Developing a Smarter Crop Forecasting
System for Food Security Assessment
and Monitoring in the Philippines
Felino P. Lansigan
Professor, Institute of Statistics and
Dean, College of Arts and Sciences
University of the Philippines Los Banos
[email protected]
Outline of Presentation
• Generation of official statistics for monitoring crop
production
• Remote Sensing in estimation of cropped area
• Crop Simulation Modeling to determine crop yields
• Smarter CFS to generate crop forecasts
• Technical issues and constraints, and Implementation
challenges
• DSS and ICT-based and CFS-related tools in Project SARAI
• Concluding remarks
Assessing Food Security involves:
• Availability –estimating and forecasting
production of the staple crops such as rice
and corn.
• Accessibility
• Affordability
• Sustainability
• Nutrition
Questions related to Food Availability:
• How sufficient are we in terms of
production of staple crops?
• How much buffer stock do we really have
considering that capacity of available
storage facilities is not enough?
• How much do we expect to harvest in the
next cropping season?
Challenge: Need reliable data on cropped area and yield
𝐴4 , 𝑌4
𝐴1 , 𝑌2
𝐴3 , 𝑌3
𝐴2 , 𝑌2
. . . 𝐴𝑛 , 𝑌𝑛
𝐴5 , 𝑌5
Pi = Ai ∗ Yi
where
𝑃𝑖 Production of the ith unit
𝐴𝑖 Area of the ith unit
𝑌𝑖 Yield of the ith unit
Average Production=
where
wi Pi
Food Security Assessment
• Food availability analysis and monitoring
requires a Crop Forecasting System.
• Official statistics on crop production are based
on the Palay and Corn Production Survey
(PCPS) c/o BAS.
Palay and Corn Production
Survey (PCPS)
• formerly Rice and Corn
Production Survey (RCPS)
• Two national quarterly
surveys: PPS and CPS
• PCPS covers all provinces
excluding Batanes.
Source: BAS
Scope of PCPS:
covers sample farming
households in sample barangays in all provinces
except Batanes.
Quarterly surveys with following reference periods:
April Round Survey: January to March
July Round Survey: April to June
October Round Survey: July to September
January Round Survey: October to December
Major outputs of the PCPS
• Final crop estimates for the immediate
past quarter.
• Crop forecasts for the current quarter
based on standing crop.
• Crop forecasts based on farmers’ planting
intentions.
Survey generates the following information
• Area planted/ harvested and production by ecosystem
(palay) and crop type (corn)
• Farm household disposition/ utilization of production
• Area with standing crop
• Planting intentions
• Use of seeds, fertilizers and pesticides
• Awareness and availability of program interventions
Estimation of Cropped Area
•
•
•
•
•
Area planted as per MAO/ Ag. Technician
Cropped area declared by Farmer
Area as per LGU’s records
Area as per Credit provider’s records
Area estimated using GPS
• Estimates of cropped area differ **depending on who is
estimating, locations, purpose, etc.
Reference: Lansigan & Salazar (2005) study in Isabela Province.
Need for Smarter CFS for Food Security
• Advances in S & T provide opportunities and tools to
develop and use knowledge-based and innovative CFS.
• Databases (weather & climate, soils, water resources, etc.)
can now be integrated, and information extracted to
improve decision-making in agricultural and food
production value chain.
• Smarter CFS tools can also be used for FS assessment and
monitoring (also in crop damage/ loss assessments, etc.)
Features of Smarter CFS Tools & Protocols
•
•
•
•
Based on advances in Science & Technology
Resource-efficient (optimal)
Reliable (accurate estimates)
Provide timely (almost real-time) data
Remote Sensing and Crop Production
Estimation of area cropped via Remote Sensing.
RS-generated Data on Crop Production
• Area planted to crop
• Stage of crop growth and development
• Extent or coverage of crop production
Crop Simulation Model to
Estimate Crop Yield
• Process-based crop simulation model
(crop growth and development)
• Model input data requirements:
- Soils data
- Weather data
- Crop management data
- Crop genetic coefficients
Crop Simulation Model Input Data
Crop genetic coefficients
Crop management data
Soil data
Weather Data
Crop genetic coefficients
of rice variety IR 64
Genetic
Coefficient
P1
P20
P2R
P5
G1
G2
G3
G4
IR 64
500 GDD (°C)
12 hours
160 GDDh-1
450 GDD (°C)
60
0.0250 g
1
1
Crop genetic coefficients of Sweet Corn
Genetic
Coefficient
Sweet Corn
P1
210 GDD (°C)
P2
0.520 hours
PHINT
38.90
P5
625 GDD (°C)
G2
907.5 g
G3
10
Some Applications of CSM
•
•
•
•
•
•
Crop yield estimation for a location
Crop forecasting given seasonal climate outlook
Yield gap analysis for specific area
Genotype (G) x Environment (E) analysis
Plant breeding screening and evaluation
Monitoring for crop losses and damages
Remote Sensing Coupled with Crop
Modeling to Assess Crop Production
Crop Forecasts
DOWNSCALING
WEATHER
ESTIMATING
CROP AREA
CROPPING STRATEGY
SIMULATING
SEASONAL
CROP YIELDS
Crop Forecasts in terms of
Probabilities of Crop Yields
Planting Dates during Dry Years in Isabela
1.00
Week 23 (1st week of June)
Week 25 (3rd week of June)
Week 27 (1st week of July)
Probability
0.75
Week 28 (2nd week of July)
0.50
0.25
0.00
0
1000
2000
3000
4000
5000
6000
7000
Yield (kg/ha)
Thursday, May 28, 2015
Probabilities of simulated yields of rice variety
PSB Rc14, Iloilo, 1983-2009 weather data
1.0000
March 29
0.9000
April 26
0.8000
May 24
0.7000
June 7
0.6000
July 5
0.5000
Aug 2
0.4000
0.3000
0.2000
0.1000
0.0000
1500
1700
1900
2100
2300
2500
Best planting of corn on May 24.
2700
2900
3100
Technical Issues and Constraints
• Accurate delineation of agroecological zones or land
management units (LMUs) based on
soils, water regime, and climate.
• Generation of crop genetic coefficients.
• Availability of Soils data
• Weather data
• Crop management data
Implementation Challenges
• Acceptance of the use of innovative and smarter
approaches in the generation of official statistics vis-àvis global standards.
• Continuing research on model development.
• Generation and collection of needed input data on
crops, soils, weather, cultural management, etc.
• Capacity building and training on data collection, and
analysis.
Key Components of Smarter CFS
• Downscaled weather forecasts/ Seasonal climate
outlook
• Crop yield estimation using process-based crop
(simulation/ summary) model (crop- and
location-specific)
• Area estimation using remote sensing
These are opportunities for collaboration.
CLIMATE
FORECAST
CROP AREA
ESTIMATION
Crop Forecasting
System for Rice
and Corn
DOWNSCALING
CROP
FORECASTS
SIMULATED CROP YIELD
Smarter Crop Forecasting System for Rice and Corn
Reference Crop Evapotranspiration and
Effective Rainfall, mm
DSS for Adaptive Planting Calendar
9
8
7
Farmers can be advised
to plant on Week 16
and Week 43 to
optimize the
evapotranspiration and
maximum effective
rainfall, thus minimize
the irrigation costs.
6
5
4
3
2
1
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Week Number (starting from March 1 - 7)
Evapotranspiration
* For irrigated farms
Effective Rainfall
Crop advisory for IPM: Locust Advisory
N
o
El Niňo
Expected?
Yes
Yes
No
Any
abnormalities
that could affect
existing
vegetation?
No
Solitary phase not
likely to be
disturbed
LOW
Situation favors the
occurrence of
gregarious and
migratory phase of
locust
Any historical
occurrence of
locust in the
vicinity?
Are the signs
of drought
apparent?
HIGH
Yes
Any locust
breeding sites
in the area?
Yes
No
Scouting/Monitorin
g for congregation
of nymphs and
adults; and egg
fields
Locust
model
Population
Situation slightly favors
the occurrence of
gregarious and
migratory phase of
locust
MEDIUM
Wind direction/
speed
Spread
direction
Host plants
Crop Advisory – Integrated Crop Management
(Corn): Site specific nutrient management (SSNM)
ISU*
Monsanto
Pioneer
Syngenta
Average
CLSU
Var13
Monsanto
Pioneer
Syngenta
Average
UPLB*
Var13
Monsanto
Pioneer
Syngenta
Average
Var13
N
120
120
120
120
100
220
220
220
220
200
170
180
180
177
180
P
40
70
30
47
30
50
80
40
57
50
80
70
50
67
80
K
30
60
50
47
30
50
70
50
57
80
30
50
40
40
60
Decision Support System Tools
Crop Water
for
Management
DSS Tools for Crop Water Management
Algorithm for Calculating Soil
Moisture Deficit and Yield
Reduction
Low-cost Soil Moisture Sensors
Ground-based Remote Sensing
for Crop Water Stress
Assessment
• Suitability based on
climate, slope,
elevation & soil
properties
• A total of
5,607,424.80 ha
highly suitable for
lowland rice
• A total of
1,812,925.00 ha
highly suitable for
corn
Crop Suitability Mapping
Tools and DSS related to
Smarter CFS in Project SARAI
•
•
•
•
•
DSS for optimal cropping calendar or planting dates
Site-Specific Nutrient Management (rice and corn)
DSS for Crop Water Management (rice & corn)
Pests and Diseases Advisories/ Bulletins
Crop/Land Suitability Mapping
Offer opportunities for collaboration.
Concluding Remarks
• Advances in S & T provide opportunities for the development
and applications of smarter approaches to gather and analyze
data for food security assessment and monitoring.
• RS, CSM and ICT offer tools to develop innovative procedures
to estimate cropped area, and to forecast crop yields.
• Coupled RS and CSM (as smarter CFS) can complement PCPS
in generation of official statistics on crop production.
Concluding Remarks
• Providing good quality and timely data and information
is crucial for food security assessment.
• Mainstreaming smarter CFS and other ICT-based tools
requires acceptance by government agency, e.g. the
Philippine Statistical Authority (PSA) and the DA
regarding the use of advances in S & T such as ICT in
generation of reliable crop forecasts.
Thank you for your attention.
<[email protected]>