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Research on children's understanding of emotion has rarely focused on children from nonindustrialised countries, who may develop an understanding at different ages as compared to children reared in industrialised countries. Quechua children from an agro-pastoralist village were given an adapted version of the Test of Emotion Comprehension (TEC) to assess their understanding of nine aspects of emotions. Older children performed better on the entire TEC than younger children. Eight- to 11-year-olds were more accurate in identifying emotions connected to individual desires and to a moral misdemeanour than were 4- to 7-year-olds. In addition, there was a trend for 8- to 11-year-olds to understand external causes of emotions better than 4- to 7-year-olds. Compared to British children, the Quechua children were less accurate overall. However, similar to the British children, certain aspects of emotion (e.g., recognition) were understood at younger ages than others (e.g., regulation), suggesting similar patterns in the sequence of emotional understanding despite the radical difference in cultural context. In contrast to children from industrialised settings, children from this Quechua village have little access to formal education. Moreover, Quechua children have fewer opportunities to engage in discussions about emotions with adults, which may also contribute to how well they performed on the TEC. Suggestions for improving the TEC and including a more naturalistic testing situation are made. (Contains 1 table.)

Three experiments examined 24- though 39-month-olds' understanding of pretend episodes, such as a puppet pouring pretend milk into a container and then tipping it over a toy animal. The children understood the linkage between the two actions and realized that the toy animal would become "wet." (MDM)

Geographically weighted regression (GWR) is used to investigate spatial relationships between freshwater acidification critical load data and contextual catchment data across Great Britain. Although this analysis is important in developing a greater understanding of the critical load process, the study also examines the application of the GWR technique itself. In particular, and unlike many previous presentations of GWR, the steps taken in choosing a particular GWR model form are presented in detail. A further important advance here is that the calibration results of the chosen GWR model are scrutinized for robustness to outlying observations. With respect to the critical load process itself, the results of this study largely agree with those of earlier research, where relationships between critical load and catchment data can vary across space. The more sophisticated spatial statistical models used here, however, are shown to be more flexible and informative, allowing a clearer picture of process heterogeneities to be revealed. [ABSTRACT FROM AUTHOR]

Abstract: In this study, geographically weighted summary statistics (GWSSs) are used to investigate spatial variation and spatial relationships in a freshwater acidification critical load data set covering Great Britain. This use of GWSSs not only provides valuable insight into the critical load process prior to a geographically weighted regression (GWR) calibration, but also helps in interpreting its output. GWSSs are similarly useful prior to the calibration of other spatial models, such as those used in geostatistics. Results agree with those of previous research, where relationships between critical load and contextual catchment data can vary across space. However the more sophisticated models used here are shown to be much more flexible and informative, allowing more spatial patterns to be revealed than before. [Copyright &y& Elsevier]

• Earth observation data and process modelling are combined to estimate grassland C dynamics. • The model-data fusion algorithm infers grazing and cutting from leaf area index data. • The algorithm was implemented at 3 managed grasslands in England for 2015–2018. • 87.5% of harvests were identified and 83% of measured yields were simulated accurately. • The in-situ estimated and simulated grazed biomass had a r = 0.8. Grasslands, natural and managed, cover a large part of the Earth's surface and play an important role in the global carbon (C) cycle. Human management strongly affects grassland C budgets through grass cutting and removal, varied grazing intensities, and organic matter additions. Thus managed grassland C cycles are highly heterogeneous and challenging to quantify. In this study, we combine a process-based model of the grassland C cycle, validated against field data on C fluxes and pools, with satellite-derived data (Proba-V and Sentinel-2) on leaf area index (LAI) in order to quantify field-scale grassland productivity and C dynamics under climatic and management conditions typical of northwest Europe. Input data on the weekly vegetation canopy anomaly (estimated from Proba-V LAI) and meteorology are used to drive the grassland C model (DALEC-Grass) that is integrated into a Bayesian model-data fusion (MDF) framework. The novelty of the MDF algorithm is that it infers weekly livestock grazing and grass cutting events based on expected canopy growth estimated by the model, and constrained by LAI observations (estimated from Sentinel-2). The MDF approach also resolves observational, parametric, and input uncertainties on C cycling estimates. We analysed four years (2015–2018) of C dynamics at three variably-managed fields of the Rothamsted Research North Wyke Farm Platform (UK). Compared against independent field data, the MDF was able to (i) identify 87.5% of the harvest events that occurred, (ii) accurately predict the annual yields in 83% of the identified harvest years and (iii) reproduce the observed grazing intensity in each field (r = 0.8 , overlap = 90%). We demonstrate that the fusion of process modelling with earth observations is an effective method for monitoring biomass removals and quantifying management impacts on field-scale C balance, without the need for frequent and laborious ground measurements. This approach can support the delivery of more robust national greenhouse gas (GHG) accounting that takes account of grassland vegetation management. [ABSTRACT FROM AUTHOR]

The British Expeditionary Force of the First World War did not fight a deliberately attritional war, despite the heavy casualties and slow gains it experienced. Before the BEF launched a single trench-warfare attack, senior officers argued for a methodical and clearly attritional operational approach, accepting limited gains and slow overall progress to get a favourable casualty exchange rate. The idea quickly had senior adherents, especially Sir William Robertson. Sir Douglas Haig, however, was consistently optimistic, thinking breakthroughs were possible; this prejudiced him against lesser, attritional, attacks. Not until mid-1917 did the BEF substantially adopt methodical step-by-step attacks. [ABSTRACT FROM AUTHOR]

The article discusses the decision of the British High Court on the issue of what constitutes an employee invention of outstanding benefit and the level of compensation for such invention in Great Britain. It focuses on the case Shanks v. Unilever Plc., in which Professor Ian Shanks sought to reverse the decision of the Comptroller-General of Patents on his employee compensation claim under the Patents Act 1977. Also cited is the case Kelly v. GE Healthcare Ltd.

• Soil moisture can be accurately simulated using field and within-field settings. • Within-field settings improve simulations of soil nutrients. • Measured k sat only marginally improves simulation in water fluxes. In agriculture, variations in soil nutrients and water are driven by soil properties, topography and agronomic practice that typically interact and change over space and time. Agroecosystem models need to capture these sources in variation, where this study's first objective was to assess the potential of using measured saturated soil hydraulic conductivity (k sat) to improve the simulation accuracy of water and soil mineral nitrogen content from the SPACSYS model for a lowland UK grazed field (6.34 ha). As a second objective, SPACSYS was run at the field level and at the within-field level to provide a further comparison of simulation accuracy. For model calibration, k sat was measured at 27 points at 0–10, 10–20 and 20–30 cm soil depths on a 50 × 50 m grid. For model validation, moisture and mineral nitrogen content in the same three soil layers, at 10 adjacent points on a 25 × 25 m grid, were measured monthly from May 2018 to April 2019, together with in situ field level water flux measurement. Measured k sat coupled with the within-field setting allowed a novel spatial investigation of SPACSYS performance. Measured k sat (as opposed to unmeasured, default values) was found to improve water flux simulation, but only slightly so, which was considered in part due to a high positive skew in the measured k sat coupled with no clear spatial structure. Field level and within-field specifications simulated soil moisture with equal accuracy, while simulation accuracy of soil ammonium and nitrate improved via the within-field setting; for water flux simulation, the field level setting should be preferred. Results provide further evidence for when a field level setting should be preferred to a within-field setting and vice-versa. [ABSTRACT FROM AUTHOR]

• The choice of estimators has an impact on the selected thresholds. • The proposed automated method based on threshold stability gives robust estimates. • It results in the highest agreement between simulated and empirical quantiles. • The modelled hourly peak flow is consistently closer to the empirical one. This study investigated core components of an extreme value methodology for the estimation of high-flow frequencies from agricultural surface water run-off. The Generalized Pareto distribution (GPD) was used to model excesses in time-series data that resulted from the 'Peaks Over Threshold' (POT) method. First, the performance of eight different GPD parameter estimators was evaluated through a Monte Carlo experiment. Second, building on the estimator comparison, two existing automated GPD threshold selection methods were evaluated against a proposed approach that automates the threshold stability plots. For this second experiment, methods were applied to discharge measured at a highly-instrumented agricultural research facility in the UK. By averaging fine-resolution 15-minute data to hourly, 6-hourly and daily scales, we were also able to determine the effect of scale on threshold selection, as well as the performance of each method. The results demonstrate the advantages of the proposed threshold selection method over two commonly applied methods, while at the same time providing useful insights into the effect of the choice of the scale of measurement on threshold selection. The results can be generalised to similar water monitoring schemes and are important for improved characterisations of flood events and the design of associated disaster management protocols. [ABSTRACT FROM AUTHOR]