Nitrogen Rates Research Articles

Optimizing vancomycin dosing in pediatrics: a machine learning approach to predict trough concentrations in children under four years of age.

Vancomycin trough concentration is closely associated with clinical efficacy and toxicity. Predicting vancomycin trough concentrations in pediatric patients is challenging due to significant inter-individual variability and rapid physiological changes during maturation. This study aimed to develop a machine learning model to predict vancomycin trough concentrations and determine optimal dosing regimens for pediatric patients < 4years of age using ML algorithms. A single-center retrospective observational study was conducted from January 2017 to March 2020. Pediatric patients who received intravenous vancomycin and underwent therapeutic drug monitoring were enrolled. Seven ML models [linear regression, gradient boosted decision trees, support vector machine, decision tree, random forest, Bagging, and extreme gradient boosting (XGBoost)] were developed using 31 variables. Performance metrics including R-squared (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) were compared, and important features were ranked. The study included 120 eligible trough concentration measurements from 112 patients. Of these, 84 measurements were used for training and 36 for testing. Among the seven algorithms tested, XGBoost showed the best performance, with a low prediction error and high goodness of fit (MAE = 2.55, RMSE = 4.13, MSE = 17.12, and R2 = 0.59). Blood urea nitrogen, serum creatinine, and creatinine clearance rate were identified as the most important predictors of vancomycin trough concentration. An XGBoost ML model was developed to predict vancomycin trough concentrations and aid in drug treatment predictions as a decision-support technology.

Tomato yield, and water use efficiency as affected by nitrogen rate and irrigation regime in the central low lands of Ethiopia

Tomato yield can be increased by the application of optimum water and fertilizer. A field experiment was conducted in Efratana Gidim district, North Shewa, Amhara, Ethiopia, during 2019 and 2020. The objective was to determine the nitrogen (N) rate and irrigation regime for optimum tomato yield and water use efficiency (WUE). The experiment consisted of three-irrigation regimes (75% ETc (Evapotranspiration from the crop), 100% ETc, and 125% ETc) and four nitrogen (N) rates (control; i.e. without N application1, 46 kg N ha−1, 92 kg N ha−1, and 138 kg N ha−1). The treatments were laid out in a split-plot design with four replications. The Irrigation regime were assigned to the main plot, while the N rate were assigned to the subplot. Data on growth, yield, and yield-related traits of tomatoes, include; plant height, number of fruit clusters per plant, fruit length, fruit diameter, number of marketable fruits, number of un-marketable fruits, the total number of fruits, marketable fruit yield, un-marketable fruit yield, total yield were collected. The data were subjected to analysis of variance using R studio. The results indicated that the experimental site had low total N content, and the application of N fertilizer significantly improved tomato yield. Increasing irrigation depth also significantly increased tomato yield. The result indicated that the highest mean marketable fruit yield (35,903 kg ha−1) was obtained from the combined application of 125% ETc with 92 kg N ha−1, while the lowest (13,655 kg ha−1) marketable fruit yield was obtained from 75% ETc with 92 kg N ha−1. The analysis of variance showed that the highest (5.4 kg m−3) WUE recorded from 75% ETc with 46 kg N ha−1 increased WUE by 77% (2.4 kg m−3) compared with the lowest (2.3 kg m−3) WUE recorded from 125% ETc with 0 kg N ha−1. The partial budget analysis also indicated that the highest net benefit (266,272 ETB (Ethiopian Birr) ha−1) and an acceptable marginal rate of return (1240%) for the invested capital was recorded from the combined application of 125% ETc with 92 kg N ha−1. Therefore, the application of 125% ETc with 92 kg N ha−1 resulted in the highest net benefit.

Integrating NDVI, SPAD, and Canopy Temperature for Strategic Nitrogen and Seeding Rate Management to Enhance Yield, Quality, and Sustainability in Wheat Cultivation.

This study explores the interplay between nitrogen doses and seeding rates on wheat yield, biomass, and protein content. Utilizing tools such as the Normalized Difference Vegetation Index (NDVI), Soil Plant Analysis Development (SPAD) measurements, and canopy temperature (CT), we conducted experiments over five growing seasons. The treatments included three nitrogen levels (0, 60, 120 kg/ha) and three seeding rates (300, 400, 500 seeds/m2) in a split-plot design with 90 plots and two replications. Our results show that an intermediate nitrogen dose (60 kg/ha) combined with a moderate seed rate (400 seeds/m2) enhances wheat yield by 22.95%. Reduced nitrogen levels increased protein content, demonstrating wheat's adaptive mechanisms under nitrogen constraints. NDVI analysis highlighted significant growth during the tillering phase with high nitrogen, emphasizing early-stage nutrient management. SPAD measurements showed that early nitrogen applications boost chlorophyll content, essential for vigorous early growth, while CT data indicate that optimal nitrogen and seed rates can effectively modulate plant stress responses. As crops mature, the predictive capacity of NDVI declines, indicating the need for adjusted nitrogen strategies. Collectively, these findings advocate for refined management of nitrogen and seeding rates, integrating NDVI, SPAD, and CT assessments to enhance yields and promote sustainable agricultural practices while minimizing environmental impacts.

Correlation of methane production with physiological traits in Trichodesmium IMS 101 grown with methylphosphonate at different temperatures.

The diazotrophic cyanobacterium Trichodesmium has been recognized as a potentially significant contributor to aerobic methane generation via several mechanisms including the utilization of methylphophonate (MPn) as a source of phosphorus. Currently, there is no information about how environmental factors regulate methane production by Trichodesmium. Here, we grew Trichodesmium IMS101 at five temperatures ranging from 16 to 31°C, and found that its methane production rates increased with rising temperatures to peak (1.028 ± 0.040 nmol CH4 μmol POC-1 day-1) at 27°C, and then declined. Its specific growth rate changed from 0.03 ± 0.01 d-1 to 0.34 ± 0.02 d-1, with the optimal growth temperature identified between 27 and 31°C. Within the tested temperature range the Q10 for the methane production rate was 4.6 ± 0.7, indicating a high sensitivity to thermal changes. In parallel, the methane production rates showed robust positive correlations with the assimilation rates of carbon, nitrogen, and phosphorus, resulting in the methane production quotients (molar ratio of carbon, nitrogen, or phosphorus assimilated to methane produced) of 227-494 for carbon, 40-128 for nitrogen, and 1.8-3.4 for phosphorus within the tested temperature range. Based on the experimental data, we estimated that the methane released from Trichodesmium can offset about 1% of its CO2 mitigation effects.

Brown patch severity of five tall fescue cultivars as influenced by summer nitrogen rates

Turf-type tall fescue [Schedonorus arundinaceus (Schreb.) Dumort., nom. cons.: TTTF] is a predominant turfgrass species for lawns throughout cool-humid regions, yet brown patch (caused by Rhizoctonia and Rhizoctonia-like species) can cause severe damage during the summer months. Hypothesized strategies to help minimize brown patch severity and decrease fungicide use includes establishing TTTFs with a high level of host resistance and minimizing summer nitrogen (N) applications. A two-year field study was conducted in West Lafayette, IN to determine the influence of late-spring and summer applied N at two application rates among five TTTF cultivars. Urea-N was applied monthly at two rates from April to July, totaling to 73.5 and 245.0 kg N ha−1. Turf performance was determined using visual ratings for turf quality, relative canopy greenness, disease severity (0–100%), and seasonal brown patch as calculated by area under the disease progress curve (AUDPC). Brown patch was generally not affected by N-rate for any cultivar in either study year. While none of the TTTF cultivars had complete brown patch resistance, cultivar differences were observed, with disease severity ranging from 9.8 to 39.0% and 20.0–51.9% in 2021 and 2022, respectively. Selecting a brown patch resistant cultivar reduced seasonal brown patch severity by 61% across study years compared to the most susceptible cultivars. This study demonstrates that summer N applications to TTTF lawns should not be completely avoided to reduce brown patch as previously suggested and emphasizes the importance of host resistance for disease management.

Influence of Different Rates of Nitrogen and Sulfur on Growth, Yield and Yield Attributes of Cabbage (Brassica oleraceae L.)

The field experiment was conducted at Lovely Professional University, Phagwara, during rabi season on the sandy loam soils to study the influence of different rates of nitrogen and sulfur on growth and yield attributes of cabbage (Brassica oleraceae L.). The experiment was laid in Factorial Randomized Block Design (FRBD) consisting of eight treatments and three replications viz. (N1: 0 kg N per ha, N2: 100 kg N per ha, N3: 125 kg N per ha, N4: 150 kg N per ha, S1: 0 kg S per ha, S2: 30 kg S per ha, S3: 60 kg S per ha and S4: 90 kg S per ha). The different growth and yield attributes viz. plant height (cm), number of unfolded leaves per plant, diameter of cabbage head (cm), weight of cabbage head (kg) and yield of cabbage (t/ha) were recorded at different growth stages and at the time of harvesting. The results revealed that the application of 150 kg nitrogen and 90 kg sulfur per hectare gave the maximum values in all the growth and yield attributes compared to all the other treatments as well as control.

Use of increasing rates of ammonia nitrogen in pot-grown blueberries and its effect on fruit yield and macronutrient concentration in leaves

Use of increasing rates of ammonia nitrogen in pot-grown blueberries and its effect on fruit yield and macronutrient concentration in leaves

Iron sulfide mineral/polylactic acid mixotrophic biofilter for simultaneous nitrate and phosphate removal

Heterotrophic denitrification based on polylactic acid (PLAHD) can remove nitrate effectively, but it is expensive and can't remove phosphate. Autotrophic denitrification based on iron sulfide (ISAD) can simultaneously remove nitrate and phosphate cost-effectively, but its nitrate rate is slow. So, iron sulfide mineral/polylactic acid mixotrophic biofilter (ISPLAB) was constructed to combine advantages of ISAD and PLAHD. ISPLAB achieved nitrogen and phosphorus removal rates of 98.04 % and 94.12 %, respectively, at a hydraulic retention time (HRT) of 24 h. The study also revealed that controlling molecular weight (Mw) of PLA improved the release of soluble organic matter; adding iron sulfide enhanced the hydrolysis of PLA and precipitated PO43− of Fe2+/Fe3+, thereby facilitated simultaneous nitrogen and phosphorus removal. Microbial community analysis resulted that denitrifying bacterias (Phaeodactylibacter and Methylotenera), sulfur-reducing bacterias (Hyphomicrobium), sulfur-oxidizing bacteria (Denitratisoma), iron-reducing bacteria (Romboutsia) and hydrolyzed bacterias (norank_f_norank_o_1–20 and norank_f_Caldilineaceae) coexisted in the ISPLAB system. Organics and iron sulfide drived the denitrification process in ISPLAB.

Spatial-temporal variability in nitrogen use efficiency: Insights from a long-term experiment and crop simulation modeling to support site specific nitrogen management

Within-field soil heterogeneity can lead to large variation in nitrogen use efficiency (NUE). Crop simulation models provide a multi-faceted approach to management considering both soil and plant interactions. However, research using crop models for investigating within field variation in NUE is limited, in part because of challenges quantifying spatially variable soil model parameters. Here soil apparent electrical conductivity (ECa) and measured soil properties were used to map spatial variations in soil characteristics across a Long-Term Experiment in Norfolk, England. The relationship between plot ECa across the 3 ha experiment and agronomic data across three different nitrogen rates (0, 110, and 220 kg N ha-1) over five wheat years (2010–2020) was quantified. The Sirius crop model was parameterized for two soils representing the extremes of ECa. Sirius was validated using recorded plot data. Site-specific optimal nitrogen and associated leaching risks were simulated across 29 years of weather data. Variation in soil properties had significant impact on measured NUE. At 220 kg N ha-1 mean observed yields across 5 years ranged from 9.0 to 10.7 t ha-1 and grain protein from 11.6% to 11% on the low EC and high EC plots, respectively. On average fertiliser grain N recovery was 19.7 kg N ha-1 lower on the low ECa plots. Sirius simulated the variation in yield, grain protein and grain N recovery to a good level of accuracy with RRMSE of 19.5%, 15.4% and 19.5%, respectively. Simulated optimal nitrogen on the low EC soils was on average 12 kg N ha-1 lower, with >1 in 4 years with optimal nitrogen <200 kg N ha-1. Our work demonstrated that using a combination of proximal soil EC scans and targeted soil sampling we can optimize the data requirements for model parameterisation to support site-specific N management.

Prediction Model of Nitrogen, Phosphorus, and Potassium Fertilizer Application Rate for Greenhouse Tomatoes under Different Soil Fertility Conditions

To reach the target yield of crops, nutrient management is essential. Selecting the appropriate prediction model and adjusting the nutrient supply based on the actual situation can effectively improve the nutrient utilization efficiency, crop yield, and product quality. Therefore, a prediction model of the NPK fertilizer application rate for greenhouse tomatoes under the target yield was studied in this study. Under low, medium, and high soil fertility conditions, a neural network prediction model based on the sparrow search algorithm (SSA-NN), a neural network prediction model based on the improved sparrow search algorithm (ISSA-NN), and a neural network prediction model based on the hybrid algorithm (HA-NN) were used to predict the NPK fertilizer application rate for greenhouse tomatoes. The experimental results indicated that the evaluation indexes (i.e., the mean square error (MSE), explained variance score (EVS), and coefficient of determination (R2)) of the HA-NN prediction model proposed in this study were superior than the SSA-NN and ISSA-NN prediction models under three different soil fertility conditions. Under high soil fertility, compared with the SSA-NN prediction model, the MSE of the ISSA-NN and HA-NN prediction models decreased to 0.007 and 0.005, respectively; the EVS increased to 0.871 and 0.908, respectively; and the R2 increased to 0.862 and 0.899, respectively. This study showed that the HA–NN prediction model was superior in predicting the NPK fertilizer application rate for greenhouse tomatoes under three different soil fertility conditions. Due to the significance of NPK fertilizer application rate prediction for greenhouse tomatoes, this technique is expected to bring benefits to agricultural production management and decision support.

Characteristics of late summer Arctic brash sea ice and its melting effect on the surface-water biogeochemistry of the Chukchi Shelf and Canada Basin

To understand the impact of the melting of late summer Arctic brash ice on the surface waters of the Chukchi Sea, we collected sea-ice samples during 2021. Floating sea ice was collected by a wire mesh pallet cage from the side of the R/V Mirai. We measured physical and biogeochemical parameters such as salinity, oxygen stable isotopic ratios, turbidity, and concentrations of chlorophyll-a and nutrients. The samples of brash ice were multiyear ice based on satellite back-trajectory analysis. Comparison of nutrient concentrations in brash ice with those of seawater samples from the temperature minimum layer similar to the water in the sea ice originated suggested that the characteristics of the brash ice were greatly affected by biogeochemical processes such as remineralization. The extremely high turbidity and concentrations of chlorophyll-a observed in the brown/green ice samples reflected the impact of sediment as well as the influence of biological activities. The N:P ratios were less than 1 because of the high phosphate concentrations, even though the ammonium concentrations were high. We hypothesized that this low N:P ratio reflected the combined effects of the accumulation of nutrients due to remineralization in the biofilm and differences of remineralization rate and adsorption features of nitrogen and phosphorus. Based on the high nitrate and ammonium concentrations in the sea-ice samples, we postulated a marked impact of sea-ice meltwater on the nitrogen cycle in the nitrate-depleted surface waters of the Chukchi Sea during late summer. We estimated that meltwater nitrogen could support 0.3%–2.6% of primary production in the northern Chukchi Sea. Our results suggest that high-turbidity ice will play an important role as a source of nutrients to the ocean during melting of sea ice, and understanding its distribution, amount, and geochemical characteristics is vital.

Improving cotton productivity and nitrogen use efficiency through late nitrogen fertilization: Evidence from a three-year field experiment in the Xinjiang

ContextPremature senescence is a major concern for crop production especially for high-density cotton in Xinjiang, China, when nitrogen (N) rates are reduced. This study aimed to examine the potential of late N fertilization in mitigating senescence, improving light and N use efficiency, and enhancing cotton productivity under reduced N rate. MethodsA three-year field experiment was conducted from 2019 to 2021, employing a total of 240 kg N ha-1, which was one-third lower than the traditional rate of 360 kg ha-1. Five different N application ratios based on cotton stages were tested, with N064 (0:6:4 ratio at early: middle: late growth stages) representing late N fertilization, and N262 representing traditional ratio in fertilization. The effects of N application timing-based ratios on yield components, N use efficiency, canopy photosynthesis, and root traits were evaluated. ResultsThe results showed that N064 had the highest lint yield 2727–3305 kg ha-1 (2019–2021) among all treatments over the three years. Late N fertilization (N064) resulted in significant increases in boll density (4.6–10.7%), seed cotton yield (5.8–8.1%), and N use efficiency (10.5–14.1%) when compared to traditional N262. During late peak boll-setting to boll opening stages, N064 decreased leaf area index (3.5–6.6%), canopy light interception rate (3.8–11.4%), and boll capacity of root (18.1%) compared to N262, while increasing SPAD (2.3–4.8%), diffuse non-interceptance (3.9–16.6%), canopy apparent photosynthetic rate (13.2–49.4%), root biomass (19.5%), root/shoot ratio (15.0%). N064 also resulted in a higher root surface area density in deep soil than N262. ConclusionsLate N fertigation enhanced post-flowering canopy photosynthetic production by improving the cotton root-shoot relationship and delaying root and canopy senescence, resulting in improved yields and N use efficiency. This N management strategy implemented under reduced N cultivation shows promise for sustainable cotton production in Xinjiang and similar ecological regions.

Impacts of Nitrogen Sources and Rates on Soil Chemical Attributes and Potato Nutrition and Yield in a Subtropical Environment

Impacts of Nitrogen Sources and Rates on Soil Chemical Attributes and Potato Nutrition and Yield in a Subtropical Environment

Ranking genotype, environment, management effects on the optimum nitrogen rate for maize: A cropping system modeling analysis

AbstractRanking the contribution of genotype, environment, and management (G × E × M) on maize's economic optimum nitrogen fertilizer rate (EONR) variability could improve understanding and predictability of EONR. We performed a simulation experiment using the Agricultural Production Systems sIMulator model with the objectives to (1) rank the effects of 24 individual G × E × M factors on the magnitude and interannual variability of the EONR across the US Midwest and (2) investigate the impact of G × M factors on the EONR variability under present and future climate scenarios. Results indicate that genetics (27%), management (31%), and environmental conditions (41%) each influence the EONR variability. Within these broad categories, the top three individual factors impacting the EONR were interannual weather variability, crop radiation use efficiency, and the soil inorganic N carryover from the previous year. The G × E × M factors influenced the yield response to N fertilizer in different ways. Soil‐related factors (e.g., organic matter and residual nitrate) influenced grain yields at the low N rates, while management factors (e.g., planting date and density) influenced yield at all N rates. Combining increases in plant density and changes in genetics synergistically increased the EONR by 15% from baseline. Future climate scenarios without adaptation decreased the EONR and yield loss, but crop adaptation was buffered against the negative climate change impacts. We concluded that 59% of the annual EONR variability is manageable (due to genetics and management) and that G × M factors could buffer climate change's negative effects on crop production. Present results can inform experimental research on N fertilizer and N rate decisions.

Oilseed Radish: Nitrogen and Sulfur Management Strategies for Seed Yield and Quality—A Case Study in Poland

Oilseed Radish: Nitrogen and Sulfur Management Strategies for Seed Yield and Quality—A Case Study in Poland

Application of resource-environmental-economic perspective for optimal water and nitrogen rate under high-low seedbed cultivation in winter wheat

High-low seedbed cultivation (HLSC), a targeted agricultural method for increasing land utilization efficiency in North China Plain, has emerged as a key practice in enhancing sustainable agriculture, particularly in the context of the region’s unique ecological challenges and water scarcity. However, comprehensive analysis of resource-environment-economy for water and nitrogen strategy in HLSC-cultivated wheat was limited. A three-year field experiment with four nitrogen rates (360, 300, 240, and 180 kg ha−1, denoted as N1, N2, N3, and N4) and three irrigation quotas (120, 90, and 60 mm, referred to as W1, W2, and W3) was conducted to investigate effects of water and nitrogen rate on water productivity (WP), agronomic efficiency of nitrogen (AEN), energy use efficiency (EUE), carbon footprint (CF), water footprint (WF), nitrogen footprint (NF), and net benefits of HLSC-cultivated wheat. Results showed that the total energy output in moderate water and nitrogen input (i.e., N3W2) was 328.0×103 MJ ha−1, which was significantly increased by 31.8% compared with lower input (i.e., N4W3) but was decreased by 17.1% compared with higher input (i.e., N1W1). The total energy input, water consumption and grain yield in N3W2 was decreased by 22.3%, 14.4 and 4.1%, relative to N1W1, respectively, and increased by 18.2%, 20.8% and 42.2% relative to N4W3, respectively. This may lead to significant increase in EUE, WP, AEN and net income in N3W2. Furthermore, the CF, WF, and NF increased as the water and nitrogen input increased, whereas minimum CF, WF and NF per unit of yield was obtained N3/N4 or W1/W2 level. 240 kg N ha−1 coupled with 90–120 mm irrigation quota was recommended by TOPSIS for the HLSC-cultivated wheat to best trade-off resource use efficiencies, environmental footprints, and net benefits. Future studies are needed to further assess the scalability and adaptability of HLSC-cultivated wheat across different climatic zones and soil types.

Brown patch severity as affected by cool‐season turfgrass species, cultivar, and nitrogen rate

AbstractBrown patch (caused by various Rhizoctonia and Rhizoctonia‐like species) is a major summer disease for several cool‐season turfgrass species, including creeping bentgrass (CBG) (Agrostis stolonifera L.), colonial bentgrass (CLBG) (Agrostis capillaris L.), perennial ryegrass (PRG) (Lolium perenne L.), and turf‐type tall fescue (TTTF; [Schedonorus arundinaceus (Schreb.) Dumort., nom. cons.]). Elevated nitrogen (N) rates are suggested to enhance development of brown patch symptoms. A controlled environment study was conducted to determine the influence of two N rates among four cool‐season turfgrass species maintained at 1.9 cm after inoculation with R. solani. Field‐grown plants of four CBG cultivars, a CLBG cultivar, a PRG blend, and two TTTF cultivars were fertilized weekly, totaling 49 and 196 kg N ha−1 across four applications. Turfgrass responses were measured as relative growth rate, brown patch severity, and seasonal brown patch using area under the disease progress curve (AUDPC). On average, peak brown patch severity among the species and cultivars was between 22.6% and 68.1% for Raptor III TTTF and Penn A‐1 CBG, respectively. Turf‐type tall fescue cultivars decreased brown patch by 61% compared to CBG, CLBG, and PRG. The lower N rate reduced brown patch AUDPC by 17% and 33% for CBG and PRG, respectively, but N rate generally had no effect for CLBG and TTTF. These results reinforce the importance of planting disease‐resistant species and cultivars. Additionally, N rate may be an important factor affecting brown patch severity for select intensively managed turfgrass species.

Residual effect of legumes on maize yield, nitrogen balance and soil organic carbon stabilization under legume – maize cropping systems

Inclusion of legumes in cereal based cropping system restores soil fertility and improves carbon storage and soil quality. In this background a field experiment was conducted in a strip-split plot design using three legume species viz., green gram, groundnut and cluster bean as vertical strip, two residue management practices i.e. residue removal (-R) and residue retention (+R) as horizontal strip, and three nitrogen levels i.e. 100%, 75% and 50% recommended rate of nitrogen (N) application in intersectional sub plots with four replications during summer 2017 and 2018. The grain and stover yield of maize, N uptake and soil properties were monitored after each crop harvest; soil nitrogen balance and soil organic carbon were estimated. Under 100% N fertilization, cluster bean – maize cropping system with residue (+R) recorded 12.78 and 9.78% higher maize grain and stover yield, respectively than without residue (–R) treatment. Highest available N (189.9 kg ha−1) and available P (54.5 kg ha−1) were registered under cluster bean- maize cropping system. Legume residue incorporation resulted 9.65, 8.46, 7.09 and 8.86% higher (p = 0.05) WBC content, WBC stock, available N and P contents over residue removal scenario. Annually, 1.71% added carbon converted into soil organic carbon. Legume residue retention and 100% recommended rate of nitrogen application enhanced nitrogen balance. Soil N balance was highest under groundnut – maize system (165 kg ha−1). Overall, for better soil health and optimum crop production, cluster bean – maize cropping system with legume residue retention and 100% N fertilization may be promoted in semi-arid Western India.

Optimizing Wheat Yield: A Comprehensive Study on the Impact of Inorganic Fertilizer Rates and Varietal Selection in Kofele, Ethiopia

Wheat is one of the globally produced and marketed cereal crops which cover 15% of the total sowing areas of cereal crops in the world. The low productivity of wheat in Ethiopia is mainly attributed to lack of improved agronomic practices. Nitrogen and phosphorus are the major important elements for crop yield. But there is lack of information on optimum fertilizer rate for wheat production in this area (Kofele district of West Arsi zone). Therefore the objective of this research was to determine the optimum nitrogen and phosphorus fertilizer rate for wheat at Kofele district. This experiment was conducted for two consecutive years in 2016 and 2017 main cropping season. The treatment has two factors (three bread wheat varieties:- Digalu, Dendea and Didase and fertilizer rates: - 73kg/ha of nitrogen and 69kg /ha of P&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O&amp;lt;sub&amp;gt;5&amp;lt;/sub&amp;gt;, 109.5kg/ha of nitrogen and 103.5kg/ha of P&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O&amp;lt;sub&amp;gt;5&amp;lt;/sub&amp;gt;, 146kg/ha of nitrogen and 138kg/ha of P&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O&amp;lt;sub&amp;gt;5 &amp;lt;/sub&amp;gt;and 46kg/ha of nitrogen and 181kg/ha of NPS:-19:38:7). The analyzed data indicated that plant height and seeds per spike of bread wheat was highly significantly affected by main effect of variety. Highest plant height (128.33cm) and seeds per spike (42.1) were recorded from variety Dendea. Spike length was significantly affected by main effect of variety. The longest spike length (8.3) was recorded from variety Hidase. Wheat grain yield was significantly affected by interaction effect at (p&amp;lt;0.05). Variety Hidase produced the highest grain yield of 6904.4kg/ha at 146kg/ha of nitrogen and 138kg/ha of P&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O&amp;lt;sub&amp;gt;5&amp;lt;/sub&amp;gt;. Above ground dry weight of wheat was significantly affected by interaction effect. The highest above ground dry weight 18.9 ton/ha of wheat was produced from variety Digalu at 73kg/ha of nitrogen and 69kg/ha of P&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O&amp;lt;sub&amp;gt;5&amp;lt;/sub&amp;gt;. Therefore, to get the highest benefit farmers should grow variety Hidase by applying 146kg/ha of nitrogen and 138kg/ha of P&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O&amp;lt;sub&amp;gt;5 &amp;lt;/sub&amp;gt;at this area.

Improving ammonia nitrogen removal and recovery by BMED stack optimization: The effect of ion exchange membrane thickness

Bipolar membrane electrodialysis (BMED) has emerged as an efficient and promising technology for the removal and recovery of ammonia nitrogen. Despite its potential, the optimization of BMED systems for enhanced performance and energy efficiency remains a critical challenge, particularly in the context of varying ion exchange membrane (IEM) thicknesses. Membranes with a thickness of < 80 µm have low resistance and are mainly used for processes requiring low energy consumption. Membranes with a thickness of 80–250 µm have moderate resistance and high ion selectivity and are widely used for electrodialysis applications. Membranes with a thickness of > 250 µm have higher ion selectivity than commercial membranes with a thickness of 80–250 µm and are suitable for processes that demand high purity. This study investigates the effect of IEM thickness ranging from 16–400 µm on BMED performance for ammonia nitrogen removal and recovery, and energy consumption. The BMED configuration comprising five cell triplets consisting of a cation exchange membrane, bipolar membrane, and anion exchange membrane was employed. The thickness of IEMs was divided into three categories: 16–17 µm (PCEM/ PAEM), 100–150 µm (CD100/AD100, InnoSep-C/InnoSep-A), and 380–400 µm (MC-3470/MA-3475). In membranes with a thickness of 100–150 µm, the ammonia nitrogen removal was exceptionally high at 99.2 %, with a recovery of 98.7 % (CD100/AD100: 100 µm) at a current density of 80 mA/cm2, and the system exhibited low specific energy consumption (SEC) of 8.87 kWh/kg-N (InnoSep-C/InnoSep-A: 150 µm) at a current density of 20 mA/cm2. However, when using the thinnest IEMs (PCEM: 16 µm, PAEM: 17 µm), an increase in total ammonia nitrogen loss (10.21–22.01 %) led to a reduction in ammonia nitrogen recovery. Conversely, when using the thickest IEMs (MC-3470: 380 µm, MA-3475: 400 µm), the SEC was extremely high (30.98–126.67 kWh/kg-N), making them unsuitable for the BMED process for ammonia nitrogen removal and recovery. Based on these experimental results, it is suggested to use an optimal thickness of IEM within the range of 80–250 µm in the BMED systems to achieve a balanced performance, maximizing ammonia nitrogen removal and recovery while minimizing the SEC. Consequently, the present study provides a comprehensive understanding of the effects of the IEM thicknesses on the BMED systems and could offer a practical perspective for optimizing the BMED stack with respect to improving the removal and recovery rate of ammonia nitrogen and reducing the energy consumption.