scholarly journals The effect of arid conditions on productivity and elements of photosynthetic activity of winter soft wheat

2021 ◽  
pp. 74-77
Author(s):  
I. A. Lobunskaya ◽  
E. V. Ionova ◽  
V. A. Likhovidova
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Vegetation Period ◽  
Wheat Varieties ◽  
Area Index ◽  
Arid Conditions ◽  
Chlorophyll Pigment ◽  
Study Results ◽  
Moisture Supply

The current paper has presented the estimating results of the effect of vegetation conditions on a leaf area index, preservation of chlorophyll pigment and productivity of the winter common wheat samples developed by the ARC “Donskoy”. The study was conducted in the laboratory of plant physiology in 2017-2020. There has been used the following methodology: the leaf areas were determined by the Nichiporovich's method (1955), the leaf area index during the periods of ear formation and flowering was estimated according to S.A. Tarasenko (2015). The chlorophyll content in the leaves of winter wheat varieties was assessed by the Shmatko's method (1976). The study results have identified that the leaf area indices and the chlorophyll content in leaves during the vegetation period changed according to the drought resistance degree of plants and depended mainly on the root moisture supply and on the studied genotype. In the conditions of insufficient moisture supply the varieties Krasa Dona, Asket, Etyud and Volny Don formed the maximum values of a leaf area index and preservation of chlorophyll pigment.

Relationship between the physiological properties and yield of winter wheat varieties on chernozem soil

2013 ◽  
Vol 61 (4) ◽  
pp. 279-292
Author(s):  
É. Szabó
Keyword(s):  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Early Period ◽  
Grain Filling ◽  
Chernozem Soil ◽  
Wheat Varieties ◽  
Area Index ◽  
Growing Seasons

The relationship between the yield, chlorophyll content and leaf area index of five winter wheat genotypes was investigated in two different growing seasons on chernozem soil. The results suggest that the genotype and the nutrient supply had a considerable influence on both the yield and the physiological traits, while the growing season modified the parameters in a significant manner. The results proved that the chlorophyll content and leaf area index had a direct influence on the yield; varieties developing larger leaf area and leaf chlorophyll content had higher yields even in different seasons, but the yield was significantly influenced by the decline in the chlorophyll content after flowering. It could be concluded that studying the chlorophyll content and leaf area values simultaneously during the more important phenological phases (especially from flowering to the early period of grain-filling) makes it possible to predict the yield from the trends.

Estimation of leaf area index and leaf chlorophyll content in Sporobolus densiflorus using hyperspectral measurements and PROSAIL model simulations

2020 ◽  
Vol 42 (4) ◽  
pp. 1181-1200
Author(s):  
Estefanía Piegari ◽  
Juan I. Gossn ◽  
Francisco Grings ◽  
Verónica Barraza Bernadas ◽  
Ángela B. Juárez ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Area Index ◽  
Model Simulations ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll

scholarly journals Seasonal Net Carbon Exchange in Rotation Crops in the Temperate Climate of Central Lithuania

2019 ◽  
Vol 11 (7) ◽  
pp. 1966 ◽  
Author(s):  
Ligita Baležentienė ◽  
Ovidijus Mikša ◽  
Tomas Baležentis ◽  
Dalia Streimikiene
Keyword(s):  
Climate Change ◽  
Exchange Rate ◽  
Soil Respiration ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Co2 Emissions ◽  
Vegetation Period ◽  
Temperate Climate ◽  
Carbon Exchange ◽  
Area Index

Intelligent agricultural solutions require data on the environmental impacts of agriculture. In order for operationalize decision-making for sustainable agriculture, one needs to establish the corresponding datasets and protocols. Increasing anthropogenic CO2 emissions into the atmosphere force the choice of growing crops aimed at mitigating climate change. For this reason, investigations of seasonal carbon exchange were carried out in 2013–2016 at the Training Farm of the Vytautas Magnus University (former Aleksandras Stulginskis University), Lithuania. This paper compares the carbon exchange rate for different crops, viz., maize, ley, winter wheat, spring rapeseed and barley under conventional farming. This study focuses on the carbon exchange rate. We measure the emitted and absorbed CO2 fluxes by applying the closed chamber method. The biomass measurement and leaf area index (LAI) calculations at different plant growth stages are used to evaluate carbon exchange in different agroecosystems. The differences in photosynthetically assimilated CO2 rates were significantly impacted by the leaf area index (p = 0.04) during the plant vegetation period. The significantly (p = 0.02–0.05) strong correlation (r = 0.6–0.7) exists between soil respiration and LAI. Soil respiration composed only 21% of the agroecosystem carbon exchange. Plant respiration ranged between 0.034 and 3.613 µmol m−2 s−1 during the vegetation period composed of a negligible ratio (mean 16%) of carbon exchange. Generally, respiration emissions were obviously recovered by the gross primary production (GPP) of crops. Therefore, the ecosystems were acting as an atmospheric CO2 sink. Barley accumulated the lowest mean GPP 12.77 µmol m−2 s−1. The highest mean GPP was determined for ley (14.28 µmol m−2 s−1) and maize (15.68 µmol m−2 s−1) due to the biggest LAI and particular bio-characteristics. Due to the highest NEP, the ley (12.66 µmol m−2 s−1) and maize (12.76 µmol m−2 s−1) agroecosystems sank the highest C from the atmosphere and, thus, they might be considered the most sustainable items between crops. Consequently, the appropriate choice of crops and their area in crop rotations may reduce CO2 emissions and their impact on the environment and climate change.

scholarly journals Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)

2015 ◽  
Vol 159 ◽  
pp. 203-221 ◽  
Author(s):  
Rasmus Houborg ◽  
Matthew McCabe ◽  
Alessandro Cescatti ◽  
Feng Gao ◽  
Mitchell Schull ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Landsat Data ◽  
Area Index ◽  
Model Inversion ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll ◽  
Regularized Model

Retrieval of leaf chlorophyll content in field crops using narrow-band indices: effects of leaf area index and leaf mean tilt angle

2015 ◽  
Vol 36 (24) ◽  
pp. 6031-6055 ◽  
Author(s):  
Xiaochen Zou ◽  
Rocío Hernández-Clemente ◽  
Priit Tammeorg ◽  
Clara Lizarazo Torres ◽  
Frederick L. Stoddard ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Tilt Angle ◽  
Narrow Band ◽  
Area Index ◽  
Leaf Chlorophyll Content ◽  
Field Crops ◽  
Leaf Chlorophyll

scholarly journals Effect of Organic Manures and Inorganic Fertilizer on the Leaf Characters of Banana

2021 ◽  
pp. 91-96
Author(s):  
Jaiz Isfaqure Rahman ◽  
D. N. Hazarika ◽  
D. Bhattacharjee
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Research Work ◽  
Inorganic Fertilizer ◽  
Microbial Consortia ◽  
Vegetative Stage ◽  
Area Index ◽  
Organic Manures ◽  
Content Index

A field experiment was carried out at Instructional cum Research Farm, Department of Horticulture, Biswanath College of Agriculture, AAU, Biswanath Chariali to study the effects of organic manures and inorganic fertilizer on leaf characters of banana cv. Amritsagar (AAA) during 2016-2017. The research work was carried out with the treatments as follows T1: FYM (Farm Yard Manure) + Microbial Consortia, T2: Enriched Compost, T3: Vermicompost, T4: Microbial Consortia, T0: RDF (FYM + NPK). Healthy suckers were planted in each plot with spacing of 2.1m x 2.1m on 27th May 2016. The treatments T1, T2, T3 and T4 were laid out in certified organic block in RBD with 5 replications while the treatment T0 was laid out outside the organic block with five replications. In the organics, T1 recorded the highest number of functional leaves (7.97, 12.46 and 5.37) in vegetative stage, shooting stage and harvesting stage respectively. Highest leaf area of 2.69 m2 at vegetative stage and 11.17 m2 at shooting stage were recorded in T1 while lowest leaf area of 2.41 m2 at vegetative stage and 8.89 m2 at shooting stage were recorded in T4. Leaf area index was highest in T1. Chlorophyll content index in both vegetative stage (45.29) and shooting stage (65.56) was also highest in T1. Comparing the leaf characters (number of functional leaves, leaf area, leaf area index and chlorophyll content index) under organic treatments with that of T0 treated plants, it was found that plants treated with inorganic fertilizer had more number of functional leaves and better leaf character than that of the plants treated with organics.

scholarly journals A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle

2021 ◽  
Vol 14 (1) ◽  
pp. 98
Author(s):  
Quanjun Jiao ◽  
Qi Sun ◽  
Bing Zhang ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
...  
Keyword(s):  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Leaf Angle ◽  
Transfer Model ◽  
Growth Monitoring ◽  
Random Forest Algorithm ◽  
Area Index ◽  
Important Indicator

Canopy chlorophyll content (CCC) is an important indicator for crop-growth monitoring and crop productivity estimation. The hybrid method, involving the PROSAIL radiative transfer model and machine learning algorithms, has been widely applied for crop CCC retrieval. However, PROSAIL’s homogeneous canopy hypothesis limits the ability to use the PROSAIL-based CCC estimation across different crops with a row structure. In addition to leaf area index (LAI), average leaf angle (ALA) is the most important canopy structure factor in the PROSAIL model. Under the same LAI, adjustment of the ALA can make a PROSAIL simulation obtain the same canopy gap as the heterogeneous canopy at a specific observation angle. Therefore, parameterization of an adjusted ALA (ALAadj) is an optimal choice to make the PROSAIL model suitable for specific row-planted crops. This paper attempted to improve PROSAIL-based CCC retrieval for different crops, using a random forest algorithm, by introducing the prior knowledge of crop-specific ALAadj. Based on the field reflectance spectrum at nadir, leaf area index, and leaf chlorophyll content, parameterization of the ALAadj in the PROSAIL model for wheat and soybean was carried out. An algorithm integrating the random forest and PROSAIL simulations with prior ALAadj information was developed for wheat and soybean CCC retrieval. Ground-measured CCC measurements were used to validate the CCC retrieved from canopy spectra. The results showed that the ALAadj values (62 degrees for wheat; 45 degrees for soybean) that were parameterized for the PROSAIL model demonstrated good discrimination between the two crops. The proposed algorithm improved the CCC retrieval accuracy for wheat and soybean, regardless of whether continuous visible to near-infrared spectra with 50 bands (RMSE from 39.9 to 32.9 μg cm−2; R2 from 0.67 to 0.76) or discrete spectra with 13 bands (RMSE from 43.9 to 33.7 μg cm−2; R2 from 0.63 to 0.74) and nine bands (RMSE from 45.1 to 37.0 μg cm−2; R2 from 0.61 to 0.71) were used. The proposed hybrid algorithm, based on PROSAIL simulations with ALAadj, has the potential for satellite-based CCC estimation across different crop types, and it also has a good reference value for the retrieval of other crop parameters.

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