Socio-economic factors influencing land-use and land-cover changes in the miombo woodlands of the Copperbelt province in Zambia

Handavu, Ferdinand; Chirwa, Paxie W.; Syampungani, Stephen

doi:10.1016/j.forpol.2018.10.010

Cited by 67 publications

(87 citation statements)

References 59 publications

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“…Zhang et al used a partial least squares regression (PLSR) model to determine the main socioeconomic indicators of cropland change [65]. Handavu et al [66] and Paudel et al [67] used a binary logistic regression model to examine the driving factors of land use and land cover change. Kundu et al [68] evaluated the land use change impact on sub-watershed prioritization using the analytical hierarchy process (AHP) method.…”

Section: Suitability Of Rbfnn Modelmentioning

confidence: 99%

Cropland Use Transitions and Their Driving Factors in Poverty-Stricken Counties of Western Hubei Province, China

Xiang

Song

2019

Sustainability

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Agriculture is important for economic development in most poverty-stricken areas in China, but cropland use is facing challenges due to rapid industrialization and urbanization, causing serious issues for poverty alleviation and sustainable socioeconomic development. Cropland Use Transition (CUT) is one way to alleviate poverty and develop the economy in poverty-stricken areas. This paper chose 16 typical poverty-stricken counties in Western Hubei province as the case area. A morphology index system was established to evaluate CUT, and geographic information system software was used to analyze the temporal-spatial variations in CUT. Using the Radial Basis Function Neural Network (RBFNN) model, contributions of driving factors of population, economy, and industrial structure to CUT were analyzed. The results show that: (1) cropland use morphology can be divided into functional morphology and spatial morphology; (2) the spatial distribution of CUT was high in the north and low in the south, the temporal variation of CUT from 1995 to 2013 showed fluctuations, and the coefficient of CUT changed from 0.460 to 0.649 with a growth rate of 41%; (3) for the driving factors, population factors most significantly contributed to CUT, followed by industrial structure and economic factors. The results obtained in this study are in line with the findings of previous studies. The RBFNN model is suitable for evaluating the contributions of driving factors, which can solve the deficiency in previous studies caused by ignoring the internal relationship and target orientation of driving factors. This study suggests that poverty-stricken counties should narrow the urban–rural divide, encourage balanced labor and investment flow into cropland by formulating relevant economic policies, motivate farmers’ agricultural engagement, and use science and technology to promote CUT and the growth of the agricultural economy, poverty alleviation, and to coordinate urban–rural development.

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Section: Suitability Of Rbfnn Modelmentioning

confidence: 99%

Cropland Use Transitions and Their Driving Factors in Poverty-Stricken Counties of Western Hubei Province, China

Xiang

Song

2019

Sustainability

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“…This explanation would be consistent with findings from Demetriou et al [78] and Borowska-Stefańska et al [79], who emphasised that excessive fragmentation can be a barrier for optimal agricultural development and can cause some difficulties in effective agricultural production. On the other hand, agricultural expansion and population growth could be also significant determinants of changes in land use [52].…”

Section: Discussionmentioning

confidence: 99%

The Impact of EU Grants Absorption on Land Cover Changes—The Case of Poland

2019

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The main goal of the paper is to verify the impact of the absorption of European Union (EU) grants on land cover changes in Polish municipalities in the years 2012–2018. The selection of the research area was justified by the fact that Poland is the largest and significantly spatially differentiated transition economy in Central-Eastern Europe, recognised as a substantial beneficiary of EU accession in 2004. The time range of analysis was set as the result of a comparison of data availability in Corine Land Cover (CLC) and Statistics Poland. The CLC dataset referring to land cover and land use changes between 2012 and 2018 was used. The focus on modifications taking place within one of the main land cover groups at level 3 of detail was applied in this research. These changes were analysed as percentages referring to the area of the municipality and to the total area of changes in the investigated period. Two categories of EU grants were considered: total and infrastructural (granted under EU Operational Programme “Infrastructure and Environment”). Moreover, some control economic, social, demographic, institutional, infrastructural, and environmental variables were applied to better explain land cover changes. Moran’s local statistic was employed to detect spatial hot-spots of EU grants absorption, as well as hot-spots of land cover changes. Then, a collection of various variables related to determinants of land cover changes was set. Economic factors, including EU grants absorption, as well as factors related to accessibility, agrarian structure, demography, environment, and spatial planning were investigated. Principal Component Analysis (PCA) was employed to convert the set of all considered variables into a set of few uncorrelated predictors. Finally, Geographically Weighted Regression (GWR) was applied to describe the spatially varied impact of investigated determinants, including EU grants, represented by estimated principal components on land cover changes.

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“…. , and x n , respectively [40]. The potential and non-potential areas for rice procued were derived from land suitability classification for rice ( Table 2).…”

Section: Methodsmentioning

confidence: 99%

Factors Driving Rice Land Change 1989–2018 in the Deli Serdang Regency, Indonesia

Siagian

Shrestha

et al. 2019

Agriculture

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The Deli Serdang Regency produces amongst the highest amounts of rice in the province of North Sumatera in Indonesia. Due to land use change and stagnant productivity, the total rice land area and its production have gradually decreased over the years. Hence, understanding this issue is crucial, especially to ensure the sustainability of rice production in the future. The objectives of this study were to identify the trends in land use change (especially regarding rice land) and to investigate the factors affecting rice land change. We classified the satellite images acquired for the years 1989, 1994, 2003, 2009, and 2018 to determine the total area of various land uses. The factors driving rice land change were analyzed using biophysical and socio-economic factors identified from the collected primary and secondary data. The primary data were derived from field surveys, soil analysis, and household surveys, and the secondary data were derived from the Statistical Institution of the Deli Serdang Regency. Correlation analysis, principle component analysis, binary logistic regression, normalization, and weighted index were used to investigate the factors driving rice land change. The results show that forest and rice land have continuously decreased, while plantations and urban areas have continuously increased over this period. We found that the majority of rice land has been converted to plantation expansion and urban development, especially from 2009 to 2018. The factors most affecting rice land change were the distance of rice land to the district capital, the distance of rice land to the provincial capital, population density, slope, and the distance of farmers’ rice land to a road. A suitability map for rice land was generated. All the outputs could help with making appropriate strategic decisions to achieve sustainable land use management, especially for rice land.

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Socio-economic factors influencing land-use and land-cover changes in the miombo woodlands of the Copperbelt province in Zambia

Cited by 67 publications

References 59 publications

Cropland Use Transitions and Their Driving Factors in Poverty-Stricken Counties of Western Hubei Province, China

Cropland Use Transitions and Their Driving Factors in Poverty-Stricken Counties of Western Hubei Province, China

The Impact of EU Grants Absorption on Land Cover Changes—The Case of Poland

Factors Driving Rice Land Change 1989–2018 in the Deli Serdang Regency, Indonesia

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