MSc

In India, rice production is an important part of the national economy. India is the second largest producer in the world with approximately 43 million hectares planted area, accounting for 22% of the total rice production in this world. World’s leading rice exporter is India, marketing about 12.5 million metric tonnes in 2018-19. Rice is grown in rainfed areas with heavy annual rainfall. Therefore it is fundamentally considered as a kharif crop. But its production mainly depends up on weather prevailing in that area. Weather has a profound influence on growth, development and yields of crop; on the incidence of pests and diseases; on water needs; and on fertilizer requirements.
The present experiment was aimed to study the crop weather relationship of rice varieties under different growing environments and to validate different crop weather models for rice varieties including statistical models and crop simulation model (DSSAT CERES-Rice model). Two varieties of rice, Jyothi and Jaya were raised at Agricultural Research Station, Mannuthy by adopting split plot design. Five planting dates such as June 5th, June 20th, July 5th, July 20th and August 5th were used as main plot treatments and the two varieties were used as sub plot treatments. The replication number used for this experiment was four.
During the field experiment, daily weather data were collected like maximum temperature, minimum temperature, relative humidity, rainfall, bright sunshine hours, wind speed and evaporation. Biometric observations like plant height, leaf area, dry matter accumulation, number of tillers per unit area, number of panicles per unit area, number of spikelets per panicle, number of filled grains per panicle, thousand grain weight, straw yield and grain yield were observed. Duration of different phenophases and physiological observations such as leaf area index, net assimilation rate, leaf area duration and crop growth rate were also calculated. Pests and diseases were noticed during different growing conditions.
Considerable variation among weather variables were noticed during the field experiment. Plant height was higher for Jyothi compared to Jaya and it showed variation among different planting dates. Maximum dry matter accumulation was recorded during 75 days after planting and it exhibited a decreasing trend with delayed planting in both the varieties. Number of spikelets per panicle, number of filled grains per panicle, thousand grain weight and straw yield were found to

be decreasing as the planting date was delayed. Highest grain yield (4698 kg ha-1) was observed in Jyothi during June 20th planting whereas, June 5th planting showed maximum grain yield (5527 kg ha-1) in Jaya. Because, continuous morning rainfall during flowering stage of Jyothi reduced its yield in June 5th planting (3021.25 kg ha-1). Maximum duration was observed during June 20th planting in Jyothi (129 days) and Jaya (139 days). Total duration was less for August 5th plantings in both the varieties (121 and 129 days for Jyothi and Jaya respectively).
Leaf area index and leaf area duration were more during 75 days after planting for both the varieties and leaf area duration showed a maximum value during 60-75 days after planting for both the varieties. In general, crop growth rate of both Jyothi and Jaya was found to be more during 45- 60 days after planting while net assimilation rate was more in the early growth stages.
Validation of statistical models for Jyothi such as models based on weekly weather variables, fortnightly weather variables, crop stage-wise weather variables and that based on composite weather variables, were carried out in which model which uses composite weather variables was selected as the best one in yield prediction of Jyothi after comparing the estimated yield and observed yield. Crop weather model using statistical methods was also developed for Jyothi and Jaya with the aid of principal component analysis. Two principal components were identified for Jaya and three for Jyothi. The regression analysis was carried out using SPSS software. This formed a better tool in predicting the yield of Jyothi and Jaya. DSSAT CERES- Rice model was also run for Jyothi as well as for Jaya after creating weather file, soil file, crop management file and experimental file for the year 2018.