Assessment of Soil Physical Quality In the Information Age

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Assessment of Soil Physical QualityIn the
Information Age
Harold M. van Es Dept. of Crop and Soil
Sciences Cornell University
SST
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Information (Precision) Agriculture
Information technologies are rapidly being
developed and employed for the purpose of
agricultural land management. Technologies such
as global positioning systems, geographical
information systems, and remote sensing, combined
with field-based data acquisition/control systems
allow for more informed decision making, more
efficient use of inputs, and better
record-keeping, thereby leading to more efficient
crop production and greater environmental
protection.
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Information AgricultureTechnology and Knowledge
IA Technology refers to the hardware and software
that allows for the collection of information,
and control of crop management tools. IA
Knowledge refers to the integration of
information through scientific methods and
experience into a set of management tools that
allow for the optimum use of PA
technology. Currently, IA technology is ahead of
the knowledge base that would allow for its
effective implementation
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Global Positioning

• Determines the geographical location of the
  equipment (georeferencing)
• In IA, global positioning systems are used
  jointly with field data acquisition (e.g., yield
  measurements) or control (e.g., fertilizer/lime
  application) systems, or sampling equipment
  (e.g., soil samples, penetrometer measurements).

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Geographical Information Systems

• A GIS allows for the integration, processing,
  and presentation of information acquired for the
  purpose of precision agriculture

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Information Agriculture Information Flow Model 1
Delayed Interpretation
Weather data
Other data?
Knowledge Base
Records
Remote sensing data
Data Processing
Prescribing
Office
Field Data
Prescriptions
Field
Data/Sample Collection
Application Equipment Control
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Information Agriculture Information Flow Model 2
On-the-go interpretation
Records
algorithm
Data Processing
Prescribing
On-board field equipment
Field Data
Prescriptions
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IA may include many components
Information
Crop Input Management

• yield mapping
• mapping of amendments
• intensive soil/crop sampling
• weather data
• remote sensing
• in field, on-the-go
• air, space-based

• variable fertilizer and lime rates
• variable organic amendments
• variable seeding rates
• differential hybrids
• variable pest control

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Information Agriculture and Soil Physical Quality

• IA has mainly focused on the variable-rate
  management of crop inputs, but it also allows for
  the inexpensive collection of georeferenced
  data with high spatial resolution related to soil
  physical properties, including
• remote sensing data
• weather information
• data from on-the-go yield monitoring
• This is generating new research needs and
  opportunities for landscape-scale research in
  soil physical behavior.

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Remote Sensing Information

• High-resolution aircraft and satellite-based
  remote sensing information is now available at
  reasonable cost that is
• multi-spectral (hyper-spectral)
• digital
• georeferenced
• fixed-wing aircraft lt1 m
• satellite lt5 m (lt 1m with Ikonos in 2000)

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Short-Wave Reflectance Patterns
Healthy crop
r
Stressed crop
Dry / low OM soil
Wet / high OM soil
1000
100
UV blue green red NIR
---------- visible --------
WAVELENGTH (nm)
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Uses of Multi-Spectral Remote Sensing Information

• Vegetation image
• VI f (r(NIR) / r(red))
• indicates relative health of the crop
• Bare-soil image
• relative reflectance is an indicator of soil
  organic matter content and wetness
• Note Soil wetness can be quantitatively assessed
  using active and passive microwave radiation, but
  these methods are not available at low cost

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CIR image bare ground May 1999
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Derived image (SoilView) May 1999
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CIR image maize crop July 1999
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CIR image bare ground May 1999
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Derived Image (SoilView) May 1999
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CIR image maize crop July 1999
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CIR image bare ground May 1999
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Derived image (SoilView) May 1999
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CIR image maize crop July 1999
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Musgrave Farm, Aurora, NY. Georeferenced
Digital Color-Infrared Image (by Emerge)
July 11, 1998
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Musgrave Farm, Aurora, NY. Georeferenced
Digital Color-Infrared Image (filtered)
July 11, 1998
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Musgrave Farm, Aurora, NY. Geo-referenced
Digital Vegetation Index Image (by Emerge)
July 11, 1998
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Commercially-Available Remote Sensing Information

• Can characterize soil variability and potential
  management zones
• Has potential for assessing crop health and pest
  incidences
• Can interrelate soil and crop variability
• Can potentially function as high-resolution
  co-variate for soil and crop variables

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Precision Agriculture Information
Weather Data

• Weather is the most significant source of
  temporal variability in agricultural systems
  and strongly affects the physical and chemical
  behavior of soils
• Accurate, high-resolution weather information is
  now available for the purpose of crop, water,
  and pest management

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Emerge Web Site
www.emergeweb.com user id e3107 password Auror
a
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Study Effects of Field Spatial Variability and
Year-to-Year Variation in Weather on Maize N
Fertilizer Needs(J.M. Sogbedji, H.M. Van Es,
S.D. Klausner and D.R. Bouldin, 2000)

• Objectives
• To determine the effects of variable drainage
  class and yearly weather variation on N
  fertilizer needs of maize
• To evaluate the performance of the LEACHM
  model in predicting seasonal N losses
• To determine the potential for real-time
  adjustment of N rates based on weather
  information

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Materials and Methods

• Three soil types within a 10 ha field
• Honeoye-Lima silt loam (moderately well drained)
• Kendaia silt loam (somewhat poorly drained)
• Lyons silt loam (poorly to very poorly drained)
• Experiment
• Four rates of N (0, 55, 110, and 220 kg ha-1)
  applied to maize with five replications for each
  soil type

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Economic optimum rates of N fertilization (kg N
ha-1) at a fertilizer-to-maize grain price ratio
of 3.37, as determined using the quadratic
model.
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Variance component analysis for economic optimum
N rates.
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Precipitation (mm)
MONTH YEAR 1978 1979 1980 1981 1982
April 68 87 89 70 34 May 55 82 25 49 74
June 112 64 144 97 137 July 47 59 82 103 32
August 119 107 72 110 47 September 90 133
73 176 93 October 87 107 87 136 31 Tota
l April-October 578 639 572 741 448 Total
May-June 167 146 169 146 211
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N Mineralization
Normal year
denitrification/ leaching
40-50 kg ha-1
Wet late spring
Early spring
Late summer
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LEACHM-N Simulations
Parameter Input values Partition coefficient,
NH4-N 3.0 L kg-1 Partition coefficient,
NO3-N 0.0 L kg-1 Denitrification half
saturation constant 10 mg L-1 Litter
Mineralization rate constant 0.01 day-1 Humus M
ineralization rate constant 7 x 10-5 day-1 Q10
factor 2.0 C N ratio for biomass and
humus 10.0 Max NO3- /NH4 in solution to
control nitrif. 8.0
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N transformation rate constants used during the
simulations. Source Sogbedji et al., (1999a).

Drainage Class Rate Constant
Nitrification Denitrification Volatilization -
----------------------------------d-1-------------
----------------- Honeoye-Lima
0.391 0.004 0.0 (mod. well
drained) Kendaia 0.240
0.106 0.0 (somewhat poorly
drained) Lyons 0.240
0.106 0.0 (poorly drained)
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LEACHM-simulated Environmental N Losses
Soil Type Mineralized Denitrified Leached Environm
ental N N1 N2 Loss (12) -----------------------
-------------kg ha-1----------------------------
Honeoye-Lima (moderate
ly well drained) 1978 36.0 5.0 14.0 19.0 1979 36
.0 5.4 14.0 19.4 1980 38.0 12.0 40.0 52.0 1981 36.
0 6.0 11.0 17.0 1982 33.0 15.0 35.0 50.0
Kendaia (somewhat poorly drained) 1978 42.0 14.
4 10.7 25.0 1979 44.0 16.0 11.0 27.0 1980 43.0 50.
8 14.6 65.4 1981 47.0 17.0 7.0 24.0 1982 46.0 54.7
11.4 65.0 Lyons (poorly
drained) 1978 50.0 16.5 9.0 25.5 1979 51.0 17.5
9.0 26.5 1980 50.0 53.0 14.0 67.0 1981 53.0 21.0
5.0 26.0 1982 52.0 56.4 11.7 68.0
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High-Resolution Real-Time Weather Information

• Can be used with simulation models to
• improve N management
• predict yields
• improve water management
• Can improve pest management by
• predicting pest occurrences
• predicting spraying conditions

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Yield Monitoring Information
Grain yield monitors are providing detailed
digital georeferenced information on the spatial
(and temporal) distribution of grain crop
yields. Yield monitors are generally the
entry-point into precision agriculture technology
for many farmers.
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Field Z - Yield Data 1998 Point File
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Yield Data- Field Z (1998)
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Measured Infiltrability
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Field M (1998)
Inset
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Field M - Inset
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Yield Monitoring Information

• provides opportunities for
• directly linking crop response to soil
  constraints (drainage, water availability,
  compaction, chemical imbalances, etc.) and
  providing economic values to them.
• Recording long-term yield trends
• Identification of management zones (in
  combination with other information?)
• Easy on-farm research (e.g., hybrid selection)

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Record Keeping

• Time, space and rate-specific records on crop
  inputs (fertilizers, pesticides, organic
  amendments) allow for better assessment of crop
  needs and environmental impacts.

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In Summary

• Information technologies provide new
  opportunities (and challenges) for the evaluation
  of the physical behavior of soils
• Georeferenced remote sensing and yield
  information allow for evaluation of spatial and
  temporal variability in fields
• Site-specific weather information can be
  effectively used to improve the efficiency of
  crop inputs and protection of the environment
• Space, time, and rate-specific information on
  crop inputs will allow for better assessment of
  agronomic and environmental impacts

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Information Agriculture and Statistics

• The effective use of georeferenced information
  for the analysis of soil physical behavior
  requires the application of sophisticated
  statistical approaches. Examples may include
• fuzzy clustering to identify management zones
  from yield monitoring data
• co-variography and co-kriging to correlate yield
  monitoring and remote sensing data with direct
  field measurements
• time-series analysis with weather data
• state-space statistics for soil water content or
  strength data