GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheep

Plaza, Javier; Sánchez, Nilda; Palacios, Carlos; Sánchez-García, Mario; Abecia, J. A.; Criado, Marco; Nieto, Jaime

doi:10.31893/jabb.22014

Cited by 8 publications

(1 citation statement)

References 26 publications

(30 reference statements)

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“…Further work is required to obtain additional quantitative data for, and examine the sensitivity and behaviour of, each of these variables. Emerging technologies including GPS monitoring and remote monitoring sensing techniques could improve the quantification of grazing activities and excreta deposition (e. g., Plaza et al 2022).…”

Section: Limitations Of This Studymentioning

confidence: 99%

Towards an integrated phosphorus, carbon and nitrogen cycling model for topographically diverse grasslands

Bilotto

Vibart

Mackay

et al. 2022

Nutr Cycl Agroecosyst

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Contemporary science on how livestock influence nutrient cycling in grazing systems is limited, particularly in topographically complex (i.e., slopes and aspects) hill country landscapes. Prominent slope and aspect variation affects primary production, animal behaviour and nutrient return. Here, we embed recent scientific advancements in nutrient dynamics across complex landscapes to (1) set up a soil organic carbon (SOC) saturation function to an existing SOC and total soil phosphorus (TSP) model (Bilotto et al. J N Z Grassl 81:171–178, 2019. https://doi.org/10.33584/jnzg.2019.81.397), (2) include total soil nitrogen (TSN) dynamics, and (3) establish if the model (herein the Grass-NEXT model) can simulate the spatial and temporal changes of TSP, SOC and TSN in hill country. A long-term P fertiliser experiment with contrasting different P fertilisation levels and associated sheep stocking regimes (herein, ‘farmlets’) was used for model testing. The Grass-NEXT model predicted TSP and SOC stocks with strong accuracy and precision (model performance), and TSN with a moderate performance across farmlets [Concordance Correlation Coefficient (CCC), 0.75, 0.72 and 0.49, respectively]. Grass-NEXT model simulated TSP, SOC and TSN distribution with moderate/strong performance across slopes (CCC, 0.94, 0.80 and 0.70) and aspects (CCC, 0.83, 0.67 and 0.51). Consistent with observed data, modelled changes in TSP and TSN were greater on low slopes and eastern aspects, but no clear pattern was observed for SOC stocks. The Grass-NEXT model provides an intuitive research tool for exploring management options for increasing SOC and TSN, as well as an instrument for monitoring and reporting on nutrient dynamics in complex landscapes.

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Section: Limitations Of This Studymentioning

confidence: 99%

Towards an integrated phosphorus, carbon and nitrogen cycling model for topographically diverse grasslands

Bilotto

Vibart

Mackay

et al. 2022

Nutr Cycl Agroecosyst

View full text Add to dashboard Cite

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Mapping livestock grazing in semi-natural areas in the European Union and United Kingdom

Malek,

Schulze,

Bartl

et al. 2024

Landsc Ecol

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Context Livestock grazing throughout Europe has resulted in high diversity of semi-natural areas in past centuries. Currently, most low intensity grazing relying on semi-natural vegetation is found primarily in marginal lands. These areas still host a high-level of biodiversity but are subject to abandonment and agricultural intensification. Objectives Spatial information on areas where semi-natural vegetation is still grazed, and how contextual geographic conditions encourage or limit grazing is missing, hindering their protection. We present an interdisciplinary approach to map the spatial distribution of grazing in semi-natural areas of the European Union (EU) and the United Kingdom (UK). Methods We first interviewed grazing experts from European countries, who provided us with estimates on grazing across selected land cover classes per environmental zone and Member State. Subsequently, we analysed the spatial distribution of grazing through maximum entropy modeling using pan-European in-situ data on grazing observations (using LUCAS, an EU wide land use survey) and a set of geographic characteristics representing the local socio-economic, terrain, soil and climatic context. Results The expert-derived estimates on grazing suggest that 20.6% (or 134 thousand km2) of semi-natural areas in the EU + UK are grazed, although with low livestock densities. In addition, we find that there is great variety across the region in the factors that explain the occurrence of grazing: while in some regions, farmers’ age and distance to markets are most important, in others terrain or climate are influencing the location of grazing. Finally, we were able to map both the grazing probability as well as actual spatial distribution of grazing on semi-natural areas for the whole of EU and UK. Conclusions These data can assist in prioritizing future conservation efforts in these unique land systems.

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