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

 

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.