Aboveground biomass relationship with canopy cover and vegetation to improve carbon change monitoring in rangelands DOI Creative Commons
Chiara Pasut, Jacqueline R. England, M. Treuting Piper

et al.

Ecosphere, Journal Year: 2025, Volume and Issue: 16(4)

Published: April 1, 2025

Abstract Rangelands cover vast areas of the global land surface and are important to terrestrial carbon budget. However, accounting in rangeland systems is often limited by lack transparent systematic methods for assessing changes aboveground biomass ( B AG ). Although relationships between canopy cover, C , have been investigated at site regional scales, there few studies across regions where impact a range vegetation types conditions has assessed. Here, results were compiled from extensive field measurements 431 Australian sites (covering an area ~6 million km 2 ) develop empirical predict other structural variables. A boosted‐regression‐tree model was trained identify relative importance predictor Then, based on these results, stepwise log‐linear relationship developed estimate . About 70% could be described using percentage large trees (stem diameter >50 cm), height. Because such detailed information not yet available sufficient spatial temporal resolution, classifications existing maps classes, as single variable, explored alternative approach For most classes assessed, estimates statistically significant, with Lin's concordance coefficients 0.67–0.79 proportional error <36% all classes. There generally little improvement performance inclusion additional explanatory Overall, this study improved our understanding systems. Additionally, combining remotely sensed woody data may offer accurate monitor stocks ecosystems scale.

Language: Английский

Soil-based carbon farming: Opportunities for collaboration DOI Creative Commons
Alex Baumber,

Rebecca Cross,

Peter Ampt

et al.

Journal of Rural Studies, Journal Year: 2024, Volume and Issue: 108, P. 103268 - 103268

Published: April 16, 2024

Soil-based carbon farming has been identified in previous research as a win-win for farm productivity and the mitigation of climate change through sequestration. However, it faces numerous barriers to adoption, including low prices, high transaction costs, information uncertainty around future outcomes, markets policy conditions. Collaboration between landholders other stakeholders proposed potential means overcoming some these barriers, while maximising benefits soil-based farming. In this article, we present results two-stage process investigating collaborative Australia, involving national-scale key informant interviews regional-scale Participatory Rural Appraisal. Fifty-three were undertaken with stakeholders, landholders, landholder groups, service providers, government, researchers financial sector. was seen offer greatest advantages relation knowledge-sharing social support, followed by its increase income enhanced bargaining power optimisation co-benefits. The collaboration less clear reducing costs or also presents new challenges risk complexity. Under current conditions, informal models best balance risks, existing cooperatives well-placed diversify into carbon. Alternative conditions locations would be needed facilitate joint projects, pooled credits, shared land management and/or creation carbon-specific cooperatives.

Language: Английский

Citations

5
Aboveground biomass relationship with canopy cover and vegetation to improve carbon change monitoring in rangelands DOI Creative Commons
Chiara Pasut, Jacqueline R. England, M. Treuting Piper

et al.

Ecosphere, Journal Year: 2025, Volume and Issue: 16(4)

Published: April 1, 2025

Abstract Rangelands cover vast areas of the global land surface and are important to terrestrial carbon budget. However, accounting in rangeland systems is often limited by lack transparent systematic methods for assessing changes aboveground biomass ( B AG ). Although relationships between canopy cover, C , have been investigated at site regional scales, there few studies across regions where impact a range vegetation types conditions has assessed. Here, results were compiled from extensive field measurements 431 Australian sites (covering an area ~6 million km 2 ) develop empirical predict other structural variables. A boosted‐regression‐tree model was trained identify relative importance predictor Then, based on these results, stepwise log‐linear relationship developed estimate . About 70% could be described using percentage large trees (stem diameter >50 cm), height. Because such detailed information not yet available sufficient spatial temporal resolution, classifications existing maps classes, as single variable, explored alternative approach For most classes assessed, estimates statistically significant, with Lin's concordance coefficients 0.67–0.79 proportional error <36% all classes. There generally little improvement performance inclusion additional explanatory Overall, this study improved our understanding systems. Additionally, combining remotely sensed woody data may offer accurate monitor stocks ecosystems scale.

Language: Английский

Citations

0