System simulation by SEMoLa

SEMoLa is a platform, developed at DISA since 1992, for system knowledge integration and modelling. It allows to create computer models for dynamic systems and to manage different types of information. It is formed by several parts, each dealing with different forms of knowledge, in an integrated way: a graphical user interface (GUI), a declarative language for modelling, a set of commands with a procedural scripting language, a specific editor with code highlighting (SemEdit), a visual modelling application (SemDraw), a data base management system (SemData), plotting data capabilities (SemPlot), a raster maps management system (SemGrid), a large library of random number generators for uncertainty analysis, support for fuzzy logic expert systems, a neural networks builder and various statistical tools (basic statistics, multiple and non-linear regression, moving statistics, etc.). The core part of the platform is the declarative modelling language (SEMoLa; simple, easy to use, modelling language). It relies on System Dynamics principles and uses an integrated view to represent dynamic systems through different modelling approaches (state/individual-based, continuous/discrete, deterministic/stochastic) without requiring specific programming skills. SEMoLa language is based on a ontology closer to human reasoning rather than computer logic and constitutes also a paradigm for knowledge management. SEMoLa platform permits to simplify the routinely tasks of creating, debugging, evaluating and deploying computer simulation models but also to create user libraries of script commands. It is able to communicate with other frameworks exchanging - with standard formats - data, modules and model components

A methodology for evaluating land suitability for medicinal plants at a regional level

Before introducing a new crop in an area, such as medicinal plant species, crop-land suitability analysis is a prerequisite to achieve an optimum exploitation of the available land resources for a sustainable agricultural production. To evaluate the land suitability it is important to take into account the habitats of the plant species. Moreover, agronomic, logistic and product quality aspects have to be considered. The importance of these aspects changes according to the stakeholders: the local government is more involved in supporting environmental suitability and production sustainability, farmers need areas which satisfy agronomic and logistic requirements, while industry is interested in the quality of production. A methodology was developed and implemented to create suitability maps for medicinal plants. Because of the generally limited information about medicinal plant adaptation, a simple methodology, based on a priori information has been developed, based on three different criteria: i) environmental suitability (point of view of local government); ii) agronomic, productivity and logistic suitability (point of view of the farmer); iii) quality suitability (point of view of industry). For each of the three criteria, a specific macro-indicator, based on land characteristics, was calculated using membership functions. Here, a methodology to create maps for the introduction of such species was developed and implemented. This methodology can be repeated by command scripts in an easy-to-use freeware GIS. The structure of the evaluation model can be easily adapted to consider more detailed land information like climate and soil. The methodology (implemented by scripts in a freeware GIS), can be easily repeated and adapted for other situations

A software application for mapping livestock waste odour dispersion

In developed Countries, coexistence of livestock production and urban settlements is a source of problematic interactions that are regulated by specific legislation, often requiring the evaluation of the potential environmental impact of livestock odour emissions. For this purpose, dispersion models are powerful tools that can be classified as dynamic (Eulerian and Lagrangian) or static (Gaussian). The latter, while presenting some limitations in condition of wind calm and complex orography, are widely adopted for their ease of use. OdiGauss is a free multilingual software application allowing to estimate odour dispersion from multiple point sources and to generate the related maps. Dispersion is calculated according to a Gaussian approach, as a function of wind speed and direction, precipitation, temperature, and solar radiation. OdiGauss incorporates a model of odour emissions from poultry farms (EmiFarm) which makes predictions based on manure production and management. Two case studies of software application on real poultry and swine farms are presented

MiniCSS: a software application to optimize crop irrigation and nitrogen fertilization strategies

Water resources are limited and agricultural input costs are steadily increasing; moreover, precipitation seems to be decreasing in amount or, at least, received in a more irregular manner. Correctly deciding irrigation and fertilization amount and time implies to simultaneously consider phenological and nutritional crop status, weather pattern during the irrigation season and taking into account the economic and energy budgets. To treat all these complexities, the use of crop simulation models is particularly indicated. Models are strictly connected to academic and research contexts and have not wide-melted farmers and agricultural technicians, despite they are strongly encouraged to optimize the use of water and fertilizer. MiniCSS, a software for the optimization of irrigation and nitrogen (N) fertilization by simulation is here presented. Its primary aim is to be easy to use, thanks to a reduced number of input parameters and a user-friendly dialog window. MiniCSS can perform i) annual/multiannual simulations, ii) simulation experiments by varying irrigation and fertilization intensity, iii) calibration of the model parameters and iv) optimization of other cultural practices. Textual and graphical results are reported as daily values, annual averages, cumulative probability and dose-response curves

The Performance and Potentiality of Monoecious Hemp (Cannabis sativa L.) Cultivars as a Multipurpose Crop

Given the growing interest in multipurpose hemp crop, eight monoecious cultivars were compared in a two-year trial for quantitative and qualitative yield in a Mediterranean environment characterized by a temperate and humid climate with hot summers. All hemp cultivars were evaluated for yield potential of (i) seed plus stem at seed maturity, and (ii) essential oil yield from inflorescences harvested at full flowering. The second goal was set to test the ability of cultivars to supply new seeds after the removal of inflorescence at full flowering. Among the cultivars, Fedora obtained the best results for seed (0.79 and 0.52 t ha1) and vegetable oil yield (0.17 and 0.09 t ha1) normally and with inflorescence removed plants, respectively. Futura, conversely, showed the best results for inflorescence (3.0 t ha1), essential oil (9 L ha1), and stem yield at seed maturity (8.34 t ha1), as means across the two years. The cultivars studied generally reached the grain-filling stage during a period that was drier and warmer than the average of the same multi-year period, and this negatively affected seed quality. The oil fatty acid composition was mainly composed of polyunsaturated fatty acids (75% on average) and not affected by the cultivar. In conclusion, although the hemp grower should always clearly know the main production objective of the crop, the monoecious cultivars available today allow a multipurpose use of hemp crop, improving the sustainability of the cultivation activity

Effect of meteorological and agronomic factors on maize grain contamination by fumonisin

Fumonisins are toxic secondary metabolites produced by fungi such as F.verticilloides. Maize is commonly colonized by several spoilage fungi both in pre- and post-harvest conditions. Field infection prevention is the best solution to contain contamination, using practices aimed at restricting plant stress and limiting the propagation of the disease. This work is focused on understanding the effect of environmental factors on the production of fumonisins in Friuli Venezia Giulia (NE Italy) on maize crops. The analysis has been performed on a dataset covering a period of 14 years (from 2000 to 2013), recording fumonisins contamination and daily meteorological data (air temperature, RH, Rain, Wind speed) for 13 different drying plants and for three different harvest times (early, medium and late). The drying plants collect grain production from an area of about 70.000-100.000 ha. Data were analyzed by full factorial ANOVA and a multiple regression approach was performed using STATA and SEMoLa software. ANOVA test pointed out a significant effect of factors \u201cyear\u201d and \u201charvest time\u201d (p<0.01) for fumonisin content. Instead, location had no significant effect. The best regression model (R2=0. 65, 2... observation) detected a significant correlation between fumonisin concentration and meteorological data in the period from 15th to 31st July. High fumonisin contents were positively correlated with daily thermal excursion, minimum temperature and wet conditions in this period. Silk drying and harvest time resulted as the key factors to contain and study fumonisins contamination in maize. Results will be used to implement a more complex dynamic model

Agro-energy supply chain planning: a procedure to evaluate economic, energy and environmental sustainability

The increasing demand for energy and expected shortage in the medium term, solicit innovative energy strategies to fulfill the increasing gap between demand-supply. For this purpose it is important to evaluate the potential supply of the energy crops and finding the areas of EU where it is most convenient. This paper proposes an agro-energy supply chain approach to planning the biofuel supply chain at a regional level. The proposed methodology is the result of an interdisciplinary team work and is aimed to evaluate the potential supply of land for the energy production and the efficiency of the processing plants considering simultaneously economic, energy and environmental targets. The crop simulation, on the basis of this approach, takes into account environmental and agricultural variables (soil, climate, crop, agronomic technique) that affect yields, energy and economic costs of the agricultural phase. The use of the Dijkstra's algorithm allows minimizing the biomass transport path from farm to collecting points and the processing plant, to reduce both the transport cost and the energy consumption. Finally, a global sustainability index (ACSI, Agro-energy Chain Sustainability Index) is computed combining economic, energy and environmental aspects to evaluate the sustainability of the Agroenergy supply chain (AESC) on the territory. The empirical part consists in a pilot study applied to the whole plain of Friuli Venezia Giulia (FVG) a region situated in the North-Eastern part of Italy covering about 161,300 ha. The simulation has been applied to the maize cultivation using three different technologies (different levels of irrigation and nitrogen fertilization: low, medium and high input). The higher input technologies allow to achieve higher crop yields, but affect negatively both the economic and energy balances. Low input levels provides, on the average, the most favourable energy and economic balances. ACSI indicates that low inputs levels ensure a more widespread sustainability of the agro-energy chain in the region. High ACSI values for high input levels are observed only for areas with very high yields or near the processing plant