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

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

nitrogen recommendations

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

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

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…..”

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

2016 Updated Scoring Functions(after 8000 sample analyses)

Aggregate Stability

new old

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

• 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

Cornell Soil Health Online Applicationhttp://soilhealthapp.cals.cornell.edu/

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

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

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

Recommendations and detailed support

Graphs provide detailed insight

VRT Recommendation

New York and Iowa Strip Trials (n=113)

Adapt-N vs Grower Rates2011-2014

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%)

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

∆ 𝑃=(𝑌 𝐴−𝑌 𝐺 )× 𝑃𝑀− (𝑁 𝐴−𝑁𝐺 )× 𝑃𝑁− 𝑃𝑆𝐷

Partial profit analysis

Avg profit gain: $65 ha-1

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

Multi-N rate Trialsdynamic vs. static N recommendation approaches for

the Northeast and Midwest

Extensive testing using multiple N rate trials

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)

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

• 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