Showing 2 results for Rice Yield
Abolfazl Faraji, Aghafakhr Mirlohi,
Volume 2, Issue 3 (10-1998)
Abstract
In order to study the effects of rate and time of nitrogen application on vegetative characters, i.e., yield and yield components of rice (Zayandeh-rood variety), an experiment was conducted at Isfahan University of Technology research farm during summer 1996. Four N rates including (60, 90, 120 and 150 KgN/ha) and four splitting form (1- all N applied before transplanting 2- 1/3 N applied before transplanting, 1/3 at the beginning of tillering and 1/3 at the emergence of first panicle in 50% hills 3- 1/2 at the beginning of tillering and 1/2 at the emergence of first panicle in 50% hills 4- 1/3 at the beginning of tillering and 2/3 at the emergence of first panicle in 50% hills) were evaluated in a factorial experiment which was arranged in a randomized complete block design with 3 replications. Plant height, number of tillers per unit area and days to heading and maturity increased with an increase in the rate of fertilizer application. Grain yield and number of panicles per square meter increased when the N rate was raised to 120 Kg N/ha, while application of 150 Kg N/ha resulted in the reduction of grain yield and number of panicles. Nitrogen rate increases did not have any significant effect on number of grains per panicle. The grain weight did not follow any particular trend at different application rates, but harvest index and percentage of filled grains were decreased as the N rate increased. The percentage of nitrogen content of plant was increased as a result of higher N - rate at heading and harvest times. Treatments containing base application of nitrogen resulted in an increase in plant height, number of tillers, plant dry matter, grain yield and number of panicles per square meter, although it caused a reduction in harvest index. The number of grains per panicle and grain weight did not follow any particular trend under the influence of time of application, although plant nitrogen content increased with a delay in time of fertilizer application.
R Amiri Chaijan, M Khosh Taghaza, Gh Montazer, S Minaee, M Alizadeh,
Volume 13, Issue 48 (7-2009)
Abstract
The objective of this research was to predict head rice yield (HRY) in fluidized bed dryer using artificial neural network approaches. Several parameters considered here as input variables for artificial neural network affect operation of fluidized bed dryers. These variables include: air relative humidity, air temperature, inlet air velocity, bed depth, initial moisture content, final moisture content and inlet air temperature. In aggregate, 274 drying experiments were conducted for creating training and testing patterns by a laboratory dryer. Samples were collected from dryer, and then dehulling and polishing operations were done using laboratory apparatus. HRY was measured at several different depths , average of which was considered as HRY for each experiment. Three networks and two training algorithms were used for training presented patterns. Results showed that the cascade forward back propagation algorithm with topology of 7- 13-7-1 and Levenberg-Marquardt training algorithm and activation function of Sigmoid Tangent predicted HRY with determination coefficient of 95.48% and mean absolute error 0.019 in different conditions of fluidized bed paddy drying method. Results showed that the input air temperature and final moisture content has the most significant effect on HRY.
