I have tried to concentrate on research that has practical applications to agriculture. Some recent and ongoing research topics are described in the following sections.

Agricultural Remote Sensing.   Research at Texas Tech has involved acquiring remote sensing data from satellites, aircraft, and ground-based sensors. One of the primary systems for getting high-resolution images of agricultural targets, such as farmer's fields, is the Texas Tech Airborne Multispectral Remote Sensing System, or "TTAMRSS". As the photos below show, TTAMRSS is flown aboard a light aircraft at altitudes ranging from 3000 to 10,000 feet above the ground. At an altitude of 5000 feet, the plane is just a speck in the sky. TTAMRSS contains three digital cameras capable of acquiring images in specific wavelengths of light, including the visible, near-infrared, and thermal infrared wavelengths. The digital cameras look down through a hole in the bottom of the airplane. TTAMRSS uses a global positioning system (GPS) receiver mounted on the aircraft to determine its location. When the locations (latitude and longitude) of targets are input into the TTAMRSS computer, it can direct the pilot to fly to each target, where it will automatically acquire an image. Imaging flights typically last from 1 to 3 hours, and may involve imaging a large number of targets.


Images acquired by TTAMRSS are used in a variety of studies. In the example shown below, images are used to derive detailed maps of the ground cover of the crops in two farmer's fields. The first field was planted half to cotton and half to corn. At the time the image was acquired, the corn had already been harvested, so there was no living vegetation in that half of the field. In the other half of the field, the density of the cotton canopy ranged from 50 to 90 percent, with most of the cotton having around 70 to 80 percent ground cover. The second field contained three crops-- cotton, alfalfa, and forage sorghum. Quite a bit of spatial variability in crop ground cover can be seen in the field, particularly the portion containing the cotton. These types of maps are useful to farmers and agricultural consultants for planning different crop management practices (such as fertilizing) and for anticipating what the yield of the crops will be at harvest.

Ground Cover

Crop Water Use.   Dwindling water resources is a major corcern for agriculture in many arid and semi-aris parts of the world, including the Texas High Plains. At Texas Tech, new approaches are being used to determine the use of water by agricultural crops for the purpose of identifying water-conserving farming practices. In the Texas Alliance for Water Conservation (TAWC) Demonstration Project, the daily and seasonal water use of crops are estimated using a combination of remote sensing imagery, weather data, and plant growth models. Remote sensing imagery can provide data on the plant canopy density for agricultural fields. As shown in the picture below, a portion of a Landsat image can provide data for many fields in a region.


Information on plant canopy density from remote sensing and daily weather data observed at stations (like the West Texas Mesonet) in the region can be input into a mathematical plant growth model to estimate how much water the crop in a particular field uses in a day. The graph below shows the daily crop water use over the growing season for a forage sorghum field. Maximum values of crop water use around mid-season were approximately 9 to 10 mm per day. The water use values vary from day to day depending on the weather (temperature, humidity, wind speed, etc.) and growth of the crop. By summing all the daily crop water use values over the entire season, a seasonal total of water used by the growing crop can be determined. In this example, the seasonal crop water use was around 18 inches of water.

CWU Sorghum

Daily estimates of crop water use can be checked by corresponding field measurements. In the picture below, an open-path eddy covariance (EC) system is set up at a field to measure the water used by the crop. The graph shows daily estimates of water use for the forage sorghum field compared to actual measurements obtained using the EC system. While there are small differences in the estimated and measured values of crop water use on each day (due to random measurement errors), there is generally good agreement between the estimated and measured values. This demonstrates that remote sensing is a good tool for use in estimating the water use of crops, particularly on the regional scale.


Precision Agriculture.   The high costs of fuel, seeds, and chemicals (fertilzer, herbicide, etc.) have made it challenging for a farmer to maintain a profit. One way of maintaining profits is to increase the efficiency of farming by decreasing the costs of farm operations. At Texas Tech, research is being conducted into "precision agriculture." Precision agriculture uses technology to reduce farming costs and increase the efficiency of farm operations. For example, the picture below shows inside the cab of a tractor equipped with "autosteer" technology. Autosteer uses the signals from orbiting satellites to determine the location and speed of the tractor. Data stored in an onboard computer tells the system how to automatically steer the tractor so that it precisely follows the furrows in the field. Instead of driving the tractor, the operator simply monitors the progress of the system. The autosteer system can pilot the tractor faster and more accurately than can be done manually. There is much less fatigue for the operator, allowing them to work more hours each day. Since the system is guided by satellites instead of by eye, work can be done even in the dark of night. This all leads to more efficient use of fuel and labor, thus reducing overall operating costs.


The information stored in the onboard computer can also be used for other aspects of precision agriculture. For example, since the computer records where the tractor (or harvester or sprayer) has already been in the field, it can prevent operations like applying fertilizer or herbicides from being done to parts of the field where they have already occurred. This saves money, and is also good for the environment.

In addition to autosteer, another popular precision agriculture operation is yield mapping. A device mounted on the harvester can measure in real-time the amount of crop (like grain or cotton) being harvested. This information, linked with the position of the harvester determined from the global positioning satellites, can allow detailed maps of the variation in crop yield across a field to be constructed. Examples of cotton yield maps constructed for a field for two years (2006 and 2007) are presented below. 2006 was a dry year, while 2007 was a rainy year. The effect of the availability of moisture is evident in the difference in yield between the two years-- the average cotton yield is 2007 was around 5 times the yield in 2006. What is also interesting in the yield maps are the spatial variations in yield. Understanding these variations might help the farmer manage the crop differently in different parts of the field, like applying more fertilizer to parts of the field with a higher yield potential.

Yield Map

Other types of spatial information can be collected for use in precision agriculture operations. The figure above shows a map of the soil electrical conductivity measured for the cotton field for which the yield maps were constructed. Electrical conductivity is measured by pulling a special sensor around the field with a tractor or truck. In the absence of soil salinity, variations in soil electrical conductivity are related to variations in soil texture. Thus, the soil electrical conductivity map is like a soil texture map for the field. The field can be divided up into different zones according to soil electrical conductivity. The map next to the electrical conductivity map shows the percent clay in the soil in each zone (determined from soil samples taken from each zone). This type of information can help explain why yield varies across a field.

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