Rice Growth Stages Research Articles

Variations in the relative abundance of hemipteran species in different seasons and stages of rice growth depending on weather variations

Rompin has played an active role in Malaysia's rice production for several decades. However, it also faces a threat, particularly from the hemipteran insect pests. There is limited documentation on the abundance of these insects and their relationship with climatic factors. Therefore, this study aimed to determine the abundance of hemipterans in different growth stages and to understand the effects of climatic factors on their population in rice fields. The study was conducted in farmers' rice fields in Rompin, Pahang, Malaysia, during the main and off-season rice planting. Sweep nets, yellow sticky traps, and yellow pan traps were utilized in each plot. Trapped insects were identified based on the rice planting seasons and growth stages. The trends of climatic factors such as rainfall, temperature, light intensity, and wind speed were analysed and correlated with insect abundance. A total of 23,023 individuals belonging to eight families and eleven species of the hemipteran order were recorded. Nine of these species were pests, namely Leptocorisa oratorius, Recilia dorsalis, Nephotettix virescens, N. nigropictus, Amaurochrous dubius, Nilaparvata lugens, Sogatella furcifera, Nezara viridula, and Scotinophara coarctata. The remaining two species were predators, Cyrtorhinus lividipennis and Polytoxus fuscoviftatus. R. dorsalis, N. lugens, S. furcifera, and A. dubius were found in significantly higher numbers and exceptionally high during rice's late vegetative and early reproductive stages. The maximum temperature significantly increased during the main season, while wind speed increased dramatically during the off-season. Wind speed significantly correlated with N. virescens, S. furcifera, L. oratorius, and A. dubius. On the other hand, rainfall exhibited a significant correlation with L. oratorius. Various pest species and their high populations in this study pose a significant threat to plants due to their preferences for different plant parts and strata.

Three-dimensional image analysis specifies the root distribution for drought avoidance in the early growth stage of rice.

Root system architecture (RSA) plays a key role in plant adaptation to drought because deep rooting enables better water uptake than shallow rooting under terminal drought. Understanding RSA during early plant development is essential for improving crop yields, as early drought can affect subsequent shoot growth. Herein, we demonstrate that root distribution in the topsoil significantly impacts shoot growth during the early stages of rice (Oryza sativa) development under drought, as assessed through three-dimensional (3D) image analysis. We used 109 F12 recombinant inbred lines (RILs) obtained from a cross between shallow-rooting lowland rice and deep-rooting upland rice, representing a population with diverse RSA. We applied a moderate drought during the early development of rice grown in a plant pot (25cm height) by stopping irrigation 14 days after sowing (DAS). Time-series RSA at 14, 21, and 28 DAS was visualized by X-ray computed tomography, and subsequently compared between drought and well-watered conditions. Following this analysis, we further investigated drought-avoidant RSA by testing 20 randomly selected RILs under drought conditions. We inferred the root location that most influences shoot growth using a hierarchical Bayes approach: the root segment depth, which positively impacted shoot growth, ranged between 1.7-3.4cm under drought conditions and between 0.0-1.7cm under well-watered conditions. Drought-avoidant RILs had a higher root density in the lower layers of the topsoil compared to the others. Fine classification of soil layers using 3D image analysis revealed that increasing root density in the lower layers of the topsoil, rather than in the subsoil, is advantageous for drought avoidance during the early growth stage of rice.

Estimating Key Phenological Dates of Multiple Rice Accessions Using Unmanned Aerial Vehicle-Based Plant Height Dynamics for Breeding

Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height (PH) dynamics are valuable for estimating phenological dates. However, research on estimating the key phenological dates of multiple rice accessions based on PH dynamics has been limited. In 2022, field traits were collected using unmanned aerial vehicle (UAV)-based images across 435 plots, including 364 rice varieties. PH, dates of initial heading (IH) and full heading (FH), and panicle initiation (PI), and growth period after transplanting (GPAT) were collected during the rice growth stage. PHs were extracted using a digital surface model (DSM) and fitted using Fourier and logistic models. Machine learning algorithms, including multiple linear regression, random forest (RF), support vector regression, least absolute shrinkage and selection operator, and elastic net regression, were employed to estimate phenological dates. Results indicated that the optimal percentile of the DSM for extracting rice PH was the 95th (R2 = 0.934, RMSE = 0.056 m). The Fourier model provided a better fit for PH dynamics compared with the logistic models. Additionally, curve features (CF) and GPAT were significantly associated with PI, IH, and FH. The combination of CF and GPAT outperformed the use of CF alone, with RF demonstrating the best performance among the algorithms. Specifically, the combination of CF extracted from the logistic models, GPAT, and RF yielded the best performance for estimating PI (R2 = 0.834, RMSE = 4.344 d), IH (R2 = 0.877, RMSE = 2.721 d), and FH (R2 = 0.883, RMSE = 2.694 d). Overall, UAV-based rice PH dynamics combined with machine learning effectively estimated the key phenological dates of multiple rice accessions, providing a novel approach for investigating key phenological dates in breeding work.

High night temperature stress on rice (Oryza sativa) - insights from phenomics to physiology. A review.

Rice (Oryza sativa ) faces challenges to yield and quality due to urbanisation, deforestation and climate change, which has exacerbated high night temperature (HNT). This review explores the impacts of HNT on the physiological, molecular and agronomic aspects of rice growth. Rise in minimum temperature threatens a potential 41% reduction in rice yield by 2100. HNT disrupts rice growth stages, causing reduced seed germination, biomass, spikelet sterility and poor grain development. Recent findings indicate a 4.4% yield decline for every 1°C increase beyond 27°C, with japonica ecotypes exhibiting higher sensitivity than indica. We examine the relationships between elevated CO2 , nitrogen regimes and HNT, showing that the complexity of balancing positive CO2 effects on biomass with HNT challenges. Nitrogen enrichment proves crucial during the vegetative stage but causes disruption to reproductive stages, affecting grain yield and starch synthesis. Additionally, we elucidate the impact of HNT on plant respiration, emphasising mitochondrial respiration, photorespiration and antioxidant responses. Genomic techniques, including CRISPR-Cas9, offer potential for manipulating genes for HNT tolerance. Plant hormones and carbohydrate enzymatic activities are explored, revealing their intricate roles in spikelet fertility, grain size and starch metabolism under HNT. Gaps in understanding genetic factors influencing heat tolerance and potential trade-offs associated with hormone applications remain. The importance of interdisciplinary collaboration is needed to provide a holistic approach. Research priorities include the study of regulatory mechanisms, post-anthesis effects, cumulative HNT exposure and the interaction between climate variability and HNT impact to provide a research direction to enhance rice resilience in a changing climate.

Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm.

Aboveground biomass (AGB) is regarded as a critical variable in monitoring crop growth and yield. The use of hyperspectral remote sensing has emerged as a viable method for the rapid and precise monitoring of AGB. Due to the extensive dimensionality and volume of hyperspectral data, it is crucial to effectively reduce data dimensionality and select sensitive spectral features to enhance the accuracy of rice AGB estimation models. At present, derivative transform and feature selection algorithms have become important means to solve this problem. However, few studies have systematically evaluated the impact of derivative spectrum combined with feature selection algorithm on rice AGB estimation. To this end, at the Xiaogang Village (Chuzhou City, China) Experimental Base in 2020, this study used an ASD FieldSpec handheld 2 ground spectrometer (Analytical Spectroscopy Devices, Boulder, Colorado, USA) to obtain canopy spectral data at the critical growth stage (tillering, jointing, booting, heading, and maturity stages) of rice, and evaluated the performance of the recursive feature elimination (RFE) and Boruta feature selection algorithm through partial least squares regression (PLSR), principal component regression (PCR), support vector machine (SVM) and ridge regression (RR). Moreover, we analyzed the importance of the optimal derivative spectrum. The findings indicate that (1) as the growth stage progresses, the correlation between rice canopy spectrum and AGB shows a trend from high to low, among which the first derivative spectrum (FD) has the strongest correlation with AGB. (2) The number of feature bands selected by the Boruta algorithm is 19~35, which has a good dimensionality reduction effect. (3) The combination of FD-Boruta-PCR (FB-PCR) demonstrated the best performance in estimating rice AGB, with an increase in R² of approximately 10% ~ 20% and a decrease in RMSE of approximately 0.08% ~ 14%. (4) The best estimation stage is the booting stage, with R2 values between 0.60 and 0.74 and RMSE values between 1288.23 and 1554.82 kg/hm2. This study confirms the accuracy of hyperspectral remote sensing in estimating vegetation biomass and further explores the theoretical foundation and future direction for monitoring rice growth dynamics.

Mathematical Modelling for Rice Blast Re-Infection

Rice is the thirdly most valued cereal crops in Kenya after maize and wheat. The demand for rice in Kenya has increased greatly over the last few years while production has still remained low. This is because rice production is affected by serious constraints especially rice diseases of which the most threatening is rice blast. Rice blast infection and re-infection can occur in different stages of rice growth and therefore need to be controlled. This study aims to developed a mathematical model for rice blast re-infection. The model employs a system of nonlinear ordinary differential equations which is analysed in details for its stability properties. Basic reproduction number <i>R<sub>o</sub></i> for rice blast re-infection was found to be less than one. Numerical simulation of the model is done using Mathematica, and graphical profile of the main variables are depicted. We conclude that rice blast re-infection reduces rice yield and necessary remedy are needed.

Mineral and Labile Organic Nitrogen Fractions in Soil Profile and Their Response to FYM and Inorganic Fertilization in Different Growth Stages of Rice Crop

Nitrogen is the key element among the major nutrients in crop production. The mineralizable soil organic nitrogen is the main contributors of soil N supply. A better understanding of soil organic nitrogen dynamics in agro-ecosystems is needed to improve N management. The present investigation was conducted in Regional Agricultural Research Station (RARS), Assam Agricultural University, Titabar, Jorhat during 2017-18. In this study, the impact of Farmyard Manure (FYM) and inorganic fertilization on soil mineral nitrogen (NO3−-N and NH4+-N) dynamics and labile organic nitrogen fractions viz. microbial biomass N (MBN), particulate organic N (PON) and water-extractable organic N (WEON) at three growth stages of rice viz. active tillering, flowering and physiological maturity stages and nitrogen stock were assessed. Six treatments viz. control (no fertilization), 100% NPK, 100% NPK + FYM 5 t ha-1, 50% NPK, 50% NPK + 50% N through FYM and FYM 10 t ha-1 were tested in randomized block design with four replications. The results showed that NO3−-N and NH4+-N were found to be significantly higher in 100% NPK+FYM 5 t ha-1 at the three growth stages of rice. The labile organic nitrogen fractions were significantly higher in FYM 10 t ha-1. All these variables were decreased with increase in crop growth stages. Integrated use of inorganic fertilizer and FYM recorded the highest nitrogen stock. Stepwise regression analysis indicated that NH4+-N was the main contributor to nitrogen stocks at the three growth stages of rice of the studied soils.

Assessing vegetation indices and productivity across nitrogen gradients: a comparative study under transplanted and direct-seeded rice

Nitrogen responses vary under diverse agronomic management practices, influencing vegetation indices (VIs) and productivity across different ecological conditions. However, the proper quantification of these responses under various crop establishment methods with varied nitrogen levels is rarely studied. Therefore, a field experiment was conducted to investigate the impact of varying nitrogen levels on VIs, growth parameters, yield attributes, yield, and economic aspects of transplanted rice (TR) and direct-seeded rice (DSR). The experiment was conducted in the randomized block design consisted seven N levels, which included 0% recommended dose of nitrogen (RDN) or no nitrogen (N0), 33.33% RDN (N1), 66.66% RDN (N2), 100% RDN (N3), 133.33% RDN (N4), 166.66% RDN (N5) and 200% RDN (N6), and replicated thrice. The plots with higher N levels demonstrated increased values of VIs and treatment N3 (120 kg N ha−1), N4 (160 kg N ha−1), N5 (200 kg N ha−1), and N6 (240 kg N ha−1) showed no statistically significant differences in NDVI (normalized difference vegetation index), RVI (ratio vegetation index), NDRE (normalized difference red edge), and GNDVI (green normalized difference vegetation index) values across the various growth stages of rice. The application of treatment N4 resulted in the highest number of panicles m−2 (348.2 in TR, 376.8 in DSR), filled grains panicle−1 (74.55 in TR, 62.43 in DSR), and a 1,000-grain weight of 26.92 g in TR and 26.76 g in DSR. The maximum yield (4.89 t ha−1) was obtained in transplanted rice at treatment N4 and, 8.15% yield reduction was noted in DSR for the same treatment, which was statistically equivalent to N3, but significantly superior to other N levels. Conversely, in DSR with RDN (120 kg N ha−1), the cost–benefit ratio surpassed that of TR by 16.96%, signifying DSR’s adaptability for more profitable rice cultivation in the region. This research provides valuable insights into optimizing nitrogen management practices for TR and DSR, thereby enhancing rice crop performance and economic returns.

Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing

Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands at narrow wavelengths for mapping LAI at various rice phenological stages, and it is functionally related to canopy spectral reflectance. Hyperspectral signatures for different phases of rice crop growth was recorded using Airborne Visible Near-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) along with corresponding ground based observations. Ground-based hyperspectral canopy spectral reflectance measurements were recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., Forsyth County, GA, USA; spectral range: 350–2500 nm) and LAI data from 132 farmer’s fields in Southern India. Among 29 hyperspectral vegetation indices tested, 8 were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, the elongation stage was the most accurately estimated using vegetation indices that exhibited a significant correlation with the airborne hyperspectral reflectance. The validation of hyperspectral vegetation indices revealed that the best fit model for estimating rice LAI was mND705 (red-edge, blue, and NIR bands) at seedling and elongation, SAVI (red and NIR bands) at tillering and WDRVI (red and NIR bands) at booting stage.

Evaluating Water Management Efficiency in Regulating Cadmium and Arsenic Accumulation in Rice in Typical Japonica Paddy Soils at Varied pH Levels

There is growing concern regarding cadmium (Cd) exposure through rice consumption. Compared with alternate wetting and drying (AWD), continuous flooding (CF) is usually considered as an effective approach for reducing Cd enrichment in rice but increases the risk of pollution from arsenic (As). In this study, the field trial was conducted to investigate remediation effects of two water management (CF and AWD) techniques on Cd pollution in rice in typical japonica rice cultivation areas with varied soil pH levels. The results indicate that soil pH was a crucial factor in regulating CF-mediated Cd/As accumulation and migration in rice plants, and grains at all stages of rice growth. In acidic fields, compared with AWD, the use of CF reduced the accumulation of Cd in plants during the tillering stage; CF during the milk stage promotes the risk of contamination of Cd in rice grains and any form of As in plants and inhibits the content of any forms of As in grains. During the mature stage, CF reduced the levels of Cd in the plants and grains while promoting the accumulation of As(V) and total As(T-As) in plants and As(III) in grains. In alkaline fields, compared with AWD, CF during the tillering stage promoted the accumulation of various forms of As in plants. During the milk stage, CF increased and decreased the Cd content in plants and grains, respectively, and reduced the accumulation of T-As in plants and As(III) in grains; during the mature stage, CF promoted the accumulation of Cd in plants and grains, induced the accumulation of T-As plants, and inhibited the accumulation of any form of As in grains. From the perspective of food safety, the impact of CF conditions on the accumulation of Cd and As in rice from acidic fields exhibited a pattern of reduction in Cd and increase in As during the maturity period, as compared to that on the AWD. Conversely, CF increased the Cd risk while simultaneously reducing the As accumulation in rice grains to a safe level in alkaline fields. CF is not recommended as a remediation strategy for Cd pollution in rice in low Cd pollution areas, but it can be considered as a potential strategy for As pollution remediation in rice in alkaline fields with low Cd pollution.

Deciphering the biometeorological influences on mycoflora dynamics in rice seeds of Indo-gangetic ecosystem

Rice (Oryza sativa L.) is a vital global food source, particularly in Asia, where it constitutes 60 to 70 per cent of cereal production. However, rice cultivation faces many challenges including seed-borne fungal pathogens. This study aimed to comprehensively assess the incidence and severity of seed-borne fungal pathogens in various rice varieties and their relationship with environmental parameters during different growth stages. Seed samples were collected from different locations in Nadia district, West Bengal, and analyzed for seed health. The prevalence of Helminthosporium oryzae, Alternaria alternata, Fusarium sp., Curvularia lunata, Penicillium sp., Aspergillus flavus, Aspergillus niger and Trichoconis padwickii was recorded. Compared to the other diseases, Fusarium sp. shows a distribution with greater kurtosis deviation from normalcy, suggesting the occurrence of more extreme values. In contrast, the tailedness of Aspergillus niger's distribution is more akin to that of a normal distribution. Eight varieties of rice were examined for their susceptibility to various seed-borne fungal pathogens, revealing that variety Shatabdi exhibited a high vulnerability to these pathogens. The isolation of seed-borne pathogens was carried out at different growth stages of rice, and it was observed that the harvesting stage is particularly susceptible to seed-borne pathogens. Temperature and humidity influence pathogen intensity differently across years, indicating the complex interplay of environmental factors in disease dynamics. This research contributes to understanding the temporal and spatial dynamics of seed-borne fungal pathogens in rice, facilitating the development of targeted management strategies for improved crop health and yield. . KEYWORDS :Biometeorological parameters, growth stages, rice, seed borne, varieties

Vertical and temporal variations in activity, abundance, and composition of nitrite-driven anaerobic methanotrophs in a paddy field

Paddy soil is an important source of methane (CH4) emissions, and the nitrite-driven anaerobic oxidation of methane (AOM) mediated by Candidatus Methylomirabilis bacteria is a crucial process for CH4 mitigation. However, the vertical and temporal fluctuations in nitrite-driven AOM activity, as well as the abundance and composition of Methylomirabilis bacteria in paddy soil, remain poorly understood. We investigated the potential rate of nitrite-driven AOM and the composition of Methylomirabilis bacteria at varying depths (0–40 cm, at 10 cm intervals) within a paddy soil during different growth stages of rice, including tillering, jointing, flowering, and milky. The potential rate of nitrite-driven AOM ranged from 0.24 to 20.6 nmol CO2 g−1 (dry soil) d−1, and the abundance of Methylomirabilis bacterial 16S rRNA genes varied from 5.8 × 106 to 4.4 × 107 copies g−1 dry soil. The 10–20 cm soil depth showed higher potential activity and bacterial abundance compared to other depths, and the tillering and jointing stages exhibited greater activity and abundance. The composition of Methylomirabilis bacteria showed significant variation across different soil depths but remained stable throughout the growth stages of rice. Soil organic carbon content was positively correlated with potential nitrite-driven AOM rate, and nitrite content was negatively correlated with the abundance of Methylomirabilis bacteria. Soil organic carbon and ammonium content had a significant impact on the composition of Methylomirabilis bacteria. The results underscore the substantial influence of soil depth and the growth stage of rice on nitrite-driven AOM in paddy ecosystems.

QTL-seq analysis identified the genomic regions of plant height and days to heading in high-latitude rice.

Introduction: Rice (Oryza sativa L.) is one of the most extensive crops in the world. China's Heilongjiang Province is the northernmost rice-growing region in the world. However, rice cultivars suitable for growth in low-latitude regions may not mature normally due to their distinct climate and short frost-free period. It is necessary to precisely determine the frost-free period for each region to make the best use of the rice growth stage so as to ensure the maturity and yield of different rice cultivars in Heilongjiang Province. The time span of the heading stage is a key parameter for evaluating the adaptability of a rice cultivar to a specific rice-growing region. Given the above facts, it is of high importance to study the associated genes and sites controlling days to heading (DH) and plant height (PH) of rice in Heilongjiang Province. Bulked segregant analysis (BSA) combined with high-throughput sequencing can effectively exclude interferences from background genomic differences, making it suitable for analyzing the associated sites of complex agronomic traits in early generations. Methods: In this study, an F3 segregating population was obtained by crossing two main cultivars that are grown under different temperatures and day-light conditions in Heilongjiang. Two pools of extreme phenotypes were built for the DH and PH of the population. For SNP and InDel variants obtained from whole-genome resequencing in the pools, an association analysis was performed using the Euclidean distance (ED) algorithm and the SNP/InDel index algorithm. Results: The intersection of SNP and InDel regions associated with the phenotypes was considered to obtain the final associated sites. After excluding interferences from the cloned genes on chromosomes 2 and 7, a total length of 6.34Mb on chromosomes 1, 3, and 10 and 3.16Mb on chromosomes 1 and 10 were left associated with PH and DH, respectively. Then, we performed a gene annotation analysis for candidate genes in the remaining regions using multiple genome annotation databases. Our research provides basic data for subsequent gene mapping and cloning. Discussion: By mining more genetic loci associated with the days to heading and plant height of rice, we may provide abundant genetic resources for refined molecular breeding in Heilongjiang Province.

Assemblages of rhizospheric and root endospheric mycobiota and their ecological associations with functional traits of rice.

The assembly processes and functions of root-associated mycobiota are among the most fascinating yet elusive topics in microbial ecology. Our results revealed that stochastic forces (dispersal limitation or ecological drift) act on fungal community assembly in both the rice rhizosphere and root endosphere at the early stage of plant growth. In addition, high covariations between the rhizosphere fungal community compositions and plant functional trait profiles were clearly demonstrated in the present study. This work provides empirical evidence of the root-associated fungal assembly principles and ecological relationships of plant functional traits with rhizospheric and root endospheric mycobiota, thereby potentially providing novel perspectives for enhancing plant performance.

Influence of ZnO Nanoparticles on Early Growth Stage of Fragrant Rice at Low Temperature (LT) Stress

Influence of ZnO Nanoparticles on Early Growth Stage of Fragrant Rice at Low Temperature (LT) Stress

Interaction between water, crop residue and fertilization management on the source-differentiated nitrogen uptake by rice

Alternate wetting and drying (AWD) is an effective water-saving practice for rice cultivation that may however promote nitrogen (N) losses compared to continuous flooding (CF). The interaction between water, crop residue and N fertilization management can influence the contribution of different N sources to plant uptake. We hypothesized that microbial processes driving the source-differentiated N supply for rice uptake during the early growth stages will depend on the interaction between water management, the timing of straw incorporation with respect to flooding and the temporal distribution of mineral N application. Rice was grown for 60 days in mesocosm experiment involving a factorial design with (i) two water regimes (CF vs. AWD) and (ii) three straw and fertilizer managements, during which soil N, porewater chemistry, plant growth and N uptake were evaluated. Source partitioning of plant N between fertilizer-, straw- and soil-derived N was achieved by means of a dual-stable isotope 15N tracing approach. Although AWD reduced total N uptake by about 4–25% with respect to CF, this could only be partly attributed to a lower uptake of fertilizer-N (and lower fertilizer-N use efficiency), suggesting that other N sources were affected by water management. Our findings evidence how the interaction between soil redox conditions and the availability of labile C and inorganic N strongly determined the supply of soil-derived N through microbial feedback and priming responses. Although incorporated straw contributed only minimally to rice N, it represented the primary driver controlling plant N nutrition through these microbial responses. These insights may contribute to identify suitable fertilization practices that favour plant N uptake during the early stages of rice growth under AWD.

Oxyfluorfen-resistant rice tolerance and weed control when using oxyfluorfen

Abstract Herbicide-resistant barnyardgrass and weedy rice control, without crop injury, is a challenge for rice producers in the United States. Herbicides not initially labeled for rice, such as oxyfluorfen, are now being evaluated as new tools for weed control. The ROXY® trait allows for the use of oxyfluorfen in rice for weed control preemergence and postemergence. Experiments were initiated in 2021 and 2022 to evaluate (1) the effectiveness of preemergence- and postemergence-applied oxyfluorfen on barnyardgrass and weedy rice, (2) the sensitivity of oxyfluorfen-resistant rice to oxyfluorfen as a function of application timing, and (3) the influence of soil moisture on oxyfluorfen-resistant rice sensitivity to oxyfluorfen. In the field, a rate response was observed for oxyfluorfen applied to weedy rice when averaged over application timings of 1-leaf, 2-leaf, 3-leaf, and tillering, with oxyfluorfen at 1,680 g ai ha−1 resulting in 81% and 72% control 7 d after application (DAA) in 2021 and 2022, respectively. Under greenhouse conditions, barnyardgrass and weedy rice control averaged by the rate of oxyfluorfen was ≥85 and ≥70%, respectfully, 7 DAA for the 1-, 2-, and 3-leaf rice growth stage timings. Preemergence applications of oxyfluorfen under 100% soil saturation resulted in 75% injury to oxyfluorfen-resistant rice, greater than all other soil moisture at 7 DAA. All postemergence applications of oxyfluorfen resulted in 63% to 70% injury to oxyfluorfen-resistant rice at 7 DAA, regardless of soil moisture. Barnyardgrass and weedy rice control with oxyfluorfen is achieved with timely applications; however, injury to oxyfluorfen-resistant rice is likely.

Biological and Chemical Management Strategy to Control Brown Spot Disease in Rice Caused by Bipolaris oryzae

The main objective of this study was to assess the effectiveness of various fungicides in managing rice brown spot, a common fungal disease in India that can reduce crop yield and seed quality. During 2020-21 crop seasons, a research project was conducted in the experimental plot of the Department of Plant Pathology at the College of Agriculture in Rewa, Madhya Pradesh, India. Different treatments involving fungicide application at various rice growth stages were implemented. The results showed that all treatments involving fungicides significantly reduced the incidence of rice brown spot in-vitro condition and increased crop yield when compared to the control group. Among the fungicides, Difenoconazole 25% EC at a concentration of 200ppm proved to be highly effective in controlling the growth of the fungal mycelium. In in-vivo condition, the treatment involving T4, which includes seed treatment with the bio-control agent Trichoderma viride (10g/kg seeds), the application of Trichoderma viride (10g/l) at 15-20 days after transplanting, and the application of propiconazole (1g/l) at the booting exhibited the highest level of reduction in the incidence of brown spot disease, amounting to 13.6 per cent. The integrated management approach are very effective to control the brown spot disease in rice.

Applying Nitrogen Fertilizer at the Full Heading Stage Has the Potential to Decrease Brown Rice Cd Accumulation

Soil contamination by cadmium (Cd) has presented a major challenge in China. The objective of the field experiments in this study was to examine the influence of nitrogen fertilizer application at the full heading and milky stages on minimizing the absorption of Cd in rice. This was achieved by affecting the distribution of Cd in root plaques and subcellular compartments of the root and flag leaf. The hydroponic culture experiments aimed to examine the effect of nitrogen and Cd interaction or deficiency on Cd accumulation in rice during the late growth stage. The findings revealed that adequate nitrogen supply during the early growth stage, coupled with nitrogen application during the full heading and milky stages, led to a notable increase in Fe concentration in the root plaques during the milk and mature stages. Furthermore, it elevated the Cd proportion in the soluble fraction of the flag leaves at the milky stage. Conversely, nitrogen deficiency during the early growth stage resulted in a significant increase in Fe concentration in the root plaques, along with a decrease in Cd concentration. Additionally, the proportion of Cd in the flag leaf cell walls increased significantly, while the proportion in the soluble fraction decreased notably. Irrespective of nitrogen supply during the early growth stage, applying nitrogen at the full heading stage significantly reduced Cd transport from shoots to brown rice, leading to a considerable reduction in the Cd concentration in brown rice. Under hydroponic culture conditions, combined Cd exposure with nitrogen supply significantly increased the Cd concentration in brown rice. Nitrogen supply had no impact on the Cd concentration in brown rice in the absence of Cd. The study showed that applying nitrogen fertilizer at the full heading stage effectively decreased the brown rice Cd concentration. This was achieved by elevating the concentration of Fe in the root plaque, thereby influencing the adsorption of Cd by the roots. Additionally, nitrogen application at the full heading stage can influence the distribution of Cd in flag leaf cells during the filling stage. Ensuring ample nitrogen supply in the early stage of rice growth is crucial, and nitrogen application at the full heading stage can effectively reduce the Cd concentration in brown rice.

Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery.

Aboveground biomass (AGB) is a crucial physiological parameter for monitoring crop growth, assessing nutrient status, and predicting yield. Texture features (TFs) derived from remote sensing images have been proven to be crucial for estimating crops AGB, which can effectively address the issue of low accuracy in AGB estimation solely based on spectral information. TFs exhibit sensitivity to the size of the moving window and directional parameters, resulting in a substantial impact on AGB estimation. However, few studies systematically assessed the effects of moving window and directional parameters for TFs extraction on rice AGB estimation. To this end, this study used Unmanned aerial vehicles (UAVs) to acquire multispectral imagery during crucial growth stages of rice and evaluated the performance of TFs derived with different grey level co-occurrence matrix (GLCM) parameters by random forest (RF) regression model. Meanwhile, we analyzed the importance of TFs under the optimal parameter settings. The results indicated that: (1) the appropriate window size for extracting TFs varies with the growth stages of rice plant, wherein a small-scale window demonstrates advantages during the early growth stages, while the opposite holds during the later growth stages; (2) TFs derived from 45° direction represent the optimal choice for estimating rice AGB. During the four crucial growth stages, this selection improved performance in AGB estimation with R2 = 0.76 to 0.83 and rRMSE = 13.62% to 21.33%. Furthermore, the estimation accuracy for the entire growth season is R2 =0.84 and rRMSE =21.07%. However, there is no consensus regarding the selection of the worst TFs computation direction; (3) Correlation (Cor), Mean, and Homogeneity (Hom) from the first principal component image reflecting internal information of rice plant and Contrast (Con), Dissimilarity (Dis), and Second Moment (SM) from the second principal component image expressing edge texture are more important to estimate rice AGB among the whole growth stages; and (4) Considering the optimal parameters, the accuracy of texture-based AGB estimation slightly outperforms the estimation accuracy based on spectral reflectance alone. In summary, the present study can help researchers confident use of GLCM-based TFs to enhance the estimation accuracy of physiological and biochemical parameters of crops.