Leaf Nitrogen Content Research Articles

Predict first–assemble later versus assemble first–predict later: Revisiting the dilemma for functional biogeography

Abstract Predicting community‐level trait indices, such as community‐weighted mean or functional dispersion, in space or time, is crucial, as they are markers of ecosystem functions, proxies for ecosystem services, and are now integral part of conservation planning. On one hand, since members of a species share similar traits, a natural way is to model species distributions as a function of the environment first (i.e. with species distribution models), and then to reconstruct trait indices for any locality of interest (predict‐first). On the other hand, community‐level trait indices can be seen as the direct result of environmental filtering, and thus their distributions may directly be modelled in response to the environment (assemble‐first). Although relatively different, these two approaches have been used interchangeably in trait‐based ecology with unknown consequences on their usability. Here, using plant community (4463 plots) and trait data (LNC, leaf nitrogen content; PLH, plant height; SLA, specific leaf area, for >800 species) covering the French Alps, we compared the two approaches to predict community mean and functional dispersion indices, accounting or not for abundance (CM, community mean; CWM, community weighted mean; FDis, functional dispersion; uFDis, unweighted functional dispersion). We tested both interpolation versus extrapolation capabilities of the two approaches. To support the empirical findings, we also run the same comparative analysis on simulated community data. While both approaches produced similar and skillful CM/CWM predictions (R2 > 0.63 for CWM PLH) for interpolation, the assemble‐first outperformed the predict‐first approach for extrapolation to unobserved environmental conditions (R2 = 0.60 against 0.54 for CWM PLH). Functional dispersion was generally less well predicted, although the assemble‐first approach again provided better predictions for both interpolation (R2 = 0.31 against 0.27 for FDis PLH) and extrapolation (R2 = 0.28 against 0.18 for FDis PLH). The predict‐first approach systematically overestimated FDis. Results were confirmed by the simulation experiments. We conclude that the direct modelling of community‐level indices provides more robust spatial predictions over space and time and should thus be preferred. This approach also does not suffer from having generally too many species predicted by the predict‐first approach.

Assessing Soil Dynamics and Improving Long-Standing Irrigation Management with Treated Wastewater: A Case Study on Citrus Trees in Palestine

Irrigation with Treated Wastewater (TWW) is a well-known agricultural practice in Palestine. The long-term use of irrigation with TWW, a source of water and nutrients, can affect plant development, soil, and groundwater quality. Consequently, the frequency and the intervals of irrigation events should be adequately scheduled, especially when nutrients (TWW-N) cannot be separated from the water. Achieving good water quality implies its immediate reuse in irrigated agriculture. In contrast, long-term soil and groundwater quality conservation is marked by the complex mechanisms that correlate the soil, water, plant, and atmosphere. Therefore, monitoring and modeling (MMA) are combined to retrieve the soil water and nitrate fluxes and identify a proper irrigation management plan in a case study in Beit Dajan-Palestine, where a schedule adapted to conventional water was applied to a 6-year-old citrus orchard continuously irrigated with TWW. Soil nitrogen concentration and water content data were collected from March to August 2021 to calibrate the Hydrus-1D model under the (1) farmer demand (F) scenario, where irrigation volumes are delivered according to the farmer experience, and to define an optimal irrigation management strategy with TWW according to the (2) model demand (M) scenario, based on the irrigation frequency. The latter respects the allowable thresholds of soil solution electrical conductivity, σe, assuming an average soil salinity profile and estimated leaf nitrogen concentrations tolerance as reference; 2021 was taken as a calibration year to retrieve water and nitrate fluxes for 2019 and 2020. In 2021, the measured soil electrical conductivity, σe, showed no salinity risk with an average value of 1.07 dS m−1 (low salinity < 2 dS m−1) but with a leaf nitrogen deficit. Although an acceptable level of available soil nitrogen was observed (ranging between 10 and 35 mg kg−1, whereas the standard value is 10–40 mg kg−1), critical concentrations were observed in the leaves (below 1%) in scenario (F) compared to scenario (M) (ranging between 1.7 and 1.9%). The latter also showed a decrease in nitrate leaching by 33% compared to the former. Overall, the comparison between the simulated and measured soil variables shows that the 1D-Hydrus model could follow the temporal variation in the monitored data, with some overestimation of the measured data during the simulation period. The simulations demonstrate that by modulating the salt tolerance threshold, the M scenario achieved better results in terms of root water and N uptake despite the stress inevitably experienced by citrus with long-term TWW irrigation. Moreover, the optimum threshold values used to assess the soil quality and citrus response under conventional water irrigation were inadequate for TWW practices. Therefore, MMA could be an alternative strategy to schedule proper TWW irrigation.

Effects of Cadmium Toxicity on Chemical Contents and Leaf Yield of Three Sun-Cured Tobacco Varieties

ABSTRACT Tobacco plants grown in cadmium (Cd)-contaminated soils accumulate high concentration of Cd in leaves and the Cd in leaves can readily be transferred to the human body through smoking of cigarettes. This study was conducted to investigate the effects of four soil Cd concentrations (0, 5, 10, and 20 mg kg−1) on leaf Cd, zinc (Zn), and nitrogen (N) concentrations and nicotine, reducing sugar and phenolic compounds of sun-cured Canik 190/5, Xanthi 81, and Birlik 124 varieties grown in a semi-arid condition of Turkey. Increasing doses of Cd did not significantly decrease leaf yield of tobacco cultivars, while significant (p < .05) increase and decrease were recorded in leaf Cd and Zn concentrations, respectively. The highest leaf Cd concentration was accumulated in Birlik 124 (60.5 mg kg−1), and the lowest in Canik 190/5 (55.35 mg kg−1) variety. Nicotine concentration of Xanthi 81 and Birlik 124 cultivars in the highest Cd dose (Cd20) increased compared to the nicotine in control application, while nicotine concentration of Canik 190/5 cultivars decreased in Cd20 dose. The results revealed that all three tobacco varieties were able to accumulate Cd in their leaves under high soil Cd levels. The effects of Cd on N uptake and nicotine, reducing sugar and phenolic compounds were different among the tobacco varieties. The result may be associated with differences in responses of tobacco cultivars to soil Cd concentration.

Intraspecific variations of leaf hydraulic, economic, and anatomical traits in Cinnamomum camphora along an urban-rural gradient

Urbanization brings numerous benefits to residents, but it also introduces complex, variable, and heterogeneous habitat conditions to urban plants, resulting in an arid and hot urban environment that decreases tree growth and the ecological service capacity of trees. In this study, we evaluated leaf hydraulic, economic, and anatomical traits and their covariations of Cinnamomum camphora along an urban-rural gradient in Hefei, Eastern China. We found that Cinnamomum camphora in urban adopted a conservative hydraulic strategy with low leaf turgor loss point (Tlp), leaf hydraulic conductance (Kleaf), and leaf water potential resulting in 50 % loss of hydraulic conductance (P50), as well as a quick investment-return economic strategy with low unit leaf dry matter content (LMA) and high leaf nitrogen content (Leaf N). P50, Kleaf and LMA were significantly positively correlated with the urban-rural gradient (PC1urban-rural gradient), while Leaf N exhibited a negative correlation with it. The results showed a trade-off between intraspecific safety and efficiency in leaf hydraulic traits along the urban-rural gradient and an intraspecific coordinated variation in leaf hydraulic and economic traits. In addition, based on the analysis of a trait coordination network, it was revealed that leaf mesophyll and stomata were key structures for trait adjustment and coordination. Furthermore, our findings offer a significant theoretical underpinning for the effective management of landscape trees and the strategic planning of urban tree species.

Intraspecific trait variation and adaptability of Stipa krylovii: Insight from a common garden experiment with two soil moisture treatments.

Understanding patterns of intraspecific trait variation can help us understand plant adaptability to environmental changes. To explore the underlying adaptation mechanisms of zonal plant species, we selected seven populations of Stipa krylovii, a dominant species in the Inner Mongolia Steppe of China, and evaluated the effects of phenotypic plasticity and genetic differentiation, the effects of climate variables on population trait differentiation, and traits coordinated patterns under each soil moisture treatment. We selected seeds from seven populations of S. krylovii in the Inner Mongolia Steppe, China, and carried out a soil moisture (2) × population origin (7) common garden experiment at Tianjin City, China, and measured ten plant traits of S. krylovii. General linear analyses were used to analyze how soil moisture and population origin affected each trait variation, Mantel tests were used to analyze population trait differentiation-geographic distance (or climatic difference) relationships, regression analyses were used to evaluate trait-climatic variable relationships, and plant trait networks (PTNs) were used to evaluate traits coordinated patterns. Both soil moisture and population origin showed significant effects on most of traits. Aboveground biomass, root-shoot ratio, leaf width, specific leaf area, and leaf nitrogen (N) content were significantly correlated with climate variables under the control condition. Specific leaf area and leaf N content were significantly correlated with climate variables under the drought condition. By PTNs, the hub trait(s) was plant height under the control condition and were aboveground biomass, root length, and specific leaf area under the drought condition. This study indicates that both phenotypic plasticity and genetic differentiation can significantly affect the adaptability of S. krylovii. In addition, soil moisture treatments show significant effects on trait-climate relationships and traits coordinated patterns. These findings provide new insights into the adaptive mechanisms of zonal species in the semiarid grassland region.

Wood-density has no effect on stomatal control of leaf-level water use efficiency in an Amazonian forest.

Forest disturbances increase the proportion of fast-growing tree species compared to slow-growing ones. To understand their relative capacity for carbon uptake and their vulnerability to climate change, and to represent those differences in Earth system models, it is necessary to characterise the physiological differences in their leaf-level control of water use efficiency and carbon assimilation. We used wood density as a proxy for the fast-slow growth spectrum and tested the assumption that trees with a low wood density (LWD) have a lower water-use efficiency than trees with a high wood density (HWD). We selected 5 LWD tree species and 5 HWD tree species growing in the same location in an Amazonian tropical forest and measured in situ steady-state gas exchange on top-of-canopy leaves with parallel sampling and measurement of leaf mass area and leaf nitrogen content. We found that LWD species invested more nitrogen in photosynthetic capacity than HWD species, had higher photosynthetic rates and higher stomatal conductance. However, contrary to expectations, we showed that the stomatal control of the balance between transpiration and carbon assimilation was similar in LWD and HWD species and that they had the same dark respiration rates.

Wood density and leaf traits independently relate to growth rate of naturally regenerated tree species in Araucaria angustifolia plantations in the Atlantic Forest, Argentina

Tree plantations can facilitate the establishment of native trees, thus providing opportunities for the ecological restoration or rehabilitation of degraded forest lands. Stand variables can influence the establishment of native tree species, but few studies have been carried out to determine how sapling growth response to stand variables varies among functionally different species. We evaluated the effect of stand age, stand basal area, tree density, and time since last logging on stem diameter growth rates, as well as the dependence of such effect on whole plant, stem, and leaf functional traits. We measured the stem diameter increment on 280 individuals of 22 species in permanent plots for a period of 2 years in araucaria ( Araucaria angustifolia (Bertol.) Kuntze) plantations in the Atlantic Forest, NE Argentina. Increasing all-species basal area and plantation age negatively affected the growth rates, and such effect tended to be higher in species with low wood density. Two functional axes independently explained growth rate differences between species in growth rate. Growth rates increase with decreasing wood density, increasing leaf phosphorus and potassium content, and decreasing specific leaf area. Among species that are similar in the traits associated with the first functional axis, plant growth increases with increasing leaf nitrogen content.

Climate change filtered out resource-acquisitive plants in a temperate grassland in Inner Mongolia, China.

Global climate change has led to the decline of species and functional diversity in ecosystems, changing community composition and ecosystem functions. However, we still know little about how species with different resource-use strategies (different types of resource usage and plant growth of plants as indicated by the spectrum of plant economic traits, including acquisitive resource-use strategy and conservative resource-use strategy) would change in response to climate change, and how the changes in the diversity of species with different resource-use strategies may influence community-level productivity. Here, using long-term (1982-2017) observatory data in a temperate grassland in Inner Mongolia, we investigated how climate change had affected the species richness (SR) and functional richness (FRic) for the whole community and for species with different resource-use strategies. Specifically, based on data for four traits representing leaf economics spectrum (leaf carbon concentration, leaf nitrogen concentration, leaf phosphorus concentration, and specific leaf area), we divided 81 plant species appearing in the grassland community into three plant functional types representing resource-acquisitive, medium, and resource-conservative species. We then analyzed the changes in community-level productivity in response to the decline of SR and FRic at the community level and for different resource-use strategies. We found that community-level SR and FRic decreased with drying climate, which was largely driven by the decline of diversity for resource-acquisitive species. However, community-level productivity remained stable because resource-conservative species dominating this grassland were barely affected by climate change. Our study revealed distinctive responses of species with different resource-use strategies to climate change and provided a new approach based on species functional traits for predicting the magnitude and direction of climate change effects on ecosystem functions.

Patterns in leaf traits of woody species and their environmental determinants in a humid karstic forest in southwest China

IntroductionLeaf functional traits constitute a crucial component of plant functionality, providing insights into plants’ adaptability to the environment and their regulatory capacity in complex habitats. The response of leaf traits to environmental factors at the community level has garnered significant attention. Nevertheless, an examination of the environmental factors determining the spatial distribution of leaf traits in the karst region of southwest China remains absent.MethodsIn this study, we established a 25 ha plot within a karst forest and collected leaf samples from 144 woody species. We measured 14 leaf traits, including leaf area (LA), leaf thicknes (LT), specific leaf area (SLA), leaf length to width ratio (LW), leaf tissue density (LTD), leaf carbon concentration (LC), leaf nitrogen concentration (LN), and leaf phosphorus concentration (LP), leaf potassium concentration (LK), leaf calcium concentration (LCa), leaf magnesium Concentration (LMg), leaf carbon to nitrogen ratio (C/N), leaf carbon to phosphorus ratio (C/P), and leaf nitrogen to phosphorus ratio (N/P), to investigate the spatial distribution of community-level leaf traits and the response of the leaf trait community-weighted mean (CWM) to topographic, soil, and spatial factors.ResultsResults showed that the CWM of leaf traits display different spatial patterns, first, the highest CWM values for LT, LTD, C/N, and C/P at hilltops, second, the highest CWM values for LA, SLA, LW, LC, LN, LP, and LK at depressions, and third, the highest CWM values for LCa, LMg, and N/P at slopes. The correlation analysis showed that topographic factors were more correlated with leaf trait CWM than soil factors, with elevation and slope being the strongest correlations. RDA analysis showed that topographic factors explained higher percentage of leaf trait CWM than soil factors, with the highest percentage of 19.96% being explained by elevation among topographic factors. Variance Partitioning Analysis showed that the spatial distribution of leaf traits is predominantly influenced by the combined effects of topography and spatial factors (37%-47% explained), followed by purely spatial factors (24%-36% explained).DiscussionThe results could improve our understanding of community functional traits and their influencing factors in the karst region, which will contribute to a deeper understanding of the mechanisms that shape plant communities.

Prediction of Seedling Oilseed Rape Crop Phenotype by Drone-Derived Multimodal Data

In recent years, unmanned aerial vehicle (UAV) remote sensing systems have advanced rapidly, enabling the effective assessment of crop growth through the processing and integration of multimodal data from diverse sensors mounted on UAVs. UAV-derived multimodal data encompass both multi-source remote sensing data and multi-source non-remote sensing data. This study employs Image Guided Filtering Fusion (GFF) to obtain high-resolution multispectral images (HR-MSs) and selects three vegetation indices (VIs) based on correlation analysis and feature reduction in HR-MS for multi-source sensing data. As a supplement to remote sensing data, multi-source non-remote sensing data incorporate two meteorological conditions: temperature and precipitation. This research aims to establish remote sensing quantitative monitoring models for four crucial growth-physiological indicators during rapeseed (Brassica napus L.) seedling stages, namely, leaf area index (LAI), above ground biomass (AGB), leaf nitrogen content (LNC), and chlorophyll content (SPAD). To validate the monitoring effectiveness of multimodal data, the study constructs four model frameworks based on multimodal data input and employs Support Vector Regression (SVR), Partial Least Squares (PLS), Backpropagation Neural Network (BPNN), and Nonlinear Model Regression (NMR) machine learning models to create winter rapeseed quantitative monitoring models. The findings reveal that the model framework, which integrates multi-source remote sensing data and non-remote sensing data, exhibits the highest average precision (R2 = 0.7454), which is 28%, 14.6%, and 3.7% higher than that of the other three model frameworks, enhancing the model’s robustness by incorporating meteorological data. Furthermore, SVR consistently performs well across various multimodal model frameworks, effectively evaluating the vigor of rapeseed seedlings and providing a valuable reference for rapid, non-destructive monitoring of winter rapeseed.

More effect of ephemeral plant species diversity on aboveground biomass than functional diversity and functional composition

AbstractThe quantification of plant diversity–productivity relationships is essential for accurate productivity assessments. Global change drivers are altering resource availability and biodiversity. However, the extent to which multiple attributes of diversity (species, functions) buffer community productivity in response to the resource availability changes and the potential driver mechanisms of the diversity‐productivity relationship still needs to be the consensus. We investigated the ephemeral plant diversity–productivity relationships of different attributes along a precipitation gradient in the Gurbantunggut Desert, China. To evaluate the potential mechanisms by which diversity and functional composition affect aboveground productivity (niche complementarity and selection effect). Our results showed that (1) the variance of species richness (SR), Rao's quadratic entropy (RaoQ), community‐weighted mean of height (CWMH), and community‐weighted mean of leaf phosphorus concentration (CWMP) was larger among sites (variation between different sites) than subplots (variation between different subplots). Among subplots, the variance of community‐weighted mean of specific leaf area (CWMSLA), community‐weighted mean of leaf carbon concentration (CWMC), and community‐weighted mean of leaf nitrogen concentration (CWMN) was larger than the sites. (2) SR, RaoQ, CWMC, CWMN, CWMH, pH, and mean annual precipitation (MAP) collectively influenced the accumulation of aboveground biomass (AGB). (3) Species diversity and MAP have strongly affected AGB and accounted for 50% and 42%. The study confirms that SR is the optimal predictor of biomass in ephemeral plants. Complementation effects may be the primary mechanism explaining the relationship between biodiversity and productivity in the Gurbantunggut Desert, and thus the effect of plant diversity cannot be downplayed.

Effects of herbivore on seagrass, epiphyte and sediment carbon sequestration in tropical seagrass bed

Herbivores strongly affect the ecological structure and functioning in seagrass bed ecosystems, but may exhibit density-dependent effects on primary producers and carbon sequestration. This study examined the effects of herbivorous snail (Cerithidea rhizophorarum) density on snail intraspecific competition and diet, dominant seagrass (Thalassia hemprichii) and epiphyte growth metrics, and sediment organic carbon (SOC). The growth rates of the herbivorous snail under low density (421 ind m−2) and mid density (842 ind m−2) were almost two times of those at extremely high density (1684 ind m−2), indicating strong intraspecific competition at high density. Herbivorous snails markedly reduced the epiphyte biomass on seagrass leaves. Additionally, the seagrass contribution to herbivorous snail as food source under high density was about 1.5 times of that under low density, while the epiphyte contribution under low density was 3 times of that under high density. A moderate density of herbivorous snails enhanced leaf length, carbon, nitrogen, total phenol and flavonoid contents of seagrasses, as well as surface SOC content and activities of polyphenol oxidase and β-glucosidase. However, high density of herbivorous snails decreased leaf glucose, fructose, detritus carbon, and total phenols contents of seagrasses, as well as surface SOC content and activities of polyphenol oxidase and β-glucosidase. Therefore, the effects of herbivorous snail on seagrass, epiphyte and SOC were density-dependent, and moderate density of herbivorous snail could be beneficial for seagrasses to increase productivity. This provided theoretical guidance for enhancing carbon sink in seagrass bed and its better conservation.

Interactive effect of branch source-sink ratio and leaf aging on photosynthesis in pistachio.

Tree source-sink ratio has a predominant and complex impact on tree performance and can affect multiple physiological processes including vegetative and reproductive growth, water and nutrient use, photosynthesis, and productivity. In this study, we manipulated the branch level source-sink ratio by reduction of photosynthetic activity (partial branch defoliation) or thinning branch fruit load early in the growing season (after fruit set) in pistachio (Pistacia vera) trees. We then characterized the leaf photosynthetic light response curves through leaf aging. In addition, we determined changes in leaf non-structural carbohydrates (NSC) and nitrogen (N) concentrations. In leaves with high source-sink ratios, there was a gradual decrease in maximum net photosynthetic rate (ANmax) over the growing season, while in branches with low source-sink ratios, there was a sharp decline in ANmax in the first two weeks of August. Branches with high-sink showed an up-regulation (increase) in photosynthesis toward the end of July (at 1,500 growing degree days) during the period of rapid kernel growth rate and increased sink strength, with ANmax being about 7 μmol m-1 s-1 higher than in branches with low-sink. In August, low source-sink ratios precipitated leaf senescence, resulting in a drastic ANmax decline, from 25 to 8 μmol m-1 s-1 (70% drop in two weeks). This reduction was associated with the accumulation of NSC in the leaves from 20 to 30 mg g-1. The mechanisms of ANmax reduction differ between the two treatments. Lower photosynthetic rates of 8-10 μmol m-1 s-1 late in the season were associated with lower N levels in high-sink branches, suggesting N remobilization to the kernels. Lower photosynthesis late in the season was associated with lower respiration rates in low-source branches, indicating prioritization of assimilates to storage. These results can facilitate the adaptation of management practices to tree crop load changes in alternate bearing species.

Correlations and path analysis of second-crop corn hybrids for maximum grain yield performance

The aim was to estimate the correlation coefficients and their direct and indirect effects through path analysis between agronomic traits that modulate the grain yield of second-crop corn hybrids. The hybrids SYN7G17 TL, 30F53VYHR, B2433PWU and AG 8700 PRO3 were employed. The following agronomic traits were evaluated: leaf nitrogen content, ear height, ear length, ear diameter, number of rows of grains per ear, number of grains per row, mass of a thousand grains, grain yield and grain protein content. Estimates of phenotypic correlation of all combinations of traits were calculated, and then the development of phenotypic correlations in direct and indirect effects was evaluated through path analysis, where grain yield was considered the main variable and the others were considered explanatory variables. Grain yield correlated strongly and positively with thousand-grain mass (0.83) and ear diameter (0.72). In the path analysis to tested conditions, no significant indirect effect was observed. However, the mass of a thousand grains and the ear diameter had a significant direct effect on the grain yield of corn, indicating that the selection of these characteristics can help to improve the grain yield in lines of corn

Semi-supervised cooperative regression model for small sample estimation of citrus leaf nitrogen content with UAV images

ABSTRACT Nitrogen is an essential nutrient element for the growth of citrus tree. The accurate estimation of leaf nitrogen content (LNC) is important to guarantee fruit quality and yield. The unmanned aerial vehicle (UAV) remote sensing has shown great potential for monitoring the LNC of crops. However, the number of training samples is always limited due to the high cost and time consumption of acquiring LNC samples. Obtaining satisfactory results is difficult for most machine-learning models with insufficient samples. Thus, a semi-supervised regression model is designed in this paper to improve the performance of estimating citrus LNC from UAV images under the condition of small samples. First, the local binary pattern (LBP) operator is employed to fully extract textural features in UAV images. Second, semi-supervised cooperative regression models based on ridge regression (Ridge), support vector regression (SVR), and random forest (RF) are constructed by combining spectral and textural features. Finally, the best learning model is used to realize the accurate limited sample LNC estimation from the available training samples. The experiments confirm that the semi-supervised cooperative regression model has better and promising results than other machine-learning methods under the condition of small samples. The RF-based cooperative regression model (CoRF) performs best among other models, with a determination coefficient (R2) of 0.716 and a root mean square error (RMSE) of 0.620 g·kg−1. The CoRF model also achieves superior performance when combining LBP textural features. The results estimated by the semi-supervised cooperative regression model is capable of providing instructions for the production and management of citrus orchard.

Morphophysiological and yield traits of soybean varieties tolerant of intercropping with maize

Abstract. Permanasari I, Sulistyaningsih E, Kurniasih B, Indradewa D. 2023. Morphophysiological and yield traits of soybean varieties tolerant of intercropping with maize. Biodiversitas 24: 3872-3880. Not all soybean varieties grow optimally when intercropped with maize. Previously, we identified seven varieties (i.e., "Demas 1", Dena 1", "Dena 2", "Derap 1", "Devon 1", "Devon 2", and "Wilis") and two (i.e., "Dega 1" and "Mahameru") as tolerant and intolerant of intercropping based on selection indices and yield. This research aims to determine the morphophysiological and yield traits of tolerant and intolerant soybean groups intercropped with maize. The study used a randomized complete block design with three blocks as replications, and conducted from October 2019 to February 2020. Results showed that light intensity in the intercropping system had decreased by 19.91% relative to that in the monoculture system at 7 weeks after planting. The tolerant group displayed significant gains in seed weight per plant (143.66%), total biomass (50.04%), harvest index (53.33%), total number of pods and seeds per plant (119.39% and 128.86%), leaf nitrogen uptake and content (20.83% and 6.44%), chlorophyll-a:b ratio (7.20%), and stem diameter (7.65%) than the intolerant group. Correlation analysis revealed that leaf nitrogen content showed the highest correlation and significantly contributed to seed weight per plant in the tolerant and intolerant groups. Seed weight in the tolerant group was mainly affected by morphological (total biomass and harvest index), physiological (leaf nitrogen content), and yield components (number of pods), whereas that in the intolerant group was influenced only by physiological parameters (nitrogen content). Nitrogen content was considered suitable as a selection indicator for determining the seed yield of intercropped soybean varieties. Furthermore, using soybean tolerant groups for intercropping may be a solution to increase soybean production in Indonesia.

A spectral decomposition method for estimating the leaf nitrogen status of maize by UAV-based hyperspectral imaging

Leaf nitrogen status plays a crucial role in characterizing maize nutrient activity, which ultimately affects both the photosynthetic efficiency and yield formation of maize. For this reason, hyperspectral imaging technology based on unmanned aerial vehicle (UAV) has emerged as a popular tool for estimating crop phenotypic traits. Canopy structure and leaf nutrition together determine the crop canopy spectrum. Efficient separation of the spectral information sensitive to the leaf nitrogen status from the canopy spectrum is considered of utmost importance for improving the estimation accuracy of maize leaf nitrogen status. Along these lines, the main goal of this work was to develop a canopy spectral decomposition method to reduce the interference of other traits on leaf nitrogen concentration (LNC) and leaf nitrogen density (LND) estimation using UAV-based hyperspectral images. First, the weights of the leaf nitrogen status, aboveground biomass (AGB), and leaf area index (LAI) contributing to the canopy spectrum were calculated by using the entropy weight method. Then, the sensitive bands of LNC and LND before and after spectral decomposition were selected by implementing the successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithm. Finally, the reflectance of all bands and sensitive bands was compared to estimate maize LNC and LND using partial least squares regression (PLSR), Gaussian process regression (GPR), support vector regression (SVR), and random forest regression (RFR). These models were systematically tested using independent samples. From the acquired results, it was demonstrated that the correlation coefficient (r) between LNC and each band increased after spectral decomposition compared to the correlation before spectral decomposition. The r between the band reflectance in the near infrared region and LNC or LND after spectral decomposition was significantly higher than that before spectral decomposition. In addition, the sensitive bands of maize leaf nitrogen status after spectral decomposition were around 470 nm, 538 nm, 638 nm, 682 nm, 710 nm, 734 nm, and 830 nm. Regardless of using the reflectance of all bands or sensitive bands, the four estimation models of LNC and LND after spectral decomposition performed better than those before spectral decomposition. The estimation models of LNC and LND constructed by CARS-SVR can successfully reproduce the estimation accuracies of the models constructed by using all-bands-SVR, with R2 on the testing set of 0.68. Theresultshighlight that spectral decomposition is an effective method to significantly improve the estimation accuracy of maize leaf nitrogen status using UAV hyperspectral images, thus effectively reducing the interference of canopy structure traits (AGB and LAI).

Aphid infestations reduce monarch butterfly colonization, herbivory, and growth on ornamental milkweed.

Anthropogenic disturbance is driving global biodiversity loss, including the monarch butterfly (Danaus plexippus), a dietary specialist of milkweed. In response, ornamental milkweed plantings are increasingly common in urbanized landscapes, and recent evidence indicates they have conservation value for monarch butterflies. Unfortunately, sap-feeding insect herbivores, including the oleander aphid (Aphis nerii), frequently reach high densities on plants in nursery settings and urbanized landscapes. Aphid-infested milkweed may inhibit monarch conservation efforts by reducing host plant quality and inducing plant defenses. To test this, we evaluated the effects of oleander aphid infestation on monarch oviposition, larval performance, and plant traits using tropical milkweed (Asclepias curassavica), the most common commercially available milkweed species in the southern U.S. We quantified monarch oviposition preference, larval herbivory, larval weight, and plant characteristics on aphid-free and aphid-infested milkweed. Monarch butterflies deposited three times more eggs on aphid-free versus aphid-infested milkweed. Similarly, larvae fed aphid-free milkweed consumed and weighed twice as much as larvae fed aphid-infested milkweed. Aphid-free milkweed had higher total dry leaf biomass and nitrogen content than aphid-infested milkweed. Our results indicate that oleander aphid infestations can have indirect negative impacts on urban monarch conservation efforts and highlight the need for effective Lepidoptera-friendly integrated pest management tactics for ornamental plants.

Monitoring leaf nitrogen content in rice based on information fusion of multi-sensor imagery from UAV

Timely and accurately monitoring leaf nitrogen content (LNC) is essential for evaluating crop nutrition status. Currently, Unmanned Aerial Vehicles (UAV) imagery is becoming a potentially powerful tool of assessing crop nitrogen status in fields, but most of crop nitrogen estimates based on UAV remote sensing usually use single type imagery, the fusion information from different types of imagery has rarely been considered. In this study, the fusion images were firstly made from the simultaneously acquired digital RGB and multi-spectral images from UAV at three growth stages of rice, and then couple the selecting methods of optimal features with machine learning algorithms for the fusion images to estimate LNC in rice. Results showed that the combination with different types of features could improve the models’ accuracy effectively, the combined inputs with bands, vegetation indices (VIs) and Grey Level Co-occurrence Matrices (GLCMs) have the better performance. The LNC estimation of using fusion images was improved more obviously than multispectral those, and there was the best estimation at jointing stage based on Lasso Regression (LR), with R2 of 0.66 and RMSE of 11.96%. Gaussian Process Regression (GPR) algorithm used in combination with one feature-screening method of Minimum Redundancy Maximum Correlation (mRMR) for the fusion images, showed the better improvement to LNC estimation, with R2 of 0.68 and RMSE of 11.45%. It indicates that the information fusion from UAV multi-sensor imagery can significantly improve crop LNC estimates and the combination with multiple types of features also has a great potential for evaluating LNC in crops.

Plant functional traits and biodiversity can reveal the response of ecosystem functions to grazing

Plant functional traits can elucidate the response of plant communities and ecosystems to biotic and abiotic disturbances. However, whether livestock consume more aboveground biomass (AGB) in communities dominated by species with ‘acquisitive’ traits or in communities where biodiversity is high is not well known. Here, we measured 22 functional traits of the grazing communities and control communities in a Mongolian Plateau desert steppe. The effects of grazing on AGB, CWM traits, species diversity, and functional diversity (FD) were analysed, furthermore, we estimated the grazing impact by using the log response ratio (LRR, an increasing value shows a higher grazing impact) and investigated the correlations between the LRR, plant growth, and community-weighted mean (CWM) traits and diversity indices. We found that grazing significantly increased the CWM dry matter content and carbon-to‑nitrogen ratio and decreased the CWM height, specific leaf area (SLA), and nitrogen and phosphorus contents. The AGB decreased, while species diversity and FD increased under grazing treatments. Additionally, we found that plant traits and biodiversity could predict the response of AGB to grazing, the LRR was higher in patches dominated by species with ‘acquisitive’ foliage and in patches with higher biodiversity; in these patches, plant growth was lower. In the study area, the response of CWM traits to grazing suggests an avoidance strategy, which may be more conducive for adapting to low resource utilization environments. Also, the relationship between the CWM traits and the LRR indicated that the effect of grazing on AGB was mainly related to the selective foraging of herbivores. In addition, patches preferred by livestock may not recover quickly, leading to slow growth and thus reduced biomass under grazing treatments after prolonged grazing.