Nutritional management and physiological responses of Atriplex nummularia Lindl. on the improvement of phytoextraction in salt-affected soil.

Soil salinity is a significant abiotic stress and poses risks to environmental sustainability. Thus, the improvement of the time for recovering the salt-affect soil is crucial for the phytoextraction process using halophytes plants, especially regarding on nutritional management. We evaluated the responses of Atriplex nummularia Lindl. to nitrogen (N) and phosphorus (P) under different salinity levels. The treatments comprised doses of N (N1 = 80 kg ha-1) and P (P1 = 60 kg ha-1): (1) without N and P (N0P0) (control); (2) with N and without P (N1P0); (3) without N and with P (N0P1); and (4) with N and P (N1P1) and five levels of electrical conductivity from irrigation water: 0.08, 1.7, 4.8, 8.6, and 12.5 dS m-1. The. We evaluated dry biomass of leaves, stems, and roots 93 days after transplantation. We also assessed the leaf and osmotic water potential, the osmotic adjustment, and the nutrient contents (N, P, Na, and K). N application increased 22.3, 17.8, and 32.8% the leaf biomass, stem biomass, and osmotic adjustment, respectively; and consequently, boosts Na extraction in 27.8%. Thus, the time of the phytoextraction process can be improved with N fertilizer at a rate of 80 kg ha-1.

Very few studies have investigated the nutrient dynamics responses in Atriplex species in salt-affected soils; thus, this study represents a novelty. We tested the management of nitrogen (N) and phosphate (P) fertilizers to increase crop yield and optimize the phytoextraction process in salt-affected soils. We believe our results contribute to the improvement of the knowledge of this relevant topic, mainly in terms of the recovery of areas degraded by salinity. There is a paucity of studies associating salinity and nutritional management of soils worldwide.

Heterotic growth of hybrids of Arabidopsis thaliana is enhanced by elevated atmospheric CO2.

PREMISE: With the global atmospheric CO2 concentration on the rise, developing crops that can thrive in elevated CO2 has become paramount. We investigated the potential of hybridization as a strategy for creating crops with improved growth in predicted elevated atmospheric CO2. METHODS: We grew parent accessions and their F1 hybrids of Arabidopsis thaliana in ambient and elevated atmospheric CO2 and analyzed numerous growth traits to assess their productivity and underlying mechanisms. RESULTS: The heterotic increase in total dry mass, relative growth rate and leaf net assimilation rate was significantly greater in elevated CO2 than in ambient CO2. The CO2 response of net assimilation rate was positively correlated with the CO2 response of leaf nitrogen productivity and with that of leaf traits such as leaf size and thickness, suggesting that hybridization-induced changes in leaf traits greatly affected the improved performance in elevated CO2. CONCLUSIONS: Vegetative growth of hybrids seems to be enhanced in elevated CO2 due to improved photosynthetic nitrogen-use efficiency compared with parents. The results suggest that hybrid crops should be well-suited for future conditions, but hybrid weeds may also be more competitive.

Decoupling of nitrogen allocation and energy partitioning in rice after flowering.

Estimation of energy partitioning at leaf scale, such as fluorescence yield (ΦF) and photochemical yield (ΦP), is crucial to tracking vegetation gross primary productivity (GPP) at global scale. Nitrogen is an important participant in the process of light capture, electron transfer, and carboxylation in vegetation photosynthesis. However, the quantitative relationship between leaf nitrogen allocation and leaf energy partitioning remains unexplored. Here, a field experiment was established to explore growth stage variations in energy partitioning and nitrogen allocation at leaf scale using active fluorescence detection and photosynthetic gas exchange method in rice in the subtropical region of China. We observed a strongly positive correlation between the investment proportion of leaf nitrogen in photosynthetic system and ΦF during the vegetative growth stage. There were significant differences in leaf energy partitioning, leaf nitrogen allocation, and the relationship between ΦF and ΦP before and after flowering. Furthermore, flowering weakened the correlation between the investment proportion of leaf nitrogen in photosynthetic system and ΦF. These findings highlight the crucial role of phenological factors in exploring seasonal photosynthetic dynamics and carbon fixation of ecosystems.

Using Machine Learning Methods Combined with Vegetation Indices and Growth Indicators to Predict Seed Yield of Bromus inermis.

Smooth bromegrass (Bromus inermis) is a perennial, high-quality forage grass. However, its seed yield is influenced by agronomic practices, climatic conditions, and the growing year. The rapid and effective prediction of seed yield can assist growers in making informed production decisions and reducing agricultural risks. Our field trial design followed a completely randomized block design with four blocks and three nitrogen levels (0, 100, and 200 kg·N·ha-1) during 2022 and 2023. Data on the remote vegetation index (RVI), the normalized difference vegetation index (NDVI), the leaf nitrogen content (LNC), and the leaf area index (LAI) were collected at heading, anthesis, and milk stages. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) regression models were utilized to predict seed yield. In 2022, the results indicated that nitrogen application provided a sufficiently large range of variation of seed yield (ranging from 45.79 to 379.45 kg ha⁻¹). Correlation analysis showed that the indices of the RVI, the NDVI, the LNC, and the LAI in 2022 presented significant positive correlation with seed yield, and the highest correlation coefficient was observed at the heading stage. The data from 2022 were utilized to formulate a predictive model for seed yield. The results suggested that utilizing data from the heading stage produced the best prediction performance. SVM and RF outperformed MLR in prediction, with RF demonstrating the highest performance (R2 = 0.75, RMSE = 51.93 kg ha-1, MAE = 29.43 kg ha-1, and MAPE = 0.17). Notably, the accuracy of predicting seed yield for the year 2023 using this model had decreased. Feature importance analysis of the RF model revealed that LNC was a crucial indicator for predicting smooth bromegrass seed yield. Further studies with an expanded dataset and integration of weather data are needed to improve the accuracy and generalizability of the model and adaptability for the growing year.

Monitoring of Nitrogen Concentration in Soybean Leaves at Multiple Spatial Vertical Scales Based on Spectral Parameters.

Nitrogen is a fundamental component for building amino acids and proteins, playing a crucial role in the growth and development of plants. Leaf nitrogen concentration (LNC) serves as a key indicator for assessing plant growth and development. Monitoring LNC provides insights into the absorption and utilization of nitrogen from the soil, offering valuable information for rational nutrient management. This, in turn, contributes to optimizing nutrient supply, enhancing crop yields, and minimizing adverse environmental impacts. Efficient and non-destructive estimation of crop LNC is of paramount importance for on-field crop management. Spectral technology, with its advantages of repeatability and high-throughput observations, provides a feasible method for obtaining LNC data. This study explores the responsiveness of spectral parameters to soybean LNC at different vertical scales, aiming to refine nitrogen management in soybeans. This research collected hyperspectral reflectance data and LNC data from different leaf layers of soybeans. Three types of spectral parameters, nitrogen-sensitive empirical spectral indices, randomly combined dual-band spectral indices, and "three-edge" parameters, were calculated. Four optimal spectral index selection strategies were constructed based on the correlation coefficients between the spectral parameters and LNC for each leaf layer. These strategies included empirical spectral index combinations (Combination 1), randomly combined dual-band spectral index combinations (Combination 2), "three-edge" parameter combinations (Combination 3), and a mixed combination (Combination 4). Subsequently, these four combinations were used as input variables to build LNC estimation models for soybeans at different vertical scales using partial least squares regression (PLSR), random forest (RF), and a backpropagation neural network (BPNN). The results demonstrated that the correlation coefficients between the LNC and spectral parameters reached the highest values in the upper soybean leaves, with most parameters showing significant correlations with the LNC (p < 0.05). Notably, the reciprocal difference index (VI6) exhibited the highest correlation with the upper-layer LNC at 0.732, with a wavelength combination of 841 nm and 842 nm. In constructing the LNC estimation models for soybeans at different leaf layers, the accuracy of the models gradually improved with the increasing height of the soybean plants. The upper layer exhibited the best estimation performance, with a validation set coefficient of determination (R2) that was higher by 9.9% to 16.0% compared to other layers. RF demonstrated the highest accuracy in estimating the upper-layer LNC, with a validation set R2 higher by 6.2% to 8.8% compared to other models. The RMSE was lower by 2.1% to 7.0%, and the MRE was lower by 4.7% to 5.6% compared to other models. Among different input combinations, Combination 4 achieved the highest accuracy, with a validation set R2 higher by 2.3% to 13.7%. In conclusion, by employing Combination 4 as the input, the RF model achieved the optimal estimation results for the upper-layer LNC, with a validation set R2 of 0.856, RMSE of 0.551, and MRE of 10.405%. The findings of this study provide technical support for remote sensing monitoring of soybean LNCs at different spatial scales.

Comparative physiological and transcriptome analysis of leaf nitrogen fluxes in stay-green maize during the vegetative stage.

In maize, nitrogen (N) stored in leaves is an important internal source for supporting subsequent growth and development. However, the regulation of N fluxes and photosynthesis and the molecular and genotypic regulations that modify them are less clear in source leaves during the vegetative stage. This knowledge is crucial for improving N use efficiency (NUE). By using 15N labeling and transcriptome methods, an analysis of the physiological and molecular basis of leaf N import and export processes and photosynthetic N use efficiency (PNUE) was conducted in two maize hybrids (XY335 and XY696) with different stay-green characteristics during the vegetative stage. Leaf N import and export in XY696 were 45% and 33% greater than those in XY335. However, the PNUE in XY335 was 17% greater than that in XY696 due to the higher net photosynthetic rate (A) and lower SLN. Correspondingly, the chlorophyll content and photosynthesis-related enzyme (PEPc, PEPck, PPDK) activities increased by 18∼30% in XY335. Transcriptome analysis indicated that the expression levels of several N and carbon metabolism-related genes encoding Rubisco, PEPc, Nir, GS and AS were significantly increased or decreased in XY696 in parallel with enzyme activities. Moreover, there was a large difference in the expression abundance of genes encoding nitrate/nitrite transporters and transmembrane proteins. Our results suggest that two hybrids modulate leaf N fluxes and photosynthesis differently by altering gene expression and enzyme activities. Our study contributes to understanding leaf N fluxes and PNUE regulation and serves as a crucial reference for NUE improvement in maize breeding research.

Optimized leaf storage and photosynthetic nitrogen trade-off promote synergistic increases in photosynthetic rate and photosynthetic nitrogen use efficiency.

A coordinated increase in the photosynthetic rate (A) and photosynthetic nitrogen use efficiency (PNUE) is an effective strategy for improving crop yield and nitrogen (N) utilization efficiency. PNUE tends to decrease with increasing N levels, but there are natural variations. Consequently, leaf functional N partitioning in Brassica napus genotypes under different N rates was measured to explore the optimized N allocation model for synchronously increasing A and PNUE values. The results showed that genotypes whose PNUE increased with increasing N supply (PNUE-I) produced an approximate A value with a relatively low leaf N content, owing to reduced storage N (Nstore ) and close photosynthetic N (Npsn ) content. Partial least squares path modeling showed that A was dominated by the Npsn content, and PNUE was directly influenced by A and Nstore . The A value increased with the Npsn content until the Npsn content exceeded the threshold value. The boundary line of PNUE varied with the Npsn and Nstore proportions, indicating that the optimum Npsn and Nstore proportions were 51.6% and 40.3%, respectively. The Nstore proportion of PNUE-I was closer to the thresholds and benefited from lower increments in Rubisco content and nonprotein form storage N content with improved N supply. Optimized Nstore and Npsn trade-off by regulating increments in Nstore content with increased N supply, thereby promoting coordinated increases in A and PNUE.

Predicting community traits along an alpine grassland transect using field imaging spectroscopy.

Assessing plant community traits is important for understanding how terrestrial ecosystems respond and adapt to global climate change. Field hyperspectral remote sensing is effective for quantitatively estimating vegetation properties in most terrestrial ecosystems, although it remains to be tested in areas with dwarf and sparse vegetation, such as the Tibetan Plateau. We measured canopy reflectance in the Tibetan Plateau using a handheld imaging spectrometer and conducted plant community investigations along an alpine grassland transect. We estimated community structural and functional traits, as well as community function based on a field survey and laboratory analysis using 14 spectral vegetation indices (VIs) derived from hyperspectral images. We quantified the contributions of environmental drivers, VIs, and community traits to community function by structural equation modelling (SEM). Univariate linear regression analysis showed that plant community traits are best predicted by the normalized difference vegetation index, enhanced vegetation index, and simple ratio. Structural equation modelling showed that VIs and community traits positively affected community function, whereas environmental drivers and specific leaf area had the opposite effect. Additionally, VIs integrated with environmental drivers were indirectly linked to community function by characterizing the variations in community structural and functional traits. This study demonstrates that community-level spectral reflectance will help scale plant trait information measured at the leaf level to larger-scale ecological processes. Field imaging spectroscopy represents a promising tool to predict the responses of alpine grassland communities to climate change.

Improving the predictions of leaf photosynthesis during and after short-term heat stress with current rice models.

In response to increasing global warming, extreme heat stress significantly alters photosynthetic production. While numerous studies have investigated the temperature effects on photosynthesis, factors like vapour pressure deficit (VPD), leaf nitrogen, and feedback of sink limitation during and after extreme heat stress remain underexplored. This study assessed photosynthesis calculations in seven rice growth models using observed maximum photosynthetic rate (Pmax ) during and after short-term extreme heat stress in multi-year environment-controlled experiments. Biochemical models (FvCB-type) outperformed light response curve-based models (LRC-type) when incorporating observed leaf nitrogen, photosynthetically active radiation, temperatures, and intercellular CO2 concentration (Ci ) as inputs. Prediction uncertainty during heat stress treatment primarily resulted from variation in temperatures and Ci . Improving FVPD (the slope for the linear effect of VPD on Ci /Ca ) to be temperature-dependent, rather than constant as in original models, significantly improved Ci prediction accuracy under heat stress. Leaf nitrogen response functions led to model variation in leaf photosynthesis predictions after heat stress, which was mitigated by calibrated nitrogen response functions based on active photosynthetic nitrogen. Additionally, accounting for observed differences in carbohydrate accumulation between panicles and stems during grain filling improved the feedback of sink limitation, reducing Ci overestimation under heat stress treatments.

Leaf photosynthetic pigment as a predictor of leaf maximum carboxylation rate in a farmland ecosystem.

The leaf maximum rate of carboxylation (Vcmax) is a key parameter of plant photosynthetic capacity. The accurate estimation of Vcmax is crucial for correctly predicting the carbon flux in the terrestrial carbon cycle. Vcmax is correlated with plant traits including leaf nitrogen (Narea) and leaf photosynthetic pigments. Proxies for leaf chlorophyll (Chlarea) and carotenoid contents (Cararea) need to be explored in different ecosystems. In this study, we evaluated the relationship between leaf maximum rate of carboxylation (scaled to 25°C; Vcmax25) and both leaf Narea and photosynthetic pigments (Chlarea and Cararea) in winter wheat in a farmland ecosystem. Our results showed that Vcmax25 followed the same trends as leaf Chlarea. However, leaf Narea showed smaller dynamic changes before the flowering stage, and there were smaller seasonal variations in leaf Cararea. The correlation between leaf Vcmax25 and leaf Chlarea was the strongest, followed by leaf Cararea and leaf Narea (R2 = 0.69, R2 = 0.47 and R2 = 0.36, respectively). The random forest regression analysis also showed that leaf Chlarea and leaf Cararea were more important than leaf Narea for Vcmax25. The correlation between leaf Vcmax25 and Narea can be weaker since nitrogen allocation is dynamic. The estimation accuracy of the Vcmax25 model based on Narea, Chlarea, and Cararea (R2 = 0.75) was only 0.05 higher than that of the Vcmax25 model based on Chlarea and Cararea (R2 = 0.70). However, the estimation accuracy of the Vcmax25 model based on Chlarea and Cararea (R2 = 0.70) was 0.34 higher than that of the Vcmax25 model based on Narea (R2 = 0.36). These results highlight that leaf photosynthetic pigments can be a predictor for estimating Vcmax25, expanding a new way to estimate spatially continuous Vcmax25 on a regional scale, and to improve model simulation accuracy.

Effect of nitrogen application levels on photosynthetic nitrogen distribution and use efficiency in soybean seedling leaves.

BACKGROUND: Nitrogen nutrition is strongly associated with crop growth and development. Nitrogen application level affects leaf size as well as nitrogen content and distribution, and thus affects photosynthetic nitrogen-use efficiency (PNUE) and yield. In this study, soybean varieties "Jinyuan 55" and "Keshan 1" were treated with nitrogen as urea at: N0, 0 kg hm-2; N0.5, 60 kg hm-2; N1, 120 kg hm-2; and N1.5, 180 kg hm-2. We compared the effect of nitrogen level on plant morphology, biomass, photosynthetic physiology, nitrogen distribution, PNUE, and other soybean seedling leaf characteristics. RESULTS: Maximum carboxylation and electron transfer, net photosynthetic rates, and PNUE of both soybean varieties showed initial significant increases with increasing nitrogen application rate and subsequent stabilization. PNUE, carboxylation system components, electron transport components, and non-photosynthetic system distribution ratios in the photosynthetic system increased and subsequently decreased with increased nitrogen application rate. The nitrogen ratio between carboxylation and electron transport systems was positively correlated with PNUE in both soybean varieties. The nitrogen ratio in light-harvesting and non-photosynthetic systems showed a linear negative correlation with PNUE. CONCLUSIONS: Overall, an appropriate nitrogen level maintained a high photosynthetic nitrogen ratio, whereas low- or high-nitrogen conditions increased or decreased the nitrogen ratio in non-photosynthetic and photosynthetic systems, respectively, thus decreasing the PNUE and photosynthetic capacity. Moreover, increased nitrogen application rate led to a decreased nitrogen ratio in the light-harvesting system and an increased nitrogen ratio of electron transport and carboxylation systems. Our results provide a theoretical basis for optimizing leaf nitrogen distribution, determining optimum nitrogen levels, and promoting soybean seedling growth.

Differential impact of liana colonization on the leaf functional traits of co-occurring deciduous and evergreen trees in a tropical dry scrub forest.

The present study was carried out to analyze the leaf functional traits of co-occurring evergreen and deciduous tree species in a tropical dry scrub forest. This study also intended to check whether the species with contrasting leaf habits differ in their leaf trait plasticity, responding to the canopy infestation by lianas. A total of 11 leaf functional traits were studied for eight tree species with contrasting leaf habits (evergreen and deciduous) and liana-colonization status (with or without liana). In the liana-free environment (L-), evergreen trees had significantly higher leaf tissue density (LTD) and total chlorophyll (CHLt) than the deciduous species. Whereas the deciduous trees had higher specific leaf area (SLA) and mass-based leaf nitrogen concentration (Nmass). The leaf trait-pair relationship in the present study agreed with the well-established global trait-pair relationships (leaf thickness (LT) vs. SLA, Nmass vs. LT, SLA vs. Nmass, and LDMC vs. SLA). There was a significant difference between L+ and L- individuals in leaf area (LA), petiole length (PL), SLA, LDMC, and CHLt in the deciduous species. On the other hand, evergreen species showed marked differences across LT, SLA, LTD, Nmass, and chlorophyll components between L+ and L- individuals of the same species. The results revealed the differential impact of liana colonization on the host trees with contrasting leaf habits. The deciduous species with the acquisitive strategy can have a competitive advantage over evergreen species in the exposed environments (L-), whereas evergreen species with shade-tolerant properties were better acclimated to the shaded environments (L+). Therefore, liana colonization can significantly impact the C-fixation strategies of the host trees by altering their light environment and further, the magnitude of such impact may vary among species of different leaf habits. The result also indicated the patterns of convergence and divergence in some of the leaf functional traits between evergreen and deciduous species explaining the patterns of species co-existence.

Stomatal, mesophyll and biochemical limitations to photosynthesis and their relationship with leaf structure over an elevation gradient in two conifers.

Photosynthetic responses across complex elevational gradients provides insight into fundamental processes driving responses of plant growth and net primary production to environmental change. Gas exchange of needles and twig water potential were measured in two widespread coniferous tree species, Pinus contorta and Picea engelmannii, over an 800-m elevation gradient in southeastern Wyoming, USA. We hypothesized that limitations to photosynthesis imposed by mesophyll conductance (gm) would be greatest at the highest elevation sites due to higher leaf mass per area (LMA) and that estimations of maximum rate of carboxylation (Vcmax) without including gm would obscure elevational patterns of photosynthetic capacity. We found that gm decreased with elevation for P. contorta and remained constant for P. engelmannii, but in general, limitation to photosynthesis by gm was small. Indeed, estimations of Vcmax when including gm were equivalent to those estimated without including gm and no correlation was found between gm and LMA nor between gm and leaf N. Stomatal conductance (gs) and biochemical demand for CO2 were by far the most limiting processes to photosynthesis at all sites along the elevation gradient. Photosynthetic capacity (A) and gs were influenced strongly by differences in soil water availability across the elevation transect, while gm was less responsive to water availability. Based on our analysis, variation in gm plays only a minor role in driving patterns of photosynthesis in P. contorta and P. engelmannii across complex elevational gradients in dry, continental environments of the Rocky Mountains and accurate modeling of photosynthesis, growth and net primary production in these forests may not require detailed estimation of this trait value.

Addressing the Relationship between Leaf Nitrogen and Carbon Isotope Discrimination from the Three Levels of Community, Population and Individual.

The carbon, nitrogen and water cycles of terrestrial ecosystems are important biogeochemical cycles. Addressing the relationship of leaf nitrogen (N) and carbon isotope discrimination (Δ) will enhance the understanding of the links between these three cycles in plant leaves because Δ can reflect time-integrated leaf-level water-use efficiency (WUE) over the period when the leaf material is produced. Previous studies have paid considerable attention to the relationship. However, these studies have not effectively eliminated the interference of environmental factors, inter-species, and inter-individual differences in this relationship, so new research is necessary. To minimize these interferences, the present work explored the relationship at the three levels of community, population, and plant individual. Three patterns of positive, negative and no relationship were observed across communities, populations, and individuals, which is dependent on environmental conditions, species, and plant individuals. The results strongly suggested that there is no general pattern for the relationship between leaf N and Δ. Furthermore, the results indicated that there is often no coupling between leaf-level long-term WUE and leaf N in the metabolic process of carbon, N and water in leaves. The main reason for the lack of this relationship is that most plants do not invest large amounts of nitrogen into photosynthesis. In addition, the present study also observed that, for most plant species, leaf N was not related to photosynthetic rate, and that variations in photosynthetic rates are mainly driven by stomatal conductance.

Within-branch photosynthetic gradients are more related to the coordinated investments of nitrogen and water than leaf mass per area.

Nitrogen (N) and water are key resources for leaf photosynthesis and the growth of whole plants. Within-branch leaves need different amounts of N and water to support their differing photosynthetic capacities according to light exposure. To test this scheme, we measured the within-branch investments of N and water and their effects on photosynthetic traits in two deciduous tree species Paulownia tomentosa and Broussonetia papyrifera. We found that leaf photosynthetic capacity gradually increased from branch bottom to top (i.e. from shade to sun leaves). Concomitantly, stomatal conductance (gs) and leaf N content gradually increased, owing to the symport of water and inorganic mineral from root to leaf. Variation of leaf N content led to large gradients of mesophyll conductance, maximum velocity of Rubisco for carboxylation, maximum electron transport rate and leaf mass per area (LMA). Correlation analysis indicated that the within-branch difference in photosynthetic capacity was mainly related to gs and leaf N content, with a relatively minor contribution of LMA. Furthermore, the simultaneous increases of gs and leaf N content enhanced photosynthetic N use efficiency (PNUE) but hardly affected water use efficiency. Therefore, within-branch adjustment of N and water investments is an important strategy used by plants to optimize the overall photosynthetic carbon gain and PNUE.

Association of maize (Zea mays L.) senescence with water and nitrogen utilization under different drip irrigation systems.

Introduction: Drip irrigation is an efficient water-saving system used to improve crop production worldwide. However, we still lack a comprehensive understanding of maize plant senescence and its association with yield, soil water, and nitrogen (N) utilization under this system. Methods: A 3-year field experiment in the northeast plains of China was used to assess four drip irrigation systems: (1) drip irrigation under plastic film mulch (PI); (2) drip irrigation under biodegradable film mulch (BI); (3) drip irrigation incorporating straw returning (SI); and (4) drip irrigation with the tape buried at a shallow soil depth (OI), and furrow irrigation (FI) was used as the control. The plant senescence characteristic based on the dynamic process of green leaf area (GLA) and live root length density (LRLD) during the reproductive stage, and its correlation with leaf N components, water use efficiency (WUE), and N use efficiency (NUE) was investigated. Results: PI followed by BI achieved the highest integral GLA and LRLD, grain filling rate, and leaf and root senescence rate after silking. Greater yield, WUE, and NUE were positively associated with higher N translocation efficiency of leaf protein responding for photosynthesis, respiration, and structure under PI and BI; whereas, no significant differences were found in yield, WUE, and NUE between PI and BI. SI effectively promoted LRLD in the deeper 20- to 100-cm soil layers, prolonged the GLA and LRLD persistent durations, and reduced the leaf and root senescence rates. The remobilization of non-protein storage N was stimulated by SI, FI, and OI, which made up for the relative inadequacy of leaf N. Discussion: Instead of persistent GLA and LRLD durations and high translocation efficiency of non-protein storage N, fast and large protein N translocation from leaves to grains under PI and BI was found to facilitate maize yield, WUE, and NUE in the sole cropping semi-arid region, and BI was recommend considering that it can reduce plastic pollution.

Identification of photosynthetic parameters for superior yield of two super hybrid rice varieties: A cross-scale study from leaf to canopy.

Enhancing photosynthetic capacity is widely accepted as critical to advancing crop yield. Therefore, identifying photosynthetic parameters positively related to biomass accumulation in elite cultivars is the major focus of current rice research. In this work, we assessed leaf photosynthetic performance, canopy photosynthesis, and yield attributes of super hybrid rice cultivars Y-liangyou 3218 (YLY3218) and Y-liangyou 5867 (YLY5867) at tillering stage and flowering stage, using inbred super rice cultivars Zhendao11(ZD11) and Nanjing 9108 (NJ9108) as control. A diurnal canopy photosynthesis model was applied to estimate the influence of key environmental factors, canopy attributes, and canopy nitrogen status on daily aboveground biomass increment (AMDAY). Results showed that primarily the light-saturated photosynthetic rate at tillering stage contributed to the advancing yield and biomass of super hybrid rice in comparison to inbred super rice, and the light-saturated photosynthetic rate between them was similar at flowering stage. At tillering stage, the higher CO2 diffusion capacity, together with higher biochemical capacity (i.e., maximum carboxylation rate of Rubisco, maximum electron transport rate (J max), and triose phosphate utilization rate) favored leaf photosynthesis of super hybrid rice. Similarly, AMDAY in super hybrid rice was higher than inbred super rice at tillering stage, and comparable at flowering stage partially due to increased canopy nitrogen concentration (SLNave) of inbred super rice. At tillering stage, model simulation revealed that replacement of J max and g m in inbred super rice by super hybrid rice always had a positive effect on AMDAY, and the averaged AMDAY increment was 5.7% and 3.4%, respectively. Simultaneously, the 20% enhancement of total canopy nitrogen concentration through the improvement of SLNave (TNC-SLNave) resulted in the highest AMDAY across cultivars, with an average increase of 11.2%. In conclusion, the advancing yield performance of YLY3218 and YLY5867 was due to the higher J max and g m at tillering stage, and TCN-SLNave is a promising target for future super rice breeding programs.

Differential defensive and nutritional traits among cultivated tomato and its wild relatives shape their interactions with a specialist herbivore.

MAIN CONCLUSION: Cultivated tomato presented lower constitutive volatiles, reduced morphological and chemical defenses, and increased leaf nutritional quality that affect its resistance against the specialist herbivore Tuta absoluta compared to its wild relatives. Plant domestication process has selected desirable agronomic attributes that can both intentionally and unintentionally compromise other important traits, such as plant defense and nutritional value. However, the effect of domestication on defensive and nutritional traits of plant organs not exposed to selection and the consequent interactions with specialist herbivores are only partly known. Here, we hypothesized that the modern cultivated tomato has reduced levels of constitutive defense and increased levels of nutritional value compared with its wild relatives, and such differences affect the preference and performance of the South American tomato pinworm, Tuta absoluta-an insect pest that co-evolved with tomato. To test this hypothesis, we compared plant volatile emissions, leaf defensive (glandular and non-glandular trichome density, and total phenolic content), and nutritional traits (nitrogen content) among the cultivated tomato Solanum lycopersicum and its wild relatives S. pennellii and S. habrochaites. We also determined the attraction and ovipositional preference of female moths and larval performance on cultivated and wild tomatoes. Volatile emissions were qualitatively and quantitatively different among the cultivated and wild species. Glandular trichomes density and total phenolics were lower in S. lycopersicum. In contrast, this species had a greater non-glandular trichome density and leaf nitrogen content. Female moths were more attracted and consistently laid more eggs on the cultivated S. lycopersicum. Larvae fed on S. lycopersicum leaves had a better performance reaching shorter larval developmental times and increasing the pupal weight compared to those fed on wild tomatoes. Overall, our study documents that agronomic selection for increased yields has altered the defensive and nutritional traits in tomato plants, affecting their resistance to T. absoluta.

On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies.

Studying the canopy spectral reflection characteristics of different N-efficient maize varieties and analyzing the relationship between their growth indicators and spectral vegetation indices can help the breeding and application of N-efficient maize varieties. To achieve the optimal management of N fertilizer resources, developing N-efficient maize varieties is necessary. In this research, maize varieties, i.e., the low-N-efficient (Zhengdan 958, ZD958), the high-N efficient (Xianyu 335, XY335), the double-high varieties (Qiule 368, QL368), and the double inefficient-type varieties (Yudan 606 YD606), were used as materials. Results indicate that nitrogen fertilization significantly increased the vegetation indices NDVI, GNDVI, GOSAVI, and RVI of maize varieties with different nitrogen efficiencies. These findings were consistent with the performance of yield, dry matter mass, and leaf nitrogen content and were also found highest under both medium and high nitrogen conditions in the double-high variety QL368. The correlations of dry matter quality, leaf nitrogen content, yield, and vegetation indices (NDVI, GNDVI, RVI, and GOSAVI) at the filling stage of different N-efficient maize varieties were all highly significant and positive. In this relationship, the best effect was found at the filling stages, with correlation coefficients reaching 0.772-0.942, 0.774-0.970, 0754-0.960, and 0.800-0.960. The results showed that the yield, dry matter weight, and leaf nitrogen content of maize varieties with different nitrogen efficiencies increased first and then stabilized with the increase in the nitrogen application level in different periods, and the highest nitrogen application level of maize yield should be between 270 and 360 kg/hm2. At the filling stage, canopy vegetation index of maize varieties with different nitrogen efficiencies was positively correlated with yield, dry matter weight, and leaf nitrogen content, especially GNDVI and GOSAVI on the leaf nitrogen content. It can be used as a means to predict its growth index.

Leaf functional traits have more contributions than climate to the variations of leaf stable carbon isotope of different plant functional types on the eastern Qinghai-Tibetan Plateau.

Elucidating the mechanisms that control the leaf stable carbon isotope values (δ13Cleaf) is the prerequisite for the widespread application of δ13Cleaf. However, the competing effects of physiological and environmental factors on δ13Cleaf variations of the different plant functional types (PFTs) have not been disentangled, and the corresponding mechanisms remain unclear. Based on large-scale δ13Cleaf measurements on the eastern Qinghai-Tibetan Plateau, the relative contributions and regulatory pathways of leaf functional traits (LFTs) and climatic factors to δ13Cleaf variations of the different PFTs were investigated. We found that δ13Cleaf of the different PFTs was correlated with annual mean precipitation negatively, but not a simple linear relationship with annual mean temperature and varied by PFTs. Leaf nitrogen content per unit area and leaf mass per area (correlated with δ13Cleaf positively) had more substantial effects on the δ13Cleaf variations of the different PFTs than other LFTs. The relative contributions of LFTs to the δ13Cleaf variations were greater than that of climatic factors, and the direct and indirect effects of climatic factors on δ13Cleaf variations varied by PFTs. Our findings provide new insights into understanding key drivers of δ13Cleaf variations at the PFT level on a regional scale.