Rice Growth Stages Research Articles

Bio-Master: Design and Validation of a High-Throughput Biochemical Profiling Platform for Crop Canopies

Accurate assessment of crop biochemical profiles plays a crucial role in diagnosing their physiological status. The conventional destructive methods, although reliable, demand extensive laboratory work for measuring various traits. On the other hand, nondestructive techniques, while efficient and adaptable, often suffer from reduced precision due to the intricate interplay of the field environment and canopy structure. Striking a delicate balance between efficiency and accuracy, we have developed the Bio-Master phenotyping system. This system is capable of simultaneously measuring four vital biochemical components of the canopy profile: dry matter, water, chlorophyll, and nitrogen content. Bio-Master initiates the process by addressing structural influences, through segmenting the fresh plant and then further chopping the segment into uniform small pieces. Subsequently, the system quantifies hyperspectral reflectance and fresh weight over the sample within a controlled dark chamber, utilizing an independent light source. The final step involves employing an embedded estimation model to provide synchronous estimates for the four biochemical components of the measured sample. In this study, we established a comprehensive training dataset encompassing a wide range of rice varieties, nitrogen levels, and growth stages. Gaussian process regression model was used to estimate biochemical contents utilizing reflectance data obtained by Bio-Master. Leave-one-out validation revealed the model’s capacity to accurately estimate these contents at both leaf and plant scales. With Bio-Master, measuring a single rice plant takes approximately only 5 min, yielding around 10 values for each of the four biochemical components across the vertical profile. Furthermore, the Bio-Master system allows for immediate measurements near the field, mitigating potential alterations in plant status during transportation and processing. As a result, our measurements are more likely to faithfully represent in situ values. To summarize, the Bio-Master phenotyping system offers an efficient tool for comprehensive crop biochemical profiling. It harnesses the benefits of remote sensing techniques, providing significantly greater efficiency than conventional destructive methods while maintaining superior accuracy when compared to nondestructive approaches.

Effects of Rice Growth Stages on the Composition and Functional Potentialities of Rhizosphere Bacterial Community in Coupled Rice-Crab System

Effects of Rice Growth Stages on the Composition and Functional Potentialities of Rhizosphere Bacterial Community in Coupled Rice-Crab System

Spray Deposition and Distribution on Rice as Affected by a Boom Sprayer with a Canopy-Opening Device

In China, rice growers predominantly use boom sprayers. However, boom sprayers have several drawbacks related to poor penetration in the middle and late stages of rice growth. Several studies have shown that the use of the canopy-opening device can improve droplet deposition in the middle and under layers of rice. Some growers doubt the efficacy of canopy-opening boom sprayers, whereas others have questions about their use. This study aimed to address both doubts and questions by evaluating the effect of spraying rice using a canopy-opening device and a canopy-opening divider combination device. The tested rice variety was “Longliangyou 2010”. Three different boom sprayers were evaluated for this purpose: (1) a boom sprayer with a canopy-opening device, (2) a boom sprayer with a canopy-opening divider combination device, and (3) a traditional boom sprayer. The effects of the position of the canopy-opening device and the spraying velocity on the deposition of droplets were considered. The experiments showed that the droplet coverage was significantly affected by boom sprayers, the position of the canopy-opening device, the spraying velocity, and the interaction of the position of the canopy-opening device and the spraying velocity. Under the spraying distance of 60 cm from the rice root and the spraying velocity of 1.2 m s−1, the spraying coverage of the paraxial surface of the whole canopy was higher than that of other treatment conditions when the canopy opening device was used. The positive spraying coverage rates of the upper, middle and lower layers were 95.18%, 88.41% and 94.99%, respectively. Compared with the traditional boom sprayer, the use of the canopy-opening device increased the average droplet coverage on the adaxial surface canopy by more than 72.91% and the average droplet coverage on the abaxial surface canopy by more than 6.88%.

Special Microbial Communities Enhanced the Role of Aged Biochar in Reducing Cd Accumulation in Rice

Biochar exhibits a good adsorption ability for heavy metals in soil and has been widely used as a remediation material in Cd-contaminated soil. However, the status of Cd uptake by rice driven by soil physicochemical properties and rhizosphere microbial communities after years of biochar application is not well understood. In this study, the relationship between the rhizosphere microbial community and soil physicochemical properties and rice Cd accumulation were investigated during the main rice growth stages. The results showed that in comparison to the non-biochar treatment (control), a noticeable reduction in Cd content in rice stem sheaths, leaves, rice husks and milled rice with different growth stages were observed in the biochar treatment after four years, which decreased by 38.76–66.18%, 40.93–70.27%, 43.64–47.92% and 31.91–34.38%, respectively. Compared to non-biochar treatment (control), the properties of the soil in different growth stages by biochar treatment of the soil pH, soil organic matter (SOM), total nitrogen (TN) and available phosphorus (AP) were significantly increased, which increased by 10.5–16.13%, 8–25%, 75–130.13% and 132.95–191.43%, respectively. The content of available Cd (ACd) concentration in different stages by biochar treatment was significantly decreased, which decreased by 26.57–44.24%. Biochar application after four years changed the rhizosphere bacterial community structure composition (phyla level) in all stages. The relative abundance of Proteobacteria, Bacteroidetes and Nitrospirae was increased, while the relative abundance of Chloroflexi, Acidobacteria and Actinobacteria was decreased. Meanwhile, the biochar application enriched Rhodocyclaceae, Burkholderiaceae, Nitrosomonadaceae, Anaerolineaceae, Ignavibacteriales and Bacteroidales, which may contribute to the reduction of Cd uptake and accumulation in rice. These results suggest that biochar treatment after four years changed the rhizosphere microbial community structure and soil physicochemical properties and promoted the colonization of specific microbial populations in the rice rhizosphere to form a special protective system in the rice rhizosphere, which reduced Cd uptake by rice.

Visible Band Index Optimation of Unmanned Aerial Vehicle for Estimating NDVI by Sentinel Imagery on Rice Vegetation

Serntinel 2A provide Normalized Difference Vegetation Index to be used as an estimate of soil fertility, plant varieties and productivity. The weakness of satellite data is that the data obtained is often inaccurate due to cloud cover, especially in tropical countries with high rainfall such as Indonesia. The use of unmanned aerial vehicle as an alternative data have limitation as it captured RGB imagery. The research was conducted from July to September 2020 at Pasir Kaliki Village, District of Rawamerta, Karawang Regency, West Java province. The study has discovered that NDVI showed higher number in result of vegetation index compared to NGRDI with correlation coefficient is 0.944625. The regression model resulted as y=4.7722x+0.3845 and MAPE value expresses as 26.74%, where the regression model with Pearson’s correlation coefficient value is 0.877885. A qualitative assessment using statistical data and a spatial assessment using sampled data from the rice vegetation map reveal a high mapping accuracy with the corresponding R2 being as high as 0.7429; however, the mapped rice vegetation accuracy might influenced by other physical factors such as water reflectant, sunlight and the RGB camera limitation itself. Nonetheless, the highest values of NGRDI only reach 0.2 while NDVI can attain at 0.9 at the peak of vegetative phase of rice growth stage. This means that Green Band have limitation in detecting vegetation index. In relation to the different approaches performed, it is noted that the average trend line on both NDVI and NGRDI shown the similarity tendency in all growth stage.

Plant growth-promoting rhizobacteria mediated moisture stress alleviation in the early stages of Rice (Oryza sativa L.) variety CO 51

Drought is one of the abiotic stresses that have a significant impact on agricultural growth across the world. Plant growth-promoting rhizobacteria (PGPR) inoculation in rice plants may be a viable and environmentally acceptable method of sustaining the development and yield of drought-stressed rice plants. The current study focused on the alleviation of drought in the early stages of rice variety CO 51 using PGPR isolated from the rhizosphere of xerophytes. The seeds were treated with bio inoculants and subjected to different moisture stress levels (10%, 20% and 30%) using PEG 6000. The seeds treated with bio inoculants exhibited higher germination percentage and growth traits such as shoot length root length and fresh weight, especially seeds treated with Bacillus velezensis VKSB5 (MT729963), and Bacillus altitudinis MLSB2 (MT729964) over uninoculated plants. This was found to be due to the increased proline accumulation and antioxidant activity in these seedlings, which plays a major role in drought alleviation by altering the osmotic potential and by its ROS scavenging mechanism. Hence this study provides evidence for the effective drought ameliorating ability of these cultures during the initial growth stages of rice. Further studies can contribute to the development of effective bio-inoculants for the mitigation of drought in rice.

Rice Growth Stage Classification via RF-Based Machine Learning and Image Processing

Rice is one of the most significant crops cultivated in Asian countries. In Taiwan, almost half of the arable land is used for growing rice. The life cycle of paddy rice can be divided into several stages: vegetative stage, reproductive stage, and ripening stage. These three main stages can be divided into more detailed stages. However, the transitions between stages are challenging to observe and determine, so experience is required. Thus, rice cultivation is challenging for inexperienced growers, even with the standard of procedure (SOP) provided. Additionally, aging and labor issues have had an impact on agriculture. Furthermore, smart farming has been growing rapidly in recent years and has improved agriculture in many ways. To lower the entry requirements and help novices better understand, we proposed a random forest (RF)-based machine learning (ML) classification model for rice growth stages. The experimental setup installed in the experiment fields consists of an HD smart camera (Speed-dome) to collect the image and video data, along with other internet of things (IoT) devices such as 7-in-1 soil sensors, a weather monitoring station, flow meter, and milometer connected with LoRa base station for numerical data. Then, different image processing techniques such as object detection, object classification, instance segmentation, excess green index (EGI), and modified excess green index (EGI) were used to calculate the paddy height and canopy cover (CC) or green coverage (GC). The proposed ML model uses these values as input. Furthermore, growth-related factors such as height, CC, accumulative temperature, and DAT are used to develop our model. An agronomist has been consulted to label the collected different stages of data. The developed optimal model has achieved an accuracy of 0.98772, and a macro F1-score of 0.98653. Thus, the developed model produces high-performance accuracy and can be employed in real-world scenarios.

Reductive dissolution of iron phosphate modifies rice root morphology in phosphorus-deficient paddy soils

Root morphology reflects plant adaptations to phosphorus (P) deficiency. We hypothesized that changes in rice root morphology reflect P deficiency decrease after ferric iron (Fe(III))-bound phosphate (Fe–P) dissolution in low-redox paddy soils. We developed a novel in-situ32P phosphor-imaging approach under flooding to estimate P uptake by rice roots released from Fe–P dissolution. 32P-labeled ferrihydrite (31 mg P kg−1) was supplied either (1) in polyamide mesh bags (30 μm mesh size) to prevent roots but not microorganisms from direct Fe–P mobilization, or (2) directly mixed with soil to enable roots and microorganisms unrestricted access to the Fe–P. The establishment of low redox conditions (Eh values between −176 and −224 mV) drove the reductive dissolution of Fe–P. Rice root-derived organic acids alone were unable to control Fe–P dissolution, and Fe(III) reduction is predominately a microbially-mediated process. Direct root access to Fe–P raised both the number and mean diameter of crown roots and root tips, and increased P uptake by 149–231%. Crown root elongation rate, 32P activities along roots and root tips were 5–133% higher when roots directly accessed Fe–P compared to Fe–P excluded from roots in mesh bags. Iron accumulation on roots depended on the rice growth stage, but not on their access to Fe–P. Roots’ access to Fe–P increased rice crown roots elongation and branching and increased P accessibility under P deficiency.

Adaptabilities of Water Production Function Models for Rice in Cold and Black Soil Region of China

Crop water production function models (WPFMs) are required methods to study the relationships between yield and water consumption under regulated deficit irrigation (RDI). In this study, a pot experiment was established to study the effect of water deficit during both individual growth stages and across two consecutive growth stages of rice on yield, water consumption, and water use efficiency (WUE) in 2017 and 2018. Light, medium, and severe water deficits were set as 80~90%, 70~80%, and 60~70% of saturated soil moisture content, respectively. The accuracies of five WPFMs were tested based on the experimental results. The results showed that yields and WUE of a light water deficit were higher than those of medium and severe water deficits at each growth stage. The yields and WUE of light drought stress treatments in the flowering and milky stages were higher than the saturated soil moisture control by 4~7.4% and 5.3~20.6%, respectively. Water consumption decreased with increasing water deficit across two consecutive growth stages. The Minhas model had the highest simulation accuracy of the five WPFMs, with relatively lower AE, RMSE, Cv, CRM, and higher R2, which were 0.0002, 0.0634, 6.9965, 0.0002, and 0.9951 in 2017 and 0.0110, 0.0760, 8.9882, 0.0131, and 0.9923 in 2018, respectively. The sensitivity indices for the Minhas model more accurately reflected the sensitivity of rice yield to water deficit at different growth stages in 2017 and 2018, compared with the Jensen model, Stewart model, Blank model, and Singh model. Rice yield was most sensitive to water deficit at the jointing and booting stage. The results indicate that the Minhas model is the most suitable WPFM for guiding rice irrigation practices in cold and black soil regions of China.

Rhizosphere soil nitrification ability controls nitrogen‐use efficiency in rice growth period

AbstractNitrification in the rhizosphere is a crucial process in controlling nitrogen‐use efficiency (NUE) in flooded paddy soils. To understand the relationship between the nitrification ability of the rhizosphere and NUE, pot experiments using 15N tracer technique were conducted to investigate the impacts of the rhizosphere soil net nitrification rate on NUE and denitrification losses at different rice growth stages in two paddy soils, which were sampled from Jurong (JR) and Yancheng (YC) in Jiangsu Province in China. The results showed that the nitrification rate in JR rhizosphere soil was lower than in YC rhizosphere soil at all rice growth stages. The abundance of ammonia‐oxidizing bacteria (AOB), ammonia‐oxidizing archaea (AOA), and pH in YC rhizosphere soils were always higher than in JR rhizosphere soils. Rice yield, biomass, NUE, leaf glutamine synthetase (GS) activity, and nitrate reductase (NR) activity were higher in JR soils with low nitrification rates than in YC soils with high nitrification rates (p < 0.05). In contrast, denitrification loss from JR soil (12.69%–23.41%) was lower than that from YC soil (26.83%–40.98%; p < 0.05) for each rice growth stage. The biomass and NUE decreased significantly as the net nitrification rate, the abundance of AOA and AOB of both the JR and YC rhizosphere soils increased (p < 0.05), and the denitrification loss was enhanced as the rhizosphere nitrification rate increased in the JR and YC soils (p < 0.05) during the rice growth period. The rhizosphere‐dominant AOB community Nitrosospira is the key factor affecting the nitrification rate and then decreasing rice NUE. In general, the rhizosphere nitrification rate in paddy soils is a primary factor controlling the rice NUE and denitrification loss.

Effects of chilling at different growth stages on rice photosynthesis, plant growth, and yield

Chilling is the main limiting factor for rice production in high-altitude and high-latitude regions. Many studies have reported the impacts of chilling on leaf photosynthesis and biomass accumulation, but few of them explored the response of photosynthesis to chilling at different growth stages of rice and whether the decrease of photosynthesis at each stage is caused by stomatal or non-stomatal limiting factor. In order to achieve high grain yield for rice, it is critical to understand how chilling affects leaf photosynthesis and plant growth. In this study, we conducted a two-year field experiment (in 2018 and 2019) to investigate the effects of chilling at booting, flowering, and grain filling stages on leaf photosynthesis, plant growth, and grain yield for a japonica rice cultivar (Songjing6). At each target growth stage, the treatment included four chilling temperatures (12, 15, 18, and 21 ℃, respectively) and four chilling durations (2, 4, 6, and 8 days, respectively). The results showed that rice photosynthesis gradually decreased as the chilling temperature was decreasing and the chilling duration was increasing. The net photosynthetic rate decreased the most at the booting stage, followed by the flowering stage, and the grain filling stage. Compared with the chilling treatment at 2 days, the chilling treatment at 12, 15, and 18 ℃ for 4, 6, and 8 days reduced net photosynthetic rate by 15.1–36.1 %, 18.7–27.2 %, and 15.2–17.2 % at the booting, flowering, and grain filling stages, respectively. The chilling-induced decrease in net photosynthetic rate was mainly attributed to stomatal limiting factor at the booting and flowering stages but non-stomatal limiting factor at the grain filling stage. During the chilling treatment, the available light for rice plants was reduced and the rice plants were impaired in their ability to use weak light. The decrease in photosynthesis lead to reduced leaf source capacity and crop growth rate, hence the reduced aboveground biomass and increased source and sink increment ratio, and ultimately decreased the grain yield for rice. Our results indicate that maintaining efficient photosynthetic functioning at the booting and flowering stages is the key to combat chilling stress for rice. • Chilling decreased the net photosynthetic rate of rice most at the booting stage, followed by the flowering stage and the grain filling stage. • Chilling-induced decrease in the net photosynthetic rate was mainly attributed to stomatal limiting factor at the booting and flowering stages, but non-stomatal limiting factor at the grain filling stage. • The simulation of light response curve showed chilling at the booting and flowering stages decreased photosynthetic capacity and photosynthetic effective time，while chilling mainly affects the ability of using strong light at the grain filling stage.

Estimating rice yield by assimilating UAV-derived plant nitrogen concentration into the DSSAT model: Evaluation at different assimilation time windows

Accurately simulating crop growth and estimating yield play a paramount role in policy decision making and agriculture management. To improve the accuracy of rice yield estimation at the field scale, plant nitrogen concentration (PNC), a highly nitrogen-related variable, was derived from the UAV-based imagery through a two-year rice experiment by using the random forest (RF) algorithm; then the PNC data was assimilated into the CERES-Rice model with a particle swarm optimization (PSO) method. In this study, the sensitivity of assimilation process to the acquisition time of UAV remotely sensed PNC data, including the vegetative stage, heading stage, ripening stage and the whole growth stage, was investigated. The results showed that the relationship between the estimated and measured PNC after data assimilation at the vegetative stage was stronger than that at other growth stages. Moreover, assimilating PNC at the early growth stage could better simulate the dynamics of PNC with no nitrogen (N) stress. Due to the assumption of soil homogeneity under various N fertilization treatments, all data assimilation strategies had the tendency to overestimate PNC in the N stress condition. In addition, the accuracy of yield estimation obtained by assimilating PNC at the vegetative stage was the highest. Accordingly, the assimilation of PNC data at the early rice growth stage could provide a great potential for improving yield estimation.

Vertical distribution and transformation of phosphorus and iron in paddy soils during the whole growth stage of rice

Paddy fields have alternating wet and dry hydrological cycles at different growth stages of rice, driving changes in soil environment and phosphorus (P) transformation and transport. In-situ measurements of dissolved oxygen (DO), iron (Fe), and P concentrations were conducted to determine the vertical distribution of these concentrations together with the hydrological and meteorological data during the whole growth stage. The results demonstrate that soil vertical DO concentrations at different growth stages were greatly influenced by the water level and temperature of the paddy field at the soil-water interface. A strong negative correlation between soil DO and DGT-labile Fe has been observed, whereas a strong positive correlation has been observed between soil Fe concentration and vertical P. In the paddy field, soil DO concentrations were lower during tillering, booting, and heading to flowering than during the other stages. Thus, soluble P fluxes from soil water interfaces (SWIs) to overlying waters were most significant during these three stages. Furthermore, soils in these three stages are capable of sustained release of P and are highly able to buffer P. Consequently, water management in paddy fields should take into account the hydrological environment during these three stages in order to minimize soil P release. The results of our study provide a valuable reference point for controlling soil P and Fe during rice growth.

Crop coefficient, reference crop evapotranspiration and water demand of dry‐season Boro rice as affected by climate variability: A case study from northeast Bangladesh

AbstractClimate variability and change significantly influence crop water demand by affecting crop coefficient (Kc), reference crop evapotranspiration (ETo, a grass reference) and potential crop evapotranspiration (ETc). The climatic effects on these hydroclimatic parameters were investigated in the past mostly for the entire crop growth period, but little or no information is available on their variation over different growth stages of the crops, specifically in Bangladesh. Therefore, this study intended to evaluate the climatic effects on these hydroclimatic parameters at the initial, development, mid‐season and late‐season growth stages of two rice varieties (Bangladesh Rice Research Institute [BRRI] dhan28 and BRRI dhan29) dominantly grown in the dry season in two northeast districts/subdistricts (Sylhet and Srimongal) as well as many other districts of Bangladesh. Kc, ET0 and ETc were analysed for the period from 1994 to 2015 using the analytical approach of the Food and Agriculture Organization (FAO) Penman–Monteith method. The results revealed that the Kc of the rice varieties increased in all growth stages over time, with the maximum Kc (1.247) in the mid‐season stage in Srimongal. ETo and ETc were found to be maximum in the mid‐season and late‐season stages, respectively. The potential crop water demand (CWRpot) greatly varied among the initial and development stages and between the two rice varieties. The initial and mid‐season growth stages of rice required more water than the other growth stages. The results on the growth stage‐wise variation of the hydroclimatic parameters pertinent to rice cultivation may greatly contribute to irrigation scheduling for dry‐season rice cultivation and planning water resources in the study region.

Effects of the Combined Application of Chinese Milk Vetch (Astragalus sinicus L.) and Red Mud to Remediate the Cadmium-Polluted Acidic Paddy Soil

In this study, we conducted field experiments to assess the effects of the combined application of Chinese milk vetch (Astragalus sinicus L., CMV) and red mud (RM) to remediate the cadmium-polluted acidic paddy soil. The results showed that the combined application increased the yield component index and improved the grain yields, compared with the control, RM1, RM2, and CMV treatments. However, the increased range of soil pH values in the CMV-RM1 and CMV-RM2 treatments was smaller than that of the RM1 and RM2 treatments at the different rice growth stages. The soil organic matter (SOM) contents of the RM-only treatments did not differ throughout the whole period, but were significantly different (p < 0.05) between CMV-RM2 and the control. Compared with the control, the combined application decreased the concentration of exchangeable Cd in the early rice soil. The combined application of CMV and RM substantially decreased the rice uptake of Cd. However, we found no significant difference (p < 0.05) between the CMV-RM1 and CMV-RM2 treatments. Therefore, compared with remediation with RM or CMV alone, we recommend the combined application of CMV and RM as an economical, highly effective, and replicable amendment for remediating acidic, Cd-polluted paddy soil. Considering the restorative effect and proper use of RM, we recommend CMV-RM1 treatment.

A Multitemporal Mountain Rice Identification and Extraction Method Based on the Optimal Feature Combination and Machine Learning

The quick and precise assessment of rice distribution by remote sensing technology is important for agricultural development. However, mountain rice is limited by the complex terrain, and its distribution is fragmented. Therefore, it is necessary to fully use the abundant spatial, temporal, and spectral information of remote sensing imagery. This study extracted 22 classification features from Sentinel-2 imagery (spectral features, texture features, terrain features, and a custom spectral-spatial feature). A feature selection method based on the optimal extraction period of features (OPFSM) was constructed, and a multitemporal feature combination (MC) was generated based on the separability of different vegetation types in different periods. Finally, the extraction accuracy of MC for mountain rice was explored using Random Forest (RF), CatBoost, and ExtraTrees (ET) machine learning algorithms. The results show that MC improved the overall accuracy (OA) by 3–6% when compared to the feature combinations in each rice growth stage, and by 7–14% when compared to the original images. MC based on the ET classifier (MC-ET) performed the best for rice extraction, with the OA of 86%, Kappa coefficient of 0.81, and F1 score of 0.95 for rice. The study demonstrated that OPFSM could be used as a reference for selecting multitemporal features, and the MC-ET classification scheme has high application potential for mountain rice extraction.

Landscape diversity influences the arthropod species diversity in the rice field

Landscape diversity is one of the key drivers for maintaining ecosystem services in agricultural production by providing vital habitats and alternative food sources for beneficial insects and pollinators within the agricultural landscapes. The landscape structure, land uses, and diversity differ between geographic locations. However, how the changes of landscape structure and land use diversity affect the arthropod diversity in a geographic area is poorly understood. Here, we tested the impact of landscape diversity on the rice locations in Bangladesh. Results ranged from highly diversified to very highly diversified in Chattogram (>7.9), to highly diversified (0.590.79) in Satkhira and moderately (0.390.59) to less diversified (0.190.39) in Patuakhali. These significant different landscape diversities influenced the arthropod diversity in rice fields. Arthropod species diversity increases with the increase in the Land Use Mix (LUM) index. The maximum tillering stage of rice growth harbored higher abundance and species diversity in rice fields. Moreover, we found that vegetation is the most important factor influencing the abundance of arthropods. Extensive agriculture and forest contributed substantially to predicting arthropod richness. Meanwhile, barren land and high-density residential land as well as intensive agriculture had large impact on species diversity. This study indicates that landscape diversity plays a vital role in shaping the species diversity in rice fields, providing guidelines for the conservation of arthropod diversity, maximizing natural pest control ecosystem service and more secure crop production itself.

Migration and Transformation of Arsenic in Rice and Soil under Different Nitrogen Sources in Polymetallic Sulfide Mining Areas.

Nitrogen (N) fertilizer affects the migration and transformation of arsenic (As) in soil and rice. We conducted pot experiments and studied the effects of 0.1, 0.2, and 0.4 g∙kg−1 N levels of NH4Cl, (NH4)2SO4, and NH4NO3 fertilizers on the As bioavailability in the As-contaminated inter-rhizosphere soil and As accumulation in the rice organs. The results showed that the concentration of bioavailable As in the rice rhizosphere soil was significantly negatively correlated with pH under the 0.4 g∙kg−1 N level of each fertilizer. At the same N level, while the As concentration was maturity stage > tillering stage in rice stems and leaves treated with NH4Cl and (NH4)2SO4, it was the opposite in roots. This suggests that the transfer of As from roots to stems and leaves mainly occurs in the late stage of rice growth under the condition of only NH4+-N fertilizer applying. The As concentration in rice aboveground organ (grains and stems–leaves) decreased with the increasing N application under the same N fertilizer treatment condition during the mature stage. In addition, the As concentration in rice grains treated with (NH4)2SO4 was the lowest. This result indicated that SO42− and NH4+-N had a significant synergistic inhibition on the As accumulation in rice grains. It was concluded that appropriate (NH4)2SO4 levels for As-contaminated paddy soils with high sulfur (S) contents would obtain rice grains with inorganic As concentrations below 0.2 mg·kg−1.

Application of Bacterial Endophytes to Control Bacterial Leaf Blight Disease and Promote Rice Growth.

Xanthomonas oryzae pv. oryzae (Xoo) causes bacterial leaf blight (BLB) disease in rice (Oryza sativa L.) and it is among the most destructive pathogen responsible for severe yield losses. Potential bacterial biocontrol agents (BCAs) with plant growth promotion (PGP) abilities can be applied to better manage the BLB disease and increase crop yield, compared to current conventional practices. Thus, this study aimed to isolate, screen, and identify potential BCAs with PGP abilities. Isolation of the BCAs was performed from internal plant tissues and rhizosphere soil of healthy and Xoo-infected rice. A total of 18 bacterial strains were successfully screened for in vitro antagonistic ability against Xoo, siderophore production and PGP potentials. Among the bacterial strains, 3 endophytes, Bacillus sp. strain USML8, Bacillus sp. strain USML9, and Bacillus sp. strain USMR1 which were isolated from diseased plants harbored the BCA traits and significantly reduced leaf blight severity of rice. Simultaneously, the endophytic BCAs also possessed plant growth promoting traits and were able to enhance rice growth. Application of the selected endophytes (BCAs-PGP) at the early growth stage of rice exhibited potential in suppressing BLB disease and promoting rice growth.

Rice Mapping and Various Stages of Rice Growth using Sentinel-1 SAR Data-A case study of Mahabubnagar District, Telangana

Space Based technology both optical and microwave SAR Data is used to monitor the Earth at regular intervals which has its application in various fields viz., agriculture, water, urban and so on. Synthetic Aperture Radar (SAR) has its advantage to penetrate through clouds, day-night imaging, all weather conditions and high resolution making it an effective system for crop monitoring, especially during Monsoon Season, as Optical Sensors doesn’t provide cloud free data. Sentinel-1 data has been used in recent studies to map and monitor rice-growing areas. The Sentinel-1 IW GRD data were used to monitor the ricegrowing area over Mahabubnagar District of Telangana State, which was processed in SNAP Software. Few plots were selected by conducting field survey during Kharif 2021. The Radar Vegetation Index (RVI) have been used to monitor the fluctuations in various stages of rice growth. This paper explains how multi temporal SAR observations can be used to monitor rice growing stages, as well as how to interpret σ°VHand σ° VV back scattering co-efficient.