Spatio-temporal evolution of agricultural land use change drivers: A case study from Chalous region, Iran

Alijani, Zohreh; Hosseinali, Farhad; Biswas, Asim

doi:10.1016/j.jenvman.2020.110326

Cited by 41 publications

(19 citation statements)

References 81 publications

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“…Rainfall fluctuations increase with time, due to afforestation and deforestation, mediated by increases in population, which leads to cutting of trees for construction and farming [37]. Similar findings were reported in a study in central Panama [38]. Rainfall in many forest-adjacent ecosystems is modulated by the forests, and this study is in agreement with a similar study in Amazon where 60% of the local precipitation is recycled with the adjacent vegetation playing a pivotal role [39].…”

Section: Discussionsupporting

confidence: 91%

Evidence of Climate Changes in a Tropical Rainforest: Case Study Kakamega Tropical Rainforest

Wanyonyi

Tsingalia

Omayio

et al. 2021

IJECC

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Aims: To assess trends in rainfall and temperature as evidence of climate change in the Kakamega tropical forest ecosystem over the last 30 years (1980-2010). Study Design: Secondary data on temperature, rainfall and other climate related phenomena that have occurred within the Kakamega forest ecosystem over the last 30 years were obtained from the Kenya Metrological Department (KMD), Kenya Agriculture and Livestock Research Organization (KALRO), and local metrological stations and analysed to assess trends. This data was supplemented with data from questionnaires and structured interviews. Place and Duration of Study: The study was carried out among forest adjacent communities in the Kakamega tropical forest ecosystem between April and December 2020. Methodology: Data on awareness and preparedness for climate change was collected using structured questionnaires, focused group discussions (FGD) and structured interviews. Three villages in the east, west, north and south of the forest that are within a 2km radius of the forest were randomly selected for sampling. From the 52,729 households, 397 households were randomly chosen from each of the three selected villages. Questionnaires targeted family heads in all the selected households. The questionnaire sought first-hand information on climate change awareness and impacts of climate change among the forest-adjacent communities. Each questionnaire comprised of open and closed-ended questions. Focused Group Discussion involved specialized groups of women, village elders, local administrators, CBOs working in the ecosystem, among others. Further information was obtained using key informants. A total of forty-eight (48) key informants were randomly selected for discussion. The interview method involved key personnel working within the forest ecosystem which included staff from KWS, County environmental officers, National Environment Officers, Agriculture officers at the national and county levels. The interviews focused on preparedness, mitigative and adaptive capacity to climate change by the forest adjacent communities. Results: Analysis of the mean rainfall trends over the last 30 years (1980-2018) reveal that, mean monthly rainfall ranged from a monthly minimum 36mm (2012) to a maximum mean of 402.30mm (2018). Similarly, analyses of the mean monthly rainfall reveal dissimilarities in amounts of rainfall in each year over the last 30 years. The mean monthly rainfall fluctuations appear to increase overtime. Analysis of temperature records for the last 30 years (1990-2018) revealed a maximum mean daily temperature of 27.720C, a minimum temperature of 25.35⁰C and a maximum of 31.96⁰C with a range of 25⁰C - 30⁰C. From 2005 to 2018, higher temperatures (above 30⁰C) are evident. When a moving cumulative mean, using data on the minimum temperature over the last 30 years (1982 – 2018) was calculated, a forecasted trend gave a mean minimum temperature of 14.41⁰C, a minimum temperature of 12.33⁰C and a maximum temperature of 18.67⁰C. In the year 1990, the minimum temperature rose from 150C to 18⁰C. The forecasted temperature for 2019 also follows the same trend, with temperatures now stabilizing above 30⁰C. Majority of the respondents (96.7%, n=290) were aware of changes in climatic conditions now, compared to previous years. Most of the respondents had moderate (53%, n=159) to high (30.7%, n=92) knowledge about changes in climate. Majority of the respondents (57.7%, n=173 ) affirmed that information on climate change was obtained from broadcast media. Majority of the respondents were very concerned about climate change 91.7% (n=275) suggesting that they understood the seriousness of the changing climate. Further analyses of the data revealed that many of the respondents were aware that natural causes (31%, n=93), human activities (34.3%, n=103) and a combination of both (34.7%, n=104) were the main causes of climate change, while many of the respondents (58%, n=174) had experienced some extreme weather events in the last five years. Conclusion: There is a clear evidence of climate changes in the Kakamega forest ecosystem as observed from the rising temperatures and variability in precipitation. Most people adjacent to the forests are aware of climate changes and its effects. The main sources of information are the electronic media. The increase in temperature may be due to deforestation, urbanization and agricultural activities.

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Section: Discussionsupporting

confidence: 91%

Evidence of Climate Changes in a Tropical Rainforest: Case Study Kakamega Tropical Rainforest

Wanyonyi

Tsingalia

Omayio

et al. 2021

IJECC

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“…In neighboring Vietnam, the impact of urbanization on agricultural land has encouraged the mass conversion and development of agricultural land, leading to surging agricultural land prices that have forced farmers to rely on nonagricultural economic activities to meet their basic livelihood needs [64]. Alijani, Hosseinali and Biswas [65] utilized a geographic information system (GIS) to examine the changes in agricultural land prices in Iran's Chalus County. They discovered that, with the economic structure undergoing significant changes, industry and commerce developing rapidly, the urban population increasing continuously and the strong demand for land among nonagricultural sectors for developmental purposes, agricultural land conversion has become a component of urban development and has contributed to the surge in agricultural land prices.…”

Section: Discussionmentioning

confidence: 99%

Multilevel Analysis of the Pressure of Agricultural Land Conversion, Degree of Urbanization and Agricultural Land Prices in Taiwan

Lee

Chen

et al. 2020

Land

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In 2000, to efficiently implement land-use policies, the Taiwanese government amended the Agricultural Development Act by easing restrictions on agricultural land purchases. As a result of increasing land development and investment needs, agricultural land prices have surged. This study aims to examine whether agricultural land prices in Pingtung County are affected by land control policy measures, the pressure of agricultural land conversion and the degree of urbanization. A multilevel analysis approach was used to analyze land price differences in townships in Pingtung County. The estimation results derived from the null model indicated significant differences between the mean land price in each administrative division. Specifically, the ratio of agricultural land prices affected by differences in Level 2 township-related factors was 21.8%, while the ratio of those affected by differences in Level 1 land-related factors was 78.2%. An empirical intercepts-and-slopes-as-outcomes regression model demonstrated that Level 2 township-related factors, such as the pressure of agricultural land conversion and the degree of urbanization, had cross-level, direct and positive impacts on agricultural land prices; while Level 1 land-related factors, such as lot size control measures on farmhouse construction and land-use zoning, had positive and significant impacts on agricultural land prices.

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“…Considering both the large area and the complex land use types in the TWL region, we made a comparative attempt to obtain more accurate classification and interpretation results using the Mahalanobis distance, the K-nearest neighbor method, maximum likelihood, and other classifiers. We adopted the maximum likelihood method classifier, which proved to have the best effect in classical supervised classification, to conduct a preliminary classification of the land use types using the TWL region's remote sensing images [64]. We used ENVI Classic software to correct the results via a visual interpretation, and the remaining unrecognized land categories were reclassified.…”

Section: Pre-treatment and Supervised Classificationmentioning

confidence: 99%

Land Use Change in the Cross-Boundary Regions of a Metropolitan Area: A Case Study of Tongzhou-Wuqing-Langfang

Dai

Zhan

Shu

et al. 2022

Land

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Since the 1980s, metropolitan areas have increased worldwide due to urbanization and regionalization. While the spatial integration of the labor and housing markets has benefitted the development of cities within metropolitan areas, they have also brought great challenges for land governance; this is particularly evident in cross-boundary regions due to the complex relations between the markets and the regulations and between governments at different levels. Extensive research has been conducted on the city-level analysis of socioeconomic integration, land use development, and urban governance within metropolitan areas; yet, it is insufficient for understanding the intricate interplay between the various forces in such regions. This study aims to reveal the dynamics of land use change from 1990–2020 and its driving forces in the recent decade in the Tongzhou-Wuqing-Langfang (TWL) region—a typical cross-boundary area between Beijing, Tianjin, and the Hebei Metropolitan Area—using Landsat imagery. We employed the land-use dynamic degree, kernel density analysis, principal component analysis, and multiple linear regression to explore the spatiotemporal patterns of land use change and its driving factors at the district/county level. The results show that the general land use changes from cultivated and forest land to urban and rural construction land across the region. The speed of the trend varies considerably over time between different areas as the land use policies and regulations of each local government change. The population growth and the tertiary and secondary industry growth are the main driving factors for the change in construction land across the whole TWL region, while the urbanization rate and fixed asset investment have different impacts across the cross-boundary region. The results suggest that expanding the integration of land use policies and regulations in the cross-boundary region is urgently required.

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Spatio-temporal evolution of agricultural land use change drivers: A case study from Chalous region, Iran

Cited by 41 publications

References 81 publications

Evidence of Climate Changes in a Tropical Rainforest: Case Study Kakamega Tropical Rainforest

Evidence of Climate Changes in a Tropical Rainforest: Case Study Kakamega Tropical Rainforest

Multilevel Analysis of the Pressure of Agricultural Land Conversion, Degree of Urbanization and Agricultural Land Prices in Taiwan

Land Use Change in the Cross-Boundary Regions of a Metropolitan Area: A Case Study of Tongzhou-Wuqing-Langfang

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