SiteSpecific and Ecologically Based Weed Management

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Site-Specific and Ecologically Based Weed
Management

• Bruce Maxwell
• Cooperating Personnel
• Lisa Rew, Land Resources and Environmental
  Science
• Nicole Wagner, Land Resources and Environmental
  Science
• Ed Luschei, University of Wisconsin
• Perry Miller, Land Resources and Environmental
  Science
• Daniel Goodman, Department of Ecology
• David Buschena, Agricultural Economic/Economics
  Department

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Outline

• The net return equation
• Cutting inputs How do you do it without
  increasing risk?
• Shifting to a more ecologically based form of
  management
• Adaptive management and the role of precision
  agriculture

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Economic return to farmer
Net return Gross returns - Costs /ha
yield (kg/ha) seed (/ha) price
(/kg) fertilizer (/ha)
insecticides (/ha) herbicides
(/ha) equipment (/ha)
insurance (/ha) technology
(/ha) Environment
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Net Return Yield (Price) (CHCTCSCFCTechCE
nv) /ha kg/ha /kg
/ha
Yield f(Nw, etc.)
Temporal Dynamics
Applied Research

• Next year yield
• Next year yield

Spatial Dynamics
Basic Research
NR
Nw
NR
Y
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Extrapolation from Small Plot Competition
Experiments (SPCE)
Ed Luschei
Other site where we want to predict weed impact
Farm
SPCE
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Process Model Description
x
x
x
x weed density z growing season precipitation
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Generation of SPCE Data
Bozeman 1993
Yield (T ha-1)
0
Do this 8 times before fitting a curve
THROUGH ALL 8
Weed density (plants m-2)
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Calculation of the Experimental and Farm
Economic Threshold (x)
Net Return with weed control
Net Return ( ha-1)
Net Return Without weed control (experimental)
xexp.
Weed density (plants m-2)
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Predict Farm Threshold at Different Location From
Experiments
Randomly choose precipitation (cm) for farm
location
Precipitation (cm)
Calculate farm weed impact and threshold
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Each SPCE uses a Randomly Drawn Precipitation
Record
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Combine Sets of 8 SPCE and Fit Predictive
Equation to Predict Farm Weed Impact
y
xWeed density
zGSP
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Experimental vs. Farm Economic Threshold
150
125
Farm Threshold
100
75
50
25
0
0
25
50
75
100
125
150
Experimental Threshold
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Experimental vs. Farm Economic Threshold
150
125
Farm Threshold
100
75
50
25
0
0
25
50
75
100
125
150
Experimental Threshold
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How to parameterize models at the field scale???
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Field Scale Research
AJ Bussan Ed Luschei Lee Van Wychen
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Experimental assessment of cost-effectiveness of
precision wild oat control in spring wheat

• E. Luschei, L.Van Wychen,
• B. Maxwell, A.J. Bussan, D. Buschena, D. Goodman

Funding Montana Noxious Weed Trust Fund
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Questions

• Does precision weed control make sense?
• Improved net return?
• reduced inputs?
• Can we use this technology for on-farm
  experimentation?

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Work from1998.

• Provided observational evidence that
• patch spray might economically outperform
  broadcast application while using less herbicides
• Wild oat control might, however, be worse with
  patch spraying

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1998 Use data prediction and simulation
to examine what if we used this strategy...
Consultant patches
No Spray
Broadcast 1X
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Blue number above box is predicted mean wild oat
density at harvest
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1999 Experiment

• Consultant maps weeds, 4 sites
• Create prescription map which has treatment
  replicates (12 reps/site, 3 treatments)
• Spray according to prescription map
• Harvest yield with yield monitor equipped combine
• Find mean yields in replicates
• Calculate mean field values for treatments from
  replicates

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From consultant wild oat map to experiment...
Broadcast spray
Consultant patch map
Control (no spray)
Patch spray
Computer tells controller to spray in purple
and green areas
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Lee Van Wychen
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Data processing -- yield monitor data
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Conclusion

• Patch spraying can increase net returns by 10/a
  for field that are 20-50 infested
• Wild oat control may be more variable with patch
  spraying (2 of 4 cases had no difference in wild
  oat rating, the other 2 had approximately twice
  the rating)
• Field-scale experiments work with current
  technology!

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Reduced Inputs Increased Risk
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Adaptive Management
Experiments
Management
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Adaptive Management
A. Bussan, L. Rew, D. Goodman, D. Buschena

• Adaptive management is based upon the premise
  that managed ecosystems are complex and
  inherently unpredictable.
• The adaptive approach embraces the uncertainties
  of system responses and attempts to structure
  management actions as "weak" experiments from
  which learning is a critical product.

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Goal Maximize Net Returns To Farmers Through
Adaptive Management of Inputs
Whole Field Input Prescription
Previous Information
Data Synthesis
Weed Control
Yield
Weeds
Seeding
Seeding
Fert.
Fertilizer
Experiment
Building the database Increases the predictive
ability
Bayesian estimation
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Proof of Concept Experiment

• Create a reasonable model to predict yield and
  net return
• Use the model to compare net return outcomes for
  site-specific, conventional high input
  full-field, and conventional full-field
  low-input approaches to management.

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Predicting Crop Response to theEnvironment and
Inputs
Decision Support Model Structure

• Weed damage function including the impact of crop
  seeding rate.
• Nitrogen and water, crop response function
• Weed response to management function (herbicide
  dose response)

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Crop Yield is a function of
Decision Support Model Structure

• Crop seeding rate (SR)
• Weed density (Nw)
• f(management intensity or herbicide rate)
• Nitrogen available in the soil (N)
• Plant available water (stored soil moisture
  growing season precipitation) (PAW)

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Decision Support Model Structure
Sensitivity Analysis
Ymax Ywf most sensitive parameter
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Herbicide Rate Response
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Decision Support Model Structure
NR Y(P-D) - (SC FC WC O)
NR Net Return (/acre) Y Yield P Price
received (/bu) D Price dockage (/bu)
SC Seed cost (/acre) FC N-Fertilizer cost
(/acre) WC Weed control cost (/acre) O All
other costs (/acre)
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Weed demographic response to predict future
population levels and subsequent crop impacts

• Seed survival in the seed bank
• Germination and emergence
• Seedling survival
• Seed production
• Migration (immigration and emigration)
• Biotic and abiotic impacts on the above
• Spatial and temporal variability in biotic and
  abiotic environment

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Spatial Analysis Input
Optimization MSU Weed Ecology Group
version 0.5 This computer
program has the minimum requirements of 1) a
georeferenced map of wild oat abundance
(density), 2) crop yield map from a yield
monitor, 3) and a map of where herbicides were
applied to the field. Other georeferenced
information can greatly improve the
predictions of the model e.g. 4) crop density
map 5) soil moisture map ...etc. ltEntergt to
continue...
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Enter field information and amount of inputs
used in the field that was mapped. ---------------
--------------------------------------------------
-------------- For estimating stored soil water
using the Paul Brown Soil Moisture Probe 1.
Predominant soil texture a) Coarse - sand
b) Coarse - loamy fine sand c) Mod. coarse -
sandy loam d) Mod. fine - clay loam, sandy
clay loam e) Fine - sandy clay, silty clay,
clay ------------------------------------------
---- Enter a letter for the soil type...?
d 2. Enter the moist soil depth determined with
the Moisture Probe in inches? 14 3. Using the
70 probability of precipitation map, enter the
expected precip. for the growing season in
inches...? 5
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Enter Economic parameter values -------------
--------------------------------------------------
------ Price expected to received for grain /bu
? 3.00 Cost of weed control at label rate
including application cost in /acre ? 18 Cost
of 60lbs of seed for planting (/acre) ? 1.20
Cost of 80lbs of N fertilizer (/acre) ? 2.50
All other crop husbandry costs in /acre ?
60 -----------------------------------------------
---------------------- Enter to
continue...
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Patchy Wild Oat Distribution
100
90
80
70
Probability of Higher ANR
60
50
40
30
20
10
0
SS better than CONV
SS better than LI
CONV better than LI
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Continuous Wild Oat Distribution
100
90
80
70
Probability of Higher ANR
60
50
40
30
SS better than CONV
CONV better than LI
SS better than LI
SSbtConv
SSbtLI
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Summary from Proof of Concept Experiment

• Logical model with reasonable estimate of
  variation can produce reasonable results.
• With inclusion of variation (Monte Carlo
  Simulation) results could be useful to producers.
• Site-specific management is likely to improve
  input management in small grain systems

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Objective
Constructing the black box
Select and refine empirical small grain yield
models to include multiple factors including
nitrogen fertilizer rate, herbicide rate and
precipitation as well as wild oat density, and
develop methods that allow on-farm
parameterization of the models for weed
management decision support. Nicole Wagner
(USDA-FSA Argentina crop predictions)
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Crop Yield as a Function of 5 Variables
Weed Density(Nw)
Crop Density(NC)
Crop Yield
Precipitation (gsp)
Available Nitrogen (N)
Herbicide Rate (r)
Nicole Wagner
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• Imagine the factorial experiment required to
  assess 5 continuous variables producing nonlinear
  responses.
• 1024 plots with no replication

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Sub-models from Disjoint Data Sets
Data Sets
Sub-models
PAW Crop Yield
PAW crop SR
N rate PAW Crop Yield
N rate PAW
herbicide rate
Herbicide rate Crop Yield
Global Model (all 5 variables)
herbicide crop SR
N rate Herbicide rate Crop Yield
Jackknife Method
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Sub-Model Selection Method

• Sum of Squares lowest SS best model
• Minimal assumptions about uncertainty
• Ymax 0.7 0.01 gsp 0.001gsp2 0.002N
  0.0006 gspN
• Likelihood Method highest likelihood best
  model
• Establishes the prior
• Characterizes error which can be incorporated
  into Monte Carlo simulation
• Use probability distributions to fit parameters

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Monte Carlo Simulation Sensitivity of Yield
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Sensitivity of Net Return
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Model Development

• Review of historical experiments and models
• Independent field data (36 site-years from around
  the world)
• Greenhouse data (full factorial experiment)
• Virtual field simulation (demonstration of
  practical application of the empirical model)

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Model Development

• Review of experiments and models
• Independent field data
• 1. gathering data
• 2. investigating data
• 3. fitting candidate models to data
• 4. assessing model performance
• Greenhouse data
• 1. conducting experiment
• 2. investigating data
• 3. fitting candidate models to data
• 4. assessing model performance
• Virtual field simulation

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Investigating individual data sets
wheat yield (kg/ha)
wild oat density (plants/m2)
Van Wychen 2004
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Results of scatter plots, standardized regression
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Results of scatter plots, standardized regression
yield
crop density
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Results of scatter plots, standardized regression
yield
yield
crop density
weed density
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Results of scatter plots, standardized regression
yield
yield
crop density
weed density
yield
soil moisture
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Results of scatter plots, standardized regression
yield
yield
crop density
weed density
yield
yield
?
fertilizer rate
soil moisture
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Candidate models
1.
Cousens 1985
rw wild oat density ywf weed-free yield
(i.e. maximum yield) i, a parameters
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Candidate models
1.
no weeds
2.
yield
high weed level
rw wild oat density rc wheat density r
intrinsic growth rate b, f parameters of
competition
crop density
Baeumer and deWit 1968, Wright 1981, Weiner 1982,
Jollife et al. 1984
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Candidate models
1.
2.
3.
Kim et al 2002
rw wild oat density ywf wheat density b0
weed competition at 0 herbicide R herbicide
rate B response rate of the herbicide
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Candidate models
1.
ymax
yield
2.
j
crop density (rc )
3.
4.
rw wild oat density rc wheat
density ymaxmaximium yield
Jasieniuk et al 2000
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Candidate models
4.
5.
Streibig et al 1993
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Candidate models
4.
5.
6.
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Candidate models
4.
5.
6.
7.
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historical models
observed yield (kg/ha)
predicted yield (kg/ha)
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Assessing model performance
Limitations of the best models

• best-fitting model does not include the history
  of agronomic modeling
• 2 does not include influence of managed inputs
• 3 does not include fertilizers influence on
  wild oat nor herbicides influence on wheat (i.e.
  crop injury)

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Why a greenhouse experiment ?

• seeking ecological 1st principles
• easier to control (e.g. water treatments, time of
  emergence, individual plant distances)
• relatively quick to repeat
• generation of hypotheses that can be further
  tested in the field (Freckleton Watkinson 2001)

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5-variable greenhouse experiment

• 9 combinations of wheat/wild oat densities
• 4 herbicide rates
• 4 nitrogen rates
• 3 water treatment levels

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Assessing model performance
best-fitting models
Model 6
Model 5a
Model 5b
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observed yield (kg/ha)
predicted yield (kg/ha)
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Model Development

• Review of experiments and models
• Independent field data
• 1. gathering data
• 2. investigating data
• 3. fitting candidate models to data
• 4. assessing model performance
• Greenhouse data
• 1. conducting experiment
• 2. investigating data
• 3. fitting candidate models to data
• 4. assessing model performance
• Virtual field simulation

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wheat density
wild oat density
soil water
Virtual Field Maps
Van Wychen, unpublished data
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maximizing yield
kg N/ha 0 22.5 45 90 112.5 135 157.5 169
herbicide rate 0 0.25 0.5 0.75 1
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Predicted Yield
Herbicide rate
Nitrogen rate
kg N/ha 157.5
full herbicide rate (1x)
High rate Inputs
kg N/ha 45
quarter herbicide rate
Low rate Inputs
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Low rate scenario
Variable rate scenario
High rate scenario
kg/ha
lt 800 801-1000 1001-1200 1201-1400 gt 1400
Predicted Yield
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Localized variable rates
High input rate
Low input rate
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Localized variable rates
High input rate
Low input rate
probability 86
100
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Spring data
crop density
Model Yield Equation
weed density
available water
refit model-- update parameters
Machinery for variable-rate application data
collection
herbicide rate
nitrogen rate
yield map
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Summary

• Overall, previously collected field data sets
  revealed a large amount of variation,
  illustrating lack of knowledge of ecological
  mechanisms upon which farmers currently make
  management decisions.
• Field data supported the development of a
  5-variable linear regression model, but a
  5-variable data set (i.e. greenhouse experiment)
  was necessary to build a 5-variable nonlinear
  model.
• Application of the 5-variable nonlinear model to
  prescribe localized variable-rate fertilizer and
  herbicide management on farms will promote
  site-specific parameter estimation and increased
  parameter stability.

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Decision Support System
Previous years data
Possible management strategies
crop density
Global Model
weed density
Yield Equation
nitrogen rate
herbicide rate
available water
Predicted Returns
On-farm data collection
Net return
Strategy
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Risk Distribution based on simulations for
predicted yield
Strategy 1 SSM
Net Return
Strategy 2 Low input
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Acknowledgements
Dr. Ed Luschei
Dr. Sharlene Sing
Perry Hofferber
Lee Van Wychen
Dr. Lisa Rew
Dr. Marie Jasieniuk
Nicole Wagner