Modelling Grazing and Burning in Communal Rangelands to Help Understand Trade-offs between Production, Carbon, and Water

Rangelands cover more than 80% of South Africa’s land area, providing critical ecosystem services, livelihoods and cultural values related to livestock. Communally owned rangelands are often overgrazed and subject to runaway fires but lack of data limits our understanding of how these threats impact production. In this transdisciplinary project, we use models to test hypotheses and predict future scenarios as a planning tool for resource-poor communal farmers. We think that moderate grazing and fire regimes will increase overall production and carbon sequestration with uncertain trade-offs for water and nutrient cycling. To test this, we trained two process-based biogeochemical models (DAYCENT and SPACSYS) with individual merits to simulate known fire returns and grazing pressures on a 40-year old long-term ecological research grassland site, and validated models with data from Mvenyane, a nearby communal livestock grazing area. DAYCENT and SPACSYS simulated observed soil organic carbon well, while accuracy for aboveground herbaceous biomass differed between models. DAYCENT projected that soil organic carbon could increase by ca. 1000 g C m-2 over ten years or 1 t C ha-1 yr-1 with moderate increases in biomass and no change in water fluxes when changing from continuous high pressure to moderate pressure grazing in a two-camp rotation, with or without fire. These and other scenarios, including future climate projections, will be used to evaluate biophysical and social trade-offs so that sustainable land use plans can be created in Mvenyane and the wider rangeland community

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

Background Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. Methods We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). Findings In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683–0·717]). Interpretation In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. Funding British Journal of Surgery Society