Economic Optimum Nitrogen Rate Research Articles

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.

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.

Optimizing machine learning-based site-specific nitrogen application recommendations for canola production

Machine learning approaches are attracting more attention in predicting the economic optimum nitrogen rate (EONR) for field crop production. However, the robustness in fertilizer recommendations appeared to be less responsive to extreme abiotic stress conditions, due to the lack of post-fertilization seasonal weather information. This study employed four machine learning models, namely, support vector machine (SVM), gradient boosting (GB), random forest (RF), and ridge regression (RR) to predict the site-specific EONR values of canola crops, based on a 22-site-year field study across eastern Canada. Model performance was assessed using the ‘leave-one-out’ approach under three scenarios, i.e., different combinations of input variables, including current seasonal weather data before N topdressing, 10-year historical weather records, and field management and crop traits. Results of this study showed that including historical weather data highly improved prediction accuracy for EONR in a variety of canola growing environments. The RF model outperformed other models in predicting site-specific EONRs, with a Pearson correlation coefficient of 0.9, a standard deviation of 23 kg N ha −1 and a relative error of 2%. For 16 of the 22 test environments, the EONR predicted using RF fell within its confidence range, indicating that the prediction has a 73% chance of being an acceptable recommendation. Overall, incorporating historical weather data is essential for successfully predicting crop N requirements under normal and stressful growing conditions. The results indicate that with a base fertilizer of 50 kg N ha −1 , the recommended topdressing application rate varied from 50 to 110 kg N ha −1 in moist seasons, but decreased to 20–50 kg N ha −1 in hot and dry years, for sustainable canola production in eastern Canada. Appropriately incorporating historical weather data and soil properties into machine learning-based algorithms can be used to precisely guide fertilizer N management on canola crops under diverse climatic stress conditions. This approach promises to optimize seed yield and reduce nitrogen losses to the environment for sustainable canola production in Canada. • Four machine learning models were compared to predict economic optimum nitrogen rate (EONR). • Historical weather conditions were essential to predict the site-specific EONR. • Random forest model performed the best in recommending EONR by integrating weather data, field management and crop features. • A rate of 50 kg N ha −1 at planting plus 20–110 kg N ha −1 at the 4–6 leaf stage is recommended for canola production.

Agronomic and Economic Evaluations of N Fertilization in Maize under Recent Market Dynamics

An online calculator is available to determine economic optimum nitrogen rate (EONR) for maize (Zea mays L.) production in the USA Corn Belt. For Minnesota, this calculator considers nitrogen (N) fertilizer cost, maize grain price and crop history, and produces a statewide N rate based on maximum economic return to N (MRTN). However, a clear precipitation and temperature gradient, and soil heterogeneity across the state, and recent changes in fertilizer cost and maize grain price require the comparison of EONR from this calculator with results from field study. The objectives of this research were to determine the agronomic and economic benefits of basal and split application of N fertilizer on maize grain yield and yield components and compare EONR from field study with N recommendation from the online calculator. The nitrogen fertilizer rate for the field study ranged from 0 to 224 kg N ha−1, either split or all applied at planting. The results showed that there were no interaction effects of N rate by time or N rate by year on maize grain yield. Maize grain yield had a quadratic response to N rate, and agronomic maximum grain yield peaked at 205 kg N ha−1. The EONR from the field study was 168 kg N ha−1 and it remained stable under a wide range of economic analysis scenarios with net benefit reaching up to 2474 USD ha−1. The N rate from the online calculator at MRTN was 151 kg N ha−1 and this rate may cause N deficiency in maize resulting in a yield penalty compared with the field study results. The field study was performed under specific soil and climatic conditions. Therefore, extensive research under various soil types, agronomic management practices and climatic conditions is warranted to evaluate the online calculator performance and its reliability as a precision tool for N fertilizer management in maize production.

Economic optimum nitrogen rate analysis for feed and malting barley

AbstractThe malt industry has specific requirements for malting barley (Hordeum vulgare L.) grain protein concentration (from 9.5 to 13%) and size (>85%), which could be affected by nitrogen (N) fertilization. We aimed to (a) define and explain the economic optimum nitrogen rate (EONR) for grain yield in feed (EONRf) and malting barley (EONRm), and (b) analyze if grain quality requirements are reached at the EONRm. We evaluated six N rates (0, 40, 80, 120, 160, and 200 kg N ha−1) in field experiments (14 site‐years). At each site‐year, the EONR was calculated using two fertilizer cost to grain price ratios, 6:1 for feed and 4:1 for malting barley. On average, maximum yield and protein response to N were 1,681 kg ha−1 and 2%, respectively. There was no consistent effect of N fertilization on grain size. The EONRf was explained by N availability and maximum yield response to N (y = 130 – 0.37N availability + 0.03N response, r2 = .89), and EONRm was explained solely by N availability (y = 320 – 0.93N availability, r2 = .75). The overall EONRm was 134 kg N ha−1. At sites with low soil N availability and high yield response to N, an average N rate of 69 kg ha−1 greater than the EONRm was necessary to reach 9.5% protein. Otherwise, the EONRf could be used (106 kg N ha−1) and grains be sold as feed barley. These results are useful to adjust N fertilizer recommendations to satisfy the malting industry demand while reducing the economic and environmental impacts of fertilization.

Canopy Indices: a Model to Estimate the Nitrogen Rate for Barley and Wheat

The objectives were to calibrate the Soil Plant Analysis Development (SPAD) and Green Seeker (GS) as tools to determine the differential of the economic optimum nitrogen rate (dEONR) at different growth stages of barley and to evaluate if a single SPAD or GS calibration model can be used for two cereal crops: barley and spring wheat. Fourteen field experiments were conducted (2016–2018) evaluating five N rates. Relative canopy indices were determined using a SPAD-502 (rSPAD) and a GS sensor (rNDVI) at Z24, Z31, and Z39 growth stages. The relationship between sensor indices and dEONR was evaluated by fitting quadratic-plateau (QP) regression models. Data from a previous study was used to evaluate if a unique QP model could predict dEONR from canopy indices for barley and wheat. Statistically significant QP models were determined for rSPAD and rNDVI at all evaluated growth stages. The sensitivity of these models was greater for rSPAD (0.0006 on average) than for rNDVI (0.0004 on average). A single QP model was developed to predict dEONR from rSPAD at Z31 and Z39 barley growth stages (R2 = 0.68). Also, a unique model was developed to predict dEONR from rSPAD at Z31 and Z39, but not only for barley but also for wheat (R2 = 0.64). A single model could be used to determine variable in-season N rates for barley and wheat, increasing the N use efficiency and limiting possible negative economic and environmental impacts of fertilization over the agroecosystems.

Computing uncertainty in the optimum nitrogen rate using a generalized cost function

A Python package, “EONR”, was developed for computing the economic optimum nitrogen rate (EONR) and its profile-likelihood confidence intervals (CIs) under economic conditions defined by the user. This work was motivated by the need to improve nitrogen fertilizer recommendations using the maximum return to nitrogen approach, specifically to make it easier for researchers and other practitioners to calculate uncertainty and consider externalities to the cost function while computing the EONR. The “EONR” package fits yield response data to a re-parameterized quadratic-plateau model, which is generally accepted as the most appropriate model for describing yield response to nitrogen in maize (the package also supports the quadratic model). Although grain price and fertilizer cost are typically the only economic factors producers consider for determining the EONR, this package allows the user to also consider variable costs and/or externalities. A general cost function may be desired if the user wishes to consider costs to the farm operation (e.g., equipment, technology, labor, etc.) or environmental costs/penalties that may result from excess fertilizer application (e.g., water treatment or health costs that result from pollution) in addition to the traditional fertilizer to grain price ratio. In addition to the development of the “EONR” Python package, the objectives of this work were to: (i) design an algorithm that utilizes a general cost function for computing the EONR and its profile-likelihood CIs for any crop and (ii) clearly document the methodology and algorithms used. The “EONR” Python package can be downloaded from the Python Package Index (https://pypi.org/), and installation instructions, tutorials, and supplementary background information can be found in the online documentation (https://eonr.readthedocs.io).

Nutrient Budget and Economic Assessment of Blended Fertilizer Use in Kenya Tea Industry

Kenya’s tea industry depends predominantly on imported compound NPK fertilizers to replenish nutrients removed through plucking. These fertilizers cannot be easily manipulated for specific soils and tea clones. They also frequently become hazardous within tea-growing environments. In this respect, two fertilizer blends containing NPKS 25 : 5 : 5 : 4 + 9Ca + 2.62Mg and NPKS 23 : 5 : 5 : 4 + 10Ca + 3Mg with trace elements have been produced commercially in the country. However, the extent to which the blended fertilizers may contribute to optimal economic gains without degrading the environment has not been determined. This was the knowledge gap that this study seeks to address. The goal of this study was to evaluate the economic efficacy of fertilizer blends with the aim of identifying optimal levels of application which would maximize tea productivity with minimal negative impacts on the environment. The study hypothesized that blended fertilizers maximize productivity of tea clones with minimal environmental damage. The fertilizer blends were evaluated in two study sites, i.e., Timbilil Estate in Kericho and Kagochi farm in Nyeri. The sites were selected purposefully, one in the eastern and the other in the western tea-growing areas. The trial was laid out in randomized complete block design with two fertilizer blends and the standard NPK 26 : 5 : 5 as control. The treatments were applied at four fertilizer rates (0 (control), 75, 150, and 225 kg·N·ha−1·yr−1), replicated thrice. Leaf samples were collected and analyzed for nutrient uptake as well as associated yields and economic trends. The economic optimum nitrogen rate (EONR) was achieved at 75 kg·N·ha−1·yr−1 at Kagochi with all fertilizers, while at Timbilil, EONR was variable, between 75 and 225 kg·N·ha−1·yr−1 with fertilizer types. This study has shown that, based on the economic point of view, Blend “A” was the most efficient and consistent fertilizer in production and economic returns across the two sites.

Application of Machine Learning Methodologies for Predicting Corn Economic Optimal Nitrogen Rate

Core Ideas A Machine Learning approach was innovatively used to predict corn EONR. Two features were created to approximate hydrological conditions for modeling EONR. Soil hydrology conditions were found essential in successful modeling in‐season EONR. Determination of in‐season N requirement for corn (Zea mays L.) is challenging due to interactions of genotype, environment, and management. Machine learning (ML), with its predictive power to tackle complex systems, may solve this barrier in the development of locally based N recommendations. The objective of this study was to explore application of ML methodologies to predict economic optimum nitrogen rate (EONR) for corn using data from 47 experiments across the US Corn Belt. Two features, a water table adjusted available water capacity (AWCwt) and a ratio of in‐season rainfall to AWCwt (RAWCwt), were created to capture the impact of soil hydrology on N dynamics. Four ML models—linear regression (LR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO) regression, and gradient boost regression trees (GBRT)—were assessed and validated using “leave‐one‐location‐out” (LOLO) and “leave‐one‐year‐out” (LOYO) approaches. Generally, RR outperformed other models in predicting both at planting and split EONR times. Among the 47 tested sites, for 33 sites the predicted split EONR using RR fell within the 95% confidence interval, suggesting the chance of using the RR model to make an acceptable prediction of split EONR is ∼70%. When RR was used to test split EONR prediction with input weather features surrogated with 10 yr of historical weather data, the model demonstrated robustness (MAE, 33.6 kg ha−1; R2 = 0.46). Incorporating mechanistically derived hydrological features significantly enhanced the ability of the ML procedures to model EONR. Improvement in estimating in‐season soil hydrological status seems essential for success in modeling N demand.

Nitrogen Fertilizer Optimization for Sugarbeet in the Red River Valley of North Dakota and Minnesota

Single fertilizer nitrogen rate of 146 kg N ha−1 for sugarbeet is outdated. Sugarbeet–fertilizer nitrogen recommendation should consider soil characteristics. Sugarbeet yield and sugar content was optimized at 112 kg N ha−1. In‐season soil nitrogen and red‐edge normalized difference vegetative index can predict yield response to nitrogen. Economic optimum nitrogen rate varies from 0 to 405 kg N ha−1. Fertilizer N application is critical to optimize sugarbeet (Beta vulgaris L.) yield and sugar concentration. Regardless of observed yields, current fertilizer recommendations in North Dakota and Minnesota suggest a single application rate of 146 kg N ha−1 (considering both fertilizer N residual soil N of 120 cm depth) for the Red River Valley (RRV) irrespective of soil type and organic matter content. Field experiments were conducted at three sites to determine sugarbeet response to N application rates, 0, 112, 146, 179, and 213 kg N ha−1 and optimal N rate during 2015 and 2016 growing seasons. Year and site had a significant effect on yield and sugar concentration. In 2015, fertilizer‐N significantly increased yield for all three sites, but in 2016, only one site responded to N. The highest N rate (213 kg N ha−1) reduced sugar concentration in 2016. In‐season soil available N (of 0–120 cm) significantly correlated with yield (R2 = 0.35) and economic return (R2 = 0.28). In season, red edge normalized difference vegetative index (RENDVI) based on the handheld optical sensor was significantly (P < 0.05) related to fertilizer‐N application rate. Economic optimum N rate for three sites varied from 0 to 405 kg N ha−1 depending on year and site characteristics. Our results suggest a revision of current sugarbeet fertilizer N recommendation to replace the single rate with N rates based on site characteristics and profitability to N applications.

Use of Multispectral Airborne Images to Improve In-Season Nitrogen Management, Predict Grain Yield and Estimate Economic Return of Maize in Irrigated High Yielding Environments

Vegetation indices (VIs) derived from active or passive sensors have been used for maize growth monitoring and real-time nitrogen (N) management at field scale. In the present multilocation two-year study, multispectral VIs (green- and red-based), chlorophyll meter (SPAD) and plant height (PltH) measured at V12–VT stage of maize development, were used to distinguish among the N status of maize, to predict grain yield and economic return in high yielding environments. Moreover, linear plateau-models were performed with VIs, SPAD and PltH measurements to determine the amount of N needed to achieve maximum maize grain yields and economic return. The available N in the topsoil (0–30 cm) was measured, and its relationship with VIs, maize yield and maize N requirements was analyzed. Green-based VIs were the most accurate indices to predict grain yield and to estimate the grain yield optimum N rate (GYONr) (216.8 kg N ha−1), but underestimated the grain yield optimum N available (GYONa) (248.6 kg N ha−1). Red-based VIs slightly overestimated the GYONr and GYONa, while SPAD highly underestimated both of them. The determination of the available N did not improve the accuracy of the VIs to determine the grain yield. The green chlorophyll index (GCI) distinguished maize that would yield less than 84% of the maximum yield, showing a high potential to detect and correct maize N deficiencies at V12 stage. The economic optimum nitrogen rate (EONr) and economic optimum nitrogen available (EONa) were determined below the GYONr and the GYONa, demonstrating that maximum grain yield strategies in maize are not normally the most profitable for farmers. Further research is needed to fine-tune the response of maize to N applications when deficiencies are detected at V12 stage, but airborne imagery could be useful for practical farming implementation in irrigated high yielding environments.

Determining corn nitrogen rates using multiple prediction models

ABSTRACTWeather uncertainty and soil spatial variability impact nitrogen (N) cycling and corn (Zea mays L.) growth, making accurate N predictions a challenge. Field studies were conducted in Lansing, Michigan, to evaluate a computer model (i.e., Adapt-N), a preseason year-based model (i.e., maximum return to N [MRTN]), and a crop sensor model (i.e., active canopy sensor with algorithm) for recommending corn N rates. To determine site-specific economic optimum N rates (EONR), five N rates were also applied (0, 33%, 66%, 133%, and 166% of the suggested MRTN) as starter + sidedress (SD) at V4. In a wet year (i.e., 2015), Adapt-N increased V8 SD N rates 35 kg N ha−1 relative to the MRTN V4 SD N application. Although the greater rate of N may have provided additional yield protection, no statistical yield differences were observed between the two models. The MRTN model increased partial factor productivity (PFP) 20% relative to Adapt-N. Limited expression of V8 corn N deficiency reduced crop sensor total N rates (21–56 kg N ha−1) and yield (0.82–1.05 Mg ha−1) relative to other models. In a drier year (i.e., 2016), N demand was reduced (EONR 64 kg N ha−1 less than 2015), resulting in similar corn response to all three models. Despite differences in actual corn N rate recommendations, all three models resulted in similar economic net returns across study years.Abbreviations: EONR, economic optimum nitrogen rate; MRTN, Maximum Return to Nitrogen; NUE, nitrogen-use efficiency; PFP, partial factor productivity; SBNRC, sensor-based nitrogen rate calculator; SD, side-dress

Corn Response to Nitrogen Management under Fully‐Irrigated vs. Water‐Stressed Conditions

Characterizing corn (Zea mays L.) grain yield (GY) response to N is critical for maximizing profits, increasing N use efficiency and minimizing environmental impacts. Although a large database of GY response to N exists for highly productive soils, few data exist for less productive soils. While changes in precipitation are expected in the future, few studies have compared GY response to varying N management practices under conditions of varying water availability. We measured GY and basal stalk nitrate nitrogen (BSN) at harvest using split‐applied urea at eight N rates under fully‐irrigated (FI) and water‐stressed (WS) conditions in a loamy sand over 2 yr (2009 and 2010). We also measured GY and BSN using single, pre‐plant applications of urea, polymer‐coated urea (PCU) and urea amended with urease and nitrification inhibitors (IU) at one or two N rates. The results showed that economic optimum nitrogen rate (EONR) and agronomic optimum nitrogen rate (AONR) did not vary by water management, in spite of significant increases in GY (up to 48%) under FI compared to WS. Modification of N fertilizer timing or N source was effective for increasing GY (by 18–41%) with FI, but did not affect GY under WS conditions. Averaged across years, BSN was greater with WS compared to FI at most N rates; however, BSN corresponding to AONR was within the optimal range for both water regimes. These findings may have important implications in areas where changes in irrigation practices or water availability are expected under future climate conditions. Better understanding of water stress effects on corn response to N is needed. We measured yield response to N management with full irrigation and under water‐stress. Optimum N rate did not vary by water management despite significant yield differences. Optimizing fertilizer timing or source increased yield but only with full irrigation. Basal stalk nitrate was higher with water‐stress than with full irrigation.

Maize Yield and Nitrogen Use Efficiency in Upper Midwest Irrigated Sandy Soils

Corn (Zea mays L.) in irrigated coarse‐textured soils can be very productive with N applications, but excess N can increase groundwater contamination. Our objectives were to (i) determine the economic optimum nitrogen rate (EONR) for continuous corn (CC) and corn after soybean [Glycine max (L.) Merr.] (CSb), (ii) evaluate corn yield and nitrogen use efficiency (NUE) of split urea‐N and single pre‐plant application of enhanced‐efficiency fertilizers, and (iii) determine the utility of canopy sensors and basal stalk nitrate‐N test to manage N. Rotations of CC in Dakota County, Minnesota, and CC, CSb, and soybean after corn (SbC) in Pope County, Minnesota, were established in 2011 to 2014 with 0, 45, 90, 135, 180, 225, 270, and 315 kg urea‐N ha−1 as split‐application (half at pre‐plant and half at V4), and pre‐plant applications of SuperU (Koch Fertilizer LLC, Wichita, KS) at 180 kg N ha−1, ESN(Agrium Advanced Technologies, Loveland, CO)/urea blend 90/90 kg Nha−1, and ESN at 180 and 225 kg N ha−1. The EONR for CC was 233 kg N ha−1. The fertilizer replacement value in CSb was 56 kg N ha−1 and the EONR was 49 kg N ha−1 less than in CC. Canopy sensors and basal stalk nitrate‐N generally under estimated N rate. A split‐urea application increased corn grain yield by 5.4% (0.63 Mg ha−1), partial factor productivity (PFP) by 6% and agronomic efficiency (AE) by 12% relative to mean single pre‐plant application of enhanced‐efficiency fertilizers. In irrigated sandy soils, applying high rates of N needed for economic optimum yield is best accomplished by splitting the application.Core Ideas In irrigated sandy soils, applying high rates of N needed for economic optimum yield is best accomplished by splitting the application. Canopy sensors and basal stalk nitrate‐N generally under estimated N rate needed to optimize grain yield. Enhanced efficiency fertilizers applied at pre‐plant are not as effective as split applications of urea in irrigated sandy soils.

Using Canopy Indices to Quantify the Economic Optimum Nitrogen Rate in Spring Wheat

In‐season N applications to spring wheat (Triticum aestivum L.) may increase profits and improve N fertilizer accuracy. The objectives were to develop a calibration tool employing normalized difference vegetative index (NDVI) and SPAD 502 chlorophyll meter (SPAD) measurements for calculating the differential from the economic optimum N rate (dEONR) at growth stages Z22, Z24, and Z31 to Z39 and provide N rate algorithms for use in applying N fertilizer at a variable rate. Sensing was conducted trials over 3 yr encompassing 10 site‐years across Southeastern Buenos Aires Province, Argentina. The relationship between sensor indices and dEONR was evaluated by fitting quadratic plateau (QP) regression models. Statistically significant QP models were determined at the Z24, Z31, and Z39 growth stages. Relative SPAD (rSPAD) and relative NDVI (rNDVI) reduced variation and improved the calibration of measured N stress with the dEONR. For Z31 and Z39, the rSPAD had the best goodness of fit statistics when compared to rNDVI [adjusted R2 (adjR2)= 0.67 and 0.57 at Z31 and 0.68 and 0.52 at Z39, respectively]. However, adjustment at Z24 was higher for rNDVI (adjR2 = 0.53 and 0.61 for rSPAD and rNDVI, respectively). A single QP model to estimate the dEONR with 58% confidence was adjusted for the Z31 and Z39 growth stages. This indicates that the same calibration for N rate determination based on rSPAD or rNDVI values can be used during stem elongation in spring wheat. This model can be used as an N rate algorithm for applying N fertilizer in‐season.

Responses of sunflower (Helianthus annuus L.) to irrigation and nitrogen fertilization rates

study was conducted to determine the effects of different nitrogen rates (0, 40, 80, 120, 160, 200 and 240 kg ha -1 N) on the yield, its components and the quality characters of sunflower (Helianthus annuus L.) and to identify the economic optimum N rate for maximum profit under both irrigated and non-irrigated conditions during a 2-year period (2007-2008). As the N rates increased, all of the traits measured significantly increased, except the crude oil content, under both irrigation treatments. Based on the 2-year average, the highest seed yield (2713.6 to 2751.2 kg ha -1 ) and crude oil yield (1012.7 to 1042.5 kg ha -1 ) were obtained using 160 kg ha -1 N and the higher N rates, with increases of 47.8% and 39.4%, respectively, compared to the control (0 kg ha -1 N). Similar trends were also obtained for the other traits, except the crude oil content, which was the highest (39-41%) at 0 kg ha -1 N but the lowest (36-38%) under the higher N rates. The highest mean values for the crude protein content and 1000-seed weight were obtained under the highest N rates (200 and 240 kg ha -1 N). In 2007, the economic optimum nitrogen rate (EONR) for sunflower was 148.5 kg ha -1 N under non-irrigated conditions, as compared with 190.0 kg ha -1 N with irrigation. In 2008, the EONRs for sunflower under non-irrigated and irrigated conditions were 145.1 and 176.7 kg ha -1 N, respectively. Although the EONR for irrigated conditions was approximately 30-40 kg ha -1 N more than that for non-irrigated conditions, the seed yield under the irrigation conditions was approximately 65-79% higher compared with the non-irrigated conditions. As a result, it was determined that the economic optimum N rate for sunflower was 145-150 kg ha -1 N under non-irrigated conditions and 177-190 kg ha -1 N under irrigated conditions.

Winter Wheat Crop Reflectance and Nitrogen Sufficiency Index Values are Influenced by Nitrogen and Water Stress

In‐season N applications to winter wheat (Triticum aestivum L.) may increase profits and improve N fertilizer accuracy. The objectives of this experiment were to determine the impact of N and water stress on crop reflectance and N sufficiency index (SI) values. The experiment contained five N rates, two water treatments, and four blocks. Crop reflectance was measured at the stem extension and flag leaf growth stages, sufficiency index (SI)‐NDVIwf was ratio between the underfertilized normalized difference vegetation index value {NDVI = [near infrared (NIR)‐red]/[NIR+red]} and the NDVI value from well fertilized and well watered treatments, while SI‐NDVImz was ratio between underfertilized NDVI values and NDVI values from well fertilized plots within a water stress treatment. Yield losses due to water and N stress were determined using 13C isotopic discrimination. This research shows that: (i) at the stem extension and flag leaf growth stages, water stress and N stress increased, green, red, and red‐edge reflectance and reduced NDVI values (ii) following the economic optimum nitrogen rate (EONR) produced grain with greater than 120 g kg−1 protein and <10 min stability; (iii) at stem elongation and flag leaf, N fertilizer induced yield gains had a stronger relationship with SI‐NDVImz (stem extension, r = 0.49*; flag leaf, r = 0.51**) than SI‐NDVIwf (stem extension, r = 0.29; flag leaf, r = 0.33); and (iv) SI‐NDVImz had greater fertilizer recommendation accuracy than SI‐NDVIwf. These findings suggest that in wheat production, SI should be referenced to well fertilized areas within a management zone.

Using Active Canopy Sensing to Adjust Nitrogen Application Rate in Corn

Remote sensing that uses active sensors continue to be tested as an N stress detection method and provide in‐season rate adjustments to corn (Zea mays L.). The objectives of the study were to use active canopy sensing to vary in‐season N application at the V10 corn growth stage and compare applied N rate, grain yield, and nitrogen use efficiency (NUE) with N applied before planting. A fertilizer N rate study was conducted in 2009 and 2010 near Ames, IA. Pre‐plant N rates were 0 to 270 kg N ha−1 and then sensor‐based rates were applied in‐season at the V10 corn growth stage. Rainfall events occurred 3 to 5 d after N application each year, providing corn response to the in‐season applied N and increasing relative corn canopy biomass by the V13 growth stage. The pre‐plant applied nitrogen (PP‐N) 270 kg N ha−1 rate used as the N‐reference did not result in the highest green normalized difference vegetative index (GNDVI) or produce the highest grain yields. The pre‐plant plus sensor applied nitrogen (PP+S‐N) recovered corn yield from plant N stress at V10. The best yield recovery with deficit pre‐plant N and sensor‐directed N application rate occurred each year when no pre‐plant N had been applied. Overall, economic optimum nitrogen rate (EONR) and NUE in PP‐N and PP+S‐N were the same. The PP+S‐N application does give corn growers options for addressing in‐season N deficiency and protect yield potential when corn N need is uncertain or when soil N losses are unavoidable.

Leaf and canopy optical characteristics as crop‐N‐status indicators for field nitrogen management in corn

AbstractThe primary constraint of predicting the economic optimum nitrogen rate (EONR) for corn (Zea mays L.) is the high variability of soil nitrogen (N) supply due to environments, soil types, manure, and cropping histories. Portable instruments have been developed to measure leaf and canopy optical characteristics for determining plant N status. The objectives of this field study were to: (1) evaluate leaf and canopy optical properties including transmittance, reflectance, and fluorescence as indicators of corn N status with soil types, developmental stages, and N‐application rates, (2) compare the efficiency of two commercial radiometers that are designed to measure canopy reflectance, and (3) assess the constraints of these crop‐based indicators as a possible guide for real‐time N sidedressing in corn. Field experiments with different levels of N, soil types, and corn hybrids were conducted at three sites in Ottawa, ON, Canada, in 2004 and 2005. Leaf chlorophyll concentrations (SPAD chlorophyll meter), chlorophyll fluorescence (OS‐30), leaf area, and canopy reflectance (NDVI measured by CropScan and GreenSeeker radiometers) were simultaneously measured at several growth stages, while grain yield was determined at harvest. Our results show that canopy reflectance (NDVI) displayed similar efficiency as an indicator of N status on both soil types and corn hybrids in the two consecutive years. The chlorophyll readings often differentiated N‐deficient from N‐sufficient plots and therefore were a promising indicator for predicting corn N requirements. The fluorometer device evaluated in this study was unable to characterize corn N status.

Using Active Canopy Sensors to Quantify Corn Nitrogen Stress and Nitrogen Application Rate

In‐season corn (Zea mays L.) N sensing with active canopy sensors can provide input variables that direct variable rate N fertilizer applications. The objectives of this study were to assess corn N stress at the V10 to V12 growth stages, establish canopy indices that relate to optimum N application, and provide N rate algorithms for use in applying variable rate N fertilizer. Corn was sensed with the GreenSeeker Green 506 (GS‐506) and Crop Circle ACS‐210 (CC‐210) active canopy sensors. Sensing was conducted in N rate trials over 3 yr encompassing 62 site‐years across Iowa. The relationship between sensor indices and differential from economic optimum nitrogen rate (dEONR) was evaluated by fitting quadratic‐plateau (QP) regression models between sensor values and dEONR. Sensing corn at the V10 to V12 stages resulted in statistically significant QP models relating sensor indices to corn N response. Relative canopy index models had greater adjR2 values than others, resulting in better representation between canopy index values and dEONR. These were the relative simple ratio index (rSRI), relative green difference vegetative index (rGDVI), and relative modified simple ratio index (rMSRI) for the GS‐506 sensor, and relative green normalized difference vegetative index (rGNDVI), rSRI, rMSRI, and rGDVI for the CC‐210 sensor. Variability in sensor prescribed N rate was greatest at slight N deficiencies (0–50 kg N ha−1 deficit dEONR). Several indices relating to canopy biomass (rGNDVI) or canopy chlorophyll (rSRI, rMSRI, or rGDVI) can be used as an N rate algorithm for applying N fertilizer in‐season.