Nutrient Management Strategies Research Articles

Application of straw-derived biochar: a sustainable approach to improve soil quality and crop yield and reduce N2O emissions in paddy soil.

The burning of agricultural straw is a pressing environmental issue, and identifying effective strategies for the rational utilization of straw resources is decisive for achieving sustainable development. Owing to its high carbon content and exceptional stability, straw biochar produced via pyrolysis has emerged as a key focus in multidisciplinary research. However, the efficacy of biochar in mitigating nitrous oxide (N2O) emissions from paddy soils is not consistent. A 2-year field experiment was conducted and investigated the impact of biochar derived from two feedstocks (rice straw and wheat straw, pyrolyzed at 450°C) on N2O emissions, global warming potential (GWP), greenhouse gas intensity (GHGI), nitrogen use efficiency (NUE), crop yield, and soil quality. The static chamber technique was used for collecting N2O gas samples, and concentrations were analyzed through gas chromatography methods. The treatment combinations included BR0 (control), BR1 (NPK at the recommended dose, 120:60:40kgha-1), BR2 (wheat straw biochar, 5 t ha-1), and BR3 (rice straw biochar, 5 t ha-1). The results exhibited that cumulative N2O emissions from BR2 and BR3 treatments decreased by 10.55% and 13.75% respectively, compared to BR1. Lower GWP and GHGI were observed under both biochar treatments compared with BR1. The highest rice grain yield (3.48Mgha-1) and NUE (76.72%) were recorded from BR3, which also exhibitedthe lowest yield-scaled N2O emission. We observed positive correlations between soil nitrate, ammonia and water-filled pore spaces, while NUE showed negative correlations with N2O emissions. Significant (p < 0.05) improvements in soil quality were also detected in both the biochar treated plots, indicated by increased soil pH, water holding capacity, porosity, and nutrient contents. Overall, the results suggest that applying biochar at a rate of 5 t ha-1 in paddy soil is a viable nutrient management strategy with the potential to reduce reliance on inorganic fertilizers, mitigate N2O emissions, and contribute to sustainable food production.

Management of Nutrients in Soybean (Glycine max) Crops: A Review

Effective nutrient management is crucial for maximizing soybean (Glycine max) yield, promoting sustainable farming methods, and satisfying worldwide food requirements. This paper analyses significant discoveries and optimal methods in nutrient management for soybean crops, with a focus on the cruciality of efficient techniques to optimize yield, quality, and environmental conservation. Soybeans have distinct nutritional needs during their many growth stages, with nitrogen (N), phosphorus (P), potassium (K), and micronutrients playing vital roles in plant growth, yield formation, and stress resistance. Optimal fertilization, determined through soil analysis and considering the specific requirements of the crops, is crucial for providing sufficient nutrients while minimizing negative environmental consequences such as nutrient runoff, leaching, and greenhouse gas emissions. Novel developments and advancements, such as precision agriculture, variable rate fertilization, and smart fertilizers, present inventive methods to enhance the efficiency of nutrient management procedures. These technological improvements allow for the precise distribution of nutrients to specific areas, using data to make informed decisions and improve the efficiency of resources. This ultimately leads to better crop performance and sustainability. In addition, efficient nutrient management enhances soil health, increases resistance to both living organisms and non-living environmental pressures and ensures economic sustainability for farmers. Soybean producers may achieve optimal yields, quality, and profitability while minimizing environmental hazards by implementing integrated nutrient management systems, including organic amendments, and closely monitoring crop nutrition. To summarise, this review emphasizes the significance of nutrient management in soybean production, emphasizing the necessity for comprehensive strategies that take into account agronomic, environmental, and economic factors. Ongoing research, innovation, and information sharing are crucial for improving nutrient management strategies and guaranteeing the long-term sustainability of soybean cropping systems.

A Comprehensive Analysis of Nutritional Profile, Cultivation Techniques, and Therapeutic Potential of Dragon Fruit (Hylocereus spp.)

Dragon fruit (Hylocereus spp.) has emerged as a globally significant fruit crop, valued for its distinctive appearance and nutritional profile. Native to Central America, this cactaceous vine has adapted to various tropical and subtropical regions worldwide. The fruit's vibrant exterior houses a white or red pulp studded with numerous edible seeds. Cultivation techniques have evolved to optimize yield and quality, with vertical or horizontal support systems and specific nutrient management strategies being crucial for successful production. Dragon fruit propagation primarily relies on stem cuttings, which can bear fruit within 14-18 months under favorable conditions. The fruit's nutritional composition is noteworthy, containing high levels of antioxidants, vitamins (particularly vitamin C), minerals, and dietary fiber. These components contribute to its potential health benefits, including cholesterol reduction, improved digestion, and enhanced immune function. Pharmacological studies have demonstrated various biological activities of dragon fruit extracts, such as anti-hyperlipidemic, hepatoprotective, and anti-ulcer effects. These properties are attributed to bioactive compounds like betalains, phenolics, and flavonoids present in the fruit. Additionally, dragon fruit shows promise in managing chronic conditions like diabetes and obesity, potentially through its effects on blood glucose regulation and lipid metabolism

Optimizing rice (Oryza sativa) growth, productivity, profitability and nutrient uptake with coated urea and organic manure in the new alluvial zone of West Bengal

A two-year field experiment was carried out at experimental farm of BCKV, Nadia, West Bengal during kharif seasons of 2019–20 and 2020–21 to study the effect of combined application of coated urea and organic manure on performance of rice. The experiment was laid-out in a randomized complete block design with four replications and treatment comprised 5 treatment combinations, such as T1 , control plot (without N); T2 , 100% recommended dose of nitrogen (RDN) through neem-coated urea (NCU); T3 , 75% RDN through NCU + 25% RDN through FYM (farm yard manure); T4, 100% RDN through polymer sulphur coated urea (PSCU); T5, 75% RDN through PSCU+25% RDN through FYM. The results showed that 75% RDN through PSCU+25% RDN through FYM re- sulted in maximum growth attributes, viz. plant height (110.8 cm), maximum number of effective tillers per square meter (416/m2 ), highest dry-matter accumulation (DMA) (740 g/m2 ), leaf area index (LAI) (4.47) and crop growth rate (CGR) (6.56 g/m2 /day), highest yield components like panicle length (23.5 cm), panicle weight (9.95 g), num- ber of filled grains/panicle (113), test-weight (21.3 g) and maximum grain yield (4.79 t/ha) and straw yield (5.53 t/ ha). Similarly, maximum amount of total nutrient uptake by crop (99.7, 40.1 and 208 kg/ha NPK), protein yield (418 kg/ha), gross returns (` 71.8 × 103 /ha), net return (` 52 × 103 /ha) and B: C ratio (2.73) were noticed under T5 treat- ment. Therefore, it is advisable to implement a comprehensive nutrient management strategy T5 (75% RDN through PSCU + 25% RDN through FYM) in rice cultivation. This approach enhances growth, yield, nutrient absorption, and quality, ultimately boosting agricultural productivity, profitability, and sustainability.

Effect of integrated nutrient management on the growth and yield of YellowSarson (Brassica rapa var. yellow sarson) under guava (Psidium guajava) basedagri-horti system

An experiment was conducted during the winter (rabi) season of 2021–22 to evaluate the effect of integrated nutrient management on the growth and yield of yellow sarson under guava based agri-horti system. The field ex- periment comprised 8 treatment, viz., T1 , Control; T2 , 100% RDF + 30 kg S + 5 kg Zn; T3 , 50% RDF + 15 kg S + 2.5 kg Zn + 2.5 t/ha FYM+ 2 spray of nano urea; T4 , 50% RDF + 15 kg S + 2.5 kg Zn + 5 t/ha FYM+ one spray of nano urea; T5 , 75% RDF + 22.5 kg S + 3.7 kg Zn + 2.5 t/ha FYM + 2 spray of nano urea; T6 , 75% RDF + 22.5 kg S + 3.7 kg Zn +5 t/ha FYM + 1 spray of nano urea; T7 , 100 % RDF + 30 kg S + 5 kg Zn + 2.5 t/ha FYM; T8 , 100% RDF + 30 kg S + 5 kg Zn + 5 t/ha FYM, was laid out in randomized block design, with 3 replications. The results revealed that application of 75% RDF + 22.5 kg S + 3.7 kg Zn +2.5 t/ha FYM + 2 spray of nano urea at 30-35 DAS and pre-flow- ering stage (T5 ) gave significantly higher growth and yield attributes such as plant height (141.03 cm), dry matter accumulation (37.76 g/plant), no. of siliqua (142/plant), siliqua length (6.67 cm), test weight (3.43 g) as well as seed yield (1.49 t/ha) and stover yield (3.54 t/ha) as compared to control. The highest amount of nitrogen, phos- phorus, potassium and sulphur uptake by plant was also observed in the treatment T5. These findings provide valuable insights for farmers and researchers aiming to optimize nutrient management strategies and improve crop productivity in similar agri-horti systems.

Response of Hydroponic Tomato Yield and Yield-correlated Morphological Characteristics to Constant or Growth Stage-based Nutrient Management Strategies

In the United States, the annual revenue attributable to tomato production is $1 billion. However, tomato production can cause negative environmental impacts, such as water pollution, often in the form of eutrophication-causing nutrient pollution. Hydroponic production can decrease excess nutrient leaching; however, optimization of nutrient management and cultivar choices could further decrease excess nutrient discharges. The objectives of this study were as follows: to evaluate and compare the responses of tomato growth characteristics, yield, and yield components to two nutrient management regimes (varying nutrient solution concentrations by growth stage and the use of a constant nutrient solution concentration from transplant to termination), and to analyze the effects of growth habits among six cultivars (Big Beef, Cherokee Purple, Heatmaster, Legend, Mountain Fresh Plus, and Tropic) on tomato yield and yield-correlated morphological characteristics. The nutrient management strategies were applied to tomato plants, and data regarding yield and related morphological characteristics were obtained. Data were analyzed using SAS PROC GLM. An analysis revealed no significant difference in the total fruit weight/plant between nutrient management regimes (P = 0.05); however, the mean fruit weight (164.26 g) and diameter (71.70 mm) were significantly greater (P &lt; 0.0001) for plants that received the constant concentration nutrient regime. Indeterminate plants had a significantly greater (P &lt; 0.0001) mean fruit weight (192.76 g) and mean fruit diameter (76.42 mm). Among cultivars, Big Beef had a significantly greater (P &lt; 0.05) total fruit weight/plant (9.25 kg). Applying a constant nutrient concentration to indeterminate cultivars, particularly Big Beef and Cherokee Purple, improved the factors analyzed and could decrease negative environmental impacts while increasing profits of the producers.

Evaluation of Nitrogen Fertilizer Supply and Soil Nitrate Thresholds for High Yields of Foxtail Millet

Foxtail millet is an important cereal crop in the North China Plain. However, excessive nitrogen fertilizer application over the years has led to declining yield and soil quality. This study investigated nutrient management strategies for foxtail millet based on crop yield levels and soil nutrient availability. In a field where targeted fertilization was conducted over six seasons, nitrogen fertilization effects and the dynamics of soil-available nitrogen were monitored continuously for two consecutive years (2022–2023) across five different foxtail millet varieties with varying yield levels. The study aimed to determine the optimal nitrogen application rate for achieving a high yield of foxtail millet, the minimum soil nitrate threshold required to maintain soil fertility, and the effective nitrogen application rate range for sustaining soil-available nitrate levels. Results showed that fertilization significantly affected dry matter weight during flowering, while variety affected dry matter weight at maturity. The average nitrogen application rate for achieving high yield across all five millet varieties was 141.3 kg·ha−1. Specifically, the average nitrogen application rate of nitrogen-efficient varieties achieving high yield (5607.32–5637.19 kg·ha−1) was 151.5 kg·ha−1, while the average nitrogen application rate of nitrogen-inefficient varieties achieving high yield (4749.77–4847.74 kg·ha−1) was 134.5 kg·ha−1. Soil NH4+-N and NO3−-N content increased when nitrogen application rate exceeded 360 kg·ha−1, posing environmental risks. To achieve high yield, soil nitrate levels would be maintained at an average of 17.23 mg·kg−1 (before sowing) and 9.75 mg·kg−1 (at maturity). A relationship between soil nitrate and nitrogen application rate was established: y = 867.5 − 50z (where y represents the optimal nitrogen application rate for high yield (kg·ha−1), and z represents soil NO3−-N content in the 0–20 cm layer before sowing, ranging from 10.0 to 17.35 mg·kg−1), which provided a practical method for nitrogen fertilization to achieve high yield of foxtail millet. In this study, the fertilization strategy was optimized according to soil nutrient level and yield targets, and the nitrogen application rate was controlled within 360 kg·ha−1 based on the soil nitrate nitrogen content, which will be instructive for reducing fertilizer use, maximizing fertilizer efficiency, and increasing yield.

Insights from a 19-year field study: optimizing long-term nutrient supply strategies for enhanced crop productivity and nutritional security in rice–wheat systems

What nutrient supply options can ensure maximum productivity and optimize the nutrient uptake of rice–wheat system (R-W system)? an experiment started in the year 1998 (19-year-old) to examine the impact of optimal nutrient supply (NS) strategies to maximize crop productivity and nutritional security in R-W system. To determine the best nutrient management strategies (BNMS), seven different NS methods were tested. These included (organic and mineral fertilizers), as well as combinations such as integrated plant nutrition system (IPNS), IPNS + B (berseem), and IPNS + C (cowpea), with the aim of enhancing the productivity and nutrient absorption of R-W system. Results showed that rice grain yield notably wide-ranging from 1.61 to 5.81 t ha−1 under different NS options and highest rice grain yield (mean of 19 years) was observed at IPNS + C (5.81 t ha−1), which was at par with IPNS (5.79 t ha−1), STCR (soil test crop response) (5.76 t ha−1) and IPNS + B (5.67 t ha−1) followed by 100% recommended dose fertilizer NPK (4.41 t ha−1), which equality with OF (organic farming) (4.04 t ha−1) and lowest was recorded in control plot (1.61 t ha−1). Wheat grain yield varied significantly from 1.43 to 5.86 t ha−1 under different NS options. The highest yield (mean of 19 years) was observed in treatments IPNS + B (5.86 t ha−1), IPNS (5.77 t ha−1), IPNS + C (5.48 t ha−1), and STCR (5.45 t ha−1), followed by OF at 4.49 t ha−1, NPK at 3.76 t ha−1, with the lowest in the control plot at 1.43 t ha−1. Additionally, total phosphorus and sulfur accumulation in rice (grain: 8.41 to 39.09 kg ha−1; straw: 6.02 to 23.54 kg ha−1) varied significantly across nutrient supply treatments. Overall, adoption of IPNS integration with legumes (IPNS + B and IPNS + C), can significantly improve productivity and nutrient accumulation in R-W systems. Incorporating legumes into farming practices is advised for sustained productivity and nutritional benefits.

Developing practical models for predicting chlorophyll levels in the Andong Reservoir using empirical and machine learning approaches based on integrated analysis of physical, chemical, and hydrological indicators

Managing excessive phytoplankton biomass is a pressing issue worldwide and requires precise predictions and efficient water quality control. In this study, we employed the random forest approach to build a practical predictive model for phytoplankton biomass as determined by chlorophyll (CHL-a) levels. Using an integrative 15-year dataset, we obtained insights into CHL-a dynamics and identified important factors influencing it. Seasonal dynamics were crucial for shaping water quality, with meteorological and hydrological fluctuations playing pivotal roles. Elevated suspended solids and phosphorus levels during the summer monsoon indicated increased runoff and nutrient loading, contributing to fluctuations in nutrient ratios. CHL-a was also responsive to seasonal variations in nutrient availability, particularly in phosphorus and ammonium-nitrogen (NH4-N). These results suggest the importance of nutrient management strategies for controlling and mitigating eutrophication risk. The random forest model had practical accuracy (R2 = 0.66, RMSE = 1.85) for predicting long-term CHL-a variation and identified phosphate-phosphorus, water temperature, and NH4-N as the most important predictors. However, our findings emphasize the importance of comprehensive analyses to identify the factors driving CHL-a variation. Combining the strengths of machine learning with multifaceted insights would enhance prediction accuracy and efficiency, ultimately facilitating informed decision-making for reservoir management and environmental conservation efforts.

Study on Nano-Zn, Inorganic, and Organic Nutrient Management Practices and Their Impact on Nutrient Availability in Kharif Maize (Zea mays L.)

A study was conducted to assess the impact of Nano-Zn, inorganic, and organic nutrient management practices on the physico-chemical properties and nutrient availability in Kharif maize (Zea mays L.) grown during 2022 and 2023 at Chandra Shekhar Azad University of Agriculture &amp; Technology, Kanpur. The experiment followed a split-plot design, with organic manures (Control, FYM at 10 t/ha, and Vermicompost at 5 t/ha) as the main plot treatments and various nutrient management strategies (Control, 75% RDF, 75% RDF + ZnSO4 at 25 kg/ha, 75% RDF + Nano-Zn at 10 ml/litre, 100% RDF, 100% RDF + ZnSO4, and 100% RDF + Nano-Zn) as the subplot treatments. Results showed that combining 100% RDF with Nano-Zn and ZnSO4 significantly enhanced soil nutrient availability and improved physico-chemical properties compared to control treatments. The organic carbon content was highest in the 100% RDF + Nano-Zn treatment (0.492%), suggesting an increase in soil organic matter. Nitrogen availability was significantly higher in this treatment, with a pooled value of 151.167 kg/ha, compared to 143.443 kg/ha in the control. Phosphorus availability also increased in the 100% RDF + Nano-Zn treatment (16.337 kg/ha), indicating better phosphorus mobilization. Potassium availability was enhanced, with the highest recorded value (216.39 kg/ha) in the 100% RDF + Nano-Zn treatment, while the control treatment had 213.597 kg/ha. Zinc availability was markedly improved, with the 100% RDF + Nano-Zn treatment reaching 0.853 mg/kg, compared to 0.79 mg/kg in the control. This study demonstrated that integrating Nano-Zn and ZnSO4 with inorganic fertilizers significantly improved soil fertility, nutrient availability, and potentially enhanced crop performance in Kharif maize. These findings indicate that combining inorganic and nano-fertilizers could be an effective nutrient management strategy, contributing to sustainable agricultural practices and improved productivity in maize cultivation, alongside better soil health.

Impact of plant nutrients and organic substances on growth, yield, and quality attributes of Anna apple cultivar under semi-arid conditions

The unique challenges posed by semi-arid climates, including soil nutrient limitations and organic matter decline, underscore the importance of effective nutrient and organic substance management to ensure optimal yields, quality, and environmental sustainability. A field experiment was conducted at Horticulture Farm of Chaudhary Charan Singh Haryana Agricultural University, Hisar during two succeeding years, 2020–21 and 2021–22, to identify optimal nutrient management strategies of different plant nutrients and organic substances on growth, flowering, quality, and yield attributes of the “Anna” apple cultivar. The experiment consisted of nineteen treatments in an RBD system. The plant growth parameters [leaf area (33.64 cm2) and trunk girth (13.16 cm)] when applied 2.0% Urea (N) + 15% Cow urine-T12. Whereas, the number of flower buds (232.83/m2) and flowering duration (39.00 days) were found better with T6. Similarly, [fruit volume (158.95 cm3), number of fruit (63.33/tree) and fruit drop (70.54%)] and [TSS: acid ratio (22.60), reducing sugars (7.17%), non-reducing sugars (2.83%), total protein (0.97 g 100 g−1), starch index (3.59)] were superior with the application of treatment T6. Higher content of boron (80.09 ppm) was estimated in treatment T6, zinc (24.69 ppm) in T9, iron (134.67 ppm) in T1, and manganese (59.94 ppm) in T12 during the experimentation. Therefore, it was concluded that treatment T12 was most effective for enhancing plant growth while treatment T6 was found superior for flowering, high yield, and quality of apple cv. Anna fruit under semi-arid conditions.

Rice Leaf Nitrogen Content Estimation Through A Methodological Framework Using Single-Sensor Multispectral Images

Using non-destructive evaluation tools based on imaging techniques, including single-sensor multispectral cameras, provides a cost-effective solution for optimizing rice nitrogen fertilization through site-specific nutrient management. However, their accuracy and precision have been identified as areas for improvement. This study aims to develop a methodology to improve the accuracy of estimations through field experiments. It utilizes multispectral images captured by MAPIR Survey3W Orange Cyan Near-Infrared and MAPIR Survey3W Red Edge cameras. The Normalized Difference Vegetation Index and Red Edge values derived from these images are correlated with Soil Plant Analysis Development values to assess rice nitrogen levels. A prediction model is then built using the Support Vector Regression algorithm. Findings from the experiments underscore the importance of addressing shadow effects, integrating the dataset on light intensity and image capture time, conducting radiometric calibration, filtering outlier data, employing image segmentation, and utilizing nonlinear Canova tests to enhance estimation accuracy. By configuring the Support Vector Regression model with RBF kernel, gamma set to 1.24, and epsilon set to 0.1, the R2 of the train data and validation data reaches 0.851, and 0.840 respectively. Meanwhile, the R2 of the test data achieves 0.793 with a mean absolute percentage error of 3.49 % and a root mean square error of 1.70. These findings underscore the potential of the proposed methodology to improve the estimation of rice nitrogen status based on single-sensor multispectral images, paving the way for more effective nutrient management strategies in rice cultivation.

Optimizing Cabbage (Brassica oleracea var. capitata) Production through Integrated Nutrient Management: Enhancing Yield, Quality, and Sustainability

This study explores the synergistic effects of integrated nutrient management (INM) on the growth, yield, and quality attributes of cabbage (Brassica oleracea var. capitata). The present study was conducted during the Rabi season of 2022-23 at Lovely Professional University at a vegetable research farm in Punjab, India. The experiment was laid out in a completely randomized block design with ten treatments replicated three times. Various combinations of organic and inorganic fertilizers, including vermicompost and neem cake were evaluated alongside the recommended dose of fertilizers (RDF). Results indicated significant enhancements in most horticultural traits of cabbage across different treatments. Notably, the combination of RDF with vermicompost and neem cake (75% RDF: 5 t/ha: 2 t/ha) exhibited the most promising results, including increased head weight (125%), head formation percentage (22.17%), enhanced head yield (65.33%), improved compactness (98.60%), elevated ascorbic acid content (24.48%), and higher dry matter content (27.56%) than of control. These findings underscore the efficacy of integrated nutrient management strategies in optimizing cabbage production, emphasizing their potential for sustainable and high-quality vegetable cultivation.

Growth Parameters of Zea mays (Maize) and Talinum triangulare (Water Leaf) in Variable Iron and Potassium Growth Media

This study investigates the effects of soil heterogeneity and nutrient fortification on the growth rates of two important food crops - maize (Zea mays) and waterleaf (Talinum triangulare). With increasing population pressures and climate change impacts posing challenges to food security, strategies are needed to improve crop yields, especially in developing economies. The research conducts greenhouse trials to measure plant growth parameters in simulated potassium and iron-fortified heterogeneous, homogeneous, and control soil treatments. Key growth parameters like number of leaves, stem height and leaf area were measured weekly for both crops across the different treatment conditions. The mean weekly longest leaf length of T.triangulaire in the control, homogenous and heterogenous treatments were 1.71cm, 1.47cm and 1.79cm respectively while that of Zea mays were 22.65cm, 18.63cm and 15.70cm respectively. Similarly, the mean weekly stem height and stem width were 0.33cm, 0.65cm, 0.56cm and 0.81cm, 1.24cm, 1.29cm for the control, homogenous and heterogenous treatments of T.triangulaire while that of Zea mays for the mean weekly stem height and leaf width were 10.00cm, 6.44cm, 6.08cm and 2.11cm, 1.42cm, 1.46cm for the control, homogenous and heterogenous treatments respectively. There was no significant difference (p &gt;0.05) in the growth parameters of Zea mays between treatments. Similarly, no significant difference was observed in the growth parameters of T.triangulaire between treatments. However, there was a significant difference (p&gt;0.05) in the growth parameters of both crops. This suggests a species-specific response to spatial distribution of nutrients. The study aimed to understand how soil heterogeneity, when combined with nutrient amendments, influences plant growth dynamics. Findings from this work could guide nutrient management strategies and inform efforts to improve crop productivity amid soil resource variability. By focusing on maize and waterleaf - staple food and vegetable crops widely consumed globally - the findings have implications for enhancing food crop cultivation and contributing to sustainable development goals around food security, nutrition, and agricultural practices resilient to environmental changes.

Nitrogen and phosphorus additions alter soil N transformations in a Metasequoia glyptostroboides plantation.

Nitrogen (N) and phosphorus (P) enrichment due to anthropogenic activities can significantly affect soil N transformations in forest ecosystems. However, the effects of N and P additions on nitrification and denitrification processes in Metasequoia glyptostroboides plantations, and economically important forest type in China, remain poorly understood. This study investigated the responses of soil nitrification and denitrification rates, as well as the abundances of nitrifiers and denitrifiers, to different levels of N and P additions in a 6-year nutrient addition experiment in a M. glyptostroboides plantation. Stepwise multiple regression analysis was used to identify the main predictors of nitrification and denitrification rates. The results showed that moderate N addition (N2 treatment, 2.4 mol·m-2) stimulated nitrification rates and abundances of ammonia-oxidizing archaea (AOA) and bacteria (AOB), while excessive N and P additions inhibited denitrification rates and reduced the abundance of nirS-type denitrifiers. AOB abundance was the main predictor of nitrification rates under N additions, whereas microbial biomass carbon and nirS gene abundance were the key factors controlling denitrification rates. Under P additions, tree growth parameters (diameter at breast height and crown base height) and AOB abundance were the primary predictors of nitrification and denitrification rates. Our study reveals complex interactions among nutrient inputs, plant growth, soil properties, and microbial communities in regulating soil N transformations in plantation forests. This study also offers valuable insights for formulating effective nutrient management strategies to enhance the growth and health of M. glyptostroboides plantations under scenarios of increasing elevated nutrient deposition.

Balancing Yield and Environmental Impact: Nitrogen Management and Planting Density for Rice in Southwest China

With growing concerns about global warming, it is crucial to adopt agronomic practices that enhance rice yields from paddy fields while reducing greenhouse gas (GHG) emissions for sustainable agriculture. An optimal nitrogen (N) fertilization rate and planting density are vital to ensure high rice yields, minimize GHG emissions, and understand emission behavior for better field management. We hypothesized that optimizing N application rates and planting density to improve nitrogen use efficiency (NUE) in rice cultivation would reduce resource losses and GHG emissions. To test this hypothesis, we implemented five treatments with a rice straw return cultural system: two planting densities (16 hills m−2 (traditional density, D1) and 20 hills m−2 (25% higher density, D2)) and three N application rates (no N fertilizer (N0), 180 kg N ha−1 (N1), and 144 kg N ha−1 (N2)). The control treatment (CK) was traditional planting density with no N fertilizer. The four new cropping modes were N1D1, N1D2, N2D1, and N2D2. We investigated the effects of N application rates and planting density on rice grain yield, NUE, and GHG emissions in multiple rice-growing seasons. The N1D2 treatment exhibited the highest grain yield over the three years, with a value of 10,452 kg ha−1, representing an increase of 12.2% compared to CK. Moreover, N uptake in N1D2 was the highest, averaging 39.2% (p &lt; 0.05) higher than CK, and 8.5%, 3.5%, and 2.8% (p &lt; 0.05) higher than N1D1, N2D1 and N2D2, respectively. N2D2 exhibited the highest NUE, with a value of 58.99 kg kg−1, surpassing all other treatments over the three years. GHG emissions, global warming potential (GWP), and greenhouse gas intensity (GHGI) in N2D2 were lower than in N1D1, N1D2, and N2D1. Additionally, reducing N application (comparing N1D1 to N2D1) and increasing plant density (comparing N1D1 to N1D2) improved N agronomic efficiency (NAE) and N partial productivity (PFPN). The negative correlation between the NAE and PFPN with GWP and GHG emissions further supports the potential for optimized N management and denser planting density to reduce environmental impact. These findings have important implications for sustainable rice cultivation practices in Southwest China and similar agroecosystems, emphasizing the need for integrated nutrient management strategies to achieve food security and climate change mitigation goals.

Postponed Application of Phosphorus and Potassium Fertilizers Mitigates the Damage of Late Spring Coldness by Improving Winter Wheat Root Physiology.

Late spring coldness (LSC) is the main limiting factor threatening wheat yield and quality stability. Optimal nutrient management is beneficial in mitigating the harms of LSC by improving wheat root physiology. This study proposed a nutrient management strategy that postponed the application of phosphorus (P) and potassium (K), effectively strengthening wheat's defense against LSC. This experiment used the winter cultivar "Yannong19" (YN 19) as plant material for two consecutive years (2021-2022 and 2022-2023). Two fertilizer treatments were used: traditional P and K fertilizers application (R1: base fertilizer: jointing fertilizer = 10:0) and postponed P and K fertilizers application (R2: base fertilizer: jointing fertilizer = 5:5); wheat plants at the anther connective formation stage shifted to temperature-controlled phytotrons for normal (T0, 11 °C/4 h) and low temperatures (T1, 4 °C/4 h; T2, -4 °C/4 h) as treatments of LSC. The results showed that under low temperature (LT) treatment, compared with R1, the R2 treatment increased the concentrations of osmotic adjustment substances (soluble sugars and soluble protein contents by 6.2-8.7% and 3.0-8.9%), enhanced activities of antioxidant enzymes (superoxide dismutase, peroxidase and catalase activities by 2.2-9.1%, 6.2-9.7% and 4.2-8.4%), balanced the hormone concentrations (increased IAA and GA3 contents by 2.8-17.5% and 10.4-14.1% and decreased ABA contents by 7.2-14.3%), and reduced the toxicity (malondialdehyde, hydrogen peroxide content and O2·- production rate by 5.7-12.4%, 17.7-22.8% and 19.1-19.1%) of the cellular membranes. Furthermore, the wheat root physiology in R2 significantly improved as the root surface area and dry weight increased by 5.0-6.6% and 4.7-6.6%, and P and K accumulation increased by 7.4-11.3% and 12.2-15.4% compared to R1, respectively. Overall, the postponed application of P and K fertilizers enhanced the physiological function of the root system, maintained root morphology, and promoted the accumulation of wheat nutrients under the stress of LSC.

Impact of Crop Residue, Nutrients, and Soil Moisture on Methane Emissions from Soil under Long-Term Conservation Tillage

Greenhouse gas emissions from agricultural production systems are a major area of concern in mitigating climate change. Therefore, a study was conducted to investigate the effects of crop residue, nutrient management, and soil moisture on methane (CH4) emissions from maize, rice, soybean, and wheat production systems. In this study, incubation experiments were conducted with four residue types (maize, rice, soybean, wheat), seven nutrient management treatments {N0P0K0 (no nutrients), N0PK, N100PK, N150PK, N100PK + manure@ 5 Mg ha−1, N100PK + biochar@ 5 Mg ha−1, N150PK+ biochar@ 5 Mg ha−1}, and two soil moisture levels (80% FC, and 60% FC). The results of this study indicated that interactive effects of residue type, nutrient management, and soil moisture significantly affected methane (CH4) fluxes. After 87 days of incubation, the treatment receiving rice residue with N100PK at 60% FC had the highest cumulative CH4 mitigation of −19.4 µg C kg−1 soil, and the highest emission of CH4 was observed in wheat residue application with N0PK at 80% FC (+12.93 µg C kg−1 soil). Nutrient management had mixed effects on CH4 emissions across residue and soil moisture levels in the following order: N150PK &gt; N0PK &gt; N150PK + biochar &gt; N0P0K0 &gt; N100PK + manure &gt; N100PK + biochar &gt; N100PK. Decreasing soil moisture from 80% FC to 60% FC reduced methane emissions across all residue types and nutrient treatments. Wheat and maize residues exhibited the highest carbon mineralization rates, followed by rice and soybean residues. Nutrient inputs generally decreased residue carbon mineralization. The regression analysis indicated that soil moisture and residue C mineralization were the two dominant predictor variables that estimated 31% of soil methane fluxes in Vertisols. The results of this study show the complexity of methane dynamics and emphasize the importance of integrated crop, nutrient, and soil moisture (irrigation) management strategies that need to be developed to minimize methane emissions from agricultural production systems to mitigate climate change.

Productivity of hybrid rice (Mestizo 27) under different water and nutrient management systems

This study examines the productivity of hybrid rice under different water and nutrient management systems. At Isabela State University, Cabagan, Isabela, we conducted a field experiment to determine the productivity of hybrid rice production under different water and nutrient management strategies. It was laid out in a split-plot design with four replications. Water management schemes as main plot consisted of alternate wetting and drying at -15 cm (A1), alternate wetting and drying at -20 cm (A2), field capacity (A3), and continuous flooding (A4), while nutrient management strategies as subplot consisted of recommended rate (B1), leaf color chart (B2), critical growth periods (B3), and rice crop management (B4). The Mestizo 27 subjected to nutrient management through the leaf color chart (B2) gained highest on the number of productive tillers, filled grains, biomass weight, and grain yield. Similarly, the interaction effect of field capacity and leaf color chart (A3xB2) resulted in numerous productive tillers, which increased the yield, net income, and return on investment. Moreover, water management through the field capacity (A3), continuous flooding (A4), and AWD-15 (A1) in combination with leaf color charts (B2) produced highest grain yield among treatments. Hence, the use of leaf color chart in combination with different water management schemes was seen as effective in increasing the yield of Mestizo 27 even beyond safe AWD-15.

Mapping agricultural tile drainage in the US Midwest using explainable random forest machine learning and satellite imagery

There has been an increase in tile drained area across the US Midwest and other regions worldwide due to agricultural expansion, intensification, and climate variability. Despite this growth, spatially explicit tile drainage maps remain scarce, which limits the accuracy of hydrologic modeling and implementation of nutrient reduction strategies. Here, we developed a machine-learning model to provide a Spatially Explicit Estimate of Tile Drainage (SEETileDrain) across the US Midwest in 2017 at a 30-m resolution. This model used 31 satellite-derived and environmental features after removing less important and highly correlated features. It was trained with 60,938 tile and non-tile ground truth points within the Google Earth Engine cloud-computing platform. We also used multiple feature importance metrics and Accumulated Local Effects to interpret the machine learning model. The results show that our model achieved good accuracy, with 96 % of points classified correctly and an F1 score of 0.90. When tile drainage area is aggregated to the county scale, it agreed well (r2 = 0.69) with the reported area from the Ag Census. We found that Land Surface Temperature (LST) along with climate- and soil-related features were the most important factors for classification. The top-ranked feature is the median summer nighttime LST, followed by median summer soil moisture percent. This study demonstrates the potential of applying satellite remote sensing to map spatially explicit agricultural tile drainage across large regions. The results should be useful for land use change monitoring and hydrologic and nutrient models, including those designed to achieve cost-effective agricultural water and nutrient management strategies. The algorithms developed here should also be applicable for other remote sensing mapping applications.