Logistic modelling to derive agricultural land use determinants: A case study from southeastern Nigeria

Gobin, Anne; Campling, Paul; Feyen, Jan

doi:10.1016/s0167-8809(01)00163-3

Cited by 57 publications

(32 citation statements)

References 26 publications

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“…Multivariate logistical regression models generate regression coefficients that are calculated using certain weighted methods that explain the probability of land use change. Thus far, multivariate logistic regression models have been used to study wildlife habitats [41,42], forest fire prediction [43], and ecological land changes [44], land use changes [45][46][47][48].…”

Section: Multivariate Logistic Regression Modelmentioning

confidence: 99%

Exploring the Dynamic Mechanisms of Farmland Abandonment Based on a Spatially Explicit Economic Model for Environmental Sustainability: A Case Study in Jiangxi Province, China

2014

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Abstract:Farmland abandonment has important impacts on biodiversity and ecosystem recovery, as well as food security and rural sustainable development. Due to rapid urbanization and industrialization, farmland abandonment has become an increasingly important problem in many countries, particularly in China. To promote sustainable land-use management and environmental sustainability, it is important to understand the socioeconomic causes and spatial patterns of farmland abandonment. In this study, we explored the dynamic mechanisms of farmland abandonment in Jiangxi province of China using a spatially explicit economical model. The results show that the variables associated with the agricultural products yield are significantly correlated with farmland abandonment. The increasing opportunity cost of farming labor is the main factor in farmland abandonment in conjunction with a rural labor shortage due to rural-to-urban population migration and regional industrialization. Farmlands are more likely to be abandoned in areas located far from the villages and towns due to higher transportation costs. Additionally, farmers with more land but lower net income are more likely to abandon poor-quality farmland. Our results support the hypothesis that farmland abandonment takes place in locations in which the costs of cultivation are high and the potential crop yield is low. In addition, our study also demonstrates that a spatially explicit economic model is necessary to distinguish between the main driving forces of farmland abandonment. Policy implications are also provided for potential future policy decisions.

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Section: Multivariate Logistic Regression Modelmentioning

confidence: 99%

Exploring the Dynamic Mechanisms of Farmland Abandonment Based on a Spatially Explicit Economic Model for Environmental Sustainability: A Case Study in Jiangxi Province, China

2014

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“…In this study, the respective ROCs for M1 through M5 were 0.850, 0.848, 0.863, 0.897, and 0.896, thus all greater than 0.7, which suggests that the selected driving factors explained well the spatial changes of the five types. The other index, which is used to evaluate the feasibility of the model for simulating the whole modes, is the Kappa index [62], which should be at least 0.85. The simulated map of 2013 was compared with the actual map via ENVI 4.8, and the Kappa index was 0.90, which indicated it could capture the future trend of green manure in developing orchards.…”

Section: The Accuracy Of the Simulation Of The Clue-s Modelmentioning

confidence: 99%

Modelling the Spatial Expansion of Green Manure Considering Land Productivity and Implementing Strategies

et al. 2018

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Abstract:In modern sustainable agriculture, green manuring is increasingly emphasized for a reasonable land use management. However, the expansion of green manure is affected by a range of factors, such as soil geophysical properties and human intervention. This paper proposes an approach of spatial modelling to understand the mechanisms that influence green manure expansion and map the future distribution of green manure intercropped in the orchards in the Pinggu District, Beijing, China. We firstly classified the orchards into five grades according to a land productivity evaluation, and then considered two strategies for implementing green manure. Two scenarios were designed to represent the strategies: prioritizing low-productivity orchards to promote green manure intercropping (scenario 1) and prioritizing high-productivity orchards to promote green manure intercropping (scenario 2). The spatial expansion of green manure for 2020 was simulated at a resolution of a 100 × 100 m grid in the CLUE-S (the Conversion of Land Use and its Effects at the Small Region Extent) model. The two strategies led to quite different spatial patterns of green manure, although they were applied to the same areas. As a result, the spatial pattern of green manuring of scenario 1 was more concentrated than that of scenario 2. To summarize, the modelled outcomes identified the driving factors that affect green manure expansion at a grid scale, whereas the implementing strategies directly determined the spatial arrangements of green manuring at a regional scale. Therefore, we argue that the assessment of the driving factors and the prediction of the future distribution of green manuring are crucial for informing an extensive use of green manure.

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“…The relationship between the probability of urban expansion and its driving factors has been evaluated by the use of logistic regression models (Geoghegan et al 2001, Gobin et al 2001, Serneels and Lambin 2001, Verburg et al 2002, Tian et al 2011. Our model took a number of proximity variables into account (Wu 2002, Li andLiu 2007).…”

Section: Linear Regression and Markov Matrixmentioning

confidence: 99%

Modeling urban expansion policy scenarios using an agent-based approach for Guangzhou Metropolitan Region of China

Tian¹,

Qiao²

2014

E&S

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ABSTRACT. Policy makers and the human decision processes of urban planning have an impact on urban expansion. The behaviors and decision modes of regional authority, real estate developer, resident, and farmer agents and their interactions can be simulated by the analytical hierarchy process (AHP) method. The driving factors are regressed with urban dynamics instead of static land-use types. Agents' behaviors and decision modes have an impact on the urban dynamic pattern by adjusting parameter weights. We integrate an agent-based model) (ABM) with AHP to investigate a complex decision-making process and future urban dynamic processes. Three policy scenarios for baseline development, rapid development, and green land protection have been applied to predict the future development patterns of the Guangzhou metropolitan region. A future policy scenario analysis can help policy makers to understand the possible results. These individuals can adjust their policies and decisions according to their different objectives.

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Logistic modelling to derive agricultural land use determinants: A case study from southeastern Nigeria

Cited by 57 publications

References 26 publications

Exploring the Dynamic Mechanisms of Farmland Abandonment Based on a Spatially Explicit Economic Model for Environmental Sustainability: A Case Study in Jiangxi Province, China

Exploring the Dynamic Mechanisms of Farmland Abandonment Based on a Spatially Explicit Economic Model for Environmental Sustainability: A Case Study in Jiangxi Province, China

Modelling the Spatial Expansion of Green Manure Considering Land Productivity and Implementing Strategies

Modeling urban expansion policy scenarios using an agent-based approach for Guangzhou Metropolitan Region of China

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