomatoes, whereby 180 kg ha-1 was the optimal nitrogen application rate, and 15 mg L-1 dissolved oxygen of irrigation water was the optimal aeration rate for tomatoes.

The Genetic Dissection of Nitrogen Use-Related Traits in Flax (Linum usitatissimum L.) at the Seedling Stage through the Integration of Multi-Locus GWAS, RNA-seq and Genomic Selection

Nitrogen (N), the most important macro-nutrient for plant growth and development, is a key factor that determines crop yield. Yet its excessive applications pollute the environment and are expensive. Hence, studying nitrogen use efficiency (NUE) in crops is fundamental for sustainable agriculture. Here, an association panel consisting of 123 flax accessions was evaluated for 21 NUE-related traits at the seedling stage under optimum N (N+) and N deficiency (N-) treatments to dissect the genetic architecture of NUE-related traits using a multi-omics approach integrating genome-wide association studies (GWAS), transcriptome analysis and genomic selection (GS). Root traits exhibited significant and positive correlations with NUE under N- conditions (r = 0.33 to 0.43, p < 0.05). A total of 359 QTLs were identified, accounting for 0.11% to 23.1% of the phenotypic variation in NUE-related traits. Transcriptomic analysis identified 1034 differentially expressed genes (DEGs) under contrasting N conditions. DEGs involved in N metabolism, root development, amino acid transport and catabolism and others, were found near the QTLs. GS models to predict NUE stress tolerance index (NUE_STI) trait were tested using a random genome-wide SNP dataset and a GWAS-derived QTLs dataset. The latter produced superior prediction accuracy (r = 0.62 to 0.79) compared to the genome-wide SNP marker dataset (r = 0.11) for NUE_STI. Our results provide insights into the QTL architecture of NUE-related traits, identify candidate genes for further studies, and propose genomic breeding tools to achieve superior NUE in flax under low N input.

Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields

Managing the nutritional status of strawberry plants is critical for optimizing yield. This study evaluated the potential of hyperspectral imaging (400-1,000 nm) to estimate nitrogen (N), phosphorus (P), potassium (K), and calcium (Ca) concentrations in strawberry leaves, flowers, unripe fruit, and ripe fruit and to predict plant yield. Partial least squares regression (PLSR) models were developed to estimate nutrient concentrations. The determination coefficient of prediction (R2P) and ratio of performance to deviation (RPD) were used to evaluate prediction accuracy, which often proved to be greater for leaves, flowers, and unripe fruit than for ripe fruit. The prediction accuracies for N concentration were R2P = 0.64, 0.60, 0.81, and 0.30, and RPD = 1.64, 1.59, 2.64, and 1.31, for leaves, flowers, unripe fruit, and ripe fruit, respectively. Prediction accuracies for Ca concentrations were R2P = 0.70, 0.62, 0.61, and 0.03, and RPD = 1.77, 1.63, 1.60, and 1.15, for the same respective plant parts. Yield and fruit mass only had significant linear relationships with the Difference Vegetation Index (R2 = 0.256 and 0.266, respectively) among the eleven vegetation indices tested. Hyperspectral imaging showed potential for estimating nutrient status in strawberry crops. This technology will assist growers to make rapid nutrient-management decisions, allowing for optimal yield and quality.

Assessing the quality of sewage sludge: CASE study of the Kumasi wastewater treatment plant

Agricultural application is the primary method of recycling sewage sludge. It is an alternative for recycling this residue, providing nutrients and organic matter to crops and soil. However, sewage treatment and management issues may impact its quality. The main objective of the research was to determine the quality of sewage sludge generated at the Kumasi Wastewater Treatment Plant (KWTP). Understanding the effects of using sludge on soil and plants is critical. To overcome this constraint, the soil microbial biomass was used to quantify the growth of microorganisms. The levels of potentially toxic elements in the sludge using atomic absorption spectrometry (AAS) are based on US EPA part 503 regulations for the disposal and management of biosolids. This study found that trace metal concentrations in the biosolids were lower than the referenced background standards threshold. Although the microbial biomass, nutrients and bacteria levels were within the accepted values for their possible use as soil fertilizer. The ecological risk index (135.10) indicated that the level of arsenic was high in the sludge. The salinity in the sludge was low, with electrical conductivity (EC) being high (60.80-436.00 μS/cm) and pH decreasing with age (6.73-7.69). The sludge produced at KWTP is of good quality and meets international standards with only a high concentration of As. This can be used for soil amendment when As is reduced in the sludge.

Nutritional Composition Profiles and Quality Evaluation of Different Cultivars of Asparagus Officinalis with Potential as Functional Foods and Health-Care Products

Asparagus officinalis is a health-care vegetable with homology value of medicine and food. The quality of A. officinalis is greatly different from various cultivars. It is essential to reveal the relationship between the variety and quality. This study investigated six nutritional compositions in ten A. officinalis cultivars, including amino acid, mineral substance, carbohydrate, vitamin C, protein and total sugar. Five chemometrics methods were further employed to evaluate their quality. The results consistently showed that ten varieties were divided into three grades as nutritional composition differences. HuaMiaoF1, JinGuan and FeiCuiMingZhu were grouped into cluster3 with the best quality, and Atlas and Jersey Giant were grouped into cluster1 with the lowest quality. Therefore, HuaMiaoF1, JinGuan and FeiCuiMingZhu can be suggested as good raw materials for medicine, food and health-care products industries. Meanwhile, the comprehensive application of five chemometrics methods was confirmed as a reliable methodology for quality evaluation of A. officinalis.

Fast screening of total nutrient contents in strawberry leaves and spent growing media using NIRS

Introduction

In closed-loop soilless cultivation, the main nutrient sinks are nutrients retained either by the crop or in spent growing media. Measurement of nutrients in spent growing media and in the aboveground vegetative plant biomass at crop termination can be a tool for assessing and optimizing nutrient efficiency. The first aim of this study was to test the potential of near-infrared reflectance spectroscopy (NIRS) to forecast the various nutrient contents in strawberry leaves, which would then allow for assessment of crop nutrient status and total nutrient uptake by strawberry plants. The second aim was to test NIRS as a high throughput technique for assessing the N, K, Ca, Mg and organic matter (OM) content and the pH, EC and C:N and C:P ratios for a dataset of composts, plant fibers and spent growing media. The NIRS prediction model for fast screening of the total nutrient contents in spent growing media was compared with a single extraction method.

Methods

A database with 369 dried and ground strawberry leaf samples with known contents of N, P, K, Ca, and Mg were scanned using NIRS. The database covered a range of leaf contents of 6-35 g N/kg dry matter (DM), 0.7-6.3 g P/kg DM and 2-29 g K/kg DM. A dataset of 458 samples of different types of materials used in growing media was validated with a dataset of 109 samples.

Results

Validation for the strawberry leaves indicated potential for this application, with R2 values of 0.90 or higher for N, K and Ca, and R2 values higher than 0.85 for P and Mg. Validation for the dataset of composts, plant fibers and spent growing media also indicated the potential for this application, with R2 values of 0.90 or higher for organic matter, and with R2 values of 0.85 or higher for total Ca, pH and C:N. A first test indicated potential for the calibration based on fresh samples of compost, plant fiber as well as spent growing media or dried (not ground) samples.

Discussion

Use of NIRS on fresh samples would eliminate the need for drying and grinding the samples and would reduce screening time. The ammonium acetate extraction is a reliable alternative to NIRS for fast screening of the total P, K, Ca, and Mg contents in composts, plant fibers and spent growing media.

Is the biotic potential, expressed as life-history features and nutritional indices, of generalized herbivore insects a function of host plants? — Spodoptera littoralis as a case study.. [DOI: 10.21203/rs.3.rs-3223926/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

The present study aimed at evaluating the impacts of castor bean, tomato, potato, and cucumber leaves on the various components of biological fitness and food consumption and utilization of S. littoralis. The levels of nitrogen, potassium, and phosphorous in the tested host plants were quantified. Feeding larvae on the tested host plants did not affect larval or pupal duration. Whereas, weight of full-grown larvae, adult emergence, and egg-hatch were significantly affected, with the highest and lowest values in case of feeding on castor bean and cucumber leaves, respectively. The highest and lowest number of eggs deposited per female were attained on castor bean and tomato leaves, respectively. Adult male and female longevity was the highest on potato and on castor bean leaves, respectively. Nutritional indices were dependent on the host plant, metamorphed instar, and age within instar, with the most profound change in case of 6th -instar larvae (last instar). Castor bean was the most nutritive host plant as it contained the highest levels of nitrogen and potassium. In contrast, tomato was the least nutritive one as it contained the lowest concentrations of nitrogen, phosphorous, and potassium. It appears that castor bean was the most suitable host plant, whereas tomato was the least suitable one. The findings obtained herein may help in understanding the biology of S. littoralis, leading to a better strategy to its control. Future studies should focus on testing a wider range of host plant species.

Plant impedance spectroscopy: a review of modeling approaches and applications

Electrochemical impedance spectroscopy has emerged over the past decade as an efficient, non-destructive method to investigate various (eco-)physiological and morphological properties of plants. This work reviews the state-of-the-art of impedance spectra modeling for plant applications. In addition to covering the traditional, widely-used representations of electrochemical impedance spectra, we also consider the more recent machine-learning-based approaches.

The role of remote sensing in tropical grassland nutrient estimation: a review

The carbon (C) and nitrogen (N) ratio is a key indicator of nutrient utilization and limitations in rangelands. To understand the distribution of herbivores and grazing patterns, information on grass quality and quantity is important. In heterogeneous environments, remote sensing offers a timely, economical, and effective method for assessing foliar biochemical ratios at varying spatial and temporal scales. Hence, this study provides a synopsis of the advancement in remote sensing technology, limitations, and emerging opportunities in mapping the C:N ratio in rangelands. Specifically, the paper focuses on multispectral and hyperspectral sensors and investigates their properties, absorption features, empirical and physical methods, and algorithms in predicting the C:N ratio in grasslands. Literature shows that the determination of the C:N ratio in grasslands is not in line with developments in remote sensing technologies. Thus, the use of advanced and freely available sensors with improved spectral and spatial properties such as Sentinel 2 and Landsat 8/9 with sophisticated algorithms may provide new opportunities to estimate C:N ratio in grasslands at regional scales, especially in developing countries. Spectral bands in the near-infrared, shortwave infrared, red, and red edge were identified to predict the C:N ratio in plants. New indices developed from recent multispectral satellite imagery, for example, Sentinel 2 aided by cutting-edge algorithms, can improve the estimation of foliar biochemical ratios. Therefore, this study recommends that future research should adopt new satellite technologies with recent development in machine learning algorithms for improved mapping of the C:N ratio in grasslands.

Improvement of tomato yield and quality using slow release NPK fertilizers prepared by carnauba wax emulsion, starch-based latex and hydrogel nanocomposite combination

The modern agriculture is working on introducing new generation of fertilizers that apt to slow down the nutrients release to be more in synchrony with plant's need throughout growth season, enhance fertilizer performance, and decrease nutrient losses into the environment. The aim of this research was to develop an advanced NPK slow-release fertilizer (SRF) and investigate its effect on yield, nutritional and morphological responses of tomato plant (Lycopersicon esculentum Mill.) as a model crop. To this goal, three water-based bio-polymeric formulations including starch-g-poly (acrylic acid-co-acrylamide) nanocomposite hydrogel, starch-g-poly(styrene-co-butylacrylate) latex, and carnauba wax emulsion were synthesized and used for production of NPK-SRF samples. Different samples of coated fertilizers (urea, potassium sulfate, and superphosphate granules) were prepared using different ratios of latex and wax emulsion, and for phosphorus and potash (R-treatment). Moreover, some of coated fertilizers (15 and 30 wt.%) was replaced with nanocomposite hydrogel containing fertilizers, named D and H treatments, respectively. The effect of SRF samples were compared with commercial fertilizers (NPK treatment) and a commercial SRF (T treatment), on the growth of tomato in the greenhouse, at two different levels (100 and 60). The efficiency of all the synthesized formulations were higher than NPK and T treatments, and among them, H100 significantly improved the morphological and physiological characteristics of tomato. For instance, amount of residual elements (nitrogen, phosphorus and potassium) as well as micro elements of calcium, iron and zinc in tomato cultivation bed and accordingly the uptake of these elements in the roots, aerial parts and fruits were increased in the R, H, and D treatments. The highest yield (1671.54 g), highest agricultural agronomy efficiency of fertilizer, and the highest dry matter percentage (9.52%) were obtained in H100. The highest amount of lycopene, antioxidant capacity and vitamin C was also observed in H100. Nitrate accumulation in tomato fruit in the synthesized SRF samples were decreased significantly compared to NPK100, and the lowest amount was observed in H100, which was 55.24% less than NPK100. Accordingly, it is suggested that combination of natural-based nanocomposite hydrogels along with coating latexes and wax emulsions can be a successful method to synthesize efficient NPK-SRF formulations for improvement of crop growth and quality.

Response of different varieties of maize to nitrogen stress and diagnosis of leaf nitrogen using hyperspectral data

Spectral technology is theoretically effective in diagnosing N stress in maize (Zea mays L.), but its application is affected by varietal differences. In this study, the responses to N stress, leaf N spectral diagnostic models and the differences between two maize varieties were analysed. The variety "Jiyu 5817" exhibited a greater response to different N stresses at the 12-leaf stage (V12), while "Zhengdan 958" displayed a greater response in the silking stage (R1). Correlation analysis showed that the spectral bands more sensitive to leaf N content were 548-556 nm and 706-721 nm at the V12 stage in "Jiyu 5817" and 760-1142 nm at the R1 stage in "Zhengdan 958". An N spectral diagnostic model that considers the varietal effect improves the model fit and root mean square error (RMSE) with respect to the model without it by 10.6% and 29.2%, respectively. It was concluded that the V12 stage for "Jiyu 5817" and the R1 stage for "Zhengdan 958" were the best diagnostic stages and were more sensitive to N stress, which can further guide fertilization decision-making in precision fertilization.

Agro-Physiological Traits of Kaffir Lime in Response to Pruning and Nitrogen Fertilizer under Mild Shading

Mild shading has been reported to increase leaf production in kaffir lime (Citrus hystrix) through the improvement of agro-physiological variables, such as growth, photosynthesis, and water-use efficiency; however, there is still a knowledge gap concerning its growth and yield after experiencing severe pruning in harvest season. Additionally, a specific nitrogen (N) recommendation for leaf-oriented kaffir lime is still unavailable due to its lesser popularity compared to fruit-oriented citrus. The present study determined the best pruning level and N dose based on agronomy and the physiology of kaffir lime under mild shading. Nine-month-old kaffir lime seedlings grafted to rangpur lime (C. limonia) were arranged in a split-plot design, i.e., N dose as a main plot and pruning as a subplot. Comparative analysis resulted in 20% higher growth and a 22% higher yield in the high-pruned plants by leaving 30 cm of main stem above the ground rather than short ones with a 10 cm main stem. Both correlation and regression analysis strongly highlighted the importance of N for leaf numbers. Plants treated with 0 and 10 g N plant^-1 experienced severe leaf chlorosis due to N deficiency, while those treated with 20 and 40 g N plant^-1 showed N sufficiency; thus, the efficient recommendation for kaffir lime leaf production is 20 g N plant^-1.

Effects of Exponential N Application on Soil Exchangeable Base Cations and the Growth and Nutrient Contents of Clonal Chinese Fir Seedlings

Nitrogen (N) is an essential macronutrient for plant function and growth and a key component of amino acids, which form the building blocks of plant proteins and enzymes. However, misuse and overuse of N can have many negative impacts on the ecosystem, such as reducing soil exchangeable base cations (BCs) and causing soil acidification. In this research, we evaluated clonal Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) seedlings grown with exponentially increasing N fertilization (0, 0.5, 1, 2 g N seedling^-1) for a 100-day trial in a greenhouse. The growth of seedlings, their nutrient contents, and soil exchangeable cations were measured. We found that N addition significantly increased plant growth and N content but decreased phosphorous (P) and potassium (K) contents in plant seedlings. The high nitrogen (2 g N seedling^-1) treated seedlings showed a negative effect on growth, indicating that excessive nitrogen application caused damage to the seedlings. Soil pH, soil exchangeable base cations (BCs), soil total exchangeable bases (TEB), soil cation exchange capacity (CEC), and soil base saturation (BS) significantly decreased following N application. Our results implied that exponential fertilization resulted in soil acidification and degradation of soil capacity for supplying nutrient cations to the soil solution for plant uptake. In addition, the analysis of plants and BCs revealed that Na⁺ is an important base cation for BCs and for plant growth in nitrogen-induced acidified soils. Our results provide scientific insights for nitrogen application in seedling cultivation in soils and for further studies on the relationship between BCs and plant growth to result in high-quality seedlings while minimizing fertilizer input and mitigating potential soil pollution.

Sodium nitroprusside ameliorates lead toxicity in rice (Oryza sativa L.) by modulating the antioxidant scavenging system, nitrogen metabolism, lead sequestration mechanism, and proline metabolism

As a toxic anthropogenic pollutant, lead (Pb) can be harmful to both plants and animals. Here, the effects of the application of nitric oxide (NO) donor, sodium nitroprusside (SNP, 0, 50, and 100 μM), on the morphological, biochemical, and molecular responses of rice plants under Pb (0, 150, and 300 μM) toxicity in hydroponic conditions were investigated. Pb stress decreased biomass, photosynthetic pigments, Fv/Fm value, and nitrogen (N) and increased the accumulation of hydrogen peroxide (H₂O₂), methylglyoxal (MG), malondialdehyde (MDA), and electrolyte leakage (EL) in rice seedlings. However, by improving the metabolism of chlorophyll and proline, SNP increased the content of chlorophyll and proline, restored the performance of the photosynthetic apparatus, and stimulated the growth of Pb-stressed rice seedlings. SNP by reducing the expression of HMA2 and increasing the expression of HMA3 and HMA4 caused the immobilization of Pb in the roots and reduced its transfer to the leaves. Adding SNP increased the activity of antioxidant enzymes and glyoxalase cycle and decreased H₂O₂, MG, MDA, and EL in the leaves of Pb-stressed rice seedlings. By upregulating the expression of genes GSH1, PCS, and ABCC1, SNP increased the accumulation of GSH and PCs in the roots and leaves and increased the plant's tolerance to Pb stress. By modulating the activity of enzymes involved in N metabolism, SNP increased the concentration of N and nitrate and decreased the concentration of ammonium in the leaves of Pb-stressed seedlings. Our study provides evidence that NO may become a promising tool for increasing the tolerance of rice plants to Pb toxicity.

Combined application of asparagine and thiourea improves tolerance to lead stress in wheat by modulating AsA-GSH cycle, lead detoxification and nitrogen metabolism

Lead (Pb), like other heavy metals, is not essentially required for optimal plant growth; however, plants uptake it from the soil, which poses an adverse effect on growth and yield. Asparagine (Asp) and thiourea (Thi) are known to assuage the negative impacts of heavy metal pollution on plant growth; however, combined application of Asp and Thi has rarely been tested to discern if it could improve wheat yield under Pb stress. Thus, this experimentation tested the role of individual and combined applications of Asp (40 mM) and Thi (400 mg/L) in improving wheat growth under lead (Pb as PbCl₂, 0.1 mM) stress. Lead stress significantly reduced plant growth, chlorophyll contents and photosystem system II (PSII) efficiency, whereas it increased Pb accumulation in the leaves and roots, leaf proline contents, phytochelatins, and oxidative stress related attributes. The sole or combined application of Asp and Thi increased the vital antioxidant biomolecules/enzymes, including reduced glutathione (GSH), ascorbic acid (AsA), ascorbate peroxsidase (APX), catalase (CAT), superoxide dismutase (SOD), glutathione S-transferase (GST), dehydroascorbate reductase (DHAR), and glutathione reductase (GR). Furthermore, the sole or the combined application of Asp and Thi modulated nitrogen metabolism by stimulating the activities of nitrate and nitrite reductase, glutamate synthase (GOGAT) and glutamine synthetase (GS). Asp and Thi together led to improve plant growth and vital physiological processes, but lowered down Pb accumulation compared to those by their sole application. The results suggest that Asp and Thi synergistically can improve wheat growth under Pb-toxicity.

Histone methyltransferases SDG33 and SDG34 regulate organ-specific nitrogen responses in tomato

Histone posttranslational modifications shape the chromatin landscape of the plant genome and affect gene expression in response to developmental and environmental cues. To date, the role of histone modifications in regulating plant responses to environmental nutrient availability, especially in agriculturally important species, remains largely unknown. We describe the functions of two histone lysine methyltransferases, SET Domain Group 33 (SDG33) and SDG34, in mediating nitrogen (N) responses of shoots and roots in tomato. By comparing the transcriptomes of CRISPR edited tomato lines sdg33 and sdg34 with wild-type plants under N-supplied and N-starved conditions, we uncovered that SDG33 and SDG34 regulate overlapping yet distinct downstream gene targets. In response to N level changes, both SDG33 and SDG34 mediate gene regulation in an organ-specific manner: in roots, SDG33 and SDG34 regulate a gene network including Nitrate Transporter 1.1 (NRT1.1) and Small Auxin Up-regulated RNA (SAUR) genes. In agreement with this, mutations in sdg33 or sdg34 abolish the root growth response triggered by an N-supply; In shoots, SDG33 and SDG34 affect the expression of photosynthesis genes and photosynthetic parameters in response to N. Our analysis thus revealed that SDG33 and SDG34 regulate N-responsive gene expression and physiological changes in an organ-specific manner, thus presenting previously unknown candidate genes as targets for selection and engineering to improve N uptake and usage in crop plants.

Development of Prediction Models for Soil Nitrogen Management Based on Electrical Conductivity and Moisture Content

A study was conducted with the goal of developing an algorithm for use in sensors to monitor available soil N. For this purpose, three different soils were selected. The soils were studied for electrical conductivity (EC) at four different moisture levels and four levels of N. The selection of moisture levels was based on optimum moisture levels between tillage moisture and field capacity. The results revealed a significant relationship between electrical conductivity and moisture level of the soil as well as between electrical conductivity and soil N content. Based on these relations, a polynomial model was developed between the EC of each selected soil sample and moisture content as well as N levels. The regression model for moisture content-based EC determination had coefficients of determination of 0.985, 0.988, and 0.981 for clay loam, sandy loam, and sandy loam soils, respectively. Similarly, the regression model for N content-based EC determination had coefficients of determination of 0.9832, 0.9, and 0.99 for clay loam, sandy loam, and sandy loam soils, respectively. An algorithm developed using a polynomial relationship between the EC of each selected soil sample at all moisture and N levels can be used to develop a sensor for site-specific N application.

Comparative estimation of nitrogen in urea and its derivative products using TKN, CHNS and hand-held refractometer

In this paper, a comparative analysis between the hand-held refractometer and other methods (TKN and CHNS) was accomplished for the estimation of nitrogen percentage (N%) in urea, nano urea fertilizer, and diesel exhaust fluid (DEF) solution. In order to compare the performance of all methods/devices, the detection of N% in different concentrations of urea, nano urea, and DEF were evaluated in terms of their linearity. The most important finding of this study was that the refractometer-based device revealed a good linear coefficient up to 40% urea solution (R² = 0.99918) among other approaches, which means the estimation of N% is more close to the theoretical value. Moreover, the refractometer has detected the urea, nano urea, and DEF samples within 3 s which were quite fast as compared to other tested methods and no requirement of any chemicals during the sample preparation and analyses. Thus, the finding of this study suggests that a hand-held urea refractometer-based portable device can be used for onsite N% determination by the fertilizer and DEF manufacturing industries and their customers due to its low cost, low power requirement, reliable estimation, rapid N% detection, and its environmental suitability.

Herbage Mass, N Concentration, and N Uptake of Temperate Grasslands Can Adequately Be Estimated from UAV-Based Image Data Using Machine Learning

Precise and timely information on biomass yield and nitrogen uptake in intensively managed grasslands are essential for sustainable management decisions. Imaging sensors mounted on unmanned aerial vehicles (UAVs) along with photogrammetric structure-from-motion processing can provide timely data on crop traits rapidly and non-destructively with a high spatial resolution. The aim of this multi-temporal field study is to estimate aboveground dry matter yield (DMY), nitrogen concentration (N%) and uptake (Nup) of temperate grasslands from UAV-based image data using machine learning (ML) algorithms. The study is based on a two-year dataset from an experimental grassland trial. The experimental setup regarding climate conditions, N fertilizer treatments and slope yielded substantial variations in the dataset, covering a considerable amount of naturally occurring differences in the biomass and N status of grasslands in temperate regions with similar management strategies. Linear regression models and three ML algorithms, namely, random forest (RF), support vector machine (SVM), and partial least squares (PLS) regression were compared with and without a combination of both structural (sward height; SH) and spectral (vegetation indices and single bands) features. Prediction accuracy was quantified using a 10-fold 5-repeat cross-validation (CV) procedure. The results show a significant improvement of prediction accuracy when all structural and spectral features are combined, regardless of the algorithm. The PLS models were outperformed by their respective RF and SVM counterparts. At best, DMY was predicted with a median RMSECV of 197 kg ha−1, N% with a median RMSECV of 0.32%, and Nup with a median RMSECV of 7 kg ha−1. Furthermore, computationally less expensive models incorporating, e.g., only the single multispectral camera bands and SH metrics, or selected features based on variable importance achieved comparable results to the overall best models.

Improving Estimation of Winter Wheat Nitrogen Status Using Random Forest by Integrating Multi-Source Data Across Different Agro-Ecological Zones

Timely and accurate estimation of plant nitrogen (N) status is crucial to the successful implementation of precision N management. It has been a great challenge to non-destructively estimate plant N status across different agro-ecological zones (AZs). The objective of this study was to use random forest regression (RFR) models together with multi-source data to improve the estimation of winter wheat (Triticum aestivum L.) N status across two AZs. Fifteen site-year plot and farmers' field experiments involving different N rates and 19 cultivars were conducted in two AZs from 2015 to 2020. The results indicated that RFR models integrating climatic and management factors with vegetation index (R² = 0.72-0.86) outperformed the models by only using the vegetation index (R² = 0.36-0.68) and performed well across AZs. The Pearson correlation coefficient-based variables selection strategy worked well to select 6-7 key variables for developing RFR models that could achieve similar performance as models using full variables. The contributions of climatic and management factors to N status estimation varied with AZs and N status indicators. In higher-latitude areas, climatic factors were more important to N status estimation, especially water-related factors. The addition of climatic factors significantly improved the performance of the RFR models for N nutrition index estimation. Climatic factors were important for the estimation of the aboveground biomass, while management variables were more important to N status estimation in lower-latitude areas. It is concluded that integrating multi-source data using RFR models can significantly improve the estimation of winter wheat N status indicators across AZs compared to models only using one vegetation index. However, more studies are needed to develop unmanned aerial vehicles and satellite remote sensing-based machine learning models incorporating multi-source data for more efficient monitoring of crop N status under more diverse soil, climatic, and management conditions across large regions.

Biomass and mineral nutrient partitioning among self-pollinated and cross-pollinated fruit on the same strawberry plant

Pollen-parent effects on fruit size and quality have been found previously among competing self-pollinated and cross-pollinated fruit on the same Redlands Joy strawberry plant. These effects occur independently of the percentage of fertilized seeds on the fruit, but the expression of these effects on fruit size and some aspects of quality are greatest when calcium is in shortest supply. Here, we aimed to clarify at what developmental stages the self-pollinated and cross-pollinated fruit diverge in size and quality and whether differences between self-pollinated and cross-pollinated fruit are due to early differences in nutrient accumulation. Fruit were harvested at 1, 2 and 3 weeks after hand-pollination and at full ripeness, approximately 4 weeks after hand-pollination. We measured fruit mass, length, diameter, colour, and the concentrations of aluminium, boron, calcium, copper, iron, nitrogen, magnesium, manganese, sodium, phospho-rous, potassium and zinc. Temporary increases in fruit mass, length or diameter due to cross-pollination were evident at 1 or 2 weeks after pollination. Consistent increases in size and skin darkness from cross-pollination emerged in the final week of fruit development. We found little evidence that self-pollinated and cross-pollinated fruit differed in mineral nutrient accumulation at any stage of fruit development. The results demonstrate that cross-pollination effects on strawberry fruit size are evident briefly during early fruit growth but emerge mainly during the final week of fruit development. The effects of cross-pollination on fruit size are not the result of early differences in mineral nutrient accumulation between self-pollinated and cross-pollinated fruit.

An applicable method for extraction of whole seeds protein and its determination through Bradford's method

Analysis of protein content of food is necessary for quality control and is essential for precise labeling. Protein analysis is an issue of great economic and social fondness. Cereals are one of the most important sources of protein in food, livestock and poultry feed. In this article, the technique of extracting protein in 4 types of grains and measuring it by the Bradford method is discussed. The results obtained from this method are compared with the data obtained by the Kjeldahl method. This comparison showed that the Bradford method is more accurate in measuring proteins. Extraction of protein using NaOH at pH 13 can be used as a modified method to release proteins in soybean meal and consequently a Fast and accurate high-performance laboratory determination method for protein content via the Bradford method. The optimum pH value was identified as that of 13 in optimum temperature 40 °C for maximum protein extraction yield (43.6%, w/w). The new method used in this paper has resulted in the measurement of grain protein in the shortest time and with the least toxicity and the highest accuracy.

A Quick Analysis Method for Protein Quantification in Oilseed Crops: A Comparison With Standard Protocol

Protein is one of the most abundant substances in plants and plays a major role in human health hence standardization of its analytical quantification method is essential. Various methods for protein quantification exist, such as Kjeldahl, Bradford, Lowry, bicinchoninic acid assay (BCA), Biuret, and total amino acid content methods. These methods are widely applied; however, the development of the rapid and efficient method is the need of the time hence the objective of this research was to analyze and comparing compare the modification of the Kjeldahl method for the determination of protein content in oilseed crops. The study was performed to improve the sample preparation method (processing and digestion) for protein quantification. Generally, the method initially requires homogenization of grains to a fine flour, which involves time and increases the risk of sample cross-contamination and partial loss of oil from the sample during grinding. Moreover at times, it becomes challenging to homogenize oil seeds to fine flour due to high oil content. However, in the present research, the whole grain was digested in place of grounded flour to accomplish quick protein quantification and compared it with the flour matrix of different oil seeds. To further reduce the digestion time and avoid frothing, we have used the modified digestion mixture. The developed method was statistically validated using analysis of variance (ANOVA), Pearson correlation reliability test, paired T-test, and different types of plot analysis. The validation of the sample preparation method in protein quantification demonstrated non-significant differences that the protein content from whole grain of all the five oilseed crops shows 100% non-significant results compared with the flour matrix in both the digestion mixtures. The developed novel method could be used to prepare the sample for protein analysis and reduces the overall analysis time while ensuring the accuracy of the results.

Electrochemical Sensors for Sustainable Precision Agriculture—A Review

Greenhouse gases released by agriculture account for 19% of global greenhouse gas emission. Moreover, the abuse of pesticides and fertilizers is a fundamental cause of soil and water pollution. Finding sustainable countermeasures for these problems requires completely new approaches and the integration of knowledge. Precision agriculture (PA) is a technology that reduces environmental pollution with minimal input (e.g., fertilizer, herbicides, and pesticides) and maximize the production of high-quality crops by monitoring the conditions and environment of farmland and crops. However, the lack of data—a key technology for realizing PA—remains a major obstacle to the large-scale adoption of PA. Herein, we discuss important research issues, such as data managements and analysis for accurate decision-making, and specific data acquisition strategies. Moreover, we systematically review and discuss electrochemical sensors, including sensors that monitor the plant, soil, and environmental conditions that directly affect plant growth.

Hyperspectral Indices for Predicting Nitrogen Use Efficiency in Maize Hybrids

Enhancing the nitrogen (N) efficiency of maize hybrids is a common goal of researchers, but involves repeated field and laboratory measurements that are laborious and costly. Hyperspectral remote sensing has recently been investigated for measuring and predicting biomass, N content, and grain yield in maize. We hypothesized that vegetation indices (HSI) obtained mid-season through hyperspectral remote sensing could predict whole-plant biomass per unit of N taken up by plants (i.e., N conversion efficiency: NCE) and grain yield per unit of plant N (i.e., N internal efficiency: NIE). Our objectives were to identify the best mid-season HSI for predicting end-of-season NCE and NIE, rank hybrids by the selected HSI, and evaluate the effect of decreased spatial resolution on the HSI values and hybrid rankings. Analysis of 20 hyperspectral indices from imaging at V16/18 and R1/R2 by manned aircraft and UAVs over three site-years using mixed models showed that two indices, HBSI1 and HBS2, were predictive of NCE, and two indices, HBCI8 and HBCI9, were predictive of NIE for actual data collected from five to nine hybrids at maturity. Statistical differentiation of hybrids in their NCE or NIE performance was possible based on the models with the greatest accuracy obtained for NIE. Lastly, decreasing the spatial resolution changed the HSI values, but an effect on hybrid differentiation was not evident.

Development of an Automated Linear Move Fertigation System for Cotton Using Active Remote Sensing

Optimum nitrogen (N) application is essential to the economic and environmental sustainability of cotton production. Variable-rate N fertigation could potentially help farmers optimize N applications, but current overhead irrigation systems normally lack automated site-specific variable-rate fertigation capabilities. The objective of this study was to develop an automated variable-rate N fertigation based on real-time Normalized Difference Vegetation Index (NDVI) measurements from crop sensors integrated with a lateral move irrigation system. For this purpose, NDVI crop sensors and a flow meter integrated with Arduino microcontrollers were constructed on a linear move fertigation system at the Edisto Research and Education Center in Blackville, South Carolina. A computer program was developed to automatically apply site-specific variable N rates based on real-time NDVI sensor data. The system’s ability to use the NDVI data to prescribe N rates, the flow meter to monitor the flow of N, and a rotary encoder to establish the lateral’s position were evaluated. Results from this study showed that the system could accurately use NDVI data to calculate N rates when compared to hand calculated N rates using a two-sample t-test (p > 0.05). Linear regression analysis showed a strong relationship between flow rates measured using the flow meter and hand calculations (R2 = 0.95), as well as the measured distance travelled using the encoder and the actual distance travelled (R2 = 0.99). This study concludes that N management decisions can be automated using NDVI data from on-the-go handheld GreenSeeker crop sensors. The developed system can provide an alternative N application solution for farmers and researchers.

The combined supplementation of melatonin and salicylic acid effectively detoxifies arsenic toxicity by modulating phytochelatins and nitrogen metabolism in pepper plants

The main objective of the study was to assess if joint application of melatonin (MT, 0.1 mM) and salicylic acid (SA 0.5 mM) could improve tolerance of pepper plants to arsenic (As) as sodium hydrogen arsenate heptahydrate (0.05 mM). The imposition of arsenic stress led to accumulation of As in roots and leaves, and increased contents of leaf proline, phytochelatins, malondialdehyde (MDA) and H₂O₂, but it reduced plant biomass, chlorophylls (Chl), PSII maximum efficiency (Fv/Fm) and leaf water potential. Melatonin and SA applied jointly or alone enhanced nitrogen metabolism by triggering the activities of glutamate synthase, glutamine synthetase, and nitrite reductases and nitrate. In comparison with a single treatment of MT or SA, the joint treatment of MT and SA had better impact on enhancing growth and key biological events and decreasing tissue As content. This clearly shows a cooperative function of both agents in enhancing tolerance to As-toxicity in pepper plants.

Dynamics and genetic regulation of leaf nutrient concentration in barley based on hyperspectral imaging and machine learning

Biofortification, the enrichment of nutrients in crop plants, is of increasing importance to improve human health. The wild barley nested association mapping (NAM) population HEB-25 was developed to improve agronomic traits including nutrient concentration. Here, we evaluated the potential of high-throughput hyperspectral imaging in HEB-25 to predict leaf concentration of 15 mineral nutrients, sampled from two field experiments and four developmental stages. Particularly accurate predictions were obtained by partial least squares regression (PLS) modeling of leaf concentrations for N, P and K reaching coefficients of determination of 0.90, 0.75 and 0.89, respectively. We recognized nutrient-specific patterns of variation of leaf nutrient concentration between developmental stages. A number of quantitative trait loci (QTL) associated with the simultaneous expression of leaf nutrients were detected, indicating their potential co-regulation in barley. For example, the wild barley allele of QTL-4H-1 simultaneously increased leaf concentration of N, P, K and Cu. Similar effects of the same QTL were previously reported for nutrient concentrations in grains, supporting a potential parallel regulation of N, P, K and Cu in leaves and grains of HEB-25. Our study provides a new approach for nutrient assessment in large-scale field experiments to ultimately select genes and genotypes supporting plant biofortification.

Spectrometric Prediction of Nitrogen Content in Different Tissues of Slash Pine Trees

The internal cycling of nitrogen (N) storage and consumption in trees is an important physiological mechanism associated with tree growth. Here, we examined the capability of near-infrared spectroscopy (NIR) to quantify the N concentration across tissue types (needle, trunk, branch, and root) without time and cost-consuming. The NIR spectral data of different tissues from slash pine trees were collected, and the N concentration in each tissue was determined using standard analytical method in laboratory. Partial least squares regression (PLSR) models were performed on a set of training data randomly selected. The full-length spectra and the significant multivariate correlation (sMC) variable selected spectra were used for model calibration. Branch, needle, and trunk PLSR models performed well for the N concentration using both full length and sMC selected NIR spectra. The generic model preformatted a reliable accuracy with R2 C and R2 CV of 0.62 and 0.66 using the full-length spectra, and 0.61 and 0.65 using sMC-selected spectra, respectively. Individual tissue models did not perform well when being used in other tissues. Five significantly important regions, i.e., 1480, 1650, 1744, 2170, and 2390 nm, were found highly related to the N content in plant tissues. This study evaluates a rapid and efficient method for the estimation of N content in different tissues that can help to serve as a tool for tree N storage and recompilation study.

Comparative Proteomics of Mulberry Leaves at Different Developmental Stages Identify Novel Proteins Function Related to Photosynthesis

Mulberry leaves at different positions are different in photosynthetic rate, nutrient substance and feeding impact to silkworms. Here, we investigated the proteomic differences of the first (L1), sixth (L6), and twentieth (L20) mulberry leaves at different stem positions (from top to the base) using a label-free quantitative proteomics approach. L1 contained less developed photosynthetic apparatus but was more active in protein synthesis. L20 has more channel proteins and oxidoreductases relative to L6. Proteins that detected in all measured leaves were classified into three groups according to their expression patterns in L1, L6, and L20. The protein group that displayed the maximum amount in L6 has the highest possibility that function related to photosynthesis. Nine function unknown proteins belong to this group were further analyzed in the light responsive expression, evolutionary tree and sub-cellular localization analysis. Based on the results, five proteins were suggested to be involved in photosynthesis. Taken together, these results reveal the molecular details of different roles of mulberry leaves at different developmental stages and contribute to the identification of five proteins that might function related to photosynthesis.

Comparison of Proximal and Remote Sensing for the Diagnosis of Crop Status in Site-Specific Crop Management

The presented paper deals with the issue of selecting a suitable system for monitoring the winter wheat crop in order to determine its condition as a basis for variable applications of nitrogen fertilizers. In a four-year (2017–2020) field experiment, 1400 ha of winter wheat crop were monitored using the ISARIA on-the-go system and remote sensing using Sentinel-2 multispectral satellite images. The results of spectral measurements of ISARIA vegetation indices (IRMI, IBI) were statistically compared with the values of selected vegetation indices obtained from Sentinel-2 (EVI, GNDVI, NDMI, NDRE, NDVI and NRERI) in order to determine potential hips. Positive correlations were found between the vegetation indices determined by the ISARIA system and indices obtained by multispectral images from Sentinel-2 satellites. The correlations were medium to strong (r = 0.51–0.89). Therefore, it can be stated that both technologies were able to capture a similar trend in the development of vegetation. Furthermore, the influence of climatic conditions on the vegetation indices was analyzed in individual years of the experiment. The values of vegetation indices show significant differences between the individual years. The results of vegetation indices obtained by the analysis of spectral images from Sentinel-2 satellites varied the most. The values of winter wheat yield varied between the individual years. Yield was the highest in 2017 (7.83 t/ha), while the lowest was recorded in 2020 (6.96 t/ha). There was no statistically significant difference between 2018 (7.27 t/ha) and 2019 (7.44 t/ha).

Contribution of Piriformospora indica on improving the nutritional quality of greenhouse tomato and its resistance against cu toxicity after humic acid addition to soil

Protected cultivation has a significant contribution in vegetable production. We investigated whether humic acid addition to soil and Piriformospora indica can improve the nutritional quality of greenhouse tomato. We conducted a pot experiment, in which the effects of P. indica inoculation, humic acid addition, and Cu spiking to soil (0, 120, 240, and 500 ppm Cu) were tested. Humic acid addition to soil spiked with 500 ppm Cu decreased the Cu concentration in the fruits of plants inoculated with P. indica from 0.65 to 0.40 mg 100 g Fw^-1, which is still above the maximum allowed limits of Cu in tomato by World Health Organization (WHO). The lycopene and ascorbic acid content of tomato fruits were consistently improved by humic acid addition and P. indica inoculation. The antioxidant enzymes' activity changed in response to humic acid addition, Cu spiking to soil, and P. indica inoculation. With increasing Cu level up to 240 ppm, the activity of superoxide dismutase (SOD) and peroxidase (POD) increased significantly. However, with spiking more Cu to soil, the activity of antioxidant enzymes reduced and the MDA content increased significantly. Addition of humic acid to soil and/or presence of P. indica increased the activity of antioxidant enzymes when the soil spiked with 500 ppm Cu. This study indicated that addition of P. indica and humic acid to the soil can enhance the nutritional quality of greenhouse tomato by reduction of Cu toxicity as a common pollutant in the greenhouse media and increasing the antioxidant content of fruits.

Mapping canopy nitrogen-scapes to assess foraging habitat for a vulnerable arboreal folivore in mixed-species Eucalyptus forests

Herbivore foraging decisions are closely related to plant nutritional quality. For arboreal folivores with specialized diets, such as the vulnerable greater glider (Petauroides volans), the abundance of suitable forage trees can influence habitat suitability and species occurrence. The ability to model and map foliar nitrogen would therefore enhance our understanding of folivore habitat use at finer scales. We tested whether high-resolution multispectral imagery, collected by a lightweight and low-cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand-scale from leaf-scale chemistry measurements across a gradient of mixed-species Eucalyptus forests in southeastern Australia. We surveyed temperate Eucalyptus forests across an elevational and topographic gradient from sea level to high elevation (50-1200 m a.s.l.) for forest structure, leaf chemistry, and greater glider occurrence. Using measures of multispectral leaf reflectance and spectral indices, we estimated N and digN and mapped N and favorable feeding habitat using machine learning algorithms. Our surveys covered 17 Eucalyptus species ranging in foliar N from 0.63% to 1.92% dry matter (DM) and digN from 0.45% to 1.73% DM. Both multispectral leaf reflectance and spectral indices were strong predictors for N and digN in model cross-validation. At the tree level, 79% of variability between observed and predicted measures of nitrogen was explained. A spatial supervised classification model correctly identified 80% of canopy pixels associated with high N concentrations (≥1% DM). We developed a successful method for estimating foliar nitrogen of a range of temperate Eucalyptus species using UAV multispectral imagery at the tree canopy level and stand scale. The ability to spatially quantify feeding habitat using UAV imagery allows remote assessments of greater glider habitat at a scale relevant to support ground surveys, management, and conservation for the vulnerable greater glider across southeastern Australia.

Role of protein phosphatases in the regulation of nitrogen nutrition in plants

The reversible protein phosphorylation and dephosphorylation mediated by protein kinases and phosphatases regulate different biological processes and their response to environmental cues, including nitrogen (N) availability. Nitrate assimilation is under the strict control of phosphorylation-dephosphorylation mediated post-translational regulation. The protein phosphatase family with approximately 150 members in Arabidopsis and around 130 members in rice is a promising player in N uptake and assimilation pathways. Protein phosphatase 2A (PP2A) enhances the activation of nitrate reductase (NR) by deactivating SnRK1 and reduces the binding of inhibitory 14-3-3 proteins on NR. The functioning of nitrate transporter NPF6.3 is regulated by phosphorylation of CBL9 (Calcineurin B like protein 9) and CIPK23 (CBL interacting protein kinase 23) module. Phosphorylation by CIPK23 inhibits the activity of NPF6.3, whereas protein phosphatases (PP2C) enhance the NPF6.3-dependent nitrate sensing. PP2Cs and CIPK23 also regulate ammonium transporters (AMTs). Under either moderate ammonium supply or high N demand, CIPK23 is bound and inactivated by PP2Cs. Ammonium uptake is mediated by nonphosphorylated and active AMT1s. Whereas, under high ammonium availability, CIPK23 gets activated and phosphorylate AMT1;1 and AMT1;2 rendering them inactive. Recent reports suggest the critical role of protein phosphatases in regulating N use efficiency (NUE). In rice, PP2C9 regulates NUE by improving N uptake and assimilation. Comparative leaf proteome of wild type and PP2C9 over-expressing transgenic rice lines showed 30 differentially expressed proteins under low N level. These proteins are involved in photosynthesis, N metabolism, signalling, and defence.

Flooding Causes Dramatic Compositional Shifts and Depletion of Putative Beneficial Bacteria on the Spring Wheat Microbiota

Flooding affects both above- and below-ground ecosystem processes, and it represents a substantial threat for crop and cereal productivity under climate change. Plant-associated microbiota play a crucial role in plant growth and fitness, but we still have a limited understanding of the response of the crop-microbiota complex under extreme weather events, such as flooding. Soil microbes are highly sensitive to abiotic disturbance, and shifts in microbial community composition, structure and functions are expected when soil conditions are altered due to flooding events (e.g., anoxia, pH alteration, changes in nutrient concentration). Here, we established a pot experiment to determine the effects of flooding stress on the spring wheat-microbiota complex. Since plant phenology could be an important factor in the response to hydrological stress, flooding was induced only once and at different plant growth stages (PGSs), such as tillering, booting and flowering. After each flooding event, we measured in the control and flooded pots several edaphic and plant properties and characterized the bacterial community associated to the rhizosphere and roots of wheat plant using a metabarcoding approach. In our study, flooding caused a significant reduction in plant development and we observed dramatic shifts in bacterial community composition at each PGS in which the hydrological stress was induced. However, a more pronounced disruption in community assembly was always shown in younger plants. Generally, flooding caused a (i) significant increase of bacterial taxa with anaerobic respiratory capabilities, such as members of Firmicutes and Desulfobacterota, (ii) a significant reduction in Actinobacteria and Proteobacteria, (iii) depletion of several putative plant-beneficial taxa, and (iv) increases of the abundance of potential detrimental bacteria. These significant differences in community composition between flooded and control samples were correlated with changes in soil conditions and plant properties caused by the hydrological stress, with pH and total N as the soil, and S, Na, Mn, and Ca concentrations as the root properties most influencing microbial assemblage in the wheat mircobiota under flooding stress. Collectively, our findings demonstrated the role of flooding on restructuring the spring wheat microbiota, and highlighted the detrimental effect of this hydrological stress on plant fitness and performance.

Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery

This study aimed to identify the optimal sets of spectral bands for monitoring multiple grapevine nutrients in vineyards. We used spectral data spanning 400–2500 nm and leaf samples from 100 Concord grapevine canopies, lab-analyzed for six key nutrient values, to select the optimal bands for the nutrient regression models. The canopy spectral data were obtained with unmanned aerial systems (UAS), using push-broom imaging spectrometers (hyperspectral sensors). The novel use of UAS-based hyperspectral imagery to assess the grapevine nutrient status fills the gap between in situ spectral sampling and UAS-based multispectral imaging, avoiding their inherent trade-offs between spatial and spectral resolution. We found that an ensemble feature ranking method, utilizing six different machine learning feature selection methods, produced similar regression results as the standard PLSR feature selection and regression while generally selecting fewer wavelengths. We identified a set of biochemically consistent bands (606, 641, and 1494 nm) to predict the nitrogen content with an RMSE of 0.17% (using leave-one-out cross-validation) in samples with nitrogen contents ranging between 2.4 and 3.6%. Further studying is needed to confirm the relevance and consistency of the wavelengths selected for each nutrient model, but ensemble feature selection showed promise in identifying stable sets of wavelengths for assessing grapevine nutrient contents from canopy spectra.

Transcriptome Analysis Reveals Different Responsive Patterns to Nitrogen Deficiency in Two Wheat Near-Isogenic Lines Contrasting for Nitrogen Use Efficiency

Simple Summary

Nitrogen (N) limitation is the key factor for wheat production worldwide. Therefore, the development of genotypes with improved nitrogen use efficiency (NUE) is a prerequisite for sustainable and productive agriculture. Exploring the molecular mechanisms of low N stress tolerance is significant for breeding wheat cultivars with high NUE. To clarify the underlying molecular mechanisms of enhanced resilience to low N in high-NUE wheat, we performed an RNA sequencing (RNA-seq) analysis. In the current research, two wheat near-isogenic lines (NILs) differing dramatically in NUE were used to measure gene expression differences under different N treatments. There was a dramatic difference between two wheat NILs in response to N deficiency at the transcriptional level, and the classification of identified candidate genes may provide new valuable insights into the resilience mechanism of wheat.

Abstract

The development of crop cultivars with high nitrogen use efficiency (NUE) under low-N fertilizer inputs is imperative for sustainable agriculture. However, there has been little research on the molecular mechanisms underlying enhanced resilience to low N in high-NUE plants. The comparison of the transcriptional responses of genotypes contrasting for NUE will facilitate an understanding of the key molecular mechanism of wheat resilience to low-N stress. In the current study, the RNA sequencing (RNA-seq) technique was employed to investigate the genotypic difference in response to N deficiency between two wheat NILs (1Y, high-NUE, and 1W, low-NUE). In our research, high- and low-NUE wheat NILs showed different patterns of gene expression under N-deficient conditions, and these N-responsive genes were classified into two major classes, including “frontloaded genes” and “relatively upregulated genes”. In total, 103 and 45 genes were identified as frontloaded genes in high-NUE and low-NUE wheat, respectively. In summary, our study might provide potential directions for further understanding the molecular mechanism of high-NUE genotypes adapting to low-N stress.

Biochemical Stress Markers, Antioxidants, and Infectious Wound-Healing Potential of UV Irradiation and Salt Stress Effects on the Pre-Treated Seed of Bitter Melon (Momordica charantia L.)

Purpose

The secondary metabolites in plants are the basis of defense and stress balance, which is an important aspect in plant growth. The UV-B treatment (a biotic stress) and salt stress on bitter melon (Momordica charantia L.) were studied, and the impact of pre-sowing seed treatment was evaluated on the basis of biochemical and enzymatic biomarkers, antioxidants, and wound-healing potential during early growth stages.

Methods

The UV-B treatment for 5 and 10 min and salt stress 250 mM and 500 mM treatments were applied, and 21-day seedling tissue were collected for total phenolic contents (TPC), total flavonoid contents (TFC), antioxidant, chlorophyll contents, hydrogen peroxide, total soluble sugar, enzymes activities, and wound-healing potential studies.

Results

The TPC, TFC, diphenyl picrylhydrazyl (DPPH), chlorophyll contents, and total soluble sugar were recorded higher at 5 min treatment with UV-B and salt stress at 250 mM concentration. Antioxidant enzymes activities were recorded higher for 10 min UV-B treatment and 500 mM salt treatment. Wound-healing potential was found significant at 5 min treatment with UV-B radiation, which was studied in vivo in rabbits. The LC-MS analysis revealed a variety of phenolic compounds in the seedlings.

Conclusion

The study concluded that treatments significantly affect the biological activities of bitter melon seeds at the seedling stage, and the seeds contain important phenolic compounds responsible for its antioxidant potential and enzymatic activities. Future studies could be focused on the later stages of growth, development, and yield characteristics subjected to salt stress along with UV-B radiation treatment.

Influence of light intensity distribution characteristics of light source on measurement results of canopy reflectance spectrometers

BACKGROUND

The characteristics of light source have an important influence on the measurement performance of canopy reflectance spectrometer. The size of the effective irradiation area and the uniformity of the light intensity distribution in the irradiation area determine the ability of the spectrometer to express the group characteristics of the measured objects.

METHODS

In this paper, an evaluation method was proposed to theoretically analyze the influence of the light intensity distribution characteristics of the light source irradiation area on the measurement results. The light intensity distribution feature vector and the reflectance feature vector of the measured object were constructed to design reflectance difference coefficient, which could effectively evaluate the measurement performance of the canopy reflectance spectrometer. By using self-design light intensity distribution test system and GreenSeeker RT100, the evaluation method was applied to evaluate the measurement results.

RESULTS

The evaluation results showed that the vegetation indices based on the arithmetic average reflectance of the measured object could be obtained theoretically only when the light intensity distribution of the light source detected by the spectrometer was uniform, which could fully express the group characteristics of the object. When the light intensity distribution of the active light source was not uniform, the measure value was difficult to fully express the group characteristics of the object. And the measured object reflectance was merely the weighted average value based on the light intensity distribution characteristics.

CONCLUSIONS

According to the research results of this paper, sunlight is the most ideal detection light source. If the passive light source spectrometer can improve the measurement method to adapt to the change of sunlight intensity, its measurement performance will be better than any active-light spectrometer.

A Special Collection: Drones to Improve Insect Pest Management

The Special Collection Drones to Improve Insect Pest Management presents research and development of unmanned (or uncrewed) aircraft system (UAS, or drone) technology to improve insect pest management. The articles bridge from more foundational studies (i.e., evaluating and refining abilities of drones to detect pest concerns or deliver pest management materials) to application-oriented case studies (i.e., evaluating opportunities and challenges of drone use in pest management systems). The collection is composed of a combination of articles presenting information first-time published, and a selection of articles previously published in Journal of Economic Entomology (JEE). Articles in the Collection, as well as selected citations of articles in other publications, reflect the increase in entomology research using drones that has been stimulated by advancement in drone structural and software engineering such as autonomous flight guidance; in- and post-flight data storage and processing; and companion advances in spatial data management and analyses including machine learning and data visualization. The Collection is also intended to stimulate discussion on the role of JEE as a publication venue for future articles on drones as well as other cybernectic-physical systems, big data analyses, and deep learning processes. While these technologies have their genesis in fields arguably afar from the discipline of entomology, we propose that interdisciplinary collaboration is the pathway for applications research and technology transfer leading to an acceleration of research and development of these technologies to improve pest management.

Incorporating Multi-Scale, Spectrally Detected Nitrogen Concentrations into Assessing Nitrogen Use Efficiency for Winter Wheat Breeding Populations

Annually, over 100 million tons of nitrogen fertilizer are applied in wheat fields to ensure maximum productivity. This amount is often more than needed for optimal yield and can potentially have negative economic and environmental consequences. Monitoring crop nitrogen levels can inform managers of input requirements and potentially avoid excessive fertilization. Standard methods assessing plant nitrogen content, however, are time-consuming, destructive, and expensive. Therefore, the development of approaches estimating leaf nitrogen content in vivo and in situ could benefit fertilization management programs as well as breeding programs for nitrogen use efficiency (NUE). This study examined the ability of hyperspectral data to estimate leaf nitrogen concentrations and nitrogen uptake efficiency (NUpE) at the leaf and canopy levels in multiple winter wheat lines across two seasons. We collected spectral profiles of wheat foliage and canopies using full-range (350–2500 nm) spectroradiometers in combination with leaf tissue collection for standard analytical determination of nitrogen. We then applied partial least-squares regression, using spectral and reference nitrogen measurements, to build predictive models of leaf and canopy nitrogen concentrations. External validation of data from a multi-year model demonstrated effective nitrogen estimation at leaf and canopy level (R2 = 0.72, 0.67; root-mean-square error (RMSE) = 0.42, 0.46; normalized RMSE = 12, 13; bias = −0.06, 0.04, respectively). While NUpE was not directly well predicted using spectral data, NUpE values calculated from predicted leaf and canopy nitrogen levels were well correlated with NUpE determined using traditional methods, suggesting the potential of the approach in possibly replacing standard determination of plant nitrogen in assessing NUE. The results of our research reinforce the ability of hyperspectral data for the retrieval of nitrogen status and expand the utility of hyperspectral data in winter wheat lines to the application of nitrogen management practices and breeding programs.

Cross-pollination affects fruit colour, acidity, firmness and shelf life of self-compatible strawberry

Cross-pollination affects the fruit characteristics of many crops but the effects of cross-pollination on fruit quality of strawberry (Fragaria × ananassa Duch.) are poorly known. This study determined how cross-pollination affects fruit quality of the strawberry cultivar, Redlands Joy, under controlled environment conditions. Plants were allocated to one of four treatments, with all flowers on each plant receiving either: (1) unassisted self-pollination (Autogamy); (2) hand-pollination with Redlands Joy pollen (Self); (3) hand-pollination with cross-pollen from a small-fruited cultivar (Sugarbaby); or (4) hand-pollination with cross-pollen from a large-fruited cultivar (Rubygem). Cross-pollination did not significantly affect plant yield or fruit mass, size, shape, firmness or shelf life. However, cross-pollination affected fruit colour and taste attributes. Cross-pollinated fruit were 3%–5% darker than self-pollinated fruit. They also had 26%–34% lower acidity and 43%–58% higher Brix:acid ratio. Cross-pollination by Sugarbaby increased fruit P, K, Ca, Fe and Mn, but decreased B, Cu and Zn, concentrations. Cross-pollination by Rubygem increased fruit Mn, but decreased K and Na, concentrations and reduced shelf life. Fruit mass, length, diameter and firmness within all treatments increased with increasing numbers of fertilized seeds per fruit. Hand self-pollinated fruit had a higher percentage of fertilized seeds than fruit arising from autogamy and they were also darker, redder, firmer, and had a longer shelf life, higher protein concentration, and lower Al and Na concentrations. The results indicate that strawberry fruit quality can be affected by both the source of pollen and the number of stigmas pollinated.