| Home | [1–10] << 11 12 13 14 15 16 17 18 19 20 >> |
|
Cassardo, C., & Andreoli, V. (2019). On the Representativeness of UTOPIA Land Surface Model for Creating a Database of Surface Layer, Vegetation and Soil Variables in Piedmont Vineyards, Italy. Applied Sciences-Basel, 9(18), 3880.
Abstract: The main aim of the paper is to show how, and how many, simulations carried out using the Land Surface Model UTOPIA (University of TOrino model of land Process Interaction with Atmosphere) are representative of the micro-meteorological conditions and exchange processes at the atmosphere/biosphere interface, with a particular focus on heat and hydrologic transfers, over an area of the Piemonte (Piedmont) region, NW Italy, which is characterized by the presence of many vineyards. Another equally important aim is to understand how much the quality of the simulation outputs was influenced by the input data, whose measurements are often unavailable for long periods over country areas at an hourly basis. Three types of forcing data were used: observations from an experimental campaign carried out during the 2008, 2009, and 2010 vegetative seasons in three vineyards, and values extracted from the freely available Global Land Data Assimilation System (GLDAS, versions 2.0 and 2.1). Since GLDAS also contains the outputs of the simulations performed using the Land Surface Model NOAH, an additional intercomparison between the two models, UTOPIA and NOAH, both driven by the same GLDAS datasets, was performed. The intercomparisons were performed on the following micro-meteorological variables: net radiation, sensible and latent turbulent heat fluxes, and temperature and humidity of soil. The results of this study indicate that the methodology of employing land surface models driven by a gridded database to evaluate variables of micro-meteorological and agronomic interest in the absence of observations is suitable and gives satisfactory results, with uncertainties comparable to measurement errors, thus, allowing us to also evaluate some time trends. The comparison between GLDAS2.0 and GLDAS2.1 indicates that the latter generally produces simulation outputs more similar to the observations than the former, using both UTOPIA and NOAH models.
|
|
Francioni, M., D’Ottavio, P., Lai, R., Trozzo, L., Budimir, K., Foresi, L., et al. (2019). Seasonal Soil Respiration Dynamics and Carbon-Stock Variations in Mountain Permanent Grasslands Compared to Arable Lands. Agriculture-Basel, 9(8), 165.
Abstract: Permanent grasslands provide a wide array of ecosystem services. Despite this, few studies have investigated grassland carbon (C) dynamics, and especially those related to the effects of land-use changes. This study aimed to determine whether the land-use change from permanent grassland to arable lands resulted in variations in the soil C stock, and whether such variations were due to increased soil respiration or to management practices. To address this, seasonal variations of soil respiration, sensitivity of soil respiration to soil temperature (Q(10)), and soil C stock variations generated by land-use changes were analyzed in a temperate mountain area of central Italy. The comparisons were performed for a permanent grassland and two adjacent fields, one cultivated with lentil and the other with emmer, during the 2015 crop year. Soil respiration and its heterotrophic component showed different spatial and temporal dynamics. Annual cumulative soil respiration rates were 6.05, 5.05 and 3.99 t C ha(-1) year(-1) for grassland, lentil and emmer, respectively. Both soil respiration and heterotrophic soil respiration were positively correlated with soil temperature at 10 cm depth. Derived Q(10) values were from 2.23 to 6.05 for soil respiration, and from 1.82 to 4.06 for heterotrophic respiration. Soil C stock at over 0.2 m in depth was 93.56, 48.74 and 46.80 t C ha(-1) for grassland, lentil and emmer, respectively. The land-use changes from permanent grassland to arable land lead to depletion in terms of the soil C stock due to water soil erosion. A more general evaluation appears necessary to determine the multiple effects of this land-use change at the landscape scale.
Keywords: ecosystem services; C stock; CO2; GHG; land use change; Q(10); temperature; vegetation; patterns; emissions; climate
|
|
De Swaef, T., Bellocchi, G., Aper, J., Lootens, P., & Roldan-Ruiz, I. (2019). Use of identifiability analysis in designing phenotyping experiments for modelling forage production and quality. J. Experim. Bot., 70(9), 2587–2604.
Abstract: Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demonstrate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model. The Pasture Simulation model (PaSim) was used to demonstrate the effectiveness of practical identifiability analysis in designing experiments and measurement protocols within phe-notyping experiments with perennial ryegrass. Virtual experiments were designed combining three factors: frequency of measurements, duration of the experiment. and location of trials. Our results demonstrate that (i) PaSim provides sufficient detail in terms of simulating biomass yield and quality of perennial ryegrass for use in breeding, (ii) typical breeding trials are insufficient to parameterize all influential parameters, (iii) the frequency of measurements is more important than the number of growing seasons to improve the identifiability of PaSim parameters, and (iv) identifiability analysis provides a sound approach for optimizing the design of multi-location trials. Practical identifiability analysis can play an important role in ensuring proper exploitation of phenotypic data and cost-effective multi-location experimental designs. Considering the growing importance of simulation models, this study supports the design of experiments and measurement protocols in the phenotyping networks that have recently been organized.
|
|
Sándor, R., Barcza, Z., Acutis, M., Doro, L., Hidy, D., Köchy, M., et al. (2016). Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance. European Journal of Agronomy, .
Abstract: • We simulate biomass, soil water content (SWC) and temperature (ST) in grasslands. • We compare nine models to the multi-model median (MMM) at nine sites. • With model calibration, we obtain satisfactory estimates of ST, less of SWC and biomass. • We observe discrepancies across models in the simulation of grassland processes. • We improve performance with multi-model approach. This study presents results from a major grassland model intercomparison exercise, and highlights the main challenges faced in the implementation of a multi-model ensemble prediction system in grasslands. Nine, independently developed simulation models linking climate, soil, vegetation and management to grassland biogeochemical cycles and production were compared in a simulation of soil water content (SWC) and soil temperature (ST) in the topsoil, and of biomass production. The results were assessed against SWC and ST data from five observational grassland sites representing a range of conditions – Grillenburg in Germany, Laqueuille in France with both extensive and intensive management, Monte Bondone in Italy and Oensingen in Switzerland – and against yield measurements from the same sites and other experimental grassland sites in Europe and Israel. We present a comparison of model estimates from individual models to the multi-model ensemble (represented by multi-model median: MMM). With calibration (seven out of nine models), the performances were acceptable for weekly-aggregated ST (R² > 0.7 with individual models and >0.8–0.9 with MMM), but less satisfactory with SWC (R² < 0.6 with individual models and < ∼ 0.5 with MMM) and biomass (R² < ∼0.3 with both individual models and MMM). With individual models, maximum biases of about −5 °C for ST, −0.3 m3 m−3 for SWC and 360 g DM m−2 for yield, as well as negative modelling efficiencies and some high relative root mean square errors indicate low model performance, especially for biomass. We also found substantial discrepancies across different models, indicating considerable uncertainties regarding the simulation of grassland processes. The multi-model approach allowed for improved performance, but further progress is strongly needed in the way models represent processes in managed grassland systems.
Keywords: Biomass; Grasslands; Modelling; Multi-model ensemble; Soil processes
Area: LiveM
|