آنالیز حساسیت و کاربرد یک مدل شبیه سازی دینامیکی از شار ازت در مسکن سازی خوک و امکانات ذخیره سازی در فضای باز

This article presents the sensitivity analysis of a deterministic model proposed by Berthiaume et al. (2005) for the prediction of daily nitrogen concentrations Nconc in kg [N] t−1[slurry] and loads Nload in kg [N] inside buildings and storage facilities at the production site scale. This model makes use of many parameters and therefore, it is important to evaluate the impact of each of these. Identification of those parameters which most affect the output values allows for the rationalisation of resources when establishing a sampling protocol for determining more precise parameter values. The most important parameters identified were the proportion of proteins in feed P, the temperature of the slurry T, the pH of the slurry h, and, the air speed over slurry v. It therefore confirmed the already acknowledged high importance of feed content and methods of distribution—information that can be easily obtained from producers and, thus, can be used in the determination of regional amounts of nitrogen produced by swine production systems (e.g, municipality, county or watershed level). In addition, this sensitivity analysis confirmed that some characteristics that are seldom known to producers—slurry pH and air speed over slurry—are also of great importance. Finally, two sets of simulation scenarios were used to illustrate potential applications of this model as a management tool and to further demonstrate its coherent behaviour over different sets of parameter values.

In recent years, intensive pig production has been associated with nitrogen non-point sources of water pollution resulting from spreading of slurry in excess of crop requirements. This situation has prompted experimental studies aiming at a better understanding of the role of feeding, genetic, slurry management and building characteristics on nitrogen loads (Canh, 1998; Canh et al., 1998a, Canh T T; Aarnink A J A; Schrama J W; Haaksma J, (1997). and Canh et al., 1998b; Dourmad and Henry, 1994; Dourmad and van Milgen, 1998; Portejoie et al., 2004; Portejoie et al., 2003; Quiniou et al., 1994). It has also inspired the development of mathematical equations for these factors but these have not been integrated simultaneously at the production site scale (Aarnink and Elzing, 1998; Dourmad et al., 1992). A deterministic mathematical model was recently developed by Berthiaume et al. (2005) to predict the effect of these factors at the production site level, and therefore facilitate management. This model allows for the prediction of daily nitrogen concentrations Nconc in kg [N] t−1[slurry] and loads Nload in kg [N] inside buildings and in storage facilities. Although the model represents a particularly well-adapted tool intended to take into account the impact of major pig farming characteristics at the production site level, it necessitates many parameter values; hence the need to evaluate the impact of the lack of precision associated to each of these. A sensitivity analysis, that is an analysis focussing on how variations in the output of a model can be apportioned to the different sources of variation and how the model depends on the information fed into it (Saltelli et al., 2000), was performed. The identification of those parameters exerting the most significant effect should allow for the rationalisation of resources when deciding on a sampling protocol for getting the parameter values. Many important characteristics in swine production such as genetics, feeding efficiency, feed content, feed distribution methods, slurry management systems and building characteristics are included in the aforementioned mathematical model. The main objectives of the sensitivity analysis presented in this paper were: (1) to ascertain the relative impact of each parameter on model outputs and (2) to study the behaviour of the model in relation to the modification of the input parameter values. The presentation of the sensitivity analysis constitutes the first section of this paper while the second section aims at illustrating the usefulness of the model as a decision support tool through simulation of two potential applications scenarios.

The object of this work was to study the behaviour of the model of nitrogen fluxes in pig production sites proposed by Berthiaume et al. (2005). Two different methods of sensitivity analysis were used to: (i) identify the most significant parameters and (ii) verify whether the model was equally sensitive to those parameters over the range of plausible values that these could take when simulating either a reproduction system or a growing-finishing system. In the reproduction system, lowering the proportion of protein in feed P from 14·7% to 12% caused a 17% reduction in the value of the output variable nitrogen load Nload while raising the proportion caused an augmentation of 15%. The impact of the parameter was even more important in the growing-finishing system where proportions of proteins in feed of 18% and 16% raised and lowered the nitrogen load in the slurry stored in the lagoon by 22%, respectively. The pH value of the slurry hslurry also caused major changes with a maximum impact of +24% for a value of 5 when compared to 7·4. The use of such a low pH value could represent the use of an acidifying diet. On the opposite, a high pH value (hslurry=8) caused a reduction of 26% of the total predicted nitrogen load in the stored slurry. The model also showed an important sensitivity to other parameters such as temperature of the slurry, air velocity over slurry or the use of water reducing system. These results are consistent with the already acknowledged importance of parameters representing feed content, slurry pH, temperature, and air velocity over slurry and constitute a confirmation of the coherence of the model. The two potential application scenarios illustrated well model flexibility and potential as a tool for management purposes. Nevertheless, further empirical validation of the model is needed specifically for farms characterised by extreme values for those significant parameters identified in sensitivity analyses.