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Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. The image features consisted of RGB-based vegetation indices (VIs), color parameters, and textures, which represented image features of different aspects and different types. To determine which N status indicators could be well-estimated, we considered two mass-based N status indicators (i.e., the leaf N concentration (LNC) and plant N concentration (PNC)) and two area-based N status indicators (i.e., the leaf N density (LND) and plant N density (PND)). Sixteen RGB-based VIs associated with crop growth were selected. Five color space models, including RGB, HSV, L*a*b*, L*c*h*, and L*u*v*, were used to quantify the winter wheat canopy color. The combination of Gaussian processes regression (GPR) and Gabor-based textures with four orientations and five scales was proposed to estimate the winter wheat N status. The gray level co-occurrence matrix (GLCM)-based textures with four orientations were extracted for comparison. The heterogeneity in the textures of different orientations was evaluated using the measures of mean and coefficient of variation (CV). The variable importance in projection (VIP) derived from partial least square regression (PLSR) and a band analysis tool based on Gaussian processes regression (GPR-BAT) were used to identify the best performing image features for the N status estimation. The results indicated that (1) the combination of RGB-based VIs or color parameters only could produce reliable estimates of PND and the GPR model based on the combination of color parameters yielded a higher accuracy for the estimation of PND (R2val = 0.571, RMSEval = 2.846 g/m2, and RPDval = 1.532), compared to that based on the combination of RGB-based VIs; (2) there was no significant heterogeneity in the textures of different orientations and the textures of 45 degrees were recommended in the winter wheat N status estimation; (3) compared with the RGB-based VIs and color parameters, the GPR model based on the Gabor-based textures produced a higher accuracy for the estimation of PND (R2val = 0.675, RMSEval = 2.493 g/m2, and RPDval = 1.748) and the PLSR model based on the GLCM-based textures produced a higher accuracy for the estimation of PNC (R2val = 0.612, RMSEval = 0.380%, and RPDval = 1.601); and (4) the combined use of RGB-based VIs, color parameters, and textures produced comparable estimation results to using textures alone. Both VIP-PLSR and GPR-BAT analyses confirmed that image textures contributed most to the estimation of winter wheat N status. The experimental results reveal the potential of image textures derived from high-definition UAV-based RGB images for the estimation of the winter wheat N status. They also suggest that a conventional low-cost digital camera mounted on a UAV could be well-suited for winter wheat N status monitoring in a fast and non-destructive way.

The nitrogen (N) nutrition index (NNI) is a reliable indicator of crop N status and there is an urgent need to develop efficient technologies for non-destructive estimation of NNI to support the practical applications of precision N management strategies. The objectives of this study were to: (i) validate a newly established critical N dilution curve for spring maize in Northeast China; (ii) determine the potential of using the GreenSeeker active optical sensor to non-destructively estimate NNI; and (iii) evaluate the performance of different N status diagnostic approaches based on estimated NNI via the GreenSeeker sensor measurements. Four field experiments involving six N rates (0, 60, 120,180, 240, and 300 kg·ha−1) were conducted in 2014 and 2015 in Lishu County, Jilin Province in Northeast China. The results indicated that the newly established critical N dilution curve was suitable for spring maize N status diagnosis in the study region. Across site-years and growth stages (V5–V10), GreenSeeker sensor-based vegetation indices (VIs) explained 87%–90%, 87%–89% and 83%–84% variability of leaf area index (LAI), aboveground biomass (AGB) and plant N uptake (PNU), respectively. However, normalized difference vegetation index (NDVI) became saturated when LAI > 2 m2·m−2, AGB > 3 t·ha−1 or PNU > 80 kg·ha−1. The GreenSeeker-based VIs performed better for estimating LAI, AGB and PNU at V5–V6 and V7–V8 than the V9–V10 growth stages, but were very weakly related to plant N concentration. The response index calculated with GreenSeeker NDVI (RI–NDVI) and ratio vegetation index (R2 = 0.56–0.68) performed consistently better than the original VIs (R2 = 0.33–0.55) for estimating NNI. The N status diagnosis accuracy rate using RI–NDVI was 81% and 71% at V7–V8 and V9–V10 growth stages, respectively. We conclude that the response indices calculated with the GreenSeeker-based vegetation indices can be used to estimate spring maize NNI non-destructively and for in-season N status diagnosis between V7 and V10 growth stages under experimental conditions with variable N supplies. More studies are needed to further evaluate different approaches under diverse on-farm conditions and develop side-dressing N recommendation algorithms.

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.

Background A greater understanding of the factors that are associated with favourable health may help increase longevity and healthy life expectancy. We examined sociodemographic, psychosocial, lifestyle and environmental exposures associated with multiple health indicators. Methods UK Biobank recruited > 500,000 participants, aged 37–73, between 2006 and 2010. Health indicators examined were 81 cancer and 443 non-cancer illnesses used to classify participants' health status; long-standing illness; and self-rated health. Exposures were sociodemographic (age, sex, ethnicity, education, income and deprivation), psychosocial (loneliness and social isolation), lifestyle (smoking, alcohol intake, sleep duration, BMI, physical activity and stair climbing) and environmental (air pollution, noise and residential greenspace) factors. Associations were estimated using logistic and ordinal logistic regression. Results In total, 307,378 participants (mean age = 56.1 years [SD = 8.07], 51.9% female) were selected for cross-sectional analyses. Low income, being male, neighbourhood deprivation, loneliness, social isolation, short or long sleep duration, low or high BMI and smoking were associated with poor health. Walking, vigorous-intensity physical activity and more frequent alcohol intake were associated with good health. There was some evidence that airborne pollutants (PM 2.5 , PM 10 and NO 2 ) and noise (L den ) were associated with poor health, though findings were not consistent across all models. Conclusions Our findings highlight the multifactorial nature of health, the importance of non-medical factors, such as loneliness, healthy lifestyle behaviours and weight management, and the need to examine efforts to improve the health outcomes of individuals on low incomes.

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 < 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions.

The optimization of nitrogen (N) fertilization is the object of intense research efforts around the world. Overfertilization is commonly used as a form of insurance against uncertain soil fertility level. However, this practice results in lower nitrogen use efficiency, high levels of residual N after harvest, and losses in the environment. Determining an N recommendation that would preserve actual crop requirements, profitability of the farm, and quality of the environment has been subjected to a number of research initiatives with a variable degree of success. On one hand, soil tests are capable of estimating the intensity of N release at any point in time, but rarely the capacity factor over a longer period. On the other hand, in the context of in-season N applications, crops are often considered good integrators of factors such as the presence of mineral N, climatic conditions, soil properties, and crop management. Strategies have been proposed with plant sensor-based diagnostic information for N recommendations, but the sensitivity of reflectance-based parameters alone do not provide complete satisfaction (delayed sensitivity, need of specific chlorophyll, biomass or cover fraction ranges, lack of specificity to the N stress). Fluorescence sensing methods have been used to monitor crop physiology for years, and they may offer solutions for N status diagnosis over reflectance-based methods. In this paper, we review three plant fluorescence components related to four sensing approaches—variable chlorophyll fluorescence, leaf chlorophyll content-related fluorescence emission ratio, blue-green fluorescence, and epidermal screening of chlorophyll fluorescence by phenolic compounds—from the perspective of their relevance to N fertilization management of agricultural crops. We examine the existence of N-induced changes in each case, together with applications and limitations of the approach. Among these approaches, the fluorescence emission ratio method is the most important and the most widely used to date. However, blue-green fluorescence and epidermal screening of chlorophyll fluorescence by phenolic compounds has also received a great deal of attention particularly with the recent commercial release of instruments which can measure in real time and in vivo both the leaf chlorophyll content and several phenolic compounds (anthocyanins, flavonoïds, hydroxycinnamic acids). Overall, our conclusion is that fluorescence-based technologies allow for highly sensitive plant N status information, independently from soil interference, leaf area, or biomass status. They also allow for probing not only the chlorophyll status but also other physiological parameters known to react to N fertility conditions. These new parameters have the potential to provide new N status indicators that can be assessed remotely in a precision agriculture context.

Studies on the zooplankton community are essential for diagnosing the health of aquatic ecosystems, as these systems respond quickly to environmental changes. Using the multimetric TRIX index for the assessment of trophic status, we assessed the trophic state and its association with zooplankton composition, distribution, and environmental variables in a tropical estuary on the Brazilian equatorial margin. The results showed that significant seasonal and sectoral environmental differences contributed to biological heterogeneity, with the second spatial sector (SII) exhibiting the greatest impact, leading to decreased alpha diversity compared to that of the first spatial sector (SI). Salinity, pH, SiO23, and NO2− exhibited significant seasonal and sectoral variations (p < 0.05). The community consisted of 65 taxa, with copepods (81.5%), mainly from the Oithonidae and Paracalanidae families, dominating species number. Overall, the community exhibited medium diversity, low richness, and heterogeneity. Beta diversity, calculated using PERMDISP, reflected environmental heterogeneity with significant seasonal differences and biological variability between rainy and dry periods. Indicator species analysis identified 15 taxa, including copepods such as Euterpina acutifrons and Clytemnestra scutellata. Of these, nine taxa (60%) were indicators for the SI, and six (40%) were indicators for the SII. This study underscores the importance of identifying environmental filters and indicator species to understand estuarine dynamics and assess ecosystem trophic states.

Aquatic food webs are spatially complex, potentially contributing to intraspecific variability in production pathway reliance of intermediate trophic level consumers. Variation in trophic reliance may be described by well-established trophic indicators, like stable isotope ratios (δ 13 C, δ 15 N), along with emerging trophic indicators, such as fatty acid composition. We evaluated stable isotope ratios and fatty acid profiles of European smelt ( Osmerus eperlanus ) among and within distinct regions of three large Swedish lakes (Hjälmaren, Mälaren, Vättern) which differed in trophic status. We expected that smelts in more oligotrophic lakes and regions would be characterized by distinct stable isotope signatures and fatty acid profiles, with particularly high polyunsaturated fatty acid (PUFA) relative levels. However, we acknowledge that frequent movement of smelts among regions may serve to spatially integrate their diet and lead to limited within-lake variation in stable isotope ratios and fatty acid composition. As expected, in comparison with more productive lakes (i.e., Hjälmaren and Mälaren), smelts from ultra-oligotrophic Vättern were characterized by low δ 15 N, high δ 13 C and high percent of a dominant PUFA, docosahexaenoic acid (DHA). Smelts from different regions of the morphometrically complex Mälaren displayed differential stable isotope ratios and fatty acid relative concentrations, which were consistent with within-lake differences in productivity and water residence times, suggesting that smelts in this lake forage locally within distinct regions. Finally, at the individual smelt level there were particularly strong and consistent associations between a well-established trophic indicator (δ 13 C) and percent DHA, suggesting that the relative concentration of this fatty acid may be a useful additional trophic indicator for smelt.

The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R2 of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications.

More and more studies have revealed a close relationship between gut microbial community and human health. However, the mechanism why changes in gut microflora structures affect shrimp health status remains unclear. In this study, we compared the differences of bacterial communities among intestine, water, and sediment collected from mariculture ponds of Penaeus japonicus in the early (30 days) and late (90 days) stage. Simultaneously, the relationship between bacterial communities and water quality indexes as well as shrimp physiological indexes was analyzed, respectively. The results showed that relative abundances of intestinal dominant phyla changed along with rearing times. And according to non-metric multidimensional scaling (NMDS), bacterial communities of shrimp gut had a closer relationship with the water library. By analyzing the relationship between water quality indexes and shrimp pond flora, it was shown that total nitrogen and ammonia nitrogen contents probably had the biggest impacts on bacterial communities of water, so did temperature and chemical oxygen demand on microbial communities of sediments and shrimp intestines. By analyzing the relationship between the dominant genus and physiological health indicators of shrimp, it was found that unidentified_Mitochondria, Photobacterium, and Acinetobacter were more closely associated with shrimp physiological health indicators such as total antioxidant capacity, superoxide dismutase activity, antibacterial activity, lysozyme activity, and agglutinating activity. Overall, these results suggested that intestinal microbiota of Penaeus japonicus has certain connections to ambient microflorae, environmental factors, and shrimp health status. These findings may be conducive to disease prevention and control by regulating gut and ambient microflorae in shrimp farming.