Improving nitrogen use efficiency in plants: effective phenotyping in conjunction with agronomic and genetic approaches

Current Progress and Future Prospect of Wheat Genetics Research towards an Enhanced Nitrogen Use Efficiency

To improve the yield and quality of wheat is of great importance for food security worldwide. One of the most effective and significant approaches to achieve this goal is to enhance the nitrogen use efficiency (NUE) in wheat. In this review, a comprehensive understanding of the factors involved in the process of the wheat nitrogen uptake, assimilation and remobilization of nitrogen in wheat were introduced. An appropriate definition of NUE is vital prior to its precise evaluation for the following gene identification and breeding process. Apart from grain yield (GY) and grain protein content (GPC), the commonly recognized major indicators of NUE, grain protein deviation (GPD) could also be considered as a potential trait for NUE evaluation. As a complex quantitative trait, NUE is affected by transporter proteins, kinases, transcription factors (TFs) and micro RNAs (miRNAs), which participate in the nitrogen uptake process, as well as key enzymes, circadian regulators, cross-talks between carbon metabolism, which are associated with nitrogen assimilation and remobilization. A series of quantitative genetic loci (QTLs) and linking markers were compiled in the hope to help discover more efficient and useful genetic resources for breeding program. For future NUE improvement, an exploration for other criteria during selection process that incorporates morphological, physiological and biochemical traits is needed. Applying new technologies from phenomics will allow high-throughput NUE phenotyping and accelerate the breeding process. A combination of multi-omics techniques and the previously verified QTLs and molecular markers will facilitate the NUE QTL-mapping and novel gene identification.

Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery

Rapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat by using the universal vegetation indices independent of growth period. Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson's correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R2, root mean square error (RMSE), and mean absolute error (MAE) of 0.85, 793.96 kg/ha, and 656.31 kg/ha, respectively. MERIS terrestrial chlorophyll index (MTCI) performed better in the vegetative period and provided the best prediction results for the N partial factor productivity (NPFP) at the jointing stage, with R2, RMSE, and MAE of 0.65, 10.53 kg yield/kg N, and 8.90 kg yield/kg N, respectively. At the same time, the modified normalized difference blue index (mNDblue) was more accurate during the reproductive period, providing the best accuracy for agronomical NUE (aNUE) assessment at the late grain-filling stage, with R2, RMSE, and MAE of 0.61, 7.48 kg yield/kg N, and 6.05 kg yield/kg N, respectively. Furthermore, the findings indicated that model accuracy cannot be improved by increasing the number of input features. Overall, these results indicate that the consumer-grade P4M camera is suitable for early and efficient monitoring of important crop traits, providing a cost-effective choice for the development of the precision agricultural system.

Root system architecture for abiotic stress tolerance in potato: Lessons from plants

The root is an important plant organ, which uptakes nutrients and water from the soil, and provides anchorage for the plant. Abiotic stresses like heat, drought, nutrients, salinity, and cold are the major problems of potato cultivation. Substantial research advances have been achieved in cereals and model plants on root system architecture (RSA), and so root ideotype (e.g., maize) have been developed for efficient nutrient capture to enhance nutrient use efficiency along with genes regulating root architecture in plants. However, limited work is available on potatoes, with a few illustrations on root morphology in drought and nitrogen stress. The role of root architecture in potatoes has been investigated to some extent under heat, drought, and nitrogen stresses. Hence, this mini-review aims to update knowledge and prospects of strengthening RSA research by applying multi-disciplinary physiological, biochemical, and molecular approaches to abiotic stress tolerance to potatoes with lessons learned from model plants, cereals, and other plants.

Hyperspectral Indices for Predicting Nitrogen Use Efficiency in Maize Hybrids

Enhancing the nitrogen (N) efficiency of maize hybrids is a common goal of researchers, but involves repeated field and laboratory measurements that are laborious and costly. Hyperspectral remote sensing has recently been investigated for measuring and predicting biomass, N content, and grain yield in maize. We hypothesized that vegetation indices (HSI) obtained mid-season through hyperspectral remote sensing could predict whole-plant biomass per unit of N taken up by plants (i.e., N conversion efficiency: NCE) and grain yield per unit of plant N (i.e., N internal efficiency: NIE). Our objectives were to identify the best mid-season HSI for predicting end-of-season NCE and NIE, rank hybrids by the selected HSI, and evaluate the effect of decreased spatial resolution on the HSI values and hybrid rankings. Analysis of 20 hyperspectral indices from imaging at V16/18 and R1/R2 by manned aircraft and UAVs over three site-years using mixed models showed that two indices, HBSI1 and HBS2, were predictive of NCE, and two indices, HBCI8 and HBCI9, were predictive of NIE for actual data collected from five to nine hybrids at maturity. Statistical differentiation of hybrids in their NCE or NIE performance was possible based on the models with the greatest accuracy obtained for NIE. Lastly, decreasing the spatial resolution changed the HSI values, but an effect on hybrid differentiation was not evident.

Enhancing Sorghum Yield Through Efficient Use of Nitrogen - Challenges and Opportunities

Sorghum is an important crop, which is widely used as food, forage, fodder and biofuel. Despite its natural adaption to resource-poor and stressful environments, increasing yield potential of sorghum under more favorable conditions holds promise. Nitrogen is the most important nutrient for crops, having a dynamic impact on all growth, yield, and grain-quality-determining processes. Thus, increasing nitrogen use efficiency (NUE) in sorghum would provide opportunities to achieve higher yield and better-quality grain. NUE is a complex trait, which is regulated by several genes. Hence, exploring genetic diversity for NUE can help to develop molecular markers associated with NUE, which can be utilized to develop high NUE sorghum genotypes with greater yield potential. Research on improving NUE in sorghum suggests that, under water-deficit conditions, traits such as stay-green and altered canopy architecture, and under favorable conditions, traits such as an optimized stay-green and senescence ratio and efficient N translocation to grain, are potential breeding targets to develop high NUE sorghum genotypes. Hence, under a wide range of environments, sorghum breeding programs will need to reconsider strategies and develop breeding programs based on environment-specific trait(s) for better adaptation and improvement in productivity and grain quality. Unprecedented progress in sensor-based technology and artificial intelligence in high-throughput phenotyping has provided new horizons to explore complex traits in situ, such as NUE. A better understanding of the genetics and molecular pathways involving NUE, accompanied by targeted high-throughput sensor-based indices, is critical for identifying lines or developing management practices to enhance NUE in sorghum.

Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat

Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability, and the ability to analyze big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack Light Detection and Ranging (LiDAR) device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis. The use of LiDAR can acquire millions of 3D points to represent spatial features of crops, and CropQuant-3D can extract meaningful traits from large, complex point clouds. In a case study examining the response of wheat varieties to three different levels of nitrogen fertilization in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in the canopy, with strong correlations with manual measurements. Hence, we demonstrate that this system could consistently perform 3D trait analysis at a larger scale and more quickly than heretofore possible and addresses challenges in mobility, throughput, and scalability. To ensure our work could reach non-expert users, we developed an open-source graphical user interface for CropQuant-3D. We, therefore, believe that the combined system is easy-to-use and could be used as a reliable research tool in multi-location phenotyping for both crop research and breeding. Furthermore, together with the fast maturity of LiDAR technologies, the system has the potential for further development in accuracy and affordability, contributing to the resolution of the phenotyping bottleneck and exploiting available genomic resources more effectively.

Agro-morphological and metabolomics analysis of low nitrogen stress response in Axonopus compressus

Axonopus compressus also known as carpet grass is a robust, stoloniferous grass that can grow in minimal fertilization and resists well to abiotic and biotic stresses including low nitrogen (LN) stress. This study aimed at characterizing the agro-morphological and metabolome responses to LN in carpet grass leaves. Under LN stress, carpet grass increased yellowness of leaves and root dry matter while reduced turf quality and shoot dry weight. The metabolome comparison between samples from optimum and LN conditions indicated 304 differentially accumulated metabolites (DAMs), which could be classified into 12 major and 31 subclasses. The results revealed that the leaf tissues accumulated more anthocyanins and other flavonoid metabolites under LN stress. Conversely, amino acids, nucleic acids and their derivatives were reduced in response to LN stress. The overall evaluation of individual metabolites and pathways, and previous studies on metabolomes indicated that carpet grass reduced its energy consumption in leaves and increased the level of organic acid metabolism and secondary metabolism in order to resist LN stress conditions.

Spectroscopic Estimation of N Concentration in Wheat Organs for Assessing N Remobilization Under Different Irrigation Regimes

Nitrogen (N) remobilization is a critical process that provides substantial N to winter wheat grains for improving yield productivity. Here, the remobilization of N from anthesis to maturity in two wheat cultivars under three irrigation regimes was measured and its relationship to organ N concentration was examined. Based on spectral data of organ powder samples, partial least squares regression (PLSR) models were calibrated to estimate N concentration (N _mass) and validated against laboratory-based measurements. Although spectral reflectance could accurately estimate N _mass, the PLSR-based N _mass-spectra predictive model was found to be organ-specific, organs at the top canopy (chaff and top three leaves) received the best predictions (R ² > 0.88). In addition, N remobilization efficiency (NRE) in the top two leaves and top third internode was highly correlated with its corresponding N concentration change (ΔN _mass) with an R ² of 0.90. ΔN _mass of the top first internode (TIN1) explained 78% variation of the whole-plant NRE. This study provides a proof of concept for estimating N concentration and assessing N remobilization using hyperspectral data of individual organs, which offers a non-chemical and low-cost approach to screen germplasms for an optimal NRE in drought-resistance breeding.

Omics resources and omics-enabled approaches for achieving high productivity and improved quality in pea (Pisum sativum L.)

Pea (Pisum sativum L.), a cool-season legume crop grown in more than 85 countries, is the second most important grain legume and one of the major green vegetables in the world. While pea was historically studied as the genetic model leading to the discovery of the laws of genetics, pea research has lagged behind that of other major legumes in the genomics era, due to its large and complex genome. The evolving climate change and growing population have posed grand challenges to the objective of feeding the world, making it essential to invest research efforts to develop multi-omics resources and advanced breeding tools to support fast and continuous development of improved pea varieties. Recently, the pea researchers have achieved key milestones in omics and molecular breeding. The present review provides an overview of the recent important progress including the development of genetic resource databases, high-throughput genotyping assays, reference genome, genes/QTLs responsible for important traits, transcriptomic, proteomic, and phenomic atlases of various tissues under different conditions. These multi-faceted resources have enabled the successful implementation of various markers for monitoring early-generation populations as in marker-assisted backcrossing breeding programs. The emerging new breeding approaches such as CRISPR, speed breeding, and genomic selection are starting to change the paradigm of pea breeding. Collectively, the rich omics resources and omics-enable breeding approaches will enhance genetic gain in pea breeding and accelerate the release of novel pea varieties to meet the elevating demands on productivity and quality.

Targeting Nitrogen Metabolism and Transport Processes to Improve Plant Nitrogen Use Efficiency

In agricultural cropping systems, relatively large amounts of nitrogen (N) are applied for plant growth and development, and to achieve high yields. However, with increasing N application, plant N use efficiency generally decreases, which results in losses of N into the environment and subsequently detrimental consequences for both ecosystems and human health. A strategy for reducing N input and environmental losses while maintaining or increasing plant performance is the development of crops that effectively obtain, distribute, and utilize the available N. Generally, N is acquired from the soil in the inorganic forms of nitrate or ammonium and assimilated in roots or leaves as amino acids. The amino acids may be used within the source organs, but they are also the principal N compounds transported from source to sink in support of metabolism and growth. N uptake, synthesis of amino acids, and their partitioning within sources and toward sinks, as well as N utilization within sinks represent potential bottlenecks in the effective use of N for vegetative and reproductive growth. This review addresses recent discoveries in N metabolism and transport and their relevance for improving N use efficiency under high and low N conditions.

High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response

The development of crop varieties with higher nitrogen use efficiency is crucial for sustainable crop production. Combining high-throughput genotyping and phenotyping will expedite the discovery of novel alleles for breeding crop varieties with higher nitrogen use efficiency. Digital and hyperspectral imaging techniques can efficiently evaluate the growth, biophysical, and biochemical performance of plant populations by quantifying canopy reflectance response. Here, these techniques were used to derive automated phenotyping of indicator biomarkers, biomass and chlorophyll levels, corresponding to different nitrogen levels. A detailed description of digital and hyperspectral imaging and the associated challenges and required considerations are provided, with application to delineate the nitrogen response in wheat. Computational approaches for spectrum calibration and rectification, plant area detection, and derivation of vegetation index analysis are presented. We developed a novel vegetation index with higher precision to estimate chlorophyll levels, underpinned by an image-processing algorithm that effectively removed background spectra. Digital shoot biomass and growth parameters were derived, enabling the efficient phenotyping of wheat plants at the vegetative stage, obviating the need for phenotyping until maturity. Overall, our results suggest value in the integration of high-throughput digital and spectral phenomics for rapid screening of large wheat populations for nitrogen response.

Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm

Genetically diverse plant germplasm stored in ex-situ genebanks are excellent resources for breeding new high yielding and sustainable crop varieties to ensure future food security. Novel alleles have been discovered through routine genebank activities such as seed regeneration and characterization, with subsequent utilization providing significant genetic gains and improvements for the selection of favorable traits, including yield, biotic, and abiotic resistance. Although some genebanks have implemented cost-effective genotyping technologies through advances in DNA technology, the adoption of modern phenotyping is lagging. The introduction of advanced phenotyping technologies in recent decades has provided genebank scientists with time and cost-effective screening tools to obtain valuable phenotypic data for more traits on large germplasm collections during routine activities. The utilization of these phenotyping tools, coupled with high-throughput genotyping, will accelerate the use of genetic resources and fast-track the development of more resilient food crops for the future. In this review, we highlight current digital phenotyping methods that can capture traits during annual seed regeneration to enrich genebank phenotypic datasets. Next, we describe strategies for the collection and use of phenotypic data of specific traits for downstream research using high-throughput phenotyping technology. Finally, we examine the challenges and future perspectives of genebank phenomics.

High-Throughput Field Phenotyping Traits of Grain Yield Formation and Nitrogen Use Efficiency: Optimizing the Selection of Vegetation Indices and Growth Stages

High-throughput, non-invasive phenotyping is promising for evaluating crop nitrogen (N) use efficiency (NUE) and grain yield (GY) formation under field conditions, but its application for genotypes differing in morphology and phenology is still rarely addressed. This study therefore evaluates the spectral estimation of various dry matter (DM) and N traits, related to GY and grain N uptake (Nup) in high-yielding winter wheat breeding lines. From 2015 to 2017, hyperspectral canopy measurements were acquired on 26 measurement dates during vegetative and reproductive growth, and 48 vegetation indices from the visible (VIS), red edge (RE) and near-infrared (NIR) spectrum were tested in linear regression for assessing the influence of measurement stage and index selection. For most traits including GY and grain Nup, measurements at milk ripeness were the most reliable. Coefficients of determination (R²) were generally higher for traits related to maturity than for those related to anthesis canopy status. For GY (R² = 0.26-0.51 in the three years, p < 0.001), and most DM traits, indices related to the water absorption band at 970 nm provided better relationships than the NIR/VIS indices, including the normalized difference vegetation index (NDVI), and the VIS indices. In addition, most indices including RE bands, notably NIR/RE combinations, ranked above the NIR/VIS group. Due to index saturation, the index differentiation was most apparent in the highest-yielding year. For grain Nup and total Nup, the RE/VIS index MSR_705_445 and the simple ratio R780_R740 ranked highest, followed by other RE indices. Among the vegetative organs, R² values were mostly highest and lowest for leaf and spike traits, respectively. For each trait, index and partial least squares regression (PLSR) models were validated across years at milk ripeness, confirming the suitability of optimized index selection. PLSR improved the prediction errors of some traits but not consistently the R² values. The results suggest the use of sensor-based phenotyping as a useful support tool for screening of yield potential and NUE and for identifying contributing plant traits-which, due to their expensive and cumbersome destructive determination are otherwise not readily available. Water band and RE indices should be preferred over NIR/VIS indices for DM traits and N-related traits, respectively, and milk ripeness is suggested as the most reliable stage.

A Robust Automated Image-Based Phenotyping Method for Rapid Vegetative Screening of Wheat Germplasm for Nitrogen Use Efficiency

Nitrogen use efficiency (NUE) in crops is generally low, with more than 60% of applied nitrogen (N) being lost to the environment, which increases production costs and affects ecosystems and human habitats. To overcome these issues, the breeding of crop varieties with improved NUE is needed, requiring efficient phenotyping methods along with molecular and genetic approaches. To develop an effective phenotypic screening method, experiments on wheat varieties under various N levels were conducted in the automated phenotyping platform at Plant Phenomics Victoria, Horsham. The results from the initial experiment showed that two relative N levels-5 mM and 20 mM, designated as low and optimum N, respectively-were ideal to screen a diverse range of wheat germplasm for NUE on the automated imaging phenotyping platform. In the second experiment, estimated plant parameters such as shoot biomass and top-view area, derived from digital images, showed high correlations with phenotypic traits such as shoot biomass and leaf area seven weeks after sowing, indicating that they could be used as surrogate measures of the latter. Plant growth analysis confirmed that the estimated plant parameters from the vegetative linear growth phase determined by the "broken-stick" model could effectively differentiate the performance of wheat varieties for NUE. Based on this study, vegetative phenotypic screens should focus on selecting wheat varieties under low N conditions, which were highly correlated with biomass and grain yield at harvest. Analysis indicated a relationship between controlled and field conditions for the same varieties, suggesting that greenhouse screens could be used to prioritise a higher value germplasm for subsequent field studies. Overall, our results showed that this phenotypic screening method is highly applicable and can be applied for the identification of N-efficient wheat germplasm at the vegetative growth phase.

Temporal Dynamics and the Contribution of Plant Organs in a Phenotypically Diverse Population of High-Yielding Winter Wheat: Evaluating Concepts for Disentangling Yield Formation and Nitrogen Use Efficiency

Enhancing crop nitrogen use efficiency (NUE) is a key requirement for both economic and ecological reasons. Consequently, the genotypic potential for NUE in winter wheat (Triticum aestivum L.) requires further exploitation. Emerging plant phenomic techniques may provide knowledge about traits contributing to grain N uptake (GNup) and grain yield (GY). However, the understanding of beneficial strategies concerning the temporal dynamics of NUE and GY formation and the role of plant organs is still scarce especially under high-yielding European conditions-particularly to discriminate interesting lines in the breeding process. Thus, screening for potentially useful NUE traits in terms of variation, stability, and contribution to target traits will be an essential prerequisite for the development of efficient phenotyping strategies. Therefore, 46 NUE and yield formation traits were assessed in a population of 75 breeding lines over 3 years from 2015 to 2017 in southern Germany, including dry matter (DM), N concentration, and N uptake at anthesis and maturity, both at the aboveground-plant and plant organ levels. Significant genotype and genotypexenvironment effects were observed for all traits. While GY was more related to post-anthesis assimilation, also DM translocation contributed substantially to GY by 31-44%. At maturity, total aboveground DM as opposed to harvest index predominantly determined GY. NUE for GY was better described by N uptake efficiency than by N utilization efficiency. GNup was greatly influenced by variation in GY, but not in grain N concentration, and by total N uptake and not the N harvest index. Post-anthesis N uptake highly depended on the year and was low in comparison to N translocation. However, post-anthesis N uptake was always correlated with GNup, suggesting the need to also consider stay-green strategies under temperate growing conditions. While anthesis traits were only moderately descriptive, GY will be enhanced by increasing total biomass and the N uptake efficiency. Similarly, targeting total N uptake, particularly at post-anthesis, seems to be a rewarding strategy to boost GNup. Thus, high-throughput phenotyping should be targeted rather toward detecting traits related to DM and N acquisition than to the internal allocation and rather to post-anthesis than to anthesis traits.

Temporal and Spectral Optimization of Vegetation Indices for Estimating Grain Nitrogen Uptake and Late-Seasonal Nitrogen Traits in Wheat

Grain nitrogen (N) uptake (GNup) in winter wheat (Triticum aestivum L.) is influenced by multiple components at the plant organ level and by pre- and post-flowering N uptake (Nup). Although spectral proximal high-throughput sensing is promising for field phenotyping, it was rarely evaluated for such N traits. Hence, 48 spectral vegetation indices (SVIs) were evaluated on 10 measurement days for the estimation of 34 N traits in four data subsets, representing the variation generated by six high-yielding cultivars, two N fertilization levels (N), two sowing dates (SD), and two fungicide (F) intensities. Close linear relationships (p < 0.001) were found for GNup both in response to cultivar differences (Cv; R2 = 0.52) and other agronomic treatments (R2 = 0.67 for Cv*F*N, R2 = 0.53 for Cv*SD*N and R2 = 0.57 for the combined treatments), notably during milk ripeness. Especially near-infrared (NIR)/red edge SVIs, such as the NDRE_770_750, outperformed NIR/visible light (VIS) indices. Index rankings and seasonal R2 values were similar for total Nup, while the N harvest index, which expresses the partitioning to the grain, was moderately estimated only during dough ripeness, primarily from indices detecting contrasting senescence between different fungicide intensities. Senescence-sensitive indices, including R787_R765 and TRCARI_OSAVI, performed best for N translocation efficiency and some organ-level N traits at maturity. Even though grain N concentration was best assessed by the red edge inflection point (REIP), the blue/green index (BGI) was more suited for leaf-level N traits at anthesis. When SVIs were quantitatively ranked by data subsets, a better agreement was found for GNup, total Nup, and grain N concentration than for several contributing N traits. The results suggest (i) a good general potential for estimating GNup and total Nup by (ii) red edge indices best used (iii) during milk and early dough ripeness. The estimation of contributing N traits differs according to the agronomic treatment.

Automated phenotyping for early vigour of field pea seedlings in controlled environment by colour imaging technology

Early vigour of seedlings is a beneficial trait of field pea (Pisum sativum L.) that contributes to weed control, water use efficiency and is likely to contribute to yield under certain environments. Although breeding is considered the most effective approach to improve early vigour of field pea, the absence of a robust and high-throughput phenotyping tool to dissect this complex trait is currently a major obstacle of genetic improvement programs to address this issue. To develop this tool, separate trials on 44 genetically diverse field pea genotypes were conducted in the automated plant phenotyping platform of Plant Phenomics Victoria, Horsham and in the field, respectively. High correlation between estimated plant parameters derived from the automated phenotyping platform and important early vigour traits such as shoot biomass, leaf area and plant height indicated that the derived plant parameters can be used to predict vigour traits in field pea seedlings. Plant growth analysis demonstrated that the "broken-stick" model fitted well with the growth pattern of all field pea genotypes and can be used to determine the linear growth phase. Further analysis suggested that the estimated plant parameters collected at the linear growth phase can effectively differentiate early vigour across field pea genotypes. High correlation between normalised difference vegetation indices captured from the field trial and estimated shoot biomass and top-view area confirmed the consistent performance of early vigour field pea genotypes under controlled and field environments. Overall, our results demonstrated that this robust screening tool is highly applicable and will enable breeding programs to rapidly identify early vigour traits and utilise germplasm to contribute to the genetic improvement of field peas.

Passive Reflectance Sensing and Digital Image Analysis Allows for Assessing the Biomass and Nitrogen Status of Wheat in Early and Late Tillering Stages

Proximal remote sensing systems depending on spectral reflectance measurements and image analysis can acquire timely information to make real-time management decisions compared to laborious destructive measurements. There is a need to make nitrogen management decisions at early development stages of cereals when the first top-dressing is made. However, there is insufficient information available about the possibility of detecting differences in the biomass or the nitrogen status of cereals at early development stages and even less comparing its relationship to destructively obtained information. The performance of hyperspectral passive reflectance sensing and digital image analysis was tested in a 2-year study to assess the nitrogen uptake and nitrogen concentration, as well as the biomass fresh and dry weight at early and late tillering stages of wheat from BBCH 19 to 30. Wheat plants were subjected to different levels of nitrogen fertilizer applications and differences in biomass, and the nitrogen status was further created by varying the seeding rate. To analyze the spectral and digital imaging data simple linear regression and partial least squares regression (PLSR) models were used. The green pixel digital analysis, spectral reflectance indices and PLSR of spectral reflectance from 400 to 1000 nm were strongly related to the nitrogen uptake and the biomass fresh and dry weights at individual measurements and for the combined dataset at the early crop development stages. Relationships between green pixels, spectral reflectance indices and PLSR with the biomass and nitrogen status parameters reached coefficients of determination up to 0.95∗∗ through the individual measurements and the combined data set. Reflectance-based spectral sensing compared to digital image analysis allows detecting differences in the biomass and nitrogen status already at early growth stages in the tillering phase. Spectral reflectance indices are probably more robust and can more easily be applied compared to PLSR models. This might pave the way for more informed management decisions and potentially lead to improved nitrogen fertilizer management at early development stages.