scholarly journals Response of Leaf Area Index, Vegetative and Reproductive Phases of Two Cotton (Gossypium Hirsutum L.) Cultivars at Different Regimes of Irrigation Scheduling

2014 ◽  
Vol 47 (1) ◽  
pp. 31-37
Author(s):  
A. Ali ◽  
T. Khalik ◽  
A. Ahmad
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Irrigation Schedule ◽  
Irrigation Scheduling ◽  
Gossypium Hirsutum L ◽  
Area Index ◽  
Cotton Crop ◽  
Minimum Number ◽  
Irrigation Effect ◽  
Reproductive Phases

ABSTRACT The experiment was conducted to investigate the effects of different regimes of irrigation schedule on various vegetative and reproductive stages of cotton crop. The results showed that irrigation effect was non significant for number of plants m-2, while I4 treatment produced maximum number of monopodial branches 2.18, but statistically it was at par with I3, I5. I4 treatment showed maximum number of sympodial branches (20.43), followed by I3 (18.63). F.H-900 showed statistically higher sympodial branches (18.34) than the F.H-901 (17.08). Maximum number of flowers per plant was formed in the I4 treatment (101.00), followed by I3 (96.31) and 15 (92.23). Significantly higher number of flowers and boll drops was recorded in treatment I1, followed by I2. Flowers and boll drop per plant decrease with increase in irrigations applied to crop. Minimum number of flowers (60.45) and bolls (16.98) dropped per plant were in I4 and 13 (65.0 and 22.72, respectively).

Cubesat Constellations Provide Enhanced Crop Phenology And Digital Agricultural Insights Using Daily Leaf Area Index Retrievals.

2021 ◽  
Author(s):  
Kasper Johansen ◽  
Matteo G. Ziliani ◽  
Rasmus Houborg ◽  
Trenton E. Franz ◽  
Matthew F. McCabe
Keyword(s):  
High Resolution ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Management Practices ◽  
Irrigation Scheduling ◽  
Growth Stages ◽  
Landsat 8 ◽  
Maize Crop ◽  
Area Index ◽  
Agricultural Revolution

Abstract Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precision-level attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multi-satellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications.

scholarly journals Estimation of Corn Leaf Area Index and Ground Cover With Vegetation Indices as a Result of Irrigation Dose

2020 ◽  
Vol 12 (12) ◽  
pp. 234
Author(s):  
C. D. Papanikolaou ◽  
M. A. Sakellariou-Makrantonaki
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Soil Cover ◽  
Vegetation Indices ◽  
Irrigation Treatment ◽  
Irrigation Scheduling ◽  
Growth Stages ◽  
Area Index ◽  
The Third ◽  
Average Maximum

New technologies have been implemented in the agricultural sector to improve crop production and resource management including irrigation water. A three-year long project was conducted to determine whether vegetation indices (VIs) could be used to estimate the leaf area index (LAI) as well as the soil cover fraction (fc) at different crop growth stages in order to use these parameters in a future study concerning irrigation through drones. A low cost unmanned aerial vehicle (drone) and a multispectral camera were used to calculate different VIs of corn (Zea mays). The irrigation scheduling based on the FAO Penman-Monteith equation and the drip irrigation method were used. Three treatments were organized in three replications and the irrigation doses were equal to 100%, 75%, and 50% of the daily evapotranspiration respectively. The Simple and Multiple Regression analysis were used and different equations were formed where the VIs were the predictor variables and the LAI and fc the predicted ones. According to the two-year period data (2018-2019), during 2018 the average maximum LAI in the full irrigation treatment (100%) was 4.1. In the medium irrigation treatment (75%) the LAI was 4.0. The LAI in the third treatment (50%) was 3.9. In 2019, the LAI was 3.7 (100% treatment). In the second treatment, the LAI was 3.3. The LAI in the third treatment was 3.0. According to the results different VIs and prediction equations could be used to estimate the LAI values with high accuracy with the in situ measurements as well as the soil cover fraction.

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
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