Land Degradation Neutrality - Potentials for its operationalisation at multi-levels in Nigeria

Speranza, Chinwe Ifejika; Adenle, Ademola A.; Boillat, Sébastien

doi:10.1016/j.envsci.2018.12.018

Cited by 30 publications

(8 citation statements)

References 15 publications

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“…A number of human-induced conditioning factors of LD have been identified in previous studies (Huber-Sannwald et al 2006;Lu et al 2007;Prăvălie et al 2017;Mekonnen et al 2018;Speranza et al 2019). We selected five of these for use in LD risk mapping in the study watershed.…”

Section: Human-induced Variablesmentioning

confidence: 99%

Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

et al. 2020

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Land degradation (LD) is a complex process affected by both anthropogenic and natural driving variables, and its prevention has become an essential task globally. The aim of the present study was to develop a new quantitative LD mapping approach using machine learning techniques, benchmark models, and human-induced and socio-environmental variables. We employed four machine learning algorithms [Support Vector Machine (SVM), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Model (GLM), and Dragonfly Algorithm (DA)] for LD risk mapping, based on topographic (n = 7), human-induced (n = 5), and geo-environmental (n = 6) variables, and field measurements of degradation in the Pole-Doab watershed, Iran. We assessed the performance of different algorithms using receiver operating characteristic, Kappa index, and Taylor diagram. The results revealed that the main topographic, geoenvironmental, and human-induced variable was slope, geology, and land use change, respectively. Assessments of model performance indicated that DA had the highest accuracy and efficiency, with the greatest learning and prediction power in LD risk mapping. In LD risk maps produced using SVM, GLM, MARS, and DA, 19.16%, 19.29%, 21.76%, and 22.40%, respectively, of total area in the Pole-Doab watershed had a very high degradation risk. The results of this study demonstrate that in LD risk mapping for a region, topographic, and geological factors (static conditions) and human activities (dynamic conditions, e.g., residential and industrial area expansion) should be considered together, for best protection at watershed scale. These findings can help policymakers prioritize land and water conservation efforts.

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Section: Human-induced Variablesmentioning

confidence: 99%

Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

et al. 2020

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“…As multiple factors are associated with LD, policy interventions aimed at achieving SLM need to be inter-sectoral. However, many policies in Nigeria, such as the Nigerian National Agricultural Policy, focus on single sectors and often do not have LD reduction as a primary objective [10]. Key policy topics related to these findings are sustainable use and management of natural resources, poverty reduction, environmental awareness and education, strategy to reduce dependence on land and natural resources for livelihoods, and inclusion of LD in land-use planning.…”

Section: Archetypes and Policy Insightsmentioning

confidence: 99%

“…With the growing LD, effective policy on land-use planning is critical for degradation response in Nigeria as the current land-use policies and practices do not adequately consider sustainable land management [10,86]. The historic lapses in the National Land Use Act (LUA) of 1978 persist, whose focus only recognizes land ownership and promotes land access without a sustainable land-use plan or governance to cater for the pressure from the growing population.…”

Section: Archetypes and Policy Insightsmentioning

confidence: 99%

“…These expose its 353 million inhabitants and their livelihoods to various impacts such as decline in ecosystem services, food insecurity, migration, and civil conflict [8]. In Nigeria, LD cases across its agroecological zones are mostly triggered by a singular factor or a combination of factors, which include: desertification, deforestation leading to biomass loss, land pollution caused by oil spillage and illegal mining, as well as extensive soil erosion [10]. Thus, agroecological zones like the Nigerian Guinean Savannah (NGS) are experiencing pressures from urbanization, agricultural expansion and an increasing population that is largely dependent on land resources for livelihoods [11,12].…”

Section: Introductionmentioning

confidence: 99%

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Social-Ecological Archetypes of Land Degradation in the Nigerian Guinea Savannah: Insights for Sustainable Land Management

Adenle

Speranza

2020

Remote Sensing

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The Nigerian Guinea Savannah is the most extensive ecoregion in Nigeria, a major food production area, and contains many biodiversity protection areas. However, there is limited understanding of the social-ecological features of its degraded lands and potential insights for sustainable land management and governance. To fill this gap, the self-organizing map method was applied to identify the archetypes of both proximal and underlying drivers of land degradation in this region. Using 12 freely available spatial datasets of drivers of land degradation—4 environmental; 3 socio-economic; and 5 land-use management practices, the identified archetypes were intersected with the Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived land-degradation status of the region, and the state administrative boundaries. Nine archetypes were identified. Archetypes are dominated by: (1) protected areas; (2) very high-density population; (3) moderately high information/knowledge access; (4) low literacy levels and moderate–high poverty levels; (5) rural remoteness; (6) remoteness from a major road; (7) very high livestock density; (8) moderate poverty level and nearly level terrain; and (9) very rugged terrain and remote from a major road. Four archetypes characterized by very high-density population, moderate–high information/knowledge access, and moderate–high poverty level, as well as remoteness from a major town, were associated with 61.3% large-area degradation; and the other five archetypes, covering 38.7% of the area, were responsible for small-area degradation. While different combinations of archetypes exist in all the states, the five states of Niger (40.5%), Oyo (29.6%), Kwara (24.4%), Nassarawa (18.6%), and Ekiti (17.6%), have the largest shares of the archetypes. To deal with these archetypical features, policies and practices that address increasing population in combination with poverty reduction; and that create awareness about land degradation and promote sustainable practices and various forms of land restoration, such as tree planting, are necessary for progressing towards land-degradation neutrality in the Nigerian Guinea Savannah.

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“…[10] assessed how the various legal provisions in Germany address actions required along the LDN response hierarchy discussed above, as well as require or allow that degradation is offset by restoration (e.g., permission for a project that would degrade a habitat is granted only if the applicant restores or upgrades land to a functionally equivalent extent). In [11], for the case of Nigeria, it not only examined the extent to which the existing laws and policies engaged with the LDN response hierarchy, but also how the current institutional arrangements and the extent to which various LDN indicators were captured in the policy documents. Both studies noted that the existing laws and policies were not conducive to facilitating implementation of the LDN target, in large part due to the fact that mechanisms to address LDN were scattered across several instruments without much coordination.…”

Section: Introductionmentioning

confidence: 99%

Opportunities and Limitations for Achieving Land Degradation-Neutrality through the Current Land-Use Policy Framework in Kenya

Gichenje

Muñoz-Rojas

Correia

2019

Land

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The United Nations Convention to Combat Desertification (UNCCD) land degradation neutrality (LDN) scientific conceptual framework underscores that LDN planning and implementation should be integrated into existing planning processes and supported by an enabling policy environment. Land-use planning, which requires the integration of different policy goals across various sectors concerned with land-use, can be an effective mechanism through which decisions with respect to LDN can be coordinated. Using Kenya as a case study, we examined current policy instruments that directly or indirectly impact on the use of land in a rural context, to assess their potential to implement LDN objectives. The qualitative content analysis of these instruments indicated that they are rich with specific legal provisions and measures to address LDN, and that there are a number of relevant institutions and structures across governance levels. However, the main shortcoming is the disjointed approach that is scattered across policy areas. Key policy improvements needed to support effective implementation of LDN include: a national soil policy on the management and protection of soil and land; a systematic and coordinated data collection strategy on soils; mobilisation of adequate and sustained financial resources; streamlined responsibilities, and governance structures across national, regional and county levels.

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Land Degradation Neutrality - Potentials for its operationalisation at multi-levels in Nigeria

Cited by 30 publications

References 15 publications

Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

Social-Ecological Archetypes of Land Degradation in the Nigerian Guinea Savannah: Insights for Sustainable Land Management

Opportunities and Limitations for Achieving Land Degradation-Neutrality through the Current Land-Use Policy Framework in Kenya

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