Multiple forecasts of kharif rice in orissa state-four year experience of fasal pilot study

Patel, N. K.; Chakraborty, Manab; Dutta, Sujay Kumar; Patnaik, C.; Parihar, J. S.; Moharana, Shreedevi; Das, Ashok; Sarangi, B K; Behera, G.

doi:10.1007/bf03030870

Cited by 9 publications

(3 citation statements)

References 11 publications

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“…It revealed strong relations (i.e., R 2 in the range 0.93 to 0.95 and RMSE in the range 30,519 to 37,451 ha, at 23 district-levels) between modeled and ground-based area estimates. Our results were also similar to other studies: Panigrahy et al (1997) obtained accuracies in the range 85.8 to 91.5% in the early estimation of rice areas in Wet Bengal, India; (ii) Patel et al (2004) found approximately 95% accuracy in forecasting Kharif rice acreage in Orissa, India; (iii) Chen et al (2011) In addition, we also compared the boro rice area estimated using only two images acquired during the initial/transplantation and peak greenness stages in the scope of this paper with those extracted in an earlier study (Mosleh & Hassan, 2014). In that study, we employed ten 16-day composite of NDVI images acquired over the entire growing season and estimated the boro rice area after the harvesting of the crop.…”

Section: Delineation Of Areas Under Rice Cultivationsupporting

confidence: 92%

Development of a remote sensing-based rice yield forecasting model

Mosleh

Hassan

Chowdhury

2016

Span. j. agric. res.

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This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh.

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Section: Delineation Of Areas Under Rice Cultivationsupporting

confidence: 92%

Development of a remote sensing-based rice yield forecasting model

Mosleh

Hassan

Chowdhury

2016

Span. j. agric. res.

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“…Kharif rice which happens to be dominant crop of the state has been chosen for demonstrating multiple forecasts at district level. Four in-season forecasts were made during kharif seasons (Patel et al, 2004); the first forecast of rice acreage at the beginning of kharif crop season using meteorological models, second forecast of district level acreage at mid growth season using two-date RADARSAT SCANSAR data and yield using meteorological models, third forecast at late growth season of district level acreage using three-date RADARSAT SCANSAR data and yield using meteorological models and revised forecast incorporating field observations at maturity. Summary results of FASAL Pilot Implementation for Kharif rice forecasts for 2006-07 are given in Table 2.…”

Section: Fasal Implementation -Orissamentioning

confidence: 99%

FASAL: an integrated approach for crop assessment and production forecasting

Parihar

Oza

2006

SPIE Proceedings

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India has a very well developed system for collection of crop statistics covering more than 50 crops at village level and aggregating it at different administrative levels. However, need for early and in-season crop production forecasting has been strongly felt. Remote sensing for crop assessment has been explored since very beginning of space applications in India. A nation-wide project called Crop Acreage and Production Estimation (CAPE) was launched at the behest of Ministry of Agriculture, Government of India in 1988. Major growing regions in the country for wheat, rice, cotton, groundnut, rapeseed/mustard and Rabi (winter) sorghum were covered. Production forecasts were made about a month before the harvesting using multi-band remote sensing data acquired at optimum bio-window and weather data. Ministry of Agriculture, satisfied with the performance of CAPE, came out with a request to target multiple crop production forecasts starting with crop sowing to end of season.Crop identification with remote sensing data requires using the data when crop has sufficiently grown. However, forecasting of crop at sowing stage would require use of weather data and information on economic factors controlling the farmer's response. Considering these things "Forecasting Agricultural output using Space, Agrometeorological and Land based observations (FASAL)" concept was devised. FASAL aims at using econometric models to forecast the area and production before the crop sowing operations. In unirrigated areas, information on amount and distribution of rainfall is being used for forecasting the crop acreage as well as yield. Remote sensing data, both optical and microwave form the core of crop area enumeration, crop condition assessment and production forecasting. Temporal remote sensing data is being used to monitor the crop through its growing period. Vegetation indices and weather parameter derived from surface and satellite observations will be used to develop the crop growth monitoring system.Components of FASAL concept have been developed, tested and implemented through a series of exercise and these are i) National wheat and winter potato production forecasting using IRS AWiFS data, ii) National Kharif rice production forecasting using Radarsat SAR data, and iii) District level FASAL implementation in Orissa state. Typically three in-season forecasts are being made. With this the FASAL concept of using the multi source data and techniques has been successfully demonstrated. FASAL implementation has been taken up to make national level multiple forecast of crops like rice, wheat, cotton, sugarcane, rapeseed/mustard, rabi-sorghum, winter-potato and jute. Procedure development for use of remote sensing, weather data -surface measurements as well as derived from satellite data, field and ancillary data to run the crop growth simulation models has been taken up. The programme is sponsored by Ministry of Agriculture, Government of India. Space Applications Centre of Indian Space Research Organisation has provided the scientific...

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“…Advanced techniques of satellite remote sensing and Geographic Information System (GIS) are now available to derive spatial pattern of crop area, cropping pattern change, crop calendar and crop rotation etc. (Navalgund et al, 1991;Parihar and Dadhwal, 2002;Rajak et al, 2002;Rajak et al, 2008;Panigrahy et al, 2005;Panigrahy & Sharma, 1997;Patel et al, 2004). High temporal resolution data is essential to analyze the crop calendar.…”

Section: Introductionmentioning

confidence: 99%

Spatial Temporal Sowing Pattern of Rapeseed-Mustard Crop in India Using Multi-Date Irs Awifs Data

Rajak

Patel

Chaudhari

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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This paper highlights the results on spatial pattern of sowing of rapeseed/mustard in four major states in India using multidate Advanced Wide Field Sensor (AWiFS) data for 2010-11 crop season. Geo-referenced, calibrated AWiFS data acquired during October 2010 to February 2011 were used to generate the Normalised Difference Vegetation Index (NDVI) image sets. Iterative Self-Organizing Data Analysis Technique (ISODATA) based clustering of the multi date NDVI dataset for mustard crop pixels was performed. The clusters were segregated to spectral emergence classes using a spectral profile matching approach with reference to ground truth data. The sowing dates were derived from the spectral emergence data using a lag period based on field observation. Analysis showed the sowing pattern in the study states is spread over around 60 days from mid October to mid December. Three distinct clusters of sowing pattern were observed. The major one (around 40%) is sown between mid October and first week of November. Around 25% area is sown from last week of November to mid December. The other 35% area is sown in between these two periods. Analysis of temperature, a key weather variable influencing the growth of this crop, showed that the crop sowing in northern Rajasthan and Haryana is delayed by about one month to avoid the frost damage during reproductive phase. In the parts of Gujarat, southern parts of Rajasthan and Madhya Pradesh (MP), an early sowing in the second fortnight of October was observed, mainly to avoid higher mean temperatures during the month of March.

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Multiple forecasts of kharif rice in orissa state-four year experience of fasal pilot study

Cited by 9 publications

References 11 publications

Development of a remote sensing-based rice yield forecasting model

Development of a remote sensing-based rice yield forecasting model

FASAL: an integrated approach for crop assessment and production forecasting

Spatial Temporal Sowing Pattern of Rapeseed-Mustard Crop in India Using Multi-Date Irs Awifs Data

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