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.

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.

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.

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.

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.

Abstract In many developing countries, crop production is achieved with little or no application of fertilizer N. Understanding grain yield trends as new winter wheat cultivars (Triticum aestivum L.) are released and grown under different N management is important for crop yield improvement. This study evaluated grain yield trends of winter wheat cultivars over time in a crop production system with and without N application. Yield data was obtained from two long‐term experiments; 502 (E502) and 222 (E222) between 1969 and 2018. Results showed a mean annual grain yield increase of 12 and 30 kg ha–1 yr–1 as new cultivars were released and grown under adequate N management in E222 and E502, respectively. However, without N application, yield declined annually by 2.4 kg ha–1 yr–1 in E222 and increased marginally by 0.6 kg ha–1 yr–1 in E502. Nonetheless, the yield increase or decrease was only significant for E502 at 112 kg N ha–1 (r2 = .145; p = .01) and its slope was significantly different from that of control treatment (p = .02). In both experiments, yield was significantly influenced by cultivar and N interaction (p < .01), an indication that yield changed according to the level of N applied. In general, when N was applied, grain yields were high as well. New cultivars released over time improved grain yield with adequate N management.

The second law of thermodynamics states that entropy or randomness in a given system will increase with time. This is shown in science, where more and more biological processes have been found to be independent. Contemporary work has delineated the independence of yield potential (YP0) and nitrogen (N) response in cereal crop production. Each year, residual N in the soil following crop harvest is different. Yield levels change radically from year to year, a product of an ever‐changing and unpredictable/random environment. The contribution of residual soil N for next years’ growing crop randomly influences N response or the response index (RI). Consistent with the second law of thermodynamics, where it is understood that entropy increases with time and is irreversible, biological systems are expected to become increasingly random with time. Consequently, a range of different biological parameters will influence YP0 and RI in an unrelated manner. The unpredictable nature that environment has on N demand, and the unpredictable nature that environment has on final grain yield, dictate the need for independent estimation of multiple random variables that will be used in mid‐season biological algorithms of the future.Core Ideas Randomness in biological systems is increasing. Many biological processes are independent. Yield levels change from one year to the next. Environments change over time and are random. Optimum fertilizer nitrogen rates change dramatically from year to year.

No-tillage (NT) can improve soil properties and crop yield. However, there are contrasting reports on its benefits compared to conventional tillage (CT). Dataset (2003–2018) from long-term continuous winter wheat (Triticum aestivum L.) experiments 222 (E222) at Stillwater and 502 (E502) at Lahoma in Oklahoma, USA, established in 1969 and 1970, respectively, was used. Both experiments were managed under CT until 2010 and changed to NT in 2011. In each tillage system, treatments included nitrogen (N) rates at E222 (0, 45, 90, and 135 kg·N·ha−1) and E502 (0, 22.5, 45, 67, 90, and 112 kg·N·ha−1). The objective was to determine the change in wheat grain yield, soil organic carbon (SOC), and total soil nitrogen (TSN) associated with the change to NT. Grain yield was recorded, and postharvest soil samples taken from 0–15 cm were analyzed for TSN and SOC. Average TSN and SOC under NT were significantly above those under CT at both locations while grain yield differences were inconsistent. Under both tillage systems, grain yield, TSN, and SOC increased with N rates. At E222, grain yield, TSN, and SOC under NT were 23%, 17%, and 29%, respectively, more than recorded under CT. At E502, grain yield was lower under NT than CT by 14% while TSN and SOC were higher by 11% and 13%, respectively. Averaged over experimental locations, wheat grain yield, TSN, and SOC were 5%, 14%, and 21%, respectively, higher under NT compared to CT. Therefore, NT positively influenced grain yield, TSN, and SOC and is likely a sustainable long-term strategy for improving soil quality and crop productivity in a continuous monocropping system.