Transcript Slide 1

Decision Support Systems in Integrated
Crop Nutrient Management
Putting the Parts Together
Paul Fixen
Potash & Phosphate Institute
www.ppi-ppic.org
Outline



Introduction
Decision Support Systems
Opportunity and Need for Improved Nutrient
Management
 Common Components of Decision Support
Systems
 Crop appearance
 Crop nutrient removal
 Soil testing, plant and grain analysis
 Nutrient response measurement
 Economic analysis
 Environmental risk assessment
 Integrating Nutrient Management
 Conclusions
Much at stake
At least 30-50% of crop yield is attributable
to commercial fertilizer nutrients
Stewart et al., 2005
A complex system involving uncertainty
Uncontrollable
Controllable
Cultural practices
Plant metabolism
Plant composition
Crop yield & quality
After Beaufils, 1973
Heightened need to get nutrient management right
as yields climb and environmental concerns intensify
 Climate change  Hypoxia
 Biodiversity
 Reactive N
 Soil erosion
 Ground water
 Desertification
 Rivers &
 Air quality
streams
 Lakes
Science has never had
a more complete set of
“knowledge nuggets”.
Industry has never had a
more impressive set of
technologies.
Adsorption Index (mg kg-1)
0
150 300 450 600 750 900 1050
0
150 300 450 600 750 900 1050
0.0
Soil Depth (m)
0.2
Wright
0.4
The challenge:
0.6
0.8
1.0
1.2
Delivering science and technology to the farm such that it
can be integrated in support of decision making
No Manure
1.4
1.6
Manure
No N or P
135 N + 0 P
135 N + 80 P
No N or P
135 N + 0 P
135 N + 80 P
1.8
Highly P fixing soil
Researchers, educators and crop advisers,
have no impact on nutrient efficiency
They impact grower decisions that
impact nutrient efficiency
Decision support systems
 Narrow definition: an interactive computer
program that helps decision makers formulate
alternatives, analyze their impacts, and select
solutions
 Broad definition: incorporates the narrow but
includes other computer-based technologies that
support decision making
 Nutrient management examples:
 See paper
Decision support systems - this paper
 Even broader definition: previous plus the
tools, computer-based or not, employed by
growers and their advisers in making nutrient
management decisions
 These tools and how they are integrated
locally to support decision making comprise
the decision support system
 The actual process used on farms today
Real world nutrient management decisions –
black box decision support
Possible site
factors
Crop
Soil
Grower
Nutrient inputs
Water quality
Climate
Weather
Technology
Decision support
Crop demand
Soil supply
Input efficiency
Economics
Environmental
Grower/Owner
Recommended rates
Event probability
Economic return
Environmental impact
Application timing
Etc.
Output
Decision
Action
Outcome
Feedback
loop
Real world nutrient management decisions –
transparent box decision support
Possible site
factors
Crop
Soil
Grower
Nutrient inputs
Water quality
Climate
Weather
Technology
Decision support
Crop demand
Soil supply
Input efficiency
Economics
Environmental
Grower/Owner
Recommended rates
Event probability
Economic return
Environmental impact
Application timing
Etc.
Output
Decision
Action
Outcome
Feedback
loop
Open support systems
 More demanding of the user
 But more powerful
 Framework
for systematic improvement
 Facilitate involvement of multiple parties
• Incorporates local knowledge and expertise
 More
conducive to modifaction as science
and technology evolves
Regional approach currently being implemented
in U.S. Corn Belt for N rate guidelines for corn
Site factors
State
Prev. crop
Fertilizer cost
Crop price
Decision support
Calculation of
avg. max.
return to N
(MRTN) based
on state trials
Rates
giving
+/- $1
of MRTN
Assumes variation in crop N demand is countered
by variation in soil N supply or efficiency such that
rate remains constant … user cannot decouple
(closed aspect) … reduces compatibility with other
components of decision making
Decision
Action
Outcome
At the same time in the region …
Hybrid Maize is being launched
After Sawyer & Nafziger, 2005
Opportunity and need for improved
nutrient management
N fertilizer recovery in major cropping systems
of the world
Crop
Crop
Research trials
trials **
** Field
Field scale
scale in
in farmer
farmer fields
fields
** Research
mostly on
on exp.
exp. stat.
stat.
mostly
%
%
Maize
Maize
65
65
37
37
NC USA
USA
NC
Rice
Rice
46
46
31
31
Asia farmer
farmer
Asia
40
40
Asia researcher
researcher
Asia
18
18
India poor
poor weather
weather
India
49
49
India good
good weather
weather
India
Wheat
Wheat
57
57
An uncontrollable factor that
markedly influences efficiency
*Ladha et al., 2005; **Cassman et al., 2002
Short term vs long term goals
 A challenge: avoid confusing true gains in
system level efficiency with practices that simply
borrow from future productivity
 Case studies in Dobermann et al., 2005:
 Soybeans in Hawaii (P)
 Rice in Philippines (P&K)
 Cotton in California (K)
 Maize in Nebraska (N)
Response of annual and cumulative seed cotton
yield to annual K application on a Vertisol
Dobermann et al., 2005
N use efficiency in irrigated maize in Nebraska
with recommended or intensive management
Recommended: 7,500 p/ha; soil test-based fertilizer rates; 2 N splits.
Intensive: 10,500 p/ha; higher fertilizer rates; 4 N splits + fall N on residue.
4-year averages
Maize yield, t/ha
Avg. Fertilizer N rate, kg/ha
N removed in grain, kg/ha
Rec.
14.0
195
167
Int.
15.8
305
198
Partial factor prod., kg grain/kg N applied
Removal efficiency, %
72
86
52
65
Measured change in soil organic N, kg/ha/yr
-58
+55
(N removal + change in soil N)/N applied, %
56
83
System level efficiency
Dobermann et al., 2005
Decision support systems should
consider both short-term and long-term
consequences
Donald Powell
Head of Gulf Coast Recovery and Rebuilding
Former FDIC Chair
$62.3 Billion Federal Budget
NPR Interview, 11/4/2005
“One of my major responsibilities is to
see to that federal dollars are spent
efficiently and effectively.”
Typical objectives of nutrient use
 Provide economically optimum nourishment to
crop
 Minimize nutrient losses from the field
 Contribute to system sustainability … soil fertility
or other soil quality components
The value of improving efficiency is dependent on
the impact on effectiveness
Can be very efficient … and totally ineffective
(Low P rate at a low soil P test)
High efficiency is not enough
Olsen soil test
at end of 5-yr: 15 ppm
42
Amount broadcast
initially, lb P2O5/A
160
Wheat yield, bu/A
40
80
8 ppm
38
0
36
5 ppm
34
Highest P efficiency
(about 30% recovery ) …
but not where you want to be
32
30
28
0
Wager et al., 1986
10
20
30
Annual seed-placed P2O5, lb/A
40
Nutrient use efficiency (NUE) vs.
land use efficiency (LUE)
100
Yield, %
75
50
25
Somewhat lower NUE
High LUE
Potentially high NUE
Low LUE
0
Increased nutrient additions
Dibb, 2000
Ecological Intensification of Agriculture
(Simultaneous pursuit of higher LUE & NUE)
“The intensification of production
systems to satisfy the anticipated
increase in food demand while meeting
acceptable standards of environmental
quality”
Success will require rigorous nutrient
management decision support
Cassman, 1999
Cassman,
1999
Kg grain per kg N .
Can NUE and productivity be increased
simultaneously?
75
U.S. Maize
59
70
65
60
43
55
50
45
40
35
30
1960 1965 1970 1975 1980 1985 1990 1995 2000
Since 1975:
39% increase in N efficiency
12% increase in fertilizer N per ha
40% increase in corn yields
Updated from Fixen & West, 2002
Common Components of Decision Support
Systems
 Crop appearance
 Crop nutrient removal
 Soil testing, plant and grain analysis
 Nutrient response measurement
 Economic analysis
 Environmental risk assessment
 Integrating Nutrient Management
Crop appearance – low tech approach.
Qualitative
Semi-quantitative
Leaf color charts
Integrated –Sensing
& Application
Crop appearance
high tech
(before yield loss)
RT200 Variable Rate Applicator
with GreenSeeker
Individual Sensors
Six individual sensor readings are used to calculate
the crops mean NDVI for the width of the applicator
and the N rate is automatically adjusted
Lafond, 2005
• Added 15% to N recovery in Oklahoma winter
wheat studies
• Growing in popularity in US Great Plains
Hand-held unit
Crop nutrient removal
 Provides a basic reference point
 Most effective when combined with soil test
information
 An old but still underutilized concept
 Example – US Corn Belt – see paper
Relative frequency (%)
Indiana precision corn field
60
50
40
30
20
10
0
60
50
40
30
20
10
0
60
50
40
30
20
10
0
1997
How have soil test levels
been changing over time
 Years 1 & 2: Number of
categories did not change
 Year 3: No samples in higher
1999
categories
 Category with most samples
moved downward:


2001
100
150
200
250
Ammonium acetate K category
upper limit (ppm)
Murrell et al., 2002
300

1997: 176 – 200 ppm
1999: 126 – 150 ppm
2001: 101 – 125 ppm
400
32.0
350
28.0
300
24.0
250
20.0
200
16.0
150
12.0
100
8.0
50
Median
4.0
CV
0
1997 1997 1998 1998 1999
CV (%)
Ammonium acetate K (ppm)
What has been happening to soil tests and variability?
0.0
1999 2000 2000 2001 2001 2002
Year
 K fertility becoming less variable but is drifting downward to yield-
limiting levels
 Reinforces the message of regional removal data
Murrell et al., 2002
Crop Nutrient Removal – low tech application
Charts showing
nutrient removal
• In adviser’s pocket
• Wall poster
A higher tech approach to determining crop
removal … PKalc.
What are the nutrient balances for a field?
Downloadable with documentation at www.ppi-ppic.org\toolbox
Soil testing
 The traditional foundation in developed countries
for over half a century
 A powerful decision aid but with limitations
 Not available in all regions
 Laboratory inaccessibility
 Lack of relevant calibration research
 Insufficient cash flow on farm or in village
Soil testing: the calibration problem in North America
 Soil testing began in mid 20th century

Extensive calibration research lead to
recommendations usually based on soil level and
expected yield
 Common belief … calibration research no longer
needed
 Soil testing remains an empirical approach - system
changes can alter interpetation
 Field: crop genetics, tillage, rotations, populations,
slow soil profile changes, other cultural practices
 Laboratory procedural changes
 Soil sampling changes
Recommendation Changes in Iowa
Soil test
% of IA soils*
category
Old
New
Very low
3
12
Low
9
24
2436
24 60
High
24
13
Very high
40
27
Optimum (Medium)
Number in red is % medium or below.
Mallarino et al., 2002
K recommended
or used in Iowa
(1000 tons K2O)
Old recs
2001-2 use
New recs
2004 use
260
440
572 (30% incr)
606 (39% incr)
•Recommendations doubled
because of new calibration
•Farmers responded to the
change
Soil testing: interpretation within decision
support systems
 Appropriate calibration in essential
 Appropriate interpretation is as well
 Replacing
general assumptions with site or
grower-specific information
 Examples of relevant information:
•
•
•
•
•
Yield potential and net crop value
Fertilizer costs
Factors influencing plant fertilizer recovery
Land tenure
Minimum acceptable return on investment
Workshop: International Symposium on Soil
Testing and Plant Analysis (Olympia, WA)
 Workshop participants were soil testing
professionals from 11 countries
 Divided into two classes of 20 each
 Each class divided into 4 groups of 5 with each
group given information on a specific farmer
 All groups given same calibration data, uptake
data, and initial soil test level
 Asked for the P rate to apply in first year and for
long-term target soil test level
Comparison of intuitively derived P
recommendations to PKMAN output
11ststyear
yearrate
rate
Class
Class
Farmer
Farmertype
type
11
22
Target
Targetsoil
soiltest
test
Class
Class
PK
PK
11
Kg
KgPP22OO55/ha
/ha
00
PK
PK
ppm
ppm
1.
1.Young
Youngrenter
renter
17
17
2.
2.Well
Wellestablished
established
56
56 45
45 55
55
26
26
25
25 22
22
3.
3.Expanding
Expanding
28
28
37
37
14
14
10
10 14
14
4.
4.Part
Parttime
time
22
22 39
39 94
94
22
22
20
20 20
20
00
12
12
22
NA
NA NA
NA
55
NA=
NA=Not
Notappropriate;
appropriate;Initial
Initialsoil
soiltest
test==10
10ppm
ppm
Computer program generated recommendations
similar to the soil testing professionals
Fixen, 1994
Within field variability …
not a new concept but one
with increased importance
 “There are dozens of soil types,
and also many man-made
variations within each soil type.”
 “Present needs within a soil type
depend so much on what has or
has not been done before.”
Johnson, 1952
Within field variability had new meaning once the
technology existed to manage it
A critical component of
modern decision support
… paper by Rosie Bryson
Plant and grain analysis
Role could increase in an informationrich support system
Maps of reference grain protein and AccuHarvest protein
for hard red spring wheat in northern Montana, U.S.
Calibrated for wheat,
barley, corn, soybean
Long and Rosenthal, 2005
Grain protein could become important feedback
Possible site
factors
Crop
Soil
Grower
Nutrient inputs
Water quality
Climate
Weather
Technology
Decision support
Crop demand
Soil supply
Input efficiency
Economics
Environmental
Grower/Owner
Output
Decision
Action
Outcome
Feedback
loop
PROTEIN
Nutrient response measurement
 Omission plots
 On-farm research
Nutrient response measurement – omission plots
N omission plot
in rice field
Used to determine indigenous nutrient supply of a specific nutrient when
others are non-limiting
A component in NuDSS
Witt and Dobermann, 2002
Flow chart of NuDSS for irrigated rice
Input parameters
Yield and soil maps
Omission plots
Climate data
Current yield level
Current fertilizer use
Yield target
Indigenous
nutrient supplies
Fertilizer sources
Prices of fertilizers
Micro nutrient needs
Timing of fertilizer
split applications
Settings
Decision support
Spatial analysis
S
I
T
E
P
R
O
F
I
L
E
Cost of all inputs
Expected income
from yield
Leaf color chart use
Crop management
Updated from Witt and Dobermann, 2004
Yield potential
Output
Recommendation
domain & indigenous
nutrient supplies
Yield target
Fertilizer Calculator
Fertilizer nutrient
requirements
Fertilizer Chooser
Meaningful and
cost effective
fertilizer sources
Fertilizer Splitter
Final fertilizer
recommendation
Profit Analyzer
Profit estimate
Promotion
Guidelines &
strategies
Nutrient response measurement - on-farm research
Yield monitors, GPS, GIS, software … expand the potential
70’
Consider the power of combining, omission plots,
350’
yield monitors, and grain protein sensing
Technical manuals available
Example: www.ppi-ppic.org\toolbox
Economic analysis
 Budget analysis software
 Example – Crop Budgeting Tool (U. of Illinois)
 Connects yield levels and input decisions to profit
and unit costs of production
 Valuable planning tool
 Illustrates the value of nutrient management in
farm profitability
Environmental risk assessment: P indices
 When is soil P a potential water quality problem?
The environmental P index
High P
Source
High
Transport
Critical Region
 Has become part of nutrient management plans in many
states in the US
 Determines whether manure application will be N
based, P based, or not allowed
 A science-based site-specific approach to targeting
efforts to improve water quality
Source integration tools.
 Assist farmers and their advisers in:

Developing manure management plans
 Keeping manure application records
 Determining supplemental fertilizer needs
 Several free excellent computer programs available
 Example: Manure Management Planner (Purdue)
www.agry.purdue.edu/mmp/
Conclusions
 Many decision aids are available as components of site-
specific decision support systems with the potential to
advance production and efficiency
 Some require minimal on-farm technology and are
appropriate for small land holders
 Others are more appropriate for regions with good
access to sophisticated technologies.
 Decision support systems should consider both short-term
and long-term consequences of management decisions.
 Their importance will increase as the demand for
improved efficiency and productivity increase.
 Open transparent support systems that facilitate adaptive
management hold the greatest promise for improving the
quality of nutrient management decision making.
Connect all 9 dots with 4 lines
without lifting your pen
“… we need to start thinking “outside the box” and to
embrace so-called “disruptive technologies” that will
transform our sector.” Luc Maene, IFA Director General
The future of the fertilizer industry lies in
“outside the box” approaches that put us back
inside the nutrient decision support box”
Will require an integrated approach
involving the scientist, the technology
developer, the adviser, and the grower