• Energy Research

Abstract Accurately predicting the impacts of higher temperatures, different precipitation rates and elevated CO2 concentrations on crop yields and GHG emissions is required in order to develop adaptation strategies. The objectives of this study were to calibrate and evaluate a regionalized denitrification-decomposition (DNDC) model using measured crop yield, soil temperature, moisture and N2O emissions, and to explore the impacts of climate change scenarios (Representative Concentration Pathways (RCP) 4.5 and RCP 8.5) on crop yields and N2O emissions in Southwestern Ontario, Canada. This simulation study was based on a winter wheat-maize-soybean rotation under conventional tillage (CT) and no tillage (NT) practices at Woodslee, Ontario, Canada. The model was calibrated using various statistics including the d index (0.85–0.99), NSE (Nash-Sutcliffe efficiency, NSE > 0) and nRMSE (normalized root mean square error, nRMSE

Abstract 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.

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.

AbstractSimulation 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 (N2O) 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 N2O 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 N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O 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 N2O emissions. Yield‐scaled N2O emissions (N2O 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 N2O emissions at field scale is discussed.

On se rend de plus en plus compte que la complexité des études d'ensembles de modèles dépend non seulement des modèles utilisés, mais aussi de l'expérience et de l'approche utilisées par les modélisateurs pour calibrer et valider les résultats, qui restent une source d'incertitude. Ici, nous avons appliqué une méthode de prise de décision multicritères pour étudier la justification appliquée par les modélisateurs dans une étude d'ensemble de modèles où 12 types de modèles biogéochimiques différents basés sur des processus ont été comparés à travers cinq étapes d'étalonnage successives. Les modélisateurs partageaient un niveau d'accord commun sur l'importance des variables utilisées pour initialiser leurs modèles pour l'étalonnage. Cependant, nous avons constaté une incohérence entre les modélisateurs lors de l'évaluation de l'importance des variables d'entrée à travers différentes étapes d'étalonnage. Le niveau de pondération subjective attribué par les modélisateurs aux données d'étalonnage a diminué séquentiellement à mesure que l'étendue et le nombre de variables fournies augmentaient. Dans ce contexte, l'importance perçue attribuée à des variables telles que le taux de fertilisation, le régime d'irrigation, la texture du sol, le pH et les niveaux initiaux des stocks de carbone organique et d'azote du sol était statistiquement différente lorsqu'elle était classée selon les types de modèles. L'importance attribuée aux variables d'entrée telles que la durée expérimentale, la production primaire brute et l'échange net d'écosystèmes variait considérablement en fonction de la durée de l'expérience du modélisateur. Nous soutenons que l'accès progressif aux données d'entrée à travers les cinq étapes d'étalonnage a influencé négativement la cohérence des interprétations faites par les modélisateurs, avec un biais cognitif dans les routines d'étalonnage « essais et erreurs ». Notre étude souligne qu'il est essentiel de négliger les attributs humains et sociaux dans les résultats des études de modélisation et de comparaison des modèles. Bien que la complexité des processus capturés dans les algorithmes et le paramétrage du modèle soit importante, nous soutenons que (1) les hypothèses du modélisateur sur la mesure dans laquelle les paramètres doivent être modifiés et (2) les perceptions du modélisateur de l'importance des paramètres du modèle sont tout aussi essentielles pour obtenir un étalonnage du modèle de qualité que les détails numériques ou analytiques. Existe una creciente conciencia de que la complejidad de los estudios de conjuntos de modelos depende no solo de los modelos utilizados, sino también de la experiencia y el enfoque utilizados por los modeladores para calibrar y validar los resultados, que siguen siendo una fuente de incertidumbre. Aquí, aplicamos un método de toma de decisiones multicriterio para investigar la justificación aplicada por los modeladores en un estudio de conjunto de modelos donde se compararon 12 tipos de modelos biogeoquímicos diferentes basados en procesos en cinco etapas de calibración sucesivas. Los modeladores compartieron un nivel común de acuerdo sobre la importancia de las variables utilizadas para inicializar sus modelos para la calibración. Sin embargo, encontramos inconsistencia entre los modeladores al juzgar la importancia de las variables de entrada en diferentes etapas de calibración. El nivel de ponderación subjetiva atribuido por los modeladores a los datos de calibración disminuyó secuencialmente a medida que aumentaba el alcance y el número de variables proporcionadas. En este contexto, la importancia percibida atribuida a variables como la tasa de fertilización, el régimen de riego, la textura del suelo, el pH y los niveles iniciales de las reservas orgánicas de carbono y nitrógeno del suelo fue estadísticamente diferente cuando se clasificaron según los tipos de modelos. La importancia atribuida a variables de entrada como la duración experimental, la producción primaria bruta y el intercambio neto de ecosistemas varió significativamente según la duración de la experiencia del modelador. Argumentamos que el acceso gradual a los datos de entrada en las cinco etapas de calibración influyó negativamente en la consistencia de las interpretaciones realizadas por los modeladores, con sesgo cognitivo en las rutinas de calibración de "ensayo y error". Nuestro estudio destaca que pasar por alto los atributos humanos y sociales es fundamental en los resultados del modelado y los estudios de intercomparación de modelos. Si bien la complejidad de los procesos capturados en los algoritmos y la parametrización del modelo es importante, sostenemos que (1) las suposiciones del modelador sobre la medida en que se deben alterar los parámetros y (2) las percepciones del modelador sobre la importancia de los parámetros del modelo son tan críticas para obtener una calibración del modelo de calidad como los detalles numéricos o analíticos. 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. هناك إدراك متزايد بأن تعقيد دراسات مجموعة النماذج لا يعتمد فقط على النماذج المستخدمة ولكن أيضًا على الخبرة والنهج اللذين يستخدمهما المصممون لمعايرة النتائج والتحقق من صحتها، والتي لا تزال مصدرًا لعدم اليقين. هنا، طبقنا طريقة صنع قرار متعددة المعايير للتحقيق في الأساس المنطقي الذي طبقه مصممو النماذج في دراسة جماعية نموذجية حيث تمت مقارنة 12 نوعًا مختلفًا من النماذج البيوكيميائية القائمة على العمليات عبر خمس مراحل معايرة متتالية. شارك مصممو النماذج مستوى مشتركًا من الاتفاق حول أهمية المتغيرات المستخدمة لتهيئة نماذجهم للمعايرة. ومع ذلك، وجدنا عدم اتساق بين صانعي النماذج عند الحكم على أهمية متغيرات المدخلات عبر مراحل المعايرة المختلفة. انخفض مستوى الترجيح الذاتي الذي يعزوه صانعو النماذج إلى بيانات المعايرة بالتتابع مع زيادة مدى وعدد المتغيرات المقدمة. في هذا السياق، كانت الأهمية المتصورة المنسوبة إلى متغيرات مثل معدل التسميد ونظام الري وقوام التربة ودرجة الحموضة والمستويات الأولية لمخزونات الكربون العضوي والنيتروجين في التربة مختلفة إحصائيًا عند تصنيفها وفقًا لأنواع النماذج. اختلفت الأهمية المنسوبة إلى متغيرات المدخلات مثل المدة التجريبية، والإنتاج الأولي الإجمالي، وصافي تبادل النظام الإيكولوجي اختلافًا كبيرًا وفقًا لطول تجربة صانع النموذج. نحن نجادل بأن الوصول التدريجي إلى بيانات الإدخال عبر مراحل المعايرة الخمس أثر سلبًا على اتساق التفسيرات التي قدمها صانعو النماذج، مع التحيز المعرفي في إجراءات معايرة "التجربة والخطأ". تسلط دراستنا الضوء على أن التغاضي عن السمات البشرية والاجتماعية أمر بالغ الأهمية في نتائج النمذجة ودراسات المقارنة بين النماذج. في حين أن تعقيد العمليات التي تم التقاطها في خوارزميات النموذج ووضع المعلمات أمر مهم، فإننا نؤكد أن (1) افتراضات صانع النموذج حول مدى ضرورة تغيير المعلمات و (2) تصورات صانع النموذج لأهمية معلمات النموذج لا تقل أهمية في الحصول على معايرة نموذج الجودة عن التفاصيل العددية أو التحليلية.

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.