R. E. Yoder

EVALUATION OF METHODS FOR ESTIMATING DAILY REFERENCE CROP EVAPOTRANSPIRATION AT A SITE IN THE HUMID SOUTHEAST UNITED STATES

Estimated daily reference crop evapotranspiration (ETo) is normally used to determine the water requirement of crops using the crop factor method. Many ETo estimation methods have been developed for different types of climatic data, and the accuracy of these methods varies with climatic conditions. In this study, pair-wise comparisons were made between daily ETo estimated from eight different ETo equations and ETo measured by lysimeter to provide information helpful in selecting an appropriate ETo equation for the Cumberland Plateau located in the humid Southeast United States. Based on the standard error of the estimate (Syx), the relationship between the estimated and measured ETo was the best using the FAO-56 Penman-Monteith equation (coefficient of determination (r2) = 0.91, Syx = 0.31 mm d-1, and a coefficient of efficiency (E) = 0.87), followed by the Penman (1948) equation (r2 = 0.91, Syx = 0.34 mm d-1, and E = 0.88), and Turc’s equation (r2 = 0.90, Syx = 0.36 mm d-1, and E = 0.88). The FAO-24 Penman and Priestly-Taylor methods overestimated ETo, while the Makkink equation underestimated ETo. The results for the Hargreaves-Samani equation showed low correlation with lysimeter ETo data (r2 = 0.51, Syx = 0.68 mm d-1, and E = 0.20), while those for the Kimberly Penman were reasonable (r2 = 0.87, Syx = 0.40 mm d-1, and E = 0.87). These results support the adoption of the FAO-56 Penman-Monteith equation for the climatological conditions occurring in the humid Southeast. However, Turc’s equation may be an attractive alternative to the more complex Penman-Monteith method. The Turc method requires fewer input parameters, i.e., mean air temperature and solar irradiance data only.

OPTIMIZATION OF FUZZY EVAPOTRANSPIRATION MODEL THROUGH NEURAL TRAINING WITH INPUT–OUTPUT EXAMPLES

In a previous study, we demonstrated that fuzzy evapotranspiration (ET) models can achieve accurate estimation of daily ET comparable to the FAO Penman–Monteith equation, and showed the advantages of the fuzzy approach over other methods. The estimation accuracy of the fuzzy models, however, depended on the shape of the membership functions and the control rules built by trial–and–error methods. This paper shows how the trial and error drawback is eliminated with the application of a fuzzy–neural system, which combines the advantages of fuzzy logic (FL) and artificial neural networks (ANN). The strategy consisted of fusing the FL and ANN on a conceptual and structural basis. The neural component provided supervised learning capabilities for optimizing the membership functions and extracting fuzzy rules from a set of input–output examples selected to cover the data hyperspace of the sites evaluated. The model input parameters were solar irradiance, relative humidity, wind speed, and air temperature difference. The optimized model was applied to estimate reference ET using independent climatic data from the sites, and the estimates were compared with direct ET measurements from grass–covered lysimeters and estimations with the FAO Penman–Monteith equation. The model–estimated ET vs. lysimeter–measured ET gave a coefficient of determination (r 2 ) value of 0.88 and a standard error of the estimate (Syx) of 0.48 mm d –1 . For the same set of independent data, the FAO Penman–Monteith–estimated ET vs. lysimeter–measured ET gave an r 2 value of 0.85 and an Syx value of 0.56 mm d –1 . These results show that the optimized fuzzy–neural–model is reasonably accurate, and is comparable to the FAO Penman–Monteith equation. This approach can provide an easy and efficient means of tuning fuzzy ET models.

Mapping agricultural fields with GPR and EMI to identify offsite movement of agrochemicals

Offsite movement of waterborne agrochemicals is increasingly targeted as a non-point source of water quality degradation. Our research has indicated that subsurface water movement is variable and site-specific, and that a small soil volume frequently conducts a large volume of flow. This concentrated flow is usually caused by soil morphology, and it often results in water moving rapidly offsite from certain areas of fields; little or no lateral subsurface flow may occur in other areas. Identifying these subsurface regions is difficult using conventional soil survey and vadose zone sampling techniques. In this study, traditional surveying is combined with electromagnetic induction (EMI) and ground-penetrating radar (GPR) mapping to identify areas with high potential for subsurface offsite movement of agrochemicals, optimizing these identification techniques, and expanding the mapping procedures to make them useful at the field-scale for agricultural production practices. Conclusions from this research are: (1) EMI mapping provides rapid identification of areas of soil with a high electrical conductivity and presumably high potential for offsite movement of subsurface water, (2) GPR mapping of areas identified by EMI mapping provides a means to identify features that are known to conduct concentrated lateral flow of water, and (3) combining the capabilities of EMI and GPR instrumentation makes possible the surveys of large areas that would otherwise be impossible or unfeasible to characterize.

Mapping shallow underground features that influence site-specific agricultural production

Modern agricultural production practices are rapidly evolving in the United States of America (USA). These new production practices present significant applications for nonintrusive subsurface imaging. One such imaging technology is GPR, and it is now being incorporated within site-specific agriculture in the detection of soil horizons, perched water (episaturation), fragipans, hydrological preferential flow paths, and soil compaction. These features traditionally have been mapped by soil scientists using intrusive measurements (e.g., soil augers, soil pits, coring tools). Rather than developing a tool for soil mapping, our studies are targeting the identification, dimensioning, and position of subsurface features that directly influence agricultural productivity. It is foreseen that this information will allow for an increase in agricultural efficiency through infield machinery automation, and it will also greatly enhance development of highly efficient crop production strategies. The field sensing methodologies that we have developed using existing geophysical technologies are highly dependent upon both the soil and site characteristics due to seasonal variations. The GPR applications presented herein were conducted primarily in a region of loess soil that extends east of the Mississippi River into western Tennessee. GPR studies were also conducted in central Tennessee on the Cumberland Plateau within a region of shallow, sandy loam soils. Additional studies were conducted on the karst area of central Kentucky. Although targeting site-specific agriculture, our results and procedures may benefit the traditional users of GPR technology. We suggest that large-scale agricultural applications of the technology would be enhanced by integrating global positioning (GPS) technology in future hardware and software products.

Forensic Application of FM-CW and Pulse Radar

Ground-penetrating radar (GPR) technology has supplied vital assistance in criminal investigations. However, law enforcement personnel desire further developments such that the technology is rapidly deployable, and that it provides both a simple user interface and sophisticated target identification. To assist in the development of target identification algorithms, our efforts involve gathering background GPR data for the various site conditions and circumstances that often typify clandestine burials. For this study, forensic anthropologists established shallow-grave plots at The University of Tennessee Anthropological Research Facility (ARF) that are specific to GPR research. These plots contain donated human cadavers lying in various configurations and depths, surrounded by assorted construction material and backfill debris. We scanned the plots using two GPR technologies: (1) a multi-frequency synthetic-aperture FM-CW radar (200–700 MHz) (GPR-X) developed by the U.S. Department of Energy’s Special Tech...

Mobilized Surveying of Soil Conductivity Using Electromagnetic Induction

Established and emerging geophysical technologies offer many promising applications for precise near–surface surveying. Scientists are investigating these non–invasive surveying techniques to enhance soil mapping and research. A non–invasive soil surveying system was developed to rapidly map soil characteristics. This system employs an all–terrain utility vehicle towing a nonmetallic carriage that cradles a commercially available ground conductivity meter. Autonomous data streams of time–stamped soil conductivity data and global positioning system (GPS) data are immediately downloaded to a computer after a survey. Both data sets are automatically merged using the time stamp data as an index. Using geographical information software (GIS), conductivity maps of increased data density are produced on–site. The mobile surveying system increased total conductivity sampling rate by a factor of >100, and increased data density by a factor of >10 over a conventional manual survey method when operating over a 1–ha open test site. For open fields that can be easily traversed with a utility vehicle, the mobile surveying system was found to greatly enhance data quality by increasing data density, and to dramatically increase both data acquisition efficiency and data post–processing speeds.

SOILWATER SENSOR PERFORMANCE

Numerous sensors are currently available to measure soil water content. Although several studies have compared relative sensor performance in the field, there have been no reports of sensor comparisons with carefully controlled soil water contents. Weighed soil columns were used to compare 23 soil water sensors representing eight sensor types. Included in the study were: a Troxler neutron gage, a Troxler Sentry® 200-AP capacitance probe, Aqua- Tel® capacitance sensors, time domain reflectometry (TDR) with two- and four-rod waveguides, gypsum blocks, Watermark® electrical resistance blocks, and Agwatronics® heat dissipation blocks. Measurement errors of the volumetric water content of the soil were determined for each sensor over a range of water contents from the maximum water holding capacity to below 5%. A loam and a sandy loam soil were wetted to the maximum water holding capacity and subsequently drained through four cycles. Sensors were calibrated using data from the first cycle and measurement errors for each sensor were determined using those calibrations in three additional cycles. Measurements outside the range of 0 to 50% volumetric soil water content were discarded. Of 64 possible readings in the test, only the neutron gage and the Aqua-Tel capacitance sensor gave 64 viable readings. The Sentry capacitance probe had the lowest measurement error and yielded 62 of 64 viable readings. Watermark sensors had measurement errors similar to the electrical capacitance sensors, but averaged 57 of 64 viable readings. In order of decreasing performance, the Aqua-tel electrical capacitance sensor, the Sentry electrical capacitance probe, the neutron gage, and the Watermark sensors performed best in this study when accuracy, reliability, durability, and installation factors were considered.

EVALUATING GPR AND EMI FOR MORPHOLOGICAL STUDIES OF LOESSIAL SOILS

Development of a rapid and nonintrusive method for obtaining accurate soil morphological information is critical for pinpointing areas that are prone to leaching. The purpose of this study was to evaluate the suitability of using ground-penetrating radar (GPR) and electromagnetic induction (EMI) techniques in combination to gather soil morphological information on loessial soils. A survey of apparent electrical conductivity (ECa) was conducted in southwestern Tennessee at 10-m increments throughout a 1-ha field. Based on variation in the EMI data, a 36-m transect was selected for further investigation by GPR using a 200-MHz antenna. A hydraulic excavator was used to trench the site to a depth of 3 m, and a complete soil morphological investigation was performed along the trench face at 6-m increments. Readings from the EMI showed a moderate correlation with percent fragic properties (r = 0.40). Average depth to the loess/alluvium interface interpreted from the GPR was 1.20 m, and to the alluvium/Tertiary sand interface, 1.88 m. The loess/alluvium and alluvium/Tertiary sand interfaces interpreted from the GPR data had strong relationships to the measured depths, R2 = 0.90 and 0.88, respectively. Results from this study show that using precursory EMI data to pinpoint GPR surveys is a precise, accurate, and rapid means of acquiring field-scale soil morphological information.

Investigation of a Fuzzy-Neural Network Application in Classification of Soils Using Ground-Penetrating Radar Imagery

Errors associated with visual inspection and interpretations of radargrams often inhibit the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this article presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profiles using GPR imagery. The classifier clusters and classifies soil profile strips along a traverse based on common pattern similarities that can relate to physical features of the soil (e.g., number of horizons; depth, texture, and structure of the horizons; and relative arrangement of the horizons, etc.). This article illustrates this classification procedure by its application on GPR data, both simulated and actual. Results show that the procedure is able to classify the profile into zones that corresponded with the classifications obtained by visual inspection and interpretation of radar grams. Application of F-NN to a study site in southwest Tennessee gave soil groupings that are in close correspondence with the groupings obtained in a previous study, which used the traditional methods of complete soil morphological, chemical, and physical characterization. At a crossover value of 3.0, the F-NN soil grouping boundary locations fall within a range of 2.7 m from the soil groupings determined by the traditional methods. These results indicate that F-NN can supply accurate real-time soil profile clustering and classification during field surveys.

Estimation of reference crop evapotranspiration using fuzzy state models

Microirrigation can potentially “spoon feed” nutrients to a crop. Accurately supplying the crop’s nitrogen (N) needs throughout the season can enhance crop yields and reduce the potential for groundwater contamination from nitrates. A 2–year study (1990–1991) was conducted on a Keith silt loam soil (Aridic Argiustoll) to examine combinations of both preplant surface application (30 cm band in center of furrow) and in–season fertigation of N fertilizer for field corn (Zea mays L.) at three different levels of water application (75%, 100%, and 125% of seasonal evapotranspiration) using a subsurface drip irrigation (SDI) system. The method of N application did not significantly affect corn yields, apparent plant nitrogen uptake, or water use efficiency, but all three factors were generally influenced by the combined total N amount. The N application method did have an effect on the amount and distribution of total soil N and nitrate–N in the soil profile following harvest. In both years, nearly all of the residual nitrate–N after corn harvest was within the upper 0.3 m of the soil profile for the treatments receiving only preplant–applied N, regardless of irrigation regime. In contrast, the nitrate–N concentrations increased with increasing rates of N injected by the SDI system and migrated deeper into the soil profile with increased irrigation. The results suggest that N applied with an SDI system at a depth of 40–45 cm redistributes differently in the soil profile than surface–applied preplant N banded in the furrow.