Modelling SOC response to land use change and management practices in sugarcane cultivation in South-Central Brazil

Flooding is a routine occurrence throughout much of the monsoonal tropics. Despite well-developed repertoires of response, agrarian societies have been ‘double exposed’ to intensifying climate change and agro-industrialization over the past several decades, often in ways that alter both the regularity of flood events and individual and community capacity for response. This paper engages these tensions by exploring everyday experiences of and responses to extreme flood events in a case study village in Southeast Sulawesi, Indonesia, which has also been the site of corporate oil palm development since 2010. We first reconstruct histories of extreme flood events along the Konawe’eha River using oral histories and satellite imagery, describing the role of these events in straining the terms of daily production and reproduction. We then outline the ways smallholder agriculturalists are responding to flood events through alterations in their land use strategies, including through the sale or leasing of flood-prone lands, the relocation of riverine vegetable production to hillside locations, and adoption of new cropping choices and management practices. We highlight the role of such responses as a driver of ongoing land use change, potentially in ways that increase systemic vulnerability to floods moving forward.

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Modelling fire probability in the Brazilian Amazon using the maximum entropy method

International Journal of Wildland Fire ◽

10.1071/wf15216 ◽

2016 ◽

Vol 25 (9) ◽

pp. 955 ◽

Cited By ~ 9

Author(s):

Marisa G. Fonseca ◽

Luiz Eduardo O. C. Aragão ◽

André Lima ◽

Yosio E. Shimabukuro ◽

Egidio Arai ◽

...

Keyword(s):

Land Use ◽

Land Use Change ◽

Maximum Entropy ◽

Forest Fires ◽

Maximum Entropy Method ◽

Management Practices ◽

Brazilian Amazon ◽

Forest Degradation ◽

Entropy Method ◽

Monthly Basis

Fires are both a cause and consequence of important changes in the Amazon region. The development and implementation of better fire management practices and firefighting strategies are important steps to reduce the Amazon ecosystems’ degradation and carbon emissions from land-use change in the region. We extended the application of the maximum entropy method (MaxEnt) to model fire occurrence probability in the Brazilian Amazon on a monthly basis during the 2008 and 2010 fire seasons using fire detection data derived from satellite images. Predictor variables included climatic variables, inhabited and uninhabited protected areas and land-use change maps. Model fit was assessed using the area under the curve (AUC) value (threshold-independent analysis), binomial tests and model sensitivity and specificity (threshold-dependent analysis). Both threshold-independent (AUC = 0.919 ± 0.004) and threshold-dependent evaluation indicate satisfactory model performance. Pasture, annual deforestation and secondary vegetation are the most effective variables for predicting the distribution of the occurrence data. Our results show that MaxEnt may become an important tool to guide on-the-ground decisions on fire prevention actions and firefighting planning more effectively and thus to minimise forest degradation and carbon loss from forest fires in Amazonian ecosystems.

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Land use, macroecology, and the accuracy of the Maximum Entropy Theory of Ecology: A case study of Azorean arthropods

10.1101/2021.09.14.460355 ◽

2021 ◽

Author(s):

Micah Brush ◽

Thomas J. Matthews ◽

Paulo A.V. Borges ◽

John Harte

Keyword(s):

Land Use ◽

Land Use Change ◽

Metabolic Rate ◽

Maximum Entropy ◽

Management Practices ◽

Entropy Theory ◽

State Variables ◽

Arthropod Communities ◽

Natural Pasture ◽

Maximum Entropy Theory

AbstractHuman activity and land management practices, in particular land use change, have resulted in the global loss of biodiversity. These types of disturbances affect the shape of macroecological patterns, and analyzing these patterns can provide insights into how ecosystems are affected by land use change. The Maximum Entropy Theory of Ecology (METE) simultaneously predicts many of these patterns using a set of ecological state variables: the number of species, the number of individuals, and the total metabolic rate. The theory’s predictions have been shown to be successful across habitats and taxa in undisturbed natural ecosystems, although previous tests of METE in relation to disturbance have focused primarily on systems where the state variables are changing relatively quickly. Here, we assess predictions of METE applied to a different type of disturbance: land use change. We use METE to simultaneously predict the species abundance distribution (SAD), the metabolic rate distribution of individuals (MRDI), and the species–area relationship (SAR) and compare these predictions to arthropod data from 96 sites at Terceira Island in the Azores archipelago across four different land uses of increasing management intensity: 1. native forest, 2. exotic forest, 3. semi-natural pasture, and 4. intensive pasture. Across these patterns, we find that the forest habitats are the best fit by METE predictions, while the semi-natural pasture consistently provided the worst fit. The intensive pasture is intermediately well fit for the SAD and MRDI, and comparatively well fit for the SAR, though the residuals are not normally distributed. The direction of failure of the METE predictions at the pasture sites is likely due to the hyperdominance of introduced spider species present there. We hypothesize that the particularly poor fit for the semi-natural pasture is due to the mix of arthropod communities out of equilibrium and the changing management practices throughout the year, leading to greater heterogeneity in composition and complex dynamics that violate METE’s assumption of static state variables. The comparative better fit for the intensive pasture could then result from more homogeneous arthropod communities that are well adapted to intensive management, and thus whose state variables are less in flux.

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Integration of GIS and Markov chain model for land use change assessment: A case study in the upstream Ba river basin, Gia Lai province

The Journal of Agriculture and Development ◽

10.52997/jad.8.04.2021 ◽

2021 ◽

Vol 20 (04) ◽

pp. 69-77

Author(s):

Huyen T. Nguyen

Keyword(s):

Land Use ◽

Markov Chain ◽

Land Use Change ◽

River Basin ◽

Land Use Planning ◽

Land Use Changes ◽

Chain Model ◽

Forest Land ◽

Change Assessment ◽

South Central

Ba river is the biggest river system in the South-Central Coast of Vietnam and plays a significant role in the socio-economic development of the region. Recently, land-use changes in Gia Lai province have been significantly transformed. Hence, to provide the information for land-use planning, there is an urgent need for land-use change assessment in the upstream Ba river basin. This study employed the Markov chain coupled with GIS to assess land-use changes between 2010 - 2015 and 2015 - 2020 periods. The results showed that during the period 2010 - 2015, there was no significant conversion of agricultural and reserve forest land. Meanwhile, a large proportion of unused (86%) and water and aquacultural land (57.5%) was converted into the other land-use types. Between 2020 and 2015, unused land decreased while the surface water and aquacultural land increased. The forest land accounted for a significant area (51.16%) during the 2015 - 2020 period. In addition, the driving forces leading to these changes were also analyzed, providing a more comprehensive of land-use change in the study area. In general, GIS and Markov were suitable for assessing land-use change. This study outcomes provide a general framework for land-use planning in Gia Lai province.

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Using Farmer Decision Rules for Mapping Historical Land Use Change Patterns from 1954 to 2007 in Rural Northwestern Vietnam

Land ◽

10.3390/land8090130 ◽

2019 ◽

Vol 8 (9) ◽

pp. 130

Author(s):

Thanh Thi Nguyen ◽

Melvin Lippe ◽

Carsten Marohn ◽

Tran Duc Vien ◽

Georg Cadisch

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Use Change ◽

Management Practices ◽

Anthropogenic Activities ◽

Decision Rules ◽

Remote Sensing Data ◽

Rural Landscapes ◽

Sensing Data ◽

Assessment Period

The present study revealed how local socioecological knowledge elucidated during participatory rural appraisals and historical remote sensing data can be combined for analyzing land use change patterns from 1954 to 2007 in northwestern Vietnam. The developed approach integrated farmer decision rules on cropping preferences and location, visual and supervised classification methods, and qualitative information obtained during various forms of participatory appraisals. The integration of historical remote sensing data (aerial photo, Landsat, LISS III) with farmer decision rules showed the feasibility of the proposed method to explain crop distribution patterns for the assessment period of 53 years. Our approach is beneficial for data-limited environments, which is a prevalent situation for many developing regions. The derived land use and crop type dataset was used for understanding how anthropogenic activities altered the study area of the Chieng Khoi commune during the assessment period of five decades, and what potential impact this can have on the natural resource base. The newly developed approach offers a methodological pathway that can be easily transferred to local government authorities for a better understanding of cropping transitions and agricultural expansion trends in data-limited rural landscapes. The detected land use change patterns and upland cropping expansion of more than two hundred percent in 53 years not only revealed the consequences of the interactions and feedback between farmers and their land, but further highlighted the urgent need for implementing sustainable land management practices in the case study watershed of the Chieng Khoi commune and northwestern Vietnam in general.

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