AbstractThe interaction between nitrogen (N), phosphorus (P), and potassium (K) fertilizers significantly impacts the uptake of micronutrients in corn, influencing their availability in soil and uptake by plants. Understanding the interaction of macro‐ and micronutrients is a prerequisite to targeting nutrient balance in crop production. Therefore, a 2‐year field experiment was conducted to determine the effect of NPK fertilization on micronutrient uptake of rain‐fed corn (Zea mays L.). A randomized complete block design was employed with 12 treatments replicated three times. Different combinations of N, P, and K fertilizer rates were investigated for micronutrient concentration and uptake in rain‐fed corn. Findings revealed the order of nutrient accumulation in corn plants: iron (Fe) > manganese (Mn) > zinc (Zn) > copper (Cu). Nitrogen application influenced nutrient concentrations and uptake. Increasing N rates increased micronutrient concentrations in corn grain, except for Cu. Interestingly, Cu content in grains exhibited no correlation with nutrient supply, biomass, or other concentrations. As the N application rate increased, micronutrient content increased at early growth stage and physiological maturity. Phosphorus application showed negligible impact on grain micronutrient concentration and uptake. However, K application notably increased Mn, Fe, and Cu uptake in grains. This study underscores the need to consider not only grain yield but also nutritional quality when determining optimal NPK rates in rain‐fed corn cultivation.

Over the past 20 years, corn plantations in the southern Great Plains of the USA have increased, especially in rain-fed environments, because of rising demand and price for corn. Although nitrogen (N) is the most important nutrient, we cannot ignore the importance of phosphorous (P) and potassium (K) in corn production. Field experiments were conducted in 2021 and 2022 to evaluate the effect of co-application of P and K with N on grain yield, biomass, nitrogen use efficiency (NUE), and nutrient uptake and removal in rain-fed corn. The results demonstrated that application of P and K did not have any significant effect on corn yields if the soil is above the established critical levels for those nutrients. It was, however, noted that the grain and biomass yield significantly relied on the amount of N applied. Nitrogen rate significantly increased plant nutrient uptake and removal. The maximum yield in our study among two site-years was 11 Mg ha-1 in the plot received 133 kg N ha-1 with 20 kg P ha-1. The maximum uptake at maturity in this treatment was 173.9 kg N ha-1, 32.28 kg P ha-1, and 128.05 kg K ha-1. Nitrogen use efficiency, agronomic efficiency (AE), and internal efficiency (IE) decreased with increasing N rate. NUE increased by 5.9% when K was present, 15.9% when P was present, and 9.7% when both were present with 133 kg N ha-1 at EFAW21. However, N rate did not affect N recovery efficiency, which is the ratio of change in plant N uptake to N rate. The results from this study confirmed that N is the most important nutrient when P and K are sufficient or near sufficient in the soils.

This paper proposes a Bayesian multilevel modeling approach to incorporate response parameters from published studies into crop yield response estimation procedures when nonlimiting or limiting treatment levels are omitted or limited in agronomic experiments. Such circumstances may be encountered when data are from farmer-led research, which may use nonstandardized experimental designs. The paper's focus is on maize yield response to nitrogen fertilizer, but the procedure is flexible enough to accommodate other factors that could affect crop yield response. A proof-of-concept Monte Carlo (MC) exercise supplements an empirical application. The MC simulation investigates the small sample properties of the proposed procedure. The empirical example uses field trial data for a maize planter experiment under different nitrogen (N) fertilizer rates. The planter trial compared mechanical planting methods to methods used in developing countries with limited access to mechanized planter technology. Some experiments had no check plots and all experiments lacked nonlimiting fertilizer rates. Linear and quadratic response functions with plateaus are used in the MC simulation and empirical application. MC results suggest that estimates were closest to true parameter values when priors for optimal N rates from published sources were used.

Effective nutrient management requires understanding nutrient uptake at various growth stages and nutrient removal by the harvested portion. Information on nutrient accumulation was provided by some older literature, and a few researchers have focused on this issue in this modern period with modern hybrids and improved corn cultivation practices. While almost all the studies were conducted in northern states of the US, information for the Southern Great Plains is still limited. To bridge this knowledge gap, a 2-year field study was conducted in a rain-fed corn production system. The study aimed to evaluate the impact of nitrogen (N), phosphorus (P) and potassium (K) fertilization on N, P, and K contents in aboveground plants at different growth stages. Pre-plant application of N (0, 67, 133 kg N ha−1), P (0 and 20 kg ha−1) and K (0 and 60 kg ha−1) fertilizers was done. Results from our study revealed that nutrient uptake values, pattern and dynamics depend on environmental conditions, soil type and management practices. N concentration in plants showed a linear response to N application rate while P and K concentrations were unaffected by NPK fertilization rates. Total N, P and K uptake was primarily driven by N application rate, showing a linear increase with higher N rates. Co-application of P and K with N did not significantly affect nutrient concentration and uptake.

Advancements in precision agriculture technologies enable producers to achieve higher yields; however, in some cases, these innovations have not reached widespread adoption despite years of availability. We sought to understand producers’ adoption experiences with two precision agriculture technologies: Nitrogen (N)-Rich Strips and the Sensor Based Nitrogen Rate Calculator (SBNRC). These technologies can help producers optimize their application of nitrogen fertilizer on growing crops, especially small grains such as wheat. Using Rogers’ (2003) diffusion of innovations theory as an explanatory framework, this descriptive-exploratory study examined the adoption behaviors of producers from two midwestern states. Rogers’ (2003) theoretical lens guided instrument development and interpretation of results. To better understand the effects of change agents’ actions and potential adopters’ behaviors during the innovation-decision process, more research is needed regarding disenchantment discontinuance and replacement discontinuance, the potential for pro-innovation bias, and of the innovation attribute compatibility. The future development of precision agriculture technology with the perceptions of potential adopters in mind, especially those averse to adoption and continuance, may assist in overcoming barriers to widespread diffusion.

Accurate winter wheat (Triticum aestivum L.) grain yield prediction is vital for improving N management decisions. Currently, most N optimization algorithms use in-season estimated yield (INSEY) as a sole variable for predicting grain yield potential (YP). Although evidence suggests that this works, the yield prediction accuracy could be further improved by including other predictors in the model. The objective of this work was to evaluate INSEY, pre-plant N rate, total rainfall, and average air temperature from September to December as predictors of winter wheat YP. An 8-yr (2012-2019) data set for grain yield was obtained from Experiment 502, Lahoma, OK. The experiment was designed as a randomized complete block with four replications and N applied at 0, 45, 67, 90, and 112 kg ha(-1). Weather data was obtained from the Oklahoma Mesonet (). The data were analyzed using R statistical computing platform. The best model was selected using least absolute shrinkage and selection operator. Root mean square error (RMSE) was obtained using k-fold cross-validation. The model selection algorithm produced the full model as the best model for yield prediction with an R-2 of .79 and RMSE of 0.54 Mg ha(-1). The best one-variable model - as expected - used INSEY as the predictor and had the highest RMSE of 0.72 Mg ha(-1) and an R-2 of .62. Mid-season YP prediction accuracy could be improved by including pre-plant N rate, mean air temperature, and total rainfall from September to December in a model already containing INSEY.

Abstract Preplant nitrogen (N) application, which involves placing nutrients in the soil before seeding, has been an integral part of crop production systems for decades. Some producers are known to apply N at least 21 d before planting. This may increase N loss and lower grain yield. This study evaluated the effect of timing and rate of N application on winter wheat (Triticum aestivum L.) grain yield and N use efficiency (NUE). An experiment with a factorial arrangement of treatments was set up in a randomized complete block design with three replications. Treatments included four N rates (0, 45, 90, and 135 kg ha–1) with each applied 7 or 30 d before planting, and at Feekes 5 (FK5). Grain N was analyzed using LECO CN dry combustion analyzer. The difference method [Grain N from (fertilized plot – check plot)]/N applied was used to compute NUE. Nitrogen application rate significantly affected grain yield (P ≤ .01). Although the rate may be temporally and spatially variable, approximately 90 kg N ha–1 was required to obtain yields that differ markedly from the check. Mid‐season applied N (FK5) had similar yields to preplant applied N in two of four site‐years and significantly increased yield at one site in 2020. Out of two sites, the timing of N application had a substantial effect on NUE in both years (P ≤ 0.11). In this case, NUE was increased by as much as 9.5% for midseason applied N compared to 30 d before planting N application time.

Plant spacing and density are important metrics in crop production because they impact the plant's ability to utilize resources and attain full yield potential. Planting sorghum (Sorghum bicolor (L.) Moench) in a more narrow spacing brings about phytochrome-mediated responses, where plants develop narrow leaves, long stems, fewer roots, and this is linked to competition that plants develop for nutrients like nitrogen (N). The Oklahoma State University hand planter (OSU-HP) can improve plant homogeneity and midseason placement of N. However, this crop production tool alongside other agronomic practices have not been adequately evaluated for improving sorghum grain yields. The objective of this work was to evaluate the response of sorghum to planting methods, the number of seeds per hole, within row spacing, and N rate. A randomized complete block design with 13 treatments replicated 3 times was used in this study. The treatments included different combinations of 3 planting methods (John Deere [JD], OSU-HP, and stick planter [check]), 3 within-row spacings (10, 30, and 60 cm), 3 different number of seeds per hole (1, 3, and 6) and 3 N rates (0, 30 and 60 kg ha(-1)). Average grain yield with 3 seeds per hole was at least 18% higher than the yield range of 0.7 to 4.6 Mg ha(-1) achieved with 1 or 6 seeds per hole. This study demonstrated that the production of sorghum using sound agronomic practices could improve yield.

Global warming continues to be discussed and debated. While this discussion continues, atmospheric carbon dioxide gradually rises and is now at 413 mg kg -1 , the highest level recorded since 1958, and an increase of 24% (315 to 413) in the last 62 years (NASA, https://climate.nasa.gov/vital-signs/carbon-dioxide/). Students in an advanced nutrient management class were assigned specific topics based on their interests as it relates to challenges present that surround Global warming. Results highlighted several findings, including increased incidence of wildfires, perils of aerosols, the value of moving to plant-based diets, and the importance of government policy, especially from the developed world. A summary statement appropriate from this student review would be that a shocking level of public resistance remains, even in the light of having overwhelming evidence of documented sources of anthropogenic contamination coming from fossil fuels, soil organic matter, nitrous oxide, and the animal industry. Abbreviations : GHG, Greenhouse gases; C, Carbon; N, Nitrogen; P, Phosphorus; K, Potassium; Ca, Calcium, Mg, Magnesium; NASA, National Aeronautical and Space Administration; CFC, chlorofluorocarbons; HFC, hydrofluorocarbons; IPCC, Intergovernmental Panel on Climate Change; UNEP, United Nations Environment Program; UNCED, United Nations Conference on Environment and development; CAM, Crassulacean acid metabolism; WUE, water use efficiency; CAFOs, Concentrated animal feeding operations; SOC, Soil Organic Carbon; EPA, Environmental Protection Agency; LAI, leaf area index; EIA, Energy Information Administration;

Method of N application in winter wheat (Triticum aestivum L.) and its impact on estimated plant N loss has not been extensively evaluated. The effects of the pre-plant N application method, topdress N application method, and their interactions on grain yield, grain protein concentration (GPC), nitrogen fertilizer recovery use efficiency (NFUE), and gaseous N loss was investigated. The trials were set up in an incomplete factorial within a randomized complete block design and replicated three times for 5 site-years. Data collection included normalized difference vegetation index (NDVI), grain yield, and forage and grain N concentration. The NDVI before and after 90 growing degree days (GDD) were correlated with final grain yield, grain N uptake, GPC, and NFUE. At Efaw location, NDVI after 90 GDDs accounted for 58% of variation in grain yield and 51% variation in grain N uptake. However, NDVI was found to be a poor indicator of both GPC and NFUE. Grain yield was not affected by the method and timing of N application at Efaw. Alternatively, at Perkins, topdress applications resulted in higher yields. The GPC and NFUE were improved with the topdress applications. Generally, topdress application enhanced GPC and NFUE without decreasing the final grain yield. The difference method used in calculating gaseous N loss did not always reveal similar results, and estimated plant N loss was variable by site-year, and depended on daily fluctuations in the environment.

AbstractGlobal nitrogen use efficiency (NUE) for cereal production is estimated to be only 33%. Providing producers with efficient methods to increase the effectiveness of their N applications is integral to agricultural sustainability and environmental quality. This study was conducted to evaluate the effect of urea ammonium nitrate (UAN) injected at different depths on grain yield and uptake of N in grain. Liquid UAN (28–0–0) was applied in bands at depths of 5 and 10 cm, along with surface applications, all at various N rates around Feekes growth stage 5. Placement depth had the most significant impact on yield at low N rates. Subsurface application at 10 cm was most beneficial in low N no‐till (NT) soils, whereas surface treatments produced higher yields in low N environments of conventional till (CT) systems. Three of the four locations experienced higher rates of N uptake from subsurface applications when compared with surface treatments. No difference in grain N uptake was apparent between application depths of 5 and 10 cm. Subsurface N applications were beneficial in reducing rates of ammonia volatilization from urea‐based fertilizers. While there was no clear separation between 5 and 10 cm application depths, subsurface depths of 10 cm provide the most significant promise in benefiting yield in low N environments of NT soils and increasing grain N across CT and NT systems.

Crop nitrogen (N) use is always affected by the variability in production environment. Dataset (2001 to 2014) from long-term winter wheat (Triticum aestivum L.) experiments at Lahoma and Stillwater, Oklahoma was used. Both experiments have a randomized complete block design with four replications, and fertilizer N was applied as urea pre-plant. Responsiveness of grain yield to maximum fertilizer N rate (112 kg ha(-1) - Lahoma; 135 kg ha(-1) - Stillwater) was compared with that from check plot (0 kg ha(-1)). The objective was to determine the relative influence of environment, management, and variety on winter wheat grain yield. The combined analysis of variance indicated that the main effect of year, treatment, location, and variety accounted for 29.3%, 21.2%, 3.1%, and 22.6%, respectively of the variance terms. Over the study period, the non-responsiveness of winter wheat to fertilizer N accounted for 29% and 23% of grain yield at Lahoma and Stillwater, respectively where yield at maximum N rate did not significantly differ from check plot. This highlights the importance of random changes in a crop production environment and its influence in dictating the response to applied N fertilizer. Nitrogen fertilizer losses could be reduced by adopting in-season variable N application techniques.

The linear response with plateau (LRP) is widely used in agronomic and agricultural economic studies of crop yield response. This empirical example uses data from an under-replicated experiment to compare maize (Zea maize L.) yield response to nitrogen under different plant and corridor row spacing. Not all replications received a 0-nitrogen rate, making estimation of the LRP difficult because data for the intercept terms is absent. We leverage information from other treatments using Bayesian methods to estimate the yield response of each treatment using a LRP function, given limited replication and absence of check plots for some treatments. We use a linearized LRP, which bypasses using the “min” operator typically required to estimate LRP functions. Economically optimal nitrogen rates were determined and net returns from treatments compared from the perspective of risk-averse producers. The wide plant/narrow row treatment was most profitable when the decision rule was to apply nitrogen. The statistical procedure used here may be useful for exploratory analyses of pilot agronomic trials that may include unbalanced and under-replicated treatments.

ABSTRACT Maize (Zea mays L.) production in the developing countries takes place on marginal landscapes using indigenous planting methods that conflict with appropriate row spacing (RS) and plant to plant spacing (PPS). A study was conducted to determine the effect of different RS, variable plant densities and different planting methods on maize grain yield. This study was conducted for two years at three locations in Oklahoma including Lake Carl Blackwell (Port silt loam), Efaw (Ashport silty clay loam), and Perkins (Teller sandy loam-fine-loamy). Fourteen treatments were evaluated at each location in a randomized complete block design with three replications. Treatments included two RS (0.51 m, 0.76 m), three nitrogen (N) application rates (0, 60, 120 kg N ha−1), two PPS (0.15 m, 0.30 m) and two planting methods (Greenseeder hand planter; farmers practice). Results showed an increase in grain yield by 34% in 2017 and 44% in 2018 for the narrow RS of 0.51 m compared to the 0.76 m RS. This was likely due to increased plant population at the narrow RS. This study suggests that maize producers in developing countries could use narrow RS (0.51 m) with wide PPS (0.30 m) to increase grain yields.

Manure phosphorus (P) accumulation in soils is of environmental concern. The objectives were to determine P concentrations and fate in soils following 119 years of manure and 89 years of chemical fertilizer application. The recovery and distribution of P were evaluated for five years in soils from the untreated check, and soils amended with manure, fertilizer-P, or NPK. Total P concentrations were significantly higher in fertilizer-treated surface soils, compared to manure application. Treated plots had significantly higher P concentrations than the check. Virtually all of the added P was accounted for, either remaining in the soil or harvested in grains. Over 50% of fertilizer-P and about 38% of manure-P were found in the top 15 cm of the soil. A majority (81–99%) of the added fertilizer-P was found in the top 30 cm, while about 40% of manure-P leached down to the 30–90 cm level of the soil profile. Following 119 years of moderate application, manure-P did not reach deeper than 90 cm, suggesting that leaching to groundwater is not a concern at this site. Preserving P in the lower soil profile could enhance the potential for plant uptake.

Correspondence Jagmandeep Dhillon, Oklahoma State University, Stillwater, OK 74078-6028. Email: jagman.dhillon@okstate.edu Abstract Variable influence of the environment on early-season plant growth leads to similarly variable yield levels from year to year. This study was conducted to determine the ideal point in the growing season when normalized difference vegetation index (NDVI) sensor readings were highly correlated with grain yield. For each site-year, NDVI readings were collected at least seven times from December through April. Readings were collected from two long-term experiments where an N response was expected in plots that historically received different N rates. The number of days from planting to sensing where growing degree days (GDD) were more than 0 (GDD > 0) was tabulated by site-year for all dates when NDVI data were collected. The r was computed for NDVI versus final grain yield at all sensing dates and plotted against the respective GDD > 0 when readings were taken. Linear plateau models were used to determine the point when the r peaked. Averaged over 3 yr (2016–2018), the optimum GDD > 0 needed to predict grain yield using NDVI in both long-term trials was between 97 and 112. Use of the GDD > 0 as a numeric metric to delineate the best time and date to collect NDVI readings and predict yield potential can then be used to formulate accurate midseason fertilizer N rates. Adhering to quantitative GDD > 0 data is much more reliable than using subjective morphological scales. These critical GDD values can be reported on a day-to-day, by-location basis (mesonet.org) for in-season producer use.

AbstractFor over 25 yr, sensor‐based Normalized Difference Vegetative Index (NDVI) data has been collected from both satellite imagery and near‐plant (3‐m) readings. Because calibrated NDVI data coming from active sensors is still relatively new, limited research has returned to evaluate databases including multiple years and environments. Composite NDVI readings and final grain yield were collected from 1999 to 2018. This included growing degree day (GDD) records for each mid‐season sensor measurement. This was attempted to potentially improve the use of a historical and subjective morphological scale. Using location‐specific‐archived‐data from the Oklahoma Mesonet, the exact number of days from planting to sensing where GDD > 0 for each date and location were compiled. The ensuing relationship between NDVI (for a predetermined GDD > 0 range) and yield was determined. Grain yield prediction was improved between 80 and 115 GDDs. These ranges further targeted a climatologically identifiable metric that precisely determined when to collect sensor readings in future years. Compared with the current composite yield prediction equation for Oklahoma, the new exponential function created from this study was higher in the lower‐yielding environments. Underestimation of fertilizer N rates has been voiced by producers in recent years. This has likely been the product of more current varieties, more efficient farming practices, and increased optimum N rates needed for higher yields. This validates the adoption of a new YP0 equation for OSU's on‐line Sensor Based Nitrogen Rate Calculator, allowing accurate yield prediction between 80 and 115 GDDs.

Improvement of nitrogen use efficiency (NUE) via active optical sensors has gained attention in recent decades, with the focus of optimizing nitrogen (N) input while simultaneously sustaining crop yields. To the authors’ knowledge, a comprehensive review of the literature on how optical sensors have impacted winter wheat (Triticum aestivum L.) NUE and grain yield has not yet been performed. This work reviewed and documented the extent to which the use of optical sensors has impacted winter wheat NUE and yield. Two N management approaches were evaluated; optical sensor and conventional methods. The study included 26 peer-reviewed articles with data on NUE and grain yield. In articles without NUE values but in which grain N was included, the difference method was employed to compute NUE based on grain N uptake. Using optical sensors resulted in an average NUE of 42% (±2.8% standard error). This approach improved NUE by approximately 10.4% (±2.3%) when compared to the conventional method. Grain yield was similar for both approaches of N management. Optical sensors could save as much as 53 (±16) kg N ha−1. This gain alone may not be adequate for increased adoption, and further refinement of the optical sensor robustness, possibly by including weather variables alongside sound agronomic management practices, may be necessary.

Biochar (B) has shown promise in improving crop productivity. However, its interaction with inorganic nitrogen (N) in temperate soils is not well-studied. The objective of this paper was to compare the effect of fertilizer N-biochar-combinations (NBC) and N fertilizer (NF) on maize (Zea mays L.) grain yield, N uptake, and N use efficiency (NUE). Trials were conducted in 2018 and 2019 at Efaw and Lake Carl Blackwell (LCB) in Oklahoma, USA. A randomized complete block design with three replications and ten treatments consisting of 50, 100, and 150 kg N ha−1 and 5, 10, and 15 Mg B ha−1 was used. At LCB, yield, N uptake, and NUE under NBC increased by 25%, 28%, and 46%, respectively compared to NF. At Efaw, yield, N uptake, and NUE decreased under NBC by 5%, 7%, and 19%, respectively, compared to NF. Generally, results showed a significant response to NBC at ≥10 Mg B ha−1. While results were inconsistent across locations, the significant response to NBC was evident at LCB with sandy loam soil but not Efaw with silty clay loam. Biochar application with inorganic N could improve N use and the yield of maize cultivated on sandy soils with poor physical and chemical properties.

The environment randomly influences nitrogen (N) response, demand, and optimum N rates. Field experiments were conducted at Lake Carl Blackwell (LCB) and Efaw Agronomy Research Station (Efaw) from 2015 to 2018 in Oklahoma, USA. Fourteen site years of data were used from two different trials, namely Regional Corn (Regional) and Optimum N rate (Optimum N). Three algorithms developed by Oklahoma State University (OSU) to predict yield potential were tested on both trials. Furthermore, three new models for predicting potential yield using optical crop sensors and climatological data were developed for maize in rain-fed conditions. The models were trained/built using Regional and were then validated/tested on the Optimum N trial. Out of three models, one model was developed using all of the Regional trial (combined model), and the other two were prepared from each location LCB and Efaw model. Of the three current algorithms; one worked best at predicting final grain yield at LCB location only. The coefficient of determination R-2=0.15 and 0.16 between actual grain yield and predicted grain yield was observed for Regional and Optimum N rate trials, respectively. The results further indicated that the new models were better at predicting final grain yield except for Efaw model (R-2=0.04) when tested on optimum N trial. Grain yield prediction for the combined model had an R-2=0.31. The best yield prediction was obtained at LCB with an R-2=0.52. Including climatological data significantly improved the ability to predict final grain yield along with using mid-season sensor data.