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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2006
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UAS Dharwad
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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.
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