Precision Agriculture: The Technology The Opportunities The Challenge

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Precision AgricultureThe TechnologyThe
Opportunities The Challenge
March, 1999
Harold M. van Es
SST
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PA What is it?
Precision Agriculture involves the application of
data acquisition/control systems and information
systems to land management and recognizes that
soil, crop, and pest-related processes are
variable in space and time within fields. The
use of Precision Agriculture is tied to new
technologies such as global positioning systems,
geographical information systems, and remote
sensing, and new statistical methods
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PA Technology and Knowledge
PA Technology refers to the hardware and software
that allows for the collection of information,
and control of crop management tools. PA
Knowledge refers to the integration of
information into a set of management tools that
allow for the optimum use of PA technology.
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PA Technology
Core Units Global Positioning System On-board
computer with data acquisition/control
software Desktop computer with data processing /
GIS software Applications On-the-go sensors
(harvest, soil, weeds, etc) Variable rate
controllers (granular, fluid, seed,
etc.) Support technology Remote sensing, etc.
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PA may include many components
Information
Variable Rate Management

• fertilizer and lime
• plant populations
• differential hybrids
• pest control
• organic amendments

• yield mapping
• previous agrichemical applications
• intensive soil/crop sampling info
• weather data
• remote sensing

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Other benefits of PA technologies

• Farm record keeping (space-time referenced)
• Quantitative information to support field
  management
• Easier on-farm research
• Potential for data mining
• Technology-driven management innovations (e.g.
  parallel swathing)
• Environmental protection

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Musgrave Farm, Aurora, NY. Georeferenced
Digital Color-Infrared Image (by Emerge)
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Musgrave Farm, Aurora, NY. Geo-referenced
Digital Vegetation Index Image (by Emerge)
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Variable Rate Management
Precision Agriculture Management

• Fertilizer and Lime
• Technology is available
• Potential benefits (economic or environmental)
  will likely vary
• Knowledge base is still inadequate
• Record keeping important side benefit

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Variable Rate Management

• Manure
• Technology is being developed
• Potential environmental benefits
• Opportunities for refinement of nutrient
  management recommendations
• Record keeping important side benefit

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Precision Agriculture Management
Variable Rate Management
Pest Management

• Targeted field scouting based on remotely-sensed
  images
• Targeted pesticide application from
  remotely-sensed images
• On-the-go pest evaluation
• Research base is limited opportunities appear to
  be great

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Precision Agriculture
Cornell Precision Ag Initiative
Leaders Harold van Es, Soil and Water
Management, Spatial Statistics Bill Cox, Grain
Crop Production Cooperators Ed McClenahan,
Research Farm Management Susan Riha, Soil-Crop
Modeling Tim Setter, Crop Stress Physiology Gary
Bergstrom, Crop Disease Management, Steve Smith,
Geographical Information Systems Wayne Knoblauch,
Farm Economics Dan Wilks, Statistical
Meteorology Andrew Landers, Equipment
Engineering Bill Philpot, Remote Sensing Ed
Harwood, Dairy - Field Crop Production James
Capron, Field Crop Production, CCE Ed Staehr,
Farm Management, CCE Keith Culver, Farmer, PA
consultant Doug Freier, Farmer Industry
collaborators include Agway (farm products),
Emerge (remote sensing), and several crop
consultants
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Cornell PA Research

• Variable fertilizer and lime application
• Variable seeding rates
• Spatial and temporal variability, their effects
  on crop growth, and their interactions with
  management practices (incl. modeling component)
• Evaluation of split-planter approach
• Use of remotely-sensed information (with Emerge)
• Statistical procedures for PA
• Economics of PA

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Musgrave Farm - Aurora, NY
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All Harvest Plots - Field Z
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Effect of Seeding Rate
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Soil Test P
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Soil Test K
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Soil Test pH
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Field M - Nitrogen x Tillage
Inset
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Field M - Nitrogen x Tillage - Inset
3142
3142
3142
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Nitrate Concentration in Soil (no crop, no
amendments)
Dry June
Soil NO3
Wet June
March
September
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Preliminary Results from Precision Agriculture
Research

• Variable seeding rates did not show promise
  based on 1998 data yields were primarily
  defined by field variability
• Distribution of soil test-based crop inputs
  appeared non- random, thereby justifying
  variable-rate application
• Optimum N rate was minimally affected by field
  variability, but greatly impacted by
  early-season precipitation
• Remotely-sensed images appear promising in
  providing useful information for soil, crop and
  pest management