• Abstract

    Traditional knowledge about the behavior of grazing livestock is about to disappear. Shepherds well know that sheep behavior follows non-random patterns. As a novel alternative to seeking behavioral patterns, this study quantified the grazing activities of two sheep flocks of Churra breed (both in the same area but separated by 10 years) based on Global Position System (GPS) monitoring and remote monitoring sensing techniques. In the first monitoring period (2009-10), geolocations were recorded every 5 min (4,240 records), while in the second one (2018-20), records were taken every 30 min (7,636 records). The data were clustered based on the day/night and the activity (resting, moving, or grazing). An airborne LiDAR dataset was used to study the slope, aspect, and vegetation height. Four visible-infrared orthophotographs were mosaicked and classified to obtain the land use/land cover (LU/LC) map. Then, GPS locations were overlain on the terrain features, and a Chi-square test evaluated the relationships between locations and terrain features. Three spatial statistics (directional distribution, Kernel density, and Hot Spot analysis) were also calculated. Results in both monitoring periods suggested that the spatial distribution of free-grazing ewes was non-random. The flocks showed strong preferences for grazing areas with gentle north-facing slopes, where the herbaceous layer formed by pasture predominates. The geostatistical analyses of the sheep locations corroborated those preferences. Geotechnologies have emerged as a potent tool to demonstrate the influence of environmental and terrain attributes on the non-random spatial behavior of grazing sheep.

  • References

    1. Aldezabal A, Garin I, Garcia-González R (1999) Activity rhythms and the influence of some environmental variables on summer ungulate behaviour in ordesa-monte perdido national park. Pirineos 145:145–156.
    2. Anderson JR, Hardy EE, Roach JT, Witmer RE (1976) A Land Use And Land Cover Classification System For Use With Remote Sensor Data, 1st Editio. Washington.
    3. Arnold GW, Dudzinski ML (1978) Ethology of Free Ranging Domestic Animals. Elsevier Scientific Publishers, Amsterdam.
    4. Baccini A, Goetz SJ, Walker WS et al (2012) Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat Clim Chang 2:182–185.
    5. Bertolozzi-Caredio D, Garrido A, Soriano B, Bardaji I (2021) Implications of alternative farm management patterns to promote resilience in extensive sheep farming. A Spanish case study. J Rural Stud 86:633–644.
    6. Castro M, Fernández-Núñez E (2016) Seasonal grazing of goats and sheep on Mediterranean mountain rangelands of northeast Portugal. Livest Res Rural Develpment 28:1–13.
    7. Chen Q, Vaglio Laurin G, Battles JJ, Saah D (2012) Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass. Remote Sens Environ 121:108–117.
    8. Clark D, Litherland A, Mata G, Burling-Claridge R (2009) Pasture monitoring from space. In: Proceedings of the South Island Dairy Event. pp 108–123.
    9. Escribano AJ (2019) The Dehesa System for Livestock Production. Evolution, Conservation Issues and Livestock Planning for Sustainability. In: Squires VR, Bryden WL (eds) Livestock: Production, Management Strategies and Challenges. NOVA.
    10. Escribano, Elghannam A, Mesias FJ (2020) Dairy sheep farms in semi-arid rangelands: A carbon footprint dilemma between intensification and land-based grazing. Land use policy 95:104600.
    11. Fernández Carmona J, Blas Ferrer E, Cervera Fras C et al (2017) Datos sobre conducta y bienestar de animales en granja. Universidad Politécnica de Valencia, Valencia.
    12. Food and Agriculture Organization of the United Nations (FAO) (2004) National forest inventory. Field manual. Template, Forestry D. Rome.
    13. Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80:185–201.
    14. Ganskopp D (2001) Manipulating cattle distribution with salt and water in large arid-land pastures: a GPS/GIS assessment. Appl Anim Behav Sci 73:251–262.
    15. Ganskopp D, Vavra M (1987) Slope Use by cattle, feral horses, deer, and bighorn shee. Northwest Sci 61:74–81.
    16. García-González R, Reiné R, Pérez S et al (2011) Comportamiento de ovinos en pastoreo libre y guiado por pastor en un puerto pirenaico. Prod. Anim. 400–407.
    17. Ghosh A, Fassnacht FE, Joshi PK, Kochb B (2014) A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales. Int J Appl Earth Obs Geoinf 26:49–63.
    18. Glimp HA, Swanson S (1994) Sheep Grazing and Riparian and Watershed Management. Sheep Res. J. 65–71.
    19. Golodets C, Boeken B (2006) Moderate sheep grazing in semiarid shrubland alters small-scale soil surface structure and patch properties. Catena 65:285–291.
    20. Handcock RN, Swain DL, Bishop-Hurley GJ et al (2009) Monitoring animal behaviour and environmental interactions using wireless sensor networks, GPS collars and satellite remote sensing. Sensors 9:3586–3603.
    21. Harris NR, Johnson DE, George MR, Mcdougald NK (2002) The Effect of Topography, Vegetation, and Weather on Cattle Distribution at the San Joaquin Experimental Range, California. In: Fifth Symposium on Oak Woodlands: Oaks in California’s Challenging Landscape. Albany, CA, pp 53–63.
    22. Herrera O (2018) Comportamiento en pastoreo del ganado bovino criollo Argentino y aberdeen angus ecotipo Riojano, en pastizales naturales del chaco árido. Universidad Nacional del Mar de Plata.
    23. Hulbert IAR, French J, Hulbert IANAR, Frencht J (2019) The Accuracy of GPS for Wildlife Telemetry and Habitat Mapping. Br Ecol Soc 38:869–878.
    24. Launchbaugh KL, Howery LD (2005) Understanding landscape use patterns of livestock as a consequence of foraging behavior. Rangel Ecol Manag 58:99–108.
    25. Lillesand T, Kiefer RW, Chipman J (2015) Remote Sensing and Image Interpretation, 7th Editio. Wiley, New York.
    26. Lim K, Treitz P, Wulder M et al (2003) LiDAR remote sensing of forest structure. Prog Phys Geogr Earth Environ 27:88–106.
    27. López IF, Hodgson J, Hedderley DI et al (2003) Selective defoliation by sheep according to slope and plant species in the hill country of New Zealand. Grass Forage Sci 58:339–349.
    28. Mora-Delgado J, Nelson N, Fauchille A, Utsumi S (2016) Application of GPS and GIS to study foraging behavior of dairy cattle. Agron Costarric 40:81–88.
    29. Nadal-Romero E, Petrlic K, Verachtert E et al (2014) Effects of slope angle and aspect on plant cover and species richness in a humid Mediterranean badland. Earth Surf Process Landforms 39:1705–1716.
    30. Pandey V, Kiker GA, Campbell KL et al (2009) GPS Monitoring of Cattle Location Near Water Features in South Florida. Appl Eng Agric 25:551–562.
    31. Percival NS, Knowles RL (1983) Combinations of Pinus radiata and pastoral agriculture in New Zealand hill country. In: Hannawey DB (ed) Foothill for Food and Forest. Oregon State University, Corvallis, pp 185–202.
    32. Priya CA, Balasaravanan T, Thanamani AS (2012) An efficient leaf recognition algorithm for plant classification using support vector machine. In: International Conference on Pattern Recognition, Informatics and Medical Engineering, PRIME 2012. pp 428–432.
    33. Putfarken D, Dengler J, Lehmann S, Härdtle W (2008) Site use of grazing cattle and sheep in a large-scale pasture landscape: A GPS/GIS assessment. Appl Anim Behav Sci 111:54–67.
    34. Saini R, Ghosh SK (2018) Crop Classification on Single Date Sentinel-2 Imagery Using Random Forest and Suppor Vector Machine. ISPRS - Int Arch Photogramm Remote Sens Spat Inf Sci XLII–5:683–688.
    35. Schieltz JM, Okanga S, Allan BF, Rubenstein DI (2017) GPS tracking cattle as a monitoring tool for conservation and management. African J Range Forage Sci 34:173–177.
    36. Schoenbaum I, Kigel J, Ungar ED et al (2017) Spatial and temporal activity of cattle grazing in Mediterranean oak woodland. Appl Anim Behav Sci 187:45–53.
    37. Senft RL, Coughenour MB, Bailey DW,et al (1987) Large Herbivore Foraging and Ecological Hierarchies. Bioscience 37:789–799.
    38. Shi Y, Wang T, Skidmore AK, Heurich M (2020) Improving LiDAR-based tree species mapping in Central European mixed forests using multi-temporal digital aerial colour-infrared photographs. Int J Appl Earth Obs Geoinf 84:1–10.
    39. Sillero N, Gonçalves-Seco L (2014) Spatial structure analysis of a reptile community with airborne LiDAR data. Int J Geogr Inf Sci 28:1709–1722.
    40. Silverman BW (1986) Estimación de densidad para las estadísticas y el análisis de datos. New York.
    41. Su Y, Guo Q, Fry DL et al (2016) A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery. Can J Remote Sens 42:1–15.
    42. Turner LW, Anderson M, Larson BT (2001) Global Positioning Systems (GPS) and Grazing Behavior in Cattle. In: Stowell RR, Bucklin R, Bottcher RW (eds) Livestock Environment VI: Proceedings of the 6th International Symposium. ASABE. St. Joseph, pp 640–650.
    43. Turner LW, Udal MC, Larson BT, Shearer SA (2000) Monitoring cattle behavior and pasture use with GPS and GIS. Can J Anim Sci 80:405–413.
    44. Vallentine JF (2001) Grazing management. Academic Press.
    45. Venter ZS, Hawkins HJ, Cramer MD (2019) Cattle don’t care: Animal behaviour is similar regardless of grazing management in grasslands. Agric Ecosyst Environ 272:175–187.
    46. Yandún Narváez FJ, Salvo del Pedregal J, Prieto PA et al (2016) LiDAR and thermal images fusion for ground-based 3D characterisation of fruit trees. Biosyst Eng 151:479–494.
    47. Zhang C, Xie Z, Selch D (2013) Fusing lidar and digital aerial photography for object-based forest mapping in the Florida Everglades. GIScience Remote Sens 50:562–573.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2022 Journal of Animal Behaviour and Biometeorology

How to cite

Plaza, J., Sánchez, N., Palacios, C., Sánchez-García, M., Abecia, J. A., Criado, M., & Nieto, J. (2022). GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheep. Journal of Animal Behaviour and Biometeorology, 10(2), 2214. https://doi.org/10.31893/jabb.22014
  • Article viewed - 81
  • PDF downloaded - 36