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  1. KRISHI Publication and Data Inventory Repository
  2. Natural Resource Management A8
  3. ICAR-Central Coastal Agricultural Research Institute L8
  4. NRM-CCARI-Publication
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Please use this identifier to cite or link to this item: http://krishi.icar.gov.in/jspui/handle/123456789/36087
Title: Comparative evaluation of linear and nonlinear weather-based models for coconut yield prediction in the west coast of India
Other Titles: Not Available
Authors: Bappa Das
Bhakti Nair
Vadivel Arunachalam
K. Viswanatha Reddy
Paramesh Venkatesh
Debashis Chakraborty
Sujeet Desai
ICAR Data Use Licennce: http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf
Author's Affiliated institute: ICAR::Central Coastal Agricultural Research Institute
ICAR::Indian Agricultural Research Institute
Published/ Complete Date: 2020-03-09
Project Code: Not Available
Keywords: Weather
Coconut yield
Prediction model
Artificial neural network
Sparse regression models
Publisher: Springer
Citation: Not Available
Series/Report no.: Not Available;
Abstract/Description: Coconut is a major plantation crop of coastal India. Accurate prediction of its yield is helpful for the farmers, industries and policymakers. Weather has profound impact on coconut fruit setting, and therefore, it greatly affects the yield. Annual coconut yield and monthly weather data for 2000–2015 were compiled for fourteen districts of the west coast of India. Weather indices were generated using monthly cumulative value for rainfall and monthly average value for other parameters like maximum and minimum temperature, relative humidity, wind speed and solar radiation. Different linear models like stepwise multiple linear regression (SMLR), principal component analysis together with SMLR (PCA-SMLR), least absolute shrinkage and selection operator (LASSO) and elastic net (ELNET) with nonlinear models namely artificial neural network (ANN) and PCA-ANN were employed to model the coconut yield using the monthly weather indices as inputs. The model’s performance was evaluated using R2, root mean square error (RMSE) and absolute percentage error (APE). The R2 and RMSE of the models ranged between 0.45–0.99 and 18–3624 nuts ha−1 respectively during calibration while during validation the APE varied between 0.12 and 58.21. The overall average ranking of the models based these performance statistics were in the order of ELNET > LASSO > ANN > SMLR > PCA-SMLR > PCA-ANN. Results indicated that the ELNET model could be used for prediction of coconut yield for the region.
Description: Not Available
Type(s) of content: Journal
Sponsors: ICAR-Central Coastal Agricultural Research Institute
Language: English
Name of Journal: International Journal of Biometeorology
NAAS Rating: 8.68
Volume No.: Not Available
Page Number: Not Available
Name of the Division/Regional Station: Natural Resource Management
Source, DOI or any other URL: 10.1007/s00484-020-01884-2
URI: http://krishi.icar.gov.in/jspui/handle/123456789/36087
Appears in Collections:NRM-CCARI-Publication

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