John Shanahan

Anaerobic potentially mineralizable nitrogen (PMN) combined with preplant nitrate test (PPNT) or pre‐sidedress nitrate test (PSNT) may improve corn (Zea mays L.) 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−1 at‐planting rate and for PMN when 0 and 180 kg N ha−1 was 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 wi...

The anaerobic potentially mineralizable N (PMN) test combined with the preplant (PPNT) and presidedress (PSNT) nitrate tests may improve corn (Zea mays L.) 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−1 preplant N treatments. Increasing NO3–N sampling depths beyond 30 cm with or without PMN improved RY predictability marginally (R2 increase up to 0.20) and reduced over‐ and under‐application frequencies up to 14%. Including PMN (prepl...

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 PMNan as a tool in corn (Zea mays L.) 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 PMNan differently based on soil and weather conditions. Preplant vs. V5 PMNan tended 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 PMNan tended to be greater than preplant PMNan. The PMNan tended to be greater in unfertilized vs. fertilized soil in sites with clay content > 9.5%, total C < 24.2 g kg−1, soil...

Estimates of mineralizable N with the anaerobic potentially mineralizable N (PMNan) test could improve predictions of corn (Zea mays L.) 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−1 were applied at planting. Evaluating across all soils, PMNan was 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 PMNan and initial soil NH4–N as separate explanatory variables improved EONR predictability (11–20%) in fine‐textured soils only. Delaying PMNan sam...

This circular is a progress report of spring small grain trials grown throughout Nebraska, and proso and sunflower variety trials conducted by the Panhandle Research and Extension Center, Scottsbluff, and the High Plains Agricultural Laboratory, Sidney. Conduct of the ...

Improving corn (Zeamays L.) 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.

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data ar...

a significant relationship was found between soluble P loss in runoff and soil test P. Eghball and Gilley (2001) Soils with high levels of P can contribute to excess P in runoff and found that erosion was the most important factor in subsequently pollute the surface water. Excess P in the soil can be removed from the system by harvesting crops. The objectives of this loss of total and particulate P, whereas runoff amount, study were to evaluate corn (Zea mays L.) P removal effects on tillage, and P source were the important factors influencsoil P reduction, and to evaluate various corn hybrids and soybean ing loss of dissolved and bioavailable P in runoff. The [Glycine max (L.) Merr.] varieties for differences in grain P concentrasoil test P levels in this study did not seem to be high tion and P removal. Soil with varying P levels as a result of annual enough (ranged from 25 to 101 mg kg 1 in the top 5 cm or biennial beef cattle (Bos taurus) feedlot manure or compost applisoil) to ...

Summary • Nitrogen (N) is typically the most yield-limiting nutrient for corn production. How to meet N requirements without over-applying is a dilemma corn producers face each year. • Low N use efficiencies are frequently due to N losses from the system but are also due to an inability to accurately estimate the economically optimum nitrogen rate (EONR). • There are several methods of developing corn N recommendations, including the “yield goal” or “mass balance” approach and “maximum return to N” (MRTN) approach. • Growers can adjust N-rate recommendations by in-season testing of N sufficiency, using soil tests or crop sensors. • All N-management approaches have advantages and disadvantages. Growers should consider various strategies and choose those that maximize profit potential while minimizing risk of over-application and nitrogen loss. Nitrogen (N) is typically the most yield-limiting nutrient for corn production, and represents one of the largest economic inputs associated w...

Precision agriculture is the name given to an innovative approach for crop production that represents a unique blend of old thoughts and new tools. Established principles and processes support a treasure of knowledge and wisdom that provides a foundation for applying new technologies to proven concepts and practices. These new technologies include innovative computer software, a new generation of sensors, and ingenious control devices, as well as instrumentation and communication technologies from the defense industry. Had it not been for the development of global positioning systems (GPS) by the military and geographical information systems (GIS) by the mining and exploration companies, precision agriculture would not be happening. Incorporating the various monitors, communication devices, and computers into agricultural equipment has resulted in a variety of sophisticated implements intended to increase profitability and protect the environment through better management decisions. These new implements not only keep track of the geographical position in fields, but are also able to monitor what is happening and control production inputs and processes on-the-go. In essence, precision agriculture tools and devices are able to collect huge amounts of site-specific data that can readily be assimilated into useful information from which management decisions can be made. In the past, producers familiar with the land, crops, and climate subjectively integrated the various factors and made decisions as appropriate. The goal of precision agriculture is to systematically make intelligent site-specific management decisions based on objective information (data) gathered from various sources and collected at different times. The success of precision agriculture depends on being able to identify meaningful relationships between factors that are economically and environmentally important and parameters that can be easily measured with a reasonable degree of spatial resolution. The relative importance of these relationships is known to change with time (within growing seasons and between years) and the process of integrating the information into reliable and consistent decisions is complicated because the interactions between climate, soil, and the crop are complex.

... of Minnesota, Southern Research and Outreach Center, 35838 120th St., Waseca, MN 56093-4521. ... In an effort to account for spatial and temporal variation in nitrogen demand, optical sensing technology is a promising tool that may capable of allowing decision makers to ...