Exploring the effects of land management change on productivity, carbon and nutrient balance: Application of an Ensemble Modelling Approach to the upper River Taw observatory, UK.

Agriculture is challenged to produce healthy food and to contribute to cleaner energy whilst mitigating climate change and protecting ecosystems. To achieve this, policy-driven scenarios need to be evaluated with available data and models to explore trade-offs with robust accounting for the uncertainty in predictions. We developed a novel model ensemble using four complementary state-of-the-art agroecosystems models to explore the impacts of land management change. The ensemble was used to simulate key agricultural and environmental outputs under various scenarios for the upper River Taw observatory, UK. Scenarios assumed (i) reducing livestock production whilst simultaneously increasing the area of arable where it is feasible to cultivate (PG2A), (ii) reducing livestock production whilst simultaneously increasing bioenergy production in areas of the catchment that are amenable to growing bioenergy crops (PG2BE) and (iii) increasing both arable and bioenergy production (PG2A + BE). Our ensemble approach combined model uncertainty using the tower property of expectation and the law of total variance. Results show considerable uncertainty for predicted nutrient losses with different models partitioning the uncertainty into different pathways. Bioenergy crops were predicted to produce greatest yields from Miscanthus in lowland and from SRC-willow (cv. Endurance) in uplands. Each choice of management is associated with trade-offs; e.g. PG2A results in a significant increase of edible calories (6736 Mcal ha-1) but reduced soil C (-4.32 t C ha-1). Model ensembles in the agroecosystem context are difficult to implement due to challenges of model availability and input and output alignment. Despite these challenges, we show that ensemble modelling is a powerful approach for applications such as ours, offering benefits such as capturing structural as well as data uncertainty and allowing greater combinations of variables to be explored. Furthermore, the ensemble provides a robust means for combining uncertainty at different scales and enables us to identify weaknesses in system understanding.

Communication and building social capital in community supported agriculture.

Community supported agriculture (CSA) schemes (programs) provide an alternative means for obtaining produce, through direct purchase from farms. They are also often driven by a vision of transforming the current mainstream food system and seek to build a community of people who support this vision. Social capital refers to the networks and ties between people and groups and the impact of these ties on access to influence, information, opportunity, and ability to organize. Social capital is built by CSAs and helps foster and stabilize the grassroots agricultural innovations that are needed for the development of sustainable food systems. Using the concept of social capital, we studied communication methods of four CSAs in the UK, examining the interactions between CSAs and their members and within each of their membership groups. We carried out in-depth interviews with 49 CSA members to establish what interactions they had with their CSA and with other members, and analyzed our data thematically to identify the characteristics of interactions that were important to participants. We consider how our research may benefit CSA organizations by enabling them to learn what their members want and to learn about the varied ways in which members conceptualize their experiences of community derived from their membership. We found that the various CSA communication strategies, which consist of frequent and varying virtual and face-to-face interactions, are able to promote development of both bridging and bonding social capital. Overall, there is a desire for social connection in CSA memberships. Furthermore, in CSAs where members can interact easily, there is potential for CSA membership to provide members with communication that is important as a source of both knowledge and social connection. CSAs can maximize both social capital and member satisfaction by using a range of communication media and methods to meet their members' circumstances and preferences.

A rapid review of influential factors and appraised solutions on organ delineation uncertainties reduction in radiotherapy.

Background and purpose.Accurate volume delineation plays an essential role in radiotherapy. Contouring is a potential source of uncertainties in radiotherapy treatment planning that could affect treatment outcomes. Therefore, reducing the degree of contouring uncertainties is crucial. The role of utilized imaging modality in the organ delineation uncertainties has been investigated. This systematic review explores the influential factors on inter-and intra-observer uncertainties of target volume and organs at risk (OARs) delineation focusing on the used imaging modality for these uncertainties reduction and the reported subsequent histopathology and follow-up assessment.Methods and materials.An inclusive search strategy has been conducted to query the available online databases (Scopus, Google Scholar, PubMed, and Medline). 'Organ at risk', 'target', 'delineation', 'uncertainties', 'radiotherapy' and their relevant terms were utilized using every database searching syntax. Final article extraction was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Included studies were limited to the ones published in English between 1995 and 2020 and that just deal with computed tomography (CT) and magnetic resonance imaging (MRI) modalities.Results.A total of 923 studies were screened and 78 were included of which 31 related to the prostate 20 to the breast, 18 to the head and neck, and 9 to the brain tumor site. 98% of the extracted studies performed volumetric analysis. Only 24% of the publications reported the dose deviations resulted from variation in volume delineation Also, heterogeneity in studied populations and reported geometric and volumetric parameters were identified such that quantitative synthesis was not appropriate.Conclusion.This review highlightes the inter- and intra-observer variations that could lead to contouring uncertainties and impede tumor control in radiotherapy. For improving volume delineation and reducing inter-observer variability, the implementation of well structured training programs, homogeneity in following consensus and guidelines, reliable ground truth selection, and proper imaging modality utilization could be clinically beneficial.

Normal lung tissue complication probability in MR-Linac and conventional radiotherapy.

PURPOSE: To study normal lung tissue (NLT) complications in magnetic resonance (MR) image based linac and conventional radiotherapy (RT) techniques. MATERIALS AND METHODS: The Geant4 toolkit was used to simulate a 6 MV photon beam. A homogenous magnetic field of 1.5 Tesla (T) was applied in both perpendicular and parallel directions relative to the radiation beam.Analysis of the NLT complications was assessed according to the normal lung tissue complication probability (NTCP), the mean lung dose (MLD), and percentage of the lung volume receiving doses greater than 20 Gy (V20), using a sample set of CT images generated from a commercially available 4D-XCAT digital phantom. RESULTS: The results show that the MLD and V20 were lower for MR-linac RT. The largest reduction of MLD and V20 for MR-linac RT configurations were 5 Gy and 29.3%, respectively. CONCLUSION: MR-linac RT may result in lower NLT complications when compared to conventional RT.

The effect of competition on the control of invading plant pathogens.

New invading pathogen strains must compete with endemic pathogen strains to emerge and spread. As disease control measures are often non-specific, that is, they do not distinguish between strains, applying control not only affects the invading pathogen strain but the endemic as well. We hypothesize that the control of the invasive strain could be compromised due to the non-specific nature of the control.A spatially explicit model, describing the East African cassava mosaic virus-Uganda strain (EACMV-UG) outbreak, is used to evaluate methods of controlling both disease incidence and spread of invading pathogen strains in pathosystems with and without an endemic pathogen strain present.We find that while many newly introduced or intensified control measures (such as resistant cultivars or roguing) decrease the expected incidence, they have the unintended consequence of increasing, or at least not reducing, the speed with which the invasive pathogen spreads geographically. We identify the controls that cause this effect and methods in which these controls may be applied to prevent it.We found that the spatial spread of the invading strain is chiefly governed by the incidence at the wave front. Control can therefore be applied, or intensified, once the wave front has passed without increasing the pathogen's rate of spread.When trade of planting material occurs, it is possible that the planting material is already infected. The only forms of control in this study that reduces the speed of geographic spread, regardless of the presence of an endemic strain, are those that reduce the amount of trade and the distance over which trade takes place. Synthesis and applications. The best control strategy depends on the presence of competing endemic strains. Applying or intensifying the control can slow the rate of spread when absent but increase it if present. Imposing trade restrictions before the epidemic has reached a given area and intensifying other control methods only when the wave front has passed is the most effective way of both slowing down spread and controlling incidence when a competing endemic strain is present and is the safest approach when its presence is unknown.

Impact of magnetic fields on calculated AAPM TG-43 parameters for 192Ir and 60Co HDR brachytherapy sources: A Monte Carlo study.

PURPOSE: The aim of this work is to investigate the influence of an external magnetic field (MF) on The American Association of Physicists in Medicine (AAPM) No. 43 Report (TG-43) parameters for 192Ir and 60Co high dose rate (HDR) brachytherapy sources using Monte Carlo (MC) simulation methods. MATERIALS AND METHODS: We used the Geant4 toolkit (version 10.1. p01) to simulate the geometry of 192Ir and 60Co brachytherapy sources. AAPM TG-43 parameters (the radial dose function, g(r), and the anisotropy function, F (r, θ)) of both 192Ir and 60Co sources were calculated in the presence of a magnetic field with strengths of 1.5T, 3T, and 7T in the X, Y, and Z directions in a voxelized water phantom. RESULTS: For the 192Ir source, the calculated values g(r) and F (r, θ) remained nearly unaffected by the magnetic field for all investigated strengths. For the 60Co source, the differences for the g(r) and F (r,θ) under the 1.5T, 3T, and 7T magnetic field strengths along the direction parallel with the MF were found to be an increase of up to 5%, 15%, and 33%, respectively. However, for the directions perpendicular with the magnetic field, there was a decrease of up to 3%, 6% and 15% under 1.5T, 3T and 7T strengths, respectively. CONCLUSION: Our results highlight the necessity of a Monte Carlo-based treatment planning system (TPS) if cobalt HDR treatments are performed under a magnetic field, especially for strengths greater than 1.5T.

Bringing forecasting into the future: Using Google to predict visitation in U.S. national parks.

In recent years, visitation to U.S. National Parks has been increasing, with the majority of this increase occurring in a subset of parks. As a result, managers in these parks must respond quickly to increasing visitor-related challenges. Improved visitation forecasting would allow managers to more proactively plan for such increases. In this study, we leverage internet search data that is freely available through Google Trends to create a forecasting model. We compare this Google Trends model to a traditional autoregressive forecasting model. Overall, our Google Trends model accurately predicted 97% of the total visitation variation to all parks one year in advance from 2013 to 2017 and outperformed the autoregressive model by all metrics. While our Google Trends model performs better overall, this was not the case for each park unit individually; the accuracy of this model varied significantly from park to park. We hypothesized that park attributes related to trip planning would correlate with the accuracy of our Google Trends model, but none of the variables tested produced overly compelling results. Future research can continue exploring the utility of Google Trends to forecast visitor use in protected areas, or use methods demonstrated in this paper to explore alternative data sources to improve visitation forecasting in U.S. National Parks.