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The accurate estimation and timely diagnosis of crop nitrogen (N) status can facilitate in-season fertilizer management. In order to evaluate the performance of three leaf and canopy optical sensors in non-destructively diagnosing winter wheat N status, three experiments using seven wheat cultivars and multi-N-treatments (0-360 kg N ha -1 ) were conducted in the Jiangsu province of China from 2015 to 2018. Two leaf sensors (SPAD 502, Dualex 4 Scientific+) and one canopy sensor (RapidSCAN CS-45) were used to obtain leaf and canopy spectral data, respectively, during the main growth period. Five N indicators (leaf N concentration (LNC), leaf N accumulation (LNA), plant N concentration (PNC), plant N accumulation (PNA), and N nutrition index (NNI)) were measured synchronously. The relationships between the six sensor-based indices (leaf level: SPAD, Chl, Flav, NBI, canopy level: NDRE, NDVI) and five N parameters were established at each growth stages. The results showed that the Dualex-based NBI performed relatively well among four leaf-sensor indices, while NDRE of RS sensor achieved a best performance due to larger sampling area of canopy sensor for five N indicators estimation across different growth stages. The areal agreement of the NNI diagnosis models ranged from 0.54 to 0.71 for SPAD, 0.66 to 0.84 for NBI, and 0.72 to 0.86 for NDRE, and the kappa coefficient ranged from 0.30 to 0.52 for SPAD, 0.42 to 0.72 for NBI, and 0.53 to 0.75 for NDRE across all growth stages. Overall, these results reveal the potential of sensor-based diagnosis models for the rapid and non-destructive diagnosis of N status.

Critical nitrogen (N) dilution curves (CNDCs) have been developed to describe the dilution dynamic of N and to diagnose N status in plants. In this study, to develop a convenient alternative CNDC determination method, four field experiments involving different N rates (0-360 kg N ha -1 ) and six wheat varieties were performed at different eco-sites from 2014 to 2019. The normalised difference red-edge (NDRE) index extracted from the RapidSCAN CS-45 (Holland Scientific Inc., Lincoln, NE, USA) sensor was used as a driving factor instead of plant dry matter (PDM) to establish a new alternative winter wheat CNDC. The newly developed CNDC was described by the equation Nc = 0.90NDRE -0.88 , when NDRE values were ≤ 0.19 and constant Nc = 3.81%, which was independent of the NDRE values. Compared to PDM-derived CNDC (R 2 = 0.73) developed with the same dataset, a comparable precision was obtained using NDRE-derived CNDC (R 2 = 0.76) and both CNDCs could accurately discriminate wheat N status. Moreover, the NDRE could be inexpensively and rapidly measured using the active sensor. The relationship between NDRE-derived CNDC and grain yield was also analysed to facilitate in-season N management, and the R 2 value reached 0.79 and 0.87 at jointing and booting stages, respectively. The NDRE-based CNDC can be used to effectively diagnose wheat N status and as an alternative approach for non-destructive determination of crop N levels.

Accurate estimation and monitoring of crop nitrogen can assist in timely diagnosis and facilitate necessary technical support for fertilizer management. Four experiments, involving three cultivars and six nitrogen (N) treatments, were conducted in southeast China to compare the two leaf-clip meters (Dualex 4 Scientific+, Force-A, Orasy, France; Soil and Plant Analyzer Development (SPAD) meter, Minolta Camera Co., Osaka, Japan) for their ability to measure nitrogen nutrient-related indicators. The results indicated that Chl had a better monitoring accuracy for chlorophyll in per unit leaf area as compared to SPAD value, and there was no saturation to appear under high leaf chlorophyll concentration status. Flavonoids (Flav) presented the advantage of early diagnosis of rice N nutrition status (about one day as compared to SPAD value). As a reliable N nutrient diagnosis indicator, it also improved the estimation accuracy compared with the classical SPAD-based method. The other Dualex value also obtained good monitoring results. Flav was positively correlated with N deficiency, and with higher R 2 in panicle initiation and booting stages with low RMSE, respectively; whereas SPAD value was negatively correlated with nitrogen deficiency. Therefore, the Flav-based nitrogen application model was found to provide an early rice nitrogen fertilizer application approach, especially in the panicle initiation and booting stages.

The critical nitrogen (N) dilution curve is a suitable analytical tool for in-season estimation of N status to implement precision N management. This study was undertaken for a comprehensive comparison of N dilution curves in Japonica and Indica rice to investigate, whether a single curve can be used for both rice ecotypes and to determine the most robust plant index for assessing N status in rice ecotypes. The different N dilution curves were developed based on plant dry matter (PDM), leaf area index (LAI), leaf dry matter (LDM) and stem dry matter (SDM) for N diagnosis in Japonica and Indica rice. The comparison of N dilution curves of two rice ecotypes showed non-significant differences, therefore a single/unified curve can be used to assess plant N status for precision N management in both rice ecotypes. The relationships between PDM based, with LAI, LDM, and SDM based N nutrition index, accumulated N deficit and N requirement, indicated that leaf based approaches could be used as substitutes for PDM approach. The lower coefficient b values of LDM based curve (due to efficient physiological N use in leaves) implied that LDM was the most appropriate approach for developing N curve as compared to other approaches.

Nitrogen (N) fertilizer management plays a crucial role in high-yield rice production. To choose a well-performing rice N nutrient diagnosis indicator for developing rice production management strategies, this research conducted five field experiments under various N treatments. The results showed that machine learning and stepwise multiple linear regression suggested a strong relationship between vegetation indexes and agronomic indicators (0.70 > R2 > 0.51). A strong correlation was obtained between red-edge based vegetation indexes and agronomic indicators (R2 > 0.40). Additionally, the all-subset regression method results demonstrated that the red-edge basis vegetation indexes were generally applied during different vegetation index combinations. The red-edge basis vegetation indexes reached an approximately 40% contribution in nitrogen nutrient index prediction and an approximately 48% contribution in leaf area index monitoring. Furthermore, this study combined the normalized difference red-edge (NDRE) basis dynamic model to calculate the N dose, which ranged from 106 to 134 kg per hectare in large-scale N management according to the NDRE from Sentinel-2B images, a decrease of approximately 46 kg N ha−1 fertilizer compared with farmers' practices. Nevertheless, more refinements are needed to ensure that this strategy can be applied to farmers' yield- and income-enhancing production. [ABSTRACT FROM AUTHOR]

Timely nondestructive estimation of crop nitrogen (N) status is crucial for in-season site-specific N management. Active crop canopy sensors are the promising tools to obtain the needed information without being affected by environmental light conditions. The objective of this study was to evaluate the potential for the GreenSeeker active crop canopy sensor to estimate rice (Oryza sativa L.) N status. Nine N rate experiments were conducted from 2008 to 2012 in Jiansanjiang, Heilongjiang Province in Northeast China. The results indicated that across site-years and growth stages, normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) obtained with the GreenSeeker sensor could explain 73%–76% and 70%–73% of rice aboveground biomass and plant N uptake variability in this study, respectively. The NDVI index became saturated when biomass reached about \bf 4\nbsp\t\nbsp\ha^ - 1 or when plant N uptake reached about \bf 100\nbsp\kg\nbsp\ha^ - 1, whereas RVI did not show obvious saturation effect. The validation results, however, indicated that both indices performed similarly, and their relative errors (RE) were still large ( \bf \gt 40\% ). Although the two indices only explained less than 40% of plant N concentration or N nutrition index (NNI) variability, the RE values were acceptable (\bf \lt 26\% ). The results indicated some potentials of using the GreenSeeker sensor to estimate rice N status nondestructively, but more studies are needed to further evaluate and improve its performance for practical applications. [ABSTRACT FROM PUBLISHER]

Understanding spatial variability of indigenous nitrogen (N) supply (INS) is important to the implementation of precision N management (PNM) strategies in small scale agricultural fields of the North China Plain (NCP). This study was conducted to determine: (1) field-to-field and within-field variability in INS; (2) the potential savings in N fertilizers using PNM technologies; and (3) winter wheat ( Triticum aestivum L.) N status variability at the Feekes 6 stage and the potential of using a chlorophyll meter (CM) and a GreenSeeker active crop canopy sensor for estimating in-season N requirements. Seven farmer's fields in Quzhou County of Hebei Province were selected for this study, but no fertilizers were applied to these fields. The results indicated that INS varied significantly both within individual fields and across different fields, ranging from 33.4 to 268.4 kg ha, with an average of 142.6 kg ha and a CV of 34%. The spatial dependence of INS, however, was not strong. Site-specific optimum N rates varied from 0 to 355 kg ha across the seven fields, with an average of 173 kg ha and a CV of 46%. Field-specific N management could save an average of 128 kg N ha compared to typical farmer practices. Both CM and GreenSeeker sensor readings were significantly related to crop N status and demand across different farmer's fields, showing a good potential for in-season site-specific N management in small scale farming systems. More studies are needed to further evaluate these sensing technology-based PNM strategies in additional farmer fields in the NCP. [ABSTRACT FROM AUTHOR]

Accurate estimation and monitoring of crop nitrogen can assist in timely diagnosis and facilitate necessary technical support for fertilizer management. Four experiments, involving three cultivars and six nitrogen (N) treatments, were conducted in southeast China to compare the two leaf-clip meters (Dualex 4 Scientific+, Force-A, Orasy, France; Soil and Plant Analyzer Development (SPAD) meter, Minolta Camera Co., Osaka, Japan) for their ability to measure nitrogen nutrient-related indicators. The results indicated that Chl had a better monitoring accuracy for chlorophyll in per unit leaf area as compared to SPAD value, and there was no saturation to appear under high leaf chlorophyll concentration status. Flavonoids (Flav) presented the advantage of early diagnosis of rice N nutrition status (about one day as compared to SPAD value). As a reliable N nutrient diagnosis indicator, it also improved the estimation accuracy compared with the classical SPAD-based method. The other Dualex value also obtained good monitoring results. Flav was positively correlated with N deficiency, and with higher R2 in panicle initiation and booting stages with low RMSE, respectively; whereas SPAD value was negatively correlated with nitrogen deficiency. Therefore, the Flav-based nitrogen application model was found to provide an early rice nitrogen fertilizer application approach, especially in the panicle initiation and booting stages. [ABSTRACT FROM AUTHOR]

Precision nitrogen (N) management requires an accurate and timely in-season assessment of crop N status. The proximal fluorescence sensor Multiplex®3 is a promising tool for monitoring crop N status. It performs a non-destructive estimation of plant chlorophyll, flavonol, and anthocyanin contents, which are related to plant N status. The objective of this study was to evaluate the potential of proximal fluorescence sensing for N status estimation at different growth stages for rice in cold regions. In 2012 and 2013, paddy rice field experiments with five N supply rates and two varieties were conducted in northeast China. Field samples and fluorescence data were collected in the leaf scale (LS), on-the-go (OG), and above the canopy (AC) modes using Multiplex®3 at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. The relationships between the Multiplex indices or normalized N sufficient indices (NSI) and five N status indicators (above-ground biomass (AGB), leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI)) were evaluated. Results showed that Multiplex measurements taken using the OG mode were more sensitive to rice N status than those made in the other two modes in this study. Most of the measured fluorescence indices, especially the N balance index (NBI), simple fluorescence ratios (SFR), blue–green to far-red fluorescence ratio (BRR_FRF), and flavonol (FLAV) were highly sensitive to N status. Strong relationships between these fluorescence indices and N indicators, especially the LNC, PNC, and NNI were revealed, with coefficients of determination (R2) ranging from 0.40 to 0.78. The N diagnostic results indicated that the normalized N sufficiency index based on NBI under red illumination (NBI_RNSI) and FLAV achieved the highest diagnostic accuracy rate (90%) at the SE and HE stages, respectively, while NBI_RNSI showed the highest diagnostic consistency across growth stages. The study concluded that the Multiplex sensor could be used to reliably estimate N nutritional status for rice in cold regions, especially for the estimation of LNC, PNC, and NNI. The normalized N sufficiency indices based on the Multiplex indices could further improve the accuracy of N nutrition diagnosis by reducing the influences of inter-annual variations and different varieties, as compared with the original Multiplex indices. [ABSTRACT FROM AUTHOR]

Active crop canopy sensors can be used for non-destructive real-time diagnosis of crop nitrogen (N) status and guiding in-season N management. However, limited studies have compared the performances of two commercially available sensors with three different wavebands: Crop Circle ACS-470 (CC-470) and Crop Circle ACS-430 (CC-430). The objective of this study was to evaluate the performances of CC-470 and CC-430 sensors for estimating winter wheat (Triticum aestivum L.) N status at different measurement heights (40 cm, 70 cm and 100 cm) and growth stages. Results indicated that the canopy reflectance values of CC-470 were more affected by height compared to the CC-430 sensor. The normalized difference red edge (NDRE) and red edge chlorophyll index (CIRE) of CC-430 were stable at the three different measuring heights. The relationships between these indices and the N status indicators were stronger at the Feekes 9–10 stages than the Feekes 6–7 stages for both sensors; however, the CC-430 sensor-based vegetation indices had higher coefficient of determination (R2) values for both stages. It is concluded that the CC-430 sensor is more reliable than CC-470 for winter wheat N status estimation due to its capability of making height-independent measurements. These results demonstrated the importance of considering the influences of height when using active canopy sensors in field measurements. [ABSTRACT FROM AUTHOR]

Various indices established on a portable SPAD-502 meter can serve as indicators of the nitrogen status of a plant. We compared di-positional SPAD readings and indices with several reliable nitrogen indicators during vegetative growth stage of rice ( Oryza sativa L.) and developed a prediction model for diagnosing nitrogen status. Three field experiments were conducted in Jiangsu province of east China during 2013 and 2014. Different nitrogen application rates were used to generate contrasting conditions of nitrogen availability in three Japonica, Wuyunjing 19, Yongyou 8, and Wuyunjing 24, and one Indica rice hybrid, Yliangyou 1. The SPAD values of the uppermost four fully expanded leaves were measured from tillering to heading stages, and these values were further used to calculate the normalized SPAD index (NSI), relative SPAD index (RSI), difference SPAD index (DSI), relative difference SPAD index (RDSI), and normalized difference SPAD index (NDSI). Five hills from each plot were simultaneously sampled, and four nitrogen indicators, including leaf nitrogen concentration (LNC), plant nitrogen concentration (PNC), plant nitrogen accumulation (NA), and nitrogen nutrition index (NNI), were measured. The results of linear correlations among the SPAD and nitrogen indicators indicated that NSI of the fourth fully expanded leaf from top (NSI4) was the most reliable and generally applicable SPAD indicator. In total, 24 potential single-stage and duration models were established, taking into consideration the fact that duration models covering the period from stem elongation to booting stages are more robust, two duration diagnostic models (Model 1: NA = 0.0279e 8.6957NSI4 , R 2 = 0.730**, n = 45; Model 2: NNI = 0.0163e 4.13NSI4 , R 2 = 0.767**, n = 45, NSI4 = 0.80–1.00) were developed and calibrated. Both models can provide accurate and appropriate N diagnosis from the stem elongation to booting growth stages for rice production. [ABSTRACT FROM AUTHOR]

Understanding the effect of nitrogen (N) vertical distribution within the rice canopy at different growth stages and development of critical nitrogen dilution curves for upper leaf layers will not only help accurate estimations of the spatial and temporal variation in N status, but will also provide a basis for analyses of N nutrition in rice. Based on a three-year field experiment with 2 rice varieties, 3 N levels and 4 planting densities, the effect of heterogeneity in N vertical distribution on the nitrogen nutrition index (NNI) was then determined, and for the first time, critical N dilution curves of upper leaf layers were constructed based on a Bayesian statistical model. The relationship for NNI entire canopy and its relationship with the other tested leaf layers was investigated. The leaf layer suitable for analyses of N nutrition and directly inversion of NNI by remote sensing is determined. The results revealed that the main contributor to plant N concentration (PNC) of entire canopy (PNC Canopy) was found to be the lower leaf layer of the rice canopy, and may lead to misdiagnosis under the non-N-limiting condition by using the canopy level based NNI. The PNC of top half leaf layer of the canopy was more stable in terms of short-term environmental changes, and more accurately representing the overall N status. The top half leaf layer was the main contributor to canopy reflectance. Thus, compared with the NNI of other upper leaf layers, the NNI of the top half leaf layer is more suitable for direct inversion by using CI (1113, 743) with the R2 = 0.70. Overall, these findings suggest that the NNI of the top half leaf layer is the optimal remote sensing indicator of N nutrition in rice. • The non-uniform distribution of nitrogen (N) affects monitoring of N status. • Critical N dilution curves for different leaf layers were therefore developed. • The NNI of the top two accumulative layer (NNI AL2) was the optimal indicator of N. • Chlorophyll index (1113, 738) performed best in direct estimation of NNI AL2. [ABSTRACT FROM AUTHOR]

Wheat and rice are the main food crops in China. Appropriate nitrogen (N) fertilizer can effectively promote the crop growth, whereas excessive use has repercussions on yield formation and environmental preservation. Therefore, timely assessment of crop N status and precise N application management are of paramount importance. The study aims to assess the impacts of N fertilizer on N accumulation and spectral dynamics in wheat and rice, and investigate the feasibility of real-time crop N status diagnosis using unmanned aerial vehicle (UAV) spectra and devise subsequent managements. Ten experiments were conducted in Xinghua City and Lianyungang City from 2017 to 2020, involving different N fertilizer rates (0–405 kg N ha−1) and various cultivars. Field sampling was carried out simultaneously with UAV image acquisition, and the crop dry matter and N concentration were obtained by indoor analysis. Normalized difference red-edge index (NDRE) and N nutrition index (NNI) demonstrated a robust power function relationship (R2 > 0.70). The time series N diagnosis curves established by critical NDRE values achieved recognition accuracy of over 89%. The validation accuracy of critical NDRE values achieved 93.84%. The probability of the calculated topdressing rate (N) falling between the agronomic optimal N rate (AONR) and economic optimal N rate (EONR) is 86%. The time series N diagnosis curve offered possibility to real-time judgement of plant N status. In addition, the subsequent N topdressing design was proved through the improvement of existing sufficiency index (SI) algorithm. The time series N diagnosis curve will provide valuable decision support for the optimal N fertilizer management of wheat and rice. Moreover, the UAV platform holds promising potential for regional-scale application in the future. • N fertilizer significantly affected dynamics of N accumulation and canopy spectra. • NDRE had a good correlation with PNA and NNI of wheat and rice. • RAGDD-based time series N diagnosis curve was established and verified. • N diagnosis curve combined well with SINR to guide field topdressing in real time. [ABSTRACT FROM AUTHOR]

Accurate and transferable wheat nitrogen status estimation is very important to plant phenotyping and smart agricultural management. The goal of this study was to establish a wheat nitrogen status estimation model across all growth stages by combining proximal sensing and meteorological data. From 2010–2020, nine multi-nitrogen rates field trials were conducted at five sites involving different wheat varieties. Proximal sensing data were acquired from a Crop Circle sensor at key growth stages and meteorological data were aggregated from planting to the corresponding sensing date. Deep neural network (DNN) and long short-term memory (LSTM) were adopted to estimate above-ground biomass, plant nitrogen uptake, plant nitrogen concentration, and the nitrogen nutrition index. Random forest (RF) was used as a benchmark regression model. Multi-task learning (MTL) based on DNN was conducted to estimate the four nitrogen indicators simultaneously. A genetic algorithm (GA) was tested to optimize the hyperparameters, connection weights, and loss function weights (for MTL) of neural networks separately. The results revealed that DNN (R2 =0.83–0.96) and MTL (R2 =0.81–0.96) achieved an overall comparable high accuracy with RF (R2 =0.83–0.97), whereas LSTM (R2 =0.76–0.93) did not improve the nitrogen status estimation in our dataset. This study presented a concise and efficient framework dedicated to exploring the transferability of phenotypic predictions and provided insights into understanding crop growth and nitrogen dynamics in response to environmental conditions. • A framework combined deep learning and evolutionary algorithm was proposed. • The proposed model performed high transferability in different space and time. • Features with cumulative effect greatly impacted the wheat N status estimation. • Water-related features were essential for wheat nitrogen content estimation. [ABSTRACT FROM AUTHOR]

Precision fertilizer management has been well proved to allow farmers to synchronize nitrogen (N) fertilizer input with crop demand to improve grain yield and N use efficiency (NUE). However, the potential environmental benefits of precision fertilizer management have been less evaluated and appreciated. In this study, we selected five most commonly used optimized N fertilizer management strategies (OPM), which are Flav-basal strategy, site-specific N management strategy, optimization algorithm, N nutrient index-based fertilizer management strategy and normalized difference red-edge based fertilizer management strategy, and compared with farmer practices (FP) in yield, resource inputs (i.e., mineral, land, labor, etc.), energy use, NUE, greenhouse gases emissions (GHGs), NH 3 , NO X and SO 2 emissions, N leaching and runoff, and economic returns in Chinese southeast paddy fields. Our results showed that the OPM strategies could increase grain yield by 13.5 % at most, NUE by 3–10 % and harvest index by 1–7 kg kg−1 compared to the FP with the optimal total N application was approximately 270–290 kg N ha−1. Agronomic benefits generally increased by $ 145–620 ha−1, with the shoot biomass increasing by $ 90–185 ha−1 of adopting through OPM for rice production. Additional benefits from the mitigation of GHGs emissions could significantly increase the environmental benefits of precision N management. Altogether, the environmental benefits of OPM are considerable, and they are likely to be adopted by farmers if new incentives from the government. • This study firstly quantized the environmental effect of various optimizing nitrogen fertilizer management strategies (OPM). • This work explored a cap-and-trade scheme in China's rice field nitrogen loss firstly. • The OPM application can increase nitrogen use efficiency while reducing the Nitrogen loss. • Approximately 1300–1500 $·ha-1 increases in OPM plots compared with farmers' practices. [ABSTRACT FROM AUTHOR]

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