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As the demand and cultivation of two‐row malting barley (Hordeum vulgareL.) increases in the Northern Great Plains, updated nitrogen (N) recommendations are increasingly necessary. Not only does N play a role in grain yield, but it also impacts grain malting characteristics, including protein and kernel plump. To determine the impacts N rate and availability have on two‐row malting barley, two experimental sites were established in eastern North Dakota during the 2020 and 2021 growing seasons. Treatments consisted of five fertilizer rates from 0 to 180 kg N ha−1and two malting barley cultivars. Soil samples to be analyzed for nitrate‐N were taken prior to planting and N credit estimates from the previous crop were considered to determine the total known available nitrogen (TKAN) in the soil. It was determined there was a strong relationship between N rate and grain yield along with a strong positive correlation between N rate and grain protein. No significant interactions between N rate and kernel plump were noted. When the relationship between relative grain yield and TKAN was modeled using a best‐fit regression, maximum yield was attained at 210 kg TKAN ha−1with a grain protein of 128 g kg−1, meeting malting quality requirements. When factoring in grain value and cost of urea fertilizer, the TKAN range needed to produce the crop at the highest profitability was lower than TKAN of maximum yield, ranging from 89 to 190 kg TKAN ha−1. Plant‐available nitrogen (N) is a driver of both yield and grain protein content of two‐row malting barley.Economic optimum N rates for two‐row malting barley promote farmer profitability and grain quality.Total N application rates can be reduced by focusing on net profitability rather than maximum yield.

Asymbiotic nitrogen fixation (N‐fixation) is a microbial process that may result in the introduction of plant‐available N into the soil. Although the process of N mineralization in soils is mediated by many microorganisms and is related mostly to soil moisture, the soil factors which regulate asymbiotic N‐fixation are relatively unknown and the number of microorganisms with the ability to fix N is small relative to the whole soil microorganism pool. Soils under long‐term no‐till management have greater asymbiotic N‐fixing organism activity compared to no‐till. This study was conducted to determine the variance of N‐fixing activity over distance, and the temporal variability of asymbiotic N‐fixing organism activity at six sites with transitional no‐till soils in eastern North Dakota over three growing seasons. Sites were sampled at the same location each month of the growing season from 2019 to 2021. At one of the sampling dates, each year additional samples were obtained at distances from the central sampling location for use in statistical analysis. The sampling over distance indicated that to characterize a large field area, multiple samples should be taken and analyzed separately or mixed together for single analysis. Monthly sampling in all 3 years indicated that peak N‐fixation activity was favored by a moist, warm environment. Dry periods and excessively wet periods resulted in low activity. Heavy rains within 48 h of sampling resulted in extremely low N‐fixation activity at the subsequent sampling date. Models were constructed relating the accumulative rainfall from the 30 days prior to sampling, and the mean air temperature from the 30 days prior to sampling. These data indicate that the activity of asymbiotic N‐fixing organisms increased with temperature and cumulative rainfall prior to measurement. Asymbiotic N‐fixing organism activity is variable over sampling locations.Asymbiotic N‐fixation was low in early spring after thaw (mid‐April to late April).Asymbiotic N‐fixation activity was favored by moist, warm soil.Asymbiotic N‐fixation activity was lowest during dry or excessively wet conditions.

Improving corn (Zea maysL.) 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. Individual corn N recommendation tools poorly estimated EONR (R2≤ 0.24) across many US Midwest states.Combining or “ensembling” two or more corn N recommendation tools improved EONR estimation (R2≤ 0.46).Both ensembling techniques evaluated proved effective at combining tools.

Based on a seminal paper published by Stanford in 1973, many land grant universities adopted N rate guidelines that used expected yield times a factor (such as 1.2 lb N/bu), with adjustments for previous crop, to formulate N rate recommendations for corn. However, discrepancies between yield‐based N rate recommendations and recent N response data led to the development of an empirical approach, using N response data to generate optimum N rates. The idea of combining crop N response data, dubbed the Maximum Return to Nitrogen (MRTN) approach, was initiated in the mid‐2000s and is today used in seven Corn Belt states. Earn 1.5 CEUs in Nutrient Management by taking the quiz for the article at https://bit.ly/35Fy683. View all CEUs at https://web.sciencesocieties.org/Learning‐Center/Courses.

Nitrogen fertilizer recommendations in corn (Zea maysL.) that match the economically optimal nitrogen fertilizer rate (EONR) are imperative for profitability and minimizing environmental degradation. However, the amount of soil N available for the crop depends on soil and weather factors, making it difficult to know the EONR from year‐to‐year and from field‐to‐field. Our objective was to explore, within the framework of hydrologic soil groups and drainage classifications (HGDC), which site‐specific soil and weather properties best estimated corn N needs (i.e., EONR) for two application timings (at‐planting and side‐dress). Included in this investigation was a validation step using an independent dataset. Forty‐nine N response trials conducted across the U.S. Midwest Corn Belt over three growing seasons (2014–2016) were used for recommendation model development, and 181 independent site‐years were used for validation. For HGDC models, soil organic matter (SOM), clay content, and evenness of rainfall distribution before side‐dress N application were the properties generally most helpful in predicting EONR. Using the validation data, model recommendations were within 34 kg N ha–1of EONR for 37 and 42% of the sites with a root mean square error (RMSE) of 70 and 68 kg N ha–1for at‐planting and side‐dress applications, respectively. Compared to state‐specific recommendations, sites needing <100 kg N ha–1or no N were better estimated with HGDC models. In contrast, for sites where EONR was >150 kg N ha–1, HGDC models underestimated N needs compared to state specific. These results show HGDC groupings could aid in developing tools for N fertilizer recommendations. Soil hydrologic classifications aid in determining corn N fertilizer rates.Economically optimal N rate was best predicted by different soil and weather properties for each hydrologic group.Soil organic matter, clay, and rainfall evenness generally helped in estimating economically optimal N rate.Compared to state N recommendations, developed models were better when economically optimal N rate <100 kg ha−1.

Improving corn (ZeamaysL.) N managementis pertinent to economic andenvironmental objectives. However, there are limited comprehensive data sources to develop and test N fertilizer decision aid tools across a wide geographic range of soil and weather scenarios. Therefore, a public‐industry partnership was formed to conduct standardized corn N rate response field studies throughout the U.S. Midwest. This research was conducted using a standardized protocol at 49 site‐years across eight states over the 2014–2016 growing seasons with many soil, plant, and weather related measurements. This note provides the data (found in supplemental files), outlines the data, summarizes key findings, and highlights the strengths and weakness for those who wish to use this dataset. Data provided from 49 corn N response trials in eight states across the U.S. Midwest.Data included a wide range of soil, plant, and weather measurements.Published manuscripts show potential methods to improve N management.

Reports of sulfur (S) deficiency symptoms in corn (Zea maysL.) fields of the Red River Valley of North Dakota and Minnesota are increasing. Current soil tests cannot predict the availability of S correctly due to the presence of gypsum in soils of this region. Field trials were conducted to determine corn yield and S uptake response to incremental applications of S (0, 11, 22, 33, and 44 kg S ha–1) in the form of ammonium sulfate [(NH4)2SO4]. Corn yield and S uptake varied significantly between sites. Out of 12 sites, only two sites had the highest corn yield without S application. Corn grain S removal ranged between 11 and 17 kg S ha–1at harvest. Fertilizer‐S (SO4) application did not result in a significant yield response. The current recommendation of 11 kg S ha–1may be necessary to reduce the risk of future S deficiency across this region and compensate for the removal of S in grain due to the uncertainty of adequate plant available S. Corn grain yield and S uptake did not respond to a fertilizer‐S application rate.Sulfur mineralization was enough to fulfill crop S demands.Soil organic matter content was not a reliable predictor for corn S response.

Improving corn (Zea maysL.) N fertilizer rate recommendation tools is necessary for improving farmers’ profits and minimizing N pollution. Research has repeatedly shown that weather and soil factors influence available N and crop N need. Adjusting available corn N recommendation tools with soil and weather measurements could improve farmers’ ability to manage N. The aim of this research was to improve publicly available N recommendation tools with site‐specific soil and weather measurements. Information from 49 site‐years of N response trials in the U.S. Midwest was used to evaluate 21 rate recommendation tools for a single (at‐planting) and split (at‐planting + sidedress) N application. Using elastic net and decision tree algorithms, the difference between each tool's N recommendation and the economically optimum nitrogen rate (EONR) was modeled against soil and weather measurements. The model's predicted values were used to adjust the tools. Unadjusted the best performing tool had r2= .24; after adjustment, the best performing tool had r2= .57. Overall tool improvement was modest and sometimes required many additional inputs. Using weather measurements (e.g., evenness of rainfall or abundant and well‐distributed rainfall) helped increase N recommendations by accounting for N loss while soil measurements (e.g., pH and total C) helped decrease N recommendations when there was sufficient available soil N. This investigation showed that incorporating soil and weather measurements is a viable approach for improving corn N recommendation tools regionally; but even with adjustments, tools still have room for additional improvement.

Splitting the N application into two or more timings may improve corn (Zea maysL.) grain yield and N recovery relative to a single‐N application. A 49 site‐year study across eight U.S. Midwestern states compared the effect of an at‐planting (single‐N application) and two split‐N applications [45 (45+SD) or 90 kg N ha−1(90+SD) at planting with the remainder of the total rate (180 or 270 kg N ha−1) applied at V9]. For split‐N applications, soil and plant responses were similar between 45+SD and 90+SD 93–98% of the time, indicating the at‐planting N rate of 45 kg N ha−1may be all that is needed in most cropping scenarios. Splitting the N application compared to a single‐N application changed soil NO3–N at VT and post‐harvest <35% of the time and plant N uptake and grain yield <15% of the time. Split‐N applications had greater grain yield in areas with uniform precipitation around the sidedress timing (Shannon Diversity Index >0.56–0.59) to incorporate N in the root zone, and in coarse‐textured soil (sand content >4–10%) that had greater potential for N loss. Single‐N applications produced greater grain yield in soils with more total N (>2.1–2.4 g kg−1) to support N mineralization and greater cation exchange capacity (CEC) (> 27–31 cmolckg−1), silt content (>66–74%), or clay content (>24–37%) to improve nutrient and water retention. Decisions on nitrogen application timing should be made based on soil parameters and typical weather conditions around the sidedress timing.

Anaerobic potentially mineralizable nitrogen (PMN) combined with preplant nitrate test (PPNT) or pre‐sidedress nitrate test (PSNT) may improve corn (Zea maysL.) N management. Forty‐nine corn N response studies were conducted across the U.S. Midwest to evaluate the capacity of PPNT and PSNT to predict grain yield, N uptake, and economic optimal N rate (EONR) when adjusted by soil sampling depth, soil texture, temperature, PMN, and initial NH4–N from PMN analysis. Pre‐plant soil samples were obtained for PPNT (0‐ to 30‐, 30‐ to 60‐, 60‐ to 90‐cm depths) and PMN (0‐ to 30‐cm depth) before corn planting and N fertilization. In‐season soil samples were obtained at the V5 corn development stage for PSNT (0‐ to 30‐, 30‐ to 60‐cm depths) at 0 kg N ha−1at‐planting rate and for PMN when 0 and 180 kg N ha−1was applied at planting. Grain yield, N uptake, and EONR were best predicted when separating soils by texture or sites by annual growing degree‐days and including PMN and initial NH4–N with either NO3–N test. Using PSNT (mean R2= .30)‐instead of PPNT (mean R2= .19)‐based models normally increased predictability of corn agronomic variables by a mean of 11%. Including PMN and initial NH4–N with PPNT or PSNT only marginally improved predictability of grain yield, N uptake, and EONR (R2increase ≤ .33; mean R2= .35). Therefore, including PMN with PPNT or PSNT is not suggested as a tool to improve N fertilizer management in the U.S. Midwest.

Soil microbes drive biological functions that mediate chemical and physical processes necessary for plants to sustain growth. Laboratory soil respiration has been proposed as one universal soil health indicator representing these functions, potentially informing crop and soil management decisions. Research is needed to test the premise that soil respiration is helpful for profitable in‐season nitrogen (N) rate management decisions in corn (Zea maysL.). The objective of this research was two‐fold: (i) determine if the amount of N applied at the time of planting effected soil respiration, and (ii) evaluate the relationship of soil respiration to corn yield response to fertilizer N application. A total of 49 N response trials were conducted across eight states over three growing seasons (2014–2016). The 4‐day Comprehensive Assessment of Soil Health (CASH) soil respiration method was used to quantify soil respiration. Averaged over all sites, N fertilization did not impact soil respiration, but at four sites soil respiration decreased as N fertilizer rate applied at‐planting increased. Across all site‐years, soil respiration was moderately related to the economical optimum N rate (EONR) (r2= 0.21). However, when analyzed by year, soil respiration was more strongly related to EONR in 2016 (r2= 0.50) and poorly related for the first two years (r2< 0.20). These results illustrate the factors influencing the ability of laboratory soil respiration to estimate corn N response, including growing‐season weather, and the potential of fusing soil respiration with other soil and weather measurements for improved N fertilizer recommendations.

The anaerobic potentially mineralizable N (PMN) test combined with the preplant (PPNT) and presidedress (PSNT) nitrate tests may improve corn (Zea maysL.) N fertilization predictions. Forty‐nine corn N response experiments (mostly corn following soybean [Glycine max(L.) Merr.]) were conducted in the U.S. Midwest from 2014–2016 to evaluate the ability of the PPNT and PSNT to predict corn relative yield (RY) and N fertilizer over‐ and under‐application rates when adjusted by PMN. Before planting and N fertilization, PPNT (0–30, 30–60, and 60–90 cm) and PMN (0–30 cm) samples were obtained. In‐season soil samples were obtained at the V5 development stage for PSNT (0–30, 30–60 cm) in all N rate treatments and PMN (0–30 cm) in only the 0 and 180 kg N ha−1preplant N treatments. Increasing NO3–N sampling depths beyond 30 cm with or without PMN improved RY predictability marginally (R2increase up to 0.20) and reduced over‐ and under‐application frequencies up to 14%. Including PMN (preplant only) with PPNT or PSNT improved RY predictability minimally (R2increase up to 0.10) only for coarse‐ and medium‐textured soils, but N fertilizer over‐ and under‐application frequencies were not substantially reduced (≤12%). These marginal improvements in RY predictability and N fertilizer over‐ and under‐application frequencies, regardless of the variables used (e.g., fertilization, sampling depth, soil texture, and growing degree‐day categories), demonstrate that including PMN with soil NO3–N alone does not improve corn N fertilization need predictions enough to recommend their use.

Understanding the variables that affect the anaerobic potentially mineralizable N (PMNan) test should lead to a standard procedure of sample collection and incubation length, improving PMNanas a tool in corn (Zea maysL.) N management. We evaluated the effect of soil sample timing (preplant and V5 corn development stage [V5]), N fertilization (0 and 180 kg ha−1) and incubation length (7, 14, and 28 d) on PMNan(0–30 cm) across a range of soil properties and weather conditions. Soil sample timing, N fertilization, and incubation length affected PMNandifferently based on soil and weather conditions. Preplant vs. V5 PMNantended to be greater at sites that received < 183 mm of precipitation or < 359 growing degree‐days (GDD) between preplant and V5, or had soil C/N ratios > 9.7:1; otherwise, V5 PMNantended to be greater than preplant PMNan. The PMNantended to be greater in unfertilized vs. fertilized soil in sites with clay content > 9.5%, total C < 24.2 g kg−1, soil organic matter (SOM) < 3.9 g kg−1, or C to N ratios < 11.0:1; otherwise, PMNantended to be greater in fertilized vs. unfertilized soil. Longer incubation lengths increased PMNanat all sites regardless of sampling methods. Since PMNanis sensitive to many factors (sample timing, N fertilization, incubation length, soil properties, and weather conditions), it is important to follow a consistent protocol to compare PMNanamong sites and potentially use PMNanto improve corn N management.

Determining which corn (Zea maysL.) N fertilizer rate recommendation tools best predict crop N need would be valuable for maximizing profits and minimizing environmental consequences. Simultaneous comparisons of multiple tools across various environmental conditions have been limited. The objectives of this research were to evaluate the performance of publicly‐available N fertilizer recommendation tools across diverse soil and weather conditions for: (i) prescribing N rates for planting and split‐fertilizer applications, and (ii) economic and environmental effects. Corn N‐response trials using standardized methods were conducted at 49 sites, spanning eight US Midwest states and three growing seasons. Nitrogen applications included eight rates in 45 kg N ha−1increments all at‐planting and matching rates with 45 kg N ha−1at‐planting plus at the V9 development stage. Tool performances were compared to the economically optimal N rate (EONR). Over this large geographic region, only 10 of 31 recommendation tools (mainly soil nitrate tests) produced N rate recommendations that weakly correlated to EONR (P≤ .10; r2≤ .20). With other metrics of performance, the Maximum Return to N (MRTN) soil nitrate tests, and canopy reflectance sensing came close to matching EONR. Economically, all tools but the Maize‐N crop growth model had similar returns compared to EONR. Environmentally, yield goal based tools resulted in the highest environmental costs. Results show that no tool was universally reliable over this study's diverse growing environments, suggesting that additional tool development is needed to better represent N inputs and crop utilization at a larger regional level.

Estimates of mineralizable N with the anaerobic potentially mineralizable N (PMNan) test could improve predictions of corn (Zea maysL.) economic optimal N rate (EONR). A study across eight US midwestern states was conducted to quantify the predictability of EONR for single and split N applications by PMNan. Treatment factors included different soil sample timings (pre‐plant and V5 development stage), planting N rates (0 and 180 kg N ha−1), and incubation lengths (7, 14, and 28 d) with and without initial soil NH4–N included with PMNan. Soil was sampled (0–30 cm depth) before planting and N application and at V5 where 0 or 180 kg N ha−1were applied at planting. Evaluating across all soils, PMNanwas a weak predictor of EONR (R2≤ 0.08; RMSE, ≥67 kg N ha−1), but the predictability improved (15%) when soils were grouped by texture. Using PMNanand initial soil NH4–N as separate explanatory variables improved EONR predictability (11–20%) in fine‐textured soils only. Delaying PMNansampling from pre‐plant to V5 regardless of N fertilization improved EONR predictability by 25% in only coarse‐textured soils. Increasing PMNanincubations beyond 7 d modestly improved EONR predictability (R2increased ≤0.18, and RMSE was reduced ≤7 kg N ha−1). Alone, PMNanpredicts EONR poorly, and the improvements from partitioning soils by texture and including initial soil NH4–N were relatively low (R2≤ 0.33; RMSE ≥ 68 kg N ha−1) compared with other tools for N fertilizer recommendations. Core IdeasAnaerobic potentially mineralizable N (PMNan) is a weak predictor of economic optimal N rate (EONR).Predictability of EONR by PMNanimproves when accounting for soil texture.For coarse‐textured soils, PMNanat V5 improves EONR predictability.Increasing incubation length does not substantially improve EONR predictability.PMNanalone is not a reliable management tool for N rate determination. Anaerobic potentially mineralizable N (PMNan) is a weak predictor of economic optimal N rate (EONR). Predictability of EONR by PMNanimproves when accounting for soil texture. For coarse‐textured soils, PMNanat V5 improves EONR predictability. Increasing incubation length does not substantially improve EONR predictability. PMNanalone is not a reliable management tool for N rate determination.

In the Northern Great Plains, sugarbeet (Beta vulgarisL.) harvesting is stretched out from August to October. Sensors mounted on unmanned aerial vehicles (UAVs) may provide the information needed to identify sugarbeet approaching an optimum harvesting date that maximizes the recoverable sugar yield. The objective of this study was to relate the vegetation indices at early‐ and late‐harvest time with corresponding sugarbeet root yield, sugar concentration, and recoverable sugar yield (RSY) at three fields in the Red River Valley of North Dakota and Minnesota during 2017 and 2018. Vegetation indices was determined based on the multispectral imagery collected using UAV. Early harvest dates (19 Sept. 2017 and 21 Aug. 2018) reduced yield and RSY in 5 of 6 site‐years, compared to late harvest dates (3 Oct. 2017, 4 Sept. 2018, and 18 Sept. 2018). In general, delaying harvest increased red normalized difference vegetation index (NDVI) and reduced red edge NDVI (RENDVI). However, Pearson correlation between yield and vegetation indices did not show consistent relationship across harvesting date, site, and growing season. Pooling all site‐year data showed a significant relationship between RENDVI and yield and RSY. Use of in‐season yield estimate (RENDVI/growing degree days) has potential to explain 46% of sugarbeet root yield and RSY. Core IdeasSugarbeet yield and sugar content is directly related to the length of growing season.Late harvest in September increased recoverable sugar yield.Sugar concentration did not change with harvest date.Association between drone‐based indices and yield varied with site‐year. Sugarbeet yield and sugar content is directly related to the length of growing season. Late harvest in September increased recoverable sugar yield. Sugar concentration did not change with harvest date. Association between drone‐based indices and yield varied with site‐year.

The recent incidence and severity of sulfur (S) deficiency due to a decrease in atmospheric S deposition and greater S removal by crops requires reevaluating current S recommendation for corn (Zea maysL.). Ten on‐farm field trials were conducted to evaluate corn response to S additions at sites in the Red River Valley of North Dakota and Minnesota between 2016 and 2017. Sulfur rates of 0, 11, 22, 33, 44 kg S ha–1as granular (NH4)2SO4were broadcasted and incorporated prior to corn planting using randomized complete block design with four replications. Plant tissue S concentration and N/S ratios were determined at two corn growth stages (V6 and V12 in 2016, V12 and R1 in 2017) and grain yield was measured at harvest. Plant tissue S did not increase with S fertilizer at V6 and V12 stages in 2016 whereas in 2017, tissue S increased with 11 kg S ha–1at two sites in the V6 growth stage. Yield was not related to both soil S and plant parameters. Corn yield increased with 11 kg S ha–1fertilizer rate at two of ten locations. Lack of response at the remaining sites suggests that S from organic matter and mineralization was enough for crop growth and hence applied fertilizer S had no effect on yield. Core IdeasCorn grain yield increased with S fertilizer at two of ten sites.No response to S fertilizer occurred due to dry early growing season.Plant tissue tests were not reliable indicators in predicting response. Corn grain yield increased with S fertilizer at two of ten sites. No response to S fertilizer occurred due to dry early growing season. Plant tissue tests were not reliable indicators in predicting response.

A series of N‐rate experiments previously conducted in spring wheat, corn, and sunflower in North Dakota indicated that less N was required when fields were in six years or more continuous no‐till compared to conventional till. The objective of this study was to determine whether part of the reason for the decreased requirement for N was the greater activity of asymbiotic N‐fixing organisms. Twelve paired‐samplings were conducted in 2018. A surface 0‐ to 5‐cm deep sample was obtained in a long‐term no‐till field directly across the fence or road from a similar soil in conventional till. Samples were incubated in an acetylene‐reduction procedure to estimate N fixation rate. Ten of twelve paired samplings had greater asymbiotic N fixation compared to the conventional till counterpart. This indicates that long‐term no‐till soils support greater N production from soil microorganisms than conventional till soils, which would result in lower input costs to no‐till farmers. Microbial communities are different between conventional till and no‐till managed soils.Asymbiotic N‐fixation was greater in farmer‐managed no‐till than neighboring conventional till.N recommendations in long‐term continuous no‐till should be separate from conventional till N recommendations.

Nitrogen provided to crops through mineralization is an important factor in N management guidelines. Understanding of the interactive effects of soil and weather conditions on N mineralization needs to be improved. Relationships between anaerobic potentially mineralizable N (PMNan) and soil and weather conditions were evaluated under the contrasting climates of eight US Midwestern states. Soil was sampled (0–30 cm) for PMNananalysis before pre‐plant N application (PP0N) and at the V5 development stage from the pre‐plant 0 (V50N) and 180 kg N ha−1(V5180N) rates and incubated for 7, 14, and 28 d. Even distribution of precipitation and warmer temperatures before soil sampling and greater soil organic matter (SOM) increased PMNan. Soil properties, including total C, SOM, and total N, had the strongest relationships with PMNan(R2≤ 0.40), followed by temperature (R2≤ 0.20) and precipitation (R2≤ 0.18) variables. The strength of the relationships between soil properties and PMNanfrom PP0N, V50N, and V5180Nvaried by ≤10%. Including soil and weather in the model greatly increased PMNanpredictability (R2≤ 0.69), demonstrating the interactive effect of soil and weather on N mineralization at different times during the growing season regardless of N fertilization. Delayed soil sampling (V50N) and sampling after fertilization (V5180N) reduced PMNanpredictability. However, longer PMNanincubations improved PMNanpredictability from both V5 soil samplings closer to the PMNanpredictability from PP0N, indicating the potential of PMNanfrom longer incubations to provide improved estimates of N mineralization when N fertilizer is applied. Relationships between mineralization estimates taken with the PMNantest and soil and weather conditions need to be improved.Soil sample timing and N fertilization minimally affected PMNanpredictability by soil and weather parameters.Soil properties predict PMNanbetter than weather conditions.Soil and weather conditions combined explain up to 69% of the variability of PMNan.Longer PMNanincubations improve the relationship between soil and weather parameters and PMNanafter N fertilization.

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 maysL.) 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 availability and fertilization can increase sunflower see yield.Nitrogen fertilization may decrease oil concentration of oilseed sunflower.Nitrogen fertilization increases sunflower lodging risk in windy regions. The N and P recommendations for sunflowers growers in North Dakota have not been changed in 30 yr. Twenty‐two N and P rate experiments were conducted during 2014 and 2015. The objective was to determine the response of seed yield, oil concentration, and lodging to available N and P. In 2014 studies were a randomized complete block split plot with N rate as main plots and P rate as subplots. Nitrogen was applied at rates of 0, 45, 90, 134, 179, and 224 kg N ha−1. Phosphorus was applied to establish P rates of 0, 13, 26, and 39 kg P ha−1. In 2015, the field design included only 0 and 26 kg P ha−1. Experiments were taken to yield and lodging was recorded at harvest. Oil seed sunflower (17 experimental locations) was also analyzed for oil concentration. The N response of sunflower seed yield was quadratic. Increased N rate resulted in lower oil concentration in half of the oilseed experiments. Increased N rate was linearly related to increasing lodging at several sites. Phosphate fertilization had little effect on seed yield, oil concentration, and lodging, despite many of the sites having soil P levels considered ‘low’. Future N rate recommendations should be based on seed yield response with increasing N and oil concentration reduction with increasing N for oilseed sunflower. A limit to maximum N rate should also be considered because of the lodging risk in this wind‐plagued region.

The geographic scope, scale, and unique collaborative arrangement warrant documenting details of this work.The purpose of this article is to describe how the research was undertaken, reasons for the research methods, and the project's potential value.The project generated a valuable dataset across a wide array of weather and soils that allows evaluation of N decision tools. Due to economic and environmental consequences of N lost from fertilizer applications in corn (Zea maysL.), considerable public and industry attention has been devoted to the development of N decision tools. Needed are research and databases and associated metadata, at numerous locations and years to represent a wide geographic range of soil and weather scenarios, for evaluating tool performance. The goals of this research were to conduct standardized corn N rate response field studies to evaluate the performance of multiple public‐domain N decision tools across diverse soils and environmental conditions, develop and publish new agronomic science for improved crop N management, and train new scientists. The geographic scope, scale, and unique collaborative arrangement warrant documenting details of this research. The objectives of this paper are to describe how the research was undertaken, reasons for the methods, and the project's anticipated value. The project was initiated in a partnership between eight U.S. Midwest land‐grant universities, USDA‐ARS, and DuPont Pioneer. Research using a standardized protocol was conducted over the 2014 through 2016 growing seasons, yielding a total of 49 sites. Preliminary observations of soil and crop variables measured from each site revealed a magnitude of differences in soil properties (e.g., texture and organic matter) as well as differences in agronomic and economic responses to applied N. The project has generated a valuable dataset across a wide array of weather and soils that allows investigators to perform robust evaluation of N use in corn and N decision tools.

Prediction of S deficiency is difficult due to poor soil test relationship to crop response. The purpose of this article is to provide evidence that the use of an N‐sufficient area established for use as a standard for active‐optical (AO) sensor directed in‐season N application could also serve to detect S deficiency in corn (Zea maysL.). Nitrogen rate experiments at Oakes and Arthur, ND, exhibited corn upper‐leaf yellowing in high N treatments while control treatments (0 N) were greenest. Two AO sensors were utilized to record red normalized differential vegetation index (NDVI) and red edge NDVI values. The high‐N treatment had the lowest NDVI readings, and the control treatments had the highest NDVI readings. Within 24 h, an application of gypsum containing 22 kg S ha−1was applied. Seven days following S application, the sites were revisited. The AO sensors were again used to record red NDVI and red edge NDVI. At both sites, the high N treatment had the highest NDVI readings and the control treatment had the lowest NDVI readings. These experiments indicate that high N treatment can increase the severity of S deficiency in corn. If a lower NDVI reading is recorded in a high N application area than in the surrounding area, S deficiency may be present. Core Ideas Sulfur deficiency in corn is difficult to anticipate using soil analysis.When N availability is low, the affect of S deficiency in corn is minimized.When N availability is high, the affect of S deficiency is intensified.Active‐optical sensors can be used along with an N‐sufficient area to reveal S deficiency. Sulfur deficiency in corn is difficult to anticipate using soil analysis. When N availability is low, the affect of S deficiency in corn is minimized. When N availability is high, the affect of S deficiency is intensified. Active‐optical sensors can be used along with an N‐sufficient area to reveal S deficiency.

Yield prediction in sugar beet (Beta vulgarisL.) is important as a basis for in‐season N application. Active optical sensors have been researched in sugar beet for yield estimation. A common field method for using active‐optical sensors is to establish an N non‐limiting area, and compare the yield predicted from sensor readings with readings from the rest of the field. Yield difference is the basis for calculation of N rate. Sugar beet gains root mass and sugar content with time. The objectives of these experiments were to utilize two active‐optical sensors at two timings with canopy height measurements and relate readings to root yield and recoverable sugar yield at consecutive harvest dates. A 2‐yr study in the Red River Valley of North Dakota and Minnesota was conducted on four sites to compare two active‐optical sensors, GreenSeeker and Holland Crop Circle, red normalized differential vegetative index (NDVI), red edge NDVI, with and without canopy height for use in sugar beet yield prediction. The red NDVI and red edge NDVI, used at V 6–8 and V 12–14 were similar in their relationship to sugar beet yield over several harvest dates. The r2of sensor measurement and yield relationships at V 6–8 improved when canopy height was considered but not at V 12–14. Active‐optical sensors when canopy height is considered could be used to predict sugar beet root yield and recoverable sugar yield over a range of harvest dates, which would be useful in developing algorithms for in‐season N fertilization.

It is important for today's farmer to manage his fields efficiently to maximize economic return and minimize environmental impacts. Managing agricultural inputs based on spatial variability in a field may help to achieve these goals. The objective of this study was to develop and compare different N management zone delineation methods and investigate the effect that variable N fertilization directed by these zones had on corn (Zea maysL.) grain yield over 4 yr. The zone delineation methods were based on: (i) supervised classification of bare soil color; (ii) cluster analysis of soil N, apparent electrical conductivity (ECa), and 1 yr of yield data; or (iii) a summation of standardized ECa, elevation, and 5 yr of yield data. The different delineation methods resulted in relatively similar zone patterns. Applied N rates were mostly affected by varying yield goals due to low variability of soil test N between zones. The effect of zone method on yield was inconsistent over the study period, with the soil color zone yields corresponding best to hypothesized productivity potential. The uniform treatment yields were not significantly different than yields from most of the N management zones. Increases in applied N for management zone treatments resulted in small yet not statistically significant yield increases in most years, with economic benefits over the uniform treatment observed in 2 yr. Yield was affected by adverse weather conditions in 2 yr, resulting in significant increases in subsurface soil N and reduced economic benefits of zone management versus conventional management.

Although pedogenesis and landscape evolution have occurred on the eastern North Dakota till plain since the inception of the Holocene, soils with argillic horizon are recognized on a very limited basis. Argillic soils are not mapped in well‐drained positions on the youngest drift prairie landscapes. Roughly half of 47 pedons examined at a precision farming research site in Barnes County, North Dakota show evidence of genetic argillic horizons based on soil structure, hand texturing, and cutan presence. This study applies physical and micromorphological evidence to verify that diagnostic argillic horizons have formed in well‐drained settings on this eastern North Dakota landscape. Total clay distributions and fine/total clay ratios show marked increases in genetic argillic horizons compared with surface horizons and parent materials. The argillic horizons average about 28 cm in thickness; only two pedons were thinner than 7.5 cm. Consequently, the increase in total clay and fine clay distributions, and the thickness criteria required to meet diagnostic horizon status as established by Soil Taxonomyare satisfied. The upper argillic horizon boundary of seven profiles was 50 cm or more below the surface, possibly indicating relict argillic horizons. Micromorphologic evidence shows the presence of illuvial clay in the three soils sampled for microscopy. Porosity variations caused by the argillic horizons likely affect subsurface flow and may be responsible for variable N levels measured for these soils. These soils formed in well‐drained landscape settings, where stratified drift may predispose the soils to illuviation.

Zone sampling for site‐specific N application has been shown to be effective in North Dakota and other areas of the Great Plains. Printed and sometimes digitized soil surveys are presently available for most agricultural counties in the USA. Order 2 soil surveys generally have scales that range from 1:12000 to 1:31680. These surveys were developed for general planning purposes. There is interest in using Order 2 soil surveys as a basis for delineating N management zone patterns, especially where the soil‐mapping units have been digitized. This study was conducted to evaluate soil survey scales at the Order 1 (scale >1:15840) and Order 2 level against grid‐ and topography‐based zone sampling to determine whether soil surveys at these scales could be used to delineate N management zones for site‐specific fertilizer application. Fields mapped at a finer scale (Order 1 survey) showed some similarity between mapping units and N management zones defined by topography. Order 2 soil‐mapping units, which are the present mapping scale of most agricultural soil surveys, were often not similar to N management zones. Published Order 2 soil surveys should not be used to develop N management zones for site‐specific agriculture unless the soil patterns are verified with other zone development tools of site‐specific management. Alternatively, a major benefit of Order 1 soil surveys would be to reinforce or redefine apparent N management zones.