Bi-Phasic Growth Patterns in Rice

Sheehy, J. E.; Mitchell, Peter; Ferrer, A.

doi:10.1093/aob/mch208

Cited by 31 publications

(27 citation statements)

References 11 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

Section: Measurement Of Crop Parametersmentioning

confidence: 91%

“…Due to intraspecific competition at the reproductive stage, the tiller number drops and the rice leaves gradually become yellow. This stage is also associated with an increase in the dry aboveground biomass of rice until maturity ( Figure 2) [36]. Fourteen rice sample plots with areas larger than 200 × 200 m 2 in Deqing County were randomly chosen in order to measure the vegetation characteristics (Figure 1).…”

Section: Measurement Of Crop Parametersmentioning

confidence: 99%

“…Furthermore, we verified the relationship between the 10-day composite VI data with AGB. The AGB curve can be described with logistic curves [36]; the particular form of the logistic equation available was written as:…”

Section: Deriving Lai and Agb Via Vismentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data

et al. 2016

View full text Add to dashboard Cite

Abstract:The high temporal resolution (4-day) charge-coupled device (CCD) cameras onboard small environment and disaster monitoring and forecasting satellites (HJ-1A/B) with 30 m spatial resolution and large swath (700 km) have substantially increased the availability of regional clear sky optical remote sensing data. For the application of dynamic mapping of rice growth parameters, leaf area index (LAI) and aboveground biomass (AGB) were considered as plant growth indicators. The HJ-1 CCD-derived vegetation indices (VIs) showed robust relationships with rice growth parameters. Cumulative VIs showed strong performance for the estimation of total dry AGB. The cross-validation coefficient of determination (R 2 CV ) was increased by using two machine learning methods, i.e., a back propagation neural network (BPNN) and a support vector machine (SVM) compared with traditional regression equations of LAI retrieval. The LAI inversion accuracy was further improved by dividing the rice growth period into before and after heading stages. This study demonstrated that continuous rice growth monitoring over time and space at field level can be implemented effectively with HJ-1 CCD 10-day composite data using a combination of proper VIs and regression models.

show abstract

Section: Measurement Of Crop Parametersmentioning

confidence: 91%

Section: Measurement Of Crop Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The bi-logistic growth model seems to appear in nature, as was shown by Sheehy et al [17], who described how rice crops can be modelled by two phases of logistic growth: vegetative growth, followed by reproductive growth. The two phases can be readily modelled as superimposedconverging bi-logistic curves.…”

Section: A Model Based On Logistic Growth Functionsmentioning

confidence: 88%

A bi-logistic growth model for conference registration with an early bird deadline

2013

View full text Add to dashboard Cite

Abstract:The recent interest in human dynamics has led researchers to investigate the processes that explain human behaviour within different contexts. Here we are concerned in modelling the human response to a deadline, and in particular we look at the process of conference registration with an early bird deadline. We provide empirical evidence from a six-year conference registration data set that the bi-logistic growth function, with the interpretation as registration with an early bird deadline, can be viewed as a social mechanism.PACS (

show abstract

“…Rice has mainly two phases of logistic growth, vegetative and reproductive wherein most of the grain yield is a function of weather during the latter phase (59)(60) (61) and it has been reported that enhanced carbon dioxide in atmosphere within temperature range of 22 0 C-32⁰ C can enhance grain yield by about 0.5 t ha -1 per 75 ppm enhancement in carbon dioxide (3) . A simplified grain equation for rice may be written as Where in Y g is grain yield, H is harvest index (HI), t i is the date of transplantation, t f is the date of harvesting and I int is the total amount of PAR (photosynthetically active radiation i.e.…”

Section: Estimating the Effects Of Installing C 4 Chassis For C 3 Ricmentioning

confidence: 99%

C4 Rice - Tweaking Rice Physiology for Second Green Revolution

Mishra¹,

Singh²,

Snehal³

et al. 2017

Int.J.Curr.Microbiol.App.Sci

View full text Add to dashboard Cite

Rice is a staple food crop for more than 50% of world"s population. The success of green revolution that began in 1960"s led to a tenfold increase in rice yield but it is clear now, that the gains from the grains of the previous green revolution have exhausted. By 2050 there would be 1309 million tonnes demand of rice and C3 rice even when best managed can yield only 915 million tones. Increasing radiation use efficiency (RUE), water use efficiency (WUE) and nutrient use efficiency (NUE) are the contemporary approaches being tested on a wider scale. C4 type photosynthesis due to its carbon dioxide tunneling system and negligible photorespiration is much more efficient than the C3 system even at tropical temperatures where rice is generally grown. Even though engineering C4 rice requires syndromic large scale tweaking in physiology, advances in genome wide deep sequencing and genome editing platforms have brought the possibility of making C4 rice closer than ever before. A selected group of C4 genes have been inserted into rice through mutagenesis and hybridization and their effects recorded in transgenics upto 3 generations. The compartmentalized overexpression of key C4 genes using Rice DNA activation tagging constitutes another approach towards C4 rice. Since C4 plants have evolved independently multiple times from C3 origins, it is being investigated whether the key genes and gene regulatory networks that regulate C4 plants have been recruited from C3 ancestors. To facilitate this, comparative transcriptomes analysis of C3 vs. C4 leaves and other C3 and C4 tissues has been done and thus the exact number of genes differentially expressing between C3 and C4 can now be calculated. The high throughput OMIC data thus generated is cross referenced with whole genome databases and this has yielded sufficient number of candidate genes for bundle sheath specific expression. Fox hunting systems and Tos 17 transposable systems have also yielded a set of interesting mutants in this regard. Identification of mutants through DHPLC and TILLING are used to track down whole genome duplication events in the evolution towards C4 rice. While the discovery of cis acting sequences in C3 to C4 transition is a favorable advance, our further studies are limited by the poor resolution of transcript profiles and epigenetic signatures. Availability of only a few models of in silico studies about performance of C4 rice under dense crop canopies is another limitation. K e y w o r d sC4 Rice, Second Green Revolution, Gene discovery, Rice yield.

show abstract

Bi-Phasic Growth Patterns in Rice

Abstract: Analysing rice growth as two components, each with a logistic curve, provides insight into the growth processes of the plant and the pattern of yield formation.

Cited by 31 publications

References 11 publications

Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data

Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data

A bi-logistic growth model for conference registration with an early bird deadline

C4 Rice - Tweaking Rice Physiology for Second Green Revolution

Contact Info

Product

Resources

About