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Resource management in sugarcane (Saccharum officinarum L.) through drip irrigation, fertigation, planting pattern and LCC based N application, and area-production estimation through remote sensing

KrishiKosh

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Title Resource management in sugarcane (Saccharum officinarum L.) through drip irrigation, fertigation, planting pattern and LCC based N application, and area-production estimation through remote sensing
 
Creator C.P.Chandrashekara
 
Contributor B.M.Chittapur
 
Subject Agronomy
 
Description Field experiments were carried out during 2004-05 and 2005-06 to study i. Performance of
sugarcane under drip irrigation, fertigation interval and planting pattern, ii. Evaluation and
standardization of leaf colour chart as a tool for nitrogen management, at Agricultural Research
Station, Arabhavi and iii. Crop acreage estimation and production forecast in sugarcane through
remote sensing in the GLBC command of Karnataka. Drip irrigation with 60-180-60 cm paired row
planting and fertigation of recommended dose of N and K in 30 equal installments at weekly
interval from 37 to 240 DAP produced higher cane yields (153.6 and 144.2 t ha-1 cane yield and
21.4 and 20.5 t ha-1 CCS yield during pre- and seasonal plantings, respectively), improved juice
quality, enhanced irrigation water (1614 and 1523 kg ha cm-1 in pre-and seasonal planting,
respectively) and fertilizer use efficiencies, besides higher economical returns over conventional
practice. Application of 50 and 60 kg N ha-1 dressing-1 coupled with LCC threshold 6 recorded
higher cane yield (150.5 and 151.7 t ha-1 during I and 123.8 and 125.0 t ha-1 during II season,
respectively), CCS yield, juice, brix, pol and lesser reducing sugars, total N, P and K uptake than
conventional practice. Higher gross return, net return and B: C ratio was observed with LCC
threshold 6 with 50 or 60 kg N ha-1 dressing-1, compared to conventional practice in both seasons.
Early, grand growth, matured stage and total sugarcane area can be delineated more accurately with
minimum error matrix and higher divergence with clear separability through proper ground truthing
and using IRS P6 LISS III multi date image by maximum likelihood supervised classification
approach. The overall seasons’ multi date model (Y= - 6.00 + 85.30 NDVIGk +18.2 LAIGk for
Gokak and Y= 27.40 + 125.90 NDVI Rbg + 5.41 LAI Rbg for Raibag taluka) with average satellite
NDVI and ground truth LAI over grand growth and matured stages predicted cane yield and
production more accurately than other models.
 
Date 2016-07-23T09:36:27Z
2016-07-23T09:36:27Z
2009
 
Type Thesis
 
Identifier http://krishikosh.egranth.ac.in/handle/1/69461
 
Format application/pdf
 
Publisher UAS Dharwad