Field validation of proximal sensors on a typical Prairie field
| dc.contributor.author | Adamolekun, Olayinka | |
| dc.contributor.examiningcommittee | Moulin, Alan (Soil Science) | en_US |
| dc.contributor.examiningcommittee | Morrison, Jason (Biosystems Engineering) | en_US |
| dc.contributor.supervisor | Akinremi, Olalekan (Soil Science) | en_US |
| dc.date.accessioned | 2019-06-05T15:25:36Z | |
| dc.date.available | 2019-06-05T15:25:36Z | |
| dc.date.issued | 2019-04-12 | en_US |
| dc.date.submitted | 2019-05-18T22:00:40Z | en |
| dc.degree.discipline | Soil Science | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | Spatial variability of soil properties across fields affect crop yield potential, available water, and other site-specific management zones. The concept of Precision Agriculture can only be achieved when operationally feasible methods of depicting spatial variability in soil properties have been devised. Hence, the use of proximal sensors to estimate soil properties at the field scale. The objectives of this study were to investigate the spatial distribution of bulk density with depth, compare the spatial pattern of soil moisture in two seasons (Spring 2014 and Fall 2017), determine the potential of using proximal sensors to estimate soil organic carbon, total carbon, total nitrogen, and soil moisture. Bulk density and soil moisture content showed strong spatial correlation from the soil surface to 75 cm depth. The spatial pattern of soil moisture content was temporally invariant as similar spatial coherent regions were found in both seasons (Spring of 2014 and Fall of 2017). Measurements made using the Veris OpticalMapper had a good correlation with soil organic carbon, total carbon, and total nitrogen that were measured in the lab. Measurements made with the ground penetrating radar was also well correlated with soil moisture content determined by the thermogravimetric method. Soil organic carbon had a root mean square error of 0.28, an R2 of 0.70, and ratio of prediction to deviation of 2.40, total carbon had a root mean square error of 0.57, R2 of 0.67, and ratio of prediction to deviation of 2.01, and total nitrogen had a root mean square error of 0.58, R2 of 0.68, and ratio of prediction to deviation of 1.80. Ground penetrating radar measurements of soil moisture content had a R2 of 0.83 and root mean square error of 0.014. Our results showed that variation of bulk density and soil moisture content exists within the field and at various depths that the stability in the spatial pattern of soil moisture content with time was due to soil texture. Both sensors have the potential to estimate soil properties with an acceptable degree of accuracy, the Veris OpticalMapper for soil organic carbon, total carbon, total nitrogen, and ground penetrating radar for soil moisture content on a Canadian Prairies field. | en_US |
| dc.description.note | October 2019 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/33950 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Proximal sensors | en_US |
| dc.subject | Spatial variability | en_US |
| dc.subject | Geostatistics | en_US |
| dc.subject | Soil properties | en_US |
| dc.title | Field validation of proximal sensors on a typical Prairie field | en_US |
| dc.type | master thesis | en_US |
| local.subject.manitoba | yes | en_US |