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A mechanistic model for estimating ammonia volatilization from slurry applied to bare soil

A mechanistic model for estimating ammonia volatilization from slurry applied to bare soil

Agricultural & Forest Meteorology 88(1-4): 145-167

The loss of ammonia by volatilization after slurry application may lead to large losses of soil-plant nitrogen. It is also a major source of atmospheric ammonia. These fluxes must therefore be accurately measured. However, volatilization depends on many features of the soil, climate and slurry, so that it is difficult to predict, or even to interpret and compare data from different experiments. We have developed a mechanistic model that simulates the influence of the various factors on volatilization, accounting for the transfers and equilibria in the topsoil and between the soil and the atmosphere. The model uses readily available input data, including soil, meteorological and slurry data. It includes energy balance and advection submodels, which make it suitable for field scale applications using simple meteorological data. Sensitivity analysis showed that soil pH has a large influence on volatilization. The model is also sensitive to soil adsorption capacity and some hydraulic characteristics (saturation water conductivity, water content at field capacity). It has been calibrated under real field conditions using experimental data for ammonia fluxes measured over two weeks after slurry application, with a micrometeorological method giving data at 15-min intervals. The soil, climate and slurry factors were also measured. The model provides a fair picture of the ammonia fluxes throughout the volatilization, including the total ammonia volatilized, the decrease in daily loss and their short-term (< 1 h) variations due to the influence of meteorological conditions on soil surface temperature and atmospheric diffusion. Lastly the model has been used to simulate the influence of various meteorological conditions and agricultural techniques on ammonia volatilization. This model will make it easier to interpret data from different experiments and will help to improve the emission submodel of atmospheric ammonia deposition models.

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Accession: 003021730

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DOI: 10.1016/s0168-1923(97)00044-0

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