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Many leaf traits strongly vary with tree size and environmental factors, but the importance of these factors to intraspecific variations of leaf traits in forest trees has rarely been simultaneously evaluated. We measured needle longevity and specific leaf area (SLA) and nitrogen (N) content of every needle age (0‐ to 4‐year old) for 65 individuals with 0.3–100 cm diameter at breast height (DBH) for an evergreen coniferous species, Pinus koraiensis Sieb. et Zucc., in Northeast China. We simultaneously evaluated the effects of tree size (DBH or tree height) and environment factors (light intensity, soil N content and water availability) on the needle longevity, SLA, foliage N content as well as the slopes of regressions of SLA and foliage N content against needle age. All of the studied leaf traits and slopes of regressions of SLA and foliage N content against needle age were significantly related to tree size. Tree height had a greater impact on SLA and area‐based leaf N content (Narea), whereas DBH was more important for needle longevity and mass‐based leaf N content (Nmass). The environment variables, light intensity, soil N content and water availability, were rather minor factors for trait variations compared with tree size. Significant influence of light intensity was found only on needle longevity, and soil N and water availability had no effects on the leaf traits. Our study clearly showed that tree size is an important driver of intraspecific variations in the key leaf traits of P. koraiensis in a natural forest. We also emphasize the importance of DBH or tree height varies depending on leaf traits, suggesting various mechanisms of size effects on the intraspecific variations in leaf traits. We suggest that ecological significance of leaf trait variations needs reconsideration incorporating tree size effect. A free Plain Language Summary can be found within the Supporting Information of this article. A free Plain Language Summary can be found within the Supporting Information of this article.

Many exotic species have little apparent impact on ecosystem processes, whereas others have dramatic consequences for human and ecosystem health. There is growing evidence that invasions foster eutrophication. We need to identify species that are harmful and systems that are vulnerable to anticipate these consequences. Species’ traits may provide the necessary insights. We conducted a global meta-analysis to determine whether plant leaf and litter functional traits, and particularly leaf and litter nitrogen (N) content and carbon: nitrogen (C: N) ratio, explain variation in invasive species’ impacts on soil N cycling. Dissimilarity in leaf and litter traits among invaded and noninvaded plant communities control the magnitude and direction of invasion impacts on N cycling. Invasions that caused the greatest increases in soil inorganic N and mineralization rates had a much greater litter N content and lower litter C: N in the invaded than the reference community. Trait dissimilarities were better predictors than the trait values of invasive species alone. Quantifying baseline community tissue traits, in addition to those of the invasive species, is critical to understanding the impacts of invasion on soil N cycling.

Corn (Zea mays L.) N use is of continued interest due to agronomic performance and environmental issues. This 2‐yr study evaluated era hybrid response to fertilizer nitrogen (FN) rate in a factorial arrangement of one popular hybrid per five decades (1960–2000 eras) and five N rates (0–224 kg N ha−1). An additional hybrid per era was grown at 168 kg N ha−1. Hybrid productivity and nitrogen use efficiency (NUE) increased across the eras, but not between the 1980 and 1990 eras. Grain yield (GY) increased 65% and total plant biomass 43%, however, total plant nitrogen uptake (PNU) increased only 19% and across N rates was only higher for the 2000 era. At the agronomic optimum nitrogen rate (AONR), there was a linear GY increase of 0.13 Mg ha−1 yr−1 and GY N response of 0.091 Mg ha−1 yr−1, indicating considerable genetic gain. There was no trend in AONR across eras. For plant N status measures, SPAD readings decreased and canopy index values increased across eras. All NUE measures indicated significant improvement in NUE. The apparent nitrogen recovery efficiency (NRE) at N rates near the AONR of each era, however, was not highest for the most recent eras. Harvest index (HI), grain nitrogen harvest index (GNHI), and fraction of total PNU accumulated by R1 were the same among eras. The grain nitrogen concentration (GNC), however, was 24% lower for the 2000 compared to the 1960 era. Corn hybrid development across the 50‐yr period improved productivity and NUE, but not the AONR.

Terrestrial ecosystems worldwide are receiving increasing amounts of biologically reactive nitrogen (N) as a consequence of anthropogenic activities. This intended or unintended fertilization can have a wide‐range of impacts on biotic communities and hence on soil respiration. Reduction in below‐ground carbon (C) allocation induced by high N availability has been assumed to be a major mechanism determining the effects of N enrichment on soil respiration. In addition to increasing available N, however, N enrichment causes soil acidification, which may also affect root and microbial activities. The relative importance of increased N availability vs. soil acidification on soil respiration in natural ecosystems experiencing N enrichment is unclear. We conducted a 12‐year N enrichment experiment and a 4‐year complementary acid addition experiment in a semi‐arid Inner Mongolian grassland. We found that N enrichment had contrasting effects on root and microbial respiration. N enrichment significantly increased root biomass, root N content and specific root respiration, thereby promoting root respiration. In contrast, N enrichment significantly suppressed microbial respiration likely by reducing total microbial biomass and changing the microbial community composition. The effect on root activities was due to both soil acidity and increased available N, while the effect on microbes primarily stemmed from soil acidity, which was further confirmed by results from the acid addition experiment. Our results indicate that soil acidification exerts a greater control than soil N availability on soil respiration in grasslands experiencing long‐term N enrichment. These findings suggest that N‐induced soil acidification should be included in predicting terrestrial ecosystem C balance under future N deposition scenarios.

Grain protein content (GPC) is an important indicator of wheat quality. Earlier estimation of wheat GPC based on remote sensing provided effective decision to adapt optimized strategies for grain harvest, which is of great significance for agricultural production. The objectives of this field study are: (i) To assess the ability of spectral vegetation indices (VIs) of Sentinel 2 data to detect the wheat nitrogen (N) attributes related to the grain quality of winter wheat production, and (ii) to examine the accuracy of wheat N status and GPC estimation models based on different VIs and wheat nitrogen parameters across Analytical Spectra Devices (ASD) and Unmanned Aerial Vehicle (UAV) hyper-spectral data-simulated sentinel data and the real Sentinel-2 data. In this study, four nitrogen parameters at the wheat anthesis stage, including plant nitrogen accumulation (PNA), plant nitrogen content (PNC), leaf nitrogen accumulation (LNA), and leaf nitrogen content (LNC), were evaluated for their relationship between spectral parameters and GPC. Then, a multivariate linear regression method was used to establish the wheat nitrogen and GPC estimation model through simulated Sentinel-2A VIs. The coefficients of determination ( R 2 ) of four nitrogen parameter models were all greater than 0.7. The minimum R 2 of the prediction model of wheat GPC constructed by four nitrogen parameters combined with VIs was 0.428 and the highest R 2 was 0.467. The normalized root mean square error (nRMSE) of the four nitrogen estimation models ranged from 26.333% to 29.530% when verified by the ground-measured data collected from the Beijing suburbs, and the corresponding nRMSE for the GPC-predicted models ranged from 17.457% to 52.518%. The accuracy of the estimated model was verified by UAV hyper-spectral data which had resized to different spatial resolution collected from the National Experimental Station for Precision Agriculture. The normalized root mean square error (nRMSE) of the four nitrogen estimation models ranged from 16.9% to 37.8%, and the corresponding nRMSE for the GPC-predicted models ranged from 12.3% to 13.2%. The relevant models were also verified by Sentinel-2A data collected in 2018 while the minimum nRMSE for GPC invert model based on PNA was 7.89% and the maximum nRMSE of the GPC model based on LNC was 12.46% in Renqiu district, Hebei province. The nRMSE for the wheat nitrogen estimation model ranged from 23.200% to 42.790% for LNC and PNC. These data demonstrate that freely available Sentinel-2 imagery can be used as an important data source for wheat nutrition and grain quality monitoring.

This paper evaluates the potential of integrating textural and spectral information from unmanned aerial vehicle (UAV)-based multispectral imagery for improving the quantification of nitrogen (N) status in rice crops. Vegetation indices (VIs), normalized difference texture indices (NDTIs), and their combination were used to estimate four N nutrition parameters leaf nitrogen concentration (LNC), leaf nitrogen accumulation (LNA), plant nitrogen concentration (PNC), and plant nitrogen accumulation (PNA). Results demonstrated that the normalized difference red-edge index (NDRE) performed best in estimating the N nutrition parameters among all the VI candidates. The optimal texture indices had comparable performance in N nutrition parameters estimation as compared to NDRE. Significant improvement for all N nutrition parameters could be obtained by integrating VIs with NDTIs using multiple linear regression. While tested across years and growth stages, the multivariate models also exhibited satisfactory estimation accuracy. For texture analysis, texture metrics calculated in the direction D3 (perpendicular to the row orientation) are recommended for monitoring row-planted crops. These findings indicate that the addition of textural information derived from UAV multispectral imagery could reduce the effects of background materials and saturation and enhance the N signals of rice canopies for the entire season.

Reducing the overapplication of nitrogen fertilizers to potatoes (Solanum tuberosum L.) can reduce production costs and their impact on the environment. One approach to produce these impacts is to reduce overapplications of fertilizers by using the nitrogen nutrition index (NNI = plant nitrogen concentration/critical nitrogen concentration) as a basis for in‐season nitrogen recommendations. The objective of this study was to create a remote sensing–based algorithm to estimate NNI. This study collected hyperspectral data (350–1830 nm) during the potato tuber formation period in 2022 and 2023. The climate regime for the study area was a mid‐temperate semiarid continental monsoon; in our study, three different spectral parameter calculation methods were employed. First, the empirical vegetation index, determined through a fixed two‐band calculation. Second, the optimal vegetation index, computed on a band‐by‐band basis. Lastly, the trilateral spectral approach, wherein the indicators are typically associated with the red edge, blue edge, and green edge. The optimum vegetation index had the highest correlation with NNI. The support vector machine, random forest (RF), and back propagation neural network models were used to create NNI prediction models. All machine learning models effectively estimated NNI, and during validation, the R2 (coefficient of determination) was >0.700. In general, the RF model outperformed the other models and during validation had an R2 of 0.869, a root mean square error of 0.052, and a relative error of 5.504%. This study demonstrates the scalability, simplicity, and cost‐effectiveness of combining hyperspectral technology and machine learning for rapid potato NNI estimation. Core Ideas Hyperspectral assessment efficiently gauges nitrogen levels in growing potatoes. Spectral parameters and machine learning enhance potato nitrogen balance accuracy. The calculated indices at 718 and 760 nm ensure precise nitrogen balance.

Understanding the sources of grain N uptake (Grain N) in maize (Zea mays L.) and especially the trade‐off between reproductive‐stage shoot N remobilization (Remobilized N) and reproductive‐stage whole‐plant N uptake (Reproductive N) is needed to help guide future improvements in yield and N use efficiency (NUE). Therefore, a literature review was performed to investigate the knowledge gap concerning changes over time in Grain N sources and on N partitioning to the grain and stover plant fractions at maturity. The synthesis–analysis was based on 100 reports, which were divided into two time intervals: (i) research conducted from 1940 to 1990—“Old Era”—and (ii) research conducted from 1991 to 2011—“New Era.” The most remarkable results were (i) Grain N concentration was the main parameter that has changed over time, (ii) Reproductive N contributed proportionally more to Grain N for the New Era while Reproductive N and Remobilized N contributed equally to Grain N for the Old Era, (iii) Remobilized N was primarily associated with vegetative‐stage whole‐plant N uptake (Vegetative N), which was constant across eras, although the proportion of the Remobilized N itself seems to be driven by the ear demand, (iv) complex plant regulation processes (source:sink) appeared to influence Reproductive N, and (v) stover N concentration gains mirrored the grain N concentration as the plant N uptake increased at maturity in both eras. This new appreciation for the changes over time may assist directed selection for yield and NUE improvements.

Remote sensing-based mapping of crop nitrogen (N) status is beneficial for precision N management over large geographic regions. Both leaf/canopy level nitrogen content and accumulation are valuable for crop nutrient diagnosis. However, previous studies mainly focused on leaf nitrogen content (LNC) estimation. The effects of growth stages on the modeling accuracy have not been widely discussed. This study aimed to estimate different paddy rice N traits—LNC, plant nitrogen content (PNC), leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA)—from unmanned aerial vehicle (UAV)-based hyperspectral images. Additionally, the effects of the growth stage were evaluated. Univariate regression models on vegetation indices (VIs), the traditional multivariate calibration method, partial least squares regression (PLSR) and modern machine learning (ML) methods, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM), were evaluated both over the whole growing season and in each single growth stage (including the tillering, jointing, booting and heading growth stages). The results indicate that the correlation between the four nitrogen traits and the other three biochemical traits—leaf chlorophyll content, canopy chlorophyll content and aboveground biomass—are affected by the growth stage. Within a single growth stage, the performance of selected VIs is relatively constant. For the full-growth-stage models, the performance of the VI-based models is more diverse. For the full-growth-stage models, the transformed chlorophyll absorption in the reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI) performs best for LNC, PNC and PNA estimation, while the three band vegetation index (TBVITian) performs best for LNA estimation. There are no obvious patterns regarding which method performs the best of the PLSR, ANN, RF and SVM in either the growth-stage-specific or full-growth-stage models. For the growth-stage-specific models, a lower mean relative error (MRE) and higher R2 can be acquired at the tillering and jointing growth stages. The PLSR and ML methods yield obviously better estimation accuracy for the full-growth-stage models than the VI-based models. For the growth-stage-specific models, the performance of VI-based models seems optimal and cannot be obviously surpassed. These results suggest that building linear regression models on VIs for paddy rice nitrogen traits estimation is still a reasonable choice when only a single growth stage is involved. However, when multiple growth stages are involved or missing the phenology information, using PLSR or ML methods is a better option.

Method of N application in winter wheat (Triticum aestivum L.) and its impact on estimated plant N loss has not been extensively evaluated. The effects of the pre‐plant N application method, topdress N application method, and their interactions on grain yield, grain protein concentration (GPC), nitrogen fertilizer recovery use efficiency (NFUE), and gaseous N loss was investigated. The trials were set up in an incomplete factorial within a randomized complete block design and replicated three times for 5 site‐years. Data collection included normalized difference vegetation index (NDVI), grain yield, and forage and grain N concentration. The NDVI before and after 90 growing degree days (GDD) were correlated with final grain yield, grain N uptake, GPC, and NFUE. At Efaw location, NDVI after 90 GDDs accounted for 58% of variation in grain yield and 51% variation in grain N uptake. However, NDVI was found to be a poor indicator of both GPC and NFUE. Grain yield was not affected by the method and timing of N application at Efaw. Alternatively, at Perkins, topdress applications resulted in higher yields. The GPC and NFUE were improved with the topdress applications. Generally, topdress application enhanced GPC and NFUE without decreasing the final grain yield. The difference method used in calculating gaseous N loss did not always reveal similar results, and estimated plant N loss was variable by site‐year, and depended on daily fluctuations in the environment.