Figure 2 - uploaded by Jan Feyen
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Schematic presentation of the modules in WAVE. Full line arrows represent obligatory 'usesrelations' , dashed lines are optional.
Source publication
La expansión de las actividades humanas tiene gran impacto en el medio ambiente, generando cambios que deben ser contrarrestados, como el cambio climático y la dispersión de contaminantes de la industria y la agricultura; a través de estrategias que permitan la conservación del suelo y de las reservas de agua. La implementación de estrategias con e...
Contexts in source publication
Context 1
... in reading and adap- ting computer codes (WAVE is written in Fortran) is however absolutely neces- sary if it's neccesary to do this. Figure 2 presents the different mo- dules and the arrows indicate the 'uses- relationship' among them. It is clear that no module can be executed without the water module. ...
Context 2
... fact, the Richards equation com- bines the Darcian flow equation with the water mass conservation law (see con- cept of water balance in figure 2). This flow equation is solved numerically (δh/δz becomes dh/dz, or within a small soil compartment and small time step, flow occurs under stationary conditions). ...
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Citations
... The WAVE model uses finite difference techniques for the solution of the physical transport equations [TIMMERMAN, FEYEN 2003].The modules currently are available in the WAVE model simulate with the following soil processes: the flow of water, transport of non-reactive solutes, the heat transport, the crop growth and the movement and transformations of nitrogen. It can deal with different soil horizons which are divided into equidistant soil compartments. ...
Applying models to interpret soil, water and plant relationships under different conditions enable us to study different management scenarios and then to determine the optimum option. The aim of this study was using Water and Agrochemicals in the soil, crop and Vadose Environment (WAVE) model to predict water content, nitrogen balance and its components over a corn crop season under both conventional tillage (CT) and direct seeding into mulch (DSM). In this study a corn crop was cultivated at the Irstea experimental station in Montpellier, France under both CT and DSM. Model input data were weather data, nitrogen content in both the soil and mulch at the beginning of the season, the amounts and the dates of irrigation and nitrogen application. The results show an appropriate agreement between measured and model simulations (nRMSE < 10%). Using model outputs, nitrogen balance and its components were compared with measured data in both systems. The amount of N leaching in validation period were 10 and 8 kgha-1 in CT and DSM plots, respectively; therefore, these results showed better performance of DSM in comparison with CT. Simulated nitrogen leaching from CT and DSM can help us to assess groundwater pollution risk caused by these two systems.
Current farming practices in Flanders, Belgium use large amounts of inorganic fertilizers to attain high yield and quality. Especially in open field vegetable production the amount of applied nitrogen fertilizer exceeds the crop demand all too often. This practice in addition to the mineralization of soil organic matter and the use of organic fertilizer results in nitrate concentrations in ground and surface water that are frequently above the thresholds set by the European Union. In order to understand the impact of these legislative norms for farmers and in search of solutions for the growers, this work proposes a crop-soil-climate interaction model that enables studying the impact and interaction of weather variability and different nitrogen fertilization schemes on yield and environment for open field cauliflower and leek production systems.
The different fertilizer scenario simulations gave a clear indication of production limits and the model allowed the estimation of plausible production outcomes under variable weather. These outcomes were used to generate on-the-go information for pre-season decision support and in-season managerial recommendations, and included a real-time estimation of the likeliness of not complying with the environmental threshold under present weather conditions. Finally, the presented model allowed determining optimal fertilizer strategies and defining best-bet solutions to the growers depending on the production priorities.
Agricultural crops include various plant species grown on the farm for food and fiber. Increase in the world population demands increase in the agricultural production as well as efficient management of resources in the form of precision agriculture through crop growth models to facilitate measured amounts of inputs to obtain desired quantity and quality of crop output. Crop growth simulation models integrate crop physiology, weather parameters, soil parameters, and management practices to simulate growth and yield of crops. These models compute growth values on a day to day basis, using relationships among input such as nutrients, water, weather parameters, etc. to predict values of crop growth parameters. Crop-specific model design results in poor modularity and prevents model sharing. A generic model uses common crop physiological processes. Validating and fine tuning the crop model is an important step before using it for actual prediction tasks. A number of crop growth models have been developed since 1980s. Future crop models should rely on improving the mechanism of interaction with environment, breeding programs, and microscale studies on individual crop growth components such as nutrients dispersion, CO2 diffusion, etc. The models should be able to predict crop yields under site‐specific soils, input, and weather conditions.
Aiming to enhance the analysis of water consumption and resulting consequences along the supply chain of products, the water accounting and vulnerability evaluation (WAVE) model is introduced. On the accounting level, atmospheric evaporation recycling within drainage basins is considered for the first time, which can reduce water consumption volumes by up to 32%. Rather than predicting impacts, WAVE analyzes the vulnerability of basins to freshwater depletion. Based on physical blue water scarcity, the water depletion index (WDI) denotes the risk that water consumption can lead to depletion of freshwater resources. Water scarcity is determined by relating annual water consumption to availability in more than 11,000 basins. Additionally, WDI accounts for the presence of lakes and aquifers which have been neglected in water scarcity assessments so far. By setting WDI to the highest value in (semi-)arid basins, absolute freshwater shortage is taken into account in addition to relative scarcity. This avoids mathematical artefacts of previous indicators which turn zero in deserts if consumption is zero. As illustrated in a case study of biofuels, WAVE can help to interpret volumetric water footprint figures and, thus, promotes a sustainable use of global freshwater resources.
