Rice Nitrogen Research Articles

Use of Nitrogen Fixing Purple Nonsulfur Bacteria to Produce Available Nitrogen for Rice (Oryza sativa L.) Cultivated in Saline Acidic Soil

This research aimed to select N2-fixation purple nonsulfur bacteria (PNSB) from saline acidic soils (SAS) in rice-shrimp system to enhance rice growth and yield. Two PNSB with highest nitrogen-releasing strains Luteovulum sphaeroides S01 and S06 were used, and the SAS was collected from Hong Dan and Thanh Phu districts. The 4 × 4 factorial experiment was carried out in a completely randomized block design, including a main factor as the liquid biofertilizers and the minor factor as the N fertilizer levels, with four replicates in a greenhouse. Both individual and mixed PNSB liquid biofertilizers significantly increased pH value and NH4 + content and reduced the exchangeable Na+ in both SAS sites. This led to 24.8–113.2% higher N uptake in rice and 6.48–30.5% lower Na accumulation in straw and 16.3–47.6% Na in grain. These results were considered as an evidence to improve rice grain yield by 27.5–56.6%. The L. sphaeroides S01 and S06 was greatly suitable for using as a biofertilizer for rice under saline acidic conditions.

Endophytic fungus regulates the root secretion of IAA and ABA to increase rice nitrogen accumulation by promoting soil nitrogen fixation

Endophytic fungus regulates the root secretion of IAA and ABA to increase rice nitrogen accumulation by promoting soil nitrogen fixation

Effect of summer legume residue incorporation and fertilizer regimes on rice growth, yield, and nutrient uptake

In a field trial at the College Farm, NAU, Navsari (Gujarat), evaluated four main plot treatments (T1: Green gram, T2: Cowpea, T3: Dhaincha, and T4: Fallow), which were grown during the summer with three replications following a randomized block design (RCBD). Green gram and cowpea were incorporated into the soil after harvest, but dhaincha was incorporated at 50% flowering. During Kharif, each main plot was split into six smaller plots with different treatments: W1 was 100% RDF, W2 was 75% RDF, W3 was 50% RDF, W4 was 75% RDF + 25% N from FYM, W5 was 50% RDF + 50% N from FYM, and W6 was no fertilizer. The results of all 24 treatment combinations were repeated three times in a split-plot design. The analysis showed significant growth, yield attributes, grain yield, and straw yield of rice in dhaincha-incorporated plots (T3), fb greengram (T1), and cowpea (T2) plots using 100% RDF, while fallow (T4) with no fertilizer application recorded significantly lower values. SPAD meter readings of rice were higher in W4 (75% RDF + 25% N from FYM), which was at par with W1 in dhaincha-incorporated plots (75% RDF + 25% N from FYM) (W4). However, applying no fertilizer (W6) resulted in lower values. The total uptake of nitrogen and phosphorus in rice was highest when it was grown in dhaincha incorporated (T3), followed by green gram (T1), cowpea (T2) incorporated plots with the usage of 100% RDF (W1) and 75% RDF + 25% N from FYM (W4), and lower values were recorded in fallow + no fertilizer treatment (T4W6). Our study revealed that incorporating summer legumes before planting rice significantly increased plant height, tillers, grain and straw yield, and total uptake. With dhaincha, inclusion has shown a greater advantage.

Rational Nitrogen Reduction Helps Mitigate the Nitrogen Pollution Risk While Ensuring Rice Growth in a Tropical Rice–Crayfish Coculture System

The incorporation of aquaculture feed within a rice–crayfish coculture system significantly enhances nitrogen cycling, thereby diminishing the reliance on chemical fertilizers. However, this benefit is often overlooked in practice, and farmers continue to use large quantities of chemical fertilizers to maximize production, resulting in excessive soil fertility and water nitrogen pollution. Thus, avoiding nitrogen pollution in rice–crayfish coculture systems has become a pressing issue. In this study, we conducted a two-year experiment with two rice cultivars, and a 33.3% reduction in nitrogen fertilizer in a rice–crayfish coculture system (RC), to systematically analyze the overall nitrogen balance, rice nitrogen nutrition, and soil fertility, as compared with a rice monoculture system (RM). Our findings revealed the following: (1) Under the 33.3% reduction in nitrogen fertilizer, the nitrogen surplus in the rice–crayfish coculture system was comparable to that in the rice monoculture, and was controlled at an environmental safety level. (2) Nitrogen utilization efficiency and the accumulation of nitrogen in the rice–crayfish coculture were comparable to those in the rice monoculture. The nitrogen cycle in this system was able to provide the nitrogen required for rice growth after nitrogen fertilizer reduction. (3) The rice–crayfish coculture significantly improved the overall soil fertility and the effectiveness of soil nitrogen nutrition. Furthermore, cutting off the application of nitrogen fertilizer after the mid-tillering stage effectively controlled the total nitrogen content in soil after rice maturity. In conclusion, reducing nitrogen fertilizer in a rice–crayfish coculture system is feasible and beneficial. It ensures rice production, reduces the risk of excessive nitrogen surplus and surface pollution, and promotes a greener, more environmentally friendly paddy field ecosystem.

Rice Leaf Nitrogen Content Estimation Through A Methodological Framework Using Single-Sensor Multispectral Images

Using non-destructive evaluation tools based on imaging techniques, including single-sensor multispectral cameras, provides a cost-effective solution for optimizing rice nitrogen fertilization through site-specific nutrient management. However, their accuracy and precision have been identified as areas for improvement. This study aims to develop a methodology to improve the accuracy of estimations through field experiments. It utilizes multispectral images captured by MAPIR Survey3W Orange Cyan Near-Infrared and MAPIR Survey3W Red Edge cameras. The Normalized Difference Vegetation Index and Red Edge values derived from these images are correlated with Soil Plant Analysis Development values to assess rice nitrogen levels. A prediction model is then built using the Support Vector Regression algorithm. Findings from the experiments underscore the importance of addressing shadow effects, integrating the dataset on light intensity and image capture time, conducting radiometric calibration, filtering outlier data, employing image segmentation, and utilizing nonlinear Canova tests to enhance estimation accuracy. By configuring the Support Vector Regression model with RBF kernel, gamma set to 1.24, and epsilon set to 0.1, the R2 of the train data and validation data reaches 0.851, and 0.840 respectively. Meanwhile, the R2 of the test data achieves 0.793 with a mean absolute percentage error of 3.49 % and a root mean square error of 1.70. These findings underscore the potential of the proposed methodology to improve the estimation of rice nitrogen status based on single-sensor multispectral images, paving the way for more effective nutrient management strategies in rice cultivation.

Developing a new active canopy sensor- and machine learning-based in-season rice nitrogen status diagnosis and recommendation strategy

ContextTraditional critical nitrogen (N) dilution curve (CNDC) construction for N nutrition index (NNI) determination has limitations for in-season crop N diagnosis and recommendation under diverse on-farm conditions. ObjectivesThis study was conducted to (i) develop a new rice (Oryza sativa L.) critical N concentration (Nc) determination approach using vegetation index-based CNDCs; and (ii) develop an N recommendation strategy with this new Nc determination approach and evaluate its reliability and practicality. MethodsFive years of plot and on-farm experiments involving three japonica rice varieties were conducted at fourteen sites in Qixing Farm, Northeast China. Two machine learning (ML) methods, random forest (RF) and extended gradient boosting (XGBoost) regression, were used to fuse multi-source data including genotype, environment, management, growth stage, normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) from portable active canopy sensor RapidSCAN. The CNDC was established using NDVI and NDRE instead of aboveground biomass (AGB) measured by destructive sampling. A new in-season N diagnosis and recommendation strategy was further developed using direct and indirect NNI prediction using multi-source data fusion and ML models. ResultsThe new CNDC based on NDVI or NDRE explained 94−96 % of Nc variability in the evaluation dataset when it was coupled with environmental and agronomic factors using ML models. The ML-based PNC and NNI prediction models explained 85 % and 21–36 % more variability over simple regression models using NDVI or NDRE in the evaluation dataset, respectively. The new in-season N diagnosis strategy using the NDVI and NDRE-based CNDCs and plant N concentration (PNC) predicted with RF model and multi-source data fusion performed slightly better than direct NNI prediction, explaining 7 % more of NNI variability and achieving 89 % of the areal agreement for N diagnosis across all evaluation experiments. Integrating this new N management strategy into the precision rice management system (as ML_PRM) increased yield, N use efficiency (NUE) and economic benefits over farmer’s practice (FP) by 7–15 %, 11–71 % and 4–16 % (161–596 $ ha−1), respectively, and increased NUE by 11–26 % and economic benefits by 8–97 $ ha−1 than regional optimum rice management (RORM) under rice N surplus status under on-farm conditions. ConclusionsIn-season rice N status diagnosis can be improved using NDVI- and NDRE-based CNDC and PNC predicted by ML modeling with multi-source data fusion. ImplicationsThe active canopy sensor- and ML-based in-season N diagnosis and management strategy is more practical for applications under diverse on-farm conditions and has the potential to improve rice yield and ecological and economic benefits.

Coated and un-coated urea incorporated with organic fertilizer improves rice nitrogen uptake and mitigates gaseous active nitrogen loss and microplastic pollution

Coated urea can improve nitrogen use efficiency (NUE) for crops, while it is also identified as a potential source of microplastics (MPs). Organic fertilizer (OF) shows potential to promote MPs degradation. However, little is known about the influence of OF incorporated with coated urea on MPs accumulation, NUE, and gaseous N loss (NH3 and N2O) in paddies. Therefore, a field experiment was conducted to address this gap, with five fertilization treatments, viz., urea (un-coated) with full rates (U), coated urea with full rates (CU), coated urea with reduced rates (RCU), 70 % coated urea blending 30 % urea with reduced rates (RBU), and RBU blending OF (ORBU). The results showed that, compared with U, the CU increased the number of MPs by 62.2–117.4 %. The ORBU significantly reduced the weight of MPs by 53.2 % through decreasing large-particle size. Additionally, the ORBU enhanced the effect of CU on increasing grain yield and NUE, mainly due to the improvement in the yield components and N uptake. The distribution of NH4+ in water and soil was a major factor driving NH3 and N2O emissions. The ORBU elevated activities of N-assimilation enzymes, decreased water NH4+ and NH3 emissions, and increased soil NH4+ and ammonium-oxidation bacteria and (nirK+nirS)/nosZ, ultimately increased soil N2O emissions compared with RBU. However, the reduction in NH3 much outweighed the increase in N2O emissions. In conclusion, this study highlighted coated urea as a source of MPs pollution, emphasized OF amendment as a strategy to diminish MPs accumulation, and found that OF incorporated with coated urea could increase grain yield and N uptake of rice while mitigating gaseous N loss in paddies.

A Precise Plot-Level Rice Yield Prediction Method Based on Panicle Detection

Accurately estimating rice yield is essential for ensuring global food security, enhancing agricultural productivity, and promoting agricultural economic growth. This study constructed a dataset of rice panicles at different growth stages and combined it with an attention mechanism and the YOLOv8 network to propose the YOLOv8s+LSKA+HorNet rice panicle detection and counting model, based on a drone remote sensing platform. Using the panicle count data collected by this model, along with the thousand-grain weight, number of grains per panicle, and actual yield data from a rice nitrogen gradient experimental field, various machine learning models were trained to ultimately propose a field-level rapid rice yield estimation model, RFYOLO. The experimental results show that the rice panicle detection and counting model can achieve an average precision (AP) of 98.0% and a detection speed of 20.3 milliseconds. The final yield estimation model achieved a prediction R2 value of 0.84. The detection and counting model significantly reduced missed and duplicate detections of rice panicles. Additionally, this study not only enhanced the model’s generalization ability and practicality through algorithmic innovation but also verified the impact of yield data range on the stability of the estimation model through the rice nitrogen gradient experiment. This is significant for early rice yield estimation and helping agricultural producers make more informed planting decisions.

Chilling at grain filling stage reduced rice grain protein content: An experimental and modeling study

Climate change has increased the trend in the intensity of global extreme weather events, including chilling. Nitrogen is one of the most essential nutrients for rice growth and development. Chilling will limit the uptake and translocation of nitrogen and the formation of grain protein in rice plants. The experiment conducted in the climate chamber with chilling stress applied at the grain filling stage revealed different effects on protein concentration in aboveground organs and caused asymmetry in grain protein contents (GPC) and grain protein yield (GPY). Chilling stress applied during the grain filling stage reduced the accumulation rate of rice grain protein by inhibiting the nitrogen uptake and the translocation from vegetative organs to grains, which resulted in a 35 % decrease in grain nitrogen accumulation and a 92 % increase in vegetative organs during grain filling. Consequently, the average nitrogen harvest index is reduced by 12 %. Nitrogen accumulation was severely affected when cooling degree days (CDD) ≥ 70 °C days. As the chilling intensity increased, the decrease of GPY was more significant than that of GPC. Moreover, we improved the grain temperature-nitrogen uptake function under chilling stress based on the relationship between CDD and the reduction in rice grains. By comparing the improved function and the modules in the existing crop model using the datasets from open field and artificial control experiments, we demonstrated that the current research on quantifying rice nitrogen uptake at extreme temperature stress needed further improvement. The effects of environmental stress on grain nitrogen accumulation are complex. Future studies should pay more attention to the ability of extreme temperature stress to affect nitrogen accumulation in various rice organs.

Optimizing nitrogen and potassium for aerobic rice (Oryza sativa L.) in elevated -temperature environments

Optimizing nitrogen and potassium for aerobic rice (Oryza sativa L.) in elevated -temperature environments

Enhanced Estimation of Rice Leaf Nitrogen Content via the Integration of Hybrid Preferred Features and Deep Learning Methodologies

Efficiently obtaining leaf nitrogen content (LNC) in rice to monitor the nutritional health status is crucial in achieving precision fertilization on demand. Unmanned aerial vehicle (UAV)-based hyperspectral technology is an important tool for determining LNC. However, the intricate coupling between spectral information and nitrogen remains elusive. To address this, this study proposed an estimation method for LNC that integrates hybrid preferred features with deep learning modeling algorithms based on UAV hyperspectral imagery. The proposed approach leverages XGBoost, Pearson correlation coefficient (PCC), and a synergistic combination of both to identify the characteristic variables for LNC estimation. We then construct estimation models of LNC using statistical regression methods (partial least-squares regression (PLSR)) and machine learning algorithms (random forest (RF); deep neural networks (DNN)). The optimal model is utilized to map the spatial distribution of LNC at the field scale. The study was conducted at the National Agricultural Science and Technology Park, Guangzhou, located in Baiyun District of Guangdong, China. The results reveal that the combined PCC-XGBoost algorithm significantly enhances the accuracy of rice nitrogen inversion compared to the standalone screening approach. Notably, the model built with the DNN algorithm exhibits the highest predictive performance and demonstrates great potential in mapping the spatial distribution of LNC. This indicates the potential role of the proposed model in precision fertilization and the enhancement of nitrogen utilization efficiency in rice cultivation. The outcomes of this study offer a valuable reference for enhancing agricultural practices and sustainable crop management.

Leaching and volatilization of nitrogen in paddy rice under different nitrogen management

Leaching and volatilization of nitrogen in paddy rice under different nitrogen management

Analysis of the Effects of Population Structure and Environmental Factors on Rice Nitrogen Nutrition Index and Yield Based on Machine Learning

With the development of rice varieties and mechanized planting technology, reliable and efficient nitrogen and planting density status diagnosis and recommendation methods have become critical to the success of precise nitrogen and planting density management in crops. In this study, we combined population structure, plant shape characteristics, environmental weather conditions, and management information data using a machine learning model to simulate the responses of the yield and nitrogen nutrition index and developed an ensemble learning model-based nitrogen and planting density recommendation strategy for different varieties of rice types. In the third stage, the NNI and yield prediction effect of the ensemble learning model was more significantly improved than that of the other two stages. The scenario analysis results show that the optimal yields and nitrogen nutrition indices were obtained with a density and nitrogen amount of 100.1 × 104 plant/ha and 161.05 kg·ha−1 for the large-spike type variety of rice, 75.08 × 104 plant/ha and 159.52 kg·ha−1 for the intermediate type variety of rice, and 75.08 × 104 plant/ha and 133.47 kg·ha−1 for the panicle number type variety of rice, respectively. These results provide a scientific basis for the nitrogen application and planting density for a high yield and nitrogen nutrition index of rice in northeast China.

Integrated Straw Return with Less Power Puddling Improves Soil Fertility and Rice Yield in China’s Cold Regions

The rice production system in China is facing challenges, including declining soil fertility and a stagnant rice yield. This study aimed to test whether integrating the return of straw to fields with less power puddling could simultaneously enhance soil fertility and rice yields. Therefore, field experiments were conducted in Heilongjiang Province, a key rice-growing region in China, from 2017 to 2021, using three different planting methods: control group (CK), straw return (SR) and straw return integrated with less power puddling (SR + LP). The results showed that small soil aggregates (particle diameter < 0.25 mm) and soil bulk density were significantly decreased when straw return was integrated with less power puddling. These changes contributed to the preservation of soil structure. Simultaneously, this approach significantly increased soil ammonium nitrogen content from 9.9 to 10.9 mg kg−1, organic matter content from 35.0 to 36.2 g kg−1, available nitrogen content from 140.5 to 147.0 mg kg−1 and available potassium content from 128.6 to 136.8 mg kg−1 at mature stage on average. Consequently, the post-heading stored assimilates accumulation of rice was increased from 6.12 to 6.43 t ha−1, and the nitrogen, phosphorus and potassium accumulation of rice were increased by 7.85 kg ha−1, 1.13 kg ha−1 and 5.68 kg ha−1, respectively. These changes ultimately resulted in a higher 1000 g weight and filled grain rate, providing the foundation for higher yields (an increase from 9.31 t ha−1 to 9.55 t ha−1). Furthermore, this approach also increased the net income for farmers by USD 14 t ha−1. In summary, this study demonstrates that integrating straw return with less power puddling can enhance soil’s nutrient supply and retention capacity. This enhancement may boost the absorption and transportation of nutrients, ultimately establishing the groundwork for higher yields and economic benefits by enhancing the 1000 g weight and filled grain rate. Future research should delve deeper into its applicability across different ecosystems and investigate the yield-increasing mechanisms.

The Use of Magnesium Fertilizer Can Improve the Nutrient Uptake, Yield, and Quality of Rice in Liaoning Province

In this study, the effects of the soil application and foliar spraying with magnesium fertilizers on rice yield and quality in Liaoning Province were investigated. Field experiments were conducted at Kaiyuan, Xinmin, and Dawa in 2022 and 2023. Magnesium fertilizers were used in the soil as magnesium sulfate monohydrate and silicon–magnesium fertilizer and on leaves was magnesium sulfate heptahydrate. The results showed that the application of 12 kg magnesium hm−2 in the soil at the Kaiyuan site can significantly increase rice yield by 14.8% compared with sites without magnesium fertilizer. The use of silicon–magnesium fertilizer showed a more obvious yield increase of 22.2%. The application of 3 kg magnesium hm−2 or 6 kg magnesium hm−2 on the leaf surface increased the rice yield at Kaiyuan by 19.4% and 21.6% and at Xinmin by 17.8% and 5.4%, respectively. The yield increase was more significant under the optimal fertilization treatment compared with the conventional fertilization treatment. The application of magnesium fertilizer increased the magnesium, nitrogen, and phosphorus contents in rice shoots and the potassium and crude protein contents in rice grains. The effect of foliar spraying with magnesium fertilizers was more obvious than soil application. Therefore, the magnesium fertilizer used on the leaf surface plays an important role in improving rice yield and quality in rice-growing areas with relatively rich soil magnesium content. Magnesium fertilizer can compensate for the yield decrease caused by the reduced use of nitrogen and phosphorus fertilizers by promoting nitrogen and phosphorus absorption in rice. Conclusively, the application of magnesium fertilizer is a promising measure to improve rice production in Liaoning province under a reduced nitrogen and phosphate fertilizer background.

UAV-based multispectral image analytics and machine learning for predicting crop nitrogen in rice

Assessment of crop nitrogen status is essential for efficient crop growth management. Existing nitrogen measurements are accurate but destructive, laborious, and time-consuming. Therefore, the soil plant analysis development (SPAD) meter approach is commonly used to address these challenges along with location-specific measurements. The study aims to develop a robust machine learning-based model for predicting rice crop SPAD values using spectral data and to generate spatial maps of SPAD values and nitrogen content. The SPAD meter data, UAV-based multispectral images, and spectroradiometer-based data were collected during Rabi 2021/22 and 2022/23 seasons. The red and red-edge bands, Normalized Difference Vegetation Index, and Normalized Pigment Chlorophyll Index correlated well with SPAD values. The random forest regressor model performed well with UAV-based data compared to support vector regression and partial least square regression and achieved good accuracy with the ground truth spectroradiometer data. This generalized model demonstrates adaptability in precisely assessing crop nitrogen status.

Estimating thresholds of nitrogen, phosphorus and potassium fertilizer rates for rice cropping systems in China.

Determining the fertilization rate plays a pivotal role in agronomic practices as they directly impact yield targets, soil fertility, and environmental risks. In this study, we proposed a method that utilizes allowed ranges of partial nutrient balance and yield to estimate the threshold of nitrogen (N), phosphorus (P), and potassium (K) fertilizer applied to rice (Oryza sativa L.) fields in China. Based on a dataset of 6792 observations from rice fields, we determined the minimum and maximum rates of N, P and K suggested for single (mono-season rice), middle (summer-season rice rotated with winter-season upland crop), early and late (double-season rice cropping system) rice, ranging between 114-146 and 220-292 kg N ha-1 per season, 56-74 and 112-149 kg P2O5 ha-1 per season, and 170-230 and 329-347 kg K2O ha-1 per season, respectively. These values serve as the lower and upper fertilization thresholds, guiding yield goals and environmental protection. Furthermore, if rice straw is returned to fields, the demand for K fertilizer can theoretically decrease by 183 kg K2O ha-1, with corresponding decreases of 50 kg N ha-1 and 26 kg P2O5 ha-1, respectively. A recommended fertilization approach, excluding returned straw nutrients from the upper fertilization thresholds, suggested average application rates of 194 kg N ha-1, 105 kg P2O5 ha-1, and 157 kg K2O ha-1, which align well with the nutrient requirements of rice. Additionally, substituting organic N for chemical N is an effective approach to conserve chemical fertilizer N, potentially reducing chemical N usage by 20%-40%. Utilizing slow-release N is also a favorable option to enhance N use efficiency and optimize N balance. This study offers valuable insights into the development of fertilization restriction indicators, aiming to achieve a delicate balance between environmental impact and agricultural productivity through the adoption of balanced fertilization rates and utilization of organic residues.

Genome‐wide identification of nitrate transporter 1/peptide transporter family (NPF) in cassava (Manihot esculenta) and functional analysis of MeNPF5.4 and MeNPF6.2 in response to nitrogen and salinity stresses in rice

AbstractNitrogen is a major driving force for the improvement of crop yield worldwide but brings detrimental effects on ecosystems. Therefore, enhancing nitrogen use efficiency (NUE) is vital for sustainable agriculture. The nitrate transporter (NRT1/NPF) family associated with nitrogen uptake and utilization is indispensable to the improvement of NUE in crops. As an important food security crop, cassava (Manihot esculenta) produces an acceptable yield in nutrient‐deficient soil. Here, we identified and systematically analyzed the NPF gene family in cassava, including phylogenetic relationship, chromosome location, gene duplication, and gene expression in response to different nitrogen supplies. The stem is involved in nitrogen transportation and remobilization. Gene expression analysis revealed that MeNPF5.4 and MeNPF6.2 were specifically expressed in the stem, and have diverse expression in different nitrogen conditions. To facilitate the functional analysis of MeNPF5.4 and MeNPF6.2, we constructed their overexpression (OE) lines in rice. A NO3− flux assay showed that MeNPF5.4 and MeNPF6.2 OE lines exhibited a marked decrease in NO3− efflux and significant NO3− influx compared with WT, which suggests that they might have contributed to the NUE improvement of rice. Notably, overexpressing MeNPF5.4 showed increased grain size, grain number, and grain weight per panicle. More importantly, the MeNPF5.4 OE line contributed to salt tolerance. Nevertheless, an obvious reduction in grain number and grain weight per panicle was detected in the MeNPF6.2 OE line compared with WT. Strikingly, the MeNPF6.2 OE line showed higher salt stress tolerance than WT in LN conditions. Taken together, our results demonstrated that MeNPF5.4 can potentially improve the NUE and salt stress tolerance of rice, which reveals valuable breeding targets to improve crop yield and stress tolerance.

Combining vegetation, color, and texture indices with hyperspectral parameters using machine-learning methods to estimate nitrogen concentration in rice stems and leaves

Context or problemNitrogen is one of the important elements of crops, which plays a decisive role in crop growth and development and the formation of yields. Monitoring of rice organ-scale nitrogen concentration based on the unmanned aerial vehicle (UAV) images is of great significance for rice field management and yield prediction. Objective or research questionPrevious studies have focused on the use of traditional statistical methods and chlorophyll-related vegetation indices to construct plant nitrogen concentration, with models lacking generalizability. MethodsIn this study, rice field trials of two varieties (NJ9108, YD6) and nitrogen fertilizer treatments (N0-N3: 0, 105, 210 and 315 kg/ha) were conducted for 3 years with manual sampling and UAV digital and hyperspectral images during key fertility periods. Based on the data of the whole growth periods and combined with vegetation indices (VIs), color indices (CIs), hyperspectral parameters (HPs), texture indices (TIs) and machine-learning algorithms, monitoring models of nitrogen concentration at the organ scale of rice were constructed and used to estimate the N content of multiple organs (leaf and stem) of rice at different periods. Field experiments were used to collect the multi-organ nitrogen concentration of rice and the remote sensing (RS) data of UAV during the critical growth period of the two years (2021, 2022), and machine-learning algorithms were used to construct the estimation models. ResultsThe results showed that VIs had good correlations with leaf nitrogen concentration (LNC), stem nitrogen concentration (SNC) and plant nitrogen concentration (PNC), with correlation coefficients (r) of 0.86, 0.74 and 0.81, respectively. Machine learning estimation models combining multiple types of RS indices were more accurate than single parameter models constructed by traditional statistical methods, with the LNC optimal model (R2 = 0.8, RMSE = 3.83 mg/g), the SNC optimal model (R2 = 0.7, RMSE = 2.43 mg/g) and the PNC optimal model (R2 = 0.7, RMSE = 3.19 mg/g). Validated using data from 2020, the machine-learning models were far more accurate than traditional methods. ConclusionsThese results show that the use of multi-source remote sensing data based on machine-learning algorithms can effectively estimate the nitrogen concentration of organs in rice. ImplicationsThis study provides an accurate, stable and universal method for estimating rice nitrogen concentration in rice organs, which can be used as a reference for estimating rice nitrogen concentration in large fields using UAV RS technology.

OsCBL1 modulates rice nitrogen use efficiency via negative regulation of OsNRT2.2 by OsCCA1

BackgroundFor cereal crop breeding, it is meaningful to improve utilization efficiency (NUE) under low nitrogen (LN) levels while maintaining crop yield. OsCBL1-knockdown (OsCBL1-KD) plants exhibited increased nitrogen accumulation and NUE in the field of low N level.ResultsOsCBL1-knockdown (OsCBL1-KD) in rice increased the expression of a nitrate transporter gene OsNRT2.2. In addition, the expression of OsNRT2.2, was suppressed by OsCCA1, a negative regulator, which could directly bind to the MYB-binding elements (EE) in the region of OsNRT2.2 promoter. The OsCCA1 expression was found to be down-regulated in OsCBL1-KD plants. At the low Nitrogen (N) level field, the OsCBL1-KD plants exhibited a substantial accumulation of content and higher NUE, and their actual biomass remained approximately as the same as that of the wild type.ConclusionThese results indicated that down-regulation of OsCBL1 expression could upregulate the expression of OsNRT2.2 by suppressing the expression of OsCCA1and then increasing the NUE of OsCBL1-KD plants under low nitrogen availability.