6.1 Explaining
Spatial Variability in Grain Yield
Tom
Mueller, Assistant Professor, Agronomy
Scott A.
Shearer, Associate Professor, Biosystems and Agricultural Engineering
Ed Perfect, Assistant Professor, Agronomy
Ken Wells, Extension Professor, Agronomy
Lloyd Murdock, Extension Professor, Agronomy
Don Nielsen, Professor, Land, Air and Water Resources, University of California,
Davis
Richard Barnhisel, Professor, Agronomy
Haluk Cetin, Mid-America Remote
Sensing Center, Murray State University
Steve Higgins, Research Specialist, Biosystems and Agricultural
Engineering
Carl
Dillon, Associate Professor, Agricultural Economics
Than Hartsock, Graduate Research Associate, Agronomy
Cooperators
Mike
Ellis, Farmer, Worth and Dee Ellis Farms, Shelby County, Kentucky
Charles Stuecker, Farmer, Stuecker Farms
Rick Murdock, Farmer, Ponderosa Farms
Introduction
The
number of yield monitoring systems in Kentucky is growing yearly because
producers recognize the great value of yield maps.
Nevertheless, growers have become frustrated because they lack the tools
and knowledge base to fully utilize multiple years of archived yield map data.
The potential is great potential for farmers to utilize yield maps to
make better agronomic decisions which can improve the economy and efficiency of
agriculture in Kentucky.
There
are three steps for utilizing yield maps. The
first is describing variability. Yield
maps allow producers to visualize and quantify variability within agricultural
fields. Profit maps can be
calculated from yield maps in order to describe the spatial variability of
economic gains and losses within agricultural fields.
The second step is understanding the causes of yield variability.
Spatial variability in grain yield is due in part to soil physical,
chemical, biological, and morphological properties.
In addition, topography, pests (e.g. weeds, insects, wildlife), micro
variations in weather, and genetic differences in seed cause yield variability
within agricultural fields. Perceived yield variability may be due to errors introduced
by yield monitoring equipment. The
third step is managing yield variability. This
may include variable rate application of fertilizers, pesticides, irrigation,
and seeds. In addition,
site-specific tillage may be used in areas where compaction is a problem.
And corn hybrids and soybean varieties may be matched to specific soils
within a given field.
The
greatest challenge for utilizing yield maps is the second step, understanding
the causes of the variability. Because
of the costs involved with sampling, producers can only measure those factors
that are expected to have the greatest impact on yield and the factors that are
manageable. The problem is
with the factors that limit yield within any particular field, farm, or
physiographic region of Kentucky vary from year to year and are not known prior
to sampling.
Objectives
This
portion of the overall investigation has two objectives:
1)
To develop an information base for use in yield map interpretation based
on the relationship between grain yield, topography, soil physical, chemical,
morphological characteristics, precipitation, and pest patterns in three
Kentucky agricultural fields for two and a half years; and
2)
To develop an economically feasible approach for producers to create
management opportunity maps. This
map will identify the yield limiting factors for each map unit.
The limitations may or may not be manageable.
If they are manageable, the producer must decide if the management
changes would be cost effective.
A
number of spatial studies have shown little correlation between grain yield and
soil chemical properties (e.g. Everett and Pierce, 1996; Blackmore et al. 1999)
although not without exception (e.g. Johnson et al., 1999).
Crops often do not respond to soil fertility for two reasons: 1) soil
nutrients may be above sufficiency levels where a crop response is expected, or
2) soil water or other factors may be even more limiting than nutrients, thereby
driving yield variability. Yield
variability often relates to various other factors that relate to plant
available water i.e., landscape position (e.g. Solohub et al., 1996; Karen et
al., 1999; Timlin et al, 1999), soil aggregate size (Cambardella et al. 1996),
and soil water storage precipitation (Runge and Hons, 1999).
Often, researchers have found that crop yield is not temporally stable (Runge
and Hons, 1999) and this is likely due to annual weather patterns and to
differences in crops. Some researchers have used simple regression or correlations
to explain yield variability (Khakural et al., 1996; Lamb, et al., 1996; Braum,
et al., 1999; Khakural, et al., 1999) while others have used multiple regression
approaches (Sudduth, et al., 1996; Cambardella et al., 1996; Blackmore, et al.,
1999; Khakural, et al., 1999; Heiniger, 1996).
A survey of the factors that control yield variability in Kentucky will
provide an information base that will be useful for yield map interpretation.
To
create management opportunity maps that are economical, a map unit should be
defined as a homogeneous productivity zone rather than a homogeneous soil unit
(e.g. soil map unit). Historical
yield maps are used to define these productivity zones.
The next step is to determine probable causes of this
variation. There are already some
existing spatial data bases available on the Internet that will be useful in
this regard for Kentucky agricultural fields (e.g. NRCS digital soil surveys,
USGS DEM’s, digital orthophotos, digitized topographic maps; http://www.state.ky.us/agencies/finance/depts/ogis/gisdept.htm).
Additional information (e.g. soil electrical conductivity sensor data,
remote sensed imagery, high resolution DEM’s) may be used if they are found to
be related to factors that influence yield variability (e.g. soil physical
properties, pest damage) and if they are not cost prohibitive.
Producers and equipment operators usually have a great deal of insight
into the causes of yield variability (e.g. planter or herbicide applicator
failure, compaction near field entrances, etc.) and should be consulted by a
crop/soil specialists in generating the management opportunity maps.
The
next step is field measurements and sampling.
This includes composite soil fertility sampling within each productivity
zone. Depending on the results from
Objective 1, soil survey evaluations may also be required (soil structure,
testing for compaction, texture by the feel method, soil drainage, depths to
limiting layers: hardpans, bedrock, fragipans).
These measurements will require training in soil survey techniques.
Once
the causes of variation are understood, the variability can be managed. This may include variable rate fertilization, seeding, or
pesticide application. It may also
include site-specific tillage or seeding. The
University of Kentucky can provide data from field trials to help make these
management decisions, but some recommendations may need to be made at the
site-specific level. Yield monitors
may be useful in this regard. They
can be used to make side-by-side management comparisons and to generate profit
maps for cost effective decision-making.
Procedures
for Objective I
- Steve Higgins, a doctoral student in Agronomy who is also a Research
Specialist in Biosystems and Agricultural Engineering will be responsible for
this objective. Scott Shearer has
initiated this research for a field in Shelby County during the 1999 growing
season. There are 3 phases to this
investigation.
Phase
1) Relate
yield with exhaustive landscape information. Elevation data is being
collected at a high intensity for several fields using Real-Time Kinematic GPS
where multiple years of yield map data exists.
Digital elevation models (e.g. Fig 1) will be
created for each field as part of a USDA sponsored project.
Topographic attributes will be extracted (e.g. slope, curvature, aspect,
wetness indices) and related with grain yield maps.
Multiple regression will be used to determine the percentage of yield
variability explained by topographic attributes.
Phase 2) Relate grain yield to soil measurements collected on a grid.
Grid sampling was done on several fields on a 100-ft grid (30.5-m; e.g. Fig.
2) at three locations in Kentucky (Shelby Co., Hardin Co., Calloway Co.),
and several other fields will be sampled at lesser intensities.
Soil test analysis will be done by UK’s division of Regulatory Services
for pH, BpH, P, K, Ca, Mg, CEC, and organic matter (OM).
In addition, particle size, topsoil depth, and depth to bedrock will be
measured at each grid point. Multiple
regression will be used to describe the relationship between grain yield,
topographic attributes, and the information described above at each grid point.
Plots of predicted versus measured will be used to visually assess the
quality of prediction.
Phase 3) Relate grain yield to information at points of interest.
A transect study along topographic and fertility gradients will be
established with between 50 and 75 sample points at each of three locations.
At each sample point, soil samples will be collected for soil fertility
analysis. Additional measurements
include, volumetric soil water content (top 40-in; weekly during growing
season), soil depth (if less than 60-inches), depth to compacted layers (e.g.
plowpan, fragipan), soil structure, and pest damage (i.e. weeds, insects,
diseases, wildlife). This data will
be analyzed with multiple stepwise regression, state space analysis (with the
assistance of Professor Donald Nielsen, University of California, Davis),
boundary layer analysis, principle components analysis, and canonical
correlations, and with neural network classifiers.
Procedures for Objective II - The second objective will be conducted by a MS
student in Agronomy, co-advised by Tom Mueller and Tasos Karathanasis.
We are requesting funding for this graduate student assistantship for 3
years beginning in August of 2000. In
addition to course work, the student will have the following
responsibilities: 1) devising and testing an approach for creating
management opportunity maps, 2) co-authoring extension publications, 3)
co-authoring refereed scientific publications, 4) writing a thesis, and 5)
assist in seeking additional research funding for utilizing yield maps in
Kentucky (specifically managing yield variability).
We believe that responsibilities involved justify a three-year masters
program. The model for creating
management opportunity maps will have two components, 1) zone delineation and 2)
explanation of limitations to grain yield.
Zone Delineation - There are a number of approaches for creating
management zones including: 1) crop yield, 2) 2nd order soil surveys
available for most counties from the NRCS, 3) intensive first order soil
surveys; 4) electrical conductivity zones, 5) remote sensed imagery (Pocknee et
al., 1996). It is our hypothesis
that zones based on historical grain yield maps in conjunction with 2nd
order soil surveys will be most efficient for creating management opportunity
maps. To test this premise, we
define efficient management zones as having more variability (in soil fertility
or grain yield) between management zones than within management zones.
This principle will be statistically tested with an F test (Steel and
Torrie, 1980). The five strategies
for creating management zones will be tested and compared to our proposed
method. The most efficient method
will be used for explaining limitations to grain yield.
Explaining Limitations to Grain Yield - We suggest a three step approach for
explaining yield variability. 1) Gathering
spatial information: free digital spatial data bases include digital second
order survey information, USGS DEM’s, digital ortho quads and more expensive
data sets which may or may not be cost effective: IR aerial images of crops,
high resolution digital elevation models (e.g. Fig 1), and measures of soil
electrical conductivity. These may
or may not be cost effective for farmers. 2)
Soil fertility sampling: composite soil samples should be collected from
each productivity zone. 3) limited
scope 1st order soil survey: A set of soil survey measurements
will be taken for each productivity zone. These
measurements may include depth to limiting layers (hardpans, perched water
tables, bedrock), soil texture, internal drainage.
The measurements that we will recommend to farmers will also depend on
the results from Objective 1.
We
will provide producers in Kentucky 1) an information base for yield map
interpretation, and 2). a set of techniques for creating management opportunity
maps. This information will be disseminated through a number of
avenues: 1) cooperative extension publications, 2) UK field days, 3) internet,
4) scientific literature, and 5) classroom.
If UK students graduate with these skills, it would be of tremendous
value to producers in the state of Kentucky.
Tom Mueller will teach these techniques to students in his “Soil Use
and Management” course. Scott
Shearer, Tom Mueller, and Carl Dillon will teach these techniques to students in
a new course: “Spatial Applications in Agriculture”.
I. An information base for yield map interpretation, II. a set of
techniques for creating management opportunity maps, III. extension publications
presenting this information (available on the internet), IV. course material for
two courses at the University of Kentucky (Soil Use and Management and Spatial
Applications in Agriculture), V. field days, and VI. refereed journal
publications.