Variable Rate Technology in Wheat www.dasnr.okstate.edu/precision_ag.

Slides:



Advertisements
Similar presentations
Do In and Post-Season Plant-Based Measurements Predict Corn Performance and/ or Residual Soil Nitrate? Patrick J. Forrestal, R. Kratochvil, J.J Meisinger.
Advertisements

History of Predicting Yield Potential TEAM VRT Oklahoma State University TEAM VRT Oklahoma State University.
RT200 >100 dealers  Asst., Assoc, Full Professors Brenda Tubana, Louisiana State University Robert Mullen, Potash Corp Wade Thomason, Virginia Tech.
O K L A H O M A S T A T E U N I V E R S I T Y Nitrogen Management for Wheat and Corn, What you Shouldn’t Do.
Plant sensing technology
1992, At What Resolution are there real biological differences.
Comparison of Active Optical Sensors
GreenSeekerTM Variable Rate Applicator Equipment and Applications
Nitrogen Use Efficiency Workshop Canopy Reflectance Signatures: Developing a Crop Need-Based Indicator for Sidedress Application of N Fertilizer to Canola.
Reverse N lookup, sensor based N rates using Weather improved INSEY Nicole Remondet.
Operating the Oklahoma State Univ. Ramp Mobile Analysis V2.0.
New Technology and Strategies for Nitrogen Management.
SOIL 4213 BIOEN 4213 History of Using Indirect Measures for detecting Nutrient Status Oklahoma State University.
GreenSeeker® Handheld Crop Sensor
Determining the Most Effective Growth Stage in Corn Production for Spectral Prediction of Grain Yield and Nitrogen Response Department of Plant and Soil.
Presented by: Keri D.Brixey
Use of By-Plot CV’s for Refining Mid-Season Fertilizer N Rates Daryl Brian Arnall Plant and Soil Sciences Department Oklahoma State University.
Sensor-based Nitrogen Application – Converting Research to Practicality Brent Rendel Rendel Farms Miami, Oklahoma.
Use of Alternative Concepts for Determining Preplant and Mid-Season N rates.
Reverse N lookup, sensor based N rates using Weather improved INSEY Nicole Remondet Rationale Weather is an aspect of agricultural sciences that cannot.
The Use of Red and Green Reflectance in the Calculation of NDVI for Wheat, Bermudagrass, and Corn Robert W. Mullen SOIL 4213 Robert W. Mullen SOIL 4213.
UTILIZATION OF CROP SENSORS TO DETECT COTTON GROWTH AND N NUTRITION
2013 NUE Conference Des Moines, Iowa August 5-7 Jacob T. Bushong.
GreenSeeker Sensor Brian Arnall Precision Nutrient Management Plant and Soil Sciences Department Oklahoma State University.
Development of a SBNRC Calculator for Cotton D. Brian Arnall Oklahoma State University W. Raun, J. Solie, M. Stone, R. Taylor, O. Walsh, D. Edmonds, C.
Economically Raising Nitrogen Use Efficiency By: Paul Hodgen.
Variable-Rate N Fertilization of Wheat and Corn in the Mid-Atlantic Variable-Rate N Fertilization of Wheat and Corn in the Mid-Atlantic Wade Thomason,
Automated Calibration Stamp Technology for Improved In-Season Nitrogen Fertilization K. Freeman, R. Teal, C. Mack, K. Martin, B. Arnall, K. Desta, J. Solie,
Plant-to-Plant Variability in Corn Production K.L. Martin, P.J. Hodgen, K.W. Freeman, Ricardo Melchiori, D.B. Arnall, R.K. Teal, R.W. Mullen, K. Desta,
John SolieIvan Ortiz-Monasterio Bill RaunCIMMYT Marv Stone Oklahoma State University John SolieIvan Ortiz-Monasterio Bill RaunCIMMYT Marv Stone Oklahoma.
Generalized Algorithm for Variable Rate Nitrogen Application on Cereal Grains John B. Solie, Regents Professor Biosystems and Agri. Engineering Dept. William.
Precision Agriculture an Overview. Precision Agriculture? Human need Environment –Hypoxia –$750,000,000 (excess N flowing down the Mississippi river/yr)
No-till Oklahoma Optimizing Nitrogen Fertilizer Through Use of Optical Sensors Brian Arnall Oklahoma State University
Three Alternative Nitrogen Management Strategies for Cereal Grain Production Brian Arnall Brian Arnall Plant and Soil Sciences Department Oklahoma State.
Nutrient Management: Ways to Save Money, From Simple to High Tech Brian Arnall Precision Nutrient Management Plant and Soil Sciences Department Oklahoma.
Generalized Algorithm for Variable Rate Nitrogen Application on Cereal Grains John B. Solie, Regents Professor Biosystems and Agri. Engineering Dept. William.
NFOA for Wheat and Corn. Yield Potential Definitions INSEYIn Season Estimated Yield = NDVI (Feekes 4 to 6)/days from planting to sensing (days.
Virginia Polytechnic Institute and State University Methods to Improve N Fertilization in Virginia Methods to Improve N Fertilization in Virginia Wickham.
Dave Mengel, Kansas State University Multi-State Winter Wheat Sensor Project,
O K L A H O M A S T A T E U N I V E R S I T Y E VOLUTION OF N ITROGEN R EFERENCE S TRIPS.
Oklahoma State University Precision Agriculture / Soil Fertility / Soil Nutrient Management Why should Nitrogen Use Efficiencies be improved? What technologies.
Expression of Spatial Variability in Corn (Zea mays L.) as Influenced by Growth Stage Using Optical Sensor Measurements By-Plant Prediction of Corn (Zea.
Theory of Predicting Crop Response to Non-Limiting Nitrogen.
Drew Tucker and Dave Mengel KSU Agronomy An update on Kansas sensor based N recommendations.
DEPARTMENT OF ENVIRONMENTAL SCIENCE & TECHNOLOGY Laboratory for Agriculture and Environmental Studies Sensor Based Variable Rate N Management in Mid-Atlantic.
Regional Project Objectives  To evaluate the performance of sensor-based N recommendation algorithms across a wide range of soil and climate conditions.
How the adoption of the N-Rich Strip has changed N Management
Components of a Variable Rate Nitrogen Rec
Exploratory Research in Corn
Nutrient Management: Ways to Save Money, From Simple to High Tech
Evolution of OSU Optical Sensor Based Variable Rate Applicator
Evaluation of the Yield Potential Based NFOA for Cotton
Development of a Response Index for Corn
Precision Agriculture an Overview
Precision Agriculture
Sensing Resolution in Corn
Precision Sensing Extension Workshop January 8, 2008
G. V. Johnson and W. R. Raun Dept. Plant & Soil Sciences
E.V. Lukina, K.W. Freeman,K.J. Wynn, W.E. Thomason, G.V. Johnson,
Predicting Yield Potential, 2007
History of Predicting Yield Potential
REVIEW.
Determining Nitrogen Application Rates with SBNRC and RAMP Calculator
Annual ASA Meeting, Indianapolis
Corn Algorithm Comparisons, NUE Workshop
OSU Corn Algorithm.
Objective: To discuss the current regional project and identify improvements needed for conducting future collaborative sensor-based research.
Late-Season Prediction of Wheat Grain Yield and Protein
Corn Algorithm Comparisons, NUE Workshop
Tastes Great. Less Filling
Presentation transcript:

Variable Rate Technology in Wheat

1993 Dr. John Solie, Dr. Marvin Stone, and Dr. Shannon Osborne collect sensor readings at ongoing bermudagrass, N rate * N timing experiments with the Noble Foundation in Ardmore, OK. Initial results were promising enough to continue this work in wheat. Dr. Marvin Stone adjusts the fiber optics in a portable spectrometer used in early bermudagrass N rate studies with the Noble Foundation, 1994.

1994 Variable N rates using an inverse N-rate, NDVI scale were applied. N rates were cut in half with no differences in grain yield compared to fixed rates. Grain N uptake levels using VRT across a 70 meter transect were less variable when compared to the fixed rates (left). John Ringer and Shannon Osborne collected sensor readings and later applied variable N fertilizer rates based on an initial bermudagrass algorithm. Initial algorithms used to spatially treat N deficiencies in wheat and bermudagrass employed an inverse N Rate-NDVI scale.

New ‘reflectance’ sensor developed. Samples were collected from every 1 square foot. These experiments helped to show that each 4ft 2 in agricultural fields need to be treated as separate farms Extensive field experiments looking at changes in sensor readings with changing, growth stage, variety, row spacing, and N rates were conducted.

Collaborative Project with CIMMYT Variety Selection/Yield Potential Spring Wheat 1995

CIMMYT Date Location Personnel Objectives Feb, 1997 Ciudad Obregon TEAM-VRT Discuss potential collaborative work Jan, 1999 Obregon & Texcoco Steve Phillips, Joanne LaRuffa, Wade Thomason, Sherry Britton, Joe Vadder, Gordon Johnson, John Solie, Dick Whitney IRSP 98, refine INSEY, 2- wheel tractor and wheat bed planter design Sep, 1999 Texcoco Erna Lukina IRSP 98, use of EY as a selection tool Aug, 2000 Texcoco Marvin Stone, Kyle Freeman, Roger Teal, Robert Mullen, Kathie Wynn, Carly Washmon, Dwayne Needham IRSP 99, applications of INSEY, sensor design for plant breeding Jan-Mar 2001 Ciudad Obregon Kyle Freeman Joint collaboration on NRI Grant Apr 2001 Ciudad Obregon Kyle Freeman Wheat harvest TOTAL July 2001 El Batan Jagadeesh Mosali, Shambel Moges Micah Humphreys, Paul Hodgen, Carly Washmon Wheat harvest Apr 2002 Ciudad Obregon Paul Hodgen NASA Grant

1996 Evaluation of management resolution at 3 locations Indices developed where we could detect differences in N and P, independent of one another. For wheat, numerator wavelengths between 705 and 735, and denominator wavelengths between 505 and 545 proved to be reliable predictors of N and P uptake. In bermudagrass, the index 695/405 proved to be reproducible from one season to the next. In, March, 1996, first variable rate N applicator demonstrated to the public Relationship between total forage N uptake and NDVI was used to apply variable N rates in turf.

In 1997, our precision sensing team put together two web sites to communicate TEAM-VRT results. Since that time, over 20,000 visitors have been to our sites. ( The first attempt to combine sensor readings over sites into a single equation for yield prediction A modification of this index would later become known as INSEY (in-season estimated yield), but was first called F45D.

Cooperative research program with CIMMYT. Kyle Freeman and Paul Hodgen have each spent 4 months in Ciudad Obregon, MX, working with CIMMYT on the applications of sensors for plant breeding and nutrient management Cooperative Research Program with Virginia Tech

1999 Applications of indirect measures of electrical conductivity were evaluated in several field experiments. This work aims to identify added input variables to refine the in- season prediction of yield. TEAM-VRT entered into discussions with John Mayfield concerning the potential commercialization of a sensor-based N fertilizer applicator for cereal crops. Increased yields at lower N rates observed at Covington. Using the in- season response index (RI NDVI ), we were able to project responsiveness to applied N, which changes from location to location based on climatic conditions specific to each parcel of land, and that changes on the same land from year to year.

RI Harvest RI NDVI Predicted potential response to applied N using sensor measurements collected in- season. Approach allowed us to predict the magnitude of response to topdress fertilizer, and in time to adjust topdress N based on a projected ‘responsiveness.’ 2000 Discovered that the N fertilizer rate needed to maximize yields varied widely over years and was unpredictable in several long-term experiments. This led to his development of the RESPONSE INDEX.

2001 N Fertilizer Optimization Algorithm (NFOA): 1. Predict potential grain yield or YP 0 (grain yield achievable with no additional N fertilization) from the grain yield-INSEY equation, where; INSEY = NDVI (Feekes 4 to 6)/days from planting to sensing (days with GDD>0) YP 0 = e (INSEY) 2. Predict the magnitude of response to N fertilization (In-Season-Response- Index, or RI NDVI ). RI NDVI, computed as; NDVI from Feekes 4 to Feekes 6 in non-N-limiting fertilized plots divided by NDVI Feekes 4 to Feekes 6 in the farmer check plots (common fertilization practice employed by the farmer). The non-N limiting (preplant fertilized) strip will be established in the center of each farmer field. 3. Determine the predicted yield that can be attained with added N (YP N ) fertilization based both on the in-season response index (RI NDVI ) and the potential yield achievable with no added N fertilization, computed as follows: YP N = (YP 0 )/ (1/R INDVI ) = YP0 * RI NDVI 4. Predict %N in the grain (PNG) based on YP N (includes adjusted yield level) PNG = YP N Calculate grain N uptake (predicted %N in the grain multiplied times YP N ) GNUP = PNG*(YP N /1000) 6. Calculate forage N uptake from NDVI FNUP = e 5.468NDVI 7. Determine in-season topdress fertilizer N requirement (FNR)= (Predicted Grain N Uptake - Predicted Forage N Uptake)/0.70 FNR = (GNUP – FNUP)/0.70 Engineering, plant, and, soil scientists at OSU release applicator capable of treating every 4 square feet at 20 mph Work with wheat and triticale plant breeders at CIMMYT, demonstrated that NDVI readings could be used for plant selection

Winter Wheat 24 locations in Oklahoma Spring Wheat 4 locations in Ciudad Obregon, MX 2001 Soft White Winter Wheat 7 locations in Virginia, 2001

Oklahoma

OctoberFebruaryJune days 50 lb N /ac 100 lb N/ac 75 lb N/ac N uptake, lb/ac INSEY: Rate of N uptake over 120 days, > ½ of the total growing days and should be a good predictor of grain yield INSEY: Rate of N uptake over 120 days, > ½ of the total growing days and should be a good predictor of grain yield 45 bu/ac, 2.5% N in the grain days with GDD>0?

NDVI at F5 In-Season Estimated Yield (INSEY)   days from planting to F5, GDD>0 +Good predictor of final grain yield +Requires only one sensor reading +Work over different regions/biotypes Units: N uptake, kg ha -1 day -1 where GDD>0 Units: N uptake, kg ha -1 day -1 where GDD>0

Fertilize whole field with 40 lbs N/ac preplant Before exiting the field, apply one strip with 80 (non-N-limiting) Fertilize whole field with 40 lbs N/ac preplant Before exiting the field, apply one strip with 80 (non-N-limiting)

Why is it important to know the RI for a field? C.V. = 31

So, what’s in it for the farmer? Ave Loss/ac/yr = $9.77

RI NDVI and RI HARVEST Strong correlation between RI NDVI (vegetative stages) and RI HARVESTStrong correlation between RI NDVI (vegetative stages) and RI HARVEST Accurately predict the crop’s ability to respond to NAccurately predict the crop’s ability to respond to N RI NDVI may refine whether or not N should be applied, how much, and expected NUERI NDVI may refine whether or not N should be applied, how much, and expected NUE Strong correlation between RI NDVI (vegetative stages) and RI HARVESTStrong correlation between RI NDVI (vegetative stages) and RI HARVEST Accurately predict the crop’s ability to respond to NAccurately predict the crop’s ability to respond to N RI NDVI may refine whether or not N should be applied, how much, and expected NUERI NDVI may refine whether or not N should be applied, how much, and expected NUE RI NDVI =NDVI-N-non-limiting/NDVI-farmer check RI Harvest RI NDVI

YP 0 YP N YP MAX

YP 0 YP N (RI=1.5) YP MAX YP N (RI=2.0)

1 2 Predict RI Predict YP 0 Predict YP N based on RI Fertilizer N = GNUP YPN –GNUP YP0 /