Estimates Of Total Biomass Research Articles

Integrating LIDAR and forest inventories to fill the trees outside forests data gap.

Forest inventories are commonly used to estimate total tree biomass of forest land even though they are not traditionally designed to measure biomass of trees outside forests (TOF). The consequence may be an inaccurate representation of all of the aboveground biomass, which propagates error to the outputs of spatial and process models that rely on the inventory data. An ideal approach to fill this data gap would be to integrate TOF measurements within a traditional forest inventory for a parsimonious estimate of total tree biomass. In this study, Light Detection and Ranging (LIDAR) data were used to predict biomass of TOF in all "nonforest" Forest Inventory and Analysis (FIA) plots in the state of Maryland. To validate the LIDAR-based biomass predictions, a field crew was sent to measure TOF on nonforest plots in three Maryland counties, revealing close agreement at both the plot and county scales between the two estimates. Total tree biomass in Maryland increased by 25.5 Tg, or 15.6%, when biomass of TOF were included. In two counties (Carroll and Howard), there was a 47% increase. In contrast, counties located further away from the interstate highway corridor showed only a modest increase in biomass when TOF were added because nonforest conditions were less common in those areas. The advantage of this approach for estimating biomass of TOF is that it is compatible with, and explicitly separates TOF biomass from, forest biomass already measured by FIA crews. By predicting biomass of TOF at actual FIA plots, this approach is directly compatible with traditionally reported FIA forest biomass, providing a framework for other states to follow, and should improve carbon reporting and modeling activities in Maryland.

Simultaneous estimation as alternative to independent modeling of tree biomass

In this paper it is shown that a simultaneous adjustment provides more efficient estimates of total tree biomass than with independent modelling for biomass estimates by compartments (canopy, bole and roots). When modeling tree biomass, it is important to consider the additivity property, since the total tree biomass must be equal to the sum of the biomass of the components. The aim of this study was to assess the simultaneous estimation performance, considering the additivity principle with respect to independent estimate when modeling biomass components and total biomass. Individual modeling of total biomass and biomass components of leaves, branches, bole without bark, bole bark, and roots was performed on Pinus elliottii Engelm trees derived from forest stands in southern Brazil. Five nonlinear models were tested, and the best performance for estimating the total biomass of each component was selected, characterizing the independent estimation. The models selected for each component were fitted using the nonlinear seemingly unrelated regression method, which characterizes simultaneous estimation. Independent fitting of coefficients for biomass components and total biomass was not satisfactory, as the sum of the biomass component estimates diverged from the total biomass. This was not observed when the simultaneous fitting was used, which takes into account the additivity principle, and resulted in more effective estimators. The simultaneous estimation method must be used in modeling tree biomass.

Selectivity and two biomass measures in an age-based assessment of Antarctic krill (Euphausia superba)

An integrated, age-structured model was fitted to different combinations of survey data using two forms of selectivity (logistic or double-logistic) with time-constant or annually varying selectivity to investigate the population dynamics of Antarctic krill (Euphausia superba) near the Antarctic Peninsula. The data were from surveys conducted by the U.S. Antarctic Marine Living Resources Program around the South Shetland Islands from 1992 to 2011. Two indices of krill biomass based on (1) trawl-net samples and (2) hydroacoustic sampling were combined with length-compositions from the nets. Sixteen model configurations using different combinations of the two biomass surveys with the various options for modeling selectivities were examined. Parameters were estimated in phases with the sequential order of the phases randomized until an invertible Hessian matrix was obtained. Model consistency for the estimates of derived quantities was tested using simulated data. Annual trends in the estimates of total biomass, spawning biomass, and recruitment were similar among different configurations assuming time-constant selectivity, but the absolute scaling ranged widely depending on which biomass indices were used. All configurations with time-constant selectivities were able to reproduce the derived quantities of the operating model when fitted to simulated data. Annually varying selectivities produced more variable estimates of the trends in population biomass, but less variable estimates of scale, compared to time-constant configurations fitted to the same data. The models with annually varying selectivities did not produce invertible Hessian matrices, and four of these configurations could not reproduce the derived parameters of their operating model when fitted to simulated data. Using AIC, the model with logistic, time-constant selectivities was selected as the best configuration to fit both sources of biomass data. The two-stage approach of first randomizing the phase order until an invertible Hessian matrix is achieved and then verifying the reproducibility of the estimates of derived quantities using simulated data could be employed in any integrated stock assessment with parameters estimated in phases.

Development of Allometric Equations for Tree Biomass in Forest Ecosystems in Paraguay

The Atlantic Forest, Humid Chaco, and Dry Chaco are major eco-regions in Paraguay, but information on forest carbon stocks in these forest types remains limited. To establish a system to measure, report and verify forest carbon change under the REDD+ mechanism, we developed new allometric equations to estimate tree biomass in each eco-region. Three models of total and aboveground biomass were developed from destructive sampling data. The models performed well, explaining ≥96% of the variation in aboveground and total biomass, although the best model differed among the eco-regions. The inclusion of height and wood density improved fit in the Atlantic Forest model, but including wood density did not enhance the Humid and Dry Chaco models. Our models will improve the estimation of biomass in Paraguay because they provide better estimates of total and aboveground biomass in each eco-region than pan-tropical generic models.

Biomass Modelling ofAndrostachys johnsoniiPrain: A Comparison of Three Methods to Enforce Additivity

Three methods of enforcing additivity of tree component biomass estimates into total tree biomass estimates forAndrostachys johnsoniiPrain were studied and compared, namely, the conventional (CON) method (a method that consists of using the same independent variables for all tree component models, and for total tree model, and the same weights to enforce additivity), seemingly unrelated regression (SUR) with parameter restriction, and nonlinear seemingly unrelated regression (NSUR) with parameter restriction. The CON method was found to be statistically superior to any other method of enforcing additivity, yielding excellent fit statistics and unbiased biomass estimates. The NSUR method ranked second best but was found to be biased. The SUR method was found to be the worst; it exhibited large bias and had a poor fit for the biomass. Therefore, we recommend that only the CON and NSUR methods should be used for further estimates, provided that their limitations are considered, that is, exclusion of contemporaneous correlations for the CON method and consideration of the significant bias of the NSUR method.

Modelling the impact of climate change on South Pacific albacore tuna

The potential impact of climate change under the IPCC AR4–A2 scenario (close to the AR5-RCP8.5 scenario) on south Pacific albacore tuna (Thunnus alalunga) is simulated with the Spatial Ecosystem And Population Dynamics Model (SEAPODYM) and environmental forcing variables provided by the Earth Climate model IPSL-CM4. Parameters controlling the habitat and dynamics of the population were optimized by fitting the model, using maximum likelihood, to a complete fishing data set for the historical fishing period since 1950. Albacore undertake clear seasonal migrations between feeding and spawning grounds, as evidenced by seasonal catch and size composition changes. This seasonality was well predicted by the SEAPODYM albacore simulations. The total biomass estimate of south Pacific albacore was predicted to have decreased from ~1.8 million tonnes (Mt) at the beginning of industrial fisheries in 1950 to 1.25Mt in 2006, in good agreement with an independent estimate from stock assessment analysis. A simulation without fishing indicated an equivalent contribution of environmental variability and fishing to the historical decrease of the stock biomass. The parameterized SEAPODYM model was used to project the dynamics of the population until the end of the 21st century with an average fishing effort based on recent years. Under this fishing and climate change scenario, the population was predicted to decrease and to stabilize after 2035 just below 0.8Mt, i.e., 55% below the initial biomass of 1960. After 2080 however, the trend was reversed when a new spawning ground emerged in the north Tasman Sea. A test simulation highlighted the sensitivity of the model results to projected dissolved oxygen concentration for which there is large uncertainty in the tropical region. A second test simulation showed that genetic selection favouring albacore with preferences for higher optimal ambient spawning temperature would maintain a reduced level of spawning in current tropical spawning areas, suppress the emergence of an area of spawning in the north Tasman Sea and therefore keep stock abundance at low 2035 levels.

Estimation of biomass and carbon storage of tree plantations in northern Iran

To estimate the aboveground biomass and carbon storage in multi-species plantations we used species-specific equations method and three other generic methods. Astara county is located in northern Iran. It has a total area of 1,757 ha. Based on species-specific allometric equations, total aboveground biomass was calculated andvaried between 81.13 and 98.21 t·ha<sup>–1 </sup>for Acer velutinum, 68.36 and 83.44 t·ha<sup>–1</sup> for Quercus castanifolia, 71.88 and 119.22 t·ha<sup>–1</sup> for Tilia begonifolia, 56.07 and 61.98 t·ha<sup>–1</sup> for Fraxinus excelsior and from 37.92 to 51.34 t·ha<sup>–1</sup> for Prunus avium. There was a significant difference between the mean values of total aboveground biomass estimation obtained by species-specific equations and the three generic methods for Alnus subcordata, Pinus taeda and Pinus nigra. Results indicated that using generalized methods produced more reliable and accurate estimations for native species than for exotic species for rapid biomass and carbon estimation in order to decide on plantation development in the area.

Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops

Leaf area index (LAI) and biomass are important indicators of crop development and the availability of this information during the growing season can support farmer decision making processes. This study demonstrates the applicability of RapidEye multi-spectral data for estimation of LAI and biomass of two crop types (corn and soybean) with different canopy structure, leaf structure and photosynthetic pathways. The advantages of Rapid Eye in terms of increased temporal resolution (∼daily), high spatial resolution (∼5m) and enhanced spectral information (includes red-edge band) are explored as an individual sensor and as part of a multi-sensor constellation. Seven vegetation indices based on combinations of reflectance in green, red, red-edge and near infrared bands were derived from RapidEye imagery between 2011 and 2013. LAI and biomass data were collected during the same period for calibration and validation of the relationships between vegetation indices and LAI and dry above-ground biomass. Most indices showed sensitivity to LAI from emergence to 8m2/m2. The normalized difference vegetation index (NDVI), the red-edge NDVI and the green NDVI were insensitive to crop type and had coefficients of variations (CV) ranging between 19 and 27%; and coefficients of determination ranging between 86 and 88%. The NDVI performed best for the estimation of dry leaf biomass (CV=27% and r2=090) and was also insensitive to crop type. The red-edge indices did not show any significant improvement in LAI and biomass estimation over traditional multispectral indices. Cumulative vegetation indices showed strong performance for estimation of total dry above-ground biomass, especially for corn (CV≤20%). This study demonstrated that continuous crop LAI monitoring over time and space at the field level can be achieved using a combination of RapidEye, Landsat and SPOT data and sensor-dependant best-fit functions. This approach eliminates/reduces the need for reflectance resampling, VIs inter-calibration and spatial resampling.

Using Small-Footprint Discrete and Full-Waveform Airborne LiDAR Metrics to Estimate Total Biomass and Biomass Components in Subtropical Forests

An accurate estimation of total biomass and its components is critical for understanding the carbon cycle in forest ecosystems. The objectives of this study were to explore the performances of forest canopy structure characterization from a single small-footprint Light Detection and Ranging (LiDAR) dataset using two different techniques focusing on (i) 3-D canopy structural information by discrete (XYZ) LiDAR metrics (DR-metrics), and (ii) the detailed geometric and radiometric information of the returned waveform by full-waveform LiDAR metrics (FW-metrics), and to evaluate the capacity of these metrics in predicting biomass and its components in subtropical forest ecosystems. This study was undertaken in a mixed subtropical forest in Yushan Mountain National Park, Jiangsu, China. LiDAR metrics derived from DR and FW LiDAR data were used alone, and in combination, in stepwise regression models to estimate total as well as above-ground, root, foliage, branch and trunk biomass. Overall, the results indicated that three sets of predictive models performed well across the different subtropical forest types (Adj-R2 = 0.42–0.93, excluding foliage biomass). Forest type-specific models (Adj-R2 = 0.18–0.93) were generally more accurate than the general model (Adj-R2 = 0.07–0.79) with the most accurate results obtained for coniferous stands (Adj-R2 = 0.50–0.93). In addition, LiDAR metrics related to vegetation heights were the strongest predictors of total biomass and its components. This research also illustrates the potential for the synergistic use of DR and FW LiDAR metrics to accurately assess biomass stocks in subtropical forests, which suggest significant potential in research and decision support in sustainable forest management, such as timber harvesting, biofuel characterization and fire hazard analyses.

A spatial assessment model for European eel (Anguilla anguilla) in a delta, The Netherlands

Given the poor status of the European eel (Anguilla anguilla) population, good assessments are essential to optimise recovery measures. Quantifying eel stock size is challenging given the fractal nature of the distribution of eels, particularly in The Netherlands with its dense interconnected network of drainage ditches, rivers and lakes. Dynamic demographic models as used by others are inappropriate in this delta with a demand for regional information on stock size. We therefore estimated the standing eel stock biomass for all freshwater water bodies, using fish monitoring data collected under the Water Framework Directive and a static GIS approach. Density data were extrapolated to total biomass estimates using GIS data concerning water type, surface area and bank length, and biological parameters obtained from biological data. The analysis yielded density and biomass estimates of the total eel and silver eel stock, with most silver eel biomass being confined to a few coastal regions. The main conclusion is that over half of the Dutch eel biomass is found in the small, regionally managed waters, revealing a crude underestimation of the eel biomass in the 2009 Dutch Eel Management Plan. The silver eel biomass estimate presented here is two to three times higher than previous estimates. Providing spatial information on eel biomass will aid in prioritising management measures to reduce anthropogenic mortality.

Above-ground biomass equations for Pinus radiata D. Don in Asturias

Aim of the study: The aim of this study was to develop a model for above-ground biomass estimation for Pinus radiata D. Don in Asturias.Area of study: Asturias (NE of Spain).Material and methods: Different models were fitted for the different above-ground components and weighted regression was used to correct heteroscedasticity. Finally, all the models were refitted simultaneously by use of Nonlinear Seemingly Unrelated Regressions (NSUR) to ensure the additivity of biomass equations.Research highlights: A system of four biomass equations (wood, bark, crown and total biomass) was develop, such that the sum of the estimations of the three biomass components is equal to the estimate of total biomass. Total and stem biomass equations explained more than 92% of observed variability, while crown and bark biomass equations explained 77% and 89% respectively.Keywords: radiata pine; plantations; biomass.

Biomass and Habitat Partitioning ofDesmognathuson Wet Rock Faces in the Southern Appalachian Mountains

Abstract The importance of plethodontid salamanders in forested ecosystems has been recognized for decades, and studies aimed at quantifying salamander biomass and determining habitat requirements have become more common. However, there is a lack of knowledge on the use and contribution of vertical structures (e.g., wet rock faces) to total salamander biomass within a forested ecosystem. The purpose of our study was to characterize the population density, biomass, and habitat use of a wet rock face by a stream-salamander assemblage. We estimated the population density to be 14.69 salamanders m−2 and the total biomass estimate to be 27.16 g m−2, which is more than two times greater than any salamander biomass reported previously in the eastern United States. We also found significant habitat partitioning of the vertical gradient by the three species of salamanders in the assemblage. The stable microclimate and increased protection from other predators (e.g., mammals, snakes, and ground-foraging birds) prov...

Elephant both Increase and Decrease Availability of Browse Resources for Black Rhinoceros

AbstractDespite the apparent dual role of elephant Loxodonta africana in shaping the food niches of large herbivores, empirical studies focus on their role in facilitating foraging opportunities, while declining resource opportunities (a necessary requirement for competition) are rarely quantified. Our study investigates the relative importance of elephant in these processes by quantifying potential browsing opportunities (using total and preferred biomass, between‐bite distances and bite mass) for black rhinoceros Diceros bicornis along a gradient of elephant utilization in the succulent thickets of the Addo Elephant National Park, South Africa. We show that browse biomass and potential between‐bite harvest rates initially increase with the intensity of elephant utilization through the formation and spread of elephant pathways in otherwise impenetrable thicket. At the maximum, modeled estimates of total and preferred biomass are on average 223 percent and 254 percent higher, respectively, than that recorded in the absence of elephant (Exclosures); potential between‐bite harvest rates are 75 percent higher. With continued elephant utilization, however, browse biomass declines and between‐bite distances increase as the pathways expand and coalesce and canopy height declines. Our model of the change in potential browsing opportunities for rhinoceros reflects the accumulated effects of elephant over time, i.e., the effects accumulate until the relationship switches from increased to reduced availability. With this we demonstrate the key role of elephant for rhinoceros foraging, linked to a potential loss of this role at higher levels of utilization.

Characterizing the distribution and rates of microbial sulfate reduction at Middle Valley hydrothermal vents

Few studies have directly measured sulfate reduction at hydrothermal vents, and relatively little is known about how environmental or ecological factors influence rates of sulfate reduction in vent environments. A better understanding of microbially mediated sulfate reduction in hydrothermal vent ecosystems may be achieved by integrating ecological and geochemical data with metabolic rate measurements. Here we present rates of microbially mediated sulfate reduction from three distinct hydrothermal vents in the Middle Valley vent field along the Juan de Fuca Ridge, as well as assessments of bacterial and archaeal diversity, estimates of total biomass and the abundance of functional genes related to sulfate reduction, and in situ geochemistry. Maximum rates of sulfate reduction occurred at 90 °C in all three deposits. Pyrosequencing and functional gene abundance data revealed differences in both biomass and community composition among sites, including differences in the abundance of known sulfate-reducing bacteria. The abundance of sequences for Thermodesulfovibro-like organisms and higher sulfate reduction rates at elevated temperatures suggests that Thermodesulfovibro-like organisms may have a role in sulfate reduction in warmer environments. The rates of sulfate reduction presented here suggest that—within anaerobic niches of hydrothermal deposits—heterotrophic sulfate reduction may be quite common and might contribute substantially to secondary productivity, underscoring the potential role of this process in both sulfur and carbon cycling at vents.

Experimental measurement of the biomass of Olea europaea L.

Olive ( Olea europaea L.) is an evergreen tree extensively cultivated for its fruit in many warm parts of Southern Europe, North Africa, South Asia and Oceania. The objective of this paper was to obtain information on the total biomass productivity of the olive grove as well as on individual tree biomass productivity and the proportions of the tree components. The study was carried out in Central Italy and estimation of tree woody biomass and total biomass was made by direct measurement and through Intergovernmental Panel on Climate Change (IPCC) methodology. To determine the biomass of all the tree parts (including fruits and prunings) of the olive grove, the forestry estimation methodology named “model tree” was adopted and one representative tree was felled and uprooted; the biomass of main trunk, branches, leaves, twigs and roots was determined separately, using their volume, weight and density; also fruits and prunings of this tree were recorded. Wood density, biomass expansion factor, root/shoot ratio, carbon fraction of dry matter and carbon content of the single tree and tree component were calculated. The study showed that “model tree” methodology is suitable for olive grove; IPCC method can be successfully used with good reliability when direct measurement of biomass expansion factor and carbon fraction are obtained from a representative tree of the olive grove. The C stock evaluation methodology made in this research and the calculation of biomass expansion factor can be considered as the first scientific contribution in estimating productivity, CO 2 sequestration, carbon stocks and yield of olive groves. Key words: Biomass, biomass expansion factor, Intergovernmental Panel on Climate Change (IPCC), tree volume, root/shoot ratio, Olea europaea, CO 2 sequestration.

Global catches, exploitation rates, and rebuilding options for sharks

Adequate conservation and management of shark populations is becoming increasingly important on a global scale, especially because many species are exceptionally vulnerable to overfishing. Yet, reported catch statistics for sharks are incomplete, and mortality estimates have not been available for sharks as a group. Here, the global catch and mortality of sharks from reported and unreported landings, discards, and shark finning are being estimated at 1.44 million metric tons for the year 2000, and at only slightly less in 2010 (1.41 million tons). Based on an analysis of average shark weights, this translates into a total annual mortality estimate of about 100 million sharks in 2000, and about 97 million sharks in 2010, with a total range of possible values between 63 and 273 million sharks per year. Further, the exploitation rate for sharks as a group was calculated by dividing two independent mortality estimates by an estimate of total global biomass. As an alternative approach, exploitation rates for individual shark populations were compiled and averaged from stock assessments and other published sources. The resulting three independent estimates of the average exploitation rate ranged between 6.4% and 7.9% of sharks killed per year. This exceeds the average rebound rate for many shark populations, estimated from the life history information on 62 shark species (rebound rates averaged 4.9% per year), and explains the ongoing declines in most populations for which data exist. The consequences of these unsustainable catch and mortality rates for marine ecosystems could be substantial. Global total shark mortality, therefore, needs to be reduced drastically in order to rebuild depleted populations and restore marine ecosystems with functional top predators.

Estimation of stem and tree level biomass models for Prosopis juliflora/pallida applicable to multi-stemmed tree species

The aim of this paper is to develop biomass models for commonly multi-stemmed Prosopis juliflora/pallida trees. The data were collected on three of the Cape Verde islands (Maio, Santiago and Santo Antao). The dataset covers 240 trees containing 1,882 stems with stem diameter at breast height over 2 cm; of that 255 individual tree stems were sampled destructively. These calibration data were used to construct stem and tree-level models for estimation of total aboveground biomass and its fine and course fractions with diameter threshold of 5 cm. A set of parameterized biomass models for multi-stemmed Prosopis spp. trees suited for biomass estimation at tree and stem levels using appropriate set of independent variables, commonly available in forest inventory programs, was created. The effect of site (island) on tree allometry was not detected. The two-phase construction of tree biomass models based on destructive sampling limited to individual stems combined with a routine field measurement of entire multi-stemmed tree specimen represents a practicable approach leading to biomass and carbon assessment that may be generally suited for tree species with complex multi-stemmed growth form similar to that of Prosopis spp.

Improving the accuracy of tree-level aboveground biomass equations with height classification at a large regional scale

A nonlinear simultaneous equations system based on diameter at breast height (DBH), used to ensure additivity for biomass of individual tree components, was fitted for Cunninghamia lanceolata. The data consisted of measurements taken from 600 sample trees in southern China. All sample trees were harvested and measured for biomass of stem wood, stem bark, branches and foliage. Tree height was classified into 2–5 levels based on the height–diameter relationship across a large geographical area. The nonlinear extra sum of squares method and the Lakkis–Jones test were used to evaluate significant differences between biomass equations with DBH only and classified equations and to assess whether height classification had a significant effect on improving the accuracy of the biomass equations. Based on the PRESS residuals, several statistical indicators, e.g. prediction determination coefficient and root mean square error, were used to evaluate model performance. The results show the height classification obviously improved model performance of the fitted equation by increasing the prediction determination coefficient, decreasing root mean square error and reducing bias and absolute bias by DBH class, especially for total aboveground biomass, stem wood biomass and stem bark biomass. Three-levels of height classification was the best for the total aboveground biomass, following by 4-levels, 5-levels and 2-levels. Although statistically significant differences between the classified equations and the equations with DBH only were found for measurements of branch biomass and foliage biomass, their proportions of total aboveground biomass was small and had only a small effect on improving the accuracy of total aboveground biomass estimates. Height classification increased the stability of parameters for the estimation of stem wood and stem bark biomass. The classified biomass equations could be applied to estimate individual tree biomass in the Chinese National Forest Inventory and the method of height classification may be used with other tree species.

Pattern of carbon allocation across three different stages of stand development of a Chinese pine (Pinus tabulaeformis) forest

AbstractThe pattern of carbon (C) allocation across different stages of stand development of Chinese pine (Pinus tabulaeformis) forests is poorly documented. In order to understand the effects of stand age on the C pool of the Chinese pine forest ecosystem, we have examined the above‐ and belowground C pools in three differently aged stands of Chinese pine in the northern mountains of Beijing, China, by plot‐level inventories and destructive sampling. Our results suggest that tree branch and foliage biomass should be estimated by age‐specific equations. Reasonably accurate estimates of tree stem, tree root, aboveground, and total tree biomass in a Chinese pine forest at different development stages were obtained using age‐independent allometric equations from tree diameter only. The ratio of belowground to aboveground tree biomass was relatively constant with stand aging, remaining around 21 %. The contribution of aboveground tree biomass C increased from 21 % of the total ecosystem C in a 25‐year‐old stand to 44 % in a 65‐year‐old stand, subsequently falling to 41 % in a 105‐year‐old stand, while the contribution of mineral soil C decreased from 64 % of the total ecosystem C in 25‐year‐old stand to 38 % in a 65‐year‐old stand, subsequently increasing to 41 % in a 105‐year‐old stand. The C stock of the total ecosystem and its aboveground tree, tree root, forest floor, and mineral soil components continuously increased with stand ageing, whereas the C stock of the understory showed a declining trend and contributed little to the total site C pool.

Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models

In this study, the performance of nine widely used and accessible crop growth simulation models (APES-ACE, CROPSYST, DAISY, DSSAT-CERES, FASSET, HERMES, MONICA, STICS and WOFOST) was compared during 44 growing seasons of spring barley (Hordeum vulgare L.) at seven sites in Northern and Central Europe. The aims of this model comparison were to examine how different process-based crop models perform at multiple sites across Europe when applied with minimal information for model calibration of spring barley at field scale, whether individual models perform better than the multi-model mean, and what the uncertainty ranges are in simulated grain yields. The reasons for differences among the models and how results for barley compare to winter wheat are discussed.Regarding yield estimation, best performing based on the root mean square error (RMSE) were models HERMES, MONICA and WOFOST with lowest values of 1124, 1282 and 1325 (kgha−1), respectively. Applying the index of agreement (IA), models WOFOST, DAISY and HERMES scored best having highest values (0.632, 0.631 and 0.585, respectively). Most models systematically underestimated yields, whereby CROPSYST showed the highest deviation as indicated by the mean bias error (MBE) (−1159kgha−1). While the wide range of simulated yields across all sites and years shows the high uncertainties in model estimates with only restricted calibration, mean predictions from the nine models agreed well with observations. Results of this paper also show that models that were more accurate in predicting phenology were not necessarily the ones better estimating grain yields. Total above-ground biomass estimates often did not follow the patterns of grain yield estimates and, thus, harvest indices were also different. Estimates of soil moisture dynamics varied greatly.In comparison, even though the growing cycle for winter wheat is several months longer than for spring barley, using RMSE and IA as indicators, models performed slightly, but not significantly, better in predicting wheat yields. Errors in reproducing crop phenology were similar, which in conjunction with the shorter growth cycle of barley has higher effects on accuracy in yield prediction.