Forging the path to achieving land degradation neutrality: Global patterns and drivers of land degradation at global scales

2020 ◽  
Author(s):  
Alex Zvoleff ◽  
Monica Noon ◽  
Gabriel Daldegan ◽  
Mariano Gonzalez-Roglich
Keyword(s):  
Land Degradation ◽  
Baseline Period ◽  
Bayesian Hierarchical ◽  
Global Challenge ◽  
Natural Processes ◽  
Effective Interventions ◽  
Productive Potential ◽  
Land Condition ◽  
Global Goal ◽  
Climate Change Drivers

<p>Land degradation – the reduction or loss of the productive potential of land – is a global challenge. More than 20% of the Earth’s vegetated surface is estimated to be degraded, affecting over 1.3 billion people, with an economic impact of up to US$10.6 trillion. Land degradation reduces agricultural productivity and increases the vulnerability of those areas already at risk of impacts from climate variability and change. Addressing land degradation, Sustainable Development Goal (SDG) target 15.3, is essential to improve the livelihoods of those most affected, and to build resilience to safeguard against the most extreme effects of climate change. Drivers of land degradation include natural processes and human activities, and understanding such drivers is key for deploying effective interventions for addressing it. The parties to the United Nations Convention to Combat Desertification (UNCCD) have adopted a framework for assessing and monitoring land degradation at national scale, by measuring three sub-indicators: Changes in land cover, changes in soil organic carbon, and changes in primary productivity. In this study, we use the framework developed by the UNCCD and Trends.Earth, the most widely tool used for producing such indicators, to assess land condition globally for the period 2001-2015, the SDG 15.3.1 baseline period. Using a Bayesian hierarchical model, we then assessed the contribution of 12 drivers of land degradation, including key biophysical and anthropogenic variables, to the observed patterns to provide insight into the main drivers of land degradation at global, regional, and national scales. These results are critical for designing locally relevant plans for assessing land degradation contributing to the global goal of achieving a land degradation neutral world by 2030. The results of this analysis allow identification of not only the significant drivers in a given region, but also of those areas where unexpected trends (either improvement or degradation) are indicative of potential policy successes or failures.</p>

scholarly journals Actual inflow of riverine sediment load into Lake Baikal: main tributaries the Selenga, Upper Angara, and Barguzin Rivers (Russia)

2021 ◽  
pp. 1111-1114
Author(s):  
T.G. Potemkina ◽  
◽  
V.L. Potemkin ◽  
Keyword(s):  
Lake Baikal ◽  
Meteorological Parameters ◽  
Sediment Load ◽  
Water Discharge ◽  
Baseline Period ◽  
Integrated Analysis ◽  
Natural Processes ◽  
Precipitation Decrease ◽  
Riverine Sediment ◽  
The Given

Abstract. The sediment load delivery into Lake Baikal from its main tributaries the Selenga, Upper Angara, and Barguzin Rivers has been reduced since the mid-1970s. This is explained by climate change and socioeconomic activities. Integrated analysis of changes in hydro-meteorological parameters (water discharge, sediment load, air temperature, precipitation) and their trends over the period 1946 1975 (baseline) and 1976 2017 (warming) is performed. Changes in natural processes and human activity were negligible during the baseline period. During the warming period, the greatest reduction of the sediment load inflow against the background of temperature rise and precipitation decrease occurred in the interval between 1996 and 2017 in the Selenga River, between 1985 and 2017 in the Upper Angara River, and between 1992 and 2017 in the Barguzin River. The flux of the sediment load into these rivers was 768 103, 88 103, and 29 103 t y 1, respectively. This is 2 3 times less than the average multiyear values for all period of 1946 2017, which are usually used when characterizing sediment load runoff from these rivers. Currently the values in the given intervals correspond to the actual sediment load flux into Lake Baikal from the main tributaries.

scholarly journals Beyond Bioproductivity: Engaging Local Perspectives in Land Degradation Monitoring and Assessment

2018 ◽  
Author(s):  
Stefanie Herrmann ◽  
Abdoul Aziz Diouf ◽  
Ibrahima Sall
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Vegetation Indices ◽  
Temporal Trends ◽  
Photo Elicitation ◽  
Field Observations ◽  
Crowd Sourcing ◽  
Vegetative Cover ◽  
Degradation Monitoring ◽  
Land Condition

Land degradation monitoring and assessment in the Sahel zone takes advantage of and relies substantially on temporal trends of remote sensing-based vegetation indices, which are proxies for the bioproductivity of the land. However, prior studies have shown that negative or positive trends in bioproductivity are not necessarily associated with degradation or improvement of land condition. We argue that remote sensing-based indices, while having contributed much to dismantling an outdated desertification narrative, are themselves falling short of capturing the whole picture and need to be accompanied by field observations that are relevant to local land users. We used the participatory photo elicitation method in three sites in order to elicit local pastoralists’ perspectives on land degradation and identify the indicators that they use to characterize pasture quality, while empowering them to lead the discussion. The discussion revealed indicators far beyond bioproductivity, including livestock performance as well as composition and quality of the herbaceous and woody vegetative cover, invasive species, soil quality and water availability. We found that the pastoralists’ interest, knowledge and field observations could potentially be harnessed using a crowd-sourcing approach in order to produce a geospatially explicit dataset of land degradation, which would be complementary to the remote sensing-based maps of trends in bioproductivity and could serve as a reference for the development of more targeted remote sensing-based indicators of land degradation

scholarly journals Application of pre-fishery abundance modelling and Bayesian hierarchical stock and recruitment analysis to the provision of precautionary catch advice for Irish salmon (Salmo salar L.) fisheries

2004 ◽  
Vol 61 (8) ◽  
pp. 1370-1378 ◽  
Author(s):  
N. Ó Maoiléidigh ◽  
P. McGinnity ◽  
E. Prévost ◽  
E.C.E Potter ◽  
P. Gargan ◽  
...  
Keyword(s):  
North Atlantic ◽  
Probability Distributions ◽  
Baseline Period ◽  
Reference Points ◽  
Model Parameters ◽  
Total Allowable Catch ◽  
Bayesian Hierarchical ◽  
The North ◽  
Salmon Fishery ◽  
Stock And Recruitment

Abstract Ireland has one of the last remaining commercial salmon driftnet fisheries in the North Atlantic, with recent catches averaging 162 000 salmon (1997–2003), approximately 20% of the total landings of salmon in the entire North Atlantic. Since 2001, the Irish commercial salmon fishery has been managed on the basis of Total Allowable Catch (TAC) in each of 17 salmon fishing districts. This has been made possible by applying a number of new and innovative techniques to the estimation of conservation limits (CLs) and pre-fishery abundance (PFA) for combined stocks in each district. Stock and recruitment parameters from well-monitored European rivers were “transported” to all Irish rivers using a Bayesian hierarchical stock and recruitment (BHSRA) model. This provided the posterior probability distributions of the model parameters and related reference points, including individual river CLs. District PFA and the number of spawners were estimated for a baseline period of 1997–2003, using district catch data, estimates of unreported catch, and exploitation rate. Harvest guidelines were established on the basis of surplus of spawning fish over the CL for the baseline period. In line with scientific advice, the commercial fishery has been reduced from 212 000 fish in 2002 to 182 000 in 2003. In 2004, a total catch (including the rod catch) of approximately 160 000 wild salmon was advised.

scholarly journals DEM- and GIS-Based Analysis of Soil Erosion Depth Using Machine Learning

2021 ◽  
Vol 10 (7) ◽  
pp. 452
Author(s):  
Kieu Anh Nguyen ◽  
Walter Chen
Keyword(s):  
Machine Learning ◽  
Soil Erosion ◽  
Land Degradation ◽  
Prediction Models ◽  
Biodiversity Loss ◽  
Gradient Boosting ◽  
Learning Models ◽  
Erosion Depth ◽  
Global Challenge ◽  
Machine Learning Models

Soil erosion is a form of land degradation. It is the process of moving surface soil with the action of external forces such as wind or water. Tillage also causes soil erosion. As outlined by the United Nations Sustainable Development Goal (UN SDG) #15, it is a global challenge to “combat desertification, and halt and reverse land degradation and halt biodiversity loss.” In order to advance this goal, we studied and modeled the soil erosion depth of a typical watershed in Taiwan using 26 morphometric factors derived from a digital elevation model (DEM) and 10 environmental factors. Feature selection was performed using the Boruta algorithm to determine 15 factors with confirmed importance and one tentative factor. Then, machine learning models, including the random forest (RF) and gradient boosting machine (GBM), were used to create prediction models validated by erosion pin measurements. The results show that GBM, coupled with 15 important factors (confirmed), achieved the best result in the context of root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE). Finally, we present the maps of soil erosion depth using the two machine learning models. The maps are useful for conservation planning and mitigating future soil erosion.

scholarly journals A UAV- and field-based investigation of the land degradation and soil erosion at opencast coal mine dumps after 5 years’ evolution of natural processes

2021 ◽  
Author(s):  
Wu Xiao ◽  
He Ren ◽  
Tao Sui ◽  
Heyu Zhang ◽  
Yanling Zhao ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Data Acquisition ◽  
Coal Mine ◽  
Land Degradation ◽  
Open Pit ◽  
Natural Processes ◽  
Opencast Coal Mine ◽  
Source Data ◽  
Mine Dump ◽  
Acquisition Method

Abstract Open-pit coal mining has a large impact on land surface, both at the mining pits themselves and at waste sites. After artificial management is stopped, a reclaimed opencast coal mine dump is affected by wind and water erosion from natural processes, resulting in land degradation and even safety incidents. In this paper, the soil erosion and land degradation after 5 years of such natural processes, at the Xilinhot open pit coal mine dump in Inner Mongolia, were investigated. A multi-source data acquisition method was applied: the vegetation coverage index was extracted from GF-1 satellite imagery, high-precision terrain characteristics and the location and degree of soil erosion were obtained using an Unmanned Aerial Vehicle (UAV), and the physical properties of the topsoil were obtained by field sampling. On this basis, the degree and spatial distribution of erosion cracks were identified, and the causes of soil erosion and land degradation were analyzed using a geographical detector. The results show that: 1) The multi-source data acquisition method can provide effective basic data for the quantitative evaluation of the ecological environment at dumps; 2) slope aspect and vegetation fractional coverage are the main factors affecting the degree of degradation and soil erosion. Based on this analysis, several countermeasures are proposed to mitigate land degradation: 1) The windward slope be designed to imitate the natural landform; 2) engineering measures should be applied at the slope to restrain soil erosion; 3) pioneer plants should be widely planted on the platform at the early stage of reclamation.

scholarly journals Developing a whole systems obesity classification for the UK Biobank Cohort

2020 ◽  
Author(s):  
Stephen Clark ◽  
Mark Birkin ◽  
Nik Lomax ◽  
Michelle Morris
Keyword(s):  
Physical Activity ◽  
Systems Approach ◽  
Multinomial Logistic Regression ◽  
Overweight And Obesity ◽  
Healthy Weight ◽  
Uk Biobank ◽  
Global Challenge ◽  
Effective Interventions ◽  
The Uk ◽  
Whole Systems

The number of people who are obese and overweight presents a global challenge, and the development of effective interventions is hampered by a lack of research which takes in to account a joined up, whole systems approach to understanding the drivers of the phenomena. We need to better understand the collective characteristics and behaviours of the overweight and obese population and how these differ from those who maintain a healthy weight. Using the UK Biobank cohort of 500 000 adults, we develop an obesity classification system using k-means clustering. Variable selection from UK Biobank is informed by the Foresight whole system obesity map across key domains (Societal Influences, Individual Psychology, Individual Physiology, Individual Physical Activity, Physical Activity Environment). This paper presents the first study of UK Biobank participants to adopt this whole systems approach. Our classification identifies six groups of people, similar in respect to their exposure to known drivers of obesity: ‘Younger, active and working hard’, ‘Retirees with good lifestyle’ , ‘Stressed, sedentary and struggling’, Older with poor lifestyle’, ‘Younger, busy professionals’ and ‘Younger, fitter families’. Pen portraits are developed to describe the characteristics of these different groups. Multinomial logistic regression is used to demonstrate that the classification can effectively detect groups of individuals more likely to be overweight or obese. The group identified as ‘Younger, fitter families’ are observed to have a higher proportion of healthy weight, while three groups have increased relative risk of being overweight or obese: ‘Younger, active and working hard’, ‘Stressed, sedentary and struggling’ and ‘Older with poor lifestyles’. This work presents an innovative new approach to better understand the whole systems drivers of obesity which has the potential to produce meaningful tools for policy makers to better target interventions across the whole system to reduce overweight and obesity.

scholarly journals FUZZY INTEGRATED DESERTIFICATION VULNERABILITY MODEL

2018 ◽  
Vol XLII-5 ◽  
pp. 395-401
Author(s):  
V. A. Dave ◽  
K. Sur
Keyword(s):  
Land Degradation ◽  
Economic Factor ◽  
Land Productivity ◽  
Analytic Hierarchy ◽  
System Parameters ◽  
Natural Processes ◽  
System A ◽  
Vulnerability Model ◽  
Grey Clustering ◽  
Hierarchy Process

<p><strong>Abstract.</strong> Desertification and Land Degradation have constantly affected the global environment over the years. Land degradation is a reduction or loss of productivity over land due to natural processes, climate change and human activities. It involves complex set of processes, which interacts over space to decrease in land productivity. In the present study, regional desertification vulnerability assessment was carried out for Bhavnagar district of Gujarat, India. A vulnerability is a basic concept which explains the flaw in any structure of a system. A vulnerability may also refer to any type of shortcoming in systems, sub system parameters or processes, or in anything that leads the system to be exposed to a threat. Fuzzy Logic (FL) method combined with remote sensing (RS) and geographic information system (GIS) techniques has several advantages for assessing vulnerability. Technically FL combines the intricate classical analytic hierarchy process and grey clustering method for estimation of coefficients. Climate, soil, vegetation and land use play a significant role in desertification of any area, hence, in this work, several indices have been generated. However, man’s intervention leads to significant changes in the environment, making socio-economic factor as a major input to assess vulnerability for desertification. Thus in this study FL has been integrated with both natural and socioeconomic factors for understanding the vulnerability to desertification in Bhavnagar region.</p>

scholarly journals Estimating Under Reporting of Leprosy in Brazil using a Bayesian Approach

2020 ◽  
Author(s):  
Guilherme L Oliveira ◽  
Juliane Fonseca Oliveira ◽  
Roberto F S Andrade ◽  
Joilda S Nery ◽  
Julia M Pescarini ◽  
...  
Keyword(s):  
Health Problem ◽  
Health Resources ◽  
Bayesian Hierarchical Model ◽  
Incidence Rates ◽  
Grouped Data ◽  
Bayesian Hierarchical ◽  
Effective Interventions ◽  
True Number ◽  
Important Health ◽  
Resouce Allocation

Leprosy remains an important health problem in Brazil - the country register the second largest number of new leprosy cases each year, accounting for 14% of the world's new cases in 2019. Although there was increasing advances in leprosy surveillance worldwide, the true number of leprosy cases is expected to be much larger than the reported. Leprosy underreporting impair planning effective interventions and thoughful decisions about the distribution of financial and health resources. In this study, we estimated leprosy underreporting for each Brazilian microregion in order to guide effective interventions and resouce allocation to improve leprosy detection in the country. We extracted the number of new cases of leprosy from 2007 to 2015 and population and socioeconomic information from the 2010 Census for each Brazilian municipality and grouped data in microregions. We applied a Bayesian hierarchical model to obtain the best explicative model for leprosy underreporing using Grade 2 of leprosy-related disabilities as a proxy to explain the incidence rates. Then, we estimated the number of missing leprosy cases (underreported cases) and the corrected leprosy incidence rates for each Brazilian microrregion.

scholarly journals Analisis Proses Degradasi Lahan dan Dampaknya Terhadap Produktifitas Lahan Pertanian di Kabupaten Klaten

2016 ◽  
Vol 21 (2) ◽  
Author(s):  
Agus Anggoro Sigit ◽  
Suharjo Suharjo
Keyword(s):  
Land Degradation ◽  
Research Result ◽  
Degradation Process ◽  
Rice Crop ◽  
Geographical Information ◽  
Survey Method ◽  
Land Productivity ◽  
Agriculture Land ◽  
Land Condition

The aim of this research are : (1) identity the process of land degradation happened in the study area; (2) evaluation of agriculture land productivity with land degradation process in the study area; (3) analysis impact process of land degradation to agriculture land productivity in the study area. This research using survey method and spatial analysis by application of Geographical Information Sistem (GIS).According to data analysis, obtained by the following research result are: (1). Have been happened by process of land degradation in regional part of the study area which tend to degrade quality of land resources by type natural process influence (subsidence, slide, crack) and anthropogenic process (making of brick). Subsidence happened in Gantiwarno; crack in Gantiwarno and Bayat; slide in Bayat. Making o brick a lot of happened in Jogonalan, Ngawen, Jatinom, Karanganom and Ceper; (2). Region with compatibility ‘harmony’ in the study area take of area for the width of 28930,10 Ha or 44,13 % (regional half almost Klaten District of productivity of its rice crop as according to its land suitability); ‘harmony (-)’ taking of area for the width of 2973,15 Ha or4,53 % (there are partial; a little region in Klaten District which manifestly do not good for effort to agriculture of rice crop, specially the paddy); ‘not harmony (+)’ taking of area for the which of 7929,25 Ha or 12,10 % (there are some region in Klaten District bad its of land condition, but its productivity is goodness); ‘not harmony’ taking of area for the which of 25724,10 Ha or 39,24 % (there are region with big enough area in Klaten District which to make a effort of agriculture of its rice crop not yet been done in an optimal fashion). Region with compatibility ‘not harmony’ need deeper attention of its land management; (3). Land degradation of most be happened to regions with compatibility status ‘not harmony’. Although is not be absolute in character, but this matter represent and existence indication of s possibility of influence of land degradation to lowering mount land productivity to rice crop in the study area. Land degradation at region of have compatibility status  ‘harmony’ as in Gantiwarno (in this time) reality not yet affect its influence to level of land productivity for the rice crop.

scholarly journals Estimating underreporting of leprosy in Brazil using a Bayesian approach

2021 ◽  
Vol 15 (8) ◽  
pp. e0009700
Author(s):  
Guilherme L. de Oliveira ◽  
Juliane F. Oliveira ◽  
Júlia M. Pescarini ◽  
Roberto F. S. Andrade ◽  
Joilda S. Nery ◽  
...  
Keyword(s):  
Delayed Diagnosis ◽  
Health Resources ◽  
Incidence Rates ◽  
Surveillance Program ◽  
Middle Income ◽  
Bayesian Hierarchical ◽  
Effective Interventions ◽  
Efficient Resource ◽  
Leprosy Case ◽  
Using Data

Background Leprosy remains concentrated among the poorest communities in low-and middle-income countries and it is one of the primary infectious causes of disability. Although there have been increasing advances in leprosy surveillance worldwide, leprosy underreporting is still common and can hinder decision-making regarding the distribution of financial and health resources and thereby limit the effectiveness of interventions. In this study, we estimated the proportion of unreported cases of leprosy in Brazilian microregions. Methodology/Principal findings Using data collected between 2007 to 2015 from each of the 557 Brazilian microregions, we applied a Bayesian hierarchical model that used the presence of grade 2 leprosy-related physical disabilities as a direct indicator of delayed diagnosis and a proxy for the effectiveness of local leprosy surveillance program. We also analyzed some relevant factors that influence spatial variability in the observed mean incidence rate in the Brazilian microregions, highlighting the importance of socioeconomic factors and how they affect the levels of underreporting. We corrected leprosy incidence rates for each Brazilian microregion and estimated that, on average, 33,252 (9.6%) new leprosy cases went unreported in the country between 2007 to 2015, with this proportion varying from 8.4% to 14.1% across the Brazilian States. Conclusions/Significance The magnitude and distribution of leprosy underreporting were adequately explained by a model using Grade 2 disability as a marker for the ability of the system to detect new missing cases. The percentage of missed cases was significant, and efforts are warranted to improve leprosy case detection. Our estimates in Brazilian microregions can be used to guide effective interventions, efficient resource allocation, and target actions to mitigate transmission.

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