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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.
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Kersebaum, K. C., Boote, K. J., Jorgenson, J. S., Nendel, C., Bindi, M., Frühauf, C., et al. (2015). Analysis and classification of data sets for calibration and validation of agro-ecosystem models. Env. Model. Softw., 72, 402–417.
Abstract: Experimental field data are used at different levels of complexity to calibrate, validate and improve agroecosystem models to enhance their reliability for regional impact assessment. A methodological framework and software are presented to evaluate and classify data sets into four classes regarding their suitability for different modelling purposes. Weighting of inputs and variables for testing was set from the aspect of crop modelling. The software allows users to adjust weights according to their specific requirements. Background information is given for the variables with respect to their relevance for modelling and possible uncertainties. Examples are given for data sets of the different classes. The framework helps to assemble high quality data bases, to select data from data bases according to modellers requirements and gives guidelines to experimentalists for experimental design and decide on the most effective measurements to improve the usefulness of their data for modelling, statistical analysis and data assimilation. (C) 2015 Elsevier Ltd. All rights reserved.
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Lai, R., Arca, P., Lagomarsino, A., Cappai, C., Seddaiu, G., Demurtas, C. E., et al. (2017). Manure fertilization increases soil respiration and creates a negative carbon budget in a Mediterranean maize (Zea mays L.)-based cropping system. Catena, 151, 202–212.
Abstract: Agronomic research is important to identify suitable options for improving soil carbon (C) sequestration and reducing soil CO2 emissions. Therefore, the objectives of this study were i) to analyse the on-farm effects of different nitrogen fertilization sources on soil respiration, ii) to explore the effect of fertilization on soil respiration sensitivity to soil temperature (T) and iii) to assess the effect of the different fertilization regimes on the soil C balance. We hypothesized that i) the soil CO2 emission dynamics in Mediterranean irrigated cropping systems were mainly affected by fertilization management and T and ii) fertilization affected the soil C budget via different C inputs and CO2 efflux. Four fertilization systems (farmyard manure, cattle slurry, cattle slurry + mineral, and mineral) were compared in a double-crop rotation based on silage maize (Zea mays L.) and a mixture of Italian ryegrass (Lolium multiflorum Lam.) and oats (Avena sativa L.). The research was performed in the dairy district of Arborea, in the coastal zone of Sardinia (Italy), from May 2011 to May 2012. The soil was a Psammentic Palexeralfs with a sandy texture (940 g sand kg− 1). The soil total respiration (SR), heterotrophic respiration (Rh), T and soil water content (SWC) were simultaneously measured in situ. The soil C balance was computed considering the Rh C losses and the soil C inputs from fertilizer and crop residues. The results showed that the maximum soil CO2 emission rates soon after the application of organic fertilizer reached values up to 12 μmol m− 2 s− 1. On average, the manure fertilizer showed significantly higher CO2 emissions, which resulted in a negative annual C balance (− 2.9 t ha− 1). T also affected the soil respiration temporal dynamics during the summer, consistently with results obtained in other temperate climatic regions that are characterized by wet summers and contrary to results from rainfed Mediterranean systems where the summer SR and Rh are constrained by the low SWC. The sensitivity of soil respiration to temperature significantly increased with C input from fertilizer. In conclusion, this research supported the hypotheses tested. Furthermore, the results indicated that i) soil CO2 efflux was significantly affected by fertilization management and T, and ii) fertilization with manure increased the soil respiration and resulted in a significantly negative soil C budget. This latter finding could be primarily explained by a reduction in productivity and, consequently, in crop residue with organic fertilization alone as compared to mineral, by the favourable SWC and T for mineralization, and by the sandy soil texture, which hindered the formation of macroaggregates and hence soil C stabilization, making fertilizer organic inputs highly susceptible to mineralization.
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Lai, R., Arca, P., Lagomarsino, A., Cappai, C., Seddaiu, G., Demurtas, C. E., et al. (2017). Manure fertilization increases soil respiration and creates a negative carbon budget in a Mediterranean maize (Zea mays L.)-based cropping system. Catena, 151, 202–212.
Abstract: Agronomic research is important to identify suitable options for improving soil carbon (C) sequestration and reducing soil CO2 emissions. Therefore, the objectives of this study were i) to analyse the on-farm effects of different nitrogen fertilization sources on soil respiration, ii) to explore the effect of fertilization on soil respiration sensitivity to soil temperature (T) and iii) to assess the effect of the different fertilization regimes on the soil C balance. We hypothesized that i) the soil CO2 emission dynamics in Mediterranean irrigated cropping systems were mainly affected by fertilization management and T and ii) fertilization affected the soil C budget via different C inputs and CO2 efflux. Four fertilization systems (farmyard manure, cattle slurry, cattle slurry + mineral, and mineral) were compared in a double-crop rotation based on silage maize (Zea mays L) and a mixture of Italian ryegrass (Lolium multiflorum Lam.) and oats (Avena sativa L). The research was performed in the dairy district of Arborea, in the coastal zone of Sardinia (Italy), from May 2011 to May 2012. The soil was a Psammentic Palexeralfs with a sandy texture (940 g sand kg(-1)). The soil total respiration (SR), heterotrophic respiration (Rh), T and soil water content (SWC) were simultaneously measured in situ. The soil C balance was computed considering the Rh C losses and the soil C inputs from fertilizer and crop residues. The results showed that the maximum soil CO2 emission rates soon after the application of organic fertilizer reached values up to 121,1111 1 111(-2) s(-1). On average, the manure fertilizer showed significantly higher CO2 emissions, which resulted in a negative annual C balance (-2.9 t ha(-1)). T also affected the soil respiration temporal dynamics during the summer, consistently with results obtained in other temperate climatic regions that are characterized by wet summers and contrary to results from rainfed Mediterranean systems where the summer SR and Rh are constrained by the low SWC. The sensitivity of soil respiration to temperature significantly increased with C input from fertilizer. In conclusion, this research supported the hypotheses tested. Furthermore, the results indicated that i) soil CO2 efflux was significantly affected by fertilization management and T, and ii) fertilization with manure increased the soil respiration and resulted in a significantly negative soil C budget. This latter finding could be primarily explained by a reduction in productivity and, consequently, in crop residue with organic fertilization alone as compared to mineral, by the favourable SWC and T for mineralization, and by the sandy soil texture, which hindered the formation of macroaggregates and hence soil C stabilization, making fertilizer organic inputs highly susceptible to mineralization. (C) 2016 Elsevier B.V. All rights reserved.
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Mansouri, M., Dumont, B., Leemans, V., & Destain, M. - F. (2014). Bayesian methods for predicting LAI and soil water content. Precision Agric., 15(2), 184–201.
Abstract: LAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was assumed to progress according to a Bayesian probabilistic state space model, in which real values of LAI and soil water content were daily introduced in order to correct the model trajectory and reach better future evolution. The chosen crop model was mini STICS which can reduce the computing and execution times while ensuring the robustness of data processing and estimation. To predict simultaneously state variables and model parameters in this non-linear environment, three techniques were used: extended Kalman filtering (EKF), particle filtering (PF), and variational filtering (VF). The significantly improved performance of the VF method when compared to EKF and PF is demonstrated. The variational filter has a low computational complexity and the convergence speed of states and parameters estimation can be adjusted independently. Detailed case studies demonstrated that the root mean square error of the three estimated states (LAI and soil water content of two soil layers) was smaller and that the convergence of all considered parameters was ensured when using VF. Assimilating measurements in a crop model allows accurate prediction of LAI and soil water content at a local scale. As these biophysical properties are key parameters in the crop-plant system characterization, the system has the potential to be used in precision farming to aid farmers and decision makers in developing strategies for site-specific management of inputs, such as fertilizers and water irrigation.
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