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.

Influence of different organic manures on growth, yield and mineral nutrient accumulation in lettuce (Lactuca sativa L.)

2021 ◽  
Vol 30 (2) ◽  
pp. 159-168
Author(s):  
Shabnur Chowdhury ◽  
MK Rahman
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Lactuca Sativa ◽  
Dry Weight ◽  
Organic Manure ◽  
Leaf Number ◽  
Nutrient Accumulation ◽  
Area Index ◽  
Organic Manures ◽  
Lactuca Sativa L

Effects of organic manures on growth and yield of lettuce (Lactuca sativa L.) and nutrient accumulation in its leaves was examined. The experiment was conducted in a completely randomized design (CRD) replicated thrice with ten treatments involving nine organic manures and a control treatment. Growth parameters viz. plant height, leaf number, leaf length, leaf area, leaf area index and fresh and dry weight of leaf, stem and root were assessed. The highest height (23.69 cm), longest leaf (32.18cm), leaf area (5883.43cm2), leaf area index (6.434), fresh weight (85.41 g) and dry weight (42.73 g) were found in Payel organic manure. The maximum leaf number (27) was recorded in Approshika organic manure. The maximum content of nitrogen (6.12%), phosphorus (1.83%), potassium (4.11%) and Sulphur (1.69%) were observed in Payel organic manure. The best growth performance and nutrient accumulation was observed in Payel organic manure. Dhaka Univ. J. Biol. Sci. 30(2): 159-168, 2021 (July)

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 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 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.

scholarly journals Relations Between Leaf Area Index and Growth Characteristics of Florunner, Southern Runner, and Sunrunner Peanut1

1991 ◽  
Vol 18 (1) ◽  
pp. 30-37 ◽  
Author(s):  
David P. Davis ◽  
Timothy P. Mack
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Fungal Pathogens ◽  
Dry Weight ◽  
Growth Characteristics ◽  
Vegetative Stage ◽  
Height Range ◽  
Area Index ◽  
Number Of Leaves ◽  
Leaf Dry Weight

Abstract Growth characteristics of three commonly planted peanut cultivars were measured during the 1988 and 1989 growing seasons at the Wiregrass Substation in Headland, Ala., to develop equations for predicting leaf area index (LAI) from other growth varibales. These equations were needed to allow rapid estimation of leaf area loss from foliar-feeding insects or foliar-fungal pathogens. Conventionally planted and tilled fields of Florunner, Sunrunner and Southern Runner peanut (Arachis hypogaea L.) were sampled for plant vegetative stage, reproductive stage, height, number of leaves, leaf area, leaf dry weight, number of pods, pod dry weight, stem dry weight, and stand density. Most growth characteristics increased linearly (p<0.05) with time in both years. LAI was significantly correlated (P<0.05) with most growth variables for each cultivar. Linear regression was used to create equations for prediction of LAI from leaf dry weight (range of R2 = 0.93 to 0.97) and number of leaves (range of R2 = 0.74 to 0.95) for each cultivar, and all cultivars combined. Equations were also developed to predict LAI from plant height (range of R2 = 0.85 to 0.96) and plant vegetative stage (range of R2 = 0.81 to 0.83). These equations should be useful to those who wish to estimate LAI from other growth variables.

Sign in / Sign up

Export Citation Format

Share Document