nitrogen management · always supplied enough n through sd. sela et al. 2019, towards applying n...

Nitrogen Managementwith Adapt-N

The Soil Health Context

Harold van Es

ESTIMATING OPTIMUM NITROGEN RATE IS CHALLENGING

• Many sources of N• Many loss pathways• Highly dynamic interactive system with uncertainties

• Highly influenced by production environments: weather, soil, and management

Precise N rate recommendations are critical due to asymmetric

producer risk

N Rate

Ris

k

Risk = Probability*Cost

EONR(uncertain)

Risk of yield lossRisk of excessfertilizer

Addressing the new reality……

• Climate change creates more weather extremes• New management practices create more diverse production environments (soil health, OM inputs, etc.)

• New technologies provide opportunities for implementation of 4R+ (stabilizers, new applicators, etc.)

• Traditional guidelines cannot adequately address all needs in nutrient management and don’t account for dynamic processes

Regional Increases in Very Heavy Precipitation Events (1958-2007)

Globalchange.gov

http://www.nutrientstewardship.com/4rs/

The 4R’s of nutrient management

Out of the 4R’s, the answer to the right N rate is probably the most challenging due to the dynamic nature of N in the soil

4R+: 4Rs and soil health

Source, Timing, and Placement

Greatly affects loss potential• Fertilizer formulation (esp. ammonia fraction)• Incorporation big factor• Use of enhanced efficiency products

• Reduces losses• Allows for lower N rates

• Timing relative to rainfall

N Supply and Uptake by Corn(base graph by Bender et al., 2013)

Cumulative Soil N Supply

(base graph by Bender et al., 2013)

230 bu/ac corn

Adapt-N Recommendations2015 vs. 2012 (Iowa)

201519” rain

2015 (19” rain): 1379 lbs/ac field total

2012 (4” rain): 790 lbs/ac field total



fertilize


Soil N Supply

Single application with N loss risk


dry

wet


In-Season Application Is Increasingly Adopted

Source: 360 Y-Drop

Resources

• Soil type• Soil health• Climate

• Precipitation• Temperature• Solar

radiation

• Cultivar• Crop rotations • Cover crops• Tillage• Irrigation and

drainage

Management Weather

Nutrient:• Source• Placement• Timing

3R’s

Dynamic-Adaptive N Rate EstimationProduction Environment Factors

Dynamic Interactions

Approach Strengths Weaknesses

Static

Yield Goal “logical”; simple to use; no cost Highly generalized; discounts

important factors; narrow management environment; not 4R or SH compatible

Empirical/MRTN experimental; simple to use; includes economic factors; no cost

Dynamic-Adaptive

Soil/tissue tests PSNT-LSNT, ear leaf, etc.

Direct measure on crop N availability; field check for N sufficiency

High labor cost; lack of time integration and modest predictability; 4R-SH compatible

Canopy sensorsYara N-Sensor,Greenseeker, OptRx

high site-specificity; equipment integration

Equipment investment; reference strips; no a-priori info; sensor constraints; relationship w/ EONR; 4R-SH compatible

Models and weather data Adapt-N, FieldView, Encirca, Farmer’s Edge

Process-based; space-time specific; in-season monitoring; low cost

Input data needed; data constraints; fees; 4R-SH compatible

General N Recommendation Approaches for Corn

Yield Goal Based Method

Generally following Stanford’s (1973) ideas:

Nrate = (Nyield– Nsoil) / Ef

e.g., for New York:Nrate = ((Yieldpotential * 1.2) – Nsoil) / Ef

Nafziger, 2006

Empirical/MRTN Approach

Morris et al., 2018

Price ratio impactMean N response

N Recommendation Systems in the US

From: Morris et al., 2018

A few states have guidelines around other technologies, e.g., sensor algorithms

Adapt-N• A Decision Support Tool to manage N• Cloud-based and highly scalable• Estimating N dynamics and crop needs in

complex production environments

Effectively addresses multiple concerns: • Farmer profitability• water quality • greenhouse gases, NH3 emissions• energy

Basically….What does Adapt-N do?

• It makes soil-crop simulations based on daily time step

• It simulates water and nitrogen dynamics in the soil (soil, management, weather, etc.)

• It simulates corn growth (soil, management, weather)

• Soil and crop models interact• It estimates supplemental N needs• It develops support information and graphs

Sela and van Es (2018), JSWC

Factors effecting the right N rate

Process based modelling

DisclosureAccording to Cornell University policy, I am disclosing that this tool was developed as part of our Cornell research program, and that Agronomic Technology Corporation (now YaraInternational ASA) received a license for the use and further development of the Adapt-N tool, and has in part sponsored associated research efforts. Conflicts of interests are managed per Cornell University policy

Features and Inputs for Adapt-N

Feature ApproachSimulation time scale

Daily time-step. Historical climate data for post-date estimates

Optimum N rate estimation

Mass balance: deterministic (pre) and stochastic (post) withgrain-fertilizer price ratio and risk factors

Weather inputs Near-real time: Solar radiation; max-min temperature;precipitation

Soil inputs Soil type or series related to NRCS database properties; rootingdepth; slope; soil organic content; artificial drainage

Crop inputs Cultivar; maturity class; population; expected yield; crop priceManagement inputs

Tillage (type, time, residue level); irrigation (amount, date);manure applications (type, N & solid contents, rate, timing,incorporation method); previous crop characteristics; covercrop

N Fertilizer inputs Multiple: Type, rate, time of application, placement depth;fertilizer price; enhanced efficiency compounds (inhibitors,slow-release)

Real-time inputs Date of emergence, soil nitrate test results

High Resolution Climate Data (4x4 km)Critical Input to Adapt-N Tool

• Gridded high-resolution climate data (Tmx, Tmin, Precip, solar radiaton), which are dynamically accessed from the NE Regional Climate Center.

• The database is derived from routines using the US National Oceanic & Atmospheric Administration's Rapid Update Cycle weather model (temperature) and operational Doppler radars (precipitation).

• Observed weather station data are used to correct NOAA estimates and generate spatially interpreted grids (DeGaetano and Belcher, 2007; DeGaetano and Wilks, 2009).

Adapt-N: Nitrogen Recommendations

How much N needed

Breakdown of recs

25

Key Value: Proven to improve financial and environmental performance

N Recommendation Methodology:Time integration through deterministic-

stochastic mass balance equation

SidedressNrate = CropNHarvest - CropNCurrent - SoilNCurrent - SoilNpostsidedress -

SoybeanNcredit + NLosspostapplication - Correctprofit

Estimated processes based on stochastic simulations

Input: Expected Yield

Near-Real-Time Simulation based on past (early-season) events

simulated & partial fixed credit

Details and Transparency

Graphical Insights

Export tools

Recs can be exported back to a source system or downloaded as a Shapefile.

Custom Fertilizers can be used, rates be adjusted, units can be rounded,

products can be mixed on the fly. Foliar products supported.

Email & Text Alerts for Tracking

Adapt-N and Soil Health Inputsaccounting for soil health in N recommendations

•Organic Matter•Rotation•Manure•Cover Crops

Tillage and Organic Matterplacement of residuemineralizationC and N pools

Cover CropsInputs for Adapt-N

Cover Crops

Cover crop type:

grass legume mix:mostly grass

mix: 50-50

mix: mostly legume

Stage at termination:

Tillering/early vegetative

stem elongation/midvegetative

boot-head/bud-flower

anthesis

Termination date:

Calendar date

Incorporation: none light full

Inputs for Adapt-N related to • biomass and C:N ratio• Placement and timing

Manure• quantity and quality• placement and timing

Rotation• biomass and C:N ratio• placement

YieldAn important part of N Management

• Important input for mass-balance models, including dynamic adaptive tools

• Affected by soil health• Yield maps provide spatially specific zones• Unknown yield potential?• Yield and N use efficiency are related

Source: EFC-AgSolver

Adapt-N History• 1980’s through early 2000’s: field research and initial

software development (Hutson, Wagenet, Sinclair Addiscott, van Es, et al.)

• 2003-2008: Adapt-N development (Melkonian, DeGaetano, van Es)

• 2008-2013: Adapt-N prototype tool available as free web interface, supported by grant funding

• 2011-current: extensive on-farm trials and model refinements:

• 2013-current: Adapt-N licensed and commercialized through Agronomic Technology Corp, now part of YaraInternational, ASA.

Model Development and Testing

Field data

Calibration

Validation

Independent evaluationon commercial farms

17 peer-reviewed publications

Modeling

Willsboro Research Farm

LEACHM Model Calibration and ValidationNitrate-N leaching from fertilizer

Sogbedji et al., 2001b, c

Manure Calibration and Validation:Soil Type and Timing

Sogbedji et al., 2006

Adapt-N on-farm test locations

125 field trials where dynamic and static N rates compared (2011-2016) Sela et al. (2018), ERL

NY IA

Results – applied N rates

In 83% of all 113 trials the Adapt-N tool recommended lower N application than the respective Grower rate, an average reduction of

40 lbs/ac (34%)

Sela et al. 2016 , Agronomy Journal

Results – measured yield


Diff = +1 bu/ac (ns)

34% additional N applied by the

farmers is in excess

Adapt–N vs Grower

Analysis by Dr. Jim Schepers for NutrientStar

Higher rates (17%) mostly justified by higher yieldsLower rates (83%) did not result in yield losses

-40

-20

0

20

40

60

80

-150 -100 -50 0 50 100Dif

fere

nce

in Y

ield

(b

u/A

)

Difference in N Rate (lb/A)

Simulated environmental losses

An average reduction of 13 lbs/ac (36%) in simulated leaching losses

An average reduction of 12 lbs/ac (39%) in simulated gaseous losses


Comparison with Economic Optimum RateCornell N Calculator

CNC default yield-based CNC realistic yield-based

avg N rate 76 lbs/ac above the OptimumRMSE = 67 lbs/ac

avg N rate 42 lbs/ac below the OptimumRMSE = 50 lbs/ac

Sela et al. 2017, Journal of Environmental Quality

Adapt-Navg N rate 6 lbs/ac below the Optimum Rate

RMSE = 30 lbs/ac

Sela et al. 2017, Journal of Environmental Quality

Comparison with Economic Optimum Rate

Leaching Losses: [NO3-N] in drain waterAdapt-N vs. Cornell N Calculator (realistic yields)

Willsboro lysimeter plots 14 sampling dates in 2014-2017 (n=448)

van Es et al., 2019

Clay LoamCNC (realistic yield) 9.7 mg/LAdapt-N 6.7 mg/L

(-31%)

Loamy SandCNC (realistic yield) 19.2 mg/LAdapt-N 12.4 mg/L

(-35%)

Future Directions?

• More in-season management• Ensemble approaches

• Models• Sensors• AI

• Industry and supply-chain incentives • performance standards (N Balance)

Atfarm AppYara International, ASA

Conclusions

• N is a dynamic nutrient• Many sources of N (organic, inorganic, etc.), some

affected by soil health management• Production environments are very diverse• New equipment offers management opportunities• Models and data improve N recommendations• Adapt-N accounts for soil health practices• Adapt-N is well tested and has known performance

What are achievable N balance targets in the US Midwest?

5 states : NE, IA, MN, IL, IN

5 locations in each state

3 types of soil texture: Sandy loam, Loam, Silty clay loam

7 seasons: 2010-2016

3 timings of N application – Fall, Spring, split

With or without nitrapyrin

Always supplied enough N through sd

Sela et al. 2019, Towards applying N balance as a sustainability indicator for the US cornbelt: realistic achievable targets, spatio-temporal variability and policy implications, ERL.

N Balance = Ninput – N output

Sela et al. 2019, Towards applying N balance as a sustainability indicator for the US cornbelt: realistic achievable targets, spatio-temporal variability and policy implications, ERL.

Applying N in better synchronization with crop N uptake substantially reduces N balance and N losses

78 kg/ha sustainable production threshold (Zhang et al. 2015)

Sela et al. 2018, ERL, Under review

Meeting Environmental TargetsTiming of Application and +/- Use of Nitrapyrin

Loam - Illinois

78 kg (Zhang et al., 2015)

PublicationsOn-Farm EvaluationSela,S., H.M. van Es, B.N. Moebius-Clune, S. R. Marjerison, J.J. Melkonian, D. Moebius-

Clune, R. Schindelbeck, and S. Gomes. 2016. Model-based N management increases Midwest maize production sustainability while enhancing economic returns. Computers and Electronics in Agriculture (accepted).

*Sela, S., H.M. van Es, B.N. Moebius-Clune, R. Marjerison, D. Moebius-Clune, R. Schindelbeck, K. Severson, E. Young. 2017. Dynamic model improves agronomic and environmental outcomes for corn N management over static approaches. J. Environm. Qual. 46(2):311-319.

*Sela,S., H.M. van Es, B.N. Moebius-Clune, S. R. Marjerison, J.J. Melkonian, D. Moebius-Clune, R. Schindelbeck, and S. Gomes. 2016. Adapt-N Outperforms Grower-Selected Nitrogen Rates in Northeast and Midwest USA Strip Trials. Agonomy J. 108: 4: 1726-1734.

Ristow, A., S. Sela, M. Davis, L. Fennell, H. van Es. 2016. Water Quality Impacts Reduced with Adapt-N Recommendations. What's Cropping Up? Vol. 26 No.2. (pre-publication; not peer-reviewed)

Moebius-Clune, B.N., H.M. van Es, and J.J. Melkonian. 2013. Adapt-N Uses Models and Weather Data to Improve Nitrogen Management for Corn. Better Crops with Plant Food. 2013 (4) 7-9.

PublicationsDevelopment, Calibration and Supportive Field Research• Marjerison, R.D. J. Melkonian, J.L. Hutson, H. M. van Es, S.Sela, L.D. Geohring, J. Vetsch. 2016. Drainage and nitrate leaching from artificially

drained maize fields simulated by the Precision Nitrogen Management model. J.Environm. Qual. 45:2044–2052 (2016).• Graham, C.J., H.M. van Es, J.J. Melkonian, and D.A. Laird. 2010. Improved nitrogen and energy use efficiency using NIR estimated soil

organic carbon and N simulation modeling. In: D.A. Clay and J. Shanahan. GIS Applications in Agriculture – Nutrient Management for Improved Energy Efficiency. pp 301-325, Taylor and Francis, LLC.

• Melkonian, J. L.D. Geohring, H.M. van Es, P.E. Wright, T.S. Steenhuis and C. Graham. 2010. Subsurface drainage discharges following manure application: Measurements and model analyses. Proc. XVIIth World Congress of the Intern. Commission of Agric. Engineering, Quebec City, Canada.

• Melkonian, J.J., H.M. van Es, A.T. DeGaetano, and L. Joseph. 2008. ADAPT-N: Adaptive nitrogen management for maize using high-resolution climate data and model simulations. In: R. Khosla, editor, Proceedings of the 9th International Conference on Precision Agriculture. Denver, CO (CD-ROM).

• DeGaetano, A.T., and B.N. Belcher. 2007. Spatial interpolation of daily maximum and minimum air temperature based on meteorological model analyses and independent observations. J. Appl. Meteorol. Climatol. 46(11): 1981–1992

• DeGaetano, A.T., and D.S. Wilks. 2009. Radar-guided interpolation of climatological precipitation data. Int. J. Climatol. 29(2): 185–196• 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.• Melkonian, J., H.M. van Es, A.T. DeGaetano, J.M.Sogbedji, and L. Joseph. 2007. Application of Dynamic Simulation Modeling for Nitrogen

Management in Maize. In: T. Bruulsema (ed.) Managing Crop Nutrition for Weather. Intern. Plant Nutrition Institute Publ. pp. 14-22.• Sogbedji, J.M., H.M. van Es, J.M. Melkonian, and R.R. Schindelbeck. 2006. Evaluation of the PNM model for simulating drain flow nitrate-N

concentrations under manure-fertilized maize. Plant and Soil 282: 343-360• van Es, H.M., J.M. Sogbedji, and R.R. Schindelbeck. 2006. Nitrate Leaching under Maize and Grass as Affected by Manure Application Timing

and Soil Type. J. Environmental Quality 35:670-679.• van Es, H.M, C.L. Yang, and L.D. Geohring. 2005. Maize nitrogen response as affected by drainage variability and soil type. Precision

Agriculture 6:281-295.• Kahabka, J.E., H.M. van Es, E.J. McClenahan, and W.J. Cox. 2004. Spatial analysis of maize response to N fertilizer in Central New York. Precision

Agriculture 5:463-476.• Sogbedji, J.M., H.M. van Es, J.L. Hutson, and L.D. Geohring. 2001. Fate of N fertilizer and green manure in clay loam and loamy sand soils: I

Calibration of the LEACHM model. Plant and Soil 229(1): 57-70.• Sogbedji, J.M., and H.M. van Es, J.L. Hutson, and L.D. Geohring. 2001. N rate and transport under variable cropping history and fertilizer rate

on loamy sand and clay loam soils: II. Performance of LEACHMN using different calibration scenarios. Plant and Soil 229(1): 71-82 • Sogbedji, J.M., H.M. van Es, S.D. Klausner, D.R. Bouldin, and W.J. Cox. 2001. Spatial and temporal processes affecting nitrogen availability at

the landscape scale. Soil & Tillage. Research 58 (3-4) 233-244.• Sogbedji, J.M., H.M. van Es, C.L. Yang, L.D. Geohring, and F.R. Magdoff. 2000. Nitrate leaching and N budget as affected by maize N fertilizer

rate and soil type. J. Environm. Qual. 29:1813-1820.

nitrogen management · always supplied enough n through sd. sela et al. 2019, towards applying n...

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