Quantifying the effect of buffer zones,crop areas and spatial aggregation on the externalities of genetically modified crops at landscape level

The development of genetically modified (GM) crops has led the European Union (EU) to put forward the concept of ‘coexistence’ to give farmers the freedom to plant both conventional and GM varieties. Should a premium for non-GM varieties emerge in the market, ‘contamination’ by GM pollen would generate a negative externality to conventional growers. It is therefore important to assess the effect of different ‘policy variables’ on the magnitude of the externality to identify suitable policies to manage coexistence. In this paper, taking GM herbicide tolerant oilseed rape as a model crop, we start from the model developed in Ceddia et al. [Ceddia, M.G., Bartlett, M., Perrings, C., 2007. Landscape gene flow, coexistence and threshold effect: the case of genetically modified herbicide tolerant oilseed rape (Brassica napus). Ecol. Modell. 205, pp. 169–180] use a Monte Carlo experiment to generate data and then estimate the effect of the number of GM and conventional fields, width of buffer areas and the degree of spatial aggregation (i.e. the ‘policy variables’) on the magnitude of the externality at the landscape level. To represent realistic conditions in agricultural production, we assume that detection of GM material in conventional produce might occur at the field level (no grain mixing occurs) or at the silos level (where grain mixing from different fields in the landscape occurs). In the former case, the magnitude of the externality will depend on the number of conventional fields with average transgenic presence above a certain threshold. In the latter case, the magnitude of the externality will depend on whether the average transgenic presence across all conventional fields exceeds the threshold. In order to quantify the effect of the relevant ‘policy variables’, we compute the marginal effects and the elasticities. Our results show that when relying on marginal effects to assess the impact of the different ‘policy variables’, spatial aggregation is far more important when transgenic material is detected at field level, corroborating previous research. However, when elasticity is used, the effectiveness of spatial aggregation in reducing the externality is almost identical whether detection occurs at field level or at silos level. Our results show also that the area planted with GM is the most important ‘policy variable’ in affecting the externality to conventional growers and that buffer areas on conventional fields are more effective than those on GM fields. The implications of the results for the coexistence policies in the EU are discussed.     

Policy And Practice Green Grass and Brown Roots: Understanding Environmental Problems in Deprived Neighbourhoods

Understanding neighbourhood conditions can play an important role in urban environmental management, especially when environmental services are lacking and new approachesare being debated. This paper describes and evaluates three research methods for assessing some of the environmentalproblems facing low income households and communities: (1) broad spectrum household surveys; (2) participatory rapid assessment; and (3) contingent valuation. As part of the study upon which this paper is based, the techniques were applied on a small scale in Jakarta. The paper finds that each technique can provide important and often complementaryinsights. The broad spectrum survey is particularly suitable to broad based planning, participatory appraisal to NGO initiatives and contingent valuation to utility pricing decisions.     

Framework for participative reflection on the accomplishment of transdisciplinary research programs

In response to the increasingly complex social–ecological issues facing society, there is a growing trend to conduct environmental research in large collaborative programs. This approach is described as transdisciplinary research as it transcends formal disciplinary boundaries, explicitly acknowledges that many different perspectives are relevant to the resolution of complex problems, and actively involves the users of research. This poses challenges for the evaluation of “impact” as any evaluation process must take into consideration the different expectations, values, culture, language and reward structures of the main participating groups, the funders, researchers and end users. How can these participating groups learn about the progress of a transdisciplinary research program in a way that is purposeful and structured, continues through the life of the program, and includes explicit feedback mechanisms that facilitate adaptation during the course of the program? This paper presents a framework for co-reflecting on the accomplishment of transdisciplinary research programs. The framework incorporates the perspectives of funders, researchers and users, and recognizes that while they place different emphasis on measures of achievement such as efficiency, rigor and relevance, ultimate accomplishment in terms of translating knowledge into practice requires that the needs and expectations of all three groups are adequately addressed. What emerges from the framework is the importance of early investment in processes, behaviors and relationships that foster social learning and the co-production of the knowledge and understanding that are required to ensure relevance; while maintaining emphasis in the traditional areas of formally testing evidence and mentoring young researchers to ensure rigor and build confidence and capacity in transdisciplinary approaches.     

Serving one another: Are shared and self‐leadership the keys to service sustainability?

Service has received increasing attention in the management literature, yet sustainability of service is often overlooked. In this Incubator we examine the potential for client involvement through shared and self‐leadership to foster empathy enhanced service and reduced costs, both of which may support a more sustainable service delivery process. Copyright © 2015 John Wiley & Sons, Ltd.     

Calibrating a Basin‐Scale Groundwater Model to Remotely Sensed Estimates of Groundwater Evapotranspiration

Remotely sensed vegetation indices correspond to canopy vigor and cover and have been successfully used to estimate groundwater evapotranspiration (ET_g) over large spatial and temporal scales. However, these data do not provide information on depth to groundwater (dtgw) necessary for groundwater models (GWM) to calculate ET_g. An iterative approach is provided that calibrates GWM to ET_g derived from Landsat estimates of the Enhanced Vegetation Index (EVI). The approach is applied to different vegetation groups in Mason Valley, Nevada over an 11‐year time span. An uncertainty analysis is done to estimate the resulting mean and 90% confidence intervals in ET_g to dtgw relationships to quantify errors associated with plant physiologic complexity, species variability, and parameter smoothing to the 100 m GWM‐grid, temporal variability in soil moisture and nonuniqueness in the solution. Additionally, a first‐order second moment analysis shows ET_g to dtgw relationships are almost exclusively sensitive to estimated land surface, or maximum, ET_g despite relatively large uncertainty in extinction depths and hydraulic conductivity. The EVI method of estimating ET_g appears to bias ET_g during years with exceptionally wet spring/summer conditions. Excluding these years improves model performance significantly but highlights the need to develop a methodology that accounts not only on quantity but timing of annual precipitation on phreatophyte greenness.