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Precision Agriculture - Intranet CATIE

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Precision Agriculture - Intranet CATIE Powered By Docstoc
					               March, 1999


Precision Agriculture:
The Technology
The Opportunities
The Challenge

Harold M. van Es


                             SST
    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
   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.
    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.
   PA may include many components
Information             Variable Rate Management
• yield mapping          •   fertilizer and lime
• previous               •   plant populations
  agrichemical           •   differential hybrids
  applications           •   pest control
• intensive soil/crop    •   organic amendments
  sampling info
• weather data
      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
Musgrave Farm, Aurora, NY.
Georeferenced Digital Color-Infrared Image
(by Emerge)
Musgrave Farm, Aurora, NY.
Geo-referenced Digital Vegetation Index Image
(by Emerge)
                        Precision Agriculture Management

      Variable Rate 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
Variable Rate
Management

Manure

• Technology is being developed
• Potential environmental benefits
• Opportunities for refinement of nutrient
    management recommendations
• Record keeping important side benefit
                        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
                                                                  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
            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
Musgrave Farm - Aurora, NY
All Harvest Plots - Field Z
Effect of Seeding Rate


                   Avg Yield per strip (lbs)
                       Chisel           Zone
Seeding Rate
          22,000         1556          1547
          29,000         1559          1612
          36,000         1588          1585
          43,000         1559          1598
Soil Test P
Soil Test K
Soil Test pH
Field M - Nitrogen x Tillage




                           Inset
Field M - Nitrogen x Tillage - Inset


                       3142
                       3142
                       3142
                                 N Fertilizer Response - Field M
                                               1998
                         1400

                         1200
Yield per s trip (lbs)




                         1000

                          800                                            chisel

                          600                                            zone

                          400

                          200

                            0
                                45 + 0   45 + 45   45 + 100   45 + 155
                                              N rate
                                  MAIZE GRAIN YIELD RESPONSE CURVES
                            160                                       160
                            140                                       140
                            120                                       120
                            100                                       100
                             80                                        80
                             60                                        60
                             40                                        40
                                                        1978                               1980
                             20                                        20
                              0                                         0
MAIZE GRAIN YIELD (bu/ac)




                      160                                             160
                      140                                             140
                      120                                             120
                      100                                             100
                            80                                         80
                            60                                         60
                            40                         1979            40
                                                                       20                   1982
                            20
                             0                                          0
                                                                            0   50   100      150   200
                            160
                            140
                            120
                                                                                 MANURE = 0
                            100
                             80
                             60                                                       MWD
                             40                                                       SWPD
                                                        1981                          PD
                             20
                              0
                                  0      50     100    150      200
                                      FERTILIZER N RATE (lb/ac)
Nitrate Concentration in Soil
 (no crop, no amendments)
               Dry June




              Wet June



March                     September
      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

				
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