Introducing a common taxonomy to support learning from failure in conservation

Although some sectors have made significant progress in learning from failure, there is currently limited consensus on how a similar transition could best be achieved in conservation and what is required to facilitate this. One of the key enabling conditions for other sectors is a widely accepted and standardized classification system for identifying and analyzing root causes of failure. We devised a comprehensive taxonomy of root causes of failure affecting conservation projects. To develop this, we solicited examples of real-life conservation efforts that were deemed to have failed in some way, identified their underlying root causes of failure, and used these to develop a generic, 3-tier taxonomy of the ways in which projects fail, at the top of which are 6 overarching cause categories that are further divided into midlevel cause categories and specific root causes. We tested the taxonomy by asking conservation practitioners to use it to classify the causes of failure for conservation efforts they had been involved in. No significant gaps or redundancies were identified during this testing phase. We then analyzed the frequency that particular root causes were encountered by projects within this test sample, which suggested that some root causes were more likely to be encountered than others and that a small number of root causes were more likely to be encountered by projects implementing particular types of conservation action. Our taxonomy could be used to improve identification, analysis, and subsequent learning from failed conservation efforts, address some of the barriers that currently limit the ability of conservation practitioners to learn from failure, and contribute to establishing an effective culture of learning from failure within conservation.     

THE FARM LEVEL EFFECTIVENESS OF SELECTED IRRIGATION POLICY MEASURES1

ABSTRACT: The on-farm economic effectiveness of government capital grants, subsidized interest rates, and the Canadian Wheat Board (CWB) delivery quota levels in terms of adoption and/or expansion of irrigation in Saskatchewan is tested. The annualized net income at 5 and 20 years of three representative farm types - a dryland grain farm, an irrigated grain farm, and an irrigated mixed farm - are used in the analysis. Tradeoffs between income levels and the risks associated with adoption/expansion of irrigation are evaluated using mean-standard deviation tradeoff and stochastic dominance. Risk differences arise due to reduced business risk through higher yields and increased financial risk through higher borrowing when adopting or expanding irrigation. Capital grants and subsidized interest rates are effective policy measures for dryland grain farms adopting irrigation because the farms are left in a similar risk position. However, these grants and interest rates are not effective policy measures in the medium run (5 years) for irrigated grain farms expanding irrigation because they lower the farm's risk efficiency. In the long run (20 years), the capital grants and subsidized interest rates need to be combined with open CWB delivery quotas before the risk position can be improved for irrigated grain farms expanding irrigation. Finally, the grants and interest rates need to be combined with increased irrigated hay production for risk efficiency to increase in both the medium and long run (5 and 20 years, respectively) on irrigated mixed farms expanding irrigation.     

Source apportionment of fine particles at urban background and rural sites in the UK atmosphere

Airborne fine particulate matter (PM_2.5) has been collected at two sites in the West Midlands conurbation, UK, representing urban background and rural locations. Chemical analyses have been carried out for major anions, trace metals, total OC and EC, and for individual organic marker species including n-alkanes, hopanes, PAHs, organic acids and sterols. Source apportionment has been conducted using both a pragmatic mass closure model and the US EPA chemical mass balance (CMB) model. The pragmatic mass closure model is well able to account for the measured PM_2.5 mass in terms of chemical/source components, and the chemical mass balance model has been used to apportion the carbonaceous component of the aerosol. The dominant components of PM_2.5 at both sites are secondary inorganic (sulphate and nitrate) and carbonaceous particles. The CMB model shows the latter to arise mainly from road traffic sources, with smaller contributions from vegetative detritus, wood smoke, natural gas, coal, and dust/soil. The CMB model also identifies an important component of the organic aerosol not associated with these primary sources, which correlates very strongly with secondary organic aerosol estimated from the OC/EC ratio. The split between different automotive source types does not relate well to UK emission inventories, and may indicate that CMB source profiles from North American studies and different carbon analysis protocols may lead to erroneous conclusions.     

Review of biosolids management options and co-incineration of a biosolid-derived fuel

This paper reviews current biosolids management options, and identifies incineration as a promising technology. Incineration is attractive both for volume reduction and energy recovery. Reported emissions from the incineration of biosolids were compared to various regulations to identify the challenges and future direction of biosolids incineration research. Most of the gaseous and metal emissions were lower than existing regulations, or could be met by existing technologies. This paper also presents the results of an experimental study to investigate the potential use of biosolids for co-incineration with wood pellets in a conventional wood pellet stove. Pilot scale combustion tests revealed that co-incineration of 10% biosolids with 90% premium grade wood pellets resulted in successful combustion without any significant degradation of efficiency and emissions.     

The potential role of stated preference methods in the Water Framework Directive to assess disproportionate costs

This paper examines the issue of disproportionate costs of Water Framework Directive (WFD) implementation using public surveys as a means to inform policy and decision making. Public taxpayers are asked their opinion regarding the implementation of the WFD and its costs. Taxpayers are expected to bear a large share of the cost of WFD implementation, be it through national taxation, local water pollution charges or higher market prices for water related goods and services. The paper's main objective is to illustrate the role of stated preference research to elicit public opinions and perceptions towards socially acceptable levels of water quality and public willingness to pay (WTP) for the expected environmental benefits of the WFD. Stated preference research can be used as a way to assess the concept of disproportionate costs to those who are expected to bear a large share of the costs of WFD implementation, and at the same time address the issue of public participation in the WFD. The survey results are used as a public consultation tool to inform policy and decision makers about public willingness and ability to pay for the implementation of the WFD. This measure can be used as one of the benchmarks to define disproportionate costs in a cost-benefit context.     

Adapting to urban challenges in the Amazon: flood risk and infrastructure deficiencies in Belém,Brazil

Regional Environmental Change - Sustainable urban infrastructure transition is perhaps the biggest challenge confronting cities in the global south in a time of climate change. Fast-growing cities...     

Dynamics and determinants of land change in India: integrating satellite data with village socioeconomics

We examine the dynamics and spatial determinants of land change in India by integrating decadal land cover maps (1985–1995–2005) from a wall-to-wall analysis of Landsat images with spatiotemporal socioeconomic database for ~630,000 villages in India. We reinforce our results through collective evidence from synthesis of 102 case studies that incorporate field knowledge of the causes of land change in India. We focus on cropland–fallow land conversions, and forest area changes (excludes non-forest tree categories including commercial plantations). We show that cropland to fallow conversions are prominently associated with lack of irrigation and capital, male agricultural labor shortage, and fragmentation of land holdings. We find gross forest loss is substantial and increased from ~23,810 km² (1985–1995) to ~25,770 km² (1995–2005). The gross forest gain also increased from ~6000 km² (1985–1995) to ~7440 km² (1995–2005). Overall, India experienced a net decline in forest by ~18,000 km² (gross loss–gross gain) consistently during both decades. We show that the major source of forest loss was cropland expansion in areas of low cropland productivity (due to soil degradation and lack of irrigation), followed by industrial development and mining/quarrying activities, and excessive economic dependence of villages on forest resources.

     

GIS-based statistical model for the prediction of flood hazard susceptibility

At present, flood is the most significant environmental problem in the entire world. In this work, flood susceptibility (FS) analysis has been done in the Dwarkeswar River basin of Bengal basin, India. Fourteen flood causative factors extracted from different datasets like DEM, satellite images, geology, soil and rainfall data have been considered to predict FS. Three heuristic models and one statistical model fuzzy Logic (FL), frequency ratio (FR), multi-criteria decision analysis (MCDA) and logistic regression (LR) have been used. The validating datasets are used to validate these models. The result shows that 68.71%, 68.7%, 60.56% and 48.51% area of the basin is under the moderate to very high FS by the MCDA, FR, FL and LR, respectively. The ROC curve with AUC analysis has shown that the accuracy level of the LR model (AUC?=?0.916) is very much successful to predict the flood. The rest of the models like FL, MCDA and FR (AUC?=?0.893, 0.857 and 0.835, respectively) have lesser accuracy than the LR model. The elevation was the most dominating factor with coefficient value of 19.078 in preparation of the FS according to the LR model. The outcome of this study can be implemented by local and state authority to minimize the flood hazard.

     

An assessment of agricultural sustainability indicators in Bangladesh: review and synthesis

The term ‘indicator’ is often vague and heterogeneous, and its dynamic characteristics make it highly variable over time and space. Based on reviews and synthesis, this study visualizes phenomena and highlights the trend of indicator selection criteria, development methods, validation evaluation strategies for improvement. In contextualization of the intensification of agriculture and climate change, we proposed a set of indicators for assessing agricultural sustainability in Bangladesh based on theoretically proposed and practically applied indicators by researchers. Also, this article raises several issues of indicator system development and presents a summary after due consideration. Finally, we underline multi-stakeholders’ participation in agricultural sustainability assessment.