Optimum Nitrogen Rate Research Articles

Fertilizer nitrogen rate effects on broiler litter–fertilized corn: Grain yield and nutrient composition

AbstractNitrogen (N) plays an important role in corn (Zea mays L.) production, but little is known of optimum fertilizer N rates for corn grain yield and nutrient composition when poultry manure such as broiler litter (BL) (Gallus gallus domesticus) is applied. Typically, farmers consider BL as a soil conditioner and often do not discount the N contribution from the BL application. Field experiments were conducted in 2020 and 2021 over three diverse environments across Alabama to determine the agronomic optimum nitrogen rate (AONR) for BL–fertilized dryland corn with five side dress fertilizer N rates (0, 84, 140, 196, and 252 kg N ha−1). The study utilized a yield response curve and evaluated the effect of fertilizer N rate on grain nutrient composition. BL (4.48 Mg ha−1 equivalent to 2‐ton acre−1) was applied to the study sites before planting each year. The AONRs for grain yield across these environments varied from 114 to 223 kg N ha−1 (0.013–0.027 kg of fertilizer N per kg of grain yield ha−1). The predicted grain yield at the AONRs ranged from 4.22 to 13.27 Mg ha−1. Grain N concentration increased with increasing fertilizer N rate. However, the weak to nonexistent correlation between fertilizer N and other grain nutrient elements suggests that raising the fertilizer N rate does not necessarily lead to higher nutrient levels in corn grain. Overall, these findings could be useful for developing N management guidelines for corn fields that receive BL in Alabama.

Mid-season nitrogen management for winter wheat under price and weather uncertainty

ContextIn-season nitrogen (N) management tools are essential for optimizing N application rates, maximizing farmers’ economic returns and minimizing adverse environmental impacts. The primary limitation to developing such tools is the risk associated with uncertainties in weather forecasts and crop price projections required to estimate yields and returns for different N rates. Therefore, characterizing the risk associated with these uncertainties is crucial for determining optimum N rates in-season. ObjectiveThis study investigated the N application decision-making process for farmers, accounting for risks associated with weather and crop price uncertainties through crop modeling and economic analysis. MethodsWe used field trial data for winter wheat in Kansas to examine how optimal nitrogen rates and economic returns vary over sites, years, and differing farmers’ risk attitudes. First, the Environmental Policy Integrated Climate (EPIC) agroecosystem model was used to simulate the distribution of final yields under different N applications during early spring. Then, an autoregressive moving average (ARMA) model estimated the wheat price distribution at harvest based on historical prices. Finally, optimal N application rates for farmers with different risk appetites were estimated using two risk decision models: the constant-absolute-risk-averse (CARA) expected utility model, which treats upside (higher-than-expected returns) and downside (lower-than-expected returns) deviations equally, and the invariant-preference, generalized-deviation (IPGD) model, which focuses on downside risk. ResultsWe found that optimal N rates vary greatly between sites and years, as well as across farmers with different risk preferences. Due to the positive skewness of economic return distribution, farmers tend to apply lower N rates when considering downside risk. On average, the optimal N rate for farmers with a CARA coefficient of 0.002 is 77 kg/ha in the CARA model and 67 kg/ha in the IPGD model. Compared to the outcome of risk-neutral N usage, risk-averse N usage for a farmer with a CARA coefficient of 0.008 could reduce the uncertainty (standard deviation) of return by 6.2 %, on average, while the expected return decreased by only 1.2 %. ConclusionsBy lowering the N rate, risk-averse farmers would reduce the uncertainty of returns and incur a minor return loss, suggesting the possibility of improving agricultural resilience while also improving N use efficiency. Our analysis also underscores the importance of yearly site-specific N management, given the substantial variation in optimal rates across years and locations. SignificanceThis study provides the foundation for an N application decision framework that considers both weather and price uncertainty. The analysis also demonstrates the potential co-benefit of enhancing agriculture’s climate and market resilience while potentially lowering N losses.

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.

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.

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<sub>2</sub>O<sub>5</sub>, 109.5kg/ha of nitrogen and 103.5kg/ha of P<sub>2</sub>O<sub>5</sub>, 146kg/ha of nitrogen and 138kg/ha of P<sub>2</sub>O<sub>5 </sub>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<0.05). Variety Hidase produced the highest grain yield of 6904.4kg/ha at 146kg/ha of nitrogen and 138kg/ha of P<sub>2</sub>O<sub>5</sub>. 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<sub>2</sub>O<sub>5</sub>. Therefore, to get the highest benefit farmers should grow variety Hidase by applying 146kg/ha of nitrogen and 138kg/ha of P<sub>2</sub>O<sub>5 </sub>at this area.

A Systematic Review on Effects of Nitrogen Fertilizer Levels on Cabbage (Brassica oleracea var. capitata L.) Production in Ethiopia.

Cabbage (Brassica oleracea var. capitata L.) holds significant agricultural and nutritional importance in Ethiopia; yet, its production faces challenges, including suboptimal nitrogen fertilizer management. The aim of this review was to review the possible effect of nitrogen fertilizer levels on the production of cabbage in Ethiopia. Nitrogen fertilization significantly influences cabbage yield and quality. Moderate to high levels of nitrogen application enhance plant growth, leaf area, head weight, and yield. However, excessive nitrogen levels can lead to adverse effects such as delayed maturity, increased susceptibility to pests and diseases, and reduced postharvest quality. In Ethiopia, small-scale farmers use different nitrogen levels for cabbage cultivation. In Ethiopia, NPSB or NPSBZN fertilizers are widely employed for the growing of various crops such as cabbage. 242 kg of NPS and 79 kg of urea are the blanket recommendation for the current production of cabbage in Ethiopia. The existing rate is not conducive for farmers. Therefore, small-scale farmers ought to utilize an optimal and cost-effective nitrogen rate to boost the cabbage yield. Furthermore, the effectiveness of nitrogen fertilization is influenced by various factors including the soil type, climate, cabbage variety, and agronomic practices. Integrated nutrient management approaches, combining nitrogen fertilizers with organic amendments or other nutrients, have shown promise in optimizing cabbage production while minimizing environmental impacts. The government ought to heed suggestions concerning soil characteristics such as the soil type, fertility, and additional factors such as the soil pH level and soil moisture contents.

GENERATING NITROGEN RECOMMENDATIONS FOR MAIZE: ECONOMIC CONSIDERATIONS

The use of excessive amounts of nitrogen fertilizers in crops usually results in nitrogen (N) losses, increasing environmental pollution and contributing to climate change, while also elevating crop production costs. This study aimed to determine in maize (Zea mays L.): (1) the economic optimal nitrogen rate (EONR), economic optimal yield (EOY), and N use efficiency (NUE) utilizing the current N/maize (grain) price ratio (N/MPR) for attainable yield levels; and (2) the effect of an increase in the N/MPR on the EONR, EOY, net income (NI), NUE, and the loss of N. Data from 140 maize response experiments to increasing rates of N, conducted between 2010 and 2019 across 18 states in Mexico under rainfed and irrigated conditions, were analyzed. In these experiments, the average maximum yields were 4.971 ± 2.716 Mg ha-1 under rainfed conditions and 11.776 ± 2.366 Mg ha-1 under irrigated conditions. The EONR, EOY, and NUE were determined with the current N/MPR for attainable yield levels. At these levels, both the EONR and NUE increased with higher EOY and decreased with increased yield without N treatment. An increase in N/MPR reduced EONR and its associated costs without significantly affecting EOY and NI. The reduction in EONR resulted in higher NUE and, consequently, lower N losses. By utilizing N/MRP in economic optimization, recommended N rates for maize can be reduced, leading to decreased N losses and environmental pollution.

Effects of different nitrogen fertilization systems on crop yield and nitrogen use efficiency – Results of a field experiment in southern Germany

The effects of the German Fertilizer Application Ordinance (GFO) on crop yield, nitrogen use efficiency and economical performance are highly controversial in science and practice in Germany. This study presents the results of a multi-year field experiment conducted at an experimental farm in southern Germany, in which the effects of different fertilizer systems on crop yield, protein concentration and nitrogen balance were analyzed.At this study site, relatively low N mineralization from the soil N pool was detected. Wheat (triticum aestivum L.) and barley (hordeum vulgare L.) showed strong yield declines from annual to multi-annual unfertilized plots, for maize (zea mays L.), this yield decrease was not observed. The recommendations according to GFO meets the fertilizer requirement at the trial site well. A 20% reduction of fertilization compared to GFO resulted in a 5% yield reduction and a decrease in protein concentration of wheat and barley.According to the quadratic N response function, the GFO treatment was slightly below the economic optimum nitrogen rate (Nopt) for wheat, and close to Nopt for winter barley on average over the trial years. For maize, a relatively high yield variability has been observed in the trial period so far. Sensor-based fertilization resulted in very high yields with high N use efficiency (up to 85%). This fertilization system can help to reduce nitrogen input and minimize nitrogen surplus.For wheat and barley, N fertilization and N uptake were well balanced, for maize clearly negative N surpluses were calculated. Despite all the discussion and criticism of GFO, the results of the plot trial show that high yields with high N use efficiency can be achieved with fertilization according to GFO.

Optimizing nitrogen application rates for winter canola in Mississippi

AbstractThe production of canola (Brassica napus L.) in the United States is low, whereas the demand is high. Most US canola research is centered in the Midwest, with minimal to no research in Southern states including Mississippi. Therefore, a study was conducted in Mississippi to assess the feasibility of canola as a double crop with soybean (Glycine max L.) rotation and determine its optimum nitrogen (N) requirement. After canola was harvested, soybean was planted within the same experimental plots. In total, six N treatments (0 [control], 34, 67, 101, 135, and 168 kg N ha−1) were applied to canola and replicated four times across all site‐years in a randomized complete block design. The application of N increased seed yield, aboveground biomass, and N content compared to the control in canola. Across all site‐years, the highest seed yield was 1726 kg ha−1 at 168 kg N ha−1. Moreover, N uptake, plant height, and test weight were highest at 135 kg N ha−1, which were 76%, 21%, and 44% greater than control, respectively. Oil content and seed weight were inversely related to N rates indicating dilution with increased N. Overall, 141 kg N ha−1 was found to be the agronomic optimum nitrogen rate, and no further supplementation was deemed necessary to maximize canola yield in Mississippi. Additionally, the following soybean crop benefitted from the N applied to canola and produced greater yields. This study establishes that canola has the potential for double cropping without adversely affecting subsequent soybean yield, provided optimum N rates are applied.

Model-averaging as an accurate approach for ex-post economic optimum nitrogen rate estimation

AbstractFinding economic optimum fertilizer rate with good accuracy is essential for optimal crop yield, efficient resource utilization, and environmental well-being. However, the prevailing incomplete understanding of input-output relationships leads to imprecise crop yield response functions, such as those for winter wheat, and potentially biased fertilizer choices. From a statistical point of view, there is uncertainity with regards to which model is most suitable to estimate the economic optimum fertilizer rate. This complexity is amplified when considering site-specific nitrogen fertilization, which factors into elements like soil attributes, topography, and crop variations within a field, as opposed to uniform application. This study undertakes a comparative analysis to evaluate biases, variance, mean squared errors and confidence intervals in Economic Optimum Nitrogen Rate (EONR) estimations across different functional forms. The goal is to uncover performance discrepancies among these forms and explore potential advantages of adopting model averaging for optimizing nitrogen use in crop cultivation. The results of simulations reveal noteworthy biases when comparing diverse yield functions with the averaged model, particularly evident in the Linear-Plateau and Mitscherlich models. Moreover, analysis of empirical data indicates that confidence intervals for the averaged model overlap with the projected ranges of all functions. This implies that the averaged model could be suitable for determining EONR and effectively address the problem of model specification without focusing on one specific functional form. The effectiveness of model averaging hinges on incorporating models that well approximate the true model. However, even if the true model is not known, the average model can provide reasonable information for determining the EONR, provided that similar model specifications are considered. This has implications for modelling of yield response for various applications and can contribute to unbiased estimations of yield response.

Effect of Nitrogen and Phosphorus Fertilization on the Growth and Yield of Wheat (Triticum aestivum L.)

A field trial was carried out as an experiment during the Rabi season of 2022–23 at the farm of R.M.P. (P.G.) College Gurukul–Narsan, Haridwar (U.K.) to identify optimum Nitrogen and phosphorus fertilizers rates. Sand loam soil with a pH of 7.5, electrical conductivity (EC) of 0.26 dSm-1, organic carbon content of 0.60 percent, and available nutrients at levels of 217.0, 19.66, and 196.66 kg ha-1 for nitrogen (N), phosphorus (P), and potassium (K), respectively, was used for the experiment. Uttar Pradesh. "Effect of Nitrogen and Phosphorus Fertilization on the Growth and Yield of Wheat (Triticum aestivum L.)" was the main topic of the study. The common dosage for the experimental design was 40 kg N + 80 kg. P2O5 was administered to every treatment. The experiment was designed using a Randomized Block Design (RBD) with 12 treatments spread across three replications, along with a control group. Based on the results, it was found that the mixture of 40 kg/ha P2O5 and 50 kg/ha (N) significantly improved yield, growth, and other yield parameters. The data for treatment 5 showed the highest values for the following parameters: plant height (97.62 cm),), number of tillers m-2 (375.00), dry matter accumulation (16.53 g /plant), test weight (44.99g, grains /ear (12.93), effective ear head per (m-2) (282.66), and straw yield (47.59 quintals/ha).

Effects of Different Irrigation Management and Nitrogen Rate on Sorghum (Sorghum bicolor L.) Growth, Yield and Soil Nitrogen Accumulation with Drip Irrigation

Sorghum (Sorghum bicolor L.) has emerged as a pivotal global food crop. Consequently, it is imperative to explore sustainable and eco-friendly strategies to achieve sustainable sorghum production with a high yield. This study aimed to reveal the effects of irrigation management and nitrogen rates and their interactions on sorghum growth traits, yield and soil nitrate-N and ammonium-N accumulation to improve irrigation and nitrogen practices under drip irrigation. A 2-year (2021 and 2022) field experiment was conducted on drip-irrigated fertilized sorghum in Heilongjiang Province to investigate the effects of three lower levels of soil moisture (80% (HI), 70% (NI), and 60% (LI) of field capacity) with four nitrogen rates at 225, 150, 75 and 0 kg/ha (designated as HN, NN, LN and WN, respectively) on sorghum growth, yield and soil nitrogen accumulation. The results indicated that irrigation management and nitrogen rate interaction had a significant effect on sorghum growth (plant height, stem diameter, leaf area index (LAI), and SPAD value), yield, aboveground biomass and 0~60 cm soil nitrogen accumulation (p < 0.05). The NNHI treatment demonstrated the highest plant height (120.9 and 121.8 cm) and LAI (2.738 and 2.645) in 2021 and 2022, and there was a significant positive correlation between plant height, LAI, and yield (p < 0.01). However, the NNNI treatment exhibited the highest yield (7477.41 and 7362.27 kg/ha) in 2021 and 2022, sorghum yield increased and then decreased with an increase in irrigation management and nitrogen rate. In addition, soil nitrate-N and ammonium-N accumulation were significantly affected by the interaction of irrigation management and nitrogen rate (p < 0.05) while irrigation management had no significant effect on the accumulation of nitrate-N and ammonium-N. Soil nitrate-N and ammonium-N accumulation increased with the increasing nitrogen rate. Although yield differences between the NNNI and HNNI treatments were not significant, the NNNI treatment with a lower soil moisture limit of 70% field capacity and a nitrogen rate of 150 kg/ha accumulated 10.4% less nitrate-N in soil than the HNNI treatment, reduced risk of nitrate nitrogen leaching. The regression analysis indicated that the optimal irrigation management and nitrogen rate management practices of 71.93% of the soil moisture lower limit and 144.58 kg/ha of nitrogen rate was an optimal strategy for favorable sorghum growth, high-yielding and low soil nitrate-N accumulation of sorghum. This study provides a scientific reference for precise water and fertilizer management in sorghum.

Effect of nitrogen and boron treatments on harvest index and nitrogen use efficiency in sugar beet

The aim of this study is to examine the effect of different doses of nitrogen (N) and boron (B) treatment on sugar harvest index (SHI), nitrogen harvest index (NHI), and nitrogen use efficiency (NUE) parameters and to determine the economic optimum nitrogen rates (EONR) in sugar beet. The experiment was set up in a randomized block factorial design with three replications. Five doses of N (0, 90, 180, 270, and 360 kg N ha-1) and four doses of B (0, 2, 4, and 6 kg B ha-1) were applied in the study. According to the results of the research, the SHI decreased statistically significantly with the increase of dose of the N treatment, but the NHI was not affected by the N treatment. Physiological efficiency of nitrogen in taproot dry matter yield (NPETDMY) and physiological efficiency of nitrogen in sugar yield (NPESY) decreased statistically significantly (p<0.01) with the increase in the dose of N treatment. A similar case was observed in the parameters of nitrogen agronomic efficiency (NAgE) and nitrogen uptake efficiency (NUpE). The increase in boron treatment doses statistically significantly (p<0.01) increased the NAgE in the first year. The EONR, calculated using the quadratic model, was found to be 240 kg N ha-1 on average of two years. As a result, the nitrogen use potential decreased with the increase of N doses applied to sugar beet. The use of EONR can be recommended for optimum yield and quality in the region.

Wheat GSPs and Processing Quality Are Affected by Irrigation and Nitrogen through Nitrogen Remobilisation.

The rheological properties and end-use qualities of many foods are mainly determined by the types and levels of grain storage proteins (GSPs) in wheat. GSP levels are influenced by various factors, including tillage management, irrigation, and fertiliser application. However, the effects of irrigation and nitrogen on GSPs remain unclear. To address this knowledge gap, a stationary split-split block design experiment was carried out in low- and high-fertility (LF and HF) soil, with the main plots subjected to irrigation treatments (W0, no irrigation; W1, irrigation only during the jointing stage; W2, irrigation twice during both jointing and flowering stages), subplots subjected to nitrogen application treatments (N0, no nitrogen application; N180, 180 kg/ha; N240, 240 kg/ha; N300, 300 kg/ha), and cultivars tested in sub-sub plots (FDC5, the strong-gluten cultivar Fengdecun 5; BN207, the medium-gluten cultivar Bainong 207). The results showed that GSP levels and processing qualities were significantly influenced by nitrogen application (p < 0.01), N240 was the optimal nitrogen rate, and the influence of irrigation was dependent on soil fertility. Optimal GSP levels were obtained under W2 treatment at LF conditions, and the content was increased by 17% and 16% for FDC5 and BN207 compared with W0 under N240 treatment, respectively. While the optimal GSP levels were obtained under W1 treatments at HF conditions, and the content was increased by 3% and 21% for FDC5 and BN207 compared with W0 under N240 treatment, respectively. Irrigation and nitrogen application increased the glutenin content by increasing Bx7 and Dy10 levels in FDC5, and by increasing the accumulation of Ax1 and Dx5 in BN207. Gliadins were mainly increased by enhancing α/β-gliadin levels. Correlation analysis indicated that a higher soil nitrate (NO3-N) content increased nitrogen remobilisation in leaves. Path analysis showed that Dy10, Dx5, and γ-gliadin largely determined wet glutenin content (WGC), dough stability time (DST), dough water absorption rate (DWR), and sedimentation value (SV). Therefore, appropriate irrigation and nitrogen application can improve nitrogen remobilisation, GSP levels, and processing qualities, thereby improving wheat quality and production.

Cultivation and Nitrogen Management Practices Effect on Soil Carbon Fractions, Greenhouse Gas Emissions, and Maize Production under Dry-Land Farming System

Effective nitrogen management practices by using two cultivation techniques can improve corn productivity and soil carbon components such as soil carbon storage, microbial biomass carbon (MBC), carbon management index (CMI), and water-soluble carbon (WSC). It is essential to ensure the long-term protection of dry-land agricultural systems. However, excessive application of nitrogen fertilizer reduces the efficiency of nitrogen use and also leads to increased greenhouse gas emissions from farming soil and several other ecological problems. Therefore, we conducted field trials under two planting methods during 2019–2020: P: plastic mulching ridges; F: traditional flat planting with nitrogen management practices, i.e., 0: no nitrogen fertilizer; FN: a common nitrogen fertilizer rate for farmers of 290 kg ha−1; ON: optimal nitrogen application rate of 230 kg ha−1; ON75%+DCD: 25% reduction in optimal nitrogen fertilizer rate + dicyandiamide; ON75%+NC: 25% reduction in optimal nitrogen rate + nano-carbon. The results showed that compared to other treatments, the PON75%+DCD treatment significantly increased soil water storage, water use efficiency (WUE), and nitrogen use efficiency (NUE) because total evapotranspiration (ET) and GHG were reduced. Under the PON75%+DCD or PON75%+NC, the soil carbon storage significantly (50% or 47%) increased. The PON75%+DCD treatment is more effective in improving MBC, CMI, and WSC, although it increases gaseous carbon emissions more than all other treatments. Compared with FFN, under the PON75%+DCD treatment, the overall CH4, N2O, and CO2 emissions are all reduced. Under the PON75%+DCD treatment, the area scale GWP (52.7%), yield scale GWP (90.3%), biomass yield (22.7%), WUE (42.6%), NUE (80.0%), and grain yield (32.1%) significantly increased compared with FFN, which might offset the negative ecological impacts connected with climate change. The PON75%+DCD treatment can have obvious benefits in terms of increasing yield and reducing emissions. It can be recommended to ensure future food security and optimal planting and nitrogen management practices in response to climate change.

Combining corn N recommendation tools for an improved economical optimal nitrogen rate estimation

AbstractImproving corn (Zea mays L.) nitrogen (N) rate fertilizer recommendation tools can improve farmers’ profits and mitigate N pollution. Numerous approaches have been tested to improve these tools, but to date improvements for predicting economically optimum N rate (EONR) have been modest. This work's objective was to use ensemble learning to improve our estimation of EONR (for a single at‐planting and split N application timing) by combining multiple corn N recommendation tools. The evaluation was conducted using 49 corn N response trials from eight states in the US Corn Belt and three growing seasons (2014–2016). Elastic net and decision tree approaches regressed EONR against three unique tools for each N application timing. Tools used in various combinations included a yield goal method, two soil nitrate tests (pre‐plant and late season), a computer simulation crop model (Maize‐N), and canopy reflectance sensing. Any combination of two or three N recommendation tools improved or maintained performance metrics (R2, root‐mean square error , and number of sites close to EONR). The best results for a single at‐planting recommendation occurred when combining the three at‐planting N recommendation tools (including interactions) with an elastic net regression model. This combined recommendation tool had a significant linear relationship with EONR (R2 = 0.46), an increase of 0.27 over the best tool evaluated alone. Combining multiple tools increased the implementation cost, but it did not reduce profitability and, sometimes, improved profitability. These results show tools can be combined to better match EONR, and thus could aid farmers in improving N management.

Predicting site-specific economic optimal nitrogen rate using machine learning methods and on-farm precision experimentation

Applying at the economic optimal nitrogen rate (EONR) has the potential to increase nitrogen (N) fertilization efficiency and profits while reducing negative environmental impacts. On-farm precision experimentation (OFPE) provides the opportunity to collect large amounts of data to estimate the EONR. Machine learning (ML) methods such as generalized additive models (GAM) and random forest (RF) are promising methods for estimating yields and EONR. Twenty OFPE N trials in wheat and barley were conducted and analyzed with soil, terrain and remote-sensed variables to address the following objectives: (1) to quantify the spatial variability of winter crops yield and the yield response to N using OFPE, (2) to evaluate and compare the performance of GAM and RF models to predict yield and yield response to N and, (3) to quantify the impact of soil, crop and field characteristics on the EONR estimation. Machine learning techniques were able to model wheat and barley yield with an average error of 13.7% (624 kg ha−1). However, similar yield prediction accuracy from RF and GAM resulted in widely different economic optimal nitrogen rates. Across sites, soil available phosphorus and soil organic matter were the most influential variables; however, the magnitude and direction of the effect varied between fields. These indicate that training a model using data coming from different fields may lead to unreliable site-specific EONR when it is applied to another field. Further evaluation of ML methods is needed to ensure a robust automation of N recommendation while producers transition into the digital ag era.

Sugarcane productivity and sugar yield improvement: Selecting variety, nitrogen fertilizer rate, and bioregulator as a first-line treatment

The improvement of sugarcane productivity depends on the crop varieties, growth environments, and management practices. In particular, the selection of the most productive variety and the use of an optimal fertilizer rate and plant bioregulator are critical for increasing sugarcane productivity and sugar yield. This study aimed to determine the high-performing sugarcane variety, optimal nitrogen rate, and potential bioregulator for improved sugarcane production, juice quality, and sugar yield. Two sugarcane varieties (D42/58 and NCo-334), three nitrogen (N) fertilizer rates (0, 100, and 150 kg/ha), and two bioregulators (Agrostemin and Crops®) were used for the study. The study revealed that sugarcane variety had a significant effect on all growth, yield, and quality parameters. Plant height was significantly influenced by variety and bioregulators, while stalk population was significantly influenced by two- and three-way interactions of varieties, bioregulators, and N fertilizer rates. Cane weight was significantly affected by variety, N fertilizer rate, and bioregulators, whereas cane yield was significantly affected by variety, N fertilizer, and their interaction effects. Sugar yield was similarly influenced by variety, bioregulator, and their interaction. The three main factors, as well as their two- and three-way interactions, had a considerable influence on cane quality parameters. Sugarcane variety D42/58 significantly outperformed variety NCo-334 in terms of sprouting, number of tillers, plant height, number of millable canes, and sucrose percentage by 7.49%, 9.50%, 12.80%, 10.50%, and 9.10%, respectively. The use of the D42/58 variety with N fertilizer (at 100 kg/ha) and/or the Agrostemin bioregulator also led to higher performance in cane population (107126), cane yield (153.34 tons/ha), Pol % (15.81%), and sugar yield (10.25 tons/ha). Most sugarcane growth, yield, juice quality, and sugar yield parameters were positively correlated; hence, high-performing varieties, appropriate N rates, and plant bioregulators could boost sugarcane productivity and sugar yield. Overall, the selection and combination of sugarcane variety D42/58 with 100 kg N/ha and/or Agrostemin bioregulator could maximize sugarcane production, juice quality, and sugar yield. To confirm the current findings, however, more research needs to be conducted across different agroecologies and seasons.

Tillage with straw incorporation reduces the optimal nitrogen rate for maize production by affecting crop uptake, utility efficiency, and the soil balance of nitrogen

AbstractUnderstanding the combined effects of tillage and straw incorporation on grain yield and nitrogen (N) fertilizer utilization is vital for sustainable agriculture in northeastern China. How various tillage patterns with straw incorporation affect soil N balance and optimal N rate for crop production remains largely unknown. A four‐year (2015–2018) field experiment was conducted to assess the impact of rotary tillage with maize straw incorporation (RTS) and plough tillage with straw incorporation (PTS) combined with different N fertilizer rates (0, 112, 187, 262, and 337 kg N ha−1) on maize yield, N uptake, soil N balance, nitrogen use efficiency, and optimal N rate. In general, higher straw N uptake and lower N partial factor productivity, N use efficiency, and recovery of applied N in grains were obtained under PTS than under RTS. Moreover, grain yield and both straw and grain N uptake increased with the N application rate. However, no further increase in grain yield was observed when the applied N exceeded 187 kg N ha−1. PTS resulted in a higher level of unaccounted N from applied N fertilizer (38–81 kg N ha−1) than RTS. A high N fertilizer input (&gt;262 kg N ha−1) combined with straw incorporation resulted in a high soil mineral N profit and substantially increased unaccounted N (614–715 kg N ha−1) over the four‐year field trial period. The optimal N input required for the highest maize yield could be reduced by 35.0%–44.6% using tillage with straw return. Nevertheless, PTS was more effective than RTS at lowering the optimal N application rate. Overall, the results of this short‐term maize field study conducted in northeastern China suggest that the amount of N fertilizer input required for maize production could be reduced by using tillage with continuous straw incorporation.

Optimal nitrogen rate strategy for sustainable rice production in China.

Avoiding excessive agricultural nitrogen (N) use without compromising yields has long been a priority for both research and government policy in China1,2. Although numerous rice-related strategies have been proposed3-5, few studies have assessed their impacts on nationalfood self-sufficiency and environmental sustainability and fewer still have considered economic risks faced by millions of smallholders. Here we established an optimal N rate strategy based on maximizing either economic (ON) or ecological (EON) performance using new subregion-specific models. Using an extensive on-farm dataset, we then assessed the risk of yield losses among smallholder farmers and thechallengesof implementing the optimal N rate strategy. We find that meeting national rice production targets in 2030 is possible while concurrently reducing nationwide N consumption by 10% (6-16%) and 27% (22-32%), mitigating reactive N (Nr) losses by 7% (3-13%) and 24% (19-28%) and increasing N-use efficiency by 30% (3-57%) and 36% (8-64%) for ON and EON, respectively. This study identifies and targets subregions with disproportionate environmental impacts and proposes N rate strategies to limit national Nr pollution below proposed environmental thresholds, without compromising soil N stocks or economic benefits for smallholders. Thereafter, the preferable N strategy is allocated to each region based on the trade-off between economic risk and environmental benefit. To facilitate the adoption of the annually revised subregional N rate strategy, several recommendations were provided, including a monitoring network, fertilization quotas and smallholder subsidies.