Adapt‐N Outperforms Grower‐Selected Nitrogen Rates in Northeast and Midwestern United States Strip Trials

Sela, Shai; Es, Harold M. van; Moebius‐Clune, Bianca N.; Marjerison, Rebecca D.; Melkonian, Jeff; Moebius-Clune, Daniel J.; Schindelbeck, Robert R.; Gomes, Simone Damasceno

doi:10.2134/agronj2015.0606

Cited by 63 publications

(36 citation statements)

References 47 publications

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“…Marginal profits were on the average $65 ha -1 higher and N inputs 45 kg ha -1 lower when Adapt-N estimated N rates. Simulated estimates of post-sidedress N losses for the four growing seasons averaged 36% lower for leaching and 39% lower for gaseous losses in this relative comparison of the growers' rate of N and the Adapt-N rate of N (Sela et al, 2016).…”

Section: Validation In On-farm Trialsmentioning

confidence: 74%

“…Adapt-N was evaluated through 113 replicated on-farm strip trials in New York and Iowa during 2011 to 2014 where recommendations from Adapt-N were compared with conventional grower practice under a variety of rotations and management practices (Sela et al, 2016). Marginal profits were on the average $65 ha -1 higher and N inputs 45 kg ha -1 lower when Adapt-N estimated N rates.…”

Section: Validation In On-farm Trialsmentioning

confidence: 99%

See 1 more Smart Citation

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

et al. 2018

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A gronomy J our n al • Volume 110 , I ssue 1 • 2 018 1 T he goal of an N recommendation system is to accurately estimate the gap between the N provided by the soil and the N required by the plant. Accurately estimating this gap depends on the ability of the recommendation system to accurately estimate fi eld or subfi eld specifi c economically optimal nitrogen rates (EONR). Current recommendation systems are not as accurate as needed to provide consistently reliable estimates of N needs across years at the fi eld or subfi eld scale. Uncontrollable factors like temperature, rainfall timing, intensity and amount, and interactions of temperature and rainfall with factors such as N source, timing and placement, plant genetics, and soil characteristics combine to make N rate recommendations for an individual fi eld or rates for subfi elds a process guided as much by science as by the best professional judgement of farmers and farm advisors.Substantial evidence has accumulated that EONRs can vary widely across fi elds, within fi elds and over years in the same fi eld for a wide range of crops and geographies. Examples ABSTRACTNitrogen fi xation by the Haber-Bosch process has more than doubled the amount of fi xed N on Earth, signifi cantly infl uencing the global N cycle. Much of this fi xed N is made into N fertilizer that is used to produce nearly half of the world's food. Too much of the N fertilizer pollutes air and water when it is lost from agroecosystems through volatilization, denitrifi cation, leaching, and runoff . Most of the N fertilizer used in the United States is applied to corn (Zea mays L.), and the profi tability and environmental footprint of corn production is directly tied to N fertilizer applications. Accurately predicting the amount of N needed by corn, however, has proven to be challenging because of the eff ects of rainfall, temperature, and interactions with soil properties on the N cycle. For this reason, improving N recommendations is critical for profi table corn production and for reducing N losses to the environment. Th e objectives of this paper were to review current methods for estimating N needs of corn by: (i) reviewing fundamental background information about how N recommendations are created; (ii) evaluating the performance, strengths, and limitations of systems and tools used for making N fertilizer recommendations; (iii) discussing how adaptive management principles and methods can improve recommendations; and (iv) providing a framework for improving N fertilizer rate recommendations.

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Section: Validation In On-farm Trialsmentioning

confidence: 74%

Section: Validation In On-farm Trialsmentioning

confidence: 99%

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

et al. 2018

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“…Adapt‐N is a dynamic, in‐season, sidedress N recommendation tool, designed to optimize N applications where the bulk of N is applied in season (Melkonian et al, 2008; Sela et al, 2016). It is currently calibrated for use on 95% of the US maize production area and is offered in a Cloud‐based environment, making it accessible through any internet‐connected device that supports a web browser.…”

Section: Methodsmentioning

confidence: 99%

Dynamic Model Improves Agronomic and Environmental Outcomes for Maize Nitrogen Management over Static Approach

Sela

Moebius‐Clune

et al. 2017

J of Env Quality

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Large temporal and spatial variability in soil nitrogen (N) availability leads many farmers across the United States to over‐apply N fertilizers in maize (Zea Mays L.) production environments, often resulting in large environmental N losses. Static Stanford‐type N recommendation tools are typically promoted in the United States, but new dynamic model‐based decision tools allow for highly adaptive N recommendations that account for specific production environments and conditions. This study compares the Corn N Calculator (CNC), a static N recommendation tool for New York, to Adapt‐N, a dynamic simulation tool that combines soil, crop, and management information with real‐time weather data to estimate optimum N application rates for maize. The efficiency of the two tools in predicting the Economically Optimum N Rate (EONR) is compared using field data from 14 multiple N‐rate trials conducted in New York during the years 2011 through 2015. The CNC tool was used with both realistic grower‐estimated potential yields and those extracted from the CNC default database, which were found to be unrealistically low when compared with field data. By accounting for weather and site‐specific conditions, the Adapt‐N tool was found to increase the farmer profits and significantly improve the prediction of the EONR (RMSE = 34 kg ha−1). Furthermore, using a dynamic instead of a static approach led to reduced N application rates, which in turn resulted in substantially lower simulated environmental N losses. This study shows that better N management through a dynamic decision tool such as Adapt‐N can help reduce environmental impacts while sustaining farm economic viability. Core Ideas Dynamic N recommendation tool reduces environmental impacts over static approach. Dynamic N recommendation tool accounts for different production environments. Dynamic N recommendation tool is successful in estimating field‐measured EONR.

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“…However, one limitation of these FUE studies is that they all use a factorial design, and only a single aspect of N fertilizer management was modified. While this is a necessary starting point, other studies of FUE using 4Rs strategy find that integrating practices that improve synchrony of N availability and crop assimilation enables reductions in N rates while maintaining yields (Meng et al 2016;Sela et al 2016). In the N 2 O studies available for this meta-analysis, when N fertilizer rate was tested, none of the other 4Rs were implemented (i.e.…”

Section: Fertilizer Managementmentioning

confidence: 99%

“…Matching crop demands with economic returns rather than applying N for maximum agronomic yields could avoid substantial N 2 O emission and achieve yieldenvironment co-benefits (McSwiney and Robertson 2005;Robertson and Vitousek 2009;Hoben et al 2011;Linquist et al 2012). Furthermore, studies of FUE suggest that comprehensive implementation of the 4Rs strategy could enable reductions in N fertilizer rates without incurring yield reductions (Meng et al 2016;Sela et al 2016). Clearly, there is great potential in achieving N 2 O and yield ''win-win'' outcomes that have yet to be explored.…”

Section: Ecologically-based Nutrient Management Practicesmentioning

confidence: 99%

N2O emissions from grain cropping systems: a meta-analysis of the impacts of fertilizer-based and ecologically-based nutrient management strategies

Han

Walter

Drinkwater

2017

Nutr Cycl Agroecosyst

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Understanding how agricultural management practices impact nitrous oxide (N 2 O) emissions is prerequisite for developing mitigation protocols. We conducted a meta-analysis on 597 pairwise comparisons (129 papers) to assess how management affects N 2 O emissions. Pairwise comparisons of practices aimed at improving fertilizer use efficiency (39%) and tillage (30%) dominated the dataset, while ecologically-based nutrient management (ENM) practices constituted 15% of the pairs. In general, across management practices, the quantity of N added was a more significant driver of N 2 O fluxes than was the form of N (fertilizer, legume biomass or animal manures). Manure interacted with soil texture so that in coarse soils, N 2 O emissions from manures tended to be higher compared to inorganic N fertilizers. The studies of ENM strategies frequently involved overapplication of N inputs in the ENM treatments. Cover crops reduced N 2 O emissions compared to bare fallows. However, during the cash crop growing season, when differences in N added and N source were confounded, the extra N inputs from cover crops were significantly correlated with the differences in N 2 O emissions between treatments with and without cover crops. Overall, in 38% of the data pairs, N 2 O emissions were reduced with limited impacts on yields; in half of these pairs, yields were maintained or increased while in the other half they were reduced by only B10%. Knowledge gaps on mitigation of agricultural N 2 O emissions could be addressed by applying an ecosystem-based, cross-scale perspective in conjunction with the N saturation conceptual framework to guide research priorities and experimental designs.

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Adapt‐N Outperforms Grower‐Selected Nitrogen Rates in Northeast and Midwestern United States Strip Trials

Cited by 63 publications

References 47 publications

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

Dynamic Model Improves Agronomic and Environmental Outcomes for Maize Nitrogen Management over Static Approach

N2O emissions from grain cropping systems: a meta-analysis of the impacts of fertilizer-based and ecologically-based nutrient management strategies

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