editedbook
Publication: IIP Proceedings Year: 2022, Month: November Page No: 250-259, ISSN/ISBN: 978-93-95632-65-2, DOI/Link: https://www.rsquarel.org/assets/docupload/rsl20233BF61766DDE5BD0.pdf
CROP SIMULATION MODEL, REMOTE SENSING, GIS AND THEIR INTEGRATION FOR YIELD MONITORING
Area/Stream: Agriculture Engineering & Food Sciences, Authors: Sujan Adak, Nandita Mandal, Khurshid Alam Keywords: Yield monitoring, crop simulation model, remote sensing, GIS, Yield monitoring scenario in India. Book Name /series: Futuristic Trends in Agriculture Engineering & Food Sciences, Volume 2, Book 9, Chapter 18Publication: IIP Proceedings Year: 2022, Month: November Page No: 250-259, ISSN/ISBN: 978-93-95632-65-2, DOI/Link: https://www.rsquarel.org/assets/docupload/rsl20233BF61766DDE5BD0.pdf
Abstract:
Numerous weathers, soil, and management factors that differ greatly in area and time considerably affect crop growth and productivity. Farmers can establish site-specific crop management practices while also learning useful information about their fields and crops through yield monitoring. The reveling of regional and temporal variability in crop yields is one of the key advantages of the yield monitoring system. The yield maps that are the end result of monitoring have a significant influence on the decision-making process. Mechanistic crop growth simulation models are helpful for predicting agricultural yield because they define crop development processes and quantify the impact of weather, soil, and management factors on crop growth and yield. Getting the spatial information on model input parameters, however, is the main obstacle to their application at the regional level. Data from remote sensing (RS), collected repeatedly over agricultural land, is useful for identifying and mapping crops as well as gauging crop vigour. In order to model and track crop growth at the regional level with inputs from remote sensing, crop simulation models (CSM) that have been successful in field-scale applications are being modified in a GIS framework with RS data. As a result, assessments are vulnerable to local soil variability, seasonal weather conditions, and crop management techniques. The leaf area index (LAI), crop phenology, crop distribution, and crop environment can all be learned from the RS data. This data is integrated with CSM in a variety of methods, including direct variable forcing, parameter re-calibration, and the use of simulation-observation discrepancies in a variable for yield monitoring correction.
Cite this: Sujan Adak, Nandita Mandal, Khurshid Alam,"CROP SIMULATION MODEL, REMOTE SENSING, GIS AND THEIR INTEGRATION FOR YIELD MONITORING", Futuristic Trends in Agriculture Engineering & Food Sciences, Volume 2, Book 9, Chapter 18, November, 2022, 250-259, 978-93-95632-65-2, https://www.rsquarel.org/assets/docupload/rsl20233BF61766DDE5BD0.pdf
Views: 4174
Download File
News
