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A new approach for estimating northern peatland gross primary productivity using a satellite-sensor-derived chlorophyll index

Carbon flux models that are largely driven by remotely sensed data can be used to estimate gross primary productivity (GPP) over large areas, but despite the importance of peatland ecosystems in the global carbon cycle, relatively little attention has been given to determining their success in these ecosystems. This paper is the first to explore the potential of chlorophyll-based vegetation index models for estimating peatland GPP from satellite data. Using several years of carbon flux data from contrasting peatlands, we explored the relationships between the MERIS terrestrial chlorophyll index (MTCI) and GPP, and determined whether the inclusion of environmental variables such as PAR and temperature, thought to be important determinants of peatland carbon flux, improved upon direct relationships. To place our results in context, we compared the newly developed GPP models with the MODIS (Moderate Resolution Imaging Spectrometer) GPP product. Our results show that simple MTCI-based models can be used for estimates of interannual and intra-annual variability in peatland GPP. The MTCI is a good indicator of GPP and compares favorably with more complex products derived from the MODIS sensor on a site-specific basis. The incorporation of MTCI into a light use efficiency type model, by means of partitioning the fraction of photosynthetic material within a plant canopy, shows most promise for peatland GPP estimation, outperforming all other models. Our results demonstrate that satellite data specifically related to vegetation chlorophyll content may ultimately facilitate improved quantification of peatland carbon flux dynamics

Aquacultural Potential of Derelict Waterbodies – A Case Study

Derelict waterbodies could be an important source to boost fish production for meeting the future fish demand of the country. The study has shown that fish yield from these waterbodies could be as high as 4.6 t/ha. Overall, net income per hectare through scientific management of derelict waterbodies has been found to be Rs 104443, with maximum and minimum net incomes per hectare being Rs 207416 and Rs 64033, respectively. Benefit–cost analysis has indicated that all waterbodies are favourable for aquaculture. Overall B-C ratio under the project has been found to be 3.82 and interestingly, scientific management of waterbodies could yield good income even from low level of investment. Such an activity can provide enormous income and employment opportunities in the rural areas. To encourage large-scale utilization of available derelict waterbodies for aquaculture, a prudent and well-conceived policy for leasing out derelict waterbodies and transfer of relevant technologies to the needy and interested farmers should be evolved. These steps would not only boost fish production in the rural areas, but would also provide much needed impetus to the growth and diversification of rural economy.Resource /Energy Economics and Policy,

Learning causal models that make correct manipulation predictions with time series data

One of the fundamental purposes of causal models is using them to predict the effects of manipulating various components of a system. It has been argued by Dash (2005, 2003) that the Do operator will fail when applied to an equilibrium model, unless the underlying dynamic system obeys what he calls Equilibration-Manipulation Commutability. Unfortunately, this fact renders most existing causal discovery algorithms unreliable for reasoning about manipulations. Motivated by this caveat, in this paper we present a novel approach to causal discovery of dynamic models from time series. The approach uses a representation of dynamic causal models motivated by Iwasaki and Simon (1994), which asserts that all “causation across time" occurs because a variable’s derivative has been affected instantaneously. We present an algorithm that exploits this representation within a constraint-based learning framework by numerically calculating derivatives and learning instantaneous relationships. We argue that due to numerical errors in higher order derivatives, care must be taken when learning causal structure, but we show that the Iwasaki-Simon representation reduces the search space considerably, allowing us to forego calculating many high-order derivatives. In order for our algorithm to discover the dynamic model, it is necessary that the time-scale of the data is much finer than any temporal process of the system. Finally, we show that our approach can correctly recover the structure of a fairly complex dynamic system, and can predict the effect of manipulations accurately when a manipulation does not cause an instability. To our knowledge, this is the first causal discovery algorithm that has demonstrated that it can correctly predict the effects of manipulations for a system that does not obey the EMC condition

Functionality in single-molecule devices: Model calculations and applications of the inelastic electron tunneling signal in molecular junctions

We analyze how functionality could be obtained within single-molecule devices by using a combination of non-equilibrium Green's functions and ab-initio calculations to study the inelastic transport properties of single-molecule junctions. First we apply a full non-equilibrium Green's function technique to a model system with electron-vibration coupling. We show that the features in the inelastic electron tunneling spectra (IETS) of the molecular junctions are virtually independent of the nature of the molecule-lead contacts. Since the contacts are not easily reproducible from one device to another, this is a very useful property. The IETS signal is much more robust versus modifications at the contacts and hence can be used to build functional nanodevices. Second, we consider a realistic model of a organic conjugated molecule. We use ab-initio calculations to study how the vibronic properties of the molecule can be controlled by an external electric field which acts as a gate voltage. The control, through the gate voltage, of the vibron frequencies and (more importantly) of the electron-vibron coupling enables the construction of functionality: non-linear amplification and/or switching is obtained from the IETS signal within a single-molecule device.Comment: Accepted for publication in Journal of Chemical Physic

Addressing Youth Perceptions of Harm in Marijuana Prevention Programming

The inverse relationship between perception of harm and substance use is clearly supported by decades of research – youth are less likely to engage in substance use when it is seen as harmful. However, despite strong theoretical and practical reasons to focus on perception of harm as a change-producer in prevention programming, little is known about what is effective in impacting perception of harm for youth marijuana use. To investigate the impact of existing prevention efforts designed to influence youth perception of harm and, consequently, youth marijuana use, we reviewed seven privately- or federally-funded online registries (e.g., Blueprints for Healthy Youth Development, Substance Abuse and Mental Health Administration’s National Registry of Evidence Based Programs and Practices) to identify evidence-based programs with marijuana-related outcomes for youth. We found 36 registry-identified programs with demonstrated impact on youth marijuana use. Although many of these programs may have actively or passively sought to alter perception of harm, only ten measured marijuana- or drug-related perception of harm as an intermediate outcome. Drawing on the commonalities of evidence-based programs with significant impacts on youth marijuana perception of harm, as well as lessons learned from other health behavior change efforts, we recommend best practices to provide state and local decision-makers with information on altering youth perception of harm for marijuana and on evaluating the impact of these efforts