1. 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 Ristow
Presenter

2. Two parts to this project:
• Comprehensive Assessment of Soil Health
• Adapt-N, a professional software tool for
nitrogen recommendations

3. Today’s soils are limited by their HEALTH
New 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

4. 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 implement
o Includes standard nutrient test
• Guide for management
decisions
o Values interpreted
with scoring functions
o Report includes written
interpretations and management
suggestions table

5. 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…..”

6. 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

7. 2016 Updated Scoring Functions
(after 8000 sample analyses)
Aggregate Stability
new old

8. SH Management Planning Process
Overview
Grower
strengths
Grower goals
Soil sampling
Evaluate
results
Define
options
Refine
options
Implement, Refine
Caveat:
Increased Increased
soil health profitability
• Identify soil limitations
• Create opportunities for synergistic
management
A B

9. • 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

10. Cornell Soil Health Online Application
http://soilhealthapp.cals.cornell.edu/

11. Soil Health Drives N Availability
Dynamically 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

12. 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

13. Summary of features and inputs for Adapt-N
Feature 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 inputs
Date of emergence, soil nitrate test results

14. Recommendations and detailed support

15. Graphs provide detailed insight

16. VRT Recommendation

17. New York and Iowa Strip Trials
(n=113)
Adapt-N vs Grower Rates
2011-2014

18. 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%)

19. 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

20. ∆𝑃 = 𝑌𝐴 − 𝑌𝐺 × 𝑃 𝑀 − 𝑁𝐴 − 𝑁 𝐺 × 𝑃 𝑁 − 𝑃𝑆𝐷
Partial profit analysis
Avg profit gain: $65 ha-1

21. 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

22. Multi-N rate Trials
dynamic vs. static N recommendation approaches for
the Northeast and Midwest
Extensive testing using multiple N rate trials

24. Midwest trials
Mean rate = 197 kg/ha
Mean EONR rate = 204 kg/ha
RMSE = 33 kg/ha
Mean rate = 222 kg/ha
Mean EONR = 204 kg/ha
RMSE = 49 kg/ha
Adapt-N decreases the RMSE by 33%
Adapt-N State N rate (MRTN)

25. New York
Mean rate = 174 kg/ha
Mean EONR rate = 181 kg/ha
RMSE = 33 kg/ha
Bias = -7 kg/ha
Mean rate = 266 kg/ha
Mean EONR rate = 181 kg/ha
RMSE = 100 kg/ha
Bias = 85 kg/ha
Adapt-N decreases the RMSE by 67% over Cornell N Calculator

26. • 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