Crop weather modelling in rice (oryza sativa.L.)

Abstract

Rice is intimately involved in the culture as well as in the food ways and economy of Indians especially Keralites. The demand of rice is expected to increase by 70 per cent over the next 30 years, primarily due to rapid population growth and by 20 1 O. the rice production has to be increased from the current level on. 7lakh tonnes to 21 lakh tonnes. To achieve this objective, the strategy should be to increase the total production. This can be accomplished by increasing the acreage under rice and by enhancing the productivity per unit area. Climate plays an important role in rice production. Rice is vulnerable to change in weather' and its cultivation continues to be a risky enterprise under unfavourable environment, despite advances made in rice production technologies. Within any season, long days and humid conditions with adequate rains or water supply help towards satisfactory vegetative growth of the rice plant while bright weather and short days with a diminishing supply of water favour the flowering phase, Cloudiness which leads to low light, extreme temperatures, variation in rainfall, and high relative humidity affect growth, yield attributes and productivity. To some extend rice has genetic potentiality to tolerate the adverse weather conditions and hence an appropriate cultivar may enable the farmer to avoid or lessen the problems from climatic stresses. Two field experiments were conducted at College of Agriculture, Vellayani during 1998' -1999 and at RARS, Pattambi during 1999-2000 to assess the performance offour high yielding rice varieties namely Kanchana, Jyothi, Matta T riveni, Kairali and one local variety Ptb-10 in three dates of planting in each season viz.,. 1 sI, l S" and 30th of June ,October and January, to find out the best varieties and best dates of planting in each season. It was also aimed to develop the best fitting crop weather model using step wise regression. In all seasons at both locations earlier dates of transplanting resulted in higher TDMP. more number of days to reach 50 per cent flowering and physiological maturity. higher grain yield and straw yield and higher harvest index. The nutrient uptake was also higher for earlier transplanting. The earlier transplanting also escaped from the severity of pests and diseases. Kanchana, Kairali and Jyothi registered similar values for number of days taken to 50 per cent flowering, physiological maturity and number of filled grains per panicle during all seasons at Vellayani. In all se~ons the nitrogen, phosphorus and potassium uptake by straw and grain were lower for local variety. Kanchana consistently out yielded other varieties followed by Kairali and the local variety Ptb-l 0 registered the lowest grain yield. At Pattambi also during all seasons number of days taken to 50 per cent flowering and physiological maturity were similar for Kanchana, Kairali and Jyothi. In all seasons the nitrogen, phosphorus and potassium uptake by straw and grain were lower for local variety. Highest grain yield was registered by Kanchana followed by Kairali in all seasons except third season where it was on par with Kairali. The lowest grain yield was registered by local variety. At both locations, all the high yielding varieties registered lower rice bug attack than local variety. Kairali and Kanchana were found resistant to sheath blight and gall midge At both locations similar trend in growth and yield attributes, nutrient uptake and pest' and disease incidence were observed in all the seasons. Result of this investigation revealed that early transplanting of rice and cultivation of high yielding varieties Kanchana would increase the productivity per unit area. Yield prediction equation with weather variables will act as a tool for improving the productivity of rice. Grain yield data for a period of twenty five years (1960-1985) from a permanent manurial experiment at Regional Agricultural Research Station, Pattambi was subjected to con-elation " analysis to develop the best fitting crop weather model using step wise regression. In virippu season 72 per cent of the variation in yield was attributed to climatological parameters viz., evaporation, maximum temperature, minimum temperature, number of rainy days, evening relative humidity and sunshine hours. In mundakan season 46 per cent of variation in yield was accounted for the climatological parameters viz., evaporation, maximum temperature and sunshine hours