Nitrogen Nutrition Research Articles

The influence of pre-sowing treatment of seeds with molybdenum on the yield of pea grain in the conditions of the Republic of Bashkortostan

Relevance. Molybdenum is an essential micronutrient element necessary for plant growth and development. In addition to the fact that molybdenum is part of hormones and enzymes, in legumes it improves nitrogen fixation and nitrogen nutrition, increases the efficiency of phosphorus-potassium fertilizers, thereby increasing productivity.The purpose of this work is to assess the effect of pre–sowing seed treatment with various doses of molybdenum on the yield of pea grain.The experiments were carried out in 2019–2023 in the conditions of the Cis-Ural steppe of the Republic of Bashkortostan. The material for the study was the pea cultivar Pamyati Popova.Methods. An ammonium molybdate solution at a dose of 10, 25, 40, 55 g of molybdenum per 1 kg of seeds was used for pre-sowing treatment. Phenological observations, determination of the completeness of germination and plant height, analysis of the yield structure were carried out in accordance with the methodology of the state cultivar testing of agricultural crops.Results. It was found that pre-sowing treatment of pea seeds of the cultivar Pamyati Popova with molybdenum at a dose of 40 and 55 g/c compared to the control resulted in a reliable (p ≤ 0.05) increase in field germination, safety of plants for harvesting, the number of nodules on 10 plants and their weight. As a result of processing pea seeds with molybdenum at doses of 25, 40 and 55 g/c, the number of beans per plant increased by 5.7–11.4%, the number of seeds per plant – by 13.2–19.5%, grain yield – by 7.3–10.7% compared with the control. Thus, in our experiments, pre-sowing treatment of seeds with molybdenum at a dose of 25–40 g/c was optimal for increasing the value of pea grain yield.

Foliar applications of a Malvaceae-derived protein hydrolysate and its fractions differentially modulate yield and functional traits of tomato under optimal and suboptimal nitrogen application.

Protein hydrolysates (PHs) can enhance plant nitrogen nutrition and improve the quality of vegetables, depending on their bioactive compounds. A tomato greenhouse experiment was conducted under both optimal (14 mM) and suboptimal (2 mM) nitrogen (N-NO3) conditions. Tomatoes were treated with a new Malvaceae-derived PH (MDPH) and its molecular fractions (MDPH1, >10 kDa; MDPH2, 1-10 kDa and MDPH3, <1 kDa). Under optimal N conditions, the plants increased biomass and fruit yield, and showed a higher photosynthetic pigment content in leaves in comparison with suboptimal N, whereas under N-limiting conditions, an increase in dry matter, soluble solid content (SSC) and lycopene, a reduction in firmness, and changes in organic acid and phenolic compounds were observed. With 14 mM N-NO3, MDPH3 stimulated an increase in dry weight and increased yield components and lycopene in the fruit. The MDPH2 fraction also resulted in increased lycopene accumulation in fruit under 14 mM N-NO3. At a low N level, the PH fractions showed distinct effects compared with the whole MDPH and the control, with an increase in biomass for MDPH1 and MDPH2 and a higher pigment content for MDPH3. Regardless of N availability, all the fractions affected fruit quality by increasing SSC, whereas MDPH2 and MDPH3 modified organic acid content and showed a higher concentration of flavonols, lignans, and stilbenes. The molecular weight of the peptides modifies the effect of PHs on plant performance, with different behavior depending on the level of N fertilization, confirming the effectiveness of fractioning processes. © 2024 Society of Chemical Industry.

Физиологические свойства перспективного грибного штамма антагониста возбудителя сухой гнили подсолнечника Т-5 Trichoderma sp.

Dry rot is one of the most dangerous and wide spreading disease on sunflower in last years. Due to the growing demand for environmentally friendly production of agricultural products, the demand for competitive microbiological products is increasing. Since 2020, there are studied and developed environmentally friendly microbiological preparations of a multifunctional type of action against the dry rot pathogen on sunflower Rhizopus oryzae Went &amp; Prins. Geerl. at the V.S. Pustovoit All-Russian Research Institute of Oil Crops. The physiological qualities of a promising antagonist strain of the dry rot pathogen T-5 Trichoderma sp. isolated as a result of stepwise screening were studied during stationary cultivation on liquid nutrient media in order to develop elements of a technological regimen for the production of a microbiological preparation in a preparative form wettable powder. Optimal conditions for surface cultivation of the fungus T-5 Trichoderma sp. on a liquid nutrient medium were determined as following: temperature +25 °C; acidic reaction of the medium (pH 3); glucose and starch as carbon source; source of nitrogen nutrition is corn extract. The media by Rudakova and No. 1 were identified as the best complicated liquid nutrient media.

Diagnostic study of nitrogen nutrition in cotton based on unmanned aerial vehicle RGB images

Nitrogen fertilizer levels significantly affect crop growth and development, necessitating precision management. Most studies focus on nitrogen nutrient estimation using vegetation indices and textural features, overlooking the diagnostic potential of color features. Hence, we investigated cotton nitrogen nutrition status using unmanned aerial vehicle (UAV) image features and the nitrogen nutrient index (NNI). Random frog algorithm - and random forest-screened image feature sets significantly correlated with the NNI, which were substituted into four machine learning algorithms for NNI estimation modeling. The composite scores (F) of optimal image feature sets were calculated using the coefficient of variation method for comprehensive cotton nitrogen nutrient diagnosis. Validation of the model for determining the critical nitrogen concentration in cotton yielded a coefficient of determination R2 = 0.89, root mean square error RMSE = 0.50 g (100 g)-1, and mean absolute error MAE = 0.44, demonstrating improved performance. Additionally, our novel NNI estimation model constructed based on the optimal image feature sets exhibited R2c = 0.97, RMSEc = 0.02, MAEc = 0.02, R2v = 0.85, RMSEv = 0.05, and MAEv = 0.04. Polynomial fitting of the composite index with NNI indicated that the model was reliable and yielded the following diagnostic criterion: 0.48 &lt; F2 &lt; 0.67 indicated nitrogen overapplication, whereas F2 &lt; 0.48 or F2 &gt; 0.67 indicated nitrogen deficiency. This study demonstrates the superior effectiveness of using UAV RGB image feature sets for NNI estimation and the quick, accurate diagnosis of cotton nitrogen levels, which will help guide nitrogen fertilizer application.

Improving Wheat Leaf Nitrogen Concentration (LNC) Estimation across Multiple Growth Stages Using Feature Combination Indices (FCIs) from UAV Multispectral Imagery

Leaf nitrogen concentration (LNC) is a primary indicator of crop nitrogen status, closely related to the growth and development dynamics of crops. Accurate and efficient monitoring of LNC is significant for precision field crop management and enhancing crop productivity. However, the biochemical properties and canopy structure of wheat change across different growth stages, leading to variations in spectral responses that significantly impact the estimation of wheat LNC. This study aims to investigate the construction of feature combination indices (FCIs) sensitive to LNC across multiple wheat growth stages, using remote sensing data to develop an LNC estimation model that is suitable for multiple growth stages. The research employs UAV multispectral remote sensing technology to acquire canopy imagery of wheat during the early (Jointing stage and Booting stage) and late (Early filling and Late filling stages) in 2021 and 2022, extracting spectral band reflectance and texture metrics. Initially, twelve sensitive spectral feature combination indices (SFCIs) were constructed using spectral band information. Subsequently, sensitive texture feature combination indices (TFCIs) were created using texture metrics as an alternative to spectral bands. Machine learning algorithms, including partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and Gaussian process regression (GPR), were used to integrate spectral and texture information, enhancing the estimation performance of wheat LNC across growth stages. Results show that the combination of Red, Red edge, and Near-infrared bands, along with texture metrics such as Mean, Correlation, Contrast, and Dissimilarity, has significant potential for LNC estimation. The constructed SFCIs and TFCIs both enhanced the responsiveness to LNC across multiple growth stages. Additionally, a sensitive index, the Modified Vegetation Index (MVI), demonstrated significant improvement over NDVI, correcting the over-saturation concerns of NDVI in time-series analysis and displaying outstanding potential for LNC estimation. Spectral information outperforms texture information in estimation capability, and their integration, particularly with SVR, achieves the highest precision (coefficient of determination (R2) = 0.786, root mean square error (RMSE) = 0.589%, and relative prediction deviation (RPD) = 2.162). In conclusion, the sensitive FCIs developed in this study improve LNC estimation performance across multiple growth stages, enabling precise monitoring of wheat LNC. This research provides insights and technical support for the construction of sensitive indices and the precise management of nitrogen nutrition status in field crops.

Analysis of the Effects of Population Structure and Environmental Factors on Rice Nitrogen Nutrition Index and Yield Based on Machine Learning

With the development of rice varieties and mechanized planting technology, reliable and efficient nitrogen and planting density status diagnosis and recommendation methods have become critical to the success of precise nitrogen and planting density management in crops. In this study, we combined population structure, plant shape characteristics, environmental weather conditions, and management information data using a machine learning model to simulate the responses of the yield and nitrogen nutrition index and developed an ensemble learning model-based nitrogen and planting density recommendation strategy for different varieties of rice types. In the third stage, the NNI and yield prediction effect of the ensemble learning model was more significantly improved than that of the other two stages. The scenario analysis results show that the optimal yields and nitrogen nutrition indices were obtained with a density and nitrogen amount of 100.1 × 104 plant/ha and 161.05 kg·ha−1 for the large-spike type variety of rice, 75.08 × 104 plant/ha and 159.52 kg·ha−1 for the intermediate type variety of rice, and 75.08 × 104 plant/ha and 133.47 kg·ha−1 for the panicle number type variety of rice, respectively. These results provide a scientific basis for the nitrogen application and planting density for a high yield and nitrogen nutrition index of rice in northeast China.

Combining spectrum, thermal, and texture features using machine learning algorithms for wheat nitrogen nutrient index estimation and model transferability analysis

The nitrogen nutrition index (NNI) has been extensively applied for the diagnosis of crop nitrogen status, providing insights into efficient nitrogen utilization and plant growth. In this study, we utilized a low-altitude unmanned aerial vehicle (UAV) platform, equipped with multispectral (MS), red–green–blue (RGB), and thermal infrared (TIR) cameras, to comprehensively capture wheat spectral information. The analysis of the relationship between NNI and relative yield revealed an initially linear relationship, which saturated for high NNI values. To enhance accuracy and minimize complexity, we employed a random forest (RF) – recursive feature elimination (RFE) method to select features as inputs for four machine learning (ML) models: back propagation neural network (BPNN), extreme learning machine (ELM), support vector regression (SVR), and Gaussian process regression (GPR). After feature selection, the prediction accuracies of single-sensor models were ranked as: MS > RGB > TIR. The R2 values for the four ML models were in the range of 0.54–0.75. Among multi-sensor combinations, the GPR with MS + RGB + TIR input features achieved the best results with R2 = 0.89 and RPD = 2.52. Further, the dataset was partitioned into six subsets based on location and cultivar variety to evaluate model transferability. The results showed that the transferability largely suffered during the bivariate conditions of different varieties at different locations; the transferability of the model was average improved by 11 % when GPR was combined with transfer component analysis (TCA). The accuracy and transferability of the NNI estimation models significantly improved, offering valuable guidance and methodological support for diagnosing the nitrogen nutrient status of wheat.

“Image-Spectral” fusion monitoring of small cotton samples nitrogen content based on improved deep forest

Accurate monitoring of nitrogen nutrition is crucial for improving cotton yield and quality, as well as the ecological environment. The mainstream method for monitoring nutrition is to establish traditional machine learning (ML) models using a single data source. However, this approach has limitations such as limited access to feature information, model-fitting problems, and limited generalization. Deep learning (DL), on the other hand, has shown promise in complex nonlinear modeling tasks due to its flexible structure. However, it has its limitations, such as the fact that agronomic sample collection and testing are usually labor- and material-intensive, resulting in sample sizes that are too small to meet its training conditions. Therefore, there is an urgent need for DL models that can effectively integrate features from multiple data sources and accurately monitor crop nitrogen content, especially in scenarios with small samples. In this study, we conducted indoor pot experiments using the cotton variety Xinluzao 53 and subjected it to six nitrogen treatments. The data sources for our analysis included hyperspectral and digital images of the cotton leaves. To enhance representation learning capabilities, we enriched the multi-class base learners within each layer of the deep forest (DF) model and introduced skip connections. These enhancements improved the quality of inversion for both hyperspectral and digital image datasets. We then developed image-spectral fusion models, which combined the DF structure with stacking ensemble learning. Our focus was on three levels of fusion: feature-level fusion, decision-level fusion, and secondary decision-level fusion. This approach aimed to further enhance the accuracy and stability of nitrogen content inversion. The DF model satisfied the training condition for small samples. Compared to traditional ML algorithms and the original DF algorithm, the improved DF model achieved an increase in validation set R2 of 13.4–28.5% and 10.9–14.9%, respectively. These findings highlight the enhanced accuracy and stability of the improved DF model. Additionally, compared to the optimal inversion model using two single data sources, the “Image-Spectral” three-level fusion models exhibited improvements in validation set R2 of 8.6–9.3%, 10.5–11.2%, and 11.8–12.5% for feature-level, decision-level, and secondary decision-level fusion, respectively. The improved DF and three-level fusion model collectively contributed to the increased accuracy of cotton nitrogen content inversion. Among these models, the secondary decision-level fusion model demonstrated the most marked improvement. This methodology provides valuable insights into monitoring crop phenotypic parameters in situations with limited sample sizes.

The Impact of Genotype and Controlled Environment Cultivation Parameters on Tomato-Leaf-Derived Exosome-like Nanoparticle Yield and Properties

Horticultural plant material offers several advantages for isolating exosomes and other natural plant-derived exosome-like nanoparticles (PDENs) due to the accessibility and affordability of plant material for widespread applications. This study aims to explore the impacts of the tomato genotype (‘Admiro’, ‘Roma’, ‘Brooklyn’, ‘Marmande’ and ‘Betalux’) and the main cultivation parameters in controlled environment agriculture on the yield and properties of their PDENs for pharmaceutical and cosmeceutical applications. The PDEN yield, size distribution, and antioxidative properties of young tomato seedlings were evaluated. The ‘Betalux’ tomato was distinguished by a remarkably higher nanoparticle concentration and a uniform size distribution and was selected for further experiments. The impact of cultivation temperature (18, 22, and 26 °C), nitrogen nutrition (0, 250, and 500 mg L−1), and the lighting photosynthetic photon flux density (PPFD; 150, 250, and 450 µmol m−2 s−1) on nanoparticle properties was investigated. Optimal conditions consisting of a temperature of 22 °C, 250 mg L−1 nitrogen nutrition, and 250 µmol m−2 s−1 lighting PPFD were used as a reference. Optimal temperature, nitrogen nutrition, and lighting intensity resulted in the highest nanoparticle yield, the most uniform particle distribution, and the highest impact of PDEN preparations on keratinocyte metabolic activity. Deviation from optimal cultivation conditions reduced the tomato biomass and the PDEN protein and yield.

Reusing livestock farming wastewater for Tifton 85 irrigation: productivity, morphological, and bromatological indicators

The utilization of wastewater from livestock farming (WLF) represents a common agricultural practice, but the absence of research on its agronomic impacts hinders its sustainable implementation. This study assesses the reutilization of WLF combined with nitrogen fertilization on the morphological, bromatological, and productivity attributes of a Tifton 85 pasture during the winter season. The study involved the application of different types of water via sprinkler: (I) stream water; (II) mixture of WLF and stream water at a 1:1 ratio; and (III) undiluted WLF combined with mineral nitrogen fertilization (0 and 45 kg N ha-1) administered at the commencement of the regrowth period. Irrigation was applied on a weekly basis, totaling 2,440 m3 ha-1. At 106 days post-regrowth, parameters including forage productivity, height, leaf-stem ratio, leaf area index, nitrogen nutrition index, crude protein content, acid detergent fiber, neutral detergent fiber, and mineral matter content were determined. Mineral fertilization at the prescribed dosage (45 kg N ha-1) did not have any discernible impact on the parameters under investigation. On average, the reuse of WLF raised the productivity from 2.50 to 5.12 t ha-1 and the Crude Protein from 12.35% to 14.58% compared to stream water. The reuse of WLF demonstrated superior outcomes (p &lt; 0.01) in terms of pasture productivity, nutritional quality, and nitrogen accumulation compared to irrigation solely with stream water. The reuse of diluted WLF (1:1) without fertilization proved adequate to sustain Tifton 85 pastures during the winter season in the Brazilian Cerrado region. Keywords: Cynodon dactylon, forage, irrigated pasture.

Nitrogen and Nod factor signaling determine Lotus japonicus root exudate composition and bacterial assembly

Symbiosis with soil-dwelling bacteria that fix atmospheric nitrogen allows legume plants to grow in nitrogen-depleted soil. Symbiosis impacts the assembly of root microbiota, but it is unknown how the interaction between the legume host and rhizobia impacts the remaining microbiota and whether it depends on nitrogen nutrition. Here, we use plant and bacterial mutants to address the role of Nod factor signaling on Lotus japonicus root microbiota assembly. We find that Nod factors are produced by symbionts to activate Nod factor signaling in the host and that this modulates the root exudate profile and the assembly of a symbiotic root microbiota. Lotus plants with different symbiotic abilities, grown in unfertilized or nitrate-supplemented soils, display three nitrogen-dependent nutritional states: starved, symbiotic, or inorganic. We find that root and rhizosphere microbiomes associated with these states differ in composition and connectivity, demonstrating that symbiosis and inorganic nitrogen impact the legume root microbiota differently. Finally, we demonstrate that selected bacterial genera characterizing state-dependent microbiomes have a high level of accurate prediction.

Diversity Patterns of Eukaryotic Phytoplankton in the Medog Section of the Yarlung Zangbo River

As one of the important biodiversity conservation areas in China, the ecosystem in the lower reaches of the Yarlung Zangbo River is fragile, and is particularly sensitive to global changes. To reveal the diversity pattern of phytoplankton, the metabarcode sequencing was employed in the Medog section of the lower reaches of the Yarlung Zangbo River during autumn 2019 in present study. The phytoplankton assemblies can be significantly divided into the main stem and the tributaries; there are significant differences in the phytoplankton biomass, alpha and beta diversity between the main stem and the tributaries. While both the main stem and the tributaries are affected by dispersal limitation, the phytoplankton assemblages in the entire lower reaches are primarily influenced by heterogeneous selection. Community dissimilarity and assembly process were significantly correlated with turbidity, electrical conductivity, and nitrogen nutrition. The tributaries were the main source of the increase in phytoplankton diversity in the lower reaches of the Yarlung Zangbo River. Such diversity pattern of phytoplankton in the lower reach may be caused by the special habitat in Medog, that is, the excessive flow velocity, and the significant spatial heterogeneity in physical and chemical factors between stem and tributaries. Based on the results and conclusions obtained in present study, continuous long-term monitoring is essential to assess and quantify the impact of global changes on phytoplankton.

Influence of mineral, organic, organomineral fertilization on winter hardiness components of columnar apple tree (&lt;i&gt;Malus domestica&lt;/i&gt; Borkh.)

Intensive biological technologies of apple-tree cultivation with application of organic and mineral fertilizers form high nitrogen nutrition, thus leading to prolonged growth of fruit plants and reducing their winter hardiness. Columnar apple tree fail to demonstrate high potential for winter hardiness. Therefore, research into regularities and mechanisms behind the influence of nutritional status of columnar apple tree on indicators of plant winter hardiness is considered relevant. The research was aimed at establishing the influence of mineral, organic, organomineral fertilization on the components of winter hardiness of columnar apple trees grown on podzolic soil. Materials and methods. The research involved the ‘Triumph’, ‘President’, ‘Ostankino’ columnar apple varieties, as well as organic and mineral fertilizers. The experiment was conducted in 2016–2021 in the agrochemical experiment station of the Federal Horticultural Center for Breeding, Agrotechnology and Nursery (Moscow region) on podzolic soil of medium-loam particle size distribution. The experimental design included 4 variants: 1) control without fertilizers; 2) N90K90; 3) organic fertilizers 100 t/ha; 4) N90K90 organic fertilizers 100 t/ha. Naa (ammonium saltpeter), KCl mineral fertilizers were applied annually, organic fertilizer (horse manure with sawdust) was applied in 2014. Winter hardiness components and plant samples were analyzed according to generally accepted methods. According to the research into the influence of mineral, organic, organomineral fertilization on the components of winter hardiness of columnar apple tree (Malus domestica Borkh.) grown on podzolic soil in the conditions of the Moscow region, the fertilizer is found inefficient in terms of providing the optimal level of nitrogen nutrition of columnar apple tree plants, except for ‘Triumph’ variety when getting organic fertilizers at a dose of 100 t/ha. In January, fructose prevailed in the sugars of annual shoots of columnar apple tree plants. The ‘Triumph’ and ‘President’ varieties showed a tendency to increase in the content of fructose and glucose as compared with the control when getting organic fertilizers and their combination with mineral fertilizers. The same tendency was observed in the ‘Ostankino’ variety for the content of ascorbic acid in annual shoots, whereas this indicator of the ‘Triumph’ and ‘President’ varieties demonstrated the opposite trend. The ascorbic acid content in annual shoots in winter appeared consistent with its content in August shoots and remained independent on the content in leaves in August. With a decrease in temperature to -40 °C (component II of winter hardiness), the ‘Triumph ‘and ‘Ostankino’ varieties suffered reversible damage to the vegetative buds from 0 to 2.0 points, the ‘President’ variety introduced with 100 tons/ha organic fertilizers demonstrated an increase in damage to the vegetative buds to 3.17 points. The greatest damage to reproductive buds in the ‘President’ and ‘Ostankino’ varieties (3.85-3.91 on the control) was observed in component II of winter hardiness. The application of mineral and organic fertilizers increased these damages to complete death of reproductive buds. Injuries to bark, cambium in all variants of the studied varieties did not exceed 2.0 points. Wood appeared more damaged in the ‘Ostankino’ variety (up to 4.03 points in the combined application of N90K90 and 100 t/ha of organic fertilizers) as well as in the Triumph variety (up to 3.80 points in the N90K90 variant). As for component III of winter hardiness, an irreversible damage was noted for reproductive buds of the studied varieties in all variants of the experiment. In terms of component IV of winter hardiness, irreversible changes occurred in reproductive buds to the greatest extent (complete death in the ‘Triumph’ and ‘President’ varieties); in the tissue under reproductive buds, to the greatest extent in the ‘President’ variety (3.07 points in the variant of 100 tons/ha of organic fertilizers) and in the ‘Ostankino’ variety (2.76 points in N90K90), at the core, the maximum damage was noted in the N90K90 variant in the ‘Triumph’ (3.22 points) and ‘Ostankino’ (2.76 points) varieties.

Halo‐hydromorphism alters nitrogen fertilization responses of tall wheatgrass pastures: Capture and use of resources, tiller dynamics and forage production

AbstractHalo‐hydromorphism limits productivity in approximately 100 million hectares worldwide. Tall wheatgrass (Thinopyrum ponticum) is a species widely used in these environments for its seeding potential, being the addition of nitrogen a considered technological tool to increase forage quality and production. The objective of the study was to determine the impact of nitrogen fertilization on the capture and use of resources (radiation, water and nitrogen) in a cool season perennial sward growing in contrasting halo‐hydromorphic conditions. Cultivated pastures from three independent sites were used. Sites were described according to the degree of halo‐hydromorphism using soil salinity and water table attributes (salinity and depth) as environmental indicators: low HHM site [electrical conductivity (EC1:2.5) 0.97 dS/m; water table salinity 2.03 dS/m; depth 85 cm], intermediate HHM site (EC1:2.5 3.86 dS/m; water table salinity 7.40 dS/m; depth 134 cm) and high HHM site (EC1:2.5 4.49 dS/m; water table salinity 7.85 dS/m; depth 31 cm). At each site, a late spring regrowth (~750°Cd) was studied by applying two treatments (n = 5): without (N0) and nitrogen fertilization (N150; 150 kg/ha of nitrogen in the form of urea). The response of tall wheatgrass to nitrogen fertilization in halo‐hydromorphic conditions depends on soil salinity and water table attributes. N150 treatments production was twice as high as in N0 in low HHM and intermediate HHM environments (from 1750 to 3500 kgDM/ha and from 1080 to 1985 kgDM/ha, respectively). Meanwhile, in high HHM conditions, forage production was only 40% higher when nitrogen was added (from 625 to 870 kgDM/ha). In low HHM the higher N150 production was related to tiller density and size, whereas in intermediate HHM and high HHM was linked only to tiller size. In N150 treatments, the nitrogen nutrition index was negatively affected with the increase in HHM conditions (0.77, 0.62 and 0.55 for low HHM, intermediate HHM and high HHM, respectively). Instead, nitrogen nutrition index of N0 was similar in all the environments (~0.42). In N150, forage production capacity analysed in terms of radiation and water use efficiency (RUE and WUE, respectively) was similar in low HHM and intermediate HHM environments (RUE ~0.81 gDM/Mj and WUE ~13 kgDM/mm). These findings emphasize the importance of conducting analyses based on resource use and capture to understand productive responses to the increase in growth‐limiting factors. Furthermore, they contribute to the identification of environments suitable for nitrogen fertilization.

Evaluation of N nutrition and optimal fertilizer rate for ridge-furrow mulched maize based on critical N dilution curve under different water conditions

Optimizing nitrogen (N) management based on the critical N dilution curve (NCDC) has been proven a precision fertilization strategy to diagnose crop N nutrition status. However, few explorations have been done to establish a NCDC for dryland maize under double ridge-furrow mulching, a planting system that has been widely adopted in semi-arid areas. Moreover, the influences of various rainfall conditions on NCDC remain unclear. Therefore, a 9-yr field nitrogen fertilization experiment from 2013 to 2021 (five nitrogen rates: 0, 75, 150, 225, and 300 kg N ha–1, respectively, represented by N0, N0.25, N0.50, N0.75, and N1) was applied to established the NCDC under two planting systems (Flatting without mulching, CK; Double ridge-furrow with mulching, DRFM) and two rainfall types (rainfall season and drought season). Compared with CK, DRFM significantly increased plant dry matter (PDM, 5.5–12.8%) and N concentration (4.2–6.9%), ultimately improved grain yield (+16.9%), cumulative nitrogen recovery efficiency (CNRE, +9.8%) and crop water productivity (WPc, +26.3%). Rainfall from jointing to silking stage was an important contributor to grain yield. Compared with rainy season, drought season triggered a 51.7% yield loss and 17.1% reduction in NRE, respectively. The NCDC of the CK and DRFM were Nc=36.6×PDM−0.37 and Nc=38.4×PDM−0.36 in rainy season, respectively; the NCDC of the CK and DRFM were Nc =34.5×PDM−0.27 and Nc=37.3×PDM−0.30 in drought season, respectively. The nitrogen nutrition index (NNI) and plant nitrogen requirement (PNR) based on the NCDC intuitively recorded the N nutritional status. The improved soil micro-environment, PDM and N uptake was recorded in N0.75 (225 kg N ha–1 yr–1) under wet season and in N0.50 (150 kg N ha–1 yr–1) under drought season, respectively, which brought an increase in crop productivity with a suitable N nutrition status. The findings highlight an accurate and effective method for assessing the N nutrition status of rainfed maize, and optimize fertilization strategy for sustainable production in semi-arid regions.

Diagnosis of Cotton Nitrogen Nutrient Levels Using Ensemble MobileNetV2FC, ResNet101FC, and DenseNet121FC

Nitrogen plays a crucial role in cotton growth, making the precise diagnosis of its nutrition levels vital for the scientific and rational application of fertilizers. Addressing this need, our study introduced an EMRDFC-based diagnosis model specifically for cotton nitrogen nutrition levels. In our field experiments, cotton was subjected to five different nitrogen application rates. To enhance the diagnostic capabilities of our model, we employed ResNet101, MobileNetV2, and DenseNet121 as base models and integrated the CBAM (Convolutional Block Attention Module) into each to improve their ability to differentiate among various nitrogen levels. Additionally, the Focal loss function was introduced to address issues of data imbalance. The model’s effectiveness was further augmented by employing integration strategies such as relative majority voting, simple averaging, and weighted averaging. Our experimental results indicated significant accuracy improvements in the enhanced ResNet101, MobileNetV2, and DenseNet121 models by 2.3%, 2.91%, and 2.93%, respectively. Notably, the integration of these models consistently improved accuracy, with gains of 0.87% and 1.73% compared to the highest-performing single model, DenseNet121FC. The optimal ensemble model, which utilized the weighted average method, demonstrated superior learning and generalization capabilities. The proposed EMRDFC model shows great promise in precisely identifying cotton nitrogen status, offering critical insights into the diagnosis of crop nutrient status. This research contributes significantly to the field of agricultural technology by providing a reliable tool for nitrogen-level assessment in cotton cultivation.

Characteristics of soil nitrogen and phosphorus fractions and microbial traits with increasing stand age in two-layered Cunninghumia lanceolata + Phoebe bournei plantations.

Soil nitrogen and phosphorus are two key elements limiting tree growth in subtropical areas. Understanding the regulation of soil microorganisms on nitrogen and phosphorus nutrition is beneficial to reveal maintenance mechanism of soil fertility in plantations. We analyzed the characteristics of soil nitrogen and phosphorus fractions, soil microbial community composition and function, and their relationship across three stands of two-layered Cunninghumia lanceolata + Phoebe bournei with different ages (4, 7 and 11 a) and the pure C. lanceolata plantation. The results showed that the contents of most soil phosphorus fractions increased with increasing two-layered stand age. The increase in active phosphorus fractions with increasing stand age was dominated by the inorganic phosphorus (9.9%-159.0%), while the stable phosphorus was dominated by the organic phosphorus (7.1%-328.4%). The content of soil inorganic and organic nitrogen also increased with increasing two-layered stand age, with NH4+-N and acid hydrolyzed ammonium N contents showing the strongest enhancement, by 152.9% and 80.2%, respectively. With the increase of stand age, the composition and functional groups of bacterial and fungal communities were significantly different, and the relative abundance of some dominant microbial genera (such as Acidothermus, Saitozyma and Mortierella) increased. The relative abundance of phosphorus solubilization and mineralization function genes, nitrogen nitrification function and aerobic ammonia oxidation function genes tended to increase. The functional taxa of fungi explained 48.9% variation of different phosphorus fractions. The conversion of pure plantations to two-layered mixed plantation affected soil phosphorus fractions transformation via changing the functional groups of saprophytes (litter saprophytes and soil saprophytes). Changes in fungal community composition explained 45.0% variation of different nitrogen fractions. Some key genera (e.g., Saitozyma and Mortierella) play a key role in promoting soil nitrogen transformation and accumulation. Therefore, the conversion of pure C. lanceolata plantation to two-layered C. lanceolata + P. bournei plantation was conducive to improving soil nitrogen and phosphorus availability. Bacteria and fungi played important roles in the transformation process of soil nitrogen and phosphorus forms, with greater contribution of soil fungi.

MORPHO-YIELD RESPONSE AND QUALITY CHARACTERIZATION OF BOTTLE GOURD (LAGENARIA SICERARIA) VARIETIES TO NITROGEN FERTILIZATION

The morpho-yield and quality traits of bottle gourd varieties were tested in response to nitrogen (N) nutrition during the year 2020 at the horticulture research area of Sindh Agriculture University Tandojam. The study was performed by applying Randomized complete block design (RCBD) with three replications. The two varieties Anmol and Long green were tested by applying six N rates that included (0 Kg N, (control), 50, 75 ,100, 125 and 150 Kg N ha-1). Both the varieties exhibited positive response to nitrogen fertilization. The maximum vine length, branches vine-1 were noted in plants, fertilized with increasing N rates (150 and 125 kg ha-1). In contrast shorter plants with less branches were noted when they were not fertilized with N nutrition.). The N nutrition also had a positive effect on fruit traits. The maximum fruit number with greater length and weight was recorded in plants supplied with increasing N fertilization. The N fertilization had beneficial influence on plant quality characteristics. The maximum pH, TSS, moisture and ash content in plants were observed, nourished with increasing N supply. The varietal comparison indicates that fruits of "Long green" had lower pH (5.88 vs. 6.0), higher in TSS (5.39 vs. 4.28%), moisture (67.74 vs. 55.52%), ash contents (19.96 vs. 19.01%), fruit yield (18178 vs. 17123 kg ha-1) than variety “Anmol”. In general, vines fertilized at 150 kg ha-1 N performed better than those fertilized at lower N levels for entire studied attributes, and "Long green" did better than "Anmol" among the evaluated varieties. The data clearly indicated severe soil N deficiency; and with each increment in applied N, the crop response was positive and significant (p&lt;0.05). Hence, the farmers are suggested that “Long Green” bottle gourd variety may preferably be adapted with N at 150 kg ha-1 due to intense performance under agro-ecological conditions of Tandojam Sindh, Pakistan

Assessment of Nitrogen and Sulphur Nutrition to Optimize the Yield and Quality of Groundnut (Arachis hypogaea L.)

A field experiment was conducted during February 2023 to June 2023 at Kerala Agricultural University, College of Agriculture, Padannakkad, Kasargod with the objective of formulating a nutrient schedule for groundnut, consisting different sources of nutrients (especially nitrogen and sulphur), time and method of application. The experiment was laid out in randomized block design with 9 treatment combinations of 2 factors, viz., Nutrient Schedule (S) and Foliar Nutrition (F), with 3 treatments each, viz., S1: nitrogen (10 kg ha-1) and sulphur (20 kg ha-1) as per package of practices as basal, S2: nitrogen and sulphur each @ 10 kg ha-1 in 2 equal splits (one as basal and the other at 20 days after sowing), S3: control (without nitrogen and sulphur), F1: foliar application of urea and potassium sulphate @ 1% each at 10 and 30 days after sowing, F2: foliar application of nano urea @ 3 ml 1-1 and nano sulphur @ 1 ml 1-1 each at 10 and 30 days after sowing, F3: control (without foliar application). The main effects of the treatments were significant with S1 recording taller plants at 20 (8.16 cm), 40 (18.05 cm) and 60 (30.34 cm) days after sowing and S2 at 80 days after sowing (37.61 cm). Foliar nutrition was found effective with application of nano fertilisers (F2), recording 11.17%, 27.02% and 18.75% taller plants than control at 40, 60 and 80 days respectively after sowing. The treatment combination S1F2 recorded the tallest plants at different growth stages with heights 18.61 cm, 34.50 cm and 41.50 cm respectively at 40, 60 and 80 days after sowing. The leaf area of groundnut varied significantly with treatments and their combinations. Application of nitrogen and sulphur @ 10 kg ha-1 each in 2 splits (S2) was superior to other treatments in recording higher leaf areas at 40, 60 and 80 days after sowing with respective values of 747.16 cm2, 1435.60 cm2 and 1216.20 cm2. Foliar nutrition with conventional urea and potassium sulphate (F1) recorded significantly higher leaf area at 40 (734.87 cm2) and 60 (1382.70 cm2) days after sowing and it was at par with foliar application of nano fertilisers (F2) at 40 days after sowing (716.70 cm2). Application of nitrogen and sulphur in 2 splits and foliar spray of nano urea and nano sulphur (S2F2) recorded maximum leaf area at 60 (1543.83 cm2) and 80 (1341.51 cm2) days after sowing. The plants which received nitrogen and sulphur in two equal splits (S2) and foliar application of nano urea and nano sulphur (F2) or their combination (S2F2) were superior in terms of kernel yield of groundnut with values 1925.87 kg ha-1, 1801.70 kg ha-1 and 2109.80 kg ha-1 respectively. The highest oil content was obtained with the treatment S2 (48.01 %) and it was on par with S1 (47.73 %). The effect of foliar nutrition was also significant and the treatments F2 and F1 were on par and they recorded 48.04 % and 47.81 %, respectively. Integrated application of nitrogen and sulphur each @10 kg ha-1as basal and foliar spray of nano fertilisers (S1F2) recorded the highest (49.30 %) oil content and it was on par with S2F1 (49.10 %) and S2F2 (48.4 %).

Comparative assessment of nano nitrogen and nano zinc nutrition on growth, yield and profitability of chilli (Capsicum annuum. L)

In contemporary agriculture, sustainability challenges like climate change, declining productivity, and nutrient deficiencies threaten food production. Judicious use of methodologies, dosages, and timing is crucial for sustainable practices. Nano-fertilizers gain importance due to their nutrient-release properties and increased use efficiencies. Chili is a commercial crop that requires high nutrients, the present field trial was carried out at AHRS, Bavikere, Shivmogga (India) during 2020 and 2021, to investigate its growth and yield responses to foliar applications of nano nitrogen (nN) and zinc (nZn) alongside conventional urea and ZnSO4. The experiment comprised combinations of recommended doses of nitrogen (RDN) (75, 100 & 125%) along with two foliar sources, nano (nN & nZn @ 0.4%) and conventional (urea @ 2% & ZnSO4@ 0.2%) against control. Foliar application of nN and nZn @0.4%+125% RDN (T3) improved growth and yield attributes & green chili yield (33.07 t ha−1), compared to RDF (T1, 23.8 t ha−1) and conventional sources (T6, 30.9 t ha−1). The utilization of nano-fertilizers in T3 resulted in a substantial enhancement of 39.0% in yield. Increments of 57.2% and 49.3% in net returns compared to conventional N and zinc sources respectively were also noted. Further, the land productivity reached $13.5 per day in T3, indicating economic viability. Application of 75% RDN with nN and nZn saved 25-50% of RDN compared to conventional sources. The results propose cutting nitrogen fertilizer by up to 50%, showcasing the eco-friendly aspect of nano-fertilizers. They boost productivity and profitability while supporting sustainable farming by reducing nitrogen usage.