Category: Nutrient Management

Bianca Moebius-Clune, Maryn Carlson, Harold van Es, and Jeff Melkonian, Department of Crop and Soil Sciences, Cornell University

Adapt-N (http://adapt-n.cals.cornell.edu) is an on-line tool that uses a simulation model to incorporate location-specific, early season weather information, as well as soil and crop management inputs, to generate precise N sidedress recommendations for corn.

We conducted a total of 84 strip trials in 2011 and 2012 (Figure 1) in NY (56), Iowa (27) and Minnesota (1) to test how well Adapt-N predicts corn N needs at sidedress time. Yield data and estimated leaching losses from all 84 trials show that, when used correctly, the Adapt-N tool significantly increased grower profits and decreased environmental losses. Thus, Adapt-N provided an economic benefit to growers, while also minimizing N losses to the environment in almost all instances. With increasing interest in Adapt-N among growers, consultants, and agricultural service providers throughout the United States and beyond, Adapt-N use has the potential to reduce corn agriculture’s contribution to greenhouse gas emissions, groundwater pollution, and hypoxia in our estuaries, while substantially increasing grower profits.

This article summarizes the results of all 84 trials (Table 1, Figure 2) and describes specific trials that provide insights into how to most effectively use Adapt-N.

Methods
We completed 18 replicated strip trials in 2011, and 42 in 2012, on commercial and research farms throughout New York. We also conducted 9 strip trials in 2011, and 19 in 2012 on commercial farms throughout Iowa (1 trial in Minnesota is included with the Iowa trials in 2012). The trials involved grain and silage corn in fields with varying management history (i.e. organic amendments, crop rotation, tillage practices, etc.). Sidedress treatments involved at least two rates of nitrogen, a conventional “Grower-N” rate based on current grower practice and an “Adapt-N” recommended rate.  A simulation was run for each field just prior to sidedressing to determine the weather-adjusted Adapt-N rate.

Yields were measured by weigh wagon, yield monitor, or in a few cases by representative sampling (two 20 ft x 2 row sections per strip). Partial profit differences between the Adapt-N recommended and Grower-N management practices were estimated through a per-acre partial profit calculation. Yields were used as measured, regardless of statistical significance, since the statistical power to detect treatment effects is inherently low for two-treatment strip trials. For corn grain, a 2011 grain price of $5.50/bu and 2012 price of $6.00/bu were assumed. For silage, $50/T was used in both 2011 and 2012, based on reported NY silage prices. A nitrogen fertilizer price of $0.60/lb was used, based on reported NY and IA fertilizer prices.

Total N losses to the environment (atmosphere and water) and N leaching losses in 2011 and 2012 were estimated for each treatment through model simulations through October 30 for 2011 NY trials, and through December, 15 in 2012 trials.

More detailed descriptions of the 2011 and 2012 methods were provided in previous WCU articles (Moebius-Clune et al., 2012; Moebius-Clune et al., 2013).

Economic Comparison
Profit gains from the use of Adapt-N were considerable.  Profits increased in 80% of all NY trials, in 75% of all IA trials, and in 79% of all 84 trials when growers followed Adapt-N recommendations. Profit gains of $27/ac on average ($31/ac in NY, $20/ac in IA) were primarily attributed to fertilizer cost savings due to lower Adapt-N recommended rates without significant yield losses. Profit gains were also achieved in some instances where Adapt-N recommended higher N rates, and consequent yield increases were achieved (3 trials). Adapt-N rates resulted in average N input reductions of 66 lbs/ac in NY, 32 lbs/ac in IA, and 54 lbs/ac overall. Yield losses decreased by only 1 bu/ac on average in the 84 trials (a statistically insignificant yield loss), indicating that Adapt-N’s reduced N recommendations were generally justified.

Because of the potential impact of field variability on the results of a single trial, analysis of all 84 trials provides the most meaningful assessment of Adapt-N performance and likelihoods for improving grower profits. A look at specific trials can provide insight into effective use of the tool. Yield losses (not always statistically significant), and sometimes profit losses, occurred in several 2012 trials where the user’s ‘expected yield’ input in Adapt-N was an underestimate of the yield achieved with the higher N rate (7 trials in 2012). Adapt-N is a precise tool that already fully accounts for the risks of uncertainty and differential losses from over and under-fertilization.  If the yield potential of the field is higher than the ‘expected yield’ provided to the model, Adapt-N is more likely to recommend insufficient N to achieve a higher yield.  Therefore, a good estimate of expected yield is crucial to attaining accurate N recommendations. Analyzing 3 to 5 years of yield history to determine the expected yield input will maximize the accuracy of yield predictions and thus improve Adapt-N recommendations.

Adapt-N recommended a higher N rate than grower practice in 10% of trials, mostly due to wet spring conditions. In 3 of these 8 trials, the higher N rate resulted in a profit increase due to corresponding yield gains, thus justifying the higher N rate. In the 5 instances where a higher Adapt-N rate resulted in profit losses, unpredictable late-season drought conditions resulted in substantial yield reductions below the expected yield in both treatments. Due to insufficient water availability, the crop was unable to make use of the additional N applied in the Adapt-N treatment, thus the additional N fertilizer cost contributed to profit losses. While such individual situations are not preventable, because post-sidedress drought cannot be predicted by tools currently available, assessment of all trials shows that use of the Adapt-N rate provided increased profitability, while decreasing N inputs, in most cases.

In 2011, Adapt-N recommendations in corn-soybean rotations were low due to a deficiency in how Adapt-N implemented soybean N crediting. However, savings from N reductions in 80% of these trials were large enough to compensate for the respective yield reductions. This error was corrected, and no further profit losses occurred in 2012 trials where corn followed soybean (Moebius-Clune et al., 2013).

Large N input reductions achieved with the use of Adapt-N can often compensate for small yield losses with the lower N rate. For example in one of the 2012 Iowa trials, Adapt-N recommended 0 lbs N/ac as compared with the conventional N rate of 75 lbs N/ac. Despite a yield reduction (9 bu/ac), the Adapt-N rate did not decrease profit (+$1/ac), due to the large reduction in sidedress fertilizer and operational expense. This trial is one of many that demonstrate that growers currently applying high rates of N can realize significant profit gains by using Adapt-N even if yields are somewhat reduced.

Environmental Benefits
Adapt-N reduced N rates by 54 lbs N/ac on average, in 90% of trials, resulting in significant reductions in N losses to the environment. By the end of the growing season, simulated N leaching losses decreased by an average of 10 lbs N/ac, and total N losses decreased by an average of 34 lbs N/ac. In 2012, simulated total N losses and particularly leaching losses of sidedress-applied excess nitrogen remained relatively low by December due to widespread dry conditions during the growing season in NY and especially in IA. Further losses of residual excess N have occurred over the winter and spring months of 2011-2012 and 2012-2013. In silage trials, the pre-plant application of manure, and consequent lower inorganic fertilizer rates at sidedress time, limits the potential magnitude for reductions in N losses in comparison with non-manured fields, although Adapt-N can nevertheless significantly reduce fertilizer application in these systems.

Conclusions
Two consecutive growing seasons of on-farm strip trial testing have shown that Adapt-N is an effective tool for N management in corn systems, resulting in profit gains in 79% of trials, on average by $27/ac ($31/ac in NY and $20/ac in IA). When accounting for the now implemented correction of a soybean credit model deficiency, and underestimated yield potential inputs, we estimate that profit gains would have been achieved in 88% of trials to date. Other pointers for attaining the most accurate Adapt-N recommendations include:

• Estimate expected yield based on 3 to 5 years of accurate yield information.
• Use representative manure test results from actual manure inputs to reduce the margin of error associated with manure applications.
• Create field locations in Adapt-N by discrete management unit. Determine management units by several key factors: i.e. soil type, historical yield data, and organic matter content.
• Take management unit specific soil samples at least every 3 years to determine an accurate organic matter content value, ideally to a 12” depth.
• Run Adapt-N on the sidedress date if possible – use the daily alert feature for automatic updated recommendations on all fields.

In summary, Adapt-N strip trial results from 2011 and 2012 have shown that using Adapt-N to predict corn N needs at sidedress time provides economic advantages to growers as well as environmental benefits due to more precise management of N. Adapt-N thus provides a strong incentive to shift N applications to sidedress time, ultimately increasing grower profits and reducing N losses to the environment in both wet and dry years.

For more information: The Adapt-N tool and training materials are accessible through any device with internet access (desktop, laptop, smartphone, tablet) at http://adapt-n.cals.cornell.edu/. Information on account setup and the recorded 3/21/2013 in-depth training webinar are posted. Adapt-N users can elect to receive email and/or cell phone alerts providing daily updates on N recommendations and soil N and water status for each field location in Adapt-N.

Acknowledgements:  The development and testing of the Adapt-N tool was supported through funds from Cornell University, USDA-NIFA Special Grants on Computational Agriculture and the Agricultural Ecosystems Program (U.S. Rep. Maurice Hinchey-NY), Northern NY Agricultural Development Program, a USDA-NRCS Conservation Innovation Program, NY Farm Viability Institute, International Plant Nutrition Institute, and MGT Envirotec. We are grateful for the cooperation in field activities from Bob Schindelbeck, Keith Severson, Kevin Ganoe, Sandra Menasha, Joe Lawrence, and Anita Deming of Cornell Cooperative Extension, from Mike Davis at the Willsboro Research Farm, from Dave DeGolyer, Dave Shearing and Jason Post at the Western NY Crop Management Association, from Eric Bever and Mike Contessa at Champlain Valley Agronomics, from Mark Ochs and Ben Lott at Mark Ochs Consulting, and from Peg Cook at Cook’s Consulting in New York, from Kevin Kuehner of Minnesota Department of Agriculture, and from Shannon Gomes, Hal Tucker, Michael McNeill, and Frank Moore at MGT Envirotec in Iowa. We also are thankful for the cooperation of the many farmers who implemented these trials on their farms. In particular we would like to acknowledge Robert and Rodney Donald for implementing farm-wide trials on most of their fields (Moebius-Clune et al., 2013b).

References
Moebius-Clune, B., M. Carlson, D. Moebius-Clune, H. van Es, and J. Melkonian. 2013. Case Study – Part II: Donald & Sons Farm Implements Adapt-N Rates on Whole Farm, Saves Money and Environment. What’s Cropping Up? TBD.

Moebius-Clune, B., M. Carlson, H. van Es, and J. Melkonian. 2013. Adapt-N Increased Grower Profits and Decreased Nitrogen Inputs in 2012 Strip Trials. What’s Cropping Up? Preview.

Moebius-Clune, B., H. van Es, and J. Melkonian. 2012. Adapt-N Increased Grower Profits and Decreased Environmental N Losses in 2011 Strip Trials. What’s Cropping Up? No. 2, 22.

Bianca Moebius-Clune, Maryn Carlson, Harold van Es, and Jeff Melkonian, Department of Crop and Soil Sciences, Cornell University

Adapt-N is an on-line tool for precision nitrogen management in corn (grain, silage, sweet), which has been available to growers in the Northeast and five Midwestern states for the past two years.  It is a computational tool based on the concept that seasonal corn N needs can be estimated much more accurately in the late spring when factoring in weather, soil and management information.  The main question for growers and other stakeholders is whether the tool provides recommendations that increase profits and reduce environmental impacts.  We are answering those questions through on-farm strip trials.  The 2011 results were reported by Moebius-Clune et al. (What’s Cropping Up? Vol. 22, No. 2, 2012) and showed very encouraging results.  We are discussing the 2012 strip trial results in this article, and a summary article for all site-years is also included in this volume.

Adapt-N (http://adapt-n.cals.cornell.edu) uses a well-calibrated computer model, and combines user information on soil and crop management with high-resolution weather information, to provide N sidedress recommendations and other simulation results on nitrogen gains and losses. As a result of 2011 beta-testing, several improvements to Adapt-N were implemented for the 2012 growing season, including adjusted soil type, previous crop, manure and irrigation input options. Model routines for soybean N contributions were adjusted to avoid artificially low N recommendations that had occurred in 2011 and an uncertainty-adjusted price-ratio correction factor to optimize profits from N application was also incorporated into the Adapt-N tool. This factor takes into account several key realities that affect farmer profit: 1) the prices of fertilizer and grain, 2) the variable risks associated with over- and under-fertilizing, and 3) the reduced uncertainty in the optimum N rate with use of this precision tool. Using a fertilizer to grain price ratio of 0.1 ($0.60/lb N: $6 bu grain) Adapt-N subtracted 8 lb N/ac from the model-predicted Agronomic Optimum N Rate (AONR) to determine the Economic Optimun N Rate (EONR).

On-Farm Strip Trials. We completed 42 replicated strip trials on commercial and research farms throughout New York and 19 replicated strip trials in Iowa (1 trial in Minnesota, included with the “Iowa trials”) on commercial farms during the 2012 growing season. The trials involved grain and silage corn, with and without manure application, and different rotations (corn after corn, corn after soybean or other; Table 1). Sidedress treatments involved at least two rates of nitrogen, a conventional “Grower-N” rate based on current grower practice and an “Adapt-N” recommended rate, based on a simulation run just prior to sidedressing. In 2012 NY trials, all but three Adapt-N rates were lower than conventional N rates (by 20 to 138 lbs/ac; Table 1). In 2012 Iowa trials, all but two Adapt-N rates were lower than the conventional N rates (by 20 to 100 lbs/ac; Table 1). Growers in IA and NY implemented field-scale strips with 2-7 (usually 4) replications per treatment.

Yields were measured by weigh wagon, yield monitor, or in a few cases by representative sampling (two 20 ft x 2 row sections per strip). Partial profit differences between the Adapt-N recommended and Grower-N management practices were estimated through a per-acre partial profit calculation:

Profit = [Adapt-N yield – Grower-N yield] * crop price – [Adapt-N rate – Grower-N rate] * price of N + Sidedress operation savings/loss

Yields were used as measured, regardless of statistical significance, since the statistical power to detect treatment effects is inherently low for two-treatment strip trials, but averaging across large numbers of trials provides good statistical power for assessing Adapt-N performance. For corn, a 2012 grain price of $6.00/bu was assumed ($7.00/bu minus $1.00/bu for drying, storing and trucking from PA Custom Rates; USDA, 2012). For silage, $50/T was used based on reported NY silage prices of $25-75/T. A nitrogen fertilizer price of $0.60/lb N was used (reported prices ranged from $0.42 – $0.80/lb N in NY and IA). When Adapt-N recommended no need for sidedressing and the Grower-N rate was greater than 0 lbs/ac, sidedress operational savings of $8/ac were added to the profit. If Adapt-N recommended an application of N and the Grower-N rate was 0 lbs/ac N, a loss of $8/ac was subtracted from the profit. Agronomic and economic outcomes of these trials were used to assess Adapt-N performance.

Results
Agronomic and economic comparisons between Grower-N and Adapt-N treatments for each trial are provided for NY and IA trials in Table 1 and Figure 1, and as averages in Table 2.

NY corn after corn trials (Figure 1 a-c). Adapt-N rates in all of the NY grain after grain trials resulted in N input reductions of 82 lb N/ac on average. In all but one trial, no significant yield loss was measured with these reduced N rates. Adapt-N rates provided a profit advantage over conventional N rates in almost all trials (by $1.62 to $88.20/acre). In 4 of the 5 instances where Adapt-N rates resulted in profit losses, actual yield achieved with the higher N rate exceeded the expected yield used in the Adapt-N simulation (by 9 bu/ac to 35 bu/ac) to estimate the sidedress rate. In each of these four cases, a more appropriate expected yield would have been available from existing yield records, and would have likely resulted in a sufficient recommendation from Adapt-N.

NY corn after soybean or other crops (Figure 1 d-f). Adapt-N rates in all NY corn grain after soybean trials (or after other crops such as wheat, oats, silage) consistently resulted in profit increases (of $7.64 to $105.30/acre). These results demonstrate that the soybean N crediting method used for 2012 successfully corrected the 2011 error. Harvest data show that, despite large N input reductions in Adapt-N treatments (average 56 lbs/ac), reductions in yields were negligible in all trials and only statistically significant in one case (Trial 34). In the only trial where Adapt-N recommended a higher rate (by 23.5 lbs/ac, Trial 22), yield increased by 6 bu/ac and a profit was realized despite higher fertilizer cost.

NY silage (Figure 1 g-i).  Adapt-N rates in 4 of 6 NY silage trials resulted in N input reductions (22.5 – 50 lbs/ac). No statistically significant yield loss with these reduced N rates was found (average 0 T/ac difference when N rates were decreased). Adapt-N rates provided a measured profit advantage over conventional N rates in two of these trials (by $48 and $58/acre) when N rates were reduced by 50 lbs/ac. Profit losses in the other four cases were due to field variability, underestimated yield potential, small N rate differences, and/or drought. Two of these trials (17 and 18) registered a profit loss due to small yield losses because yields were higher than the ‘expected yield’ entered into Adapt-N, in addition to artificially low Grower-N rates as the grower was already reducing N rates to near Adapt-N rates (55-75 lb/ac below standard recommendations that use current yield potentials). In comparison to standard recommendations, these trials would constitute profit gains with Adapt-N use. In two trials, Adapt-N rates were higher by 10-11 lbs/ac, justified by the expected yield input of 22 and 24 T/ac respectively (Trials 14 and 15). Due to drought, measured yields were well below expected yields (by 5.3 and 11.8 tons), and due to field variability, measured yields were lower in plots with the higher Adapt-N rate, resulting in a calculated profit loss.

Silage trials were less numerous, and exhibited greater yield variability than non-manured grain trials, making it more difficult to assess Adapt-N performance. Factors contributing to such variability are low precision in manure testing and application (in comparison to synthetic fertilizer), unevenness of spreading, and the effects of drought in several trial locations. An overall assessment of the currently available data in silage trials for 2011 and 2012 (Moebius-Clune et al. 2013a) suggests that the model is handling these well, and that profit gains are achieved particularly with large N use reductions at sidedress, but further testing is desirable.

IA corn grain trials (Figure 1 j-l). The majority of 2012 Iowa trial results were impacted by abnormally droughty conditions. Still, Adapt-N rates in all but two of the 19 IA trials resulted in N input reductions (by 36 lb/ac on average). Except for one trial, no significant yield loss was measured with these reduced N rates. Adapt-N rates provided a profit advantage over conventional N rates in 74% of the trials (by $1.49 to $81.20/acre). In one of the three trials where N reductions resulted in profit losses, actual yield achieved with the higher N rate exceeded expected yield used in Adapt-N by 9 bu/ac (Trial 62), resulting in a profit loss despite N savings. In the 2 trials (65 and 73) where Adapt-N recommended a higher rate than the conventional N rate (by 30 lb/ac and 40 lb/ac respectively; the latter was accidentally implemented as 70 lb/ac), the expected yield was not attained due to mid-season drought, resulting in profit losses from unnecessary N application.

Conclusions
Our 2012 Adapt-N trials affirm our 2011 conclusions: The value of the Adapt-N tool is substantial, resulting in significant N input and loss reductions and in profit savings in 77% of all trials (Table 2, Figure 2). Of the 2012 recommendation errors, half (7 trials) were preventable with better expected yield inputs, and only 5% were unexplained. Recommendation errors in 2012 resulting in profit losses mostly occurred in instances where expected yield either exceeded or underestimated actual yield, thereby demonstrating the importance of a good estimate of the expected yield in generating accurate N recommendations using Adapt-N. Drought conditions during the growing season resulted in abnormally low yields in several trials. Obviously, Adapt-N or other N recommendation methods are unable to account for abnormal weather events that occur after the window for sidedressing has passed. The tool was, however, successful in adjusting for the significant effects of early season conditions to recommend N fertilizer needs more precisely.

Over the 2012 growing seasons, 61 trials indicate that:

• Grower profits increased on average by $32/ac in NY, and by $17/acre in IA trials with the improved 2012 model version.
• N application rates were significantly reduced in almost all cases, by 54 lbs N/ac on average, and thus post-growing season losses of excess N to the environment were decreased substantially.
• Yield losses were generally negligible (-1 bu/ac average across all trials), despite the reduced N inputs
• Higher N recommendations were justified by higher yields when drought was not the greater limiting factor.
• 77% of Adapt-N recommendations provided increased grower profits over current rates in 2012, when including inadequate expected yield inputs, 87% when these are excluded.
• Model inputs, especially yield expectation, must be carefully chosen to represent field-specific conditions.

In all, growers can realize large savings with the use of Adapt-N, which also provides strong incentives to shift the bulk of N applications to sidedress time, and will in the long term decrease environmental losses.

For more information: The Adapt-N tool and training materials are accessible through any device with internet access (desktop, laptop, smartphone, tablet) at http://adapt-n.cals.cornell.edu/. Information on account setup and the recorded 3/21/2013 in-depth training webinar are posted there. Adapt-N users can elect to receive email and/or cell phone alerts providing daily updates on N recommendations and soil N and water status for each field location in Adapt-N.

Acknowledgements:  The development and testing of the Adapt-N tool was supported through funds from Cornell University, USDA-NIFA Special Grants on Computational Agriculture and the Agricultural Ecosystems Program (U.S. Rep. Maurice Hinchey-NY), Northern NY Agricultural Development Program, a USDA-NRCS Conservation Innovation Program, NY Farm Viability Institute, International Plant Nutrition Institute, and MGT Envirotec. We are grateful for the cooperation in field activities from Bob Schindelbeck, Keith Severson, Kevin Ganoe, Sandra Menasha, Joe Lawrence, and Anita Deming of Cornell Cooperative Extension, from Mike Davis at the Willsboro Research Farm, from Dave DeGolyer, Dave Shearing and Jason Post at the Western NY Crop Management Association, from Eric Bever and Mike Contessa at Champlain Valley Agronomics, from Mark Ochs and Ben Lott at Mark Ochs Consulting, and from Peg Cook at Cook’s Consulting in New York, from Kevin Kuehner of Minnesota Department of Agriculture, and from Shannon Gomes, Hal Tucker, Michael McNeill, and Frank Moore at MGT Envirotec in Iowa. We also are thankful for the cooperation of the many farmers who implemented these trials on their farms. In particular we would like to acknowledge Robert and Rodney Donald for implementing farm-wide trials on most of their fields (Moebius-Clune et al., 2013b).

References
Moebius-Clune, B., M. Carlson, H. van Es, and J. Melkonian. 2013a. Adapt-N Proves Economic and Environmental Benefits in Two Years of Strip-Trial Testing in New York and Iowa. What’s Cropping Up? Preview.

Moebius-Clune, B., M. Carlson, D. Moebius-Clune, H. van Es, and J. Melkonian. 2013b. Case Study – Part II: Donald & Sons Farm Implements Adapt-N Rates on Whole Farm, Saves Money and Environment. What’s Cropping Up? TBD.

Moebius-Clune, B., H. van Es, and J. Melkonian. 2012. Adapt-N Increased Grower Profits and Decreased Environmental N Losses in 2011 Strip Trials. What’s Cropping Up? 22. [URL verified 4/27/13].

Julia Knight¹, Quirine Ketterings¹, Karl Czymmek^1,2, and Rich Wildman^3
1Nutrient Management Spear Program, Department of Animal Science, Cornell University, ²PRODAIRY, Dept. of Animal Science, Cornell University, and ³Agricultural Consulting Service Inc.

Introduction
Phosphorus enrichment of surface waters leading to algal blooms and other issues related to eutrophication continues to be an issue in a number of locations.  Runoff from agricultural fields can contribute to P runoff and management tools and policies have been developed to manage runoff risk.   In 1999, New York (NY) introduced its first Concentrated Animal Feeding Operation (CAFO) Permit. This was followed by release of the NY Phosphorus Index (NY-P Index; USEPA, 1999; Czymmek et al., 2003) and establishment of a statewide on-farm research partnership in 2001. State policy requires implementation of the Natural Resources Conservation Service (NRCS)-NY 590 nutrient management standard on all farms with a CAFO Permit as well as animal feeding operations (AFOs) receiving state or federal cost share funds for manure storage and other related practices. Since 2001, the NY-P Index has been a required element of the NY 590 nutrient management standard.

In May of 2009 President Obama signed an Executive Order to intensify efforts to protect and restore the Chesapeake Bay and its watershed. This Order resulted from the belief that there had not been sufficient progress in restoring the health of the Bay and its watershed in the past 25 years. As a result of the Order, USEPA published Guidance for Federal Land Management in the Chesapeake Bay Watershed (“Guidance document”) on May 12, 2010. This document states that managing P through state-based P runoff indices is flawed and results in over-application of P to cropland. In the Guidance document, USEPA replaced the P index approach with a Psat approach based on a 20% Psat cutoff for manure or fertilizer application (USEPA, 2010) (Figure 1).  While only applicable on federal lands at this point, it is viewed by some as a potential precursor to more widespread implementation on private lands.

During the review period for the Guidance document, USEPA received input from numerous organizations, including academic members of SERA-17. This group consists of research scientists, policy makers, extension personnel, and educators with the mission to develop and promote innovative solutions to minimize P losses from agriculture by supporting: (1) information exchange between research, extension, and regulatory communities; (2) recommendations for P management and research; and (3) initiatives that address P loss in agriculture (http://www.sera17.ext.vt.edu/). The SERA-17 scientists questioned the validity of the use of a Psat based cutoff for land application of manure and/or fertilizer, raised concerns that the Psat approach does not consider landscape position (a critical component of P loss), and pointed out that various Psat methodologies provide significantly different results. Despite these comments, USEPA published the 20% Psat cutoff in the Guidance document (http://www.epa.gov/owow_keep/NPS/chesbay502/pdf/chesbay_responsetocomments.pdf):

 “EPA recognizes that Psat is an important feature that could improve the usability of the P index in long term nutrient management planning, particularly where P leaching is the primary environmental concern. EPA does not recommend any one methodology for determining Psat. We understand that the methods used to determine Psat are depended upon the chemical features of the extracts and do not provide conversion factors between the methods mentioned. EPA understands that the method of P analysis should always be clearly described in any presentation of Psat or soil test P. Also, while Psat and soil P are correlated, by determining the P application based on P-Sat, EPA’s recommendation will still allow application beyond realistic yield goals in areas where Psat is lower than 20 percent; soil P is a more conservative estimate for P applications.”

The implementation of the Psat cutoff for P application to federal land, and the potential for implementation of a similar cutoff for all agricultural land, motivated a project to compare the impact of use of a Psat approach on P fertilizer and manure application cutoffs as compared to our current NY P index approach. Specifically, our goal was to evaluate if a Mehlich-3  derived Psat (P/[Fe+Al]) could be converted to a particular Cornell Morgan P and if so, determine the potential 20% Psat cutoff for manure application.

What Did We Do?
In total, 91 soil samples were tested for Cornell Morgan (Morgan, 1941) and Mehlich-3 (Mehlich, 1984) extractable P, Fe, Al, and Ca. The Psat was determined as P/[Fe+Al]*100 (molar ratios) according to Kleinman et al. (2002). As mentioned, there are different methods for estimating Psat. The ratio of Mehlich-3 extractable P over Fe+Al was selected as a most likely candidate for implementation, because it is a commonly available agronomic test, despite evidence that this method (1) is unsuitable for calcareous soils found in parts of NYS, and (2) requires soil specific calibrations. Samples were collected from New York farms identified in conjunction with Agricultural Consulting Services, Inc. (ACS). Samples were air-dried and ground to pass a 2 mm sieve prior to laboratory analysis. Regression analyses were performed to determine if Morgan data could be correlated to Psat and if so, at what Cornell Morgan soil test level a Mehlich-3 derived P saturation of 20% was obtained.

What Did We Find?
Across all soil samples, a P saturation of 20% corresponded to a Cornell Morgan P of 86 lbs/acre (Figure 2). This Cornell Morgan value was somewhat higher than the 56 lbs P/acre (Cornell Morgan test) reported for 59 soil samples from the Delaware River Watershed in 1999 (Kleinman et al., 1999; assuming that Psat based on Mehlich-3 equals 0.7 times Psat derived from the oxalate extraction according to Kleinman and Sharpley, 2002), and similar to the 80 lbs P/acre (Maine Modified Morgan test) for 106 soil samples submitted to the Maine Soil Testing Service (Ohno et al., 2007). The New York data also show a wide range in soil test P equivalents; for example, of the 7 soils with a Psat of 20%, corresponding Cornell Morgan P levels ranged from 56 to 172 lbs P/acre with a median value of 71 lbs P/acre. Similarly, soils with a Cornell Morgan P of 75-85 lbs/acre corresponded to a Psat ranging anywhere from 16 to 38%.

Implications
The implementation of a Psat cutoff of 20% for manure application instead of the NY-P Index will not impact manure application to high risk fields with a Cornell Morgan soil test of 80 lbs/acre or more, as the current P Index will not allow manure application to those fields as the NY-PI score will be 100 or more if the the transport factor is 1.0. Given that a very low percentage of NY fields test greater than 80 lbs P/acre (about 5%), implementation of a Psat in NY will have minimal effect on manure application practices. However, it could adversely impact farms with fields with very high soil test P but low transport risk. Such Psat based policy purports to address manure disposal (i.e. application beyond what would be most optimal for P resource management) but will increase the use of purchased fertilizer as it does not account for fertilizer value of N and K in the manure. Further, we do not believe implementing the Psat cutoff in NY offers real environmental benefit because as a chemical test alone, it fails to account for key, field specific risk considerations of landscape position and relationship of the field to surface waters.

Conclusions
Implementation of a Psat approach will cause restrictions on P application for very high P fields with a low NY-PI transport risk. On average, across all soils in the study, a Psat of 20% corresponded to a Morgan soil test P level of 86 lbs/acre, just above the current cutoff for P application for fields with a high transport risk. This means that implementation of a Psat approach would eliminate manure and fertilizer application to fields with a Cornell Morgan P of 86 lbs/acre, independent of the risk of transport of this soil test P to surface or groundwater. We do not recommend the application of the Psat approach in NY as it will increase costs for some farms while unlikely to offering corresponding environmental benefit.

References

• Czymmek, K.J., Q.M. Ketterings, L.D. Geohring, and G.L. Albrecht. 2003. The New York Phosphorus Index User’s Guide and Documentation. CSS Extension Bulletin E03-13. 64 pp. Available: http://nmsp.cals.cornell.edu/publications/extension/PI_User_Manual.pdf [29 January 2012].
• United States Department of Agriculture and Environmental Protection Agency (USDA-EPA). 1999. Unified National Strategy for Animal Feeding Operations. Washington DC. Available: http://www.epa.gov/npdes/pubs/finafost.pdf [23 April 2012].
• United States Department of Agriculture and Environmental Protection Agency (USDA-EPA). 2010. Guidance for Federal Land Management in the Chesapeake Bay Watershed. Chapter 2. Agriculture. EPA841-R-10-002. Washington DC. Available: http://www.epa.gov/owow_keep/NPS/chesbay502/pdf/chesbay_chap02.pdf [23 April 2012].
• Ohno, T., B. R. Hoskins, and M.S. Erich. 2007. Soil organic matter effects on plant available and water soluble phosphorus. Biology and fertility of soils 43: 683-690.
• Kleinman, P.J.A., R.B. Bryant, and W.S. Reid. 1999. Development of pedotransfer functions to quantify phosphorus saturation of agricultural soils. Journal of Environmental Quality 28: 2026-2030.
• Kleinman, P.J.A., and A.N. Sharpley. 2002. Estimating soil phosphorus sorption saturation from Mehlich-3 data. Communications in Soil Science and Plant Analysis 33: 1825-1839.

Acknowledgments
This work was supported by the Cornell University Agricultural Experiment Station (CUAES) and in-kind contributions by Agricultural Consulting Service Inc. For questions about these results contact Quirine M. Ketterings at 607-255-3061 or qmk2@cornell.edu, and/or visit the Cornell Nutrient Management Spear Program website at: http://nmsp.cals.cornell.edu/.

Quirine Ketterings¹ and Karl Czymmek^1,2

¹Nutrient Management Spear Program, ²PRODAIRY, Department of Animal Science, Cornell University

Introduction
The New York Phosphorus Index (NY-PI) was introduced in 2001. Since then, phosphorus (P) fertilizer sales (farm use) declined from 36,506 tons of P₂O₅ in 2001 (19.5 lbs P₂O₅/acre) to 18,610 tons P₂O₅ in 2009 (10.2 lbs P₂O₅/acre). In 2011, we surveyed Certified Nutrient Management Plan (CMNP) developers certified through the New York State Agricultural Environmental Management (AEM) program to evaluate their perceptions of the drivers for this change in P use. All 24 planners responded to the survey allowing us to document: (1) farms and acres covered by CNMPs and changes in management practices and soil test levels; and (2) planner perceptions of the drivers of these changes since the introduction of the NY-PI in 2001. The survey contained questions related to (1) farms and acres for which CNMPs were developed in 2010; (2) time and effort needed to do a NY-PI assessment for a field; (3) impact of NY-PI field assessment on changes in manure and/or fertilizer practices; and (4) changes in soil test P levels after 2001 when the NY-PI was introduced. In addition, planners were asked what they would tell policy makers about why farmers made changes and what policies and programs are needed to continue progress. The 24 CNMP planners consisted of 18 from the private sector, 5 from Soil and Water Conservation Districts (SWCD) and one from Cornell Cooperative Extension based in the New York City Watershed. One of the SWCD planners works with a private sector planner and their joint response is included in the private sector planner category.

Results and Discussion
Farm Sizes
The private sector planners were responsible for CNMPs covering 88% of all CNMP cropland and 76% of all farms with a CNMP (Table 1). Although private sector planners also planned most of the new plans in 2010 (74% of all acres, 62% of all farms), 22% of all acres newly planned in 2010 were farms in the NYC Watershed. The SWCDs planned less than 10% of all farmland and farms.

The private sector and the SWCD planners worked primarily with CAFO-farms (200 cows or more) with average farm size exceeding 800 acres/farm. The planner from the NYC Watershed worked primarily with smaller operations (<200 acres/farm and 50-80 cows per farm) (Table 2).

About 1/3^rdof all the farms that CNMPs were developed for in 2010 did not meet the minimum size requirements to be qualified as a medium or large CAFO but were in state or federal programs that required a CNMP. Most of the farms in the NYC Watershed are included in this category. For both private sector planners and SWCD planners, new plans developed in 2010 tended to be for smaller farms (Table 2), consistent with the 100% compliance for CAFO farms in NY and expansion of CNMP planning to smaller farms involved in federal or state programs.

Time Required for NY-PI
The time needed to complete an NY-PI assessment for a field varied from 10 to 90 min, mostly dependent on whether the assessment was for a new field (and included determination of dominant slope and flow distance to streams), or if the assessment was an update from a previous year. Averaged across all planner responses, 40 min per field was needed, although 50% of all planners indicated assessments could be done within 30 min. About 40% estimated they needed 30-60 min per field, while 10% said more than 1 hour per field was needed. These differences might reflect differences in field topography (complex slopes, multiple flow paths etc.).

Fields Impacted by NY-PI
The planners estimated that management of 17% of acres under nutrient management planning was altered because of an initially very high or high NY-PI score. As a result of NY-PI implementation, manure was reallocated to fields that would otherwise not have received manure (as indicated by 77% of the planners). The most frequent changes made in manure management were changes in timing and rate (86% of the planners ranked timing and rate as the top two changes made). Changes in method of application were less common (ranked in the top two by 13% of the planners only). According to 65% of the planners, the introduction of the NY-PI resulted in an increase in both acres per farm and amount of exported manure. Forty three percent of the planners indicated that NY-PI based planning decreased the average soil test P levels over time and 48% said the percentage of fields classified as very high in soil test P decreased. The introduction of the NY-PI did not change cow numbers per farm or poultry litter use over time, according to 57% and 78% the planners, respectively.

Soil Test P Trends
Only 5% of the fields represented in the assessment tested above 80 lbs/acre Morgan extractable P, the level at which the NY-PI exceeds 100 if the transport risk from the field is high, and slightly less than ten times the agronomic critical level for most crops. Of the total cropland area, 4% could not receive manure under NY regulations because the NY-PI already exceeded 100 without the manure application.

Perceptions of Drivers
The two most important drivers for the changes in fertilizer use observed by NY planners were the price of fertilizer and the on-farm research partnership that showed that no additional starter P was needed if the soil test was classified as high or very high in P (Figure 1).

The reply related to fertilizer sales is most likely reflecting recent memory of the peak in fertilizer prices in 2008, as actual fertilizer sales decreased over time, prior to the 2008 price spike. Other reasons included greater use of soil testing for fertilizer use decisions, the expansion of manure application options in the state, awareness of the link between animal numbers and acres needed to apply the manure generated by the animals, improvements in herd nutrition, and the onset of a regulatory environment. One planner pointed out the importance of involving stakeholders when addressing environmental concerns:

“The history of collaboration and trust between the public, academic, and private sector stakeholders in New York State has led to a track record of efficient problem solving. Involve stakeholders in the process and hold them accountable to create real solution.”

 Policy Message
Some planners pointed to improvements made in NY, the farms’ investment in protection of the environment, and the role of the NY-PI in achieving such improvements. Others pointed to the need for partnership, science-based guidelines, and funding for applied research and planner and farmer training:

“The bottom line for the success that we have seen in NYS is because of the “systems” approach taken by the state and not just focusing on one problem area. Not only was phosphorus looked at, but, nitrogen and now potassium research is ongoing. On-farm research is one of the major “keys” to have “real data” from a true farm field setting with actual weather and field conditions with specified goals being measured. This approach has proven to be successful within all farming regions of NYS.”

“Another “key” to the success in NYS is that ALL agencies have collectively worked together in providing funding for research, data collection and analysis, training and educational programs for certified CNMP planners and farmers along with assistance for implementation of all needed conservation practices. Funding at the state and federal levels is the life blood for continued success that NYS has experienced thus far.”

The role of qualified professionals was stressed by several of the planners:

“The support of a skilled and knowledgeable planner using good information and effective tools applying the right strategies in the right places at the right times has been critical in helping NY farmers achieve reduced environmental impact. The P-Index applied by trained Nutrient Management Planners helps farms implement practices that are both environmentally effective and economically feasible.”

Also pointed out were the needs for farm-specific solutions and flexibility to address the challenges in nutrient management:

“Farmers need to know why changes are required, but they need flexibility to manage with day to day changes.  Economics will continue to be major driver.”

In addition, the need for research and improvement of tools for management in general was pointed out in the planner responses:

“We need to continue to use science based technology such as the P index, N index, etc. rather than using broad restrictions to nutrient management planning (i.e. no winter spreading)”

“Keep supporting our farms with research and training programs.”

Planners referred to benefits of the collaborative approach to P management among dairy farms in NY for other industries, and/or called for action by other sectors of agriculture:

“The system is working!! Good research coupled with effective communication and on-farm planning has brought incredible benefits to New York agriculture. It goes beyond livestock agriculture. I know of several successful landscape businesses that never apply any P fertilizer to lawns anymore.”

Enforcement of regulations was identified as a key component as well in achieving improvements at the farm level:

“I think however that the biggest driver of changes in terms of nutrient management, amount of manure applied and reduction of overall P applications is due to the fact that the DEC is enforcing the CNMP. We have been doing CNMPs in NY since […], most (and maybe all) of our medium and large CAFO clients have been inspected several times, and as the competence of the inspectors increase, and the inspections became more thorough, the attitude of the farmers was to look for the recommendations and to make sure that they actually applied what was there. […]”

Others indicated the need for continued support for planners, training, and on-farm research:

“With the success that has been obtained in NYS, I would recommend continuing the current programs that are in place with more funding devoted to enhancing our farm producers viability, farmers know their farms and fields better than anyone else, including government officials, but they continually need assistance with improving production and lessening environmental risk through state and federal programs so new technology and implementation practices or best management practices can be adopted in a timely and financially stable manner.”

Conclusions
The key ingredients for success identified by the CNMP planners were: (1) statewide awareness of environmental issues driven by both regulations and extension programming/training; (2) development and implementation of science-based and practical tools (like the NY-PI) that allow for farm-specific solutions to the challenges; (3) demonstrated need for or benefits of alternative management practices (i.e. an on-farm research partnership that addresses relevant questions and on-farm research that results in credible answers); (4) accountability; (5) state enforcement of regulations; and (6) the presence of economically feasible solutions. The success story of NY reflects a recognition of the need for change by both farmers and farm advisors, an interest in exploring management alternatives while looking for win-win approaches (i.g. reduced fertilizer use, re-evaluation of dairy rations, etc.), and a willingness by farmers and farm advisors to contribute to on-farm research that generated reliable data and believable results (with as the foundation a trust-based farmer-advisor-researcher relationship). We conclude that the NY-PI contributed to the successful reduction in P use in NY by being acceptable to farmers and farm advisors as a risk assessment tool, by being directionally correct (it made sense) and by allowing farms to design farm-specific solutions. The story of NY shows that change can be obtained via policy, incentives, measuring and monitoring.

Acknowledgments
For questions about these results contact Quirine Ketterings at 607-255-3061 or qmk2@cornell.edu, and/or visit the Cornell NMSP website at: http://nmsp.cals.cornell.edu/.

 

Bianca Moebius-Clune, Harold van Es, and Jeff Melkonian, Department of Crop and Soil Sciences, Cornell University

For over fifteen years, the corn stalk nitrate test (CSNT; Binford et al., 1992; Blackmer & Mallarino, 1996) has been promoted as a tool to determine whether a corn crop received deficient, adequate, or excessive nitrogen (N) amounts during a growing season. In recent years, the test has been strongly promoted as part of the adaptive N management approach, and its adoption has increased geographically beyond Iowa where it was initially developed. Little attention has been given to whether the test is sufficiently precise for field-level N management.

The Test
The basic concept of the CSNT is that the nitrate-N concentration of the lower corn stalk at the end of the season is indicative of whether sufficient N was applied to the corn crop, as plants suffering from N deficiency remove more N from the lower stalk than those with adequate or excess N supply. Universities and grower associations generally suggest the following interpretations of the test:

• Low (less than 250 ppm nitrate-N, in some states 450 or 750):  high probability that the crop was N deficient.
• Optimal (generally between 250 and 2000 ppm nitrate-N, in some states also including a “marginal” range when below 750): high probability that yields were not limited by N, and no apparent excess.
• Excessive (>2000 ppm nitrate-N): high probability that N uptake exceeded plant requirement and that N was applied at excessive rates.

The CSNT is promoted as a tool that provides a post–mortem evaluation, but concerns have emerged about its utility to growers. All reported data on the CSNT in journal articles and fact sheets show that yield adequacy is often observed with CSNT values in the “low” range, which raises doubts about whether the test is a powerful indicator of N deficiency. Indeed, a recent Iowa report based on a large data set of N rate trials (Sawyer, 2010) indicated that 15% of CSNT values in the “low” range were false positives, while of cases with field-verified N deficits, 30% of CSNT results were false negatives. A recent Maryland study involving 10 experiments (Forrestal et al., 2012) found about a third of “low” CSNT values to be false positives for deficiencies.

Arguably, the primary value of the CSNT is related to determining excessive N rates, because N deficiencies can also be determined from leaf yellowing during the growing season. Recent New York research reports suggest that fields with high excessive N applications may still show low or optimum CSNT values (What’s Cropping Up?, Vol.21 No.3) and that site differences affect CSNT values more than excess or deficient fertilizer rates (Katsvairo et al., 2003). The above-mentioned Iowa report (Sawyer, 2010) indicated that 33% of cases with field-verified excess N applications were not identified through the test, i.e., one third were false negatives for excess N. Moreover, the Maryland study (Forrestal et al., 2012) found as much as half the CSNT results to be false negatives for excess N. These results challenge the notion that the CSNT is an effective tool for adaptive nitrogen management in corn production.

Methods
As part of an Adapt-N beta testing effort (http://adapt-n.cals.cornell.edu/), we conducted 35 replicated strip trials on commercial and research farms throughout New York (17 trials) and Iowa (18 trials) in 2011. They involved two rates of N (a conventional “Grower” rate and an “Adapt-N” rate), which resulted in field-scale strips with N rate differences ranging from 15 to 140 lbs/ac. Trials had 3 to 8 replications for each treatment (except for 2 of the trials, NY8 and NY9, with only single strip yield measures, but replicated CSNT values). Trials were distributed across both states under a wide range of weather conditions, and involved grain and silage corn, with and without manure application, and rotations of corn after corn, corn after soybean, and corn after a clover cover crop (Table 1). New York yield results were reported in a recent What’s Cropping Up? article (Vol.22 No.2).

To allow for comparison across all trials, silage yield values were converted to grain equivalents (8.14 bu grain per ton silage, calculated by using a harvest index of 0.55). The yield results from a majority of the trials showed unambiguous over-fertilization associated with the higher N rate (same yields for both rates). In these cases, the amount of “effective excess N applied” was set to the N rate difference between treatments (Table 1). In some cases the low rate provided insufficient N (reduced yields), and the optimum N level appeared to be between the high and low rates. In these cases, the amount of excess N applied was estimated by subtracting a conservative 1.25 lb N from the N rate difference between the treatments per bushel of yield lost due to the lower rate.

Fifteen corn stalk sections, sampled from each replicate strip, were dried, ground, and analyzed for nitrate content, according to published protocols. Means for each treatment are presented in Table 1. The utility of the CSNT was then assessed by evaluating the relationship between N rates, test values, and yield losses, and determining whether it accurately diagnosed field-demonstrated deficient or excessive N levels.

Results
Figure 1 shows the relationship between yield loss and CSNT results (the critical 250 and 2000 ppm levels are indicated on the graph). This pattern is similar to those in the original publications, but our data also indicate that:

• While yield losses are strongly associated with low CSNT values, the reverse does not hold: Low stalk nitrate levels do not necessarily imply yield losses.
• Adequate N rates (no yield losses) can result in a wide range of CSNT values.  i.e. the power of the test to detect adequate or excess N rates is limited because low CSNT values may be observed when yield losses did not occur.
• Conversely, high CSNT values correctly imply a high probability of excess N rates.

  

In most trials, but not all, CSNT values for the upper N rates were higher than for the lower ones, indicating that the test shows some sensitivity to N levels (Table 1). However, in only 8 out of 35 trials (6 of them from Iowa) did the CSNT for the upper rate fall into a higher category than the CSNT for the lower rate.

N Deficient Cases:  An evaluation of the power of the CSNT to detect N deficiencies is presented in Table 1 and Figure 2. Of all CSNT values in the “low” range (25 instances), 60% were measured when N rates were in fact known to be adequate or even excessive (i.e., more than half were false positives of deficiency; Fig 2a). For only six of these trials, yield reductions were statistically significant and the CSNT correctly supported the results (highlighted in green in Table 1). Of the 11 trials where significant yield losses were measured with the lower N rate (and deficiencies occurred), the CSNT identified six (54%) correctly in the “low” range (Fig 2b), while CSNT results for the remaining 46% of trials were false negatives for deficiency.

N Excess Cases:  Instances with excessive CSNT values (>2000 ppm) were in fact known to have excess N or there was no evidence to the contrary (Table 1, Fig 2c). Therefore, the test was 100% accurate when showing excessive CSNT values, similar to Sawyer’s (2010) results. However, the opposite was not the case. We found that in only 11 of 35 cases (31%, 24% of those in NY) where unambiguous surplus N applications occurred, the CSNT correctly identified excess N levels (CSNT>2000 ppm, Fig. 2d). Conversely, for 24 of these 35 cases (69% overall; 76% for those in NY) the CSNT erroneously diagnosed non-excessive levels (i.e. more than two-thirds were false negatives). Many of these could be considered serious misdiagnoses (highlighted in red in Table 1, excess of 30 lb N or more). This includes Trial NY4 where at least 140 lbs N/ac excess were applied, Trial NY18 with an excess of at least 106 lb N/ac, and Trial NY27 and NY28 where at least 75 lbs N/ac excess were applied. In the latter case, the CSNT values suggested deficiency when in fact N was applied in considerable excess.

Conclusions
We used 35 strip trials to make an assessment of the utility of the CSNT for corn nitrogen management on a field-by-field basis. We conclude from this year’s data and other published work that the test has limited ability to support management decisions. The primary question is whether the test can effectively detect excessive N applications. The answer appears to be “no.” Over two-thirds of the cases with substantially over-fertilized crops (up to 140 lbs/ac) did not show CSNT values in the excessive range (>2000 ppm), i.e. a majority of those cases were false negatives. Since the test’s primary need is related to determining excessive N rates, it appears to perform weakly in serving its main purpose. A second issue is whether the CSNT precisely determines N deficiencies. In this case the problem is with high rates of false positives, i.e., low CSNT values while N rates were in fact adequate or even excessive.

An additional concern is that end-of-season evaluations of the current growing season have limited value for the predictability of N needs in future growing seasons. Research has demonstrated (summarized by van Es et al., 2007) that weather conditions during the early growing season greatly affect N losses and are a critical factor in determining optimum N rates. This implies that an interpretation of CSNT values requires an evaluation of the complex growing conditions of the past season, and that test results from one growing season have limited value for predicting N needs for the next year when the weather may be very different.

Overall, we conclude from previous research reports and our own 35 strip trials that the CSNT is not an effective tool for use in field-specific adaptive N management, especially in the Northeast. We suggest that users of the test recognize its inherent weaknesses, and we recommend caution with the adoption of the CSNT for field-level adaptive N management.

Acknowledgements
This work was supported by grants from the New York Farm Viability Institute and the USDA-NRCS Conservation Innovation Grants Program. We are grateful for the cooperation in field activities from Bob Schindelbeck, Keith Severson, Kevin Ganoe, Sandra Menasha, and Anita Deming of Cornell Cooperative Extension, from Dave DeGolyer, Dave Shearing and other staff at the Western NY Crop Management Association, from Eric Bever and Mike Contessa at Champlain Valley Agronomics, and from Shannon Gomes, Hal Tucker, Michael McNeil and Frank Moore of MGT Envirotec in Iowa. We also are thankful for the cooperation of the many farmers who implemented these trials.

References
Binford, G.D., A.M. Blackmer, and B.G. Meese. 1992. Optimal concentrations of nitrate in cornstalks at maturity. Agron. J. 84:881–887.

Blackmer, A.M. and A.P. Mallarino. 1996. Cornstalk Testing to Evaluate Nitrogen Management (PM-1584). Iowa State Univ. Extension. Available on the Web at: http://www.extension.iastate.edu/Publications/PM1584.pdf. [URL verified 2/14/12].

Forrestal, P.J., R.J. Kratochvil, and J.J. Meisinger. 2012.  Late-Season Corn Measurements to Assess Soil Residual Nitrate and Nitrogen Management.  Agron. J. 104:148–157 (2012)

Katsvairo, T.W., W. J. Cox, and H. M. van Es. 2003.  Spatial Growth and Nitrogen Uptake Variability of Corn at Two Nitrogen Levels.  Agron. J. 95:1000–1011

Sawyer, John. 2010. Corn Stalk Nitrate Interpretation.  Integrated Crop Management News.  Iowa State University Extension and Outreach. http://www.extension.iastate.edu/CropNews/2010/0914sawyer.htm [URL verified 3/11/12].

van Es, H.M., B.D. Kay, J.J. Melkonian, and J.M. Sogbedji. 2007. Nitrogen Management Under Maize in Humid Regions: Case for a Dynamic Approach.  In: T. Bruulsema (ed.) Managing Crop Nutrition for Weather. Intern. Plant Nutrition Institute Publ. pp. 6-13. http://adapt-n.cals.cornell.edu/pubs/pdfs/vanEs_2007_Managing N for Weather_Ch2.pdf . [URL verified 2/22/12].