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Gillins, Elizabeth Reducing the Cost of Soil Mapping In Precision Agriculture Faculty Mentor: Ruth Kerry, Geography Department Using electrical conductivity data (ECa) in soil survey is related to several soil properties. Particular use has been made of ECa data in precision agriculture because it is related to properties that could affect crop yield. Although theoretically sound reasons exist to explain these relationships, they vary spatially, making ECa data interpretation difficult, even within fields. Some soil scientists have attempted to establish relationships with ECa for which there is no theoretical basis. McBratney et al. (2005) suggest that theoretically sound relationships developed between high frequency devices can be used to inform interpretation of the lower frequency ECa data. A three part theory was presented: 1. If the soil is hyper-electrolytic, ECa measures salinity. 2. If the profile thickness is thinner than the effective depth of measurement and the underlying material has a smaller ECa, ECa indicates soil depth. 3. If there is no compaction and the electrolyte concentration balances with soil charge, ECa represents variations in clay and moisture content. Our aim was to determine whether the following model provides insights into the causes of patterns of ECa in several fields with soil likely to meet the different conditions mentioned above. Data from several fields in the United Kingdom collected by Dr. Kerry were statistically analyzed to test parts two and three of the theory above and a local field with salinity problems was sought to test part one of the theory. A suitable testing field site located in Lakeshore, Utah, was identified through contacts in the Brigham Young University Plant and Animal Sciences Department and at Utah State University Soil Extension Office. The use of the field and equipment was coordinated with the help of a professor in the Extension Office. One hundred stakes were placed 15 meters apart across the field using a GPS unit. Each stake marked a collection point for a soil sample. At each point, six samples from within one meter of the stake were collected and mixed in a bucket. The mixed sample was placed in a bag and marked according to location in the field. Each soil sample was tested for pH and electrical conductivity in the soil lab in the Plant and Animal Sciences Department at BYU. The texture of the soil samples was determined in connection with a course Dr. Kerry taught in fall semester. Electrical conductivity data and soil sample locations were collected using the EM38 instrument connected to a GPS. For each data point where soil had been collected, the lab results were used to make a determination of which the condition of the theory above was met. Moving correlation analysis was then used to determine the relationship between ECa and relevant soil properties in the vicinity of each data point. Each step in this process was a challenging experience. I made several trips to the field site only to find the site flooded with irrigation water. Precise conditions were required to collect the samples. I learned how to use the electrical conductivity equipment and GPS unit after several attempts to complete the data collection. Lab work took several weeks to finish by myself and I learned the necessity of careful, accurate analysis. Results showed that where a condition from the theory was met, there was a strong relationship between ECa and the relevant soil property mentioned in the theory. However, within most fields different conditions from the model apply in different sections of the field. Therefore, assuming that a site is salty or has shallow soil etc. will not give a true interpretation of what EC a is measuring in the entire field. The cost of soil mapping cannot be reduced based on the model proposed by McBratney et al. (2005) alone because of the inconsistencies in which soil properties are correlated with ECa data within a field. More cost could be incurred by incorrectly mapping a field based on the model. A promising approach seems to be that the ECa data can be used to divide the field into regions with similar ECa values. Six soil samples can then be collected in each region and be used to determine which conditions of the model are met in that region. However, this needs some further investigation. The preliminary results of this research were presented by myself at the Western Society of Soil Science Regional Meeting in Park City, Utah in June 2006. I received an award for my presentation and was the only undergraduate among graduate students and professors presenting at the conference. This research project will soon be fully written up as a journal article and submitted to the Precision Agriculture Journal where I will be listed as a co-author. My personal growth from this project has been educational both academically and personally. Previous to this project, I had little experience with soil science. However, at the research conference, I discovered I understood the research other soil scientists were presenting. Our professorial contact at Utah State University was impressed at my progress in soil science and even offered me a stipend-paid Master’s student slot at USU. BYU received positive exposure from my participation in the conference. Many conference attendees were unfamiliar with the university, while several other scientists were familiar and quite impressed with the research presented. The ORCA experience was positive for me also. I grew and was stretched as I prepared and presented the research, then interacted with professionals in the field in an academic setting. Reference McBratney, A. B., Minasny, B. & Whelan, B. M. (2005) Obtaining ‘useful’ high-resolution soil data from proximally-sensed electrical conductivity/resistivity (PSEC/R) surveys. In: J. V. Stafford ed. Precision Agriculture ’05. p. 503-511. Wageningen Academic Publishers, Wageningen, The Netherlands.