What Maturity Group to Plant - When and Where Larry Purcell, Michael Popp, and Montserrat Salmeron MSSB Meeting January 12, 2016 Memphis,

Slides:



Advertisements
Similar presentations
Soybean seed quality response among maturity groups to planting dates in the Midsouth Larry C. Purcell & Montserrat Salmeron MidSouth Soybean Board Meeting,
Advertisements

Climate Data and Crop Modeling Joel Paz, Gerrit Hoogenboom, Axel Garcia y Garcia, Larry Guerra, Clyde Fraisse and James W. Jones The University of Georgia.
© Crown copyright Met Office 2011 Climate impacts on UK wheat yields using regional model output Jemma Gornall 1, Pete Falloon 1, Kyungsuk Cho 2,, Richard.
Arkansas Rice Variety Program C.E. Wilson, Jr. Director, RREC /Professor / Extension Rice Agronomist.
Crop Modeling, The 2012 “Flash Drought” & Irrigation Demand Cameron Handyside University of Alabama in Huntsville Earth Systems Science Center September,
Morteza Mozaffari Soil Testing and Research Laboratory, Marianna Efforts to Improve N Use Efficiency of Corn in Arkansas Highlights of Research in Progress.
Results and discussion Results and Discussion Figure 3. Observed (symbols) and simulated (lines) V-Stages of soybean cultivars (MG 3.0 to 3.9) grown at.
Evaluation of Various Insecticide Regimes in Sweetpotato Production for Sugarcane Beetle Control in the Mid-South Larry Adams 1, Randall Luttrell 1 and.
Current Status of Soil Test Calibration in Mississippi Bobby R. Golden Delta Research and Extension Center
Effects of Introduction of Feed Grains into Mid South Soybean Production Systems Effects of Introduction of Feed Grains into Mid South Soybean Production.
 Materials: Twelve rice cultivars (Starbonnet, Cypress, Wells, Bluebonnet, Lemont, Mars, Carolina Gold, Magnolia, Drew, Guichao, C-GL-13, and Lagrue)
Rationale and Objectives  Summer fallow is a common practice in the western portion of the Central Great Plains.  Summer fallow is inefficient at storing.
A Case Study of Crop Model Applications in an Increasing Diversity of Genetically Modified Traits Girish Badgujar 1, V.R. Reddy 1, K. Raja. Reddy 2, David.
Effects of Introduction of Feed Grains into Mid South Soybean Production Systems Effects of Introduction of Feed Grains into Mid South Soybean Production.
The Nitrogen Requirement and Use Efficiency of Sweet Sorghum Produced in Central Oklahoma. D. Brian Arnall, Chad B. Godsey, Danielle Bellmer, Ray Huhnke.
Use of Multispectral Imagery for Variable Rate “Application-zone” Identification in Cotton Production Tim Sharp Beltwide Cotton Conference January 6-10,
Relationships Between NDVI and Plant Physical Measurements Beltwide Cotton Conference January 6-10, 2003 Tim Sharp.
Soybean Agronomics Eric P. Prostko Department of Soil & Crop Science The University of Georgia.
Impact of Climate Change on Flow in the Upper Mississippi River Basin
Development of tillering pattern under transplanting and direct sowing methods in spring planted sugarcane M. O. A. Galal *, A. M. Abou-Salama **, E. A.
Vulnerability and Adaptation Assessments Hands-On Training Workshop Impact, Vulnerability and Adaptation Assessment for the Agriculture Sector – Part 2.
Influence of Planting Date, Harvest Date, Soil Type, Irrigation and Nematicides on Pest Numbers, Yield and Quality of Sweetpotatoes in the Mississippi.
Rate and Duration of Seed Component Accumulation in Water Stressed Soybeans José L. Rotundo & Mark Westgate Iowa State University, 1301 Agronomy Hall,
Nitrogen Use Efficiency Workshop Canopy Reflectance Signatures: Developing a Crop Need-Based Indicator for Sidedress Application of N Fertilizer to Canola.
1 Cotton 2005 Ouachita Fertilizer River Parishes.
Scaling up Crop Model Simulations to Districts for Use in Integrated Assessments: Case Study of Anantapur District in India K. J. Boote, Univ. of Florida.
DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.
Terence Robinson, Alan Lakso, Leo Dominguez, Mario Miranda and Mike Fargione Dept. of Horticulture, Cornell University Geneva, NY Precision Irrigation.
Effects of Introduction of Feed Grains into Mid South Soybean Production Systems Effects of Introduction of Feed Grains into Mid South Soybean Production.
Upscaling disease risk estimates Karen Garrett Kansas State University.
Efficiency of data presentation & utilization in state soybean variety trials Lingxiao (Ling) Zhang Delta Research and Extension Center Mississippi State.
Climate impacts on UK wheat yields using regional model output
Agro-Climate Tools Indiana Certified Crop Adviser Program Indianapolis, Indiana Dec. 18, 2013 Chad Hart Iowa State University
DRAINMOD APPLICATION ABE 527 Computer Models in Environmental and Natural Resources.
Utilization of NAA as a Seed Treatment to Control Stem Number in Russet Burbank Andrew P. Robinson, North Dakota State University / University of Minnesota.
Field Calibration of Plant Disease Models Using Smartphones Joe Russo Midwest Weather Working Group 3 rd Annual Meeting Charlotte, North Carolina August.
Impact of 2012 Drought & Corn Production Research Update Agronomy In-service January 4, 2013 Peter Thomison Horticulture and Crop Science Ohio State University.
Louisiana Variety Testing 2010 SCC-33/UCTA St. Pete Beach, FL January 26-29, 2010 Rick Mascagni Northeast Research Station St. Joseph, LA.
Yield Loss Prediction Tool for Field-Specific Risk Management of Asian Soybean Rust S. Kumudini, J. Omielan, C. Lee, J. Board, D. Hershman and C. Godoy.
Introduction Soybeans are an important crop on many cotton farms in the Midsouth. Rapidly rising input prices have increased the cost of soybean production.
1 Texas Liquid Fertilizer Corn TLF Commitment to you Increase yields Lower Costs Help solve those production problems that limit profitability.
David Krueger, President AgRenaissance Software LLC Raleigh, NC A Simplified Approach to Recordkeeping with FieldRecon Beltwide Cotton Conference January.
Woody Woodruff Method University of Missouri –a chart !!! –a “checkbook” method –uses historic water use data –originally for corn on claypan soils took.
1 Cotton 2005 Ouachita Fertilizer Red River. 2 Ouachita Commitment to you Increase yields Lower Costs / Unit Produced Help solve specific production problems.
Dr. Joe T. Ritchie Symposium : Evaluation of Rice Model in Taiwan Authors : Tien-Yin Chou Hui-Yen Chen Institution : GIS Research Center, Feng Chia University,
Enhanced Pest Control Systems for Mid-South Soybean Production Tom Allen, Mississippi State Blair Buckley, LSU AgCenter Pengyin Chen, University of Arkansas.
Corn Yield Comparison Between EPIC-View Simulated Yield And Observed Yield Monitor Data by Chad M. Boshart Oklahoma State University.
Soybean Maturity Groups and Selection
CERES-Wheat a dynamic model to simulate the effects of cultivar, planting density, weather, soil water and nitrogen on crop growth, development and yield.
Conservation Tillage in Cotton: A Mississippi Delta Perspective
EVALUATION OF PLANT GROWTH ENHANCEMENT PRODUCTS ON LOW DESERT COTTON
RISER Row-crop Irrigation Science Extension and Research
Food Soybean Varieties in Low-Input Conditions
and No-Tillage under Various Crop Rotations.
Nutritional Study of N.C. Blackberry Cultivars
Soil test results and how to use them
Managing Irrigation Using the STAMP Irrigation Tool
Economics of Soybean Maturity Groups’ Yield Response to Insecticides Seed Treatments with Early Planting 1Normie Buehring, 2Angus Catchot, 3Don Cook, and.
Irrigated Corn VT VT Irrigated Corn
Effects of the Introduction of Feed Grains Into Mid-South Soybean Production Systems John Orlowski1, Bobby Golden1, Jeremy Ross2, Gene Stevens3, Ronnie.
History of Predicting Yield Potential
Making Climate Information Usable to the Ag Community
What Is Up with Soybean Yields?
Conference/Meeting Name
Initial Research Directions for Mapping Chilling Statistics
Ascend Idea Starters.
Gurpreet Kaur, Dan Reynolds, B. R. Golden, T. Irby, W. J. Ross, G
National Agricultural Statistics Service
Presentation transcript:

What Maturity Group to Plant - When and Where Larry Purcell, Michael Popp, and Montserrat Salmeron MSSB Meeting January 12, 2016 Memphis, TN

 Project description  Yield results 2012 – 2014  Economics  Modeling  Decision-support tool Outline

 3-year study ( )  10 locations  Irrigated  4 planting dates (PD)  MG 3 to 6 soybeans (16 cultivars) ( > 6000 plots) ❶ Columbia, MO ❷ Portageville, MO ❸ Fayetteville, AR ❹ Keiser, AR ❺ Milan, TN ❻ Verona, MS ❼ Rohwer, AR ❽ Stoneville, MS ❾ St. Joseph, LA ❿ College Station, TX Participants: ❶ Felix Fritschi, Bill Wiebold; ❷ Earl Vories, Grover Shannon; ❸ Larry Purcell, Montse Salmeron, Ed Gbur; ❹ Fred Bourland; ❺ David Verbree, Angela McClure; ❻ Normie Buehring; ❼ Larry Earnest; ❽ Bobby Golden; ❾ Josh Lofton; ❿ Travis Miller, Clark Neely, Daniel Hathcoat Project Description

Yield Seed quality (AA, germ, grade, oil/protein) Developmental stages Stand counts, plant height, node number Lodging, shattering, green stem At all locations, we measured:

 Project description  Yield results 2012 – 2014  Economics  Modeling  Decision-support tool Outline

MG 4 and MG 5 soybeans were the best choices for early plantings. MG 4 best choices for late plantings, followed by MG 3 soybeans. Average Yield Response Over Locations Salmeron et al Agron. J. 106:1893

Yield Response by Location Salmeron et al Crop Sci. (in press)

Day of LocationMGRelY max RelY max Apr 1Apr 15May 1May 15Jun 1Jun 15 Rohwer Apr0.97 a0.98 a0.95 a0.91 a0.82 a0.72 b Apr1.00 a1.00 a0.98 a0.94 a0.87 a0.78 a Mar0.96 a0.92 b0.86 b0.82 b0.76 b0.71 b b0.75 c0.76 c0.77 c0.77 b0.78 a Salmeron et al Crop Sci. (in press) Yield Response by Location

Optimum Planting Window

Planting date x MG management guide by state. Guide for Arkansas available at MidSouth Soybean Board’s and Arkansas Soybean Board’s websites. Other states available soon. Limited number of hardcopies available in back.

 Project description  Yield results 2012 – 2014  Economics  Modeling  Decision-support tool Outline

Identify PD x MG combinations that reduce production risks as opposed to planting only the profit-maximizing PD x MG selection.

Expected returns vs. return risk at Rohwer, AR. Highest expected returns for MG 4 at first planting date Greatest risk also for MG 4 at first planting date By combining different MGs and planting dates, risk can be greatly decreased while returns are only slightly decreased Weeks, Popp, Purcell et al Field Crops Res. (in review)

MG 4, PD1, 100% MG 3, PD1, 21% MG 4, PD1, 39% MG 5, PD1, 12% MG 3, PD2, 7% MG 4, PD2, 21% Returns: 16% decrease Risk: 41% decrease Expected returns vs. return risk at Rohwer, AR.

 Project description  Yield results 2012 – 2014  Economics  Modeling  Decision-support tool Outline

What is a crop model? Predicts crop growth for each day throughout season Uses daily weather data Uses soil characteristics Predicts yield, development stages based upon crop characteristics Once calibrated, models can predict responses to different ‘what ifs’… Our approach: Calibrate CropGro model using data from all locations for 2012 and 2013 (4480 observations) Compare predicted vs observed results using independent data from 2014 (2368 observations) Decision Support System for Agrotechnology Transfer

Model Prediction for R1 CalibrationEvaluation

Model Prediction for R7 CalibrationEvaluation

Model Prediction for Yield CalibrationEvaluation

 Project description  Yield results 2012 – 2014  Economics  Modeling  Decision-support tool Outline

Decision Support Tool Development Collect daily weather data from 30 years from 11 locations in the MidSouth Run model simulations for: MGs 3 through 6 in half-MG intervals weekly planting dates from Mar 15 to June 30 (16 weeks) 30 years 11 sites between 29 and 39 o N total of 73,920 simulations Create interface in Excel that allows user to make comparisons among these 73,920 predictions

Link available to this site from MSSB’s and ASPB’s websites SOYMAP is available online for downloading