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Crops & Soils Magazine - Article




Methods of making variable-rate nitrogen recommendations

  1. Brian Arnalla
  1. a Nutrients for Life Foundation Professorship of Soil & Food Crop Nutrition, Precision Nutrient Management, Oklahoma State University, Stillwater

Photo courtesy of CaseIH



While a variable-rate N strategy that works across all regions, landscapes, and cropping systems has yet to be developed, those methods that are capable of determining the three inputs of the Stanford equation while incorporating regional specificity will capture the greatest level of accuracy and precision. Earn 0.5 CEUs in Nutrient Management by reading this article and taking the quiz at www.certifiedcropadviser.org/certifications/self-study/811

This article in CNS

  1. Vol. 49 No. 6, p. 24-27
    unlockOPEN ACCESS
    Published: November 21, 2016

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Variable-rate nitrogen management (VRN) is a fairly hot topic right now. The outcome of VRN promises improved efficiencies, economics, yields, and environmental sustainability. As the scientific community learns more about the crop’s response to fertilizer nitrogen and the soil’s ability to provide nitrogen, the complexity of providing VRN recommendations, which both maximize profitability and minimize environmental risk, becomes more evident.

The components of nitrogen fertilizer recommendations are the same whether it is for a field flat rate or a variable-rate map. The basis for all N recommendations can be traced back to the Stanford equation (Stanford, 1973). At first glance, the Stanford equation is very basic and fairly elegant with only three variables in the equation.

Historically, this was accomplished on a field level through yield goal estimates and soil test nitrate values. The generalized conversions such as 1.2 lb N/bu of corn and 2.0 lb N/bu of winter wheat took account for Ncrop and efert to simplify the process.

However, many challenges are created as recommendations move from a field or farm level to a zone or even sub-acre resolution. Schmidt et al. (2011), described the range in economical optimum N rate (EONR) across a corn field on a research station in central Pennsylvania to be 131, 62, and 131 lb/ac in Years 1, 2, and 3 of the study, respectively. Many other papers have documented the significance in field variance of EONR for multiple crops in many environments in both field-scale and small-plot research (Schmidt et al., 2002; Malzer et al., 1996; Mamo et al., 2003; Harrington et al., 1997; Lark and Wheeler, 2003; Scharf et al., 2005). When a N recommendation is made on a field level, the producer immediately has to accept a certain level of error. To reduce potential yield losses, the field recommendation needs to be at or above the N rate that maximizes yield on the majority of the field. If the yield of the field is normally distributed, there is opportunity loss on both sides of the curve. There will be a small percent of the field that does not reach maximum yield, and there is a high percentage of the field that will receive more N than needed.

Variable-rate nitrogen management allows the opportunity to take advantage of the inherent variability of the soil and environment by increasing inputs and yields in the areas and environments likely to respond and reducing inputs in those areas where yield is restricted. However, as concluded by Ferguson et al. (2002), the spatial application of the existing recommendation algorithm developed for uniform application may be inappropriate for VRN, and unique recommendation equations for major soils and climatic regions may be necessary to achieve substantial increases in N use efficiency.

The mechanisms and inputs for VRN recommendations may vary greatly but, as mentioned earlier, can be related back to the Stanford equation.


The basis for Ncrop is grain yield × grain N concentration. As grain N is fairly consistent, the goal of VRN methods is to identify grain yield. As such, the use of yield monitor data to determine yield zones was quickly utilized. With access to multiple years of yield data, yield zones and yield stability parameters are easily identified. Commonly, these yield zones can then be coupled with a regionally specific conversion factor to determine N rate by zone. If multiple years of grain yield data are not available, then crop reflectance can be substituted as a proxy for grain yield (Tucker et al., 1980; Raun et al., 2001). With the increasing accessibility of remotely sensed (satellite, aerial, low-altitude UAV, and ground-based) data, in-season biomass zones can be developed.

Many regions have been able to identify primary yield-driving soil factors such as texture, organic matter (OM), and depth to limiting layer. Khosla et al. (2002) and Koch et al. (2004) documented that N management using site-specific management zones that accounted for both soil variability and productivity led to N recommendations with increased yields and maximized N fertilizer use efficiency.


The N provided by, or in some cases removed by, the soil is dynamic and often weather dependent. Kindred et al. (2014) documented the amount of N supplied by the soil varied spatially by 107, 67, and 54 lb/ac across three studies. Much of the soil N concentration is controlled by OM. For every 1% OM in the top 6 inches of the soil profile, there is approximately 1,000 lb N/ac. Technologies including bare soil imagery and multiple ground-based machines are providing the opportunity to map OM on a field scale. Then OM can be used as a factor in reducing total N demand such as a factor the University of Nebraska uses in their standard fertilizer recommendation (Shapiro et al., 2008). The flow of N into and out of the OM system is continuous when the soil has enough moisture and is warm enough for microbial activity. The ability to predict the processes of immobilization (conversion of mineral nitrogen into organic nitrogen by micro-organisms) and mineralization (conversion of organic nitrogen into mineral nitrogen form) would significantly improve N management. A great deal of effort has been placed in the development of laboratory procedures and computer models. It has been quite challenging to accurately predict in situ mineralization, partially due to the significant impact weather has on the process.

Currently, models incorporating weather and soil information to determine in-season nitrogen losses are being calibrated and applied on the farm. The nitrogen cycle is a very leaky system with three primary loss pathways of nitrate leaching, ammonia volatilization, and denitrification. Weather and soil parameters determine the amount of loss through any given pathway. The ability to predict in situ losses in season provides the capability of accurately accounting for the environment’s impact on Nsoil.


Historically, the efficiency at which N fertilizer is utilized was integrated into N recommendations and not provided as an input option, e.g., the general conversion factor for corn of 1.2 lb N/bu. Nitrogen concentration in corn grain ranges from 1.23–1.46% with an average of 1.31% (Heckman et al., 2003) or 0.73 lb N/bu. Therefore, the 1.2-lb value is assuming a 60% fertilizer use efficiency. More recently, recommendations have been to incorporate application method or timing factors in attempt to account for efficiencies.

There are many soil parameters that may lead to changes in nutrient use efficiencies. Soil texture may be one of the most important (Liang and MacKenzie, 1994; Cambouris et al., 2016). When VRN methods involve soil type or soil texture, they may inherently account for changes in efert.

Integration of parameters

While the parameters of a nitrogen recommendation seem quite clear, Douglas Beegle and T. Scott Murrell provided great insight at a symposium entitled “Stanford’s Equation as a Framework for Making N Recommendations and for Improving N Recommendations” at the 2012 Annual Meeting of the American Society of Agronomy, Crop Science Society, and Soil Science Society of America. Figure 1 from their presentation provides a graphic representation of Stanford’s simple mass balance equation. The presenters then took it further to partition components of Nsoil (Fig. 2). The true complexity of a fully integrated N recommendation was outlined in the final step of a proposed framework for improved N recommendations. In Fig. 3, the framework of the components needed and the sources of the data are outlined in the final theoretical equation. However, this extremely complex and in-depth analysis of N recommendation did not partition Ncrop for efert, but instead left them as single variables and focused on Nsoil only. Many VRN approaches focus on one parameter even if the technique involves multiple inputs.

Fig. 1.
Fig. 1.

Graphic representation of Stanford’s equation for nitrogen recommendations. Figure adapted from a 2012 presentation by Douglas Beegle and T. Scott Murrell.

Fig. 2.
Fig. 2.

Expanded Stanford equation to include organic and inorganic forms and other sources such as manure and legumes. Figure adapted from a 2012 presentation by Douglas Beegle and T. Scott Murrell.

Fig. 3.
Fig. 3.

Proposed framework for improving nitrogen recommendations based upon the expanded Stanford equation.


An early attempt to integrate the components Ncrop and Nsoil was the use of ground-based remote sensing and N reference strips described by Lukina et al. (2001) and Raun et al. (2002). In this approach, canopy reflectance data (NDVI) is utilized to predict potential grain yield. An N-rich reference strip, an area of the field that received a rate of N higher than the rest of the field, is compared with the rest of the field. The difference between the two is described as a response index (RI). Johnson and Raun (2003) proposed that since response to N fertilizer strongly depends on supply of non-fertilizer N in a given year, any N management strategy that includes a reliable in-season predictor of RI should dramatically improve N use efficiency in cereal production. Thus, this approach of utilizing in-season crop reflectance actively incorporated both Ncrop and Nsoil.

The integrations of the three components in N is becoming more common as data acquisition is becoming common practice and access to powerful data processing systems much easier. Today, many groups offer VRN recommendations based upon yield data (either multiple years of yield data or multiple years of reflectance data), soil data (soil survey, soil sample, EC, EM, etc., data), historical weather data (to provide likelihood of future weather), and daily weather data (to provide real-time precipitation and temperatures) all incorporated into computer models that will predict crop growth patterns, OM mineralization, and probability of N losses and plant stresses. All of these are incorporated into N management strategies.


While a VRN strategy that works across all regions, landscapes, and cropping systems has yet to be developed, the process of nitrogen management has greatly improved and is evolving almost daily. Those methods that are capable of determining the three inputs of the Stanford equation while incorporating regional specificity will capture the greatest level of accuracy and precision. Ferguson et al. (2002) suggested that improved recommendation algorithms may often need to be combined with methods (such as remote sensing) to detect crop N status at early, critical growth stages followed by carefully timed, spatially adjusted supplemental fertilization to achieve optimum N use efficiency. As information and data are gathered and incorporated and data-processing systems improve in both capacity and speed, the likelihood of significantly increasing nitrogen use efficiency for the benefit of the society and industry improves. The goal of all practitioners is to improve upon the efficiencies and economics of the system, and this should be kept in mind as new techniques and methods are evaluated. This improvement can be as small as a few percentages.




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