Studies on optimization of agro-techniques to maximize productivity of winter mize (Zea mays L.) and evalution of dassat V.3.5 ceres maize model
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Date
2006
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UAS Dharwad
Abstract
Two field experiments were conducted at Agricultural College farm Dharwad during
rabi seasons of 1998-99 and 1999-2000 under irrigated condition. The results revealed that
the single cross maize hybrid DMH-2 planted during II and / or I fortnight of October
recorded significantly higher grain yield (8553 and 8255 kg ha-1 respectively), net returns
(Rs.30165 and 29145 ha-1 respectively) and B: C ratio (3.09 and 3.02 respectively) over other
treatment combinations.
Irrigation scheduling at 0.9 IW: CPE ratio coupled with plant density of 111111 plants
ha-1 and 150 per cent recommended nitrogen application (225 kg ha-1 ) produced significantly
higher grain yield (8894 kg ha-1), net returns (Rs 31959 ha-1) and B:C ratio (2.96) as
compared to other treatment combinations.
The required genetic co-efficients for maize varieties were generated with the help of
GENCALC programme. The simulation studies on growth and yield due to the effect of
genotypes and planting dates, irrigation scheduling coupled with plant densities and nitrogen
levels carried out by making use of minimum data sets such as weather, soil and experimental
details. The CERES maize simulation results on growth and yield of maize viz., LAI, days
to anthesis and maturity, grain, stover and biomass yield, harvest index, grain weight, grains
ear-1, grain number m-2, water used and nitrogen uptake were very accurate and within the
permissible tendency (less than 10 percent) towards over estimation and/ or under estimation
except for leaf number plant-1 where in CERES model over estimated to the extent of more
than ten percent which emphasized the need for precise estimation of PHINT value. In
addition, the simulated NO3 nitrogen accumulation, NO3 nitrogen leaching and drainage
water helped in the interpretation of input use such as water and nitrogen. It is concluded that
DSSAT v 3.5 maize model is very robust in predicting the growth and yield of maize as
influenced by agro-techniques and could be used in wider perspective.