Precision Agriculture an Overview. Precision Agriculture? Human need Environment –Hypoxia –$750,000,000 (excess N flowing down the Mississippi river/yr)

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Presentation transcript:

Precision Agriculture an Overview

Precision Agriculture? Human need Environment –Hypoxia –$750,000,000 (excess N flowing down the Mississippi river/yr) Developed vs Developing Countries High vs Low yielding environments

Many research & development practices are not designed to foster site-specific management Continued success in wheat germplasm and technology dissemination worldwide depends on the free and uninhibited flow of genetic materials and information. Restrictions imposed on such movement due to intellectual property protection could have serious consequences on the ability of developing countries to sustain wheat productivity growth. …. further gains would have to come from specifically targeting breeding efforts to the unique characteristics of marginal environments

What is Precision Agriculture? Treating small areas of a field as separate management units for the purpose of optimizing crop production based on in-field variability

Site Specific Management The application of an input to a specific area based on the evaluation of variability of the need for that input. Richardson, Recognition of site-specific differences within fields and tailoring management accordingly, instead of managing an entire field based on some hypothetical average. Emmert, 1995.

Definitions of Precision Agriculture Using information to better manage farms at the field level or finer resolution. Optimizing inputs to produce the largest net income. Combine yield monitors, GPS, Grid Soil Sampling.

What is Precision Farming? Management by the Field Management by the foot Global Positioning Systems Yield Monitors Sensor Based Weed Control Grid Sampling Variable Rate Fertilizer Application

Oklahoma State University’s Definition of Precision Agriculture Variable rate application of fertilizers, pesticides or other materials based on the sensed needs of the crop within the following constraints: –Available Technology –Agronomic –Economic

Large Scale (Macro) Variability Within a Field

Intermediate Scale Variability Within a Field IKONI Imagery 4 m Resolution

Small Scale (Micro) Variability Within a Field

Variability in Weed Populations

Variability in Grain Yield

Map Based - Precision Farming Map Based - Precision Farming

On-the- Go Sensing of Plant Needs and Variable Rate Treatment

Map Based vs. Real Time Map Based: 1.Treat next season’s crop 2.Historic information 2. Slowly changing variables e.g. pH 3. Coarse resolution 4. Can directly measure variables e.g. pH Transition: Aerial/Satellite Imagery 1.Sense and treat current crop 2.Near real-time 3.Variables that change rapidly, e.g. N 4.Resolution limited by ability to accurately and precisely locate position Real-Time: 1.Sense and treat current crop 2.Real-Time, sense and treat on-the-go 3.Variables that change rapidly, e.g. N 4.High resolution, 1 m 5.Indirect measurement

History Oklahoma State University Optical Sensor Based Nitrogen Fertilizer Application 1991

History Oklahoma State University Optical Sensor Based Nitrogen Fertilizer Application First discussion between the Departments of Plant and Soil Sciences and Biosystems and Agricultural Engineering concerning the possibility of sensing biomass in wheat and bermudagrass. Biomass was to be used as an indicator of nutrient need (based on removal).

History Oklahoma State University Optical Sensor Based Nitrogen Fertilizer Application 1994

History Oklahoma State University Optical Sensor Based Nitrogen Fertilizer Application 1995

History Oklahoma State University Optical Sensor Based Nitrogen Fertilizer Application 1996

History Oklahoma State University Optical Sensor Based Nitrogen Fertilizer Application

History Oklahoma State University Optical Sensor Based Nitrogen Fertilizer Application TEAM-VRT entered into discussions with John Mayfield, Patchen, Inc., concerning the potential commercialization of a sensor-based N fertilizer applicator for cereal crops. 2001

History Oklahoma State University Optical Sensor Based Nitrogen Fertilizer Application 2002 TreatmentPre- Trt. N, lb/ac Trt. N, lb/ac Total N, lb/ac Yield, lb/ac Net Rtn, $/ac N-Rich Strip Var. Rate Fixed Rate A Fixed Rate B Field Rate SED Treated March 20 to April 10, 2002 Net Revenue = grain yield * $3.00 / bu – N Fertilizer * $0.25 / lb SED = Standard Error of the Difference

History Oklahoma State University Optical Sensor Based Nitrogen Fertilizer Application 2003