Assessing the Impact of Geospatial Susceptibility on the Rural Agricultural Landscapes of Village Settlements in Southwestern Nigeria

A theoretical framework for agricultural landscape pattern transitions in rural areas is proposed to fill the gap of current research with an empirical study in the Alabata farm settlement in Ogun State, Southwest Nigeria. Adopting the use of ArcGIS 10.8 and Surfer 27.3 the surface area and volume of the ground surface degraded and exposed to harsh weather conditions due to both natural and man-made anthropogenic activities causing runoffs and continuous soil erosion were calculated, which have affected agricultural production and sent some of the farmers packing, abandoning their farmlands. The farm settlement within the total sampled frame area of 1.50 km2 covers a total distance of 5.11 km. The sample frame was divided into 36 frames with similar ecological microclimates, topographical features, terrain, and soil types that fell into either of the two identified soil types: 1 and 2. The study involved the completion of a questionnaire onsite using three major criteria and only 23 farmers responded positively to the few questions as farmers who were either living on the farm or working as hired laborers on-site at the time of the visit. The transition characteristics of agricultural landscape patterns as represented in abandoned, degraded farmlands and orchards are analyzed from a local perspective.

Authors

Institutions

Publication Details

Journal
Land
Published
2024-10-20
DOI
https://doi.org/10.3390/land13101719
Citations
1
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
0.28
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Assessing the Impact of Geospatial Susceptibility on the Rural Agricultural Landscapes of Village Settlements in Southwestern Nigeria

Xiaolan Tang, Yuanyuan Liu, Yujie Ren, John Adekunle Adesina
1 citations
Land
Land Use and Ecosystem Services
0.28
article

Assessing the Impact of Geospatial Susceptibility on the Rural Agricultural Landscapes of Village Settlements in Southwestern Nigeria

Xiaolan Tang, Yuanyuan Liu, Yujie Ren, John Adekunle Adesina
article en
1 citations

Abstract

A theoretical framework for agricultural landscape pattern transitions in rural areas is proposed to fill the gap of current research with an empirical study in the Alabata farm settlement in Ogun State, Southwest Nigeria. Adopting the use of ArcGIS 10.8 and Surfer 27.3 the surface area and volume of the ground surface degraded and exposed to harsh weather conditions due to both natural and man-made anthropogenic activities causing runoffs and continuous soil erosion were calculated, which have affected agricultural production and sent some of the farmers packing, abandoning their farmlands. The farm settlement within the total sampled frame area of 1.50 km2 covers a total distance of 5.11 km. The sample frame was divided into 36 frames with similar ecological microclimates, topographical features, terrain, and soil types that fell into either of the two identified soil types: 1 and 2. The study involved the completion of a questionnaire onsite using three major criteria and only 23 farmers responded positively to the few questions as farmers who were either living on the farm or working as hired laborers on-site at the time of the visit. The transition characteristics of agricultural landscape patterns as represented in abandoned, degraded farmlands and orchards are analyzed from a local perspective.

LandVol. 13(10)
Nanjing Forestry University (CN), University of Lagos (NG)
Openalex Percentile: Top 41%
Land Use and Ecosystem Services
0.28
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.