Rural Land Use and Land Tenure in New Zealand

Tímár, Levente

doi:10.2139/ssrn.1970100

Cited by 10 publications

(19 citation statements)

References 54 publications

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“…Socio-economic and bio-geophysical variables elucidate the role of anthropogenic forces in forest transitions, altering landscape structures and their composition (Ramachandra et al 2014a). These geophysical variables and socio-economic factors aid as drivers of land use changes in the landscape (Nelson et al 1999;Barbier 2001;Timar 2011;Cho et al 2015).…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Geospatial analysis of forest fragmentation in Uttara Kannada District, India

Ramachandra

Setturu²,

Chandran³

2016

For. Ecosyst.

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Background: Landscapes consist of heterogeneous interacting dynamic elements with complex ecological, economic and cultural attributes. These complex interactions help in the sustenance of natural resources through bio-geochemical and hydrological cycling. The ecosystem functions are altered with changes in the landscape structure. Fragmentation of large contiguous forests to small and isolated forest patches either by natural phenomena or anthropogenic activities leads to drastic changes in forest patch sizes, shape, connectivity and internal heterogeneity, which restrict the movement leading to inbreeding among Meta populations with extirpation of species. Methods: Landscape dynamics are assessed through land use analysis by way of remote sensing data acquired at different time periods. Forest fragmentation is assessed at the pixel level through computation of two indicators, i.e., P f (the ratio of pixels that are forested to the total non-water pixels in the window) and P ff (the proportion of all adjacent (cardinal directions only) pixel pairs that include at least one forest pixel, for which both pixels are forested). Results: Uttara Kannada District has the distinction of having the highest forest cover in Karnataka State, India. This region has been experiencing changes in its forest cover and consequent alterations in functional abilities of its ecosystem. Temporal land use analyses show the trend of deforestation, evident from the reduction of evergreensemi evergreen forest cover from 57.31 % (1979) to 32.08 % (2013) Forest fragmentation at the landscape level shows a decline of interior forests 64.42 % (1979) to 25.62 % (2013) and transition of non-forest categories such as crop land, plantations and built-up areas, amounting now to 47.29 %. PCA prioritized geophysical and socio variables responsible for changes in the landscape structure at local levels. Conclusion: Terrestrial forest ecosystems in Uttara Kannada District of Central Western Ghats have been experiencing threats due to deforestation with land use changes and fragmentation of contiguous forests, as is evident from the decline of interior forests and consequent increases in patch, transitional, edge and perforated forests. Interior or intact forest cover in this ecologically fragile region is now 25.62 %. Considering the accelerating rates of forest fragmentation in recent times, the focus should be on reforestation and regeneration of natural vegetation to sustain food and water security and the livelihood of local populations. This requires innovation with holistic approaches in the management of forests by involving all local stakeholders to minimize the encroachment of forests, and improvements in regeneration.

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Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Geospatial analysis of forest fragmentation in Uttara Kannada District, India

Ramachandra

Setturu²,

Chandran³

2016

For. Ecosyst.

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“…It is implemented via two interacting revealed-preference econometric models: a dynamic national land-use share model [11] and a static geographic land-use allocation model [12]. LURNZ simulates land use, rural production and greenhouse gas emissions for all private rural land in New Zealand annually at a 25-hectare resolution; the current model builds on earlier foundational work in Hendy et al [15].…”

Section: Model Descriptionmentioning

confidence: 99%

“…The national-level changes in land use are allocated spatially across New Zealand based on coefficients from a multinomial choice model relating land-use decisions to geophysical characteristics, location and land tenure [12]. The overall structure of the choice model follows, with some modifications, other studies in the discrete choice land-use literature, for example, Chomitz and Gray [20] and Nelson et al [21].…”

Section: Model Descriptionmentioning

confidence: 99%

“…Our econometric framework may represent overall land-use responses to changes in returns better, because we estimate them directly. We also account for more geophysical dimensions of land quality, as well as for location and land tenure in modeling land-use decisions [12], and we consider emission reductions achieved by displacing agricultural activities. Finally, our model is spatially explicit and can therefore be applied to study expected impacts on regional economic outcomes and local ecosystem services, such as water quality.…”

Section: Model Descriptionmentioning

confidence: 99%

“…The appendix contains details on how LURNZ models national-level land-use change. Details for other components of the LURNZ model are in Kerr and Olsen [11], Timar [12], Timar [13] and Anastasiadis et al [14].…”

Section: Introductionmentioning

confidence: 99%

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Spatial and Temporal Responses to an Emissions Trading Scheme Covering Agriculture and Forestry: Simulation Results from New Zealand

Kerr

Anastasiadis

Olssen

et al. 2012

Forests

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Abstract:We perform simulations using the integrated Land Use in Rural New Zealand (LURNZ) model to analyze the effect of various New Zealand emissions trading scheme (ETS) scenarios on land use, emissions and output in a temporally and spatially explicit manner. We compare the impact of afforestation to the impact of other land-use change on net greenhouse gas emissions and evaluate the importance of the forestry component of the ETS relative to the agricultural component. We find that the effect of including agriculture in the ETS is small relative to the effect of including forestry. We also examine the effect of land-use change on the time profile of net emissions from the forestry sector. Finally, we present projections of future agricultural output under various policy scenarios.

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Land-Use Modelling in New Zealand: Current Practice and Future Needs

Hendy

Ausseil

Bain³

et al. 2018

SSRN Journal

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This paper was prepared under Motu's Shaping New Zealand's Low-Emission Future programme, with funding support from the Ministry for Primary Industries and the Aotearoa Foundation. It was informed by the Motu Researcher Workshop on Land Use and Climate Change held in Wellington on 30 April 2018, and we gratefully acknowledge the contributions by presenters and other participants in that process. We also wish to acknowledge Susan Walker from Manaaki Whenua-Landcare Research, who reviewed parts of this paper. Disclaimer Contributions to the researcher workshop and this report were made in individuals' personal capacity and their inclusion in this paper does not imply any recommendation, consensus or endorsement by all participants, their affiliated organisations or the programme funders. While every effort has been made to ensure the information in this publication is accurate, the Ministry for Primary Industries does not accept any responsibility or liability for error of fact, omission, interpretation or opinion that may be present, nor for the consequences of any decisions based on this information.

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Rural Land Use and Land Tenure in New Zealand

Cited by 10 publications

References 54 publications

Geospatial analysis of forest fragmentation in Uttara Kannada District, India

Geospatial analysis of forest fragmentation in Uttara Kannada District, India

Spatial and Temporal Responses to an Emissions Trading Scheme Covering Agriculture and Forestry: Simulation Results from New Zealand

Land-Use Modelling in New Zealand: Current Practice and Future Needs

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