by T.G. Mueller
Dep. of Agronomy
University of Kentucky
Farmers in the United States and throughout the world have the ability to quantify crop productivity across agricultural fields at very small scales; however, yield maps will only be of value if they can be used to make better soil and crop management decisions. This can only be possible if the causes of yield variability are known and understood . The causes may include spatial differences in management, precipitation, soil fertility status, soil water storage capacity, and pest infestation. Once the causes are know, the next step is management. If it is not possible to overcome some of these factors that cause low yields in certain areas, then it may be wise to reconsider the land use system. For example, removing land from production and possibly into CRP (Remember Dr. Dillon's lecture). Some limitations, however, may be manageable. For example, all we may need to do in some cases is to add more fertilizer to optimize crop productivity. These two steps of first assessing soil variability and then managing it are taken from discussions in a paper by Pierce and Nowak (1999).
The fundamental premises being made by those who practice site-specific management are that the causes of yield variability known and that once we know the causes, appropriate economical management strategies are also known. Are these reasonable assumptions? Consider the the site-specific soil sampling lecture. There we saw that it was not always possible to assess the fertility status at economically reasonable scales of management (i.e., > 200 foot grid ). There is a great deal of research that has been done to support whole field management practices based on known field conditions. But do these recommendations recommendations translate to site-specific management? Maybe, maybe not. We are not going to get into the recommendation issue during this lecture other than to say what I've already said. We are going to focus on the cause step. Anyway, you have to know the causes before you can manage.
Understanding the causes for site-specific variation in productivity will be challenging. There is a need methods for assessing important agronomic factors site specifically. These methods need to be relatively cheap. What about sensors? Well, sensors are usually cheap (relatively speaking). But are they related to factors of agronomic importance? Next week, we will consider sensors that work from afar. I'm referring to our lectures on remote sensing. This week, however, we are discussing ground based sensors. There are many kinds (we just purchased a ground based optical sensor for nitrogen management last week), but for this learning module, we are focusing on soil electrical conductivity (EC) sensors.
There are two kinds of soil EC sensors: induction and direct contact sensors. As I described on Monday, The induction sensors induce a field through the soil which results in the flow of electrons in the soil. As those electrons flow, they develop their own secondary electrical field which is proportional to the movement of the electrons (i.e., conductivity) in the soil. The sensor uses a detector coil to measure the secondary field which is related to the soil electrical conductivity. The sensor that we will use in class is a direct contact sensor. Look at Mueller et al. (2003) for a detailed description about how this sensor operates.
Soil electrical conductivity (induction and ground based) sensors has been found to be related to many factors of of agronomic importance. Look through Mueller et al. (1993) how soil EC relates to soil and landscape properties in Kentucky soils. EC is also related to crop productivity. Look at the paper by Kitchen et al. (1999) to see what these relationships are like. You will learn more about both topics in the lecture on Wednesday and you will work with yield-EC data for your assignment.
Understand how bulk soil EC sensors operate.
Be able to describe the relationship between EC and soil and landscape properties particularly for Kentucky Landscapes.
Be able to create EC maps plotted with other spatial data.
Understand how EC relates to crop productivity.
Monday (March 31, 2003) - Explanation of EC. Field
demonstration.
Wednesday (April 2, 2003) - Lecture on EC, Begin on assignment.
Friday (April 4, 2003) - EC Assignment.
Why is it important to understand the nature and the causes of yield variability?
What are the two major classes of soil electrical conductivity sensors?
Describe how induction sensors are used to measure soil electrical conductivity.
How is EC measured using the direct contact method?
Describe the three components of EC map variability.
What are the factors that govern EC variability?
What are some of the factors or agronomic importance that EC has been related with?
Summarize the methods used in this paper.
How did the variance of EC measurements behave over time and with depth?
What factors were the authors able to relate with EC? Do any of these have agronomic significance?
What role did mapping techniques play in overall variability?
Do you agree with the conclusion?
What is “Soil Water Storage” and why is it important?
How do
the authors define claypans (In Kentucky we have similar soils by they have <
35% clay below the topsoil)?
What is
boundary line analysis?
How does
Webb (1972) suggest that boundary line analysis can be used?
How did EC relate with yield?
Hand in your map of EC on top of the DOQQ (see Mapping EC data from the field by Gluck with ArcView 3.2. in the methods section below). .
Hand in your plots of the EC-yield relationships.
Interpret your EC plots.
How does the traditional crop and soil paradigm differ from the precision agriculture paradigm?
How did we calculate yield potential?
What is the significance of the following quantity: Yield Potential - Actual Yield. How might it be used for management.
For the two nitrogen studies, how did EC relate to nitrogen response.
.Go to this directory: C:\ESRI\AV_GIS30\ARCVIEW\EXT32 and paste those three files into it. If it says they already exist, cancel out.
Click HERE. Extract the file.
Open MS Excel. Open the file VERIS274.DAT that you downloaded. Click delimited, tab. Click Finish.
Now you need to clean the negative EC values out. Select every thing by holding <control> and <shift> down. Now push the number 8. To sort by shallow EC hold down <alt>, then hit the D key and (while still holding down alt) hit the S key. Select Column C hit enter. Delete any negative values (there were none). When we do large fields, we usually get several negative deep and shallow EC values. But there were none for this field because we collected so few points.
Next thing is to set the file up in a dbf format to bring it into arc/view. Select columns A and B. Format the those two columns as numbers with seven decimal place.
Select Columns C and D and format those to one decimal place.
Now make the column headings as follows: LON LAT SHALLOW DEEP.
Select all cells with numbers in them by holding <control> and <shift> down and then pushing the number 8.
Save the file by holding the <alt> key down and pressing F and then press A. Change Filename to VERIS274 (don't use quotes). The save as type should be DBF4. Click save, then yes.
Click excel. But don't save changes.
Open ArcView 3.2.
Go to file, extensions. Make sure Projector, Spatial Analyst, and Mr. SIDS viewers are on. Click OK.
Click on tables. Then click Add. Navigate to the DBF file you just created. Close this table by clicking on the little x in the upper right hand corner of the table.
Click on View. Double click on View1.
Go to the View menu and select Add Event Theme. Look at the fields and select the defaults. Click OK.
You should see where we collected EC points.
Go to to the Theme menu bar and select Convert to Shapefile.
Save the name as EC.shp in a directory you can navigate too. Click ok and then Add to view.
Go to view, properties. Set map units to decimal degrees.
Click on EC.shp.
Below your menu bar, there should be an an icon on the far right of the tool bar with an arrow pointing up. Click on it.
For output units select feet. Click OK.
Under Catagory, pick State Plane-1983.
For type, select Kentucky North.
Click OK
Click yes to recalculate area. Click yes to add as a shape file.
Click on new view. Click OK.
For the file name, you can call it EC_KYNORTH.shp. Click OK.
Next step is to add DOQQ data.
Go to the Kentucky Office of GIS (http://ogis.state.ky.us). Click on download or order Kentucky spatial data. Under Digital Ortho Imagery, click download or order CD Rom.
Click on DOQQ image server. Click enter. Either apply for a password or or click the word "here". Click "Download individual files or order CD-ROMs". Click on Fayette Co.
We are in the Lexington West (J41) Geological quadrant. The field is in the southeastern section of this quad .
Hold your mouse over the different yellow boxes. When you do so you can see the name of your individual files at the bottom of the window. Move your cursor around till "J41SE.zip" appears at the bottom of the screen. Look at the box the courser is over. This is the one you will download. Click on it. If you have trouble and don't feel like figuring it out, go to this site to get the data. http://kyvenutian1.state.ky.us/ogis/sid/distribution/north/j41se.zip (you may have to re-enter you OGIS username and password).
In either case, click open. It will download. You need to unzip the file and store it somewhere.
In Arcview 3.2. Click the add data button. Set data source type to "image data source".
navigate to where you downloaded the image. Select j41se.
Now move your GPS layer up so you can see both on the screen at the same time.
Now zoom out.
You can see that changes have been made in that area.
Export this image for printing (file, export). Use the JPEG file type. Under options, change resolution to 144 and quality to 100. Click ok. Click OK again.
You can insert this into PowerPoint or word for printing.
Click here.
Down load the EC-yield data from HERE.
Plot the relationships between EC (deep and shallow) and yield for all three files.
On the plots, draw where you think the boundary lines occur.
Interpret the plots. Consider the different years and crops.
Kitchen, N.R., K.A. Sudduth, and S.T. Drummond. 1999. Soil electrical conductivity as a crop productivity measure for claypan soils. J. Prod. Agric. 12:607-617.
Mueller, T.G., N.J. Hartsock, T.S. Stombaugh, S.A. Shearer, P.L. Cornelius, and R.I. Barnhisel. 2003. Soil Electrical Conductivity Map Variability: Case studies in Kentucky. Agronomy Journal. IN PRESS.
Pierce, F.J., and P. Nowak. 1999. Aspects of precision agriculture. Adv. Agron. 87:1-85.