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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 689687
Decision Support Systems for nitrogen pollution
reduction in agriculture systems. A case of Valencian
Community (Spain)
José Miguel de Paz
Instituto Valenciano de Investigaciones Agrarias-IVIA
Moncada-Valencia (Spain)
depaz_jos@gva.es
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 689687
Rockström J. et al. 2009. A safe operating space for humanity. Nature, Vol 461|24, 472-475
Planetary boundaries for the safe/sustainable operating of the planet.
Biodiversity loss
Atmospheric aerosol loading
Chemical pollution
Climate change – Carbon cycle
Ocean acidification
Stratospheric Ozone Depletion
Nitrogen cycle
Phosphorus cycle
Global freshwater use
Change in land use
1.- INTRODUCTION
PLANET SUSTAINABILITY
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 689687
< 25
25 - 50
50 - 75
75 - 100
100 - 125
125 - 150
150 - 175
175 - 200
200 - 270
NITRATO (mg/l)
Source: Database Conselleria Agricultura
Comunidad Valenciana (2009).
NO3 groundwater
(mg/L)
1.- INTRODUCTION
NITRATE POLLUTION
Zonas vulnerables
2000
2004
2009
2018 (propuestos)
2018 eliminados
no vulnerables
Nitrate Vulnerable zones.
DOGV 5/7/2018
Nitrate Vulnerable
zones
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 689687
VALENCIAN AGRICULTURAL SYSTEM
CLIMATE
SOIL
FERTILIZER
SOCIO-
ECONOMIC
CROP
WATER
HIGH COMPLEX SYSTEM
o High factors variability
o Multiple factors interactions
COMPLEX MANAGEMENT
SUSTAINABILITY PROBLEMS
o Nitrate pollution
o Low NUE
1.- Irrig. system
Flood (30%)
Drip (70%)
2.- Water quality
Groundwater
Surface
Alternative
 Mineral (72%)
 Organic (28%)
 Fertigated
 Centralized fertigation (> 25000 has)
Crop Area
(ha)
Citrus 161 000
Vegetable 25 000
Fruit tree 43 000
Vine 22 000
Olive 11 000
Arid
Sem i-arid
Dry-subhum id
Subhum id
Humid
Low income
Farmers aging
Low tech. profile
4 / 471.- INTRODUCTION
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 689687
NITROGEN USE EFFICIENCY-NUE
ESTIMATION
N lost
(kg N/ha)
NUE
(%)
MAGRAMA (2015) 211 46*
de Paz et al. (2009) 183 45**
*NUE = Noutput/Ninput * 100
**NUE = Ncrop / (Nmin + Nirri + Nferti) * 100
NITROGEN MANAGEMENT
o Fixed rates
o Organic fertilization is
not considered
SITE-SPECIFIC N MANAGEMENT-SSNM
(MacCarthy et al. 2018)
RECOMMENDATION SYSTEMS
o Based on DSS-models
INTEGRATED SOIL FERTILITY MANAGEMENT
o Integrate organic matter in fertilization
recommendation
EUROSTAT:
NUE 2009-2014
1.- INTRODUCTION 5 / 47
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 689687
DSS-BASED ON SIMULATION MODELS
- Nextr = Prod * Kupt* Fnu
- ETc = Kc * ETo
- %N = C1 * TDM-C2
- NO3lix = Nmin* (1-e(-k*dren/Por))
- Nmin = No * (1-ekt)
-
6 / 47
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 689687
DEFINITION OF DECISION SUPPORT SYSTEM
DSS: A interacting computer-based system that helps the decision maker in the
use of data and models in the solution of unstructured problems. (Scott-Norton )
DSS groups scientific and expert knowledge in a structured way to make it
available to users by an interface.
2.- DSS-MODEL
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 689687
PROBLEMS TO BE CONSIDERED IN THE
DEVELOPMENT OF A DSS-MODEL
– Model complexity
• Models are not adapted to Valencian agricultural
systems. (farmer skills, data availability, etc.)
– High uncertainty
• Model
• Input data
– Unfriendly interface
• Non implemented in WEB or App.
2.- DSS-MODEL
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 689687
 To develop a simple model adapted to
valencian agricultural system
 Crops (vegetables, citrus, persimmon,
vines etc.)
 Low technologic profile
 Low data requirement
 Friendly graphical user interface-GUI
 Simple and intuitive GUI. Farmers
should participate in the design.
 Supported databases
 Climate
 Soil
 Water
 Crop
 Implemented in WEB or/and App
CHALLENGES TO EXTEND THE USE OF DSS IN
VALENCIAN COMMUNITY
2.- DSS-MODEL
DSS-SALTIRSOIL
agrosal.ivia.es
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 689687
D Nmin soil
-500
-400
-300
-200
-100
0
100
200
-500 -400 -300 -200 -100 0 100 200
D Nmin soil observed (kg N/ha)
DNminsoilNITIRSOIL(kgN/ha)
RMSE = 55.1 kg N/ha
NITIRSOIL MODEL
- Simulate main N balance terms
- Low data requirements
- Simple GUI.
- Supported by databases (soil, water,climate, crop,fertilizers, wastes etc.)
- Calibrated-validated for main valencian crops
- Developed in visual studio .net 2012 and adapted to be implemented in a
WEB site.
Calibration: N uptake
oLettuce, potato, artichoke, onion,
cauliflower, citrus.
Validation: N leaching, D Nmin soil.
N leaching
0
50
100
150
200
250
300
350
400
0 100 200 300 400
Observed (kg N/ha)
NITIRSOILSimulated(kgN/ha)
RMSE = 39.4 kg N/ha
N uptake
0
50
100
150
200
250
300
0 100 200 300
Observed (kg N/ha)
NITIRSOILSimulated(kgN/ha)
Calibration
Validation
RMSE = 17.4 kg N/ha
De Paz et al 2012. 17th N workshop Innovations for
Sustainable Use of Nitrogen Resources.
2.- DSS-MODEL
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 689687
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3.- DSS-DATABASES
Soil database
- 480 points
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Climate database
- 54 weather
stations
Water database
Control points:
- 1040 groundwater
- 145 surface
Crop database:
- 55 annual crops
- 22 fruit trees
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 689687
Graphical user interface-DSS
NITIRSOIL model
SoilWater Crop
MANAGEMENT
- Reports
- Warnings
Databases
Climate
Interpretation of model results
and decision algorithms
5.- DSS-WEB (or App) STRUCTURE
DATA INPUT
(PHP, javascript,
html)
ANALYSIS AND DATA
INTERPRETATION
(Visual studio 2012 .
Net)
RECOMENDATION
(PHP, javascript, html)
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 689687
THANKS FOR YOUR ATTENTION

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Decision Support Systems for nitrogen pollution reduction in agriculture systems

  • 1. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689687 Decision Support Systems for nitrogen pollution reduction in agriculture systems. A case of Valencian Community (Spain) José Miguel de Paz Instituto Valenciano de Investigaciones Agrarias-IVIA Moncada-Valencia (Spain) depaz_jos@gva.es
  • 2. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689687 Rockström J. et al. 2009. A safe operating space for humanity. Nature, Vol 461|24, 472-475 Planetary boundaries for the safe/sustainable operating of the planet. Biodiversity loss Atmospheric aerosol loading Chemical pollution Climate change – Carbon cycle Ocean acidification Stratospheric Ozone Depletion Nitrogen cycle Phosphorus cycle Global freshwater use Change in land use 1.- INTRODUCTION PLANET SUSTAINABILITY
  • 3. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689687 < 25 25 - 50 50 - 75 75 - 100 100 - 125 125 - 150 150 - 175 175 - 200 200 - 270 NITRATO (mg/l) Source: Database Conselleria Agricultura Comunidad Valenciana (2009). NO3 groundwater (mg/L) 1.- INTRODUCTION NITRATE POLLUTION Zonas vulnerables 2000 2004 2009 2018 (propuestos) 2018 eliminados no vulnerables Nitrate Vulnerable zones. DOGV 5/7/2018 Nitrate Vulnerable zones
  • 4. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689687 VALENCIAN AGRICULTURAL SYSTEM CLIMATE SOIL FERTILIZER SOCIO- ECONOMIC CROP WATER HIGH COMPLEX SYSTEM o High factors variability o Multiple factors interactions COMPLEX MANAGEMENT SUSTAINABILITY PROBLEMS o Nitrate pollution o Low NUE 1.- Irrig. system Flood (30%) Drip (70%) 2.- Water quality Groundwater Surface Alternative  Mineral (72%)  Organic (28%)  Fertigated  Centralized fertigation (> 25000 has) Crop Area (ha) Citrus 161 000 Vegetable 25 000 Fruit tree 43 000 Vine 22 000 Olive 11 000 Arid Sem i-arid Dry-subhum id Subhum id Humid Low income Farmers aging Low tech. profile 4 / 471.- INTRODUCTION
  • 5. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689687 NITROGEN USE EFFICIENCY-NUE ESTIMATION N lost (kg N/ha) NUE (%) MAGRAMA (2015) 211 46* de Paz et al. (2009) 183 45** *NUE = Noutput/Ninput * 100 **NUE = Ncrop / (Nmin + Nirri + Nferti) * 100 NITROGEN MANAGEMENT o Fixed rates o Organic fertilization is not considered SITE-SPECIFIC N MANAGEMENT-SSNM (MacCarthy et al. 2018) RECOMMENDATION SYSTEMS o Based on DSS-models INTEGRATED SOIL FERTILITY MANAGEMENT o Integrate organic matter in fertilization recommendation EUROSTAT: NUE 2009-2014 1.- INTRODUCTION 5 / 47
  • 6. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689687 DSS-BASED ON SIMULATION MODELS - Nextr = Prod * Kupt* Fnu - ETc = Kc * ETo - %N = C1 * TDM-C2 - NO3lix = Nmin* (1-e(-k*dren/Por)) - Nmin = No * (1-ekt) - 6 / 47
  • 7. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689687 DEFINITION OF DECISION SUPPORT SYSTEM DSS: A interacting computer-based system that helps the decision maker in the use of data and models in the solution of unstructured problems. (Scott-Norton ) DSS groups scientific and expert knowledge in a structured way to make it available to users by an interface. 2.- DSS-MODEL
  • 8. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689687 PROBLEMS TO BE CONSIDERED IN THE DEVELOPMENT OF A DSS-MODEL – Model complexity • Models are not adapted to Valencian agricultural systems. (farmer skills, data availability, etc.) – High uncertainty • Model • Input data – Unfriendly interface • Non implemented in WEB or App. 2.- DSS-MODEL
  • 9. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689687  To develop a simple model adapted to valencian agricultural system  Crops (vegetables, citrus, persimmon, vines etc.)  Low technologic profile  Low data requirement  Friendly graphical user interface-GUI  Simple and intuitive GUI. Farmers should participate in the design.  Supported databases  Climate  Soil  Water  Crop  Implemented in WEB or/and App CHALLENGES TO EXTEND THE USE OF DSS IN VALENCIAN COMMUNITY 2.- DSS-MODEL DSS-SALTIRSOIL agrosal.ivia.es
  • 10. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689687 D Nmin soil -500 -400 -300 -200 -100 0 100 200 -500 -400 -300 -200 -100 0 100 200 D Nmin soil observed (kg N/ha) DNminsoilNITIRSOIL(kgN/ha) RMSE = 55.1 kg N/ha NITIRSOIL MODEL - Simulate main N balance terms - Low data requirements - Simple GUI. - Supported by databases (soil, water,climate, crop,fertilizers, wastes etc.) - Calibrated-validated for main valencian crops - Developed in visual studio .net 2012 and adapted to be implemented in a WEB site. Calibration: N uptake oLettuce, potato, artichoke, onion, cauliflower, citrus. Validation: N leaching, D Nmin soil. N leaching 0 50 100 150 200 250 300 350 400 0 100 200 300 400 Observed (kg N/ha) NITIRSOILSimulated(kgN/ha) RMSE = 39.4 kg N/ha N uptake 0 50 100 150 200 250 300 0 100 200 300 Observed (kg N/ha) NITIRSOILSimulated(kgN/ha) Calibration Validation RMSE = 17.4 kg N/ha De Paz et al 2012. 17th N workshop Innovations for Sustainable Use of Nitrogen Resources. 2.- DSS-MODEL
  • 11. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689687 ii i ii i i iii iiiii ii i iiii ii i ii iiii ii ii iiiiiiiiiiiiiiii i i iii i ii ii ii i i iiiii iii ii i i ii ii ii iiiii i i i ii i iiiiiiiii ii iiiii i ii i ii ii ii i ii iii i ii i i i i ii iiiiii i iiiii i i i iiii i ii i iii iiiiiiiii i i ii i ii i i ii i iiiii i i i iiiiii iiii iii ii i i ii i ii i ii i i ii ii iiiii i ii ii i i i iii i i ii iiii i i i i ii i i i ii iiii i iiii i ii ii i i i ii i i i i iiiiiiiiii ii ii i i i i i i iiiii ii i ii iiiiii i i ii i iiii i i ii i i i i iiiiiii i i i i iii ii i i iiiiiiiiiii iii i i i i i i i i i i i i ii i ii i i ii i i i ii i ii i i i i i ii iii i ii iiii iiii i i iiii i ii i ii i i i i i iiiiiiiiiii iiiiiiiiiii i ii i iii i i i ii iiiiiiiii ii ii i ii iiiiiiiiiiiiiii i iiiiiiiiiiiiiiiiiiiiiiiiiiiii ii iii i i i i i i i i i i i i ii i i i i i i i i i i ii i i i i ii i i ii i ii iiii ii i i i ii i i i i i ii i i i iii ii i i i i i i iii i i i i ii i i ii i ii i iiii iii i i i i iii i i i iii i i iii i ii ii ii i i ii i i i iii i i iii i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i ii ii i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i iii i i i i i i i i i i i i i i i i i i ii ii ii i i iii iiiiii i i i i i iii ii i i i iii ii ii iii i i ii i iiiiii i ii iiiiii i i i i i i iiiiiii i iiii i iiiii i i ii i iiii ii i i ii iiiii i i i i iiii iiiiii i i i i i i ii ii i i i ii iiii ii i i iii iii iiii i ii ii ii ii ii i ii ii i iii ii i ii iii i i i i i i i iii ii i ii i i iiii ii i i i i i i i ii i i iii i i ii ii ii i i ii i ii ii ii iiiii ii ii i iii iiiiiiiiiiiii i i iii ii i i i iii iiiiii ii iiii iiiii i i iiii i iiiiiiii iiii i iiiiiiiii i i iiii i iiiiiiiii i iiii ii iiii i i iiiii i iiiiiii i i i iiii ii ii i i iiii i iii iiiiiiiii iiiiiiii i i i ii i iii ii i iiii iii iiiii iiii iiii 3.- DSS-DATABASES Soil database - 480 points !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e !e ^_^_ ^_^_^_ ^_ ^_ ^_^_ ^_ ^_ ^_ ^_ ^_ ^_^_^_ ^_ ^_^_ ^_ ^_ ^_ ^_^_ ^_^_ ^_ ^_^_ ^_^_^_ ^_ ^_^_^_ ^_ ^_ ^_^_ ^_ ^_^_ ^_^_ ^_^_ ^_ ^_ ^_^_^_ ^_ ^_^_ ^_ ^_^_^_^_ ^_ ^_^_^_^_^_^_^_^_ ^_^_^_^_^_^_ ^_ ^_^_^_ ^_^_ ^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_ ^_^_ ^_ ^_^_^_^_ ^_ ^_ ^_^_ ^_ ^_ ^_^_^_ ^_ ^_^_^_ ^_ ^_^_ ^_ ^_ ^_ ^_ ^_^_ ^_ ^_ ^_ ^_ ^_^_ ^_^_ ^_^_ ^_^_^_ ^_^_ ^_ ^_ ^_^_ ^_^_ ^_^_ ^_ ^_ ^_^_ ^_ ^_ ^_ ^_^_ ^_ ^_ ^_ ^_ ^_ ^_ ^_ ^_ ^_^_^_ ^_^_^_ ^_^_^_ ^_^_^_^_ ^_ ^_ ^_^_ ^_^_^_^_ ^_^_^_ ^_^_^_ ^_ ^_^_ ^_ ^_ ^_^_ ^_ ^_^_ ^_ ^_ ^_ ^_ ^_ ^_^_ ^_ ^_ ^_^_ ^_^_ ^_ ^_ ^_^_ ^_ ^_ ^_ ^_^_ ^_^_^_^_ ^_^_^_^_ ^_ ^_^_ ^_^_^_ ^_^_^_^_ ^_^_ ^_ ^_ ^_ ^_ ^_^_^_ ^_ ^_^_^_ ^_ ^_^_^_^_^_^_^_^_^_^_^_^_ ^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_ ^_^_^_^_ ^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_ ^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_ ^_ ^_^_ ^_ ^_^_^_^_^_ ^_^_ ^_ ^_^_ ^_^_^_^_^_ ^_^_ ^_ ^_^_ ^_ ^_^_^_^_ ^_^_ ^_^_^_^_ ^_^_^_^_^_^_^_^_ ^_^_^_ ^_^_ ^_ ^_ ^_ ^_^_^_^_ ^_^_ ^_ ^_^_^_ ^_^_ ^_ ^_^_^_^_^_ ^_ ^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_ ^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_^_ Climate database - 54 weather stations Water database Control points: - 1040 groundwater - 145 surface Crop database: - 55 annual crops - 22 fruit trees
  • 12. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689687 Graphical user interface-DSS NITIRSOIL model SoilWater Crop MANAGEMENT - Reports - Warnings Databases Climate Interpretation of model results and decision algorithms 5.- DSS-WEB (or App) STRUCTURE DATA INPUT (PHP, javascript, html) ANALYSIS AND DATA INTERPRETATION (Visual studio 2012 . Net) RECOMENDATION (PHP, javascript, html)
  • 13. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689687 THANKS FOR YOUR ATTENTION

Hinweis der Redaktion

  1. In a study performed by Rockstrom in 2009, were identified nine earth processes affected by human activities. Three of them have been transgressed their boundaries indicating the non-sustainability of the system. The first is the biodiversity loss, which is more than 100-1000 times above the normal rate of the species lost. The second is climate change which is affected by our high CO2 atmospheric levels. The third is the disruption of the N cycle due to agricultural activities, contaminating the water bodies and the atmosphere. The C and N cycle are closely related and they are also linked to agricultural activities, thus I will focus on these two problems from the agricultural point of view. One analysis identified three earth-system processes as the main problems for planet sustainability. The biodiversity loss, the climate change and the interference of agriculture in the nitrogen cycle. Modern agriculture is a main cause of environmental pollution, including the large-scale nitrogen induced environmental change. Identifying and quantifying planetary boundaries that must not be transgressed could help prevent human activities from causing unacceptable environmental change. We have found nine such processes for which we believe it is necessary to define planetary boundaries. This analysis suggests that three of the Earth-system processes — climate change, rate of biodiversity loss and interference with the nitrogen cycle — have already transgressed their boundaries. Today, the rate of extinction of species is estimated to be 100 to 1,000 times more than what could be considered natural. Modern agriculture is a major cause of environmental pollution, including large-scale nitrogen- and phosphorus-induced environmental change. At the planetary scale, the additional amounts of nitrogen and phosphorus activated by humans are now so large that they significantly perturb the global cycles of these two important elements. Si el carbono y el cambio climático parecen encabezar la agenda pública, considere que la contaminación por nitrógeno afecta a ambos, y al mismo tiempo causa contaminación del aire y el agua que reduce la esperanza de vida y amenaza la biodiversidad. Lo hace aumentando la formación del óxido nitroso de los gases de efecto invernadero (N2O); afectando las tasas de crecimiento y descomposición de las plantas, alterando la competitividad y la diversidad de las especies; y formando gases y partículas dañinas en el aire que inhalamos con cada respiración.
  2. In order to develop the CAP approaches to grow we have to know the main characteristics of the valencian agricultural system. These characteristics are high variability of soils, climate, crops, water, fertilizer management, and socio-economic factors. Additionally, there are very complex interactions among all these factors. This means that the management of the valencian agricultural system is very complex. Since this complexity is not well addressed, several sustainability problems have arisen: Nitrate pollution, low nitrogen use efficiency and soil degradation and greenhouse gas emissions. Low Dreal income: 22% (1990-2015) Old age farmers Interactions between all the agricultural system’s components should be considered when designing and managing the system (Canali et al. 2012. In book: Advances in Citrus Nutrition Chapter: Organic Citrus: Soil Fertility and Plant Nutrition Management. Organic Citrus: Soil Fertility and Plant Nutrition Management) El principal proposito de un sistema agrario es la produccion de alimentos. Hay factores humanos y físicos. MOS suelo, media 1.22, SD 2.57, CV. 105% 484 puntos Irrigation systems (ESRYCE, 2017) Renta agraria por UTA precios constantes 1990-2015: 122%
  3. What is the main cause of this nitrate pollution? The low NUE in valencian community close to 45-46% in agreement of MAGRAMA and de Paz. This means we are loosing high amount of N polluting the water bodies and the atmosphere. Our agriculture is almost at the end of the NUE ranking in comparison with other european countries. This low NUE is mainly due to the farmers follow a fixed rates for nitrogen management and they don’t include the organic fertilization in the fertilization plans. In order to improve this NUE we should apply the concepts of site-specific N management considering the recommendation systems based on soil measurements, foliar measurements of based on the use of DSS-models. The second concept that should be applied is the integrated soil fertility management considering the organic matter in fertilization recommendation. These two systems are included in the new version of the “Code for the Use and Management of Fertilizers” which is going to be presented in the FAO Committee on Agriculture (COAG) next September 2018. 75% de programas de fertilizacion en invernaderos almeria no incluyen la aplicación N por materia orgánica Fertilización orgánica no suele incluirse en el plan abonado N del cultivo en zona huerta valencia. Recomendaciónes de abonado nitrogenado se basan tradicionalmente en campos experimentales. Fuera de estos campos estas recomendaciones general una incertidumbre. Por lo que se deben aplicar metodos de site-specific fertilizer recommendation (MacCarthy DS et al. 2018) What is the main cause of this nitrate pollution? The misuse of nitrogen fertilization by farmers that produces loss of great amounts of nitrate by leaching, polluting the groundwater, and on the other hand the NUE is very low, incurring monetary loss in addition to harming the environment. Valencian community is in the last positions of NUE ranking in comparison with other European countries. The main cause of this low NUE is the farmers use fixed rates for fertilizer planification and is not considered the organic fertilization application in the fertilizer planification. How to improve our NUE? Applying the concepts of site-specific nitrogen management, using recommendation systems based on soil measurements, foliar measurements and DSS-models, and the integrated soil fertility management integrating the organic fertilizer in the fertilization recommendations. What are the main causes of this low I NUE in valencian agriculture systems?. Number one fixed rates farmers use for crop Fertilization, and number two is the organic fertilization in usually not considered in the fertilization plans. In order to increase the NUE we have to adopted in our agricultural systems the concepts of Site-specific nitrogen management. This is to say, implement recomendation systems based on scientific knowledge as is the based on soil measurements, on foliar measurements and on DSS-models, and also apply the concept of integrated soil fertility management, intagrating the organic matters in fertilizer recommendation. But currently there is a scope for improvement, each time there is an increment of fertigation area centraly controlled by irrigation communities, in which more than 25000 ha are control in this way. Poco eficiente, por explotaciones intensivas pequeñas, se junta con CCRR con fertirrigación centralizada. Y no se adapta el abonado a las condiciones específicas de la zona.
  4. Los modelos de simulacion son una alternativa rapida y de bajo coste para evaluar estrategias de manejo de riego y abonado nitrogenado ademas de permitir una evaluacion previa del impacto ambiental de las estrategias de manejo, reduciendo asi el riesgo en la toma de decisiones y el tiempo de respuesta.
  5. Dirección general de la tecnología de la información y las comunicaciones-DGTIC