Pre-harvest wheat yield and production estimation for the Punjab, India

Dubey, Rachana; Ajwani, N. D.; Kalubarme, Manik H.; Sridhar, V.; Navalgund, R. R.; Mahey, R. K.; Sidhu, S. S.; Jhorar, O. P.; Cheema, S. S.; Rang, R. S. Na

doi:10.1080/01431169408954232

Cited by 22 publications

(10 citation statements)

References 4 publications

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“…GDD is computed as the accumulation of daily average temperature since the seeding day or the emergence day, depending on the model. It is largely used for local scale crop modeling (e.g., Claverie et al, 2012 andLiu et al, 2010;Lobell et al, 2003;Ma et al, 2013), but has also been employed in global or regional crop forecasting models (Dubey et al, 1994;Idso, Pinter, Hatfield, Jackson, & Reginato, 1979;Qian, De Jong, Warren, Chipanshi, & Hill, 2009;Raun et al, 2001;Walker, 1989). Additionally, the GDD has been used to smooth the time series of various biophysical variables by providing a better temporal consistency and coherence (Duveiller, Baret, & Defourny, 2013).…”

Section: Introductionmentioning

confidence: 99%

Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information

Franch

Vermote

Becker‐Reshef

et al. 2015

Remote Sensing of Environment

162

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Section: Introductionmentioning

confidence: 99%

Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information

Franch

Vermote

Becker‐Reshef

et al. 2015

Remote Sensing of Environment

162

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“…It is reported that the climate effects reflect in regional weather and which in turn affects the crop yield (Solow et al, 1998). In this context the work of Yokoyama Shigeki (2002) is worth mention because the El Niño induced drought is a major agricultural risk in tropical Asia and that the remotely sensed images offer the great potential in estimating the crop yields over large areas (Pinter et al, 1981;Dubey et al, 1994;Kallubarme et al, 2003 andPatel et al, 2006). Zubair (2002) studied on the impact of El Niño Southern Oscillation (ENSO) which led to a decline in the rice yield for the Yala season and an increase in the Maha season in Srilanka.…”

Section: Introductionmentioning

confidence: 99%

Variability of agro climatic regime over homogeneous monsoon regions of India — El Niño and La Niña events

Rao

Kumar

Rao

2012

Global and Planetary Change

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“…The early attempts directed towards wheat production forecast in India using remote sensing based acreage estimates and yield from Crop Cutting Experiments (CCE) were in Karnal in 1983-84 [9] and in Patiala Tehsil (Punjab State) during 1984-85 cropping season [10]. The operational utility of CCE based yield relation was found limited, therefore, further studies were carried out, using direct regression of district yields with corresponding area weighted average spectral indices [11]. During the last decade, considerable work has been carried out in India in the spectral response and yield relationships of different crops at Space Application Centre, Ahmedabad, under Crop Acreage and Production Estimation (CAPE) project.…”

Section: Introductionmentioning

confidence: 99%

Yield estimation in farmers' fields at Alipur Block of Delhi by using crop model and satellite data

Sehgal¹,

Kalra²,

Singh³

et al. 2006

SPIE Proceedings

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A study was undertaken to validate the Wheat Growth Simulator (WTGROWS) in the farmers' fields of Alipur Block of Delhi and linking satellite derived vegetation index with the simulation model to estimate the wheat yield. Date of sowing, management practices and cultivars varied widely among the study sites. Leaf area index (LAI), phenological development and agronomic management (fertilizers and irrigation) were monitored at regular intervals for the 25 field sites selected in the study area. Above ground biomass and grain yield were recorded at harvest. Using the parameters derived for these sites, WTGROWS was run for each of the individual 25 sites. Crop phenology, temporal course of LAI and grain yield of each site was compared with the actual observations. The simulated and actual LAI temporal profile matched well for sites with different dates of sowing, excepting larger deviation noticed in the later stages of the crop growth. The simulated pre-anthesis duration and total above ground biomass were also correlated well with the observed values being mostly within ± 15%. There were large discrepancies in simulated and observed grain yield. A satellite image near anthesis of IRS 1D LISS-3 was acquired for the study area. The sites were identified on the image and their vegetation indices were derived. Average grey value in Infrared (IR) and Red (R) band, Ratio Vegetation Index (RVI), Soil Adjusted Vegetation Index (SAVI) andNormalized Difference Vegetation Index (NDVI) were giving significant relation with measured LAI of 5 th February which corresponded to crop anthesis stage. The relation between vegetation indices and LAI was logarithmic in nature. This logarithmic relation was incorporated into the WTGROWS to force the LAI to the equation-derived value at particular growth stage and model yield was computed and compared with actual observations.

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Pre-harvest wheat yield and production estimation for the Punjab, India

Cited by 22 publications

References 4 publications

Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information

Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information

Variability of agro climatic regime over homogeneous monsoon regions of India — El Niño and La Niña events

Yield estimation in farmers' fields at Alipur Block of Delhi by using crop model and satellite data

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