Empirical test of an agricultural landscape model: the importance of farmer preference for risk aversion and crop complexity

Developing models to predict the effects of social and economic change on agricultural landscapes is an important challenge. Model development often involves making decisions about which aspects of the system require detailed description and which are reasonably insensitive to the assumptions. However, important components of the system are often left out because parameter estimates are unavailable. In particular, measurements of the relative influence of different objectives, such as risk, environmental management, on farmer decision making, have proven difficult to quantify. We describe a model that can make predictions of land use on the basis of profit alone or with the inclusion of explicit additional objectives. Importantly, our model is specifically designed to use parameter estimates for additional objectives obtained via farmer interviews. By statistically comparing the outputs of this model with a large farm-level land-use data set, we show that cropping patterns in the United Kingdom contain a significant contribution from farmer’s preference for objectives other than profit. In particular, we found that risk aversion had an effect on the accuracy of model predictions, whereas preference for a particular number of crops grown was less important. While nonprofit objectives have frequently been identified as factors in farmers’ decision making, our results take this analysis further by demonstrating the relationship between these preferences and actual cropping patterns.

Development of a food-exchange model to replace saturated fat with MUFAS and n-6 PUFAS in adults at moderate cardiovascular risk

The recommendation to reduce saturated fatty acid (SFA) consumption to ≤10% of total energy (%TE) is a key public health target aimed at lowering cardiovascular disease (CVD) risk. Replacement of SFA with unsaturated fats may provide greater benefit than replacement with carbohydrates, yet the optimal type of fat is unclear. The aim was to develop a flexible food-exchange model to investigate the effects of substituting SFAs with monounsaturated fatty acids (MUFAs) or n-6 (ω-6) polyunsaturated fatty acids (PUFAs) on CVD risk factors. In this parallel study, UK adults aged 21-60 y with moderate CVD risk (50% greater than the population mean) were identified using a risk assessment tool (n = 195; 56% females). Three 16-wk isoenergetic diets of specific fatty acid (FA) composition (%TE SFA:%TE MUFA:%TE n-6 PUFA) were designed using spreads, oils, dairy products, and snacks as follows: 1) SFA-rich diet (17:11:4; n = 65); 2) MUFA-rich diet (9:19:4; n = 64); and 3) n-6 PUFA-rich diet (9:13:10; n = 66). Each diet provided 36%TE total fat. Dietary targets were broadly met for all intervention groups, reaching 17.6 ± 0.4%TE SFA, 18.5 ± 0.3%TE MUFA, and 10.4 ± 0.3%TE n-6 PUFA in the respective diets, with significant overall diet effects for the changes in SFA, MUFA, and n-6 PUFA between groups (P < 0.001). There were no differences in the changes of total fat, protein, carbohydrate, and alcohol intake or anthropometric measures between groups. Plasma phospholipid FA composition showed changes from baseline in the proportions of total SFA, MUFA, and n-6 PUFA for each diet group, with significant overall diet effects for total SFA and MUFA between groups (P < 0.001). In conclusion, successful implementation of the food-exchange model broadly achieved the dietary target intakes for the exchange of SFA with MUFA or n-6 PUFA with minimal disruption to the overall diet in a free-living population. This trial was registered at clinicaltrials.gov as NCT01478958.

A toxicokinetic model for thiamethoxam in rats: implications for higher-tier risk assessment

Risk assessment for mammals is currently based on external exposure measurements, but effects of toxicants are better correlated with the systemically available dose than with the external administered dose. So for risk assessment of pesticides, toxicokinetics should be interpreted in the context of potential exposure in the field taking account of the timescale of exposure and individual patterns of feeding. Internal concentration is the net result of absorption, distribution, metabolism and excretion (ADME). We present a case study for thiamethoxam to show how data from ADME study on rats can be used to parameterize a body burden model which predicts body residue levels after exposures to LD50 dose either as a bolus or eaten at different feeding rates. Kinetic parameters were determined in male and female rats after an intravenous and oral administration of 14C labelled by fitting one-compartment models to measured pesticide concentrations in blood for each individual separately. The concentration of thiamethoxam in blood over time correlated closely with concentrations in other tissues and so was considered representative of pesticide concentration in the whole body. Body burden model simulations showed that maximum body weight-normalized doses of thiamethoxam were lower if the same external dose was ingested normally than if it was force fed in a single bolus dose. This indicates lower risk to rats through dietary exposure than would be estimated from the bolus LD50. The importance of key questions that should be answered before using the body burden approach in risk assessment, data requirements and assumptions made in this study are discussed in detail.

Managing governance, risk and compliance for post-crisis regulatory change: a model of IS capabilities for financial organizations

The 2008-2009 financial crisis and related organizational and economic failures have meant that financial organizations are faced with a ‘tsunami’ of new regulatory obligations. This environment provides new managerial challenges as organizations are forced to engage in complex and costly remediation projects with short deadlines. Drawing from a longitudinal study conducted with nine financial institutions over twelve years, this paper identifies nine IS capabilities which underpin activities for managing regulatory themed governance, risk and compliance efforts. The research shows that many firms are now focused on meeting the Regulators’ deadlines at the expense of developing a strategic, enterprise-wide connected approach to compliance. Consequently, executives are in danger of implementing siloed compliance solutions within business functions. By evaluating the maturity of their IS capabilities which underpin regulatory adherence, managers have an opportunity to develop robust operational architectures and so are better positioned to face the challenges derived from shifting regulatory landscapes.

Risk taking for oneself and others: a structural model approach

Economic theory makes no predictions about social factors affecting decisions under risk. We examine situations in which a decision maker decides for herself and another person under conditions of payoff equality, and compare them to individual decisions. By estimating a structural model, we find that responsibility leaves utility curvature unaffected, but accentuates the subjective distortion of very small and very large probabilities for both gains and losses. We also find that responsibility reduces loss aversion, but that these results only obtain under some specific definitions of the latter. These results serve to generalize and reconcile some of the still largely contradictory findings in the literature. They also have implications for financial agency, which we discuss.

Biases in comparative analyses of extinction risk: mind the gap

1. Comparative analyses are used to address the key question of what makes a species more prone to extinction by exploring the links between vulnerability and intrinsic species’ traits and/or extrinsic factors. This approach requires comprehensive species data but information is rarely available for all species of interest. As a result comparative analyses often rely on subsets of relatively few species that are assumed to be representative samples of the overall studied group. 2. Our study challenges this assumption and quantifies the taxonomic, spatial, and data type biases associated with the quantity of data available for 5415 mammalian species using the freely available life-history database PanTHERIA. 3. Moreover, we explore how existing biases influence results of comparative analyses of extinction risk by using subsets of data that attempt to correct for detected biases. In particular, we focus on links between four species’ traits commonly linked to vulnerability (distribution range area, adult body mass, population density and gestation length) and conduct univariate and multivariate analyses to understand how biases affect model predictions. 4. Our results show important biases in data availability with c.22% of mammals completely lacking data. Missing data, which appear to be not missing at random, occur frequently in all traits (14–99% of cases missing). Data availability is explained by intrinsic traits, with larger mammals occupying bigger range areas being the best studied. Importantly, we find that existing biases affect the results of comparative analyses by overestimating the risk of extinction and changing which traits are identified as important predictors. 5. Our results raise concerns over our ability to draw general conclusions regarding what makes a species more prone to extinction. Missing data represent a prevalent problem in comparative analyses, and unfortunately, because data are not missing at random, conventional approaches to fill data gaps, are not valid or present important challenges. These results show the importance of making appropriate inferences from comparative analyses by focusing on the subset of species for which data are available. Ultimately, addressing the data bias problem requires greater investment in data collection and dissemination, as well as the development of methodological approaches to effectively correct existing biases.