Study for the development of a common framework for the quantitative advice of crop nutrient requirements and greenhouse gas emissions and removal assessment at farm level : FaST-Navigator : executive summary.
Date: 2022-05-25
Institutions: Directorate-General for Agriculture and Rural Development
Subjects: EU Member State, agricultural performance, agronomic research, animal production, carbon capture and storage, crop production, digital technology, farming sector, fertiliser, greenhouse gas, means of agricultural production, simulation, soil conditioning, sustainable agriculture

Study for the development of 
a common framework for the 
quantitative advice of crop 
nutrient requirements and 
greenhouse gas emissions and 
removal assessment at farm 
level 
FaST-Navigator 

AGRI-2020-0316 

Executive summary 

Edited by Anna Osann 
December 2021 

EUROPEAN COMMISSION 

Directorate-General for Agriculture and Rural Development 
Directorate DG-AGRI — Directorate A - Strategy and Policy Analysis 
Unit A.3 — Policy Performance 

E-mail: 

AGRI-EVALUATION@ec.europa.eu 

European Commission 
B-1049 Brussels 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EUROPEAN COMMISSION 

FaST-Navigator 

Executive summary 

Edited by Anna Osann (AgriSat),  

with contributions from  

Anna Osann, Carmen Plaza, María-Llanos López (AgriSat), 

Horacio López, Almudena Olivas, Fran Jara, Daniel Vicente (ITAP),  

Ana Pilar Armesto, Iñaki Arozarena (INTIA), 

Alfonso Calera, David Cifuentes, Raúl Moreno, Javier Sánchez (UCLM), 

Susanne Köppen (IFEU), 

Stefano Fabiani, Tiziana Pirelli, Pasquale Nino, Silvia Vanino, Antonella di Fonzo, 
Antonio Giampaolo (CREA), 

André Chanzy, Elena Pareja (INRAE), 

Jerzy Kozyra, Piotr Skowon, Rafal Wawer (IUNG), 

Carlo De Michele (Ariespace) 

2022 

Directorate-General for Agriculture and Rural Development   

 
 
 
 
 
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Luxembourg: Publications Office of the European Union, 2022 

PDF 

ISBN 978-92-76-44621-7   

doi: 10.2762/099578 

KF-08-21-358-EN-N 

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Reproduction is authorised provided the source is acknowledged. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EXECUTIVE SUMMARY 

The new Common Agricultural Policy (CAP), which starts in 2023, aims to foster a sustainable and 
competitive agricultural sector that can support the livelihoods of farmers and provide healthy and 
sustainable food for society, as well as vibrant rural areas. 

Among other innovations, the new CAP strategic plans indicate that Member States shall establish 
a  system  for  supplying  farmers  with  the  Farm  Sustainability  Tool  for  Nutrients  (FaST).  The  FaST 
was  defined  as  an  electronic  tool  for  on-farm  decision  support  starting  from  minimum  crop 
fertilization  management  functionalities  and  extendable  to  further  sustainability  objectives.  The 
main objective of FaST is the wider adoption of nutrient management plans and takes advantage of 
the digital technologies to facilitate their computation. 

The  FaST  Navigator  (Nutrient  management  Algorithms,  Valorisation  of  Inputs  and  GHG 
Assessment – Tool for Optimization of Resources) study addresses one of the main challenges for 
the implementation of the FaST: the availability of nutrient management algorithms adapted to the 
different  characteristics  and  operational  under  different  conditions  of  data  availability  while 
standardised and consistent. 

For  this  purpose,  it  describes  the  methodological  frameworks  for  the  quantitative  advice  for 
fertilizer use and GHG emissions/removal parameterized in real systems representing the diversity 
of  agricultural  systems  in  the  European  Union.  These  frameworks  are  accompanied  by  the 
assessment  of  the  economic  performance  of  possible  alternatives  of  farm  management  on  these 
topics. The frameworks facilitate the development of the FaST providing a standardised reference 
for  the  calculation  procedures  and  are  open  to  facilitate  the  adaptation  of  the  methodologies  in 
different environments. 

The FaST Navigator framework covers the assessment of crop nutrient requirements at field 
scale, as well as the assessment of GHG emissions/removals and economic performance at 
farm scale. The frameworks have also been implemented in a stand-alone digital tool (“Navigator 
Tool”). 

A comprehensive review has been conducted of existing methodologies. It covers the description of 
the variables and the optimum time scale to be considered in the mass balances necessary for the 
estimation  of  the  crop  nutrient  requirements  at  field  level  for  nitrogen  (N),  phosphorus  (P)  and 
potassium (K), and the estimation of the GHG emissions and removals at farm level. The proposed 
methodology optimizes the use of data and variables, which are useful both for nutrients and GHG 
assessment. 

The FaST Navigator framework and the Navigator Tool have been designed to work with four lines 
of  methodological  approach  depending on the  precision or accuracy of  the available  data.  The 
objectives of the user and the availability of data will define the FaST Navigator lines where the tool 
will  operate,  from  the  most  complex  Line  1  (measured  data)  to  the  simplest  Line  4  (default 
parameters) over quantitative (Line 2 and qualitative (Line 3) models. It is important to note that 
the lines 1 to 4 work both inside and outside Nitrate Vulnerable Zones (NVZ).  

The assessment of crop nutrient requirements at field scale is based on the nutrient balance, 
which  is  defined  as  the  difference  between  the  nutrient  inputs  entering  a  field  (mainly  livestock 
manure and fertilisers) and the nutrient outputs leaving the field (the uptake of nutrients for crop 
and pasture production).  

The  Navigator  nutrient  framework  integrates  different  models  for  different  variables  and  lines 
(which  have  been  dubbed  F1,  F2,  F3,  F4,  where  F  stands  for  fertilization).  The  only  variables 
considered  in  lines  F3  and  F4  are  uptake,  fixation,  and  mineralization,  while  the  lines  F1  and  F2 
consider  all  variables  (i.e.  also  volatilization,  nitrification,  denitrification,  and  leaching).  The 
relevant time scales depend on the lines, where F1 and F2 are compatible with daily time scale, F3 
requires  seasonal  and  F4  annual  time  scale.  The  F1  and  F2  models  distinguish  three  different 
operating  modes,  according  to  the  monitoring  phases  of  the  crop  before,  during,  and  after  the 
growing  campaign.  This  allows  for  adjusting  the  top  dressings  more  precisely  to  the  actual 
evolution of the crop and the weather conditions in each field. 

The spatial unit for the GHG emissions and removals assessment is the farm. The framework 
includes crop production, livestock farming, carbon cycle as well as energy use. All inputs into the 
farm as well  as all outputs (in terms of  gases  emitted)  are  considered. The emissions cover CO2, 
N2O  and  CH4  emissions  and  are  expressed  as  CO2  equivalents.  The  balancing  of  emissions  and 
removals (calculated on the basis of activity data and their corresponding emission factors) yields 
the net GHG emissions. 

 
 
 
Activity  data  originate  from  all  activities  at  farm  level.  In  the  agricultural  sector,  they  cover, 
among others, the amount of fertilizers applied, the number of animals, the share of organic soils, 
the amount of diesel used for field works. In the land-use, land-use change, and forestry (LULUCF) 
sector, they cover the conversion from a land use to another.  Emission factors are the emissions 
associated  with  these  activities.  They  can  come  from  livestock  management  (e.g.  methane  from 
enteric fermentation, manure storage) as well as from the production and use of agricultural inputs 
(e.g.  mineral  fertilizers,  diesel,  animal  feed).  The  system  boundaries  include  two  elements:    (A) 
direct  emissions  and  removals  occurring  at  farm  level  and  (B)  upstream  emissions  from  material 
and energy use.  

The  GHG  framework  combines  a  life  cycle  based  approach  with  IPCC  methodologies.  Whenever 
possible, it follows the IPCC Tier 2 approach with regard to pools and emission factors calculated. 
The lines (dubbed G1, G2, G3, G4) differ in two ways. First with regard to the activity data: lines 
that  are  more  complex  require  a  more  fine-grained  data  input  by  the  farmer  since  a  higher 
disaggregation allows a more specific calculation of emissions. Second, different lines use different 
emission factors. They are adapted to the level of detail of the corresponding line and are derived 
from the IPCC guidelines as well as other scientific sources.  

The framework for the economic performance assessment (EPA) is built on the definition of 
economic  performance  of  a  farm  expressed  through  a  set  of  indicators  retrieved  from  the  Farm 
Accountancy  Data  Network  (FADN)  database.  Those  economic  indicators  are  based  on  costs  and 
income  linked  to  farm  activities  (e.g.,  fertilizer  management)  and,  according  to  FADN  typological 
classification (type of farming and economic size), show the impact of the activities adopted (e.g., 
fertilization practices) on the farm’s financial results. All costs for inputs and management practices 
implemented and all revenues realized at farm level are included, both on a seasonal or an annual 
reference time scale. 

Five  main  economic  indicators  are  considered,  based  on  and  fully  compatible  with  the  FADN 
database, namely: Total gross value, unit cost of production, gross margin per hectare, unit gross 
margin,  production  cost  per  hectare.  Optional  environmental  indicators  are  water  use  efficiency, 
energy use efficiency, and land productivity. 

The  EPA  framework  considers  two  lines,  E1  and  E2.  Both  use  the  same  variables  and  indicators 
(consistent  with  the  FADN  survey  and  based  on  the  same  typological  classification).  Line  E1 
calculates the value of variables from comprehensive information directly provided by the farmer. 
It shows the effective costs and incomes of farmers, according to their management strategy. Line 
E2 considers only data coming from FADN and thus, allows for benchmarking against farms of the 
same typological classification, i.e., it shows the current level of efficiency of the farm compared to 
the average. 

The web-based Navigator Tool implements the three modules of the methodological frameworks 
at  each  level.  The  tool  version  for  farmers’  use  is  conceived  as  a  stand-alone  tool,  without  any 
connection to online databases. Therefore, it runs only on two categories of data: one is the input 
data  provided  by  the  farmer  (typed  into  a  screen  form  or  read  from  a  pen  drive),  the  other  is 
tables of parameters (e.g., default values) stored inside the tool architecture. No data from users 
will be recorded by the tool after the session ends, according to the tool conceptual design. 

A benchmarking analysis has been performed to assess the accuracy of the different modelling 
strategies according to their level of complexity and their level of information (corresponding to the 
different lines in the framework). This analysis has relied on (a) existing comprehensive databases 
from  previous and ongoing projects covering  different  agro systems and climate zones, providing 
agro-climatic  conditions,  nutrient  balances,  impacts  on  yield  as  reference  data  together  with 
variables  and  parameters  required  to  implement  the  different  FaST  Navigator  lines;  (b)  a  crop 
simulator based on a crop model calibrated against the data mentioned before. The benchmarking 
analysis has also validated the framework for a wide range of real cases across Europe. A traffic 
light code indicates the accuracy of the results. 

The  benchmarking  analysis  points  to  the  importance  of  the  target  yield.  In  general,  the  use  of 
measured  values  of  crop  and  soil  inputs  improves  the  recommendation  accuracy.  The  main 
advantage of F1 and F2 lines (over F3 and F4) is the assessment of the crop N requirements along 
the  growing  cycle  and  the  possibility  to  adapt  the  N  doses  depending  on  the  events  registered 
during  the  campaign.  This  difference,  in  addition  to  the  fact  that  these  lines  are  based  on  actual 
measured data is being considered also in the definition of the traffic lights indicator. 

The conclusion of our analysis of the methodological frameworks, selection of models and available 
parameterizations  is  that  the  following  conditions  of  applicability  are  well  covered:  Cereals, 
maize,  legumes,  industrial  crops,  and  grassland  in  intensive  monocultures  or  rotations  (to  some 
degree  also  organic  farming)  in  Mediterranean,  Atlantic,  and  Continental  climate  zones  and 

livestock  in  the  same  climate  zones.  Other  crops  (essentially  woody  crops  like  vineyard,  almond 
trees, and fruit trees) are more difficult to model due to non-input factors influencing yield. Also, in 
this same case, the scientific knowledge base and the available comprehensive datasets are often 
more at research than operational stage.  

The  extension  of  the  FaST  Navigator  frameworks  to  other  areas,  crops,  management 
approaches or practices, land uses and land use change, will require some degree of research and 
(further)  development  of  methodologies  and  their  demonstration  in  real  environment  (thus  also 
generating the necessary datasets for operational use). In parallel and in a simple, practical way, 
the frameworks and the digital tool can be adapted and fine-tuned to local or regional conditions by 
drawing from specific databases available at and within Member States. 

 
 
 
 
 
 
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doi: 10.2762/099578 
ISBN 978-92-76-44621-7