scholarly journals Physiological and Ecological Studies of Rice Plant in Well-drained and Ill-drained Field. : (9) Changes of phosphate and potassium content in internode of rice plant in ripening process.

1963 ◽  
Vol 31 (3) ◽  
pp. 241-244 ◽  
Author(s):  
Mituo KIDO ◽  
Shozo YANATORI
Keyword(s):  
Rice Plant ◽  
Potassium Content ◽  
Ecological Studies ◽  
Ripening Process

scholarly journals Ecological Studies on the Establishment of Regionality in Early Planting Culture and Great Harvest of Rice Plant

1962 ◽  
Vol 17 (3) ◽  
pp. 115-118
Author(s):  
I. ABE ◽  
K. ONO ◽  
J. WADA ◽  
M. ODAGIRI
Keyword(s):  
Rice Plant ◽  
Ecological Studies ◽  
Early Planting

scholarly journals Ecological Studies on the Establishments of Regionality in Early Planting Culture and Great Harvest of Rice Plant

1962 ◽  
Vol 17 (3) ◽  
pp. 111-114
Author(s):  
I. ABE ◽  
K. ONO ◽  
J. WADA ◽  
K. TORIYAMA
Keyword(s):  
Rice Plant ◽  
Ecological Studies ◽  
Early Planting

scholarly journals Ecological Studies on the Establishment of Regionality in Early Planting Culture and Great Harvest of Rice Plant

1960 ◽  
Vol 16 (3) ◽  
pp. 99-105 ◽  
Author(s):  
I. ABE ◽  
K. ONO ◽  
K. TORIYAMA ◽  
J. WADA
Keyword(s):  
Rice Plant ◽  
Ecological Studies ◽  
Early Planting

scholarly journals Ecological Studies on the Establishment of Regionality in Early Planting Culture and Great Harvest of Rice Plant

1961 ◽  
Vol 17 (1) ◽  
pp. 21-29
Author(s):  
I. ABE ◽  
K. ONO ◽  
J. WADA ◽  
K. TORIYAMA
Keyword(s):  
Rice Plant ◽  
Ecological Studies ◽  
Early Planting

scholarly journals Ecological Studies on the Establishment of Regionality in Early Planting Culture and Great Harvest of Rice Plant

1962 ◽  
Vol 18 (1) ◽  
pp. 23-26
Author(s):  
I. ABE ◽  
K. ONO ◽  
J. WADA ◽  
K. TORIYAMA
Keyword(s):  
Rice Plant ◽  
Ecological Studies ◽  
Early Planting

scholarly journals Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation

2021 ◽  
Vol 13 (17) ◽  
pp. 3502
Author(s):  
Jingshan Lu ◽  
Jan Eitel ◽  
Jyoti Jennewein ◽  
Jie Zhu ◽  
Hengbiao Zheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Least Squares ◽  
Spectral Index ◽  
Rice Plant ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Potassium Content ◽  
Estimation Accuracy ◽  
Average Temperature ◽  
Daily Average

Potassium (K) plays a significant role in the formation of crop quality and yield. Accurate estimation of plant potassium content using remote sensing (RS) techniques is therefore of great interest to better manage crop K nutrition. To improve RS of crop K, meteorological information might prove useful, as it is well established that weather conditions affect crop K uptake. We aimed to determine whether including meteorological data into RS-based models can improve K estimation accuracy in rice (Oryza sativa L.). We conducted field experiments throughout three growing seasons (2017–2019). During each year, different treatments (i.e., nitrogen, potassium levels and plant varieties) were applied and spectra were taken at different growth stages throughout the growing season. Firstly, we conducted a correlation analysis between rice plant potassium content and transformed spectra (reflectance spectra (R), first derivative spectra (FD) and reciprocal logarithm-transformed spectra (log [1/R])) to select correlation bands. Then, we performed the genetic algorithms partial least-squares and linear mixed effects model to select important bands (IBs) and important meteorological factors (IFs) from correlation bands and meteorological data (daily average temperature, humidity, etc.), respectively. Finally, we used the spectral index and machine learning methods (partial least-squares regression (PLSR) and random forest (RF)) to construct rice plant potassium content estimation models based on transformed spectra, transformed spectra + IFs and IBs, and IBs + IFs, respectively. Results showed that normalized difference spectral index (NDSI (R1210, R1105)) had a moderate estimation accuracy for rice plant potassium content (R2 = 0.51; RMSE = 0.49%) and PLSR (FD-IBs) (R2 = 0.69; RMSE = 0.37%) and RF (FD-IBs) (R2 = 0.71; RMSE = 0.40%) models based on FD could improve the prediction accuracy. Among the meteorological factors, daily average temperature contributed the most to estimating rice plant potassium content, followed by daily average humidity. The estimation accuracy of the optimal rice plant potassium content models was improved by adding meteorological factors into the three RS models, with model R2 increasing to 0.65, 0.74, and 0.76, and RMSEs decreasing to 0.42%, 0.35%, and 0.37%, respectively, suggesting that including meteorological data can improve our ability to remotely sense plant potassium content in rice.

scholarly journals On the Varietal and Cultural Differences of Ripening Process in Rice Plant

1978 ◽  
Vol 47 (2) ◽  
pp. 235-242
Author(s):  
Masahiko YOSHIHARA ◽  
Tetsuo TAKAGI ◽  
Tsuyoshi SHIROTO ◽  
Yasuo OTSUKA ◽  
Masaki HORIE
Keyword(s):  
Cultural Differences ◽  
Rice Plant ◽  
Ripening Process
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