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 fplansigan@up.edu.ph
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 A 4, Y 4 A 1, Y 2 A 3, Y 3 A 5, Y 5 A 2, Y 2... A n, Y n
P i = A i Y i where P i Production of the ith unit A i Area of the ith unit Y i Yield of the ith unit Average Production= w i P i where
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 Soil data Crop management data Weather Data
Crop genetic coefficients of rice variety IR 64 Genetic Coefficient IR 64 P1 500 GDD ( C) P20 12 hours P2R 160 GDDh -1 P5 450 GDD ( C) G1 60 G2 0.0250 g G3 1 G4 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 0.75 Week 23 (1st week of June) Week 25 (3rd week of June) Week 27 (1st week of July) Week 28 (2nd week of July) Probability 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 0.9000 0.8000 0.7000 0.6000 0.5000 0.4000 March 29 April 26 May 24 June 7 July 5 Aug 2 0.3000 0.2000 0.1000 0.0000 1500 1700 1900 2100 2300 2500 2700 2900 3100 Best planting of corn on May 24.
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 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) 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. Evapotranspiration Effective Rainfall * For irrigated farms
Crop advisory for IPM: Locust Advisory El Niňo Expected? Yes N o Any historical occurrence of locust in the vicinity? Yes Yes Situation favors the occurrence of gregarious and migratory phase of locust HIGH No Any abnormalities that could affect existing vegetation? No Solitary phase not likely to be disturbed LOW Yes Are the signs of drought apparent? No Situation slightly favors the occurrence of gregarious and migratory phase of locust MEDIUM Wind direction/ speed Any locust breeding sites in the area? Scouting/Monitorin g for congregation of nymphs and adults; and egg fields Locust model Population Spread direction Host plants
Crop Advisory Integrated Crop Management (Corn): Site specific nutrient management (SSNM) ISU* CLSU UPLB* N P K Monsanto 120 40 30 Pioneer 120 70 60 Syngenta 120 30 50 Average 120 47 47 Var13 100 30 30 Monsanto 220 50 50 Pioneer 220 80 70 Syngenta 220 40 50 Average 220 57 57 Var13 200 50 80 Monsanto 170 80 30 Pioneer 180 70 50 Syngenta 180 50 40 Average 177 67 40 Var13 180 80 60
Decision Support System Tools for Crop Water 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
Crop Suitability Mapping 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
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. <fplansigan@up.edu.ph>