%0 Journal Article
%T Biomass Estimation Models for Cocoa (<i>Theobroma cacao</i>) Plantations in Ghana, West Africa
%A Emmanuel Donkor
%A Stephen Adu-Bredu
%A Edward Matthew Osei Jnr
%A Samuel A. Andam-Akorful
%A Yakubu Mohammed
%J Open Journal of Applied Sciences
%P 1588-1618
%@ 2165-3925
%D 2023
%I Scientific Research Publishing
%R 10.4236/ojapps.2023.139126
%X The role of
cocoa systems for climate change mitigation and adaptation has increased
substantially because of their capability to trap carbon dioxide from the
atmosphere and deposited in the cocoa trees as carbon. Development of
aboveground biomass (AGB) models for cocoa plantations is crucial for accurate
estimation of carbon stocks in the cocoa systems, however, allometric models
for estimating AGB for cocoa plantations remain a challenge for cocoa producing
countries in Sub-Saharan Africa especially Ghana. The aim of this study is to
develop allometric model that can be used for the estimation of AGB for cocoa
plantations in Ghana, as well as West Africa. Destructive sampling was carried
out on 110 cocoa trees obtained from the cocoa rehabilitation exercise for the
development of the allometric models. Diameter at breast height (</span><i><span style=\"font-family:Verdana;\">D</span></i><span style=\"font-family:Verdana;\">), total tree height (</span><i><span style=\"font-family:Verdana;\">H</span></i><span style=\"font-family:Verdana;\">) and wood density (</span><i><span style=\"font-family:Verdana;\">¦Ñ</span></i><span style=\"font-family:Verdana;\">) were used as predictors to develop
seven models. The best model was selected based on coefficient of determination
(</span><i><span style=\"font-family:Verdana;\">R</span></i><sup><span style=\"font-family:Verdana;\">2</span></sup><span style=\"font-family:Verdana;\">), index of agreement (</span><i><span style=\"font-family:Verdana;\">I</span><sub><span style=\"font-family:Verdana;\">A</span></sub></i><span style=\"font-family:Verdana;\">), root mean squared error
(RMSE), bias (E%), mean absolute error (MAE) and corrected akaike information
criterion (AIC</span><sub><span style=\"font-family:Verdana;\">C</span></sub><span style=\"font-family:Verdana;\">) and percentage relative standard error (PRSE) of
the estimated parameters. The selected model, which was the one with the
predictors </span><i><span style=\"font-family:Verdana;\">D</span></i><span style=\"font-family:Verdana;\"> and </span><i><span style=\"font-family:Verdana;\">¦Ñ</span></i><span style=\"font-family:Verdana;\">, was given as; AGB = 0.7217</span><i><span style=\"font-family:Verdana;\">¦Ñ</span></i><span style=\"font-family:Verdana;\">(</span><i><span style=\"font-family:Verdana;\">D</span></i><sup><span style=\"font-family:Verdana;\">2</span></sup><span style=\"font-family:Verdana;\">)</span><sup><span style=\"font-family:Verdana;\">0.921</span></sup><span style=\"font-family:Verdana;\">. It was
compared with the Yuliasmara
%K Carbon Stocks
%K Diameter at Breast Height
%K Wood Density
%K Tree Height
%K Cocoa Landscape
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=128087