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Principal Investigator:
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Cooperators:
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Primary
Research Location: Bushland,
TX. Project Title: Use of Precision Irrigation and Remote Sensing to Manage and Monitor Disease in Pathogen Infested Soils |
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Reporting Period: January 2000 - December 2000 Objectives:
A. Summary of Progress: Irrigation and Disease: Take-all is a root disease of wheat caused by the soil borne fungus Gaeumannomyces graminis var. tritici. The disease is most prevalent under center pivot irrigation in continuous wheat fields that are heavily irrigated. Such fields are common in the 1N-reporting district of the Texas Panhandle. Unlike many diseases, take-all typically occurs in the same field locations year after year. Because take-all develops in reoccurring spots and is strongly influenced by irrigation, we felt it was an ideal disease for management using precision agriculture technologies. A study
was established under the center pivot irrigation system at the TAES
research site in Bushland. The center pivot is equipped with LEPA nozzles
and on-off valves. In conjunction with the on-off valves, water application
can be managed by regulating pivot speed. The pivot is also equipped
with thirty-two infrared transducers (IRTs) that are capable of measuring
crop canopy temperature. In 1998, the continuous wheat wedge of the
Bushland center pivot research site was infested with the take-all fungus.
Disease developed in infested spots in 1998 and again in 1999. In April
of 1999, take-all spots were geo-referenced for studies to be conducted
during the 2000 growing season. During 2000, differential irrigation
was applied to take-all areas and also to areas that were not infested
with the pathogen. Take-all areas that had been geo-referenced in 1999
were located and spectral readings were made weekly using the Crop Scan
hand held radiometer, and canopy temperature was recorded using the
pivot-mounted IRTs. We also contracted with a commercial remote sensing
firm, Earth Scan, to take satellite remote sensing images of our research
plots.
Fig. 1. Spectral image from the ICONOS satellite provided by Earth Scan, a commercial remote sensing provider. Plots irrigated at 50% PET are recognized as dark spots in the field. However, based on these images, differentiation between drought stress and root rot was not possible. Remote
Sensing - Differentiation between Biotic and Abiotic Stresses: If remote
sensing is to be of value for detection of plant diseases we must be
able to detect diseases before the symptoms are readily visible and
differentiate between disease and abiotic stress. Existing technologies
that are commercially available, and in use today, have limited resolution.
Thermal infrared transducers only read in the thermal infrared region,
and satellites, most handheld radiometers, and aerial imaging platforms
only use a few broad wavebands. Higher resolution data will be needed
if we are to detect spectral signatures that differentiate biotic and
abiotic stresses.
Fig. 2- Hyperspectral radiometer (A) and an integrating sphere (B) supplied by Resource 21 and Boeing. These imaging tools were used during the 2000 growing season to evaluate different remote sensing technologies for their ability to differentiate between biotic and abiotic stresses. Beet necrotic yellow vein virus (BNYVV) causes a yellowing of sugar beet foliage that mimics nitrogen stress. We collected samples throughout the growing season that exhibited yellowing symptoms as well as apparently healthy samples. Readings were taken in the field with a 9-band hand-held radiometer and airplane and satellite images were arranged when possible. Once the samples were in the lab they were tested for the presence of BNYVV through ELISA. The leaves were read in the integrating sphere and then frozen. Pigment extracts have been made on several preliminary samples and read with a scanning spectrophotometer. When complete, the tissue will be analyzed for nitrogen content. The hand held radiometer was unable to differentiate between asymptomatic plants that tested positive for BNYVV and asymptomatic plants that tested negative for BNYVV. Preliminary analysis of the hyperspectral data from the integrating sphere also appeared unable to differentiate between asymptomatic plants that tested positive for BNYVV and asymptomatic plants that tested negative for BNYVV. However, preliminary analysis of the data from pigment extracts showed differences between asymptomatic plants that tested positive for BNYVV and asymptomatic plants that tested negative for BNYVV (Figure 3). This indicates that the potential exists to remotely detect disease in the field but we are limited by our instrumentation. The sizes of the datasets are quite large and make analysis difficult. We are currently developing a database system, which will allow us to store and manipulate the large datasets generated by these technologies. It will be a network accessible, multi-user system, which will facilitate collaboration and sharing of data between all Precision Ag faculty. Remote Sensing - Disease Quantification: In a separate study, we investigated remote sensing as a means of quantitatively measuring Cercospora leaf spot incidence and severity. The study was conducted in a Cercospora disease nursery containing a fungicide evaluation trial; with treatments ranging from highly effective fungicide rotations to disease check plots with no control. An individual with years of experience in visually rating cultivars for disease severity rated each replicated treatment in the nursery while we used the Crop Scan handheld multi-spectral radiometer for ratings. Preliminary results indicated that the hand-held radiometer was a more effective means of relating disease severity to final yield than visual disease ratings made by an experienced individual. Visual ratings showed the highest variability at intermediate levels of disease where individuals are less adept at estimating percentages. This work will be repeated in 2001.
Fig. 3. Reflectance and absorbance data from healthy and diseased Sugar beets. Only spectrophometer readings could differentiate between Green virus-infected tissue from green virus -free tissue. Remote Sensing - Definition of Management Zones - Ergot: Development of a model for ergot risk assessment, based on historic weather records, has been attempted before with mixed results. Although these models can be useful, predictions are often inaccurate because of the extreme spatial and temporal variability in environmental factors that impact disease development. One of the basic problems with this approach is that weather data often does not correlate well with disease incidence because weather stations may be far removed from the fields of interest. For instance, in the Texas Panhandle, it is not uncommon for one field to have a high incidence of disease while fields one to five miles away might be disease free. The weather station nearest any of these fields could be 25-50 miles away. In such a case, it is clear that strong correlations between disease incidence and any particular weather parameter would be a matter of chance. Therefore, the distance of a sorghum seed production field from the nearest weather station defines the management zone for sorghum ergot. However, with the advent of Doppler radar, it is possible to record meteorological events with a resolution of only a few square kilometers, a degree of accuracy that is normally unavailable with ground weather stations (Figure 4). National Weather Service (NWS) stations throughout the Southern Region maintain archives of past weather events from Doppler radar. In cooperation with the NWS in Amarillo and hybrid sorghum seed producers, we have identified sorghum fields that were infested with ergot during the last three years and are in the process of relating specific weather events, as recorded by Doppler radar, with disease. Seed companies have identified and recorded the location of diseased fields using GPS technology. Therefore, we now have the capability to input GPS coordinates of diseased fields into the Doppler radar program archives and search for weather events for those specific locations over any particular period of time. In the Texas Panhandle, widely scattered showers frequently occur during sorghum flowering periods and these showers may be the explanation for the variability in disease incidence. We believe that Doppler radar technology will provide the answer to this question.
Fig. 4. Doppler radar images of area surrounding Amarillo, Texas. A) One hundred mile image provides complete coverage of most of the hybrid seed sorghum production areas in the Texas Panhandle. B) Images can be magnified to approximately one square mile resolution, and meteorological events for individual geo-referenced fields can be identified. Remote Sensing - Definition of Management Zones - Soilborne Viruses. In a second study, we studied the distribution of BSBMV and BNYVV in sugar beet fields in Minnesota, Colorado, and Texas. Agriculturalist working for the various sugar companies are interested in identifying the best method of sampling for these pathogens because plants growing in heavily infested areas of a field can be plowed out. This selective removal of diseased plants can result in an overall improvement in crop yield and quality. Therefore, the size of the management zone is only limited by our ability to describe distribution of the pathogen in the field. Fields
infested with BNYVV, BSBMV, or both viruses were identified and grid
sampled. Grid size was one acre, for the entire field, and smaller areas
were intensively sampled with grid sizes of approximately 10'. The location
of each soil sample taken from each grid was geo-referenced using a
Satloc GPS receiver. BNYVV and BSBMV was baited from each soil sample
and bait plants tested for the presence of each virus using ELISA. ELISA
data for each geo-referenced soil sample was recorded into ArcView and
used to generate geo-referenced field maps. Data was further analyzed
using the GS+ geostatistical program to determine spatial distribution
patterns of each virus. In only one field, auto correlation existed
at a separation distance of approximately 30', but beyond this distance,
there was no observable distribution pattern (Figure 5).
Fig. 5. Variograms of spatial distribution of BNYVV from intensively sampled (small grid) section of a field (A), and the whole field in an acre-sized (large) grid (B). For plot-based studies, we must also be able to locate the boundaries of our plots to segregate these factors according to treatments and replicates. With rectangular plots this is easily accomplished with any GIS tool. However, with the Bushland center pivot, crops are planted in a circle to maximize irrigation efficiency and reduce runoff. Therefore, plots are not rectangular but small sections of a circle. By using a combination of drawings from AutoCAD along with ArcView, we have been able to generate plot boundaries for any given plot design (Figure 6). Whether our plots are six rows wide or twelve, we can take the appropriate AutoCAD template, overlay a geo-referenced ArcView image of our plots and quickly retrieve the data of interest. This technique allows us to convert rapidly from geo-referenced data to plot based data, thereby permitting easy statistical analysis of any particular data set.
Fig. 6. Data from a yield monitor combine superimposed on a plot map. Each plot is labeled with the treatment and block. A spatial join of these images results in a table identifying each yield point from the combine with the treatment and block it came from. B. Education/technology transfer: During the year, members of the plant pathology project gave numerous PA presentations at field days, crop tours, commodity research meetings, and growers meetings. We also routinely provide tours to various groups such as scouts, schools, visiting scientists, etc. during which we give overviews of our PA project. C. Milestones achieved: We have made significant progress over the last three years in elucidating the effect of various PET-based irrigation levels on crop yields, water use efficiency and disease development. Corn, sorghum, sugar beets, and wheat have been included in the studies. We have found that with all grain crops, irrigation at 75% PET instead of 100% PET has typically resulted in equal or greater potential profit to growers because of reduced input costs. Water has been conserved and energy costs reduced. In addition, we have found that irrigation management has the potential to reduce the incidence and severity of several plant diseases and insect pests. In general, reduced irrigation frequency reduces incidence and severity of many soilborne plant pathogens more than reducing the total amount of irrigation water applied. The most significant aspect of this research has been the demonstration that producers cannot irrigate crops growing in pathogen-infested soils in the same manner they irrigate crops growing in pathogen-free soils. Soilborne plant pathogens reduce crop yield and quality and have an adverse effect on crop water use efficiency. D. Publications: Michels,
Jr., G. J., G. Piccinni, C. M. Rush, and D. A. Fritts. 1999 Sensing
Greenbug (Homoptera: Aphididae) Infestations In Winter Wheat With Infrared
Transducers. Southwestern Entomology 24(4):269-279. Piccinni, G., C.M. Rush, K.M. Vaughn, and Lazar, M. D. 2000. Lack of Relationship Between Susceptibility to Common Root Rot and Drought Tolerance Among Several Closely Related Wheat Lines. Plant Disease. 83:25-28. Piccinni, G., J.M. Shriver, and C.M. Rush. 2001. The relationship among seed size, planting date and common root rot in hard red winter wheat. Plant Disease. (accepted) Identification
and differentiation of biotic and abiotic stresses using multispectral
remote sensing for application in IPM production systems. USDA-IPM $99,000. F. Precision Agriculture meetings attended/papers (posters) presented: G. J. Michels, G. Piccinni, C. M. Rush, and D. A. Frits. 2000. Using infrared transducers to sense greenbug infestations in winter wheat. Proceedings of the 5th International Conference on Precision Agriculture. Bloomington, MN. July 16-19,2000. (in press) Piccinni, G., J.K. Burk, C.M. Rush, and G.J. Michels. 1999. Development of an Automated System for Infrared Detection of Plant Stress. p. 109. In Agronomy Abstracts. ASA, Madison, WI. ASA/CSSA/SSSA Joint 91st Annual Meeting, Salt Lake City, UT, Oct. 31 - Nov. 4, 1999. G. J. Michels,
G. Piccinni, C. M. Rush, and D. A. Frits. 2000. Using infrared transducers
to sense greenbug infestations in winter wheat. 5th International Conference
on Precision Agriculture. Bloomington, MN. July 16-19,2000. Staff Writer. 2000. Satellite images of land take root with farmers. Albuquerque Journal. August 14, 2000 pg. 2. Rush, C.M. 2000. Precision Agriculture. 2000 Spring Crops Field Day. AREC Bushland, TX.
Giovanni
Piccinni, an assistant research scientist who had been with the plant
pathology PA project since it began, also left early in 2000 and took
a job with TAES in Uvalde. Giovanni, a plant stress physiologist, had
taken leadership of the PA irrigation studies under the Bushland center
pivot. His departure constituted a big loss to our program but we were
still able to complete the field studies he had supervised. |
