A hierarchical dataset of vegetative and reproductive growth in apple tree organs under conventional and non-limited carbon resources

A monitoring of apple fruit, shoot and trunk growth was performed on 15 trees, equally split according to three treatments, which determined heavily contrasting carbon assimilate availability: unmanipulated trees (FRU), thinned trees (THI) and defruited trees (DEF). Several variables describe the vegetative growth on FRU and DEF trees (shoot length, base diameter, number of fruits on shoot, and height, diameter, pruning intensity and number of fruits of the branch carrying the shoot; trunk circumference), as well as the fruit growth on FRU and THI trees (3 fruit diameters). Additional measurements from ancillary shoots (apical diameter, number of leaves, leaf dry weight, stem dry weight, fresh mass, volume) and fruits (3 diameters, dry weight) from trees undergoing the same treatments, provide a more complete (destructive) characterization of organs growth, thanks to several measurements performed across the growing season. Organs are provided with categorical variables indicating the treatment, tree, canopy height, orientation (for both shoots and fruit), as well as branch and shoot identifiers, so that hierarchical modeling of the dataset can be performed. The dataset is completed with dates and day of the year of the measurements and the accumulated growing degree days from full bloom. Data can be used to calculate apple tree absolute and relative growth rates, maximum potential growth rates, as well as shoot growth responses to thinning and pruning. The dataset can also be used to calibrate allometric relationships, estimate structural apple tree growth parameters and their variabilit

Delineation of individual tree crowns from ALS and hyperspectral data: A comparison among four methods

In this paper four different delineation methods based on airborne laser scanning (ALS) and hyperspectral data are compared over a forest area in the Italian Alps. The comparison was carried out in terms of detected trees, while the ALS based methods are compared also in terms of attributes estimated (e.g. height). From the experimental results emerged that ALS methods outperformed hyperspectral one in terms of tree detection rate in two of three cases. The best results were achieved with a method based on region growing on an ALS image, and by one based on clustering of raw ALS point cloud. Regarding the estimates of the tree attributes all the ALS methods provided good results with very high accuracies when considering only big trees

Activity budget and movement patterns of Brown Swiss and Alpine Grey lactating cows during summer grazing in alpine pastures

We used GPS tracking to monitor the grazing patterns of Brown Swiss and Alpine grey lactating cows on an alpine summer pasture (2038 m a.s.l.; SD = 146) in the Dolomites. The pasture (171 ha) was managed with a continuous grazing system (0.52 LU/ha) with morning and evening milking in the barn, guided grazing during the ‘day’, and free grazing at ‘night’. GPS positions were collected from 8 Brown Swiss multiparous and 9 Alpine Grey (4 primiparous and 5 multiparous) cows every two minutes. We inferred behaviours (grazing, resting, walking) from movement metrics, activity sensors and direct behavioural observations. After excluding milking periods, the cows grazed for 8 h/d, rested 10–11 h/d, and walked for 1.5/d. Grazing extended into late evening after milking, and resting prevailed throughout the ‘night’ until the morning milking. When grazing and resting, cows mainly used grasslands as the preferred habitat, but forest and sparse shrub were also used remarkably without consistent negative or positive selection. The pasture use was highly heterogeneous, with higher animal loads close to the barn, especially at night, and in areas with gentler slopes. Alpine Grey primiparous cows were less limited by slope and distance from the barn in their movement but were more selective in habitat use than multiparous cows. Differences between multiparous cows of the two breeds were less marked. Further studies should help understand the internal and external drivers of cattle grazing patterns to devise management practices combining animals’ productivity and welfare with the conservation of the grassland ecosystem services

Many-core applications to online track reconstruction in HEP experiments

Interest in parallel architectures applied to real time selections is growing in High Energy Physics (HEP) experiments. In this paper we describe performance measurements of Graphic Processing Units (GPUs) and Intel Many Integrated Core architecture (MIC) when applied to a typical HEP online task: the selection of events based on the trajectories of charged particles. We use as benchmark a scaled-up version of the algorithm used at CDF experiment at Tevatron for online track reconstruction - the SVT algorithm - as a realistic test-case for low-latency trigger systems using new computing architectures for LHC experiment. We examine the complexity/performance trade-off in porting existing serial algorithms to many-core devices. Measurements of both data processing and data transfer latency are shown, considering different I/O strategies to/from the parallel devices.Comment: Proceedings for the 20th International Conference on Computing in High Energy and Nuclear Physics (CHEP); missing acks adde

A user-friendly forest model with a multiplicative mathematical structure: a Bayesian approach to calibration

Forest models are being increasingly used to study ecosystem functioning, through the reproduction of carbon fluxes and productivity in very different forests all over the world. Over the last two decades, the need for simple and “easy to use” models for practical applications, characterized by few parameters and equations, has become clear, and some have been developed for this purpose. These models aim to represent the main drivers underlying forest ecosystem processes while being applicable to the widest possible range of forest ecosystems. Recently, it has also become clear that model performance should not be assessed only in terms of accuracy of estimations and predictions, but also in terms of estimates of model uncertainties. Therefore, the Bayesian approach has increasingly been applied to calibrate forest models, with the aim of estimating the uncertainty of their results, and of comparing their performances. Some forest models, considered to be user-friendly, rely on a multiplicative or quasimultiplicative mathematical structure, which is known to cause problems during the calibration process, mainly due to high correlations between parameters. In a Bayesian framework using a Markov Chain Monte Carlo sampling this is likely to impair the reaching of a proper convergence of the chains and the sampling from the correct posterior distribution. Here we show two methods to reach proper convergence when using a forest model with a multiplicative structure, applying different algorithms with different number of iterations during the Markov Chain Monte Carlo or a two-steps calibration. The results showed that recently proposed algorithms for adaptive calibration do not confer a clear advantage over the Metropolis–Hastings Random Walk algorithm for the forest model used here. Moreover, the calibration remains time consuming and mathematically difficult, so advantages of using a fast and user-friendly model can be lost due to the calibration process that is needed to obtain reliable results

MuSCA: A multi-scale source-sink carbon allocation model to explore carbon allocation in plants. An application to static apple tree structures

Background and aims: Carbon allocation in plants is usually represented at a topological scale, specific to each model. This makes the results obtained with different models, and the impact of their scales of representation, difficult to compare. In this study, we developed a multi-scale carbon allocation model (MuSCA) that allows the use of different, user-defined, topological scales of a plant, and assessment of the impact of each spatial scale on simulated results and computation time. Methods: Model multi-scale consistency and behaviour were tested on three realistic apple tree structures. Carbon allocation was computed at five scales, spanning from the metamer (the finest scale, used as a reference) up to first-order branches, and for different values of a sap friction coefficient. Fruit dry mass increments were compared across spatial scales and with field data. Key Results: The model was able to represent effects of competition for carbon assimilates on fruit growth. Intermediate friction parameter values provided results that best fitted field data. Fruit growth simulated at the metamer scale differed of ~1 % in respect to results obtained at growth unit scale and up to 60 % in respect to first order branch and fruiting unit scales. Generally, the coarser the spatial scale the more predicted fruit growth diverged from the reference. Coherence in fruit growth across scales was also differentially impacted, depending on the tree structure considered. Decreasing the topological resolution reduced computation time by up to four orders of magnitude. Conclusions: MuSCA revealed that the topological scale has a major influence on the simulation of carbon allocation. This suggests that the scale should be a factor that is carefully evaluated when using a carbon allocation model, or when comparing results produced by different models. Finally, with MuSCA, trade-off between computation time and prediction accuracy can be evaluated by changing topological scales

Continuous monitoring of tree responses to climate change for smart forestry: a cybernetic web of trees

6openBothTrees are long-lived organisms that contribute to forest development over centuries and beyond. However, trees are vulnerable to increasing natural and anthropic disturbances. Spatially distributed, continuous data are required to predict mortality risk and impact on the fate of forest ecosystems. In order to enable monitoring over sensitive and often remote forest areas that cannot be patrolled regularly, early warning tools/platforms of mortality risk need to be established across regions. Although remote sensing tools are good at detecting change once it has occurred, early warning tools require ecophysiological information that is more easily collected from single trees on the ground. Here, we discuss the requirements for developing and implementing such a treebased platform to collect and transmit ecophysiological forest observations and environmental measurements from representative forest sites, where the goals are to identify and to monitor ecological tipping points for rapid forest decline. Long-term monitoring of forest research plots will contribute to better understanding of disturbance and the conditions that precede it. International networks of these sites will provide a regional view of susceptibility and impacts and would play an important role in ground-truthing remotely sensed data.openTognetti, Roberto; Valentini, Riccardo; Belelli Marchesini, Luca; Gianelle, Damiano; Panzacchi, Pietro; Marshall, John D.Tognetti, R.; Valentini, R.; Belelli Marchesini, L.; Gianelle, D.; Panzacchi, P.; Marshall, J.D

Montane ecosystem productivity responds more to global circulation patterns than climatic trends

Ajuts: we thank the support of KIT IMK-IFU, the University of Wisconsin sabbatical leave program, and the Helmholtz Society/MICMOR fellowship program. We also thank the DWD for German weather data. Phenology data were provided by the members of the PEP725 project. We are indebted to the providers and funders of the eddy covariance flux tower observations, the FLUXNET program, and its database. The sites in Graswang, Rottenbuch and Fendt belong to the TERENO and ICOS-ecosystems networks, funded by Bundesministerium für Bildung und Forschung(BMBF)and the Helmholtz Association. The modeling study of SOLVEG was partially supported by Grant-in-Aid for Scientific Research, no. 21120512, provided by the Japan Society for the Promotion of Science(JSPS). This study was financially supported by the Austrian National Science Fund(FWF) under contract P26425 to GW.Regional ecosystem productivity is highly sensitive to inter-annual climate variability, both within and outside the primary carbon uptake period. However, Earth system models lack sufficient spatial scales and ecosystem processes to resolve how these processes may change in a warming climate. Here, we show, how for the European Alps, mid-latitude Atlantic ocean winter circulation anomalies drive high-altitude summer forest and grassland productivity, through feedbacks among orographic wind circulation patterns, snowfall, winter and spring temperatures, and vegetation activity. Therefore, to understand future global climate change influence to regional ecosystem productivity, Earth systems models need to focus on improvements towards topographic downscaling of changes in regional atmospheric circulation patterns and to lagged responses in vegetation dynamics to non-growing season climate anomalies

Arachidonic Acid/ppara Enhancement of Ca2+-Regulated Exocytosis in Antral Mucous Cells of Guinea Pig

N is known to be the most limiting element for vegetation growth in temperate and boreal forests. The expected increases in global temperature are predicted to accelerate N mineralization, therefore incrementing N availability in the soil and affecting the soil C cycle as well. While there is an abundance of C data collected to fulfill the requirements for national GHG accounting, more limited information is available for soil N accumulation and storage in relation to forest categories and altitudinal gradients. The data collected by the second Italian National Forest Inventory, spanning a wide range of temperature and precipitation values (10° latitudinal range), represented a unique opportunity to calculate N content and C/N ratio of the different soil layers to a depth of 30 cm. Boosted Regression Tree (BRT) models were applied to investigate the main determinants of soil N distribution and C/N ratio. Forest category was shown to be the main explanatory factor of soil N variability in seven out of eight models, both for forest floor and mineral soil layers. Moreover latitude explained a larger share of variability than single climate variables. BRT models explained, on average, the 49% of the data variability, with the remaining fraction likely due to soil-related variables that were unaccounted for. Accurate estimations of N pools and their determinants in a climate change perspective are consequently required to predict the potential impact of their degradation on forest soil N pools