Wheat yield modelling using remote sensing and agrometeorological data in Harayana state

Spectral-trend-agrometeorological yield models for four zones in Harayana state were developed using district level area weighted Normalized Difference Vegetation Index (NDVI), trend predicted yield and meteorological indices like Growing Degree Days (GDD), Temperature Diference (TD) and Rainfall Ac...

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Bibliographic Details
Published in:Journal of the Indian Society of Agricultural Statistics 2003-08, Vol.56 (2)
Main Authors: Verma, U, Rahul, D.S, Hooda, R.S, Yadav, M, Khera, A.P, Singh, C.P, Kalubarme, M.H, Hooda, I.S. (Chaudhary Charan Singh Harayana Agricultural University, Hisar (India))
Format: Article
Language:English
Subjects:
AGROCLIMATIC ZONES
BLÉ
CROP YIELD
FORECASTING
RENDEMENT DES CULTURES
RENDIMIENTO DE CULTIVOS
TECHNIQUE DE PRÉVISION
TEMPERATURA
TEMPERATURE
TEMPÉRATURE
TRIGO
TÉCNICAS DE PREDICCIÓN
WHEATS
ZONAS AGROCLIMÁTICAS
ZONE AGROCLIMATIQUE
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Summary:Spectral-trend-agrometeorological yield models for four zones in Harayana state were developed using district level area weighted Normalized Difference Vegetation Index (NDVI), trend predicted yield and meteorological indices like Growing Degree Days (GDD), Temperature Diference (TD) and Rainfall Accumulated over critical growth phases of wheat. Meteorological indices calculated were integrated over seven phenological stages of wheat viz. (i) Crown Root Initiation Stage (ii) Tillering Stage and (vii) Maturity stage. Districts in Haryana were grouped into four zones (clusters of districts) based on their physiography/soils and agro-climatic conditions. Trend predicted yields were obtained using historical yield time series data and spectro-trend yield relationship incorporated in the agrometeorological yield models. Remote sensing based model predicted yield were compared with Bureau of Economic & Statistics (BES) by computing Relative Deviation (RD%). The results indicated that the prediction capability for district level wheat yield has improved significantly using these zonal yield models.
ISSN:0019-6363