EC/FAO Programme on Information Systems to Improve Food Security Decision-Making in the European Neighborhood Policy (ENP) East Area
Proposal to improve crop forecasting in Armenia
Workshop, October 13, 2010 Armstatehydromet Service, Yerevan, Armenia
- WORSHOP REPORT -
1. Background
This half day Workshop was jointly organized by Armstatehydromet (hereinafter: Hydromet) and FAO under the “EC/FAO Programme on Information Systems to Improve Food Security Decision-Making in the European Neighborhood Policy (ENP) East Area” on October 13, 2010. The Programme is financed by the European Commission and implemented by FAO. The Programme aims at improving food security by enhancing the national capacity to generate, analyze, communicate and mainstream more relevant and reliable information into policies and programmes. The Workshop took place in Hydromet’s meeting room.
2. Workshop Objectives
The objective of the workshop was to build consensus on the priorities to be addressed under the Programme to improve crop forecasting and the dissemination of information. This was achieved by presenting and discussing with stakeholders the options identified by an international consultant to improve crop forecasting and the dissemination of information to Marz Regional Centers. The Workshop also provided an opportunity to present the potential of improved crop forecasting for food security policies and programmes to concerned staff from the Ministry of Agriculture (MoA). The Workshop was organized for the stakeholders of the Programme who are interested in agro-meteorological forecasting, i.e. producers and users of agro-meteorological data, namely staff from the Ministry of Agriculture, Hydromet and the National Statistical Service (NSS). The Workshop was designed in such a way as to engage all participants in discussions and reflect on appropriate recommendations for all state institutions involved in crop forecasting activities in Armenia. The international consultant, Bernard Tychon, was on a mission in Armenia for 10 days, from 6 to 15 October 2010. His tasks were the following:
Review current activities related to agrometeorological forecasting and operational service in Hydromet and the dissemination of information (including through information products) to MoA;
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Identify weaknesses and limitations of current agro-meteorological forecasting and dissemination of information;
Propose options for addressing users’ demand and needs, taking into account the resources available under the Programme to improve capacity of Hydromet and MoA;
Organize a Workshop to present and discuss with Hydromet and the users of agrometeorological information the options identified to address users’ demand and needs;
Finalize a proposal to provide support under the Programme with different options, integrating the comments received during the Workshop, with a budget and an agenda for implementation.
The agenda of the Workshop was established in consultation with the international consultant and Hydromet staff. It is provided in Annex 1. All participants were provided with a package of documents:
list of participants
agenda
brief description of the EC/FAO Programme
hard copy of the presentation delivered by Nelly Arakelyan (HYROMET)
hard copy of the presentation delivered by Bernard Tychon
agrometeorological bulletin (produced by Hydromet)
3. Participation
The Workshop was attended by 26 participants, including the international consultant and the Country Coordinator. Participants belong to a number of national institutions: Ministry of Agriculture (represented by the Crop Production and Agricultural Planning Departments), National Statistical Service (Food Security Unit), Hydromet and the Armenian state Agricultural Academy. The list of Workshop participants is presented in Annex 2.
4. Process
The Workshop was opened by a Welcoming speech of the Hydromet Director (presented by his Deputy, H. Melkonyan). He noted that the objectives of the Programme were similar to the activities of their Service. He also underlined the importance of the improvement of crop forecasting in Armenia.
The first presentation was devoted to the EC/FAO Programme, its objectives, areas of intervention, main activities and expected results (M. Tapaltsyan, Country Coordinator). The Country Coordinator also thanked the Director of Hydromet for all the support provided to organize the Workshop.
The next presentation on the use of agrometeorological data and delivery of information to the users was delivered by N. Arakelyan from Agrometerological Forecasting Division. The presentation focused on the observation, products, problems and proposals on improvement of agromet forecasting. She presented the activities of their Division, the list of users and the ways of delivering the information to the users.
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The last presentation on options to improve crop forecasting in Armenia was made by B. Tychon. The presentation comprised 3 main parts: introduction to operational crop yield forecasting techniques (a new crop-forecasting system to be adjusted to Armenia, present status of the crop yield forecast in Armenia and proposed improvements).
The presentation of the international consultant is provided in Annex 3.
Each presentation was followed by an active discussion among Participants. The most active discussion followed the presentation of B. Tychon.
5. Main results and follow-up activities The main options proposed by the consultant to improve crop forecasting and the dissemination of the information were the following:
Organize an Interinstitutional Working Group, including MoA, Hydromet and NSS representatives, to strengthen the collaboration between the national institutions; this will allow improving communication and collaboration between Hydromet, MoA, farmers and other users of agrometeorological information.
Introduce a new crop yield forecasting system; there is high demand and need for improving agrometeorological forecasts. A new system on crop forecasting was presented by the Consultant.
Train relevant staff from MoA, Hydromet and NSS on a new crop yield forecasting system and remote sensing. Probably, 2-3 people from each institution.
Post the Agrometerological Bulletin currently developed by Hydromet on the Hydromet Website (www.meteo.am); this will ensure access to all users; there is currently quite a limited access to agrometeorological bulletins. For instance, Marz support centres do not receive the bulletins, which is missed opportunity.
Increase the interest and capacity of Hydromet in Plant Diseases; the consultant has identified high need for using a prediction model for crop disease development while, according to MoA, 30% yield reductions were observed in years with high disease pressure (in case plant protection measures are not implemented in due course).
Design booklets with recommendations to farmers in emergencies and unfavourable climatic conditions.
Procure required relevant equipment (including both specific meteorological and IT equipment, e.g. a laptop for Hydrometeorological station) for Hydromet to improve agromet forecasting.
The implementation of these recommendations will result in mitigating the negative impacts of climate vagaries and generating positive impacts. It will also assist the decision-makers in better planning their activities to support farmers. The Workshop clearly showed that there is a strong interest from the national institutions in improving crop yield forecasting. The participants agreed with the recommendations of the international consultant. The options proposed were explained in details. Improving crop forecasting is based on a system concept which will have to be adapted to the Armenian context. It will require a strong investment from Hydromet and a partnership with MoA (and with NSS at the beginning at least). All parties are now informed of the proposal and there is a consensus on the options proposed by the consultant. This is the main result of the Workshop.
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Based on the mission of the international consultant and the inputs provided by Workshop participants, Programme staff will finalize a proposal, integrating the comments received during the Workshop, with a budget and an agenda for implementation.
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Annex 1: Workshop Agenda
1300 – Snacks, sandwiches, beverage
1400- Opening speech, Levon Vardanyan, Hydromet, Director (Presented by Hamlet Melkonyan, Deputy Director)
1415 - Presentation on Programme, Mane Tapaltsyan, EC/ FAO Programme on Food Security Information Systems to Improve Decision Making in East area, Country Coordinator
1440 – Presentation on Use of Agrometerological data and Delivery of Information to the users, Nelly Arakelyan, Hydromet, Agrometereological Forecasting Division
1515 - Proposals on Improvement of Crop Forecasting in Armenia, Bernard Tychon, Liege University, Professor, Agrometeorologist
1700 - Discussions
1745 - Visit to the Divisions of Hydromet
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Annex 2. List of Workshop Participants
1. L. Vardanyan, RA Ministry of Emergency, Armstatehydromet, Director 2. H. Melkonyan, RA Ministry of Emergency, Armstatehydromet, Deputy Director 3. Z. Petrosyan, Armstatehydromet Operational Hydrometerological Centre, Head 4. V. Grigoryan, Advisor to the Arstatehydromet, Director 5. D. Avagyan, European Commission (not attended) 6. M. Tapaltsyan, EC/ FAO Programme on Food Security Information Systems, Country Cordinator 7. L. Grigoryan, Agrometeorological Forecasts, Head of Division 8. N. Hakobyan, Hydrometeorological Center of Information and Marketing Division, Head 9. L. Simonyan, Agrometeorology Division, Head 10. N. Arakelyan, Agrometeorological Forecasts Division, Leading Specialist 11. K. Yesayan, RA Ministry of Agriculture, Horticulture Division, Head 12. G. Harutyunyan, RA Ministry of Agriculture, Land Management and Use Division, Head 13. H. Lemberyan, RA Ministry of Agriculture, Agricultural Planning Division, Head 14. A. Petrosyan, RA Ministry of Agriculture, Agricultural Planning Division, Chief Specialist 15. A. Hakobdjanyan, RA National Statistical Service, Food Security Division, Head 16. L. Aleksanyan, Agricultural Support Marz Center of Armavir, Head 17. Kh. Mkrtchyan, Agricultural Support Marz Center of Aragotsotn, Head 18. G. Yeghiazaryan, Agrogitaspyur Department, Armenian State Agricultural Academy 19. B. Zakaryan, Armstatehydromet, Hydrography Division, Head 20. G. Surenyan, Meteorological Forecasts Division, Head 21. A. Hovsepyan, Climate Research Division, Head 22. D. Hovhannisyan, Climatology Division, Head 23. V. Badalyan, Agrometeological Division, Chief Specialist
A few more specialists from Operational Hydrometeorological Centre, Hydrometeorological Center of Information and Marketing, Climate Research Division, Climatology Division of Armstatehydromet were also present at the Workshop.
Annex 3. Presentation of the international consultant
Crop yield forecasting proposal for Armenia
Bernard TYCHON([email protected])
FAO ConsultantHydromet, Yerervan, 13 October 2010
Content
• Operational Crop yield forecasting techniques
• Present status of the Crop Yield Forecast in Armenia
• Proposed improvements• Conclusions
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Operational crop yield forecasting techniques
Objectives of crop production and crop yield forecasting systems
• Pricing• Market stability• Food security• Control of supply
} data in real time!
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EU-15 Cereals balance sheet:Marketing year: 2002/2003Common (Mio t)
wheat Barley Durum Maize Rye Sorghum Oats Triticale Others EUR 15Beginning stocks (01.07.2002)
Market 12.7 7.3 1.0 5.3 1.0 0.0 0.6 0.6 0.1 28.5Intervention 0.5 2.5 0.0 0.0 5.1 0.0 0.0 0.0 0.0 8.1
Total 13.2 9.8 1.0 5.3 6.1 0.0 0.6 0.6 0.1 36.6Usable production 93.9 47.7 9.4 40.0 4.7 0.7 6.8 5.2 0.7 209.2Import 6.2 0.1 0.4 3.0 0.0 0.1 0.0 0.0 0.2 10.0TOTAL AVAILABILITIES 113.2 57.6 10.9 48.3 10.8 0.8 7.4 5.8 1.0 255.8USE - Human 33.0 0.0 7.0 2.5 1.5 0.2 1.3 0.0 0.0 45.5 - Seed 2.9 2.0 0.8 0.2 0.2 0.0 0.3 0.2 0.2 6.8 - Industrial 6.3 7.4 0.0 4.4 0.2 0.0 0.2 0.0 0.1 18.6 -Ultra peripheral islands 0.3 0.2 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.9 - Animal feed 41.6 31.8 1.0 32.0 2.0 0.7 4.1 4.8 0.4 118.4TOTAL USE 84.1 41.4 8.8 39.5 3.9 0.8 5.9 5.0 0.7 190.0Solde disponible 29.2 16.2 2.1 8.8 6.9 0.0 1.5 0.8 0.3 65.8Export (1) 16.5 9.0 0.9 2.3 * 1.5 0.0 0.7 0.0 0.0 30.9End stocks (30.06.2003)
Market 12.7 6.7 1.2 6.5 1.0 0.0 0.8 0.8 0.3 29.9Intervention 0.0 0.5 0.0 0.0 4.4 0.0 0.0 0.0 0.0 5.0
Total 12.7 7.2 1.2 6.5 5.4 0.0 0.8 0.8 0.3 34.9Change in stocks -0.5 -2.6 0.1 1.2 -0.7 0.0 0.2 0.2 0.2 -1.8Change in public stocks -0.5 -2.0 0.0 0.0 -0.7 0.0 0.0 0.0 0.0 -3.1
(1) Grains equivalent. *) Maize includes 1.8 mio. t processed products and animal feedMaximum W T O: 2002/2003 ESTIMATED EXPORT QUANTITIES 2002/2003
Wheat incl. durum 14.438 mio t +0,5 mio t food aid 17.40 mio t (food aid included and refund-free)Coarse grains 10.8432 mio t.(inclu. 0,4 mio t potato starch) 13.50 mio t (inclu 1.8 mio t maize products, but excl. 0,4 mio t potato starch)
Production = Yield X Area
Basic relation
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Yield assessment
Yield variability in cereals
0
1
2
3
4
5
6
7
1961 1965 1969 1973 1977 1981 1985 1989 1993
Yiel
d (to
ns/h
a)
Kyrgyzstan
Egypt
Romania
SAUDI
USA
Gommes, FAO, 2003
10
Yield factors of variability
30
40
50
60
70
80
90
1960 1965 1970 1975 1980 1985 1990 1995
Yiel
d (a
rbitr
ary
units
)
0
5
10
15
20
25
30
Fact
ors
F1 to
F5
(arb
itray
uni
ts)
YieldF1 (innovation) F2 (policy)F3 (trend)F4 (extreme factor) F5 (weather)
In Gommes, FAO, 2003
Crops
Soils
Weatherstations
Remotesensing
0
2000
4000
6000
8000
10000
120001975197619771978197919801981198219831984198519861987198819891990199119921993199419951996199719981999
Harvest year
Yiel
d (k
g/ha
)
Vegetationindices
(NDVI/DMP)
Vegetationindices
(NDVI/DMP)
Climate dataClimate data
Crop, soil & management parameters
Crop, soil & management parameters
Crop growthsimulation model(s)
Cultivatedarea
Cultivatedarea
Water balance parameters
Water balance parameters
Yieldprediction
moduleProductionProduction
Country yieldAgricultural statistics
Crop monitoring & Yield forecasting: General Flowchart
Estimates for each YEAR x REGION x CROP on:- Yield = fcal(4 types of Indicators)
Trend, Meteorology, Crop growth model, Remote Sensing- Production = Yield x Area
FAO, modified
11
Water balance at station level
NDVI or other gridded data
ETA /grid
Agricultural statistics
ETA District/marz
Yield district (Marz)
1987
2002
1985
Updated general methodology
INPUTS
AMS ETP
Actual rainfall
STATISTICA
NOAA GAC
V A S T
Yield prediction model at
departmental level
OUTPUT
Temperature, RR, RH,… Yield prediction
model at national level
W I N D I S P
MATLAB
Initial Water Holding Content
Soil Water Satisfaction Index
Water excess, deficit
Actual ETA
…
NDVI
INDEPENDENT VARIABLES
CROP YIELD DATA
Historical crop yield data at departmental level
Yield agregation at national level
METEO
Starting date
NDVI max
Time peak
…
Cumulated actual rainfall
ExplanatoryVariables
Spot-VEGETATION
AGROMET SHELL (AMS)
12
Yield and forthcoming weather…
Present status of the Crop Yield Forecasting in Armenia
13
• Information provided by Hydromet– Field observations
• Meteorological data (3-hourly data)• Phenological data (bi-weekly data)• Dense agromet observation network (36 stations !)
– Crop yield forecasting for major crops• Winter wheat, Barley, Apricots, Grape, Onion +
Pasture/Grassland• Based on agromet models developed a long time
ago (input data = temperature, rainfall, development stage, most of the time)
• Information provided by Ministry of Agriculture (MoA)– Based on agricultural research/advise centers
field observations (Mars Centers)– Crop Yield Forecast based on expertise inside
the MoA
14
Communication
By email or telephone to- MoA- Private companies (Beer, Sugar,…)
Proposed improvements
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Proposal
• Communication improvement:– Set up of a multidisciplinary working group
combining Hyrdromet and MoA expertise in order to provide an improved yield assessment/forecast of major crops (sharing of data and experiences)
• Ten daily meeting/Monthly meeting/Emergency meeting
• Led by Hydromet or another rotating management – Website development (Hydromet activities
show-case with a connection to MoA website)
Proposal• Technical improvements:
– Adopt the Crop Yield Forecasting (CYF) general methodology applied in many countries by adapting it to the Armenian context
– Integrate remote sensing data into the Armenian CYF system
– Improve spatial interpolation through adapted interpolation techniques, especially for crop development stages
– Develop plant diseases model for plant protection activities that are also crucial for crop yield forecasting
– Consider extreme events in the crop yield forecast (early frost, hail, locusts invasion,…)
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Methodology4 types of explanatory variables
Agrometeorologicalfactors
Meteorologicalfactors
RS derived factors
Output of an agrometmodel AgrometShell Sum of met. variables
Output of Chronos programme and cumulated NDVI
Chronos metricsNDVI image processing tool
Calculation of metrics of the different time series
Input data:
- 10-daily NDVI time series for the considered period
With a spreadsheet
Sum of some met data that could explain yield: rain, ET0, radiation,…
AgrometShellFAO Tool
Simulation of Crop specific soil water balance and calculation of a set of parameters.
Input data:- Rainfall- Potential Evapotranspiration- Phenological parameters
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Other factors
Methodology4 types of explanatory variables
Agrometeorologicalfactors
Meteorologicalfactors
RS derived factors
Output of an agrometmodel AgrometShell Sum of met. variables
Output of Chronos programme and cumulated NDVI
Chronos metricsNDVI image processing tool
Calculation of metrics of the different time series
Input data:
- 10-daily NDVI time series for the considered period
With a spreadsheet
Sum of some met data that could explain yield: rain, ET0, radiation,…
AgrometShellFAO Tool
Simulation of Crop specific soil water balance and calculation of a set of parameters.
Input data:- Rainfall- Potential Evapotranspiration- Phenological parameters
21
Other factors
17
Crop diseases modelCase of W. Wheat (Septoriosis) – Proculture model
100806040
1/10/99
1/10/99
18
t°
1/12/99
1/12/99
1/02/00
1/02/00
31/03/00
31/03/00
31/05/00
31/05/00
31/07/00
31/07/00
Données climatiques journalières
102
3020100
% hr
3020100
Pluie
102
101
100
102
101
100
102
101
100
102
101
100
101
100
F5F4F3F2F1
Sensitive variety
Infectionsjournalières
(mm)
Meteo hourly data
Phytophtora Infestans (Mildew) on Potato
Time in hour
No infection
Light infection
Aver
age
Tem
p w
ith R
H>9
0%
Moderate infection
High infection
Very high infection
-Wind-Rain
4 – 10 days-Opt T°: 18 to 22 °C-Min 8h with RH > 90%
Infection-free water-opt T° : 12 to 16°C-min 5°C
-opt T°: 17 to 20°C
Guntz-Divoux curves
Potential Infection
Relative Humidity
DECEMBER 2002
JANUARY 2003
FEBRUARY 2003
MARCH 2003
APRIL 2003
-30
-20
-10
0
10
20
30
40
1-Jan 1-Mar 1-May
°C
19
T max T min
POLAND - Western
0
5
10
15
20
25
30
35
1-Jan 1-Feb 1-Mar 1-Apr 1-May
mm snowrain
Frost kill risk
Frost Risk monitoringFrost Risk monitoringFrost Risk monitoring
In Genovese, 2005 (JRC)
Example for Armenia
Ararat Marz
0
20
40
60
80
100
120
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Time (dekade)
Rain
fall
(mm
)
2006-bad2007-good2003-bad
Ararat Marz
-15
-10
-5
0
5
10
15
20
25
30
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Time (dekade)
Tem
pera
ture
(°C)
2006- bad2007-good2003-bad
Example of weather conditions for 3 years in one Marzof Armenia
20
Erevan
Cold
Temperature
(Average)
In NewLocClim
Agromet explanatory variables
April 2006 dek 3 Rain
Actual evapotranspiration
Wheat 2006Water excess during wheat crop growth period (2006)
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SPOT-VEGETATION IMAGERY (Average 10-daily data)
Available at FAO
2007 – Good for wheat
2006 – Bad for wheat
Armenia Marz Vegetation index(2007)
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Time (Dekade)N
DVI
KotaikAragatsotnArmavirAraratErevanChirakLoriVaiots DzorSiunikGhergharkounikTavouchAverage
Armenia Marz Vegetation index (2006)
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Time (Dekade)
NDVI
KotaikAragatsotnArmavirAraratErevanChirakLoriVaiots DzorSiunikGhergharkounikTavouchAverage
First trial for potato
• Simplification: – 10 years of data (meteo, RS, agricultural
statistics)– 1 station per Marz– 1 single model for the whole country– Agrometeo and Remote sensing explanatory
variables only• 26 potential explanatory variables• 4 variables retained after statistical analyses:
– WEXv, ETAf, SVAL and SLOP
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Potato Yield ForecastModel Cross Validation Results
Trend CY Model
R2 = 0.7796
0.00000
5.00000
10.00000
15.00000
20.00000
25.00000
0.000000 5.000000 10.000000 15.000000 20.000000
Official Statistics (T/ha)
Tren
d (T
/ha)
Series1
Linear (Series1)
Agrometeorological CY Model
R2 = 0.8027
0.000000
5.000000
10.000000
15.000000
20.000000
25.000000
0.000000 5.000000 10.000000 15.000000 20.000000
Official statistics (T/ha)
Agr
omet
Mod
el (T
/ha)
Series1
Linear (Series1)
Potato Yield
0.000000
5.000000
10.000000
15.000000
20.000000
25.000000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Time (year)
Yiel
d (t/
ha)
Official figuresAgromet modelSimple trend
Conclusions• Create a CYF working group including both Hydromet
and MoA• Train experts from both institutions in new
agrometeorolgical tools (team building and capacity building)
• Adapt the General CYF flowchart to Armenia in order to provide the first CYF before 20 months
• Equip both teams with hardware and software material• Acquire 3 automatic weather stations for plant disease
forecasting and early warning• Website?
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