CROP-WEATHER RELATIONSHIP IN RABI MAIZE (Zea mays L.) AND TESTING OF CERES MAIZE MODEL FOR THE MIDDLE GUJARAT AGROCLIMATIC ZONE

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Date
1992
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AAU, Anand
Abstract
Maize (Zea mays L.) is one of the most important cereals both for human and animal consumption. Maize is grown in climates ranging from temperate to tropics during the period when mean daily temperatures are above 15 c and frost free. Weather variables affect the crop growth differently in different phenophases during its growth cycle. Field experiments during the rabi seasons of 1989-90 and 1990-91 were conducted with cv Ganga Safed-2 and were laid out in split plot design with three dates of sowing as main plot and four irrigation regimes as sub-plot, replicated six times. The results obtained in the study revealed that, the lower temperatures observed in silk emergence to physiological maturity phase gave higher grain yields. The crop had the maturity periods ranging from 104 to 112 and 121 to 131 days from emergence in 1989-90 and 1990-91, respectively. Higher crop growth rate coupled with lower leaf weight ratio contributed towards higher grain yield. The peak leaf area index beyond certain threshold value did not contribute to higher grain yields. Radiation use studies indicated higher intercepted solar radiation and intercepted photosynthetic active radiation use efficiencies for first date of sowing (October 5) in both the years of study; the higher values being noticed in 1990-91. The extinction coefficient (K) calculated by using incident solar radiation and transmitted solar radiation showed a value of 0.78. Grain yields were significantly higher in first date of sowing in the year 1990-91. This was because of lengthening in grain filling period due to lower temperatures. Irrigation scheduling technique by infrared thermometry could save 20-25% of irrigation water over farmers' method. Yield attributes such as test weight, ear weight and ear numbers positively contributed towards grain yields. Correlation studies between grain yield and weather parameters revealed that lower temperatures from tasseling and morning and afternoon vapour pressures throughout the growing season played a major role in deciding final grain yield. During silk emergence to physiological maturity both solar radiation (SR) and photosynthetic active radiation (PAR) showed significant positive correlation with grain yield indicating better source and sink relationship. Agrometeorological indices like accumulated growing degree days (GDD),helio-thermal units (HTU) and thermal interception rate (TIR) had significant positive correlation with grain yield during silk emergence to physiological maturity. Stepwise regression analysis selected afternoon vapour pressure and mean temperature during silk emergence to dough stage for predicting the grain yield SO-30 days before maturity. For even early prediction, accumulated PAR in emergence to tassel emergence stage was selected with lower p R value (0.73), which predict the grain yield 50-60 days before maturity. Path analysis of grain . yield and weather parameters observed in important phenophases indicated that, low maximum temperature in tassel emergence to dough stage and higher SR in silk emergence to physiological maturity are favourable for higher grain yields. Prediction models were also developed by using stepwise regression technique for predicting stover yield and total biomass yields. The models include accumulated HTU in tassel emergence to silk emergence for stover yield prediction and temperature range in emergence to silk emergence for predicting total biomass respectively. The models obtained could predict 40-50 days before maturity. CERES-maize model was corrected for genetic coefficients and was found to be' good for prediction for this region. CERES-maize model could predict the silking and maturity date with minimum error. However, the grain yields predicted by CERES-maize model showed larger error compared to that of yields predicted by regression models obtained in the study. In addition to the larger error, the CERES-maize model has the limitation of predicting the yield at the end of growing season. However, prediction models obtained in the study could predict the grain yield, stover yield and biomass production well in advance.
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AGRICULTURAL METEOROLOGY, AGRICULTURE, A STUDY
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