 Stochastic uncertainty and sensitivities of nitrogen flows on dairy farms in The NetherlandsJouke Oenema, Saskia Burgers, Herman van Keulen and Martin van Ittersum Agricultural Systems, 2015, vol. 137, issue C, 126-138 Abstract: Nutrient management decisions and environmental policy making must be based on sound data and proper analysis. Annual data collection and monitoring of farm and nutrient performance are wrought with uncertainties. Such uncertainties need to be addressed as it may lead to ambiguities and wrong conclusions. We developed an input-output N balance model to describe and quantify N flows in dairy farming systems. Input for this model was based on monitored data for one year (2005) from one experimental (detailed monitoring) and 14 pilot commercial dairy farms (less detailed monitoring). A Monte Carlo approach was used to quantify effects of uncertainty of input data on annual farm N surplus, soil surface N surplus and N intake during grazing, followed by a sensitivity analysis to apportion the different sources of uncertainty. Uncertainties in data input were described with probability density functions. Farm N surplus of the 14 pilot farms ranged between 81 and 294 kg ha−1, soil surface N surplus between 35 and 256 kg ha−1, and N intake during grazing between 27 and 108 kg ha−1. The uncertainties of N flows – both relative and absolute – increased from farm N surplus (CV = 8%; SD = 15 kg N ha−1) to soil surface N surplus (CV = 12%; SD = 16 kg N ha−1) to N intake during grazing (CV = 49%; SD = 28 kg N ha−1). Variation in uncertainty among farms in farm and soil surface N surplus and N intake during grazing was substantial and was related to the farm structure and farm characteristics such as production intensity, N fixation by clover and annual changes in stocks of roughage and manure. We found that a monitoring program based on more measurements instead of estimates and/or fixed rate values from literature will not always result in a better quantification of farm and soil surface N surplus on clover-based dairy farms. However, on farms with no N fixation, an intensive monitoring program reduced the uncertainty in farm and soil surface N surplus by 23% and the uncertainty of N intake during grazing was reduced by more than 30%. Knowledge about uncertainties of N flows is necessary to correctly interpret the N performance on dairy farms and its evolution through time. A first step is to get insights into the most uncertain N flows on a dairy farm. The next step, where possible, is to improve the estimation of the most uncertain N flows. Based on the insights from this study, these steps will underpin the validation of trends in N performance and justify decisions in environmental policy making and/or decisions for making on-farm improvements. Keywords: Nutrient management; Dairy farming system; N cycling; Monitoring data; Monte Carlo; Ordinary random sampling (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:agisys:v:137:y:2015:i:c:p:126-138 DOI: 10.1016/j.agsy.2015.04.009 Access Statistics for this article Agricultural Systems is currently edited by J.W. Hansen, P.K. Thornton and P.B.M. Berentsen More articles in Agricultural Systems from Elsevier |
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