Advancing Climate-Adaptive Decision Tools To Reduce Nutrient Pollution From Agricultural Fields
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Transcript of Advancing Climate-Adaptive Decision Tools To Reduce Nutrient Pollution From Agricultural Fields
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Advancing Climate-Adaptive Decision Tools to Reduce Nutrient Pollution from Agricultural Fields
S. Sela, H.M. van Es, B.N. Moebius-Clune, R. Marjerison, D. Moebius-Clune, R. Schindelbeck, K. Severson, E. Young
Section of Soil and Crop Science, School of Integrative Plant Science, Cornell University
Aaron RistowPresenter
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Two parts to this project:• Comprehensive Assessment of Soil Health• Adapt-N, a professional software tool for
nitrogen recommendations
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Today’s soils are limited by their HEALTHNew approach to measuring limitations:
• We are talking about it!• Beyond nutrient limitations and excesses • Interacting biological and physical limitations:
• Limit resilience to drought and extreme rainfall, pests• Impact crop quality, yield• Demand expensive inputs
• Need to understand agro-ecosystems with many interconnected parts
• Need to understand constraints and manage them
Physical processes
Biological processes
Chemical processes
Soil Health
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Cornell Soil Health Assessment Framework
• Publically available since 2006• Identifies soil constraints • Measures 16 indicators
o Representing agronomically important soil processes
o Consistent and easy to implemento Includes standard nutrient test
• Guide for management decisionso Values interpreted
with scoring functionso Report includes written
interpretations and management suggestions table
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Soil Health Testing• Quantification• Soil Health can’t be measured directly• Awareness• Diagnosing problems for targeted
management• Monitoring current status
and improvements“What gets measured, gets done…..”
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Biological Indicators Soil Processes
Organic Matter Water and nutrient storage/release, long-term energy storage, C sequestration
Active Carbon C easily available as short-term microbial food source; biol. Activity
Soil Proteins Primary N-containing fraction of organic matter; N release
Respiration Integrates microbial abundance and metabolic activity; nutrient release
Potentially Mineralizable N
From microbial release during decomposition of organic matter, N release capacity
Root Rot Bioassay Soil-borne disease pressure/suppressiveness of microbial community
Cornell Soil Health Test ties Indicators to Soil Processes
Chemical Indicators: Processes as per standard soil test: nutrient availability, reaction, toxicity, pollution
Physical Indicators Soil Processes
Aggregate Stability Resistance to dispersal; aeration, infiltration, crusting, germination, rooting, runoff & erosion
Available Water Capacity Plant available water; water storage, drought resistance, prevent leaching
Surface Hardness Penetration resistance 0”- 6” (compaction); aeration, surface rooting, infiltration, water transmission, germination, runoff & erosion
Subsurface Hardness Penetration resistance 6” - 18” (compaction); deep rooting, drought resistance, water movement and drainage, extreme precipitation resilience
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2016 Updated Scoring Functions(after 8000 sample analyses)
Aggregate Stability
new old
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SH Management Planning Process Overview
Growerstrengths
Grower goalsSoil sampling
Evaluate results
Define options
Refine options
Implement, Refine
Caveat: Increased Increasedsoil health profitability
• Identify soil limitations• Create opportunities for synergistic management
A B
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• Overview of Soil Health concepts
• Field sampling• Description of indicators• Brief laboratory
methodology• How indicator values are
“scored”• Soil Health Report• Soil Health Report
Interpretation• Linkages to Management
Available online at http://soilhealth.cals.cornell.edu
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Cornell Soil Health Online Applicationhttp://soilhealthapp.cals.cornell.edu/
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Soil Health Drives N AvailabilityDynamically interacting with weather:• Poor soil health = less N available, less N buffering, higher risks• Biologically: Microbial Activity, OM content and quality determine
potential contribution• Physically: Compaction, infiltration, available water capacity,
aggregation, etc., determine loss, access, crop stress
Poor soil health is costly in many ways
Integrating soil health information into N recommendations from Adapt-N to promote short-term and long-term incentives to manage for better soil health
Cornell Soil Health Team soilhealth.cals.cornell.edu
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Adapt-N• Developed at Cornell University; rolled out in 2008;
licensed and commercialized in 2013 through Agronomic Technology Corp as a partnership
• Recognized in multiple sustainability initiatives• Linked to several industry data platforms
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Summary of features and inputs for Adapt-NFeature Approach
Simulation time scale Daily time-step. Historical climate data for post-date estimates
Optimum N rate estimation
Mass balance: deterministic (pre) and stochastic (post) with grain-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; rooting depth; slope; SOC; artificial drainage
Crop inputs Cultivar; maturity class; population; expected yield; crop price; Management inputs Tillage (type, time, residue level); irrigation (amount, date); manure
applications (type, N & solid contents, rate, timing, incorporation method); previous crop characteristics; cover crop (2016)
N Fertilizer inputs Multiple: Type, rate, time of application, placement depth; fertilizer price; enhanced efficiency compounds (inhibitors, slow-release).
Real-time inputsDate of emergence, soil nitrate test results
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Recommendations and detailed support
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Graphs provide detailed insight
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VRT Recommendation
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New York and Iowa Strip Trials (n=113)
Adapt-N vs Grower Rates2011-2014
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NY IA
Results – applied N rates
• In 83% trials Adapt-N recommended lower N rate than Grower
• Average reduction of 45 kg ha-1(34%)
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Yield is not significantly different between Adapt-N and Grower rates (p=0.185 for NY and 0.541 for IA)
NY IA
Results – measured yields
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∆ 𝑃=(𝑌 𝐴−𝑌 𝐺 )× 𝑃𝑀− (𝑁 𝐴−𝑁𝐺 )× 𝑃𝑁− 𝑃𝑆𝐷
Partial profit analysis
Avg profit gain: $65 ha-1
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Simulated environmental losses
An average reduction of 14.3 kg ha-1 (36%) in simulated leaching losses
An average reduction of 13.5 kg ha-1 (39%) in simulated gaseous losses
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Multi-N rate Trialsdynamic vs. static N recommendation approaches for
the Northeast and Midwest
Extensive testing using multiple N rate trials
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Midwest trials
Mean rate = 197 kg/ha Mean EONR rate = 204 kg/haRMSE = 33 kg/ha
Mean rate = 222 kg/ha Mean EONR = 204 kg/haRMSE = 49 kg/ha
Adapt-N decreases the RMSE by 33%
Adapt-N State N rate (MRTN)
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New York
Mean rate = 174 kg/ha Mean EONR rate = 181 kg/haRMSE = 33 kg/haBias = -7 kg/ha
Mean rate = 266 kg/ha Mean EONR rate = 181 kg/haRMSE = 100 kg/haBias = 85 kg/ha
Adapt-N decreases the RMSE by 67% over Cornell N Calculator
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• Healthy soil is more resilient• Soil Health drives N availability • Validated with 200+ on-farm experiments• Proven win-win opportunities:
• Farmer savings by $60-90 per hectare• Reduced leaching impacts by 35%• Reduced greenhouse gas impacts by 40%
In summary
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