Part II: Section A
 
Using GIS Technology
3. Data Analysis

Data analysis is the second major step in assessing goose damage on crop yields (Figure IIA-25). The goal of data analysis is to derive as much information from the given data as possible to assist in separating goose damage from other factors. GIS provides the tools for data analysis. Establishing relationships between the data and the yield, comparing data, or calculating a statistical summary are examples of GIS data analysis techniques that can be applied.

Figure IIA-25: Required steps for assessing goose damage on crop yields.

 

What is GIS?
Geographic information systems (GIS) provide a systematic approach to managing large amounts of data, along with the tools necessary for analysis and interpretation. This guideline reviews some example data sets used to characterize a field, showing how GIS can help organize and manage the data so as to be used more effectively in various management decisions .

What is a query?
The query function is an integral part of any database. It is used to select a group of records from the tables within a database. Several applications use queries to select yield features by their value or location. Queries can be performed on the yield data to determine whether a relationship exists between certain soil types and crop yield.

Importance of a query
Selecting records from a database or map allows further analysis of the retrieved items. A small area of exceptional yield might be explained by soil conditions found in the same location. Within the GIS environment, locating areas of high or low yield can be done by running a query. Following the query on yield levels, one can define a query that identifies specific soil conditions. Running these two queries on the yield data is the first step to seeking a relationship between yield amounts and soil types.

There are several other reasons to query your GIS data. Among these reasons are:

-
  To use the results for further analysis
-
  To use the results to select other features
-
  To make edits to the selected data
-
  To calculate statistics on the selected data
-
  To create a graph or report that describes your selection
-
  To export your selection to a new file
 

For example, it is possible to generate summary statistics on the yield points that were selected previously through a query. After the statistics are compiled, it is possible to select the soils that intersect the selected yield points. No matter which results are desired, most spatial analysis starts with some form of query.

 
Performing a Query
 
There are two ways to perform a query:
1.
  Query using the Select by Theme
2.
  Query using the Editing mode
   
1. Query using the Select by Theme:

Step 1 : Select by Theme

  • Select the yield points that intersect the selected soils or the time of grazing
  • Drag the wheat yield (point) theme to the top of the table of contents and make it the active theme
  • Choose Select by Theme from the Theme menu



Step 2 : Enter Input Data

  • From the first drop-down list (Select features of active themes that), choose Intersect

  • Click New Set

Based on the choices, ArcView selects the yield points that touch or are within the boundary of the selected polygons in the Soil Map theme.

Step 3 : Open Attribute Table

  • View the selection in the attribute table
  • It is also possible to view the selection in the theme's attribute table
  • Make the Yield data (point) active
  • Click the Open Theme Table button
  • To make it easier to view the selection, promote the selection to the top of the table by clicking the Promote button
  • The selected records are all highlighted and grouped together at the top of the table
2. Query using the editing mode

Step 1 : Start an Editing Cession

  • Highlight the yield data theme
  • From the menu bar, select Theme and click on Start Editing


Step 2 : Open Attribute Table

  • Open the table by clicking on this button
  • It is possible to make a new theme containing only the yield inside exclosures

Step 3 : Start a Query

  • Start a query by clicking on the button pictured
  • For instance, yield inside the exclosures was flagged with number 2
  • Click on New Set
  • Change selection by clicking on

Step 4 : Delete Record

  • From the editing tool bar select Delete Records

Step 5 : Save Edits and Display the New Shapefile

  • From the table menu choose Save Edits As (give it a new name).

  • Display the new shapefile.

 

Sorting Yield Data

Whether using method 1,Query by theme, or Method 2, Query using edit mode, it is possible to perform the following:

 
1.
  Remove data impacted by confounding factors
2.
  Sort data by soil type
3.
  Sort data by intensity of grazing
4.
  Get estimate of yield from each exclosure and its pair
 

1. Remove confounding factors

Several factors may affect yield other than goose grazing. The extent of impact from these factors should be carefully delineated and their yield separated before any further analysis.

For instance, water inundation (ponded area) has a negative impact on yield. Areas impacted by water should be clipped out and not considered in the evaluation.

Step 1 :Display Areas Impacted by Water

Step 2 : Add the Yield Data Layer

 

Step 3 : Clip (delete) Yield in Water Impacted Areas

  • Need ArcView extensions XTools (clip with polygons, erase…)
  • From the XTools menu, click on Erase Features
  • Step 4 : Display Yield Data

    After clipping out the areas impacted by water, the yield data map should look like this:

     
    2. Test for soil differences

    One of the most basic sources of potential yield variability is the difference in soil types within the field. The data files associated with the soil types provide a detailed database of soil characteristics that can be related to other observations and measurements.

    Depending on field characteristics for each exclosure location per soil type, there are two ways to test for soil differences:

    Perform a t-test

    For field having only two major soil types, perform a t-test compare yield inside exclosure for each soil type (Figure IIA-26).

    Figure IIA-26: Map showing exclosure locations in comparison to soil type (top). Summary table for a t-test analysis (below).

     
    t-Test: Two-Sample Assuming Equal Variances
    Suavie Silty Clay Loam
    Sauvie Silty Loam
    Mean
    522.33
    507.1614286
    Variance
    2167.4528
    3585.437848
    Observations
    2
    7
    Pooled Variance
    3382.868555
    Hypothesized Mean Difference
    0
    Df
    7
    t Stat
    0.325270654
    P(T<=t) one-tail
    0.377241209
    t Critical one-tail
    1.894577508
    P(T<=t) two-tail
    0.754482418
    t Critical two-tail
    2.36462256
    Perform an ANOVA

    For fields having more than two soil types, perform an ANOVA using the yield from each exclosure. In many cases, it may not be possible to perform an ANOVA based on the exclosure yield. However, it is still possible to obtain reliable information about the differences in yield based on soil types by collecting data from a representative subset or sample. Sampling is simply the technique used to choose representative yield data points for analysis from a larger population. In a GIS environment, it is possible to randomly select a sub-sample from the population using bootstrapping.

    What is Bootstrapping?

    Bootstrapping is a resampling procedure that pulls random observations from an entire data set. This procedure generally is applied to large datasets with many observations. Electronic, automatic sampling such as yield mapping often generates this type of data. Bootstrap methods are computer-intensive processes of obtaining a subset of the data that meets statistical assumptions and can be used to calculate standard errors, confidence intervals, and significance tests. Each subsample is a random sample with replacement from the full sample.

    How it is done in Arc/View?

    Step 1: From the movement menu select create bootstrap file

    Step 2: Insert input data

    A bootstrap file creator window will open.

    Specify the theme name, the number of iteration to be performed, and the sample size.

    Step 3: Name and save the output bootstrap feature.

    No soil Difference

    In case there is no significant yield difference due to soil type, we can combine the yield data from all exclosures for further analysis.

     

    3. Sort data by intensity of grazing

    Since most of the necessary data has been collected in the field or subsequently generated, it is possible to use the data to determine whether relationships exist between goose grazing and yields.

    This step can be performed using a query or a clip command. Figure IIA-27 illustrates the process of using the clip command. (a) Polygon theme (areas grazed in March) used to clip yield data point. (b) Output map representing yield data within a particular class of interest (in this case, areas grazed in March).

    Figure IIA-27: Map showing March grazing (left) and yield data clipped for the area grazed in March (right).


     

    Using GIS to extract yield data in a series of steps from within zones of impact, we can determine yields from areas grazed in April, areas grazed in March but not April, areas grazed in January but not later, areas in exclosures, and other nongrazed areas (Figure IIA-28). This procedure allows us to develop a complete picture of goose grazing impact on yields and to evaluate the effects of seasonal grazing.

    Figure IIA-28: Summary table comparing the impact of timing of grazing on yield.

    0
    Exclosure
    No to Light Grazing
    Moderate Grazing
    Heavy Grazing
    Water Impact
    # of Observations
    85
    2454
    6831
    2435
    464
    Yield (g/m2)
    513
    483
    452
    374
    303
    STDV
    82
    63
    54
    86
    83
     

    4. Obtain estimate of yield using paired plot comparisons

    The same exclosure (control) can be used for more than just one-pair comparisons.

    Step 1 : Overlay Exclosure and Paired Plots on Top of the Grazing Map

    Both maps showing paired plot location in relation to respective exclosure.

    Step 2 : Zoom in to an Exclosure and its Paired Grazed Plot(s)

    Step 3 :Zoom in to Individual Yield Data Points

    Step 4 : Extract the Yield Data Point for Each Exclosure and its Paired Plot
    Yield in Paired Plots (g/m squared)
    Exclosure ID
    Exclosure
    Heavily Grazed
    Moderately Grazed
    1
    555
    273
    569
    2
    490
    -
    429
    3
    432
    316
    394
    4
    480
    -
    459
    5
    526
    -
    437
    6
    472
    377
    -
    7
    471
    393
    -
    8
    583
    428
    374
    9
    588
    -
    341
     
    5. Perform Statistical Analysis

    Using the data gathered from the previous section perform appropriate statistical analysis (Figures IIA-29 and IIA-30).

    Figure IIA- 29: Using a paired t-test to compare yield inside exclosures to paired plots grazed in March.

    t-Test: Paired Two Sample for Means
    0
    Exclosure
    Heavliy Grazed
    Mean
    502.4
    357.4
    Variance
    4020.175
    3868.3
    Observations
    5
    5
    Peason Correlation
    0.152066444
    00
    Hypothesized Mean Difference
    0
    00
    df
    4
    00
    t Stat
    3.96431676
    00
    P(T<=t) one-tail
    0.008308745
    00
    t Critical one-tail
    2.131846486
    00
    P(T<=t) two-tail
    0.01661749
    00
    t Critical two-tail
    2.776450856
    00

    Figure IIA- 30: Using a paired t-test to compare yield inside exclosures to paired plots grazed through April.

    t-Test: Paired Two Sample for Means
    0
    Exclosure
    Heavliy Grazed
    Mean
    521.74
    428.7857143
    Variance
    3332.5232
    5430.571429
    Observations
    7
    7
    Peason Correlation
    -0.100401973
    000
    Hypothesized Mean Difference
    0
    000
    df
    6
    000
    t Stat
    2.507789035
    00
    P(T<=t) one-tail
    0.023020325
    00
    t Critical one-tail
    1.943180905
    00
    P(T<=t) two-tail
    0.046040649
    00
    t Critical two-tail
    2.446913641
    00
     
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    1. Introduction
    2. Data Collection
    3. Data Analysis
    4. Findings/Summary
     
    All information, data, design, and graphics are copyright (c) 2003 Dept of Rangeland Resources, Oregon State University. All rights reserved.