Updating Soil Based Yield and Productivity Data in Ohio:
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Transcript of Updating Soil Based Yield and Productivity Data in Ohio:
Updating Soil Based Yield and Productivity Data in Ohio:
Evaluation of CERES-Maize for Creating a Reliable and Transparent System for Updating Soil-Based Yield and
Productivity Data at the Series Level
Jenette GoodmanDr. Brian Slater – AdvisorThe Ohio State University
School of Environment and Natural Resources
INTRODUCTION
Rational
In the state of Ohio, there is a need for reliable, accurate, and up to date soil yield and productivity data.
Yield/Productivity data is a tool used to aid in:•Land use and management decisions.
(agricultural vs. commercial /residential)
•Cropping and fertilization strategies.
•Assessment of a soil’s economic value.
•Differential Tax Assessment Programs
•All 50 states have some sort of tax relief.
•Ohio: CAUV Program.
the Current Agricultural Use Value Program
•Agricultural land values in Ohio are assessed based on soil productivity and yield potential under the fairly controversial Current Agricultural Use Value (CAUV) program.
•CAUV is a differential tax assessment program which allows farm land to be taxed according to agricultural and not full market value.
•Rational behind CAUV assessment:- Corrects an inherent “unfairness” in the real estate tax
system, which places a large tax burden on farmland owners, who typically own large areas of land, yet use a small amount of community resources
- Preserves farmland by potentially helping offset increasing operating costs and such.
INTRODUCTION
CAUV Calculation
Farms Net Income CAUV = __________________________
Adjusted Capitalization Rate
Net Income = (projected gross income) - (projected production costs)
•Projected gross income: computed starting with typical cropping patterns for the soil types found on a farm, applying the previous five year's statewide average yields per acre for each crop in the pattern, then multiplying these average yields by the previous five years' average price for each crop (Jeffers and Libby, 2008).
INTRODUCTION
CHANGES IN THESE YIELD NUMBERS HAVE SIGNIFICANT IMPACTS ON FARM OWNER AND COMMUNITIES ALIKE. FAMERS’ INCOMES AND
COMMUITIES’ TAX REVINUES ARE DEPENDENT ON HOW THESE VALUES ARE DETERMINED. THEREFORE THE METHOD OF ASSESSMENT SHOULD TRANSPARENT,
RELABLE, AND JUSTIFIABLE.
Approaches to Yield Prediction
•Empirical/regression based approach
•Educated “estimations” based on expert soil and productivity knowledge
•Reporting of yields (too little soil information)
•Computer simulation of plant growth i.e. Models
Our Method of Choice: Crop Growth Models
INTRODUCTION
Why Take a Computer Model Approach?
•Issue to Consider: Not enough yield data associated with each specific soil series for a purely empirical approach.
•Crop models are a well tested and generally accepted method for evaluating crop yield potential with limited data.
•Many different crop growth models including : CERES, RZWQM
•Models look mainly at carbon, nitrogen, water, and temperature dynamics at critical growth stages of plants
•For this study, the CERES-Maize model (within the DSSAT cropping modeling software package) was selected to predict maize yield values for a number of soil series in Ohio .
INTRODUCTION
Overview of DSSAT
•A Decision Support System for Agrotechnology Transfer (DSSAT) is a software packagecontaining various crop growth models for a variety of crops
•DSSAT is a software product that combines crop, soil and weather data bases into standard formats for access by crop models and applications (an integrated system which assists in generating appropriately formatted data for input into models) .
•The user can simulate multi-year outcomes of crop management strategies for different crops at any location in the world.
•It has been in use for more than 15 years by researchers in over 100 countries.
INTRODUCTION
The Goal
The object of this research project is to:
1.) Use existing soil, climate, and yield data to create a transparent and reliable system for generating yield and productivity data for Ohio soils at the soil series level;
2.) Evaluate the feasibility of using existing Ohio soil and maize yield data in combination with A Decision Support System for Agrotechnology Transfer (DSSAT) to generate accurate and reliable maize yields at the soil series level;
3.) To develop a method for generating soil profile input data representative of a typical series profile.
INTRODUCTION
Overview of DSSAT
(Jones, et al., 2003)
Overview of DSSAT
(Jones, et al., 2003)
The CERES – Maize Model
•A process-level, comprehensive model that uses a daily time-step simulation to calculate and integrate growth and rates of change within the soil-plant-atmosphere system (Quiring and Legates, 2008).
•Soil temperature is used to modify plant processes (emergence) and SOM decomposition.
- Highly dependent on soil / water relation and availability.
•Predicts plant biomass and final grain yield
INTRODUCTION
Generalized Modeling Process
1. Acquisition of minimum datasets
-For DSSAT models this includes soil, weather, and management factor associated with specific yield data.
2. Generation of Appropriately formatted input files.
-DSSAT provides software for this
3. Initial running of model
4. Model Calibration
5. Model Prediction and and Validation.
INTRODUCTION
In a perfect world we would have test site for each soil series with a specific set of management practices.
Data Requirements
Soil – Profile analysis by horizon (texture, %OC, pH)
Weather – Daily: max/min temp, precipitation
Management – Row spacing, seeding rates, cultivars, etc.
Yield – Yields for each soil series (calibration/validation)
INTRODUCTION
Generation of Weather Input Data
Required Minimum Weather Dataset 1. latitude and longitude of the weather station2. daily values of incoming solar radiation (MJ/m²-day)3. maximum and minimum air temperature (ºC)4. rainfall (mm).
SourceWeather datasets are available from the National Oceanic and Atmospheric Administration (NOAA), National Climatic Data Center. This online resource, free to academic institutions, gives access to years of weather data from thousands of weather stations across the United States.
INTRODUCTION
Defining Crop Management Inputs
Required Minimum Management DatasetManagement data: planting date, dates when soil conditions were measured prior to planting, planting density, row spacing, planting
depth, crop variety, irrigation, and fertilizer practices.
Sources Suggested management practices from the Ohio Agronomy Guide, 14th edition as well as agricultural practices commonly implemented in Ohio (Thomison et al., 2005).
Actual, know management practices implemented at specific sites during specific growing seasons.
INTRODUCTION
Generation of Soil Input Data
Required Minimum Soil DatasetGeneral:
Surface slope, soil color, permeability, and drainage class.
By Horizon:upper and lower horizon depths (cm)
percentage sand, silt, and clay content1/3 bar bulk density
organic carbonpH in water
aluminum saturation, andinformation on abundance of roots..
Sources: OSU Characterization Data, NRCS Official Descriptions
INTRODUCTION
Model Calibration
•Ideally uses a combination of field experiments and computer simulations to most accurately represent the system being modeled.
•Make necessary adjustments to model parameters as well as simulation results.
•DSSAT does not offer any automated procedures for calibration.
•Changes to parameters of the model in order to calibrate it for conditions specific to Ohio must be done manually, by making quantitative comparisons of model outputs to “fine tune” the model parameters.
•Typically involves adjustment of cultivar parameters and soil profile data (water related) to minimize the RMSE between observed and simulated yields
INTRODUCTION
Model Validation
Model validation involves taking the calibrated model and applying it to a new set of parameters. With DSSAT, calibration and
validation dataset are usually associated with years of weather data. So one year(s) yield data will be used to calibrate the model, while another set of year(s) will be used for validation, with only the
weather data and pest management for that year being changed (Thorp et al., 2008; Quiring and Legates, 2008).
Sources of Yield Data
County Soil Surveys: Out of Date, described as map units, not individual soil series.
CAUV: Computed using soil characteristics and production information contained in the 1989 Bulletin 685, entitled ‘Ohio Soils with Yield Data and Productivity Index’
Performance Trials: Actual, real yields, on known soils, with known management strategies.
INTRODUCTION
Overview
•A Decision Support System for Agrotechnology Transfer (DSSAT) was used to predict maize yield for 30 soil series, implementing the CERES-MAIZE model.
•Minimum datasets for soil, management, and weather data were aquired and input to DSSAT’s Sbuild, Xbuild, and Weatherman respectively to generate inputs for the CERES-Maize model.
•The largest challenge of this study was to create a representative soil profile for input into CERES-maize model from existing soil profile data, without resampling.
METHODS
Soil Profile Selection / Generation
1. Reprehensive Soil Profile: Profiles chosen based on how well they appeared to represent the soil series within the range of values described in the official soil series descriptions.
Limitations: Number and kind of profiles and lab analysis is available. Risk of selection of non repetitive profile.
2. Generic Profile Generation: The SSURGO database offers a range for physical and chemical properties of each soil series, along with a generic profile.
Limitations: Discrete definition of attributes from a range of values. To calibrate each profile for best prediction would require a very large effort.
METHODS
Modeling/Scenario Strategies
1. Concept Validation: Use Ohio Soils with Yield Data and Productivity Index numbers for a number of “benchmark” soil series in Ohio.
2. Soil Survey map unit yield estimates: Use most recent and reliable numbers from Wood, Hancock, and Allen County.
- Model each soil series, then construct yield estimates based on component proportion of map unit.
3. Performance Trials
METHODS
Scenario 1: Concept Validation
Weather • 30 years •NAEW Coshocton, Ohio.
Management•Suggested management practices of the Ohio Agronomy Guide, 14th edition (Thomison et al., 2005).
Yield: Ohio Soils with Yield Data and Productivity Index’ .
SeriesSeries Drainage ClassDrainage Class TextureTexture Slope %Slope %
blountblount so,wwhatso,wwhat silsil 33
pewamopewamo very poorlyvery poorly siclsicl 11
glynwoodglynwood wellwell silsil 11
haskinhaskin somewhat poorlysomewhat poorly slsl 11
crosbycrosby somewhat poorlysomewhat poorly silsil 00
miamianmiamian wellwell silsil 66
lewisburglewisburg wellwell silsil 11
eldeaneldean wellwell silsil 11
avonburgavonburg somewhat poolysomewhat pooly silsil 00
brookstonbrookston poorlypoorly siclsicl 00
benningtonbennington somwhat poorlysomwhat poorly silsil 11
cardingtoncardington wellwell silsil 44
clermontclermont poorlypoorly silsil 00
hoytvillehoytville very poorlyvery poorly siclsicl 00
loudonvilleloudonville wellwell silsil 44
nappaneenappanee somewhatsomewhat silsil 11
METHODS
Soil: Representative profiles chosen from OSU Characterization Database.
Yield estimates published in 1989
Scenario 2: The 3 CountiesWeather: • 10 years •Wood, Hancock, and Allen County.
Management: Ohio Agronomy Guide, 14th edition (Thomison et al., 2005).
Soil: OSU Database
Yield: Wood, Hancock, and Allen County Soil Survey (SSURGO)
METHODS
Soil Series Slope Texture
Spinks 4 loamy fine sand
Milton 0 silt loam
Latty 0 clay
Toledo 0 silty clay
Hoytville 0 silty clay loam
Mermill 0 clay loam
Haskin 1 loam
Del Rey 0.5 silt loam
Gilford 0 fine sandy loam
Glynwood 1 silt loam
Fox 3 fine sandy loam
Eldean 1 silt loam
Sloan 0 silty clay loam
Pewamo 1 silty clay loam
Millgrove 0 clay loam
Nappanee 1 silty clay
Elliot 0.5 silty clay loam
Blount 3 silt loam
Morley 0.5 loam
Ottokee 1 loamy fine sand
Scenario 3: Performance Trials
Weather: • 1997 to 2009•Performance trial location.
Management• Actual management information provided (fertilization, planting/harvesting, etc.)
Soil: Representative profiles chosen from OSU Characterization Database.
Yield: Location average yield form 1997 to 2009
Use half the years for calibration and half for validation.
Site Soil Series Texture
Bucyrus Blount Silt Loam
Wooster Canfield Silt Loam
Beloil Fitchville Silt Loam
Van Wert Hoytville Clay
Hoytville Hoytville Clay
Upper Sandusky Blount Silt Loam
South Charleston Kokomo Silt Loam
Washington C.H. Pewamo Silt Loam
Coshocton Chagrin Loam
Greenville Kokomo Loam
METHODS
Scenario 1: Concept Validation
RESULTS/DISCUSSION
Observed vs. Simulated Maize Yields
R2 = 0.6191
0
2000
4000
6000
8000
10000
12000
14000
0 2000 4000 6000 8000 10000 12000 14000
Observed (Kg/ha)
Sim
ula
ted
(K
g/h
a)
Observed Simulated
lewisburg loudonville
haskin haskin
loudonville cardington
glynwood avonburg
bennington lewisburg
miamian bennington
eldean glynwood
blount crosby
avonburg blount
clermont miamian
cardington clermont
crosby pewamo
pewamo eldean
hoytville hoytville
brookston brookston
Yield Rankings
Scenario 2: County Soil Survey
RESULTS/DISCUSSION
Soil Series
Simulated
Observed
milton 4105.2 6450.24
latty 10466.2 6853.38
toledo 7171.6 8197.18
Mermill 10398.5 9406.6
Haskin 8160 6987.76
Gilford 7497.3 8734.7
Glynwood 10775.8 7122.14
Hoytville 10948.3 9070.65
Del Rey 9811.8 7390.9
Spinks 6206.8 5375.2
Fox 7533.2 5912.72
Eldean 10009 7256.52
Sloan 11139.9 7189.33
Pewamo 11490.8 8734.7
Millgrove 10225.7 9406.6
Nappanee 5057.5 6517.43
Elliot 10174.7 7323.71
Blount 10336.4 7659.66
Morley 10346.7 6315.86
Ottokee 5931.7 6584.62
Series Component Percent
Simulated Yield
(Kg/ha)
Blount 90 10336.4
Pewamo 10 10225.7
Total Map Unit 10325.33
Hancock County Estimate for Map Unit
(BoA)= 9,541 Kg/ha
Scenario 3: Performance Trials
In Progress…
RESULTS/DISCUSSION
•These preliminary results confirm that it may be possible to use the DSSAT CERES-maize model in conjunction with existing soil data to characterize maize yield and productivity of Ohio soils at the soil series level.
•The model exhibited sensitivity to soil profile data input that the soil series level.
•Model is only as accurate as the data to which it is calibrated.
•More and better yield data is needed.
•Definition of representative soil profile is essential and difficult.
•Model exhibits sensitivity which may not be expressed in the generalized yield numbers provided by the CAUV and NRCS.
•Lack sufficient data to reliably calibrate and validate for each soil series, but perhaps if the model is proven effective for a number of soil series, it may be acceptable to apply it to the remaining…. Is this not the point of modeling?
CONCLUSIONS
•Thomison, P., et al. OSU Extension Bulletin 472-05: Ohio Agronomy Guide . 14th ed. Vol. Chapter 4. The Ohio State University Extension, 2005.
•Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., et al. (2003). The DSSAT cropping system model. European Journal of Agronomy, , 18(3-4) 235-265.
•Brown, D. M. (1987). CERES-maize: A simulation model of maize growth and development: C.A. jones and J.R. kiniry (editors), texas A & M university press, college station, TX, 1986, 194 pp. + two diskettes. U.S. $33.50 (cloth). Agricultural and Forest Meteorology, 41(3-4), 339-339
•Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., et al. (2003). The DSSAT cropping system model. European Journal of Agronomy, , 18(3-4) 235-265.
•Quiring, S. M., & Legates, D. R. (2008). Application of CERES-maize for within-season prediction of rainfed corn yields in delaware, USA. Agricultural & Forest Meteorology, 148(6), 964-975.
•Thorp, K. R., DeJonge, K. C., Kaleita, A. L., Batchelor, W. D., & Paz, J. O. (2008). Methodology for the use of DSSAT models for precision agriculture decision support. Computers and Electronics in Agriculture, 64(2), 276-285.
REFERENCES
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ACKNOWLEGEMNTS
Sincere thanks are offered to NRCS, Columbus for their financial support, Jeff Glanville from NRCS for his advice and assistance, Peter Thomison from OSU Extension for
help with performance trial data.
THANK YOU!