India rice bull background
Upcoming SlideShare
Loading in...5
×
 

India rice bull background

on

  • 247 views

 

Statistics

Views

Total Views
247
Views on SlideShare
247
Embed Views
0

Actions

Likes
0
Downloads
3
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

India rice bull background India rice bull background Document Transcript

  • INDIA RICE BULLETIN BACKGROUND DOCUMENT Date: 21/11/2008 About this product……1. OverviewThe India Rice Bulletin is targeted at quantitative rice yield forecasts at State level for India. It is pro-duced at the JRC-IPSC-AGRI4CAST action and adds a new Crop Yield forecasting bulletin to the existingones (MARS European Rice bulletin, MARS Rice bulletin for China, MARS Crop yield forecast for Europe,MARS European Pasture bulletin http://mars.jrc.it/mars/About-us/AGRI4CAST). It is meant to be issuedtwice a year. Crop growth simulation, agro-meteorological analysis, yield forecasts and remote sensinganalysis are done in-house, area statistics are taken from publically available sources.1.1 World rice productionRice is the staple food for half of the global population and the second largest produced cereal worldwide.At the beginning of the 1990s, annual production was around 350 million tons and by the end of the cen-tury it reached 410 million tons. In 2006, world production totaled 635 million tons of milled rice.Production is geographically concentrated in Western and Eastern Asia, with more than 90% of world to-tal production. China and India, which account for more than one-third of global population, supply overhalf of the worlds rice. Brazil is the most important non-Asian producer, followed by the United States.The increase in world production is mainly due to Western and Eastern Asia producers. World rice production Philippines, 2% Indonesia, Bangladesh, Vietnam, Thailand, 1 China, 28% India, 22% 9% 7% Other countries, 17% 6% 5% Mayarmar, 4% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%1.2 India rice productionAt country level, yields have strongly increased since 1950/51, passing from 0.668 t/ha to 2.084 t/ha forthe 2006/07 campaign (Directorate of Rice Development; Government of India; Ministry of Agriculture(Dept. of Agriculture and Cooperation); http://drdpat.bih.nic.in/). Among the main producers, West Ben-gal, Andhra Pradesh, Uttar Pradesh increased significantly their productivity levels, although there aresignificant differences in the 5-year average yields, with Andhra Pradesh almost reaching 3 t/ha and UttarPradesh yielding about 2 t/ha (see map following page).___________________________________________________________________________2. MethodologyThe core of the system is the CropML-WARM modelused to simulate rice crop growth. The crop yieldforecasting approach and agro-meteorological analy-sis follows closely the approach applied for Europe (atthe moment only scenario analysis is performed). In-stead of observed meteorological data, ECMWF data isused. http://mars.jrc.ec.europa.eu/mars/About-us/AGRI4CAST/Crop-yield-forecast.2.1. Weather dataAs crop growth simulation models require meteoro-logical information and at the current stage no ob-served meteorological data is available to the actionECMWF (European Centre for Medium-Range WeatherForecast; http://www.ecmwf.int/) daily meteorologi-cal forecast data is used. Data resolution is one de-gree latitude × one degree longitude. The archive iscreated using the ERA 40 data set. The Figure shows,as an example, the yearly maximum of the maximumdaily temperature for the long term average.© European Communities 2008 JRC48190Editorial staff: R. Confalonieri, A.S. Rosenmund, C.G. Conte, B. Baruth, I. Savin, L. Nisini Agriculture Unit – Agri4cast /JRC.Data production: D. Fanchini, P.A. Bianchi, A. Klisch Agriculture Unit – Agri4cast /JRC. Pag. 1
  • Editorial staff: R. Confalonieri, A.S. Rosenmund, C.G. Conte, B. Baruth, I. Savin, L. Nisini Agriculture Unit – Agri4cast /JRC.Data production: D. Fanchini, P.A. Bianchi, A. Klisch Agriculture Unit – Agri4cast /JRC. Pag. 2
  • 2.2 Simulation modelA new system (WOCS; WOrld Crop Simulator) has been developed and used for running the simulations.WOCS is a generic environment suitable for long term, spatially distributed simulations of the impact ofdriving factors (e.g. weather variables) on crops’ behavior and on crop-pathogens interactions.The WOCS architecture allows to easily adapt thesystem to new conditions, regions, crops or to ac- Graphical user interfacecount for new processes. WOCS is used for ricesimulations also in Europe and China. The model Adapter model 1 Model 1used for simulating the crop growth and develop-ment of rice is WARM, in the version included in the Framework Adapter model 2 Model 2crop model library CropML.WARM (Confalonieri et al., 2006 ) is the model for 1 Adapter model 3 Model 3rice simulation developed by AGRI4CAST in col- … …laboration with other Institutions and used for the Data layerEU-27 MARS rice bulletin since 2005.Data used for WARM parameterization over India Databasecomes from five published field experiments carriedout in India between 2001 and 2005, where crop development stage, aboveground biomass accumulationand leaf area index were monitored.CropML (Crop Modelling Library) is a new platform for simulating crops growth developed by AGRI4CAST(Agriculture Unit, JRC). CropML implements a multi-model approach for crops growth, aiming at providingthe user with a set of modelling solutions to be run in parallel or individually according to the specificneeds/situation. CropML currently implements the CropSyst model, a generic crop simulator for herba-ceous crops and WARM.2.3 Model preparation: Agro climatic zonation and spatial schematizationEight agroclimatic zones were considered, according to the Directorate of Rice Development; Governmentof India; Ministry of Agriculture (Dept. of Agriculture and Cooperation); http://drdpat.bih.nic.in/. Theyare: The Arid Western Plains (light brown in the map below), The Humid Bengal-Assam Basin (yellow),The Humid-Eastern Himalayan Region (olive-green), The Humid to Semi-Arid Western Ghats and Karna-taka Plateau (light green), the Humid Western Himalayan Region (dark green), The Semi-Arid Lava Pla-teau and Central Highlands (dark brown), The Sub-Humid Sutlei-Ganga Alluvial Plains (light blue), andThe Sub-Humid to Humid Eastern and South-Eastern Uplands (dark green).The elementary simulation units were obtained by intersecting the rice mask obtained from Xiao et al.(2006)2 re-projected to SPOT-VGT with administrative boundaries and the ECMWF grid weather. A qualitycheck of the rice mask was carried out using different approaches, by analysis of SPOT-VGT time series1 Confalonieri, R., Acutis, M., Bellocchi, G., Cerrani, I., Tarantola, S., Donatelli, M., Genovese, G., 2006. Exploratory sensitivity analysis of CropSyst, WARM and WOFOST: a case-study with rice biomass simulations. Italian Journal of Agrometeorology, 11, 17-25.2 Xiao, X., Boles, S., Frolking, S., ChangSheng, L., Jagadeesh, Y.B., Salas, W., Moore, B., 2006. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sensing of Environment, 100, 95-113.Editorial staff: R. Confalonieri, A.S. Rosenmund, C.G. Conte, B. Baruth, I. Savin, L. Nisini Agriculture Unit – Agri4cast /JRC.Data production: D. Fanchini, P.A. Bianchi, A. Klisch Agriculture Unit – Agri4cast /JRC. Pag. 3
  • and by visual comparison with GLOBCOVER.2.4 Satellite analysisThe satellite analysis employs 10-daily NDVI data of SPOT-Vegetation. The dataset comprises the fulltime series starting in 1998 covering the extent of India. The satellite data are atmospherically and geo-metrically corrected. The 10 daily values are retrieved by means of ‘maximum value compositing’. Miss-ing values are interpolated and smoothed afterwards by a modified Swets approach.The cluster analysis is based on the isodata algorithm extracting 7 clusters. Only pixels, that contain riceproduction areas according to the rice mask, are included in this analysis. The clustering is performed forthe current year of 2008, but also for percentage differences between the current year and an averageyear, that is calculated as the average of all previous years of the time series (1998 – 2007).Further developments• The modules for the simulation of cold-induced spikelet sterility and blast disease (already used for the EU-27 MARS rice bulletin) will be analyzed and the suitability of their parameterizations evaluated for a possible future use for rice simulation in India.• Crop calendars will be derived from NDVI profiles, thus accounting for inter-annual variability in sowing dates. Currently they derive from average statistics, like for the other MARS bulletin.• ECMWF data is ingested as is, in the future we will seek for calibration and bias reduction.Next bulletinIn case of interest, the next rice bulletin for India could be issued in June 2009. It would include theanalysis and simulations of the last part of the 2008/09 campaign and some of the improvements justmentioned.Contacts: Agri4cast Action, bettina.baruth@jrc.itMARS bulletins for European rice can be found under: http://mars.jrc.ec.europa.eu/mars/Bulletins-PublicationsDisclaimer: The geographic borders are purely a graphical representation and are only intended to be indicative. These boundaries do not necessar-ily reflect the official EC position. Legal Notice: Neither the European Commission nor any person acting on behalf of the commission is responsi-ble for the use, which might be made of the following information.Editorial staff: R. Confalonieri, A.S. Rosenmund, C.G. Conte, B. Baruth, I. Savin, L. Nisini Agriculture Unit – Agri4cast /JRC.Data production: D. Fanchini, P.A. Bianchi, A. Klisch Agriculture Unit – Agri4cast /JRC. Pag. 4