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Technologies that separate manure or digestate into fractions with different solids and nutrient contents present interesting options to mitigate manure storage emissions (by reducing the quantity of carbon stored anaerobically) and to improve nutrient distribution (by reducing the quantity of water transported with nutrients). In this study, the dairy farm model, DairyCrop-Syst, was used to simulate storage emissions of methane (CH4), nitrous oxide (N2O), and ammonia (NH3), and to simulate nutrient distribution for a case-study farm in Canada. The farm used several types of manure processing, including: anaerobic digestion (AD), solid-liquid separation (SLS), and nutrient recovery (NR). Simulations were done with combinations of the above technologies, i.e., a baseline with only AD that produced a single (unseparated) effluent, compared to AD+SLS, and AD+SLS+NR that produced two separate fractions. With AD+SLS+NR, the processing system isolated a solid fraction with a high concentration of N and P, and a liquid fraction containing less nutrients. Compared to the baseline system, the addition of solid liquid separation and nutrient recovery (i.e. SLS+NR) reduced CH4 emissions from outdoor liquid digestate storage by 87%, with only a small offset from higher N2O and NH3 emissions from storing the solid fraction. The solid fraction was simulated to be transported to fields at least 30 km away from the dairy barns, while the liquid fraction was transported by dragline to fields adjacent to the barn. The advanced nutrient separation system resulted in much lower transport costs for manure nutrients and the ability to transport N and P to greater distances.

Abstract Representative subsets of global climate models (GCMs) are often used in climate change impact studies to account for uncertainty in ensemble climate projections. However, the effectiveness of such subsets has seldom been assessed for the estimations of either the mean or the spread of the full ensembles. We assessed two different approaches that were employed to select 5 GCMs from a 20-member ensemble of GCMs from the CMIP5 ensemble for projecting canola and spring wheat yields across Canada under RCP 4.5 and 8.5 emission scenarios in the periods 2040–2069 and 2070–2099, based on crop simulation models. Averages and spreads of the simulated crop yields using the 5-GCM subsets selected by T&P and KKZ approaches were compared with the full 20-GCM ensemble. Our results showed that the 5-GCM subsets selected by the two approaches could produce full-ensemble means with a relative absolute error of 2.9–4.7% for canola and 1.5–2.2% for spring wheat, and covers 61.8–91.1% and 66.1–80.8% of the full-ensemble spread for canola and spring wheat, respectively. Our results also demonstrated that both approaches were very likely to outperform a subset of randomly selected 5 GCMs in terms of a smaller error and a larger range.

In this study, Life Cycle Assessment (LCA) methodology was combined with the agro-ecosystem model DNDC to assess the climate and eutrophication impacts of perennial grass cultivation at five different sites in Sweden. The system was evaluated for two fertilisation rates, 140 and 200 kg N ha−1. The climate impact showed large variation between the investigated sites. The largest contribution to the climate impact was through soil N2O emissions and emissions associated with mineral fertiliser manufacturing. The highest climate impact was predicted for the site with the highest clay and initial organic carbon content, while lower impacts were predicted for the sandy loam soils, due to low N2O emissions, and for the silty clay loam, due to high carbon sequestration rate. The highest eutrophication potential was estimated for the sandy loam soils, while the sites with finer soil texture had lower eutrophication potential. According to the results, soil properties and weather conditions were more important than fertilisation rate for the climate impact of the system assessed. It was concluded that agro-ecosystem models can add a spatial and temporal dimension to environmental impact assessment in agricultural LCA studies. The results could be used to assist policymakers in optimising use of agricultural land.

Study region: This study used data collected from three cropland sites (two in Manitoba and one in Prince Edward Island) in Canada. Study focus: In efforts to accurately describe the water dynamics in agricultural soils, most of the agri-hydrological models developed are highly complex, such that they require detailed input data, of which most of them are not always measured or otherwise readily available. However, the comprehensive and complex representations of the processes may not be justified when data is scarce. Thus, this study developed a soil water budget model that could describe the movement of water through Canadian agricultural soils using a few parameters only. The model was developed by selecting algorithms from various existing models, whose data requirements are readily available in literature and public Canadian databases. New hydrological insights: The model developed in this study can simulate evapotranspiration, runoff, deep percolation, and soil moisture content for various seasons (i.e., growing, non-growing/dormant, and pre-planting/post-harvest) with minimum data requirement, which addresses the limited availability of data in the country for crop and hydrological modeling. Moreover, the model is simple and easy to use, and can work without calibration. It was tested using three site-specific datasets, and results show that despite its simplicity, its overall performance was comparable with those of other agricultural models that use cascade flow framework for hydrology.

Biophysical agricultural models are needed for assessing science-based mitigation options to improve the efficiency and sustainability of agricultural cropping systems. It is crucial that they can accurately simulate soil hydrology and nutrient flows which strongly influence crop growth, biogeochemical processes and water quality. The purpose of this study was to compare the performance of the DeNitrification DeComposition model (DNDC), which utilizes simplified hydrologic processes, to a more comprehensive water flow model, the Root Zone Water Quality Model (RZWQM2), to determine which processes are sufficient for simulating water and nitrogen dynamics and recommend improvements. Both models were calibrated and validated for simulating soil hydrology, nitrogen loss to tile drains and crop biomass using detailed observations from a corn (Zea mays L.) -soybean (Glycine max (L.) Merr.) rotation in Iowa, with and without cover crops. DNDC performed adequately across a wide range of metrics in comparison to a more hydrologically complex model. Soybean and corn yield, and corn biomass over the growing season were well simulated by both models (NRMSE

Internal climate variability (ICV) is one of the major sources of uncertainty in climate projections, yet it is seldom quantified for projections of crop production. Our study focuses on quantifying the uncertainty due to ICV in projections of crop productions in Canada. We utilize climate scenarios from two large ensembles (LEs, CanESM2-LE and CanRCM4-LE with 25 members each) as inputs to the crop models in the Decision Support System for Agrotechnology Transfer. We simulate crop yields for canola, maize and spring wheat under the future climates of four global warming levels. The coefficient of variation (CV) of the projected crop production across the LE members is used to quantify the uncertainty related to ICV and this is compared with the CVs generated using the 20 GCMs in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Crop production in Canada could increase with global warming, e.g. spring wheat production could increase by up to 21% at the warming level of 3.0 °C. The projections often produce larger uncertainty associated with the GCMs than from ICV at all warming levels above 2.0 °C. The results from an asymptotic test for the equality of CVs show a significant difference in CVs of projections of canola production between CanESM2-LE/CanRCM4-LE and CMIP5 for the warming level of 3.0 °C. However, the test results do not indicate a significant difference among the ensembles at all four warming levels for maize and spring wheat. The uncertainty due to ICV is often comparable to that associated with GCMs at the warming level of 1.5 °C, e.g. a CV of 6.0 and 6.4% for CanESM2-LE and CanRCM4-LE and 6.6% for CMIP5 in the projections of spring wheat production. We conclude there is a need to account for uncertainty related to ICV in projections of Canadian crop production, especially at lower warming levels.

The challenges of accurately measuring in situ ammonia (NH3) losses from agricultural systems are well known. Using an open path laser coupled with a backward Lagrangian stochastic dispersion model is a promising approach for quantifying both point- and area-sources; however, this approach requires the open path laser to detect low NH3 concentrations and small concentration differences. In this study, we compared the new GasFinder3 open path laser (Boreal laser Inc., Edmonton, Canada) with the GasFinder2 sensor, the previous version. The study took place at two locations: an outdoor open-air manure compost site, and a field of wheat stubble which was fertilized with urea ammonium nitrate. Results showed the two lasers reported similar concentrations during three days of measurements at the compost site, but differed at the field site, where concentrations were close to the minimum detection limit. The GasFinder3 had a lower standard deviation under all conditions, especially with low wind speed and high relative humidity.

Science-based assessments of climate change impacts on cropping systems under different levels of global warming are essential for informing stakeholders which global climate targets and potential adaptation strategies may be effective. A comprehensive evaluation of climate change impacts on Canada’s crop production under different levels of global warming is currently lacking. The DayCent, DNDC and DSSAT models were employed to estimate changes in crop yield and production for three prominent crops including spring wheat, canola and maize in current agricultural regions of Canada. Four warming scenarios with global mean temperature changes of 1.5 °C, 2.0 °C, 2.5 °C and 3.0 °C above the pre-industrial level were investigated. Climate scenarios from 20 Global Climate Models, included in the Coupled Model Intercomparison Project Phase 5 and downscaled with a multivariate quantile mapping bias correction method, were used to drive the crop simulation models. Simulated yield changes demonstrate a potentially positive impact on spring wheat and canola yields at all four temperature levels, particularly when shifting planting date is considered in the simulations. There was less consensus for the currently utilized short-season maize cultivars, as yields were only projected to increase by DNDC compared to a slight decrease by DayCent and a slight increase up to 2.5 °C followed by a decrease at 3.0 °C by DSSAT. These findings indicate that climate at the global warming levels up to 3.0 °C above the pre-industrial level could be beneficial for crop production of small grains in Canada. However, these benefits declined after warming reached 2.5 °C.

Abstract We describe a new species of kukri snake (Oligodon Fitzinger, 1826) from the limestone karst formations of Satun and Trang Provinces in southern Thailand. Phylogenetic analyses based on three mitochondrial DNA fragments (12S–16S ribosomal rRNA and cytochrome b) recover the new species within the Oligodon cinereus species complex, where it forms a deeply divergent yet poorly supported clade sister to Oligodon saiyok Sumontha et al., 2017 and another unnamed lineage currently referred to Oligodon cinereus (Günther, 1864) from southwest Myanmar. Morphologically, the new species is distinguished from all other members of the genus by the following combination of characters: ventral scales 189–193 with distinct lateral keeling; subcaudal scales 47–54, paired; anterior dorsal scale rows 17–19, with the reduction from 19 to 17 rows occurring above the 28th–30th ventral scale when present; maxillary teeth 8, blade-like and laterally compressed; dorsum olive–gray, plain; ventral surface white anteriorly, dark gray posteriorly; underside of tail dark gray, smeared with white. We briefly discuss the natural history and conservation status of this new species and provide observations of other kukri snakes inhabiting limestone karst habitats. Our study also incorporates genetic samples of four recently described Oligodon endemic to Thailand, all of which are recovered in the O. cinereus species complex. In agreement with previous studies, we demonstrate that species-level diversity within the O. cinereus species complex is underestimated, and additional sampling is necessary to revise this taxonomically challenging clade.

Introduction: Mental health conditions (MHC) among adolescents in low- and middle-income countries, including South Africa, are estimated to be high. Adaptive emotion regulation (ER) skills can protect against MHC among adolescents. In South Africa, there is limited adolescent mental health prevalence data as well as little understanding of the associations between MHC and ER among adolescents. This study aimed to address these gaps by describing the psychosocial characteristics of older South African adolescents from low-income settings as well as investigating associations between depression and anxiety symptoms and ER. Methods: We selected 12 schools in collaboration with two NGOs. Learners aged 15–18-years were recruited to complete a tablet-based survey. ER, depression, anxiety, and other psychosocial measures were included. Two multiple linear regression models were used to determine associations between depression symptoms, anxiety symptoms, other psychosocial factors, and ER. Results: Of the 733 participants from 12 Western Cape schools, 417 (56.90%) screened at risk for clinically significant anxiety symptoms, 423 (57.70%) participants for depression symptoms, 229 (31.40%) participants for PTSD symptoms and 263 (35.90%) for risky alcohol use. Depression and anxiety scores were found to be significantly positively correlated with ER difficulties and adolescents struggled most with identifying and utilizing adaptive ER strategies. The adjusted linear regression model reported that female gender, clinically significant depressive, anxiety, post-traumatic stress symptoms and risky-alcohol use were all significantly associated with poorer ER scores, while self-esteem was significantly associated with better ER scores. Conclusion: These findings contribute to the South African adolescent mental health literature and to the research gap on the links between depression and anxiety and ER. Future research should consider further exploration of the relationships between psychosocial factors and ER to inform the urgent development and testing of appropriate adolescent interventions in this setting.