Modelling the bioaccumulation of persistent organic pollutants in agricultural food chains for regulatory exposure assessment

New models for estimating bioaccumulation of persistent organic pollutants in the agricultural food chain were developed using recent improvements to plant uptake and cattle transfer models. One model named AgriSim was based on K OW regressions of bioaccumulation in plants and cattle, while the other was a steady-state mechanistic model, AgriCom. The two developed models and European Union System for the Evaluation of Substances (EUSES), as a benchmark, were applied to four reported food chain (soil/air-grass-cow-milk) scenarios to evaluate the performance of each model simulation against the observed data. The four scenarios considered were as follows: (1) polluted soil and air, (2) polluted soil, (3) highly polluted soil surface and polluted subsurface and (4) polluted soil and air at different mountain elevations. AgriCom reproduced observed milk bioaccumulation well for all four scenarios, as did AgriSim for scenarios 1 and 2, but EUSES only did this for scenario 1. The main causes of the deviation for EUSES and AgriSim were the lack of the soil-air-plant pathway and the ambient air-plant pathway, respectively. Based on the results, it is recommended that soil-air-plant and ambient air-plant pathway should be calculated separately and the K OW regression of transfer factor to milk used in EUSES be avoided. AgriCom satisfied the recommendations that led to the low residual errors between the simulated and the observed bioaccumulation in agricultural food chain for the four scenarios considered. It is therefore recommended that this model should be incorporated into regulatory exposure assessment tools. The model uncertainty of the three models should be noted since the simulated concentration in milk from 5th to 95th percentile of the uncertainty analysis often varied over two orders of magnitude. Using a measured value of soil organic carbon content was effective to reduce this uncertainty by one order of magnitude.

A new three-locus model for rootstock-induced dwarfing in apple revealed by genetic mapping of root bark percentage

Rootstock-induced dwarfing of apple scions revolutionized global apple production during the twentieth century, leading to the development of modern intensive orchards. A high root bark percentage (the percentage of the whole root area constituted by root cortex) has previously been associated with rootstock induced dwarfing in apple. In this study, the root bark percentage was measured in a full-sib family of ungrafted apple rootstocks and found to be under the control of three loci. Two QTL for root bark percentage were found to co-localise to the same genomic regions on chromosome 5 and chromosome 11 previously identified as controlling dwarfing, Dw1 and Dw2, respectively. A third QTL was identified on chromosome 13 in a region that has not been previously associated with dwarfing. The development of closely linked 3 Sequence-tagged site STS markers improved the resolution of allelic classes thereby allowing the detection of dominance and epistatic interactions between loci, with high root bark percentage only occurring in specific allelic combinations. In addition, we report a significant negative correlation between root bark percentage and stem diameter (an indicator of tree vigour), measured on a clonally propagated grafted subset of the mapping population. The demonstrated link between root bark percentage and rootstock-induced dwarfing of the scion leads us to propose a three-locus model that is able to explain levels of dwarfing from the dwarf ‘M.27’ to the semi-invigorating rootstock ‘M.116’. Moreover, we suggest that the QTL on chromosome 13 (Rb3) might be analogous to a third dwarfing QTL, Dw3 that has not previously been identified.

Anticipating future risk in social-ecological systems using fuzzy cognitive mapping: the case of wildfire in the Chiquitania, Bolivia

Understanding complex social-ecological systems, and anticipating how they may respond to rapid change, requires an approach that incorporates environmental, social, economic, and policy factors, usually in a context of fragmented data availability. We employed fuzzy cognitive mapping (FCM) to integrate these factors in the assessment of future wildfire risk in the Chiquitania region, Bolivia. In this region, dealing with wildfires is becoming increasingly challenging due to reinforcing feedbacks between multiple drivers. We conducted semi-structured interviews and constructed different FCMs in focus groups to understand the regional dynamics of wildfire from diverse perspectives. We used FCM modelling to evaluate possible adaptation scenarios in the context of future drier climatic conditions. Scenarios also considered possible failure to respond in time to the emergent risk. This approach proved of great potential to support decision-making for risk management. It helped identify key forcing variables and generate insights into potential risks and trade-offs of different strategies. All scenarios showed increased wildfire risk in the event of more droughts. The ‘Hands-off’ scenario resulted in amplified impacts driven by intensifying trends, affecting particularly the agricultural production. The ‘Fire management’ scenario, which adopted a bottom-up approach to improve controlled burning, showed less trade-offs between wildfire risk reduction and production compared to the ‘Fire suppression’ scenario. Findings highlighted the importance of considering strategies that involve all actors who use fire, and the need to nest these strategies for a more systemic approach to manage wildfire risk. The FCM model could be used as a decision-support tool and serve as a ‘boundary object’ to facilitate collaboration and integration of different forms of knowledge and perceptions of fire in the region. This approach has also the potential to support decisions in other dynamic frontier landscapes around the world that are facing increased risk of large wildfires.