Rice Developmental Stages Research Articles

Rice developmental stages modulate rhizosphere bacteria and archaea co-occurrence and sensitivity to long-term inorganic fertilization in a West African Sahelian agro-ecosystem

BackgroundRhizosphere microbial communities are important components of the soil-plant continuum in paddy field ecosystems. These rhizosphere communities contribute to nutrient cycling and rice productivity. The use of fertilizers is a common agricultural practice in rice paddy fields. However, the long-term impact of the fertilizers usage on the rhizosphere microbial communities at different rice developmental stages remains poorly investigated. Here, we examined the effects of long-term (27 years) N and NPK-fertilization on bacterial and archaeal community inhabiting the rice rhizosphere at three developmental stages (tillering, panicle initiation and booting) in the Senegal River Delta.ResultsWe found that the effect of long-term inorganic fertilization on rhizosphere microbial communities varied with the rice developmental stage, and between microbial communities in their response to N and NPK-fertilization. The microbial communities inhabiting the rice rhizosphere at panicle initiation appear to be more sensitive to long-term inorganic fertilization than those at tillering and booting stages. However, the effect of developmental stage on microbial sensitivity to long-term inorganic fertilization was more pronounced for bacterial than archaeal community. Furthermore, our data reveal dynamics of bacteria and archaea co-occurrence patterns in the rice rhizosphere, with differentiated bacterial and archaeal pivotal roles in the microbial inter-kingdom networks across developmental stages.ConclusionsOur study brings new insights on rhizosphere bacteria and archaea co-occurrence and the long-term inorganic fertilization impact on these communities across developmental stages in field-grown rice. It would help in developing strategies for the successful manipulation of microbial communities to improve rice yields.

A new canopy chlorophyll index-based paddy rice critical nitrogen dilution curve in eastern China

Abstract Nitrogen (N) plays an essential role in the crop growing cycle. Canopy vegetation indexes and chlorophyll concentration were important tools in-crop N status diagnosis and provided a crucial guide for agricultural managers. This work focused on evaluating the performance of different chlorophyll-related indexes (canopy chlorophyll index, CCI) and Greenseeker RT 200 values (GS values, normalized difference vegetation index, NDVI & Ratio vegetation index, RVI) in crop N nutrient diagnosis. Four experiments were conducted with multiple N fertilizer rates (0 ∼ 390 kg N ha−1) and seven Japonica rice (Oryza sativa L.). Results suggested that the relationship between CCI value and tissue nitrogen accumulation [including leaf nitrogen accumulation (LNA) and plant N accumulation (PNA)] was more reliable than that of GS values. Compered GS values with CCI values-based N concentration dilution curves, CCI-based Nc curve showed better accuracy and lower bias than GS values-based curves. And this curve lower than the reference LAI basis Nc curve and higher than the dry matter-based curve. The N nutrition index (NNI), calculated by the CCI-based curve, ranged from 0.31 to 1.33 during the vegetative stage. A significant positive relationship between CCI values and accumulated N deficit (AND) applications over the rice developmental stages. This present study also proved that CCI could be used to predict grain yield in total growth periods. These developed canopy level vegetation indexes-based Nc curve as a novel tool to offer more choices for evaluating crop N nutrition status and precision N topdressing management in Japonica rice of China.

Assessment of Rice Developmental Stage Using Time Series UAV Imagery for Variable Irrigation Management.

Rice is one of the three major crops in the world and is the major crop in Asia. Climate change and water resource shortages may result in decreases in rice yields and possible food shortage crises. In this study, water-saving farming management was tested, and IOT field water level monitoring was used to regulate water inflow automatically. Plant height (PH) is an important phenotype to be used to determine difference in rice growth periods and yields using water-saving irrigation. An unmanned aerial vehicle (UAV) with an RGB camera captured sequential images of rice fields to estimate rice PH compared with PH measured on site for estimating rice growth stages. The test results, with two crop harvests in 2019, revealed that with adequate image calibration, the correlation coefficient between UAV-PH and field-PH was higher than 0.98, indicating that UAV images can accurately determine rice PH in the field and rice growth phase. The study demonstrated that water-saving farming is effective, decreasing water usage for the first and second crops of 2019 by 53.5% and 21.7%, respectively, without influencing the growth period and final yield. Coupled with an automated irrigation system, rice farming can be adaptive to water shortage situations.

16S rRNA Gene Amplicon Based Metagenomic Signatures of Rhizobiome Community in Rice Field During Various Growth Stages.

Rice is a major staple food across the globe. Its growth and productivity is highly dependent on the rhizobiome where crosstalk takes place between plant and the microbial community. Such interactions lead to selective enrichment of plant beneficial microbes which ultimately defines the crop health and productivity. In this study, rhizobiome modulation is documented throughout the development of rice plant. Based on 16S rRNA gene affiliation at genus level, abundance, and diversity of plant growth promoting bacteria increased during the growth stages. The observed α diversity and rhizobiome complexity increased significantly (p < 0.05) during plantation. PCoA indicates that different geographical locations shared similar rhizobiome diversity but exerted differential enrichment (p < 0.001). Diversity of enriched genera represented a sigmoid curve and subsequently declined after harvest. A major proportion of dominant enriched genera (p < 0.05, abundance > 0.1%), based on 16S rRNA gene, were plant growth promoting bacteria that produces siderophore, indole-3-acetic acid, aminocyclopropane-1-carboxylic acid, and antimicrobials. Hydrogenotrophic methanogens dominated throughout cultivation. Type I methanotrophs (n = 12) had higher diversity than type II methanotrophs (n = 6). However, the later had significantly higher abundance (p = 0.003). Strong enrichment pattern was also observed in type I methanotrophs being enriched during water logged stages. Ammonia oxidizing Archaea were several folds more abundant than ammonia oxidizing bacteria. K-strategists Nitrosospira and Nitrospira dominated ammonia and nitrite oxidizing bacteria, respectively. The study clarifies the modulation of rhizobiome according to the rice developmental stages, thereby opening up the possibilities of bio-fertilizer treatment based on each cultivation stages.

Phytohormone Profiling Method for Rice: Effects of GA20ox Mutation on the Gibberellin Content of Japonica Rice Varieties.

Gibberellins (GAs) are a very important group of phytohormones involved in seed germination, vegetative growth, flowering, and fruit development, being only 4 of the 136 known bioactives: GA1, GA3, GA4, and GA7. It has been evidenced that mutations in the OsGA20ox-2 gene produce rice (Oryza sativa) dwarf varieties, which were one of the main pillars of the green revolution. In this work two main objectives were proposed: (i) develop a rapid and broad phytohormone profiling method and (ii) to study the effects on the GA content of the GA20ox-2 mutation in several rice developmental stages using three varieties (tall variety, elite variety, mutated variety). A phytohormone extraction using an SPE step and HPLC-MS/MS detection using a QqQ instrument was determined which resulted in limits of detection (LOD) and limits of quantification (LOQ) for GAs that varied between 0.1–0.7 and 0.3–2.3 pg ⋅ g-1 (f.w.) of rice sample, respectively, allowing highly sensitive phytohormones detection in samples. Moreover, a good reproducibility was obtained for the GAs as relative standard deviations (RSD) for a 40 ng ⋅ mL-1 pattern varied between 0.3 and 0.9%. Notoriously, GA1 was absent in the coleoptile and GA4 was the GA with higher content in the majority of developmental stages. We also observed a large content increase of the four bioactive GAs in the internode of the flag leaf of the mutated variety allowing to reach same height as the elite variety. Therefore, we provide a rapid and broad phytohormonal profiling method and evidence that the GA20ox-2 mutation is not the only factor generating dwarf varieties. To our knowledge, this is the first study that it has been reported such a high number of simultaneously analyzed gibberellins in rice samples (Oryza sativa ssp. japonica) in different tissues of different growth stages.

Wireless Channel Propagation Characteristics and Modeling Research in Rice Field Sensor Networks.

Wireless channel propagation characteristics and models are important to ensure the communication quality of wireless sensor networks in agriculture. Wireless channel attenuation experiments were carried out at different node antenna heights (0.8 m, 1.2 m, 1.6 m, and 2.0 m) in the tillering, jointing, and grain filling stages of rice fields. We studied the path loss variation trends at different transmission distances and analyzed the differences between estimated values and measured values of path loss in a free space model and a two-ray model. Regression analysis of measured path loss values was used to establish a one-slope log-distance model and propose a modified two-slope log-distance model. The attenuation speed in wireless channel propagation in rice fields intensified with rice developmental stage and the transmission range had monotone increases with changes in antenna height. The relative error (RE) of estimation in the free space model and the two-ray model under four heights ranged from 6.48–15.49% and 2.09–13.51%, respectively, and these two models were inadequate for estimating wireless channel path loss in rice fields. The ranges of estimated RE for the one-slope and modified two-slope log-distance models during the three rice developmental stages were 2.40–2.25% and 1.89–1.31%, respectively. The one-slope and modified two-slope log-distance model had better applicability for modeling of wireless channels in rice fields. The estimated RE values for the modified two-slope log-distance model were all less than 2%, which improved the performance of the one-slope log-distance model. This validates that the modified two-slope log-distance model had better applicability in a rice field environment than the other models. These data provide a basis for modeling of sensor network channels and construction of wireless sensor networks in rice fields. Our results will aid in the design of effective rice field WSNs and increase the transmission quality in rice field sensor networks.

Microbial explanations for field-aged biochar mitigating greenhouse gas emissions during a rice-growing season.

Knowledge about the impacts of fresh and field-aged biochar amendments on greenhouse gas (CH4, N2O) emissions is limited. A field experiment was initiated in 2012 to study the effects of fresh and field-aged biochar additions on CH4 and N2O emissions and the associated microbial activity during the entire rice-growing season in typical rice-wheat rotation system in Southeast China. CH4 and N2O fluxes were monitored, and the abundance of methanogen (mcrA), methanotrophy (pmoA), ammonia-oxidizing archaea (AOA), ammonia-oxidizing bacteria (AOB), nitrite reductase (nirS, nirK), N2O reductase (nosZ), and potential soil enzyme activities related to CH4 and N2O were simultaneously measured throughout different rice developmental stages. There were three treatments: control (urea without biochar), fresh BC (urea with fresh biochar added in 2015), and aged BC (urea with 3-year field-aged biochar added in 2012). Results showed that field-aged biochar significantly decreased seasonal CH4 emissions by 16.8% in relation to the fresh biochar, though no significant differences were detected between biochars and control treatment. The structural equation model indicated that soil pH, microbial biomass carbon (MBC), pmoA, and mcrA were the main factors directly influenced by fresh and aged biochar amendments; aged biochar showed a negative effect while fresh biochar showed positive effects on CH4 fluxes. Both fresh and field-aged biochar obviously increased AOA and AOB abundances and reduced the (nirS+nirK)/nosZ ratio during the entire rice-growing season, although no significant effects were observed on seasonal N2O emissions. Therefore, biochar amendment produced long-term effects on total CH4 and N2O emissions through observed influences of soil pH and functional gene abundance. The figure shows how fresh and field-aged biochar differentially affected CH4 production and oxidation and N2O production and reduction through related functional gene abundances. Blue arrows indicate suppressing while pink arrows indicate promoting effect.

Support vector machine-based open crop model (SBOCM): Case of rice production in China.

Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM) was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability.

As contamination alters rhizosphere microbial community composition with soil type dependency during the rice growing season

Rhizosphere microbes play a cardinal role in transformation and crop uptake of arsenic (As), thereby relieving or intensifying the risk of As contamination in the food webs. How rhizosphere microbiomes respond to As contamination in different paddy soils and rice growth stages is still unclear. Here, we conducted a rice pot experiment to address the effects of rice developmental stage and As contamination on the rhizosphere microbial communities in two contrast paddy soils, a yellow clayey paddy soil (YP, pH 5.1, soil organic matter 20.8 g/kg) and red paddy soil (RP, pH 6.2, soil organic matter 46.1 g/kg). The rhizosphere microbial communities were investigated using phospholipid fatty acids analysis at tillering, panicle initiation, and maturity stages. The results showed that rice growing in YP soil accumulated 2-10 times higher contents of As in root than that in RP soil. There was a significant effect of rice growing stage, independent of soil types and As treatment, on rhizosphere microbial community composition in both YP and RP soils as depicted by canonical correspondence analysis. As contamination significantly altered rhizosphere microbial community composition only in YP soil, which showed the soil type dependency of the As contamination effect. In RP soil, the higher content of soil organic matter reduced the impact of As contamination. Soil pH explained more percentage of variation in microbial community composition than soil DOC and DON did. These influences of soil physiochemical properties on heavy metal available and rhizosphere microbial community may lay the foundation for exploration of bioremediation potential.

Molecular and Bioinformatic Characterization of the Rice ROOT UV-B SENSITIVE Gene Family.

Background ROOT UV-B SENSITIVE (RUS) genes exist in most eukaryotic organisms, and encode proteins that contain a DUF647 (domain of unknown function 647). Although the RUS genes are known to play essential roles in Arabidopsis seedling development, their precise functions are not well understood in other plants, including rice.FindingsIn this study, six OsRUS genes were cloned from rice root and leaf cDNA libraries. Our analysis showed that the sequence and open reading frame of cloned OsRUS3 cDNA differs from the predictions reported in the RAP-DB and RGAP databases. Public microarray, MPSS, and EST databases were used to analyze the expression profiles of the six OsRUS genes. Expression profiles for all OsRUS genes at different rice developmental stages were also analyzed by qRT-PCR. The signal peptide, GPI-anchor, transmembrane domain and subcellular localization of OsRUS proteins were predicted by various bioinformatics tools. Furthermore OsRUS1 was determined to be localized to the chloroplast by a protoplast experiment.ConclusionsAll the characterization of the OsRUS family generated from this study will provide a crucial foundation from which to further dissect how OsRUS genes function in rice development.Electronic supplementary materialThe online version of this article (doi:10.1186/s12284-016-0127-0) contains supplementary material, which is available to authorized users.

Comparative Transcriptional Profiling of Melatonin Synthesis and Catabolic Genes Indicates the Possible Role of Melatonin in Developmental and Stress Responses in Rice.

As a well-known animal hormone, melatonin (N-acetyl-5-methoxytryptamine) is also involved in multiple plant biological processes, especially in various stress responses. Rice is one of the most important crops, and melatonin is taken in by many people everyday from rice. However, the transcriptional profiling of melatonin-related genes in rice is largely unknown. In this study, the expression patterns of 11 melatonin related genes in rice in different periods, tissues, in response to different treatments were synthetically analyzed using published microarray data. These results suggest that the melatonin-related genes may play important and dual roles in rice developmental stages. We highlight the commonly regulation of rice melatonin-related genes by abscisic acid (ABA), jasmonic acid (JA), various abiotic stresses and pathogen infection, indicating the possible role of these genes in multiple stress responses and underlying crosstalks of plant hormones, especially ABA and JA. Taken together, this study may provide insight into the association among melatonin biosynthesis and catabolic pathway, plant development and stress responses in rice. The profile analysis identified candidate genes for further functional characterization in circadian rhythm and specific stress responses.

Changes in lipid substances in rice during grain development

Lipid substances, such as fatty acids, γ-oryzanol, policosanols, and tocol (tocopherol+tocotrienol), were investigated in two rice cultivars, Ilpum and Dasan, during rice development. In both cultivars, the lipid extract level decreased steadily after it reached its maximum level. Additionally, there were differences in the fatty acid composition, depending on the rice developmental stage, between the two cultivars. The γ-oryzanol level increased during rice development, and there were differences in the composition of γ-oryzanol between the two cultivars. The levels of policosanols drastically decreased during the early stage of rice development in the two cultivars. The total tocol level showed a downward trend during rice development. The predominant tocol isomer in Ilpum was α-tocopherol during rice development. In Dasan, the predominant tocol isomer was α-tocopherol at the early stage, but γ-tocotrienol at the later stage. This study provided information on the levels and composition of lipid substances, such as fatty acids, γ-oryzanol, policosanols, and tocol during rice development.

Influence of host plant odours on invasion of the rice leaf bug Trigonotylus caelestialium into paddy fields

The host‐odour preferences of the rice leaf bug Trigonotylus caelestialium between the rice plant Oryza sativa L. and four species of graminaceous weeds, Poa annua, Alopecurus aequalis, Digitaria ciliaris and Eleusine indica, were investigated with an olfactometer aiming to clarify the influence of these odours on invasion of the bug to paddy fields at the flowering stage of rice. Both female and male adults significantly preferred the graminaceous weed A. aequalis in the flowering stage to rice in the fifth‐leaf stage. The bugs also significantly preferred flowering P. annua and A. aequalis to rice in the panicle‐formation stage. However, the bugs showed no preferences between rice in the flowering and grain‐filling stages and the flowering graminaceous weeds P. annua, D. ciliaris and E. indica. The preference of the rice leaf bug for the flowering graminaceous weeds before rice flowering coincides with the fact that these bugs mainly live on these weeds before rice flowering. It is considered that the bug's similar preference for flowering rice panicles as the flowering graminaceous weeds causes the intense invasion of the bug into paddy fields at this rice developmental stage.

Small RNAs Originated from Pseudogenes: cis- or trans-Acting?

Pseudogenes are significant components of eukaryotic genomes, and some have acquired novel regulatory roles. To date, no study has characterized rice pseudogenes systematically or addressed their impact on the structure and function of the rice genome. In this genome-wide study, we have identified 11,956 non-transposon-related rice pseudogenes, most of which are from gene duplications. About 12% of the rice protein-coding genes, half of which are in singleton families, have a pseudogene paralog. Interestingly, we found that 145 of these pseudogenes potentially gave rise to antisense small RNAs after examining ∼1.5 million small RNAs from developing rice grains. The majority (>50%) of these antisense RNAs are 24-nucleotides long, a feature often seen in plant repeat-associated small interfering RNAs (siRNAs) produced by RNA-dependent RNA polymerase (RDR2) and Dicer-like protein 3 (DCL3), suggesting that some pseudogene-derived siRNAs may be implicated in repressing pseudogene transcription (i.e., cis-acting). Multiple lines of evidence, however, indicate that small RNAs from rice pseudogenes might also function as natural antisense siRNAs either by interacting with the complementary sense RNAs from functional parental genes (38 cases) or by forming double-strand RNAs with transcripts of adjacent paralogous pseudogenes (2 cases) (i.e., trans-acting). Further examinations of five additional small RNA libraries revealed that pseudogene-derived antisense siRNAs could be produced in specific rice developmental stages or physiological growth conditions, suggesting their potentially important roles in normal rice development. In summary, our results show that pseudogenes derived from protein-coding genes are prevalent in the rice genome, and a subset of them are strong candidates for producing small RNAs with novel regulatory roles. Our findings suggest that pseudogenes of exapted functions may be a phenomenon ubiquitous in eukaryotic organisms.

Modelling and prediction of developmental process in rice. I. Structure and method of parameter estimation of a model for simulating developmental process toward heading.

イネの幼穂分化, 出穂および成熟などの発育ステージを環境要因の経過から予測するモデルの基本構造とパラメータの推定法を提案し, その考え方のもとに出穂期の気象的予測モデルを導き, 水稲品種日本晴に適用した。本モデルでは, de Witらの発育速度の慨念を適用して, 出芽後n日目の発育指数 (Developmental Index, DVI) はその間の発育速度 (Developmental Rate, DVR) を積算したものとして与える。さらに出芽時のDVIを0, そして出穂時のそれを1と定めることによって, 出芽から出穂に到る発育過程をDVI=0～1の間の連続的な数値として表すことができる。このようなDVIの制約条件下で, DVRと気温および日長との関係を与える数式を導き, かつそのパラメータを, 筑波と京都での日本晴の作期移動試験および人工気象室実験から得られた出穂日のデータを用いて, シンプレックス法によって決定した。得られたパラメータの値から, 日本晴の出芽から出穂までの最小日数 (基本栄養生長性) は51.4日, 限界日長は15.6時間, 発育の最低温度は12～13℃, 同最適温度は30～32℃, そして日長に感応し始める時期はDVI=0.20と推定された。本モデルによる出穂データの推定精度は標準誤差で3.6日であったが, 従来の有効積算温度法によるそれは6.5日であった。したがって, 本報のモデルは従来の方法に比較して高い予測精度を得ることが可能と考えられる。