Effect of inorganic fertilizer on nodulation and leaf area index ofArachis hypogea and Voandzeia subterranea using indigenous rhizobium

2009 ◽  
Vol 1 (3) ◽  
Author(s):  
OE Ngwu ◽  
IE Osahume ◽  
MAN Ankwe
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Inorganic Fertilizer ◽  
Area Index ◽  
Arachis Hypogea

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 Effect of Combined Application of Organic and Inorganic Fertilizers on Peanut Production

2006 ◽  
Vol 6 (1) ◽  
pp. 31-46
Author(s):  
Rosalia Briones ◽  
◽  
Pedro Pascual ◽  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Inorganic Fertilizer ◽  
Index Number ◽  
Net Income ◽  
Area Index ◽  
Combined Application ◽  
Peanut Production ◽  
Fertilizer Combination ◽  
Organic And Inorganic Fertilizers

Plant height, leaf area index, number of pods per plant, and seed yield were significantly influenced by the application of organic and inorganic fertilizer combination. Plant which received inorganic fertilizer alone (T1) grew taller (97.38 cm), developed larger leaf area index (1.06) and more pods per plant (102.93), and produced higher yield (1.80 t ha1) than those subjected to T0, T3 and T4 treatments. Plots applied solely with inorganic fertilizer generated the highest net income among the different treatments used.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

Hyperspectral characteristics and leaf area index monitoring of rice (Oryza sativa L.) under carbon dioxide concentration enrichment

2021 ◽  
Vol 54 (3) ◽  
pp. 231-243
Author(s):  
Chao Liu ◽  
Zhenghua Hu ◽  
Rui Kong ◽  
Lingfei Yu ◽  
Yuanyuan Wang ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Oryza Sativa ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Oryza Sativa L ◽  
Carbon Dioxide Concentration ◽  
Area Index

scholarly journals Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Agriculture ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 208
Author(s):  
Daniel Queirós da Silva ◽  
André Silva Aguiar ◽  
Filipe Neves dos Santos ◽  
Armando Jorge Sousa ◽  
Danilo Rabino ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Optical Sensors ◽  
Precision Agriculture ◽  
Real Data ◽  
Relevant Information ◽  
Normal Operation ◽  
Area Index ◽  
Sensory Data ◽  
The Right

Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.

Sign in / Sign up

Export Citation Format

Share Document