Bill Cox, Phil Atkins and Geoff Reeves, Department of Crop and Soil Sciences, Cornell University
March 2012 was the warmest March on record across much of the USA (13 degrees above normal for most of NY). Surprisingly, a couple of growers in NY planted limited corn acreage during the week of March 19th when daytime temperatures averaged about 75 degrees. Farmer testimony indicated satisfactory emergence for the March-planted corn. Many other growers, however, elected to wait until the next warm spell, which occurred during the week of April 15th when daytime temperatures averaged about 70 degrees. Farmer testimonies, however, were somewhat mixed for the corn planted during this week with some replanting reported, especially in poorly drained areas of a field. We planted two studies that week: our corn silage hybrid trial with 82 entries on April 20th at the Aurora Research Farm in Cayuga County and a 10-acre seeding rate study on April 18th just northwest of Auburn in Cayuga County.
Table 1. Weather conditions at the Aurora Research Farm and the Auburn airport from April 15-April 30th in 2012. Emboldened date indicates the weather conditions on the day of planting for studies discussed in this article.
Weather conditions (daily weather is recorded the morning after at 8:00 AM so the April 20th data at Aurora is recorded as April 21st data when the high temperature was 78) for the first 10 days after planting at Aurora changed drastically (Table 1). At Aurora, the high temperature the day after planting was 54 and then only 2 days above 50 degrees were recorded over the next 8 days (64 on April 26th, reported as April 27th data, and 53 on April 29th, reported as April 30th data). More importantly, only 24 hours after planting, Aurora received a cold 0.6 inches of rain followed by 0.86 inches of precipitation in the form of a 5-inch snow storm 48 hours after planting. Another 0.20 inches of precipitation occurred the following day, 72 days after planting, when the high temperature was only 36 degrees. Also, note that low temperatures dipped down to 26 degrees for two nights about a week after planting. Obviously, weather conditions were conducive for imibitional chilling damage during the initiation of the emergence process, cold stress during the emergence process, and drowning out of corn seeds shortly after planting in poorly-drained areas of a field.
When averaged across the 82 hybrids entered in the study, the stand establishment rate (number of established plants in 2 rows of the 20 foot plot length at the V4 stage/86 seeds in each seed packet planted) averaged 85.4% (Table 2). Stand establishment averaged from about 84 to about 89% for the 12 seed companies that entered hybrids. Of the 82 hybrids entered in the study, only six hybrids had stand establishment rates of less than 80% on this drained Lima silt loam soil. Obviously, most modern hybrids can withstand the rigors of cold and wet weather conditions, even 5 inches of snow, shortly after planting (Fig.1 and 2).
Table 2. Stand establishment rates of 82 hybrids from 12 seed companies planted on April 20th, 2012, 2 days before a 5-inch snow storm.
At the field-scale study where soil conditions are more variable, we counted the number of established corn plants at the V5 stage along the entire length of one row (~800 feet) at each seeding rate for the two hybrids (9807HR from Pioneer and DKC49-94 from DEKALB) evaluated in this study. When averaged across hybrids and seeding rates, stand establishment rate averaged 84.6%. Stand establishment varied from about 83 to 87% between hybrids and from about 84 to 87% across seeding rates (Table 3). This site did experience two warm days (highs of 74 and 76, Table 1) 2 days after planting so conditions were not quite as harsh. On the other hand, low temperatures dipped down to 24 degrees for two nights and 26 degrees another night about 10 days after planting. In addition, this site received about 4 inches of snow a few days after planting. So the 84% stand establishment rate on this production field was quite satisfactory given the conditions. I will add that in a 50 by 100 foot low spot in the third replication of the study no corn emerged (not accounted for in the data because it was a seeding rate study ) so certainly the excessively wet conditions after planting had a major impact on stand establishment rates.
Table 3. Plant populations at the fifth leaf stage (V5) of a DEKALB and a Pioneer hybrid at four seeding rates in a field-scale study planted on April, 18th, 2012 a few miles northwest of Auburn, NY in Cayuga County.
So, what does the stand establishment data from 2012 tell us? First, most if not all modern hybrids have excellent cold tolerance and perhaps tolerance to imbitional chilling (an elusive phenomenon that I am not sure that I have ever observed). On the other hand, modern hybrids have limited tolerance to flooded soil conditions shortly after planting as observed in the field-scale study. So obviously, soil drainage conditions should be a major factor when considering early planting dates (an early planting date lengthens the time that corn is in the vulnerable period to flooded soil conditions, from planting to about the V5 stage). Another factor to consider is planting depth. We only plant at about a 1.5 inch depth in April, especially when cool and wet conditions are forecasted for the immediate future. Many growers mentioned that their planting depth was at the 2-inch soil depth when planting the week of April 15th, which may have contributed to poor stand establishment reported by some farmers in some poorly drained areas of a field or on heavy soils.
Fig.1. Aurora corn silage hybrid trial on May 11th, 2012, planted on April 20th.
What happens if soil conditions are dry in mid-April next year and soil conditions are once again ideal for planting? I will again recommend to begin planting anytime after April 10-15, provided your location does not experience late spring killing frosts (< 28 degrees after May 15th or so) and your soils are well-drained and do not readily flood. In other words, I recommend to plant fields with good drainage that are not in frost pockets anytime after April 10-15that a soil depth of about 1.5-1.75 inches. I wouldn’t plant much deeper in April unless you are looking for moisture.
Fig.2. Counting emerged corn plants at the V4 stage in the Aurora corn silage hybrid trial on May 31, 2012.
Julia Knight1, Quirine Ketterings1, Karl Czymmek1,2, and Rich Wildman3
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.
Figure 1. Guidance for Federal Land Management in the Chesapeake Bay Watershed: 1.2.2 Implementation Measures for Agriculture in the Chesapeake Bay Watershed to Control Nonpoint Source Nutrient and Sediment Pollution, USEPA.
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%.
Figure 2: Relationship between the Cornell Morgan P test and P saturation derived from Mehlich-3 data (P/(Fe+Al) in molar ratio).
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.
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/.
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.
Table 1: Percent of all acres and farms under nutrient management planning in 2010 in New York planned by Soil and Water Conservation Districts (5 planners), Cornell Cooperative Extension (1 planner, New York City Watershed), and private sector planners (18 planners).
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).
Table 2: Total acres and number of farms as well as farm size for farms with certified nutrient management plans in 2010 planned by Soil and Water Conservation Districts (SWCD, 5 planners), Cornell Cooperative Extension (CCE, 1 planner, working in the New York City (NYC) Watershed), and private sector planners (18 planners).
About 1/3rdof 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.
Figure 1: Planner perceptions of the drivers of the drastic reduction in P fertilizer sales for on-farm use in New York. Planners were asked to ranks the drivers from 1-5 with 1-2 considered important, while 4-5 was not an important contributor to the change over time. Cost of fertilizer and on-farm research were identified as the most important drivers (23 respondents).
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).
Table 1. Assessment of CSNT performance, based on strip trial results involving two fertilizer rates. CSNT values less than 250 (low) are presumed to indicate N deficiencies and values greater than 2000 (high) are presumed to indicate excess N.
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.
Figure 1. Yield losses (bu/ac) and CSNT values (ppm) from the lower N rate treatments in all trials, and for the higher N rate treatments in those trials where excess was unambiguous (implying no yield gain with further added N).
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.
Figure 2. Proportion of CSNT values that correctly or incorrectly identified field-demonstrated deficiency or excess status of the number of a) CSNT values measured in the low range, b) known deficiency scenarios, c) CSNT values measured in the excessive range, and d) known excess scenarios.
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 ofnitrate 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
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].
Bianca Moebius-Clune, Harold van Es, and Jeff Melkonian, Department of Crop and Soil Sciences, Cornell University
Research has demonstrated (summarized by van Es et al., 2007) that soil and crop management practices, combined with weather conditions during the early growing season, greatly affect N losses and are therefore critical factors in determining optimum N rates. The difference in fertilizer N needs from one year to the next could easily be 100 lb N, and generalized N recommendations are inherently imprecise. In a recent case study, we highlighted the impact of early- vs. late planting on recommended N rates (What’s Cropping Up?, Vol. 21, No 4).
It is not possible to accurately determine at the beginning of the growing season how much N fertilizer will be needed for that year’s crop, because some critical processes that affect N losses have not yet passed. Most growers fertilize for a worst-case scenario and apply “insurance fertilizer” – they put on in excess of what is needed in most years. This reduces farm profits and causes high environmental losses. Seasonal corn N needs can be estimated much better in the late spring to guide sidedress applications. Adapt-N is an online decision support tool (http://adapt-n.cals.cornell.edu) designed to help farmers precisely manage nitrogen (N) inputs for grain, silage, and sweet corn. It 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. We have completed the first year of beta-testing through on-farm strip trials in New York, which are presented in this article.
Methods
We completed 18 replicated strip trials on commercial and research farms throughout New York during the 2011 growing season. They involved grain and silage corn, with and without manure application, and different rotations (corn after corn, corn after soybean, and corn after a clover cover crop; Table 1). Treatments involved two rates of nitrogen, a conventional “Gower-N” rate based on current grower practice and an “Adapt-N” recommended rate. A simulation was run for each field prior to sidedressing to determine the Adapt-N rate. In 2011, due to seasonal weather conditions, all Adapt-N rates were lower than conventional N rates (by 15 to 140 lbs/ac; Table 1). Growers then implemented field-scale strips with 3 or 4 replications for each treatment (except NY8 and NY9, where only single yield strips were implemented due to time and equipment constraints).
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 N use – Grower N use] * price of N
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, a grain price of $5.50/bu was assumed ($6.50/bu minus $1.00/bu for drying, storing and trucking from PA Custom Rates; USDA, 2011). For silage, $50/T was assumed based on reported NY silage prices of $25-75/T. The price of N fertilizer was assumed at $0.60/lb N (prices ranged from $0.49 – $0.75/lb N in NY). Total N losses to the environment (atmosphere and water) and N leaching losses were estimated for each treatment by running model simulations with all N inputs through the end of the growing season (30 October). Agronomic, economic and environmental outcomes of these trials were then used to assess Adapt-N performance.
Results
Errors were made in model and/or trial implementation in a few cases (labeled with * in Table 1): A clover cover crop was improperly simulated as an incorporated sod, resulting in a low Adapt-N recommendation and substantial yield losses. In other cases, Adapt-N fertilizer and manure inputs did not reflect real field applications, or N applications were made too late in the season. The lesson here is that correct input information is, of course, needed for Adapt-N to provide an accurate recommendation. The resulting yields and simulations from the above four trials were not representative of 2011 Adapt-N performance, and these trials were therefore removed from further analysis.
Agronomic, economic and environmental comparisons between Grower-N and Adapt-N treatments for each trial are provided in Figure 1, and as averages in Table 2. A comparison of grain and silage harvest data (Fig. 1a & 1b) shows that differences in yields were negligible and statistically not significant for almost every trial, despite substantially reduced N rates applied for the Adapt-N treatment (Tables 1 and 2). A case study describing one of these trials, conducted at Donald & Sons Farm, in Moravia, NY, where 140 lb of N were saved without yield loss, is described in a companion article in the current issue of What’s Cropping Up?.
When the previous crop was soybean (3 trials), yield losses were found in every case (Fig. 1a), although the grower N rates were well above economic optimum N rates. We determined that Adapt-N overestimated the soybean N contribution, and thus provided low N recommendations in these three cases. The 2011 version of Adapt-N used a flat 30 lb soybean N credit, but also simulated immobilization of N in stover in corn-after-corn rotations, effectively almost doubling the N credit for corn following soybean. We believe that part or all of the soybean ‘N credit’ should mostly be regarded as an absence of an immobilization penalty for corn-corn rotations. Changes will be made to the Adapt-N tool to reflect these findings for the 2012 growing season.
Estimated leaching losses (Fig. 1c & d), as well as total N losses (Table 2) decreased as a result of reduced N application rates for the Adapt-N treatment. On average, leaching losses decreased by 38 lb N/ac in grain trials, and by 11lb/ac in silage trials. There was less room for improvement in silage trials because lower fertilizer rates were used after manure applications.
Most trials resulted in profit gains from the use of Adapt-N, ranging from $1 – $80/acre, (Fig. 1e & f). Average profit gains were $35/acre for corn after corn and $39/acre for silage corn (Table 2). Corn after soybean trials registered an average loss of $11/acre due to one trial with high yield loss (NY3). This was the only trial out of 14 (7%) where profit loss was significant. Fig. 2 indicates the low risk of profit loss (<14% overall before the correction of the soybean N credit), and high probability of improved profits (86%) of using Adapt-N in 2011.
Our data suggest that after minor adjustments of the Adapt-N tool, it will be even better equipped to give accurate recommendations. Growers who tend to use high amounts of nitrogen will realize large savings. In a much wetter year, increased profitability would come from appropriately applying more N at sidedress time in order to prevent yield reductions from N losses. In the long term we expect that environmental losses will decrease in both dry and wet years, because this tool provides strong incentives to shift N applications to sidedress time.
Conclusions
From beta-testing on commercial farms throughout NY State in 2011, we determined that the value of the Adapt-N tool was substantial. The tool was quite successful in adjusting for the effects of seasonal conditions to accurately recommend N fertilizer needs. Also,
• N application rates were significantly reduced (15 to 140 lb/acre).
• Grower profits increased on average by $35/acre, except in corn after soybean (due to model inaccuracies that are being corrected for the 2012 growing season).
• N losses to the environment were decreased substantially (5 to 120 lb/acre).
Adjustments to the Adapt-N tool will improve ease of use and accuracy for the 2012 growing season. The Adapt-N tool and information about it is accessible to stakeholders through any device with internet access (desktop, laptop, smartphone, and tablet) at http://adapt-n.cals.cornell.edu/, where information on account setup is also available.
Acknowledgments
This work was supported by grants from the NY Farm Viability Institute and the USDA-NRCS Conservation Innovation 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 Jason Post at the Western NY Crop Management Association, and from Eric Bever and Mike Contessa at Champlain Valley Agronomics. We also are thankful for the cooperation of the many farmers who implemented these trials.
References
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].
EPA, 2010. Inventory of U.S. Greenhouse Gas Emissions and Sinks (1990-2008). U.S. Environmental Protection Agency, Washington, DC.
Marlene van Es (1), Bianca Moebius-Clune (1), Harold van Es(1), Jeff Melkonian(1), and Keith Severson (2), (1) Department of Crop and Soil Sciences, Cornell University and (2) Cornell Cooperative Extension Cayuga County
Growers across the country have used a wide range of methods to decide on nitrogen (N) application rates for corn, from mass balances to a variety of soil and plant tissue tests, to maximum return to nitrogen curves, to… simply… rules of thumb. But most are frustrated by the lack of accuracy of these methods. Early-season weather can greatly impact how much N fertilizer is needed year to year, and this variability has been difficult to manage. The amount of N fertilizer required could easily differ by 100 lbs from one year to the next. This variability results in average N recommendations that are higher than needed in many years, leading to profit loss for growers and environmental damage through N losses to water as nitrate and to the air as nitrous oxide, a potent greenhouse gas.
The web-based Adapt-N tool has the potential to change the way N management is done. Soil data, along with crop and soil management information are supplied by the grower. The Adapt-N tool uses these data in combination with newly available high-resolution climate data to simulate N availability and losses due to weather, and thus provide more accurate sidedress N recommendations. The tool is undergoing beta-testing in on-farm strip trials across New York and Iowa in the 2011 and 2012 growing seasons. Once fully validated, Adapt-N will, over the long term, help reduce N losses to the environment that contribute to air and water pollution, while saving farmers money through the optimization of fertilizer purchases and application rates.
One of the New York agricultural enterprises collaborating with the Adapt-N team is Donald and Sons Farm located in Moravia, NY. The farm has been in the family for several generations and currently encompasses 1500 acres of land. In 2011, 1050 acres were in corn and 250 in soybeans.
The Donald brothers, Robert and Rodney, are no strangers to on-farm research and have collaborated with Cornell University and private companies many times over the years. When asked why they keep getting involved in research Rodney replied, “Money! Some [projects] take you down a dead end street, but if we see, for example, that we can save putting 100 lbs [of N] on, that’s a lot of money.” So, although the on-farm research can be time consuming for Robert and Rodney, they see the value in the important benefits it can generate.
The Donald brothers’ acreage varies greatly in soil type, and organic matter contents range from about 1 to 5%. The farm currently bases its N application rates on recommendations from A&L Great Lakes Laboratories, generated based on soil tests by management unit. Robert and Rodney practice variable rate application, taking advantage of their RTK-GPS system for soil sampling, input application and yield monitoring. The bulk of their fertilizer N application occurs at sidedress time, as they have found that early season applications run the risk of losses during wet springs. They experimented for a few years with putting anhydrous ammonia on at preplant, and considered slow-release and inhibitor technology, but decided to return to sidedressing. The amount the Donalds spend on N fertilizer has nearly quadrupled since 2000, and in 2011 they spent $107,000 – a strong incentive for them to seek new tools to help optimize application rates. As Rodney puts it, “money talks … and with what we are getting in corn for what we are putting on in ammonia, we’re not gaining.”
This past spring, Robert and Rodney identified 10 acres of a 100-acre field to implement a replicated strip trial to test the Adapt-N tool. The field was planted with corn on May 21st with 22lbs of N from monoammonium phosphate starter. In early June, Keith Severson of Cayuga Cooperative Extension used Adapt-N, inputting the Donald brothers’ field information, such as organic matter content, expected yield, tillage, fertilizer inputs, etc., to generate an N sidedress recommendation of 80 lb N/acre.
When asked what he thought when he heard of the 80 lb recommendation, Rodney said, “it was hard for me to chew on 80. … It was a little hard for me to chew on!” On June 19th, two sidedress treatments were applied in eight, 16-row-wide strips. Four of the strips received the standard N rate based on the recommendation from A&L labs, which was 220 lbs, and the remaining 4 strips received the Adapt-N rate of 80 lbs. Throughout the growing season, the brothers still felt very unsure about the low Adapt-N rate compared to their usual practice. They kept their eyes on the field after sidedressing, taking note that the Adapt-N strips appeared to be a lighter shade of green. “We thought, uh oh, this is going to be a blow, here we go.”
However, as the season came to a close the results indicated otherwise. There was no loss in yield despite the 140 lb application rate difference. The Donald’s yield monitor data showed spot-yields between about 120 and 230 bu/ac. The average yields for the conventional plots were 174.1 bu/ac, while Adapt-N average yield was 173.6 bu/ac. Robert and Rodney were shocked by the results stating, “it wasn’t until we were combining that we realized the yield wasn’t really different even though there was a 140 lb N difference [in sidedress rate]”.
The results show great promise for the Adapt-N tool and for the Donald brothers’ ability to save on N fertilizer. Assuming that the trial field was fairly representative of the rest of the farm, the Donalds would have saved approximately $70,000 in fertilizer in 2011. A post-season Adapt-N simulation estimated that they had also reduced their N leaching losses in 2011 by about 77%, from 142 to 32 lbs/ac.
Robert and Rodney intend to collaborate on more extensive testing of the Adapt-N tool next year and see whether different weather conditions affect the recommendations. In addition to another fully replicated strip trial, they may use variable-rate recommendations provided by Adapt-N in strips next to those provided by A&L Laboratories on multiple fields. When discussing variable rate application with the brothers, using rates with drastically higher N amounts than needed by the crop was likened to “aiming for the bull’s eye in the opposite direction of the target,” to which Rodney laughingly replied, “I’ve been doing that all my life.” Variable rate application using Adapt-N should allow for a more accurate AND precise accounting of the effects of organic matter-derived N and texture in interaction with that year’s weather on overall N availability.
Overall the trial suggests that more accurate N recommendations based on weather impacts, in addition to soil and management information, could lead to substantially higher profits for farmers, while reducing environmental losses in most years. This creates a win-win situation as farmers face higher costs for fertilizer and we search for feasible and effective ways to reduce detrimental losses to the environment.
Acknowledgments
This work was supported by grants from the NY Farm Viability Institute and the USDA-NRCS. Thank you to Robert and Rodney Donald for their cooperation in diligently implementing this trial, and taking the time to share their thoughts. For more information about the Adapt-N tool, visit http://adapt-n.cals.cornell.edu/.
