Evaluation of variability and environmental stability of grain quality and agronomic parameters of pigmented rice (O. sativa L.)

Sanghamitra, Priyadarsini; Sah, Rameswar Prasad; Bagchi, Torit Baran; Sharma, Srigopal; Kumar, Anjani; Munda, Sushmita; Sahu, Rabindra Kumar

doi:10.1007/s13197-017-2978-9

Cited by 30 publications

(20 citation statements)

References 25 publications

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“…One of the important findings is that the released varieties had lower PA compared to the pigmented rice. This could be because the released varieties were selected for high yield during their development and grain yield had highly negative correlation with PA . Thus, high‐yielding varieties of non‐pigmented rice were found to be lower in PA content compared to the lower‐yielding pigmented rice which developed through natural recombination and selection during the course of evolution.…”

Section: Resultsmentioning

confidence: 99%

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Phytic acid content may affect starch digestibility and glycemic index value of rice (Oryza sativa L.)

et al. 2019

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BACKGROUND: Phytic acid (PA) is an anti-nutrient present in cereals and pulses.It is known to reduce mineral bioavailability and inhibit starch-digesting -amylase (which requires calcium for activity) in the human gut. In principle, the greater the amount of PA, the lower is the rate of starch hydrolysis. It is reflected in the lower glycemic index (GI) value of food. People leading sedentary lifestyles and consuming rice as a staple food are likely to develop type 2 diabetes. Hence, this study was planned to understand how PA content of different rice varieties affects the GI. RESULTS: Rice Khira and Mugai which had very low PA (0.30 and 0.36 g kg −1 , respectively) had higher GI values and -amylase activity, while Nua Dhusara and the pigmented rice Manipuri black rice (MBR) which had high PA (2.13 and 2.98 g kg −1 , respectively) showed low -amylase activity and GI values. This relationship was statistically significant, though a weak relationship was found for the pigmented rice. Expression levels of MIPSI, IPKI and GBSSI markedly increased in the middle stage of grain development in all of the six genotypes having contrasting PA and GI. Maximum expression of MIPSI and IPKI was observed in Nua Dhusara and MBR (which had high PA) while that of GBSSI was observed in Khira and Mugai (with higher GI) at middle stage showing a negative correlation between PA and GI. CONCLUSIONS:The data indicate that high PA content in rice might have an adverse effect on starch digestibility resulting in slower starch digestion in the human gut and consequently low glycemic response.

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Section: Resultsmentioning

confidence: 99%

“…Overall, the pigmented rice had lower GI in comparison to released varieties, which is one of the important findings of this experiment. Most of the grain quality traits were reported to be negatively correlated with grain yield . Thus pigmented rice is a low yielder but good in terms of nutrition for diabetics (Fig.…”

Section: Resultsmentioning

confidence: 99%

Phytic acid content may affect starch digestibility and glycemic index value of rice (Oryza sativa L.)

et al. 2019

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“…SOC as the main soil fertilizer influences the soil structure and crop growth [36]. With the improvement of remote sensing technology, increasing studies have focused on recording the growth status of crops in different phenological periods [37][38][39]. VI is designed to enhance the contribution of vegetation properties and allow the reliable spatial and temporal intercomparisons of terrestrial photosynthetic activity and canopy structural variations [40].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

et al. 2019

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High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSE P ) and coefficient of determination of prediction (R 2 P ) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSE P of 3.718 and highest R 2 P of 0.391, followed by SVM (RMSE P = 3.753, R 2 P = 0.361) and PLSR (RMSE P = 4.087, R 2 P = 0.283). The SLR model was the worst model, with the lowest R 2 P of 0.281 and highest RMSE P of 3.930. ANN and SVM were better than OK (RMSE P = 3.727, R 2 P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

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“…Our data, however, showed much wider variation on PA, which is much important for the study and it is because of diverse rice genotypes chosen with different genetic constitution, different parental background and ecological conditions for rice cultivation. Screening the landraces and other cultivated rice may provide a better opportunity to identify rice germplasm with relatively low PA content, as the released varieties are normally selected on the basis of higher economic yield (Sanghamitra et al., 2018). Despite the work done so far, the information available on the effect of genetic and environmental factors on P uptake, translocation, remobilization, partitioning and its correlation with PA accumulation in the rice grain is rather elusive.…”

Section: Resultsmentioning

confidence: 99%

“…Any change in the function of these transporters affects the P distribution in different tissues, particularly the seeds, as P is majorly stored here as PA. Several mutants with the lpa phenotype have been reported in rice (Larson et al., 2000). Many of them have been selected based on the decrease in the percentage of PA and the high amount of inorganic P in seeds through alteration in the gene function associated with PA biosynthesis (Sanghamitra et al., 2018). To understand the sequence variation between Phalguni (highest PA content: 23.7 g/kg) and the Khira (lowest in PA content: 3.0 g/kg), the sequence of SDPT between the Khira and Phalguni was compared using BioEdit software (https://bioedit.software.informer.com/) through CLUSTALW tool.…”

Section: Resultsmentioning

confidence: 99%

A single nucleotide substitution in the SPDT transporter gene reduced phytic acid and increased mineral bioavailability from Rice grain ( Oryza sativa L.)

et al. 2021

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Phosphorus (P) flow in agricultural land depends on the P taken off from harvested product, its losses through runoff and fertilizer applied to balance the removed P. Phytic acid (PA), the major storage form of phosphorus (P) in cereal grains is a key anti‐nutrient for human and non‐ruminants leads to eutrophication of waterways. As the natural non‐renewable P reserves are limited, enhancing P use efficiency is needed for field crops. SULTR‐like phosphorus distribution transporter (SPDT) is a novel rice transporter transfer P to the grain. Any alteration in transporter gene reduce grain P with concomitant rise in the leaves. A low PA (3.0 g/kg) rice Khira was identified where a single nucleotide mutation in LOC_Os06g05160 gene encoding SPDT showed low P transportation to grain. An amino acid change was detected as Valine‐330 to Alanine at the 3′ end of fifth exon. Highest expression of SPDT was observed in node I of rice as compared to low PA genotype. The mutation in SPDT could significantly affect P and PA accumulation in the grains with increased mineral bioavailability. Practical applications Excessive P application in crop leads to higher production cost as well as rapid depletion of limited rock phosphate. Alteration of P transporter function in the rice lower PA and total P accumulation in the grains with increased mineral bioavailability. The re‐distributed P in the straw can be applied as manure to the rice field. Thus, less P will be removed from the field, result in the decreased requirement for P fertilizer.

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Evaluation of variability and environmental stability of grain quality and agronomic parameters of pigmented rice (O. sativa L.)

Cited by 30 publications

References 25 publications

Phytic acid content may affect starch digestibility and glycemic index value of rice (Oryza sativa L.)

Phytic acid content may affect starch digestibility and glycemic index value of rice (Oryza sativa L.)

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

A single nucleotide substitution in the SPDT transporter gene reduced phytic acid and increased mineral bioavailability from Rice grain ( Oryza sativa L.)

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