Pasture Productivity Assessment under Mob Grazing and Fertility Management Using Satellite and UAS Imagery
Pasture management approaches can determine the productivity, sustainability, and ecological balance of livestock production. Sensing techniques potentially provide methods to assess the performance of different grazing practices that are more labor and time efficient than traditional methods (e.g.,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/6/9/232 |
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| author | Worasit Sangjan Lynne A. Carpenter-Boggs Tipton D. Hudson Sindhuja Sankaran |
| author_facet | Worasit Sangjan Lynne A. Carpenter-Boggs Tipton D. Hudson Sindhuja Sankaran |
| author_sort | Worasit Sangjan |
| collection | DOAJ |
| description | Pasture management approaches can determine the productivity, sustainability, and ecological balance of livestock production. Sensing techniques potentially provide methods to assess the performance of different grazing practices that are more labor and time efficient than traditional methods (e.g., soil and crop sampling). This study utilized high-resolution satellite and unmanned aerial system (UAS) imagery to evaluate vegetation characteristics of a pasture field location with two grazing densities (low and high, applied in the years 2015–2019) and four fertility treatments (control, manure, mineral, and compost tea, applied annually in the years 2015–2019). The pasture productivity was assessed through satellite imagery annually from the years 2017 to 2019. The relation and variation within and between the years were evaluated using vegetation indices extracted from satellite and UAS imagery. The data from the two sensing systems (satellite and UAS) demonstrated that grazing density showed a significant effect (<i>p</i> < 0.05) on pasture crop status in 2019. Furthermore, the mean vegetation index data extracted from satellite and UAS imagery (2019) had a high correlation (<i>r</i> ≥ 0.78, <i>p</i> < 0.001). These results show the potential of utilizing satellite and UAS imagery for crop productivity assessment applications in small to medium pasture research and management. |
| first_indexed | 2024-03-10T00:15:19Z |
| format | Article |
| id | doaj.art-c38bdae3185b4d9597214b9311612d29 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-10T00:15:19Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-c38bdae3185b4d9597214b9311612d292023-11-23T15:53:37ZengMDPI AGDrones2504-446X2022-09-016923210.3390/drones6090232Pasture Productivity Assessment under Mob Grazing and Fertility Management Using Satellite and UAS ImageryWorasit Sangjan0Lynne A. Carpenter-Boggs1Tipton D. Hudson2Sindhuja Sankaran3Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USADepartment of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USADepartment of Animal Science, Washington State University Extension, Kittitas County, Ellensburg, WA 98926, USADepartment of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USAPasture management approaches can determine the productivity, sustainability, and ecological balance of livestock production. Sensing techniques potentially provide methods to assess the performance of different grazing practices that are more labor and time efficient than traditional methods (e.g., soil and crop sampling). This study utilized high-resolution satellite and unmanned aerial system (UAS) imagery to evaluate vegetation characteristics of a pasture field location with two grazing densities (low and high, applied in the years 2015–2019) and four fertility treatments (control, manure, mineral, and compost tea, applied annually in the years 2015–2019). The pasture productivity was assessed through satellite imagery annually from the years 2017 to 2019. The relation and variation within and between the years were evaluated using vegetation indices extracted from satellite and UAS imagery. The data from the two sensing systems (satellite and UAS) demonstrated that grazing density showed a significant effect (<i>p</i> < 0.05) on pasture crop status in 2019. Furthermore, the mean vegetation index data extracted from satellite and UAS imagery (2019) had a high correlation (<i>r</i> ≥ 0.78, <i>p</i> < 0.001). These results show the potential of utilizing satellite and UAS imagery for crop productivity assessment applications in small to medium pasture research and management.https://www.mdpi.com/2504-446X/6/9/232grazing densitynutrientpasture managementforage grassremote sensing |
| spellingShingle | Worasit Sangjan Lynne A. Carpenter-Boggs Tipton D. Hudson Sindhuja Sankaran Pasture Productivity Assessment under Mob Grazing and Fertility Management Using Satellite and UAS Imagery Drones grazing density nutrient pasture management forage grass remote sensing |
| title | Pasture Productivity Assessment under Mob Grazing and Fertility Management Using Satellite and UAS Imagery |
| title_full | Pasture Productivity Assessment under Mob Grazing and Fertility Management Using Satellite and UAS Imagery |
| title_fullStr | Pasture Productivity Assessment under Mob Grazing and Fertility Management Using Satellite and UAS Imagery |
| title_full_unstemmed | Pasture Productivity Assessment under Mob Grazing and Fertility Management Using Satellite and UAS Imagery |
| title_short | Pasture Productivity Assessment under Mob Grazing and Fertility Management Using Satellite and UAS Imagery |
| title_sort | pasture productivity assessment under mob grazing and fertility management using satellite and uas imagery |
| topic | grazing density nutrient pasture management forage grass remote sensing |
| url | https://www.mdpi.com/2504-446X/6/9/232 |
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