Approaches and uncertainties in nutrient budgets: implications for nutrient management and environmental policies

Abstract

Nutrient budgets of agroecosystems are constructed either (i) to increase the understanding of nutrient cycling, (ii) as performance indicator and awareness raiser in nutrient management and environmental policy, or (iii) as regulating policy instrument to enforce a certain nutrient management policy in practice. This paper explores nutrient budgeting approaches and summarizes sources of uncertainty associated with these approaches. Possible implications of uncertainties associated with the different methodologies and approaches for nutrient management and environmental policy are discussed. Three types of nutrient budgets have been distinguished, i.e. farm-gate, soil surface and soil systems budgets. A farm-gate budget is the most integrative measure of environmental pressure, and seems most suitable as environmental performance indicator. A soil surface budget is appropriate for estimating the net loading of the soil with nutrients. Soil system budgets account for nutrient inputs and outputs, recycling of nutrients within the system, nutrient loss pathways and changes in soil nutrient pools; it is the most detailed budget and provides detailed information for nutrient management. Case studies for the dairy farm De Marke indicate that the three budgeting approaches supplement each other. The accuracy and precision of the nutrient budget depend on budgeting approach, data acquisition strategy and type of agroecosystem. There is often a considerable amount of uncertainty in the nutrient budget, due to various possible biases and errors, notably in the partitioning of nutrient losses. Possible sources of biases are personal bias, sampling bias, measurement bias, data manipulation bias and fraud. Sources of errors are sampling and measurement errors. Both biases and errors in nutrient budget estimates may lead to confusion and wrong conclusions. Yet, there is little published evidence that uncertainties are taken into account in decision making. Uncertainties are usually smaller for a farm-gate budget than for a soil surface budget. Therefore, farm-gate budgets are preferred over soil surface budgets as policy instrument. Quantifying uncertainties requires: (a) system identification and analysis, (b) classification of uncertainties, (c) specification of distributions of probabilities of the various sources using Monte Carlo simulation, and (d) monitoring of the nutrient pools, inputs and outputs over time. Analyses of uncertainties in nutrient budgets may provide information about the weakest chain in establishing agri-environmental cause–effect relationships and, therefore, may assist to better focus research efforts. Data for the nitrogen budget of The Netherlands indicate relatively large uncertainties for the items denitrification and leaching, with coefficients of variation >30%. In conclusion, there is a need for standard procedures and guidelines in nutrient budgeting and uncertainty analyses, to improve the confidence in and applicability of nutrient budgets.