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Quantifying the output uncertainty and tracking down its origins is key to interpreting the results of modelling studies. We performed such an uncertainty analysis on the predictions of forest growth and yield under climate change. We specifically focused on the effect of the interannual climate variability. For that, the climate years in the model input (daily resolution) were randomly shuffled within each 5-year period. In total, 540 simulations (10 parameter sets, nine climate shuffles, three global climate models, and two mitigation scenarios) were made for one growing cycle (80 years) of a Scots pine (Pinus sylvestris L.) forest growing in Peitz, Germany. Our results show that, besides the important effect of the parameter set, the random order of climate years can significantly change results such as basal area and produced volume, as well as the response of these to climate change. We stress that the effect of weather variability should be included in the design of impact model ensembles and in the accompanying uncertainty analysis. We further suggest presenting model results as likelihoods to allow risk assessment. For example, in our study, the likelihood of a decrease in basal area of >10% with no mitigation was 20.4%, whereas the likelihood of an increase >10% was 34.4%. Key words: forest model, uncertainty, weather variability, basal area, production., Resume : Quantifier l'incertitude d'un resultat et en retrouver les origines est essentiel pour interpreter les resultats des etudes de modelisation. Nous avons effectue une telle analyse d'incertitude sur les [...]

Biomass from short‐rotation coppice (SRC) of woody perennials is being increasingly used as a bioenergy source to replace fossil fuels, but accurate assessments of the long‐term greenhouse gas (GHG) balance of SRC are lacking. To evaluate its mitigation potential, we monitored the GHG balance of a poplar (Populus) SRC in Flanders, Belgium, over 7 years comprising three rotations (i.e., two 2 year rotations and one 3 year rotation). In the beginning—that is, during the establishment year and during each year immediately following coppicing—the SRC plantation was a net source of GHGs. Later on—that is, during each second or third year after coppicing—the site shifted to a net sink. From the sixth year onward, there was a net cumulative GHG uptake reaching −35.8 Mg CO2 eq/ha during the seventh year. Over the three rotations, the total CO2 uptake was −51.2 Mg CO2/ha, while the emissions of CH4 and N2O amounted to 8.9 and 6.5 Mg CO2 eq/ha, respectively. As the site was non‐fertilized, non‐irrigated, and only occasionally flooded, CO2 fluxes dominated the GHG budget. Soil disturbance after land conversion and after coppicing were the main drivers for CO2 losses. One single N2O pulse shortly after SRC establishment contributed significantly to the N2O release. The results prove the potential of SRC biomass plantations to reduce GHG emissions and demonstrate that, for the poplar plantation under study, the high CO2 uptake outweighs the emissions of non‐CO2 greenhouse gases. [ABSTRACT FROM AUTHOR]

The availability of nutrients is one of the factors that regulate terrestrial carbon cycling and modify ecosystem responses to environmental changes. Nonetheless, nutrient availability is often overlooked in climate-carbon cycle studies because it depends on the interplay of various soil factors that would ideally be comprised into metrics applicable at large spatial scales. Such metrics do not currently exist. Here, we use a Swedish forest inventory database that contains soil data and tree growth data for > 2500 forests across Sweden to (i) test which combination of soil factors best explains variation in tree growth, (ii) evaluate an existing metric of constraints on nutrient availability, and (iii) adjust this metric for boreal forest data. With (iii), we thus aimed to provide an adjustable nutrient metric, applicable for Sweden and with potential for elaboration to other regions. While taking into account confounding factors such as climate, N deposition, and soil oxygen availability, our analyses revealed that the soil organic carbon concentration (SOC) and the ratio of soil carbon to nitrogen (C : N) were the most important factors explaining variation in "normalized" (climateindependent) productivity (mean annual volume increment - m3 ha-1 yr-1) across Sweden. Normalized forest productivity was significantly negatively related to the soil C :N ratio (R2 = 0.02-0.13), while SOC exhibited an empirical optimum (R2 = 0.05-0.15). For the metric, we started from a (yet unvalidated) metric for constraints on nutrient availability that was previously developed by the International Institute for Applied Systems Analysis (IIASA - Laxenburg, Austria) for evaluating potential productivity of arable land. This IIASA metric requires information on soil properties that are indicative of nutrient availability (SOC, soil texture, total exchangeable bases - TEB, and pH) and is based on theoretical considerations that are also generally valid for nonagricultural ecosystems. However, the IIASA metric was unrelated to normalized forest productivity across Sweden (R2 = 0.00- 0.01) because the soil factors under consideration were not optimally implemented according to the Swedish data, and because the soil C:N ratio was not included. Using two methods (each one based on a different way of normalizing productivity for climate), we adjusted this metric by incorporating soil C :N and modifying the relationship between SOC and nutrient availability in view of the observed relationships across our database. In contrast to the IIASA metric, the adjusted metrics explained some variation in normalized productivity in the database (R2 = 0.03-0.21; depending on the applied method). A test for five manually selected local fertility gradients in our database revealed a significant and stronger relationship between the adjusted metrics and productivity for each of the gradients (R2 = 0.09-0.38). This study thus shows for the first time how nutrient availability metrics can be evaluated and adjusted for a particular ecosystem type, using a large-scale database. [ABSTRACT FROM AUTHOR]

The availability of nutrients regulates terrestrial carbon cycling and modifies ecosystem responses to environmental changes. Nonetheless, nutrient availability is often overlooked in climate-carbon cycle studies because it depends on the interplay of various soil factors that would ideally be comprised into one metric. Such a metric does not currently exist. Here, we used a Swedish forest inventory database that contains soil and tree growth data for > 2500 forests across Sweden to test which combination soil factors best explains variation in plant growth, and to take the first steps in developing a nutrient availability metric. For the latter, we started from a (yet unvalidated) metric for constraints on nutrient availability that was previously developed by IIASA (Laxenburg, Austria). This IIASA-metric was developed for crops and uses only indirect indicators of nutrient availability. Our analyses revealed that soil organic carbon content (SOC) and the soil carbon to nitrogen (C : N) ratio were the most important factors explaining variation in normalized (climate-independent) productivity. Normalized productivity increased with decreasing soil C : N ratio (R2 = 0.02-0.13), while SOC exhibited an empirical optimum (R2 = 0.05-0.15). The IIASA-metric was unrelated to normalized productivity (R2 = 0.00-0.01), because the soil factors under consideration were not well implemented, and because the C : N ratio was not included. We upgraded this metric by incorporating soil C : N and adjusting the relationship between SOC and nutrient availability in view of the observed relationship across our database. This upgraded metric explained a significant fraction of the variation (R2 = 0.03-0.21; depending on the applied method) and thus opens up new opportunities to further validate and improve it with other datasets, from forests and from other ecosystem types, to ultimately develop a generic global nutrient availability metric. [ABSTRACT FROM AUTHOR]

We calibrated and evaluated the agricultural model AquaCrop for the simulation of water use and yield of a short-rotation coppice (SRC) plantation with poplar ( Populus) in East Flanders (Belgium) during the second and the third rotation (first 2 years only). Differences in crop development and growth during the course of the rotations were taken into account during the model calibration. Overall, the AquaCrop model showed good performance for the daily simulation of soil water content ( R2 of 0.57-0.85), of green canopy cover ( R2 > 0.87), of evapotranspiration (ET; R2 > 0.76), and of potential yield. The simulated, total yearly water use of the SRC ranged between 55% and 85% of the water use of a reference grass ecosystem calculated under the same environmental conditions. Crop transpiration was between 67% and 93% of total ET, with lower percentages in the first than in the second year of each rotation. The observed (dry mass) yield ranged from 6.61 to 14.76 Mg ha−1 yr−1. A yield gap of around 30% was observed between the second and the third rotation, as well as between simulated and observed yield during the third rotation. This could possibly be explained by the expansion of the understory (weed) layer; the relative cover of understory weeds was 22% in the third year of the third rotation. The agricultural AquaCrop model simulated total water use and potential yield of the operational SRC in a reliable way. As the plantation was extensively managed, potential effects of irrigation and/or fertilization on ET and on yield were not considered in this study. [ABSTRACT FROM AUTHOR]