• Energy Research

AbstractWe have read with interest an opinion paper recently published in the European Journal of Soil Science (Berthelin et al., 2022). This paper presents some interesting considerations, at least one of which is already well known to soil scientists working on soil organic carbon (SOC), that is, a large portion (80%–90%) of fresh carbon inputs to soil is subject to rapid mineralization. The short‐term mineralization kinetics of organic inputs is well‐known and accounted for in soil organic matter models. Thus, clearly, the long‐term predictions based on these models do not overlook short‐term mineralization. We point out that many agronomic practices can significantly contribute to SOC sequestration. If conducted responsibly whilst fully recognising the caveats, SOC sequestration can lead to a win‐win situation where agriculture can both contribute to the mitigation of climate change and adapt to it, whilst at the same time delivering other co‐benefits such as reduced soil erosion and enhanced biodiversity.Highlights Rapid mineralization of organic inputs is an important factor for soil carbon sequestration. Mineralization kinetics of organic inputs are well‐known and accounted for in soil organic matter models. Many agronomic practices can contribute significantly to SOC sequestration. SOC sequestration can lead to a win‐win situation where agriculture can both contribute to the mitigation of climate change and adapt to it.

Using organic fertilizers on sugarcane mulches is a potentially interesting substitute for mineral fertilization in terms of economic and environmental impacts. However, no general agreement exists regarding the short-term effect of combining mulching and organic fertilization on greenhouse gas (GHG) emissions. Therefore, we studied different mixtures in the field by combining two amounts of sugarcane mulch (5 and 10 Mg ha−1) with different N fertilizers (urea, pig slurry, and digested sewage sludge). We measured CO2 and N2O emissions shortly after application of the mixtures (0–14 days) and the mulch decomposition dynamics from 0 to 120 days after application. We hypothesized that the relative amount of N to C modifies the decomposition dynamics and GHG fluxes. The emitted N2O-N and CO2-C were measured using static chambers. Mulch-C decomposition was measured using litterbags. Our results showed that the proportion of mulch-C remaining on the soil on day 120 was not altered by either the type of N fertilizer or the mulch amount. On a shorter time scale (0–49 days), the different N treatments affected the mulch-C and mulch-N losses and the C:N ratios, indicating a transient interaction between the dynamics of the mulch and the added N. The intensity of N2O-N emission was ranked as pig slurry > urea > digested sewage sludge, underscoring the effect of the physical form of N fertilizer. This work highlights the need to jointly study carbon and nitrogen dynamics and consider both soil carbon and gas emissions to assess the GHG balances of sugarcane farming practices.

AbstractA potential strategy for mitigating nitrous oxide (N2O) emissions from permanent grasslands is the partial substitution of fertilizer nitrogen (Nfert) with symbiotically fixed nitrogen (Nsymb) from legumes. The input of Nsymb reduces the energy costs of producing fertilizer and provides a supply of nitrogen (N) for plants that is more synchronous to plant demand than occasional fertilizer applications. Legumes have been promoted as a potential N2O mitigation strategy for grasslands, but evidence to support their efficacy is limited, partly due to the difficulty in conducting experiments across the large range of potential combinations of legume proportions and fertilizer N inputs. These experimental constraints can be overcome by biogeochemical models that can vary legume‐fertilizer combinations and subsequently aid the design of targeted experiments. Using two variants each of two biogeochemical models (APSIM and DayCent), we tested the N2O mitigation potential and productivity of full factorial combinations of legume proportions and fertilizer rates for five temperate grassland sites across the globe. Both models showed that replacing fertilizer with legumes reduced N2O emissions without reducing productivity across a broad range of legume‐fertilizer combinations. Although the models were consistent with the relative changes of N2O emissions compared to the baseline scenario (200 kg N ha−1 yr−1; no legumes), they predicted different levels of absolute N2O emissions and thus also of absolute N2O emission reductions; both were greater in DayCent than in APSIM. We recommend confirming these results with experimental studies assessing the effect of clover proportions in the range 30–50% and ≤150 kg N ha−1 yr−1 input as these were identified as best‐bet climate smart agricultural practices.

Abstract Crop residue affects nitrous oxide (N2O) emissions in no-tillage systems, but the magnitude of the emissions depends on soil drivers that are directly influenced by crop residue quality and quantity. We conducted a one-year study to investigate how mulch chemical characteristics and mass affect N2O emissions during their decomposition in the field under subtropical conditions. The mulch treatments consisted of vetch and wheat crop residues applied to the soil at quantities of 0, 3, 6 and 9 Mg ha−1 dry matter. We followed the kinetics of mulch carbon (C) and nitrogen (N), soil temperature, moisture and inorganic N, the denitrification end-product ratio [N2O/(N2O + N2)] at days 15 and 30, and N2O and carbon dioxide (CO2) emissions with a static chamber method. Mulch decomposition and C and N dynamics were very rapid for vetch and much more gradual for wheat, reflecting the differences in their initial chemical compositions, but for both residues, the initial mulch mass had no effect on the decomposition rate. The presence of mulches increased soil moisture in the 0–10 cm soil layer, with the results for water-filled pore space by treatment as 0 = 3

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.

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.