192 Outcomes of Simulated Combinations of Rangeland Management Scenarios on Soil and Nutrient Processes

The relationships among rangeland management practices and ecosystem on soil and nutrient processes are complex. Simulation models can provide valuable insight into this complexity to guide future research and management decisions for sustainable grazing systems. The objective of this analysis was to evaluate the combinations of grazing management (continuous vs. rotational), stocking rate (low, moderate, or high), and burning (none vs. annual spring burn) on soil erosion, nutrient losses, and carbon sequestration. Soil, weather, and forage species data were collected for 3 locations in the Central Great Plains (Konza Prairie Biological Station, Kansas; Agricultural Research Center-Hays, Kansas; Central Plains Experimental Range, Wyoming). These data were used to simulate combinations of rangeland management scenarios using the Agricultural Policy/Environmental eXtender (APEX) model. A stocker cattle operation was simulated at Hays, Kansas and Wyoming locations, and a cow-calf operation at Konza, Kansas location. Burning increased soil loss from water erosion regardless of stocking rate or grazing management. Rotational grazing increased soil loss at the Kansas locations, but decreased soil loss at Wyoming compared with continuous grazing. Stocking rate had little effect on nitrogen losses, but rotational grazing decreased nitrogen loss compared with continuous grazing. Greater stocking rates slightly decreased phosphorus losses with continuous grazing, but not rotational grazing. Burning decreased phosphorus losses with continuous grazing at Konza, but not the other locations. Greater stocking rates decreased the deposition of soil organic carbon especially with continuous grazing, and burning resulted in losses of soil organic carbon at Konza and Wyoming. In conclusion, rangeland management practices differentially affect nutrient processes among ecosystems such that a one-size-fits-all management scheme will not maximize retention of all nutrients in all ecosystems.

How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

Lying on the Road Before Being Run Over: Vehicular Manslaughter, Suicide, or Accident? Two Case Reports and Literature Review

We present two apparent hit-and-run cases where two women were run over. The vehicles involved were subsequently traced and their owners charged with manslaughter. Autopsy evidence, scientific investigation of the scene and circumstances of the deaths, technical inspection of the vehicles, and DNA analysis strongly suggested that both victims were lying on the road before the accident. Case 1 was a suicide. In Case 2, the victim had fallen to the ground following acute alcohol intoxication. Victimological analysis was pivotal in reconstructing the dynamics of the events. We suggest that a hit-and-run fatality should not be regarded as a manslaughter case until each piece of evidence has been carefully considered. We also propose an interdisciplinary method of reconstructing run over occurrences based on the following three steps: (i) identify whether there was a primary impact when the victim was in an upright position; (ii) identify victim drug/alcohol intoxication and/or presence of acute or chronic disease or injury, which may have contributed to the impact; and (iii) consider suicide intent.

Criminological and Medico-legal Aspects in Homicidal and Suicidal Sharp Force Fatalities

The interpretation of sharp force fatality dynamics may be difficult in some cases, but a contribution to analysis of the phenomenon may be provided by case studies. Therefore, the purpose of our study is focused on identifying, in observed sharp force fatalities, reliable parameters that can differentiate a homicidal and suicidal manner of death, with particular reference to criminological parameters. Data derived from sharp force fatality cases in Padua and Venice from 1997 to 2019, anonymized and collected in Excel, included personal, circumstantial, clinical, and psychopathological-criminological data, as well as crime scene investigation, necroscopic, and toxicological data. Statistical analyses were performed using chi-square and Wilcoxon rank-sum tests. Possible predictors of homicide were analyzed by logistic regression. Six parameters (bloodstains distant from the body, clothing lacerations, hesitation/defense wounds, number of injuries, and potential motives) were significantly different in the two groups (p < 0.05). An independent statistical association between potential motives explaining the crime (p < 0.001; OR 27.533) and homicide on multiple logistic regression analysis was highlighted. The absence of clothing lacerations was inversely related to homicide (p = 0.002, OR 0.092). To the best of our knowledge, this is one of very few Italian studies concerning the differential diagnosis between homicidal and suicidal sharp force fatalities. The dynamics of the event is established in most cases by the integrated evaluation of data from crime scene investigation and the autopsy. Nevertheless, in an atypical scenario, a psychopathological-criminological analysis may provide essential elements, and particular attention should be given to the identification of potential explanatory motives.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2 O emissions

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N₂ O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N₂ O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N₂ O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N₂ O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N₂ O emissions. Yield-scaled N₂ O emissions (N₂ O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N₂ O emissions at field scale is discussed.

Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.