Fertilizer Deep Placement Significantly Affected Yield, Rice Quality, 2-AP Biosynthesis and Physiological Characteristics of the Fragrant Rice Cultivars

The management of fertilizers in a context of climate change and the preservation of the environment is strongly related to the regulation and accumulation of 2-acetyl-1-pyrroline (2AP) in fragrant rice. However, the feasibility of such management strategies in terms of enhancing the accumulation of 2AP has not yet been explored in aromatic cultivars. Here, we investigated the impact of the application of two fertilizers at three depth (surface, 5 cm and 10 cm) levels of placements to improve the aromatic rice quality, including such aspects as the 2AP content-, protein-, amylose- and yield-related traits. For this purpose, two known rice cultivars, Basmati 385 (B-385) and Yunjingyou (YJY), were grown in pots during 2019 and 2020 under fluctuating climates. The deep application of fertilizer at 10 cm significantly affected the 2AP content with such values as 127.53 μg kg−1 and 111.91 μg kg−1 obtained for Fragrant Fertilizer (FF) and Urea in B-385 cultivar, and 126.5 μg kg−1 and 114.24 μg kg−1 being observed for FF and Urea in YJY, respectively, during 2019. In addition, values of 108.41 μg kg−1 and 117.35 μg kg−1 were recorded for FF and Urea in B-385, while 125.91-μg kg−1 and 90.71-μg kg−1 were measured for FF and Urea in YJY, respectively, during 2020. Similarly, B-385 had better 2AP content and yield-related traits, as well as amylose content and cooked rice elongation, as compared to the YJY rice cultivar. The 2AP accumulation and its related biochemical parameters, and their relationships in different plant tissues at different growth stages under FF and Urea treatments, were also improved. Further, the 2AP content and the P5C activity demonstrated strong correlations during the grain filling periods in both fragrant rice cultivars. In conclusion, our findings have the potential to provide useful information to farmers and agriculture extension workers in terms of the saving of fertilizers and the improvement of rice grain quality under fluctuating climate conditions.

Rice quality and its impacts on food security and sustainability in Bangladesh

Rice market efficiency is important for food security in countries where rice is a staple. We assess the impact of rice quality on rice prices, food security, and environmental sustainability in Bangladesh. We find that while price varies as expected for most quality attributes, it is unaffected by a broken percentage below 24.9 percent. This reveals a potential inefficiency, considering the average 5 percent broken rate observed in the market. An increase in the broken rate of milled rice within the limits supported by our findings can, ceteris paribus, increase rice rations by 4.66 million a year, or conversely, yield the current number of rice rations using 170.79 thousand fewer hectares and cutting emissions by 1.48 million metric tons of CO2 equivalent. Thus, producing rice based on quality assessment can improve food security and its sustainability.

Prediction modeling of isoscapes for stable isotopes in China’s rice based on environmental similarity

Rice quality is directly related to human health, so it is important to have traceability systems that can trace inferior or contaminated rice back to its geographical origin. This ensures farming practices in substandard regions become better regulated to improve rice quality, origin labelling and consumer trust. However, tracing the origin of rice on the marketplace requires an accurate database benchmarking the isotope distribution over areas of rice production. Large stable isotope data sets can be used to determine the geographical origin of rice through predictive isoscape models. This study presents the first rice isoscape based on environmental similarity to predict the geospatial distribution of δ13C, δ2H and δ18O values of Chinese rice and provides uncertainty at every location such prediction is made. For this study, 794 rice samples were collected in 2017 from primary rice production regions of China. An independent verification shows that the predicted isotope distribution from this new approach is of high accuracy, with a root mean squared error (RMSE) of 0.51‰, 7.09‰ and 2.06‰ for δ13C, δ2H and δ18O values respectively. In addition, uncertainty in the spatial distribution of isotopes can be used to indicate the prediction accuracy and to guide future sampling. Our results indicate that an isoscape prediction method based on environmental similarity is effective to predict the spatial distribution of stable isotope in rice, and is an effective tool for building isotope distribution in rice over large areas with complex environment. This method could also be used to predict potential isotopic variations in future years due to climate change.

Impact of Elevated CO2 and Reducing the Source-Sink Ratio by Partial Defoliation on Rice Grain Quality – A 3-Year Free-Air CO2 Enrichment Study

Evaluating the impact of increasing CO2 on rice quality is becoming a global concern. However, whether adjusting the source-sink ratio will affect the response of rice grain quality to elevated CO2 concentrations remains unknown. In 2016–2018, we conducted a free-air CO2 enrichment experiment using a popular japonica cultivar grown at ambient and elevated CO2 levels (eCO2, increased by 200 ppm), reducing the source-sink ratio via cutting leaves (LC) at the heading stage, to investigate the effects of eCO2 and LC and their interactions on rice processing, appearance, nutrition, and eating quality. Averaged across 3 years, eCO2 significantly decreased brown rice percentage (−0.5%), milled rice percentage (−2.1%), and head rice percentage (−4.2%) but increased chalky grain percentage (+ 22.3%) and chalkiness degree (+ 26.3%). Markedly, eCO2 increased peak viscosity (+ 2.9%) and minimum viscosity (+ 3.8%) but decreased setback (−96.1%) of powder rice and increased the appearance (+ 4.5%), stickiness (+ 3.5%) and balance degree (+ 4.8%) of cooked rice, while decreasing the hardness (−6.7%), resulting in better palatability (+ 4.0%). Further, eCO2 significantly decreased the concentrations of protein, Ca, S, and Cu by 5.3, 4.7, 2.2, and 9.6%, respectively, but increased K concentration by 3.9%. Responses of nutritional quality in different grain positions (brown and milled rice) to eCO2 showed the same trend. Compared with control treatment, LC significantly increased chalky grain percentage, chalkiness degree, protein concentration, mineral element levels (except for B and Mn), and phytic acid concentration. Our results indicate that eCO2 reduced rice processing suitability, appearance, and nutritional quality but improved the eating quality. Rice quality varied significantly among years; however, few CO2 by year, CO2 by LC, or CO2 by grain position interactions were detected, indicating that the effects of eCO2 on rice quality varied little with the growing seasons, the decrease in the source-sink ratios or the different grain positions.

Use of Artificial Neural Network Model for Rice Quality Prediction Based on Grain Physical Parameters

The main goal of this study was to test the ability of an artificial neural network (ANN) for rice quality prediction based on grain physical parameters and to conduct a comparison with multiple linear regression (MLR) using 66 samples in duplicate. The parameters used for rice quality prediction are related to biochemical composition (starch, amylose, ash, fat, and protein concentration) and pasting parameters (peak viscosity, trough, breakdown, final viscosity, and setback). These parameters were estimated based on grain appearance (length, width, length/width ratio, total whiteness, vitreous whiteness, and chalkiness), and milling yield (husked, milled, head) data. The MLR models were characterized by very low coefficient determination (R2 = 0.27–0.96) and a root-mean-square error (RMSE) (0.08–0.56). Meanwhile, the ANN models presented a range for R2 = 0.97–0.99, being characterized for R2 = 0.98 (training), R2 = 0.88 (validation), and R2 = 0.90 (testing). According to these results, the ANN algorithms could be used to obtain robust models to predict both biochemical and pasting profiles parameters in a fast and accurate form, which makes them suitable for application to simultaneous qualitative and quantitative analysis of rice quality. Moreover, the ANN prediction method represents a promising approach to estimate several targeted biochemical and viscosity parameters with a fast and clean approach that is interesting to industry and consumers, leading to better assessment of rice classification for authenticity purposes.

Comprehensive Evaluation of 17 Qualities of 84 Types of Rice Based on Principal Component Analysis

Rice quality is a complex indicator, and people are paying more and more attention to the quality of rice. Therefore, we used seven rice varieties for twelve nitrogen fertilizer treatments and obtained eighty-four rice types with seventeen qualities. It was found that 17 quality traits had different coefficients of variation. Among them, the coefficient of variation of chalkiness and protein content was the largest, 44.60% and 17.89% respectively. The cluster analysis method was used to define four categories of different rice qualities. The principal component analysis method was used to comprehensively evaluate 17 qualities of 84 rice. It was found that rice quality was better under low nitrogen conditions, Huanghuazhan and Lvyinzhan were easier to obtain better comprehensive rice quality during cultivation. Future rice research should focus on reducing protein content and increasing peak viscosity.

Analysis on Spectral Matching of Biochemical Component for Rice

The protein, starch and amylose are three important indexes to characterize rice quality. The starch, crude protein and amylose of rice were extracted by experiments. The hyperspectral reflectance of starch, crude protein and amylose, their mixed samples and rice samples were determined by a ASD FieldSpec Pro FR™ The spectral characteristics of starch, crude protein, amylose and their correlation with component content were analyzed by using spectral matching technique and multivariate statistical method. The results showed that the spectra of three biochemical components were significantly different, and the spectral peaks and valleys of the mixed samples showed “red shift” or “blue shift”. The contents (%) of crude protein, starch and amylose in rice flour were significantly related to the absorption area S between 2020nm and 2235nm on their spectral curve. The results showed that the hyperspectral method could be used to estimate the contents of crude protein, starch and amylose content in rice, and then to detect rice quality.

Effects of High Temperature on Rice Grain Development and Quality Formation Based on Proteomics Comparative Analysis Under Field Warming

With the intensification of global warming, rice production is facing new challenges. Field evidence indicates that elevated temperature during rice grain-filling leads to the further deterioration of grain quality. In order to clarify the potential regulatory mechanism of elevated temperature on the formation of rice quality, the DIA mass spectrometry method under the background of field warming was conducted to investigate the regulatory effects of high temperature on grain development and material accumulation pathways. The results showed that a total of 840 differentially expressed proteins were identified during the grain-filling process under elevated temperature. These differentially expressed proteins participated in carbon metabolism, amino acid biosynthesis, signal transduction, protein synthesis, and alternately affected the material accumulation of rice grains. The significant up-regulation of PPROL 14E, PSB28, granule-bound starch synthase I, and the significant down-regulation of 26.7 kDa heat shock protein would lead to the component difference in grain starch and storage proteins, and that could be responsible for the degradation of rice quality under elevated temperature. Results suggested that proteins specifically expressed under elevated temperature could be the key candidates for elucidating the potential regulatory mechanism of warming on rice development and quality formation. In-depth study on the metabolism of storage compounds would be contributed in further proposing high-quality cultivation control measures suitable for climate warming.

Physical and Milling Quality of Milled Rice in Indonesia

More than 250 rice varieties are grown in Indonesia from new superior varieties and local varieties with aromatic, non-aromatic aromas and pigmented rice. Rice quality is one factor that must be considered in addition to high productivity. Generally, the quality of grain and rice quality in Indonesia have met the quality standards set based on SNI 0224-1987/SPI-TAN/01/01/1993 and SNI 6128:2020. Ricequality is determined by several factors such as grain appearance, nutritional value, cooking and eating quality and become one of the foremost considerations for rice buyers. Such information can be utilized as basic data of character of genetic resources on assembling of new superior varieties by rice breeders. Those information can be used as a descriptive information for farmers and rice productivity, high yield, and better rice quality. Good quality local cultivar can be used as parents for the formation of new superior varieties.