scholarly journals Leaf traits and gas exchange in saplings of native tree species in the Central Amazon

2010 ◽  
Vol 67 (6) ◽  
pp. 624-632 ◽  
Author(s):  
Keila Rego Mendes ◽  
Ricardo Antonio Marenco
Keyword(s):  
Gas Exchange ◽  
Nitrogen Content ◽  
Leaf Area ◽  
Tree Species ◽  
Leaf Traits ◽  
Leaf Nitrogen ◽  
Dry Season ◽  
Leaf Thickness ◽  
Rainfall Regime ◽  
Leaf Nitrogen Content

Global climate models predict changes on the length of the dry season in the Amazon which may affect tree physiology. The aims of this work were to determine the effect of the rainfall regime and fraction of sky visible (FSV) at the forest understory on leaf traits and gas exchange of ten rainforest tree species in the Central Amazon, Brazil. We also examined the relationship between specific leaf area (SLA), leaf thickness (LT), and leaf nitrogen content on photosynthetic parameters. Data were collected in January (rainy season) and August (dry season) of 2008. A diurnal pattern was observed for light saturated photosynthesis (Amax) and stomatal conductance (g s), and irrespective of species, Amax was lower in the dry season. However, no effect of the rainfall regime was observed on g s nor on the photosynthetic capacity (Apot, measured at saturating [CO2]). Apot and leaf thickness increased with FSV, the converse was true for the FSV-SLA relationship. Also, a positive relationship was observed between Apot per unit leaf area and leaf nitrogen content, and between Apot per unit mass and SLA. Although the rainfall regime only slightly affects soil moisture, photosynthetic traits seem to be responsive to rainfall-related environmental factors, which eventually lead to an effect on Amax. Finally, we report that little variation in FSV seems to affect leaf physiology (Apot) and leaf anatomy (leaf thickness).

Leaf Nitrogen Content Indirectly Estimated by Leaf Traits Derived From the PROSPECT Model

2015 ◽  
Vol 8 (6) ◽  
pp. 3172-3182 ◽  
Author(s):  
Zhihui Wang ◽  
Andrew K. Skidmore ◽  
Roshanak Darvishzadeh ◽  
Uta Heiden ◽  
Marco Heurich ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Leaf Traits ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Content

Leaf Photosynthesis and Drought Adaptation in Field-Grown Oilseed Rape (Brassica napus L.)

1996 ◽  
Vol 23 (5) ◽  
pp. 631 ◽  
Author(s):  
CR Jensen ◽  
VO Mogensen ◽  
G Mortensen ◽  
MN Andersen ◽  
JK Schjoerring ◽  
...  
Keyword(s):  
Brassica Napus ◽  
Nitrogen Content ◽  
Leaf Area ◽  
Leaf Nitrogen ◽  
C3 Plants ◽  
Soil Drying ◽  
Leaf Nitrogen Content ◽  
Drought Adaptation ◽  
Brassica Napus L ◽  
Use Efficiency

Photosynthesis and drought adaptation in leaves of field grown rape (Brassica napus L. cv. Global) were investigated in 1992 under temperate climatic conditions in plants grown in lysimeters in a sand and in a loam soil. Light-saturated net photosynthesis (Amax), leaf conductance to water vapour (ge), leaf water potential (Ψe), leaf osmotic potential at full turgor (Ψπ100), specific leaf area (SLA), spectral reflection index (RI) used as a measure of leaf area, and leaf nitrogen content, were determined in irrigated plants and in plants exposed to soil drying. In the early growth stages before flowering, Amax was 35-45 μmol m-2 s-1 and ge was 1-1.5 mol m-2 s-1. Maximum rates of CO2 assimilation greater than 30 μmol m-2 s-1 were obseved for up to 19 days. Stomata partly closed in ageing leaves maintaining a constant CI/Ca ratio. Both photosynthetic nitrogen use efficiency (NUE; Amax per unit of nitrogen) and photosynthetic water use efficiency (WUE; Amax/ge) were high compared with efficiencies of stems and husks and of other C3 plants. In bracts Amax and ge were 10-15 μmol m-2 s-1 and 0.2-0.7 mol mol m-2 s-1, respectively. Both Amax and ge varied linearly with leaf nitrogen content. When soil water was depleted, both Ψπ100 and RI decreased relative to controls on both soil types before any significant decrease in Ψπ occurred. On loam with slow soil drying SLA, ge and Amax decreased before any significant decrease in Ψe occurred. We suggest that these responses might have been triggered by a non-hydraulic signal transmitted from the roots. When water was more depleted, rape maintained positive turgor down to Ψe of -1.6 MPa. Rape had a high TW/DW ratio (9-11) and a 6 limited ability to adjust osmotically, ΔΨe100 being at most 0.3-0.4 MPa.

scholarly journals Sensitivity of Functional Traits to Environmental factors in a Karst Plant Community

2022 ◽  
Author(s):  
Yang Wang ◽  
Limin Zhang ◽  
Jin Chen ◽  
Ling Feng ◽  
Fangbing Li ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Nitrogen Content ◽  
Bulk Density ◽  
Leaf Area ◽  
Plant Community ◽  
Functional Traits ◽  
Specific Leaf Area ◽  
Soil Bulk Density ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Content

In this study, the plant communities at five succession stages (herbage, herbage-shrub, shrub, tree-shrub, and tree) in the Zhenning Karst Plateau area of Guizhou were examined. The changes of plant functional characteristics in different succession stages were analyzed, as was the relationship between functional traits and environmental factors. The main results include the following. (1) During the succes-sion process, plant height, leaf dry matter mass, leaf area, leaf nitrogen content, and leaf phosphorus content gradually increased, whereas leaf thickness and specific leaf area decreased, and leaf C:P ratio and leaf N:P ratios did not change significantly. (2) Soil organic matter, soil total nitrogen, soil total phosphorus, soil C:N, soil C:P, and soil C:K increased at first and then decreased, reaching a peak at the tree-shrub stage. Soil total potassium fluctuated and soil bulk density gradually decreased and reached the lowest value at the tree-shrub stage. (3) Redundancy analysis (RDA) showed that the plant community shifted from a nutri-ent-poor soil environment to a nutrient-rich environment. Soil total phosphorus, soil C:K, soil organic mat-ter, soil C:N, and soil bulk density were the key environmental factors affecting the change of functional traits. (4) Structural equation modeling suggests that that specific leaf area and leaf nitrogen content had more sensitive responses to soil nutrient resources and environmental factors, respectively.

scholarly journals Estimating the Leaf Nitrogen Content with a New Feature Extracted from the Ultra-High Spectral and Spatial Resolution Images in Wheat

2021 ◽  
Vol 13 (4) ◽  
pp. 739
Author(s):  
Jiale Jiang ◽  
Jie Zhu ◽  
Xue Wang ◽  
Tao Cheng ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Precision Agriculture ◽  
Field Experiments ◽  
Mean Squared Error ◽  
Growing Season ◽  
Leaf Nitrogen ◽  
Textural Analysis ◽  
Hyperspectral Images ◽  
Leaf Nitrogen Content ◽  
New Feature

Real-time and accurate monitoring of nitrogen content in crops is crucial for precision agriculture. Proximal sensing is the most common technique for monitoring crop traits, but it is often influenced by soil background and shadow effects. However, few studies have investigated the classification of different components of crop canopy, and the performance of spectral and textural indices from different components on estimating leaf nitrogen content (LNC) of wheat remains unexplored. This study aims to investigate a new feature extracted from near-ground hyperspectral imaging data to estimate precisely the LNC of wheat. In field experiments conducted over two years, we collected hyperspectral images at different rates of nitrogen and planting densities for several varieties of wheat throughout the growing season. We used traditional methods of classification (one unsupervised and one supervised method), spectral analysis (SA), textural analysis (TA), and integrated spectral and textural analysis (S-TA) to classify the images obtained as those of soil, panicles, sunlit leaves (SL), and shadowed leaves (SHL). The results show that the S-TA can provide a reasonable compromise between accuracy and efficiency (overall accuracy = 97.8%, Kappa coefficient = 0.971, and run time = 14 min), so the comparative results from S-TA were used to generate four target objects: the whole image (WI), all leaves (AL), SL, and SHL. Then, those objects were used to determine the relationships between the LNC and three types of indices: spectral indices (SIs), textural indices (TIs), and spectral and textural indices (STIs). All AL-derived indices achieved more stable relationships with the LNC than the WI-, SL-, and SHL-derived indices, and the AL-derived STI was the best index for estimating the LNC in terms of both calibration (Rc2 = 0.78, relative root mean-squared error (RRMSEc) = 13.5%) and validation (Rv2 = 0.83, RRMSEv = 10.9%). It suggests that extracting the spectral and textural features of all leaves from near-ground hyperspectral images can precisely estimate the LNC of wheat throughout the growing season. The workflow is promising for the LNC estimation of other crops and could be helpful for precision agriculture.

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