Multiple alleles at Early flowering 1 locus making variation in the basic vegetative growth period in rice (Oryza sativa L.)

2009 ◽  
Vol 119 (2) ◽  
pp. 315-323 ◽  
Author(s):  
Hiroki Saito ◽  
Qingbo Yuan ◽  
Yutaka Okumoto ◽  
Kazuyuki Doi ◽  
Atsushi Yoshimura ◽  
...  
Keyword(s):  
Oryza Sativa ◽  
Vegetative Growth ◽  
Oryza Sativa L ◽  
Growth Period ◽  
Early Flowering ◽  
Multiple Alleles ◽  
Basic Vegetative Growth

scholarly journals Agrobacterium Tumefaciens Mediated Transformation of rolC::Hd3a-GFP in Black Rice (Oryza Sativa L. cv. Cempo Ireng) to Promote Early Flowering

2015 ◽  
Vol 14 ◽  
pp. 469-473 ◽  
Author(s):  
Yekti Asih Purwestri ◽  
Resta Dewi Komala Sari ◽  
Lisa Novita Anggraeni ◽  
Aries Bagus Sasongko
Keyword(s):  
Oryza Sativa ◽  
Agrobacterium Tumefaciens ◽  
Oryza Sativa L ◽  
Early Flowering ◽  
Black Rice

A Novel Gene ef1‐h Conferring an Extremely Long Basic Vegetative Growth Period in Rice

Crop Science ◽  
2002 ◽  
Vol 42 (2) ◽  
pp. 348-354 ◽  
Author(s):  
Hidetaka Nishida ◽  
Hiromo Inoue ◽  
Yutaka Okumoto ◽  
Takatoshi Tanisaka
Keyword(s):  
Vegetative Growth ◽  
Growth Period ◽  
Novel Gene ◽  
Basic Vegetative Growth

scholarly journals Effect of Microbial (Jeevatu) Treatment on Rice (Oryza sativa L.) Production

2013 ◽  
Vol 1 (4) ◽  
pp. 184-188
Author(s):  
Suraj Raj Adhikari ◽  
Khadga Bhakta Paudel ◽  
Kusum Pokhrel ◽  
Amrita Paudel
Keyword(s):  
Oryza Sativa ◽  
Seed Production ◽  
Vegetative Growth ◽  
Rice Plant ◽  
Oryza Sativa L ◽  
Liquid Form ◽  
Cultivated Plant

JeevatuTM is a consortium of beneficial natural microbes, available in liquid form. The yield of rice was observed vigorously high in Jeevatu based rice cultivated plant. The seed production in per spikelet is 237.1±44.92 in Jeevatu based and 179.4±25.26 in chemical based rice plant.  In case of vegetative growth, 173.1±6.34 cm and 140.9±11.11 cm in Jeevatu based rice plant and chemical based methods respectively. Similarly the length of stalk of spikelet is 27±1.63cm and 22.1±2.23 cm was observed in Jeevatu based and chemical based respectively. The number of stalk in lumps is 16.2±1.75 and 12.2±2.20 in Jeevatu and chemical based paddy plant respectively under the same environment and physical factors.DOI: http://dx.doi.org/10.3126/ijasbt.v1i4.9134 Int J Appl Sci Biotechnol, Vol. 1(4): 184-188

scholarly journals Rice (Oryza sativa L.) Growth Modeling Based on Growth Degree Day (GDD) and Artificial Intelligence Algorithms

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 59
Author(s):  
Li-Wei Liu ◽  
Chun-Tang Lu ◽  
Yu-Min Wang ◽  
Kuan-Hui Lin ◽  
Xing-Mao Ma ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Oryza Sativa ◽  
Oryza Sativa L ◽  
Gene Expression Programming ◽  
Model Performance ◽  
Growth Period ◽  
Rice Production ◽  
Growth Prediction ◽  
Rice Growth ◽  
Degree Day

Rice (Oryza sativa L.) growth prediction is key for precise rice production. However, the traditional linear rice growth forecasting model is ineffective under rapidly changing climate conditions. Here we show that growth rate (Gr) can be well-predicted by artificial intelligence (AI)-based artificial neural networks (ANN) and gene-expression programming (GEP), with accumulated air temperatures based on growth degree day (GDD). In total, 10,246 Gr from 95 cultivations were obtained with three cultivars, TK9, TNG71, and KH147, in Central and Southern Taiwan. The model performance was evaluated by the Pearson correlation coefficient (r), root mean square error (RMSE), and relative RMSE (r-RMSE) in the whole growth period (lifecycle), as well as the average and specific key stages (transplanting, 50% initial tillering, panicle initiation, 50% heading, and physiological maturity). The results in lifecycle Gr modeling showed that ANN and GEP models had comparable r (0.9893), but the GEP model had the lowest RMSE (3.83 days) and r-RMSE (7.24%). In stage average and specific key stages, each model has its own best-fit growth period. Overall, GEP model is recommended for rice growth prediction considering the model performance, applicability, and routine farming work. This study may lead to smart rice production due to the enhanced capacity to predict rice growth in the field.

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