Nitrogen land budget/gross nitrogen budget

The land budget approach aims to estimate the total nutrient at risk of pollution (air, soil and water). The land budget therefore requires data on excretion. The term ''gross'' refers to the fact that the result of the land budget, the Gross Nitrogen Surplus (GNB), includes all N emissions to the air (Eurostat, 2013).

The GNB as AEI is on national/EU-level calculated as follows (Eurostat, 2018a).

Table 5.2: Elements of the Gross Nitrogen Budget as AEI (Eurostat, 2018a)

Inputs	Outputs
Fertilisers inorganic fertilisers, organic fertilisers (excluding manure) Gross manure input, which is calculated from manure production (nitrogen excretion; according to the current methodology no reductions are made for nitrogen losses due to volatilisation in stables, storages and with the application to the land) manure withdrawals (manure export, manure processed as industrial waste, non-agricultural use of manure, other withdrawals) change in manure stocks manure import Other nitrogen inputs, which consist of seeds and planting material biological nitrogen fixation by leguminous crops and grass-legume mixtures atmospheric deposition	Total removal of nitrogen with the harvest of crops (cereals, dried pulses, root crops, industrial crops, vegetables, fruit, ornamental plants, other harvested crops) Total removal of nitrogen with the harvest and grazing of fodder (permanent grassland and fodder from arable land including temporary grassland) Crop residues removed from the field

Eurostat (2018a) points out, that the current national budgets quoted are not comparable between different countries due to differences in definitions, methodologies and data sources used by countries.
OECD suggests the GNB as an appropirate indicator to calculate comparable indicators on regional and national scale (1993; 2007).
In Austria, Wick et al. (2012) used the nitrogen land budget to compare agricultural budgets with the concentration of nitrates in corresponding catchments. They found a good statistical correlation.
In the Netherlands, Nitrate leaching is estimated from the N surplus and leaching fractions that are depending on land use and soil type. Through calculations based on experimental data from various sources, the limits on the use of cattle slurry and mineral fertiliser in grass and silage maize production on sandy soils were calculated (Schröder et al, 2007).
In France, the CORPEN budget (2006) was developed to measure nitrogen surplus on farm level. This indicator allows to identify farms with a risk of environmental enrichment in nitrogen. This indicator could measure
1) cumulative phenomena (enrichment in nitrogen by temporary storage in a nonleachable form) for which the nitrogen budget can be an acceptable indicator (with the limits related to gaseous losses) but cannot measured,
2) instant phenomena (a stock of nitrogen in mineral form leached by rain).
Both phenomena lead to water pollution. The CORPEN budget indicator is a relevant long term indicator which cannot bring out short term risks of pollution.
In Germany, both, the nitrogen farm budget (StofBilV, 2017) and the nitrogen soil (surface) budget (DüV, 2017) are legally binding implemented in national legislation. The fertilising ordinance (DüV, 2017) functions as national implementation of the Nitrates Directive.

Wick et al. (2012) report, that a couple of authors doubt the applicability of the soil surface budget for the reflection of the actual nitrate leaching. The budget is a theoretical concept which describes only a potential for a contamination of groundwater (de Ruijter et al., 2007; Lord and Antony, 2002, Sieling and Kage, 2006). Wick et al. (2012) further explain, that some authors find only poor statistical relationship between the soil surface budget result and nitrate leaching, using correllation analysis (Buczko et al., 2010), analysis of covariance (Lord and Antony, 2002; Rankinen et al., 2007) and regression analysis (Buczko et al., 2010; Rankinen et al., 2007; Sieling and Kage, 2007). According to Wick et al, these statistical evaluations posess the weakness of being limited geographical and temporary.

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