Presentation on theme: "Rice and poverty coincide in Asia, home to over 70% of the world’s poor (900 million people) and where almost 90% of the world’s rice is produced and."— Presentation transcript:
Rice and poverty coincide in Asia, home to over 70% of the world’s poor (900 million people) and where almost 90% of the world’s rice is produced and consumed. Rice and poverty coincide in Asia, home to over 70% of the world’s poor (900 million people) and where almost 90% of the world’s rice is produced and consumed. The Indonesian population continues to grow rapidly, meanwhile commodity prices are rising and the available arable land area is decreasing. The Indonesian population continues to grow rapidly, meanwhile commodity prices are rising and the available arable land area is decreasing. In most of the developing world, rice availability is equated with food security and closely connected to political stability: Rice price increases have caused social unrest in several countries, most recently during the food crisis of In most of the developing world, rice availability is equated with food security and closely connected to political stability: Rice price increases have caused social unrest in several countries, most recently during the food crisis of Background
Rice for Asia and INDONESIA Dominated by Asian countries Distributed in wide range of climatic zones FAOSTAT  indispensable staple food crop for billions of people Consumption 139 kg/capita/year (Indonesia) Asian countries are responsible for approximately 90% of the world rice production and consumptions.
Rice is still the Main Components of Food Security (80% of national carbohydrate needs) Paddy field in Indonesia (8,106,562 million ha (MoA, 2012)) 40% Indonesian paddy field located in Java Island (60% of national food production) Increasing of Demand (Quantity) + 1.5% / year in 2025 : the need of rice in Indonesia is around 58.6 million tons (Dry Milled Rice) →12.91 million ha Paddy field 2010 – 2014: Target is to maintain self-sufficiency of 5 commodities (rice and corn, soybean, sugar, & meat) Surplus target of rice : 10 million tons in 2014 Cont...
Rice production per-province (1993 – 2011) Source: BPS, 2012 Approximately 40.33% of those in Indonesia, which is an area of 3.25 million hectares are in Java. Approximately 40.33% of those in Indonesia, which is an area of 3.25 million hectares are in Java. To support nearly 53.82% (36,831,357 tons) of national food production. To support nearly 53.82% (36,831,357 tons) of national food production. in 2011 the harvested area increased to million hectares with productivity of 5.02 tons / hectare. in 2011 the harvested area increased to million hectares with productivity of 5.02 tons / hectare.
Agricultural information esp. cultivated area, growth and yield of major crops → important for food policy and economic planning. Agricultural information esp. cultivated area, growth and yield of major crops → important for food policy and economic planning. At present: crop acreage, production & other agricultural information → National Bureau of Statistics of Indonesia : At present: crop acreage, production & other agricultural information → National Bureau of Statistics of Indonesia : ‐ Report summary of several administrative levels ‐ Sampling based on classical statistics (ubinan). ‐ Rice productivity data collected through the Crop Cutting Survey using SUB-S form based on household approach by a direct measurement in 2.5x2.5m crop cutting plot. ‐ The data collection is conducted in every subround (four- monthly) with Sub-district Statistic Coordinator (called KSK as coordinator of Sub Disrict Statistik) and KCD being the enumerator.
Remote sensing data is expected can support crop monitoring and strengthening food security Remote sensing data is expected can support crop monitoring and strengthening food security IAARD is going to determine to research strategy to use remote sensing for food security program IAARD is going to determine to research strategy to use remote sensing for food security program
Identification of present landuse → paddy field and upland agriculture in crop production centers, for supporting the implementation and enforcement of UU Lahan Pertanian Berkelanjutan. Updating and auditing land resources mapping & identification to support : OPTIMIZING EXISTING AGRICULTURAL LAND Monitoring paddy growing stages, and estimating paddy planting and harvesting area. Detecting flood or drought affected area → providing recommendations for mitigation. Providing near real time information of paddy field condition by integrating with other information (such as Cropping Calendar) in an Integrated System Information for food stock policy. State of the Art of Remote Sensing Utilization for National Agriculture Development
Updating more detailed map of paddy field by verifying and using more precise satellite data Completing agricultural landuse planning for special commodities and precision farming, Site selection of local food potential for food diversification based on resources and post harvest handling, Auditing tertiary irrigation system for water management Monitoring agricultural crops production management, Agricultural hazards monitoring related to crop insurance.
Information System for Agriculture Monitoring In Indonesia Operationally used : Since 1972 : Land Resources Mapping (ICALRD, MoA)→ Recommendation for Agric. Planning → Aerial photograph, Landsat, etc Since 2007 : Integrated / Dinamic Cropping Calendar (IAHRI - ICALRD - IAARD – MoA) based on Rainfall data Vegetation (Greeness) monitoring (LAPAN & BPPT) → MODIS On going project since 2012 : Agricultural Land Resources Information System part of Agricultural Resources Management Information System (ICALRD – IAARD – MoA) → Optic and SAR data Indonesia Rice Crop Monitoring from Space webgms.iis.u- tokyo.ac.jp/DMEWS/INDONESIA (Collaboration ICALRD of MoA – JAXA & Univ. Of Tokyo – LAPAN)
MOVING FORWARD Ministry of Agriculture has envisaged that RS and GIS have a prominent role in promoting efforts for supporting food security. Accurate information is indispensable for making policies related to the spatial distribution of rice fields, water resource management, annual production projections, and market predictions. The major challenge of using remote sensing for estimating rice and crop production area: 1) increasing accuracy and 2) standardization of models.
1. LAPAN : (Indonesian Agency for Aeronautic and Aerospace) Supply satellite data Supply satellite data Methodology Methodology Support the availability UAV Support the availability UAV 2. Universities (IPB, UGM, IPB, ITS, UI, BINUS, dll): Methodology Methodology Capacity building Capacity building 3. BPPT : (Agency Assessment for Application Technology) Methodology Methodology 4. Central Beureau Statistic & PUSDATIN of MoA: Field sampling methodology Field sampling methodology Field data measurement and field work for collecting data Field data measurement and field work for collecting data
Project Name Institutions1. Asia Rice – GEOGLAM : Group Earth Observation Global Agriculture Monitoring Team: JAXA (Japan), ISRO (India), GISTDA (Thailand),, VAST/STI (Vietnam), LAPAN (Indonesia), ICALRD/MOA (Indonesia), MRC, IRSA (China) Supported by : SIMBIOSS (Australia), and RESTEC (Japan) 2. SAFE – APRSAF : Satellite Aplication for Environment – Asia Pasific Regional Space Agency Forum ICALRD of MoA, LAPAN (Indonesia) JAXA, Univ of Tokyo, National Institute for Agro-Environmental Sciences - NIAES (Japan) 3. RIICE : RS-Based Information & Insurance for Crop in Emerging Economies ICALRD/IAARD-MoA (Indonesia), IRRI (Phillipines), SARMAP (Switzerland)
Objective: To assess existing model using multi-spectral optical remote sensing data as well as SAR data, for estimating planted area, yield, and production of paddy. To apply methods or model to obtain a map of planted area, yield, and production of rice derived from remote sensing data Expected Output of Year I (2013/14): Selected Satellite based model of estimating planted, harvested area, and production of paddy in the center of paddy area validated using ground truth data Paddy yield information at sub district level of acreage, area harvested, and production of paddy derived from remote sensing data analysis in in the center of paddy area.
Collaboration with Agencies Executing AgencyActivities Excuting Agencies LAPAN PUSDTAIN of MoA BPS Developing models of paddy cropping pattern in Java island. Examining model to identify paddy growing stage and its abilty to estimate yield. Conducting workshop & training Supporting Agencies JAXA National Institute for Agro-Environmental Sciences (NIAES), Japan: Satellite data provision (ALOS AVNIR-2 and PALSAR, ALOS-2) Remote Sensing Application and Training: (1) Rice yield estimation by SAR data backscatter, (2) The use of empirical model which use relationship between vegetation index and yield. Supporting for fieldwork & the use of rice monitoring software Training, workshop, and seminar are matters to be considered during the activity Technical supporter, Model development Assist the automatic data processing needed for the continuous monitoring. End Users AIAT – IAARD of MoA Local Government Conducting field surveys for validation of model Conducting workshop & seminar
Satellite data analyzed : 16 days-composite MODIS data. Methods used : EVI, using LAPAN Model to estimate planted, harvested area, and production of paddy in the center of paddy area validated using ground truth data. Data field collected from Subang area
Framework of operational use after this prototyping Satellite data preparation : Inventory and selection of basic analytical methods Satellite data processing : digital analysis of satellite imagery, identifying paddy growing stage estimating paddy harvested area and production of paddy ICALRD – IAARD – MoA LAPAN Data Provider: LAPAN JAXA Data & Information Updating Web-GIS based Information Preparation of information (spatial & tabular): paddy planted area, harvested area, and estimates of paddy production. IAARD, LAPAN, BPS, PUSDATIN of MoA User: BPS, PUSDATIN of MoA, Agric. Local Office for Food Crop feedback
Remote sensing technology could be used as a second opininon information for predicting rice area and rice yield. Improvement of accuracy and standardization of models urgently needed for research Need sinergism of national institutional support the vision of a successful RS +GIS/ Geo-spatial Technology in any government organization.