Ecology and the Environment

Trivedi, Priya Ranja

doi:10.1007/978-1-4614-7612-2

Cited by 16 publications

(1 citation statement)

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“…In most parts of Africa, fuel wood collection for firewood and charcoal production, and, to a lesser extent, livestock grazing in forests are the most significant drivers of forest degradation. Underlying or indirect causes are complex connections of social, economic, political, cultural and technological processes that affect the direct drivers to cause deforestation or forest degradation (Abay 2014;Aragão et al 2014;Bele et al 2014;Jabbour and Hunsberger 2014;Kalema et al 2014;Medrilzam et al 2013;Monson 2013). They act at multiple scales: international (markets, commodity prices), national (population growth, domestic markets, national policies, governance) and local circumstances (subsistence, poverty).…”

Section: Introductionmentioning

confidence: 99%

Modelling forest loss and other land use change dynamics in Ashanti Region of Ghana

Koranteng

Zawiła-Niedźwiecki²

2015

Folia Forestalia Polonica

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Forest losses amid land use dynamics have become issues of outermost concern in the light of climate change phenomenon which has captivated the world’s attention. It is imperative to monitor land use change and to forecast forms of future land use change on a temporal and spatial basis. The main thrust of this study is to assess land use change in the lower half of the Ashanti Region of Ghana within a 40 year period. The analysis of land use change uses a combination method in Remote Sensing (RS) and Geographic Information System (GIS). Cellular Automata and Markov Chain (Cellular Automata-Markov) are utilized to predict for land use land cover (LULC) change for 2020 and 2030. The processes used include: (i) a data pre-processing (geometric corrections, radiometric corrections, subset creation and image enhancement) of epoch Landsat images acquired in 1990, 2000, and Disaster Monitoring Constellation (DMC) 2010; (ii) classification of multispectral imagery (iii) Change detection mapping (iv) using Cellular Automata-Markov to generate land use change in the next 20 years. The results illustrate that in years 2020 to 2030 in the foreseeable future, there will an upsurge in built up areas, while a decline in agricultural land use is envisaged. Agricultural land use would still be the dominant land use type. Forests would be drastically reduced from close to 50% in 1990 to just fewer than 10% in 2030. Land use decision making must be very circumspect, especially in an era where Ghana has opted to take advantage of REDD+. Studies such as this provide vital pieces of information which may be used to monitor, direct and influence land use change to a more beneficial and sustainable manner

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Section: Introductionmentioning

confidence: 99%

Modelling forest loss and other land use change dynamics in Ashanti Region of Ghana

Koranteng

Zawiła-Niedźwiecki²

2015

Folia Forestalia Polonica

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Delineate Soil Characteristics and Carbon Pools in Grassland Compared to Native Forestland of India: A Meta-Analysis

Padbhushan

Rana²,

Kumar³

et al. 2020

Agronomy

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Grassland is a highly dynamic land use system and it provides vital ecosystem services, mainly consisting of carbon storage in the tropics and subtropics. The objective of this study was to delineate grassland in India according to soil characteristics and carbon pools in comparison to native forestland, and to discuss management strategies for improving soil carbon (SC) storage in grassland. A total of 675 paired datasets from studies on grassland and forestland in India generated during the period of 1990–2019 were used for meta-analysis study. The analysis shows that soil pH and bulk density (BD) in grasslands were greater by 1.1% and 1.0% compared to forestlands while soil organic carbon (SOC) declined by 36.3% (p < 0.05). Among carbon pools, labile carbon (LC), non-labile carbon (NLC), and microbial biomass carbon (MBC) were 35.5%, 35.3% and 29.5% lower, respectively, in the grassland compared to the forestland. Total carbon (TC) was 35.0% lower in the grassland than the forestland (p < 0.05). Soil carbon stocks (SCS) were 32.8% lower in the grassland compared to the forestland. In the grassland, MBC/SOC (%) from the surface layer and subsurface layer were lower by 2.4% and 8.5%, respectively compared to forestland. The percentage effect size was found to have decreased from surface soil to subsurface soil. Relative SCS loss and carbon dioxide equivalent emission from the grassland compared to forestland were 15.2% and 33.3 Mg ha−1, respectively (p < 0.05). Proper management strategies like agroforestry, legume introduction, silvipastoral system, fertilization, irrigation, and quality grass species could improve SC storage and reduce SCS loss in grassland. Overall, this study gives an idea that conversion of native forestland into grassland in India has declined the SC content and hence it is necessary to adapt proper strategies to manage the soil-atmosphere carbon balance.

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Unveiling Innovations in Grasslands Productivity and Sustainability

Abdalla

2023

Agronomy

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Ecology and the Environment

Cited by 16 publications

References 1 publication

Modelling forest loss and other land use change dynamics in Ashanti Region of Ghana

Modelling forest loss and other land use change dynamics in Ashanti Region of Ghana

Delineate Soil Characteristics and Carbon Pools in Grassland Compared to Native Forestland of India: A Meta-Analysis

Unveiling Innovations in Grasslands Productivity and Sustainability

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