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Parameterization and Testing of AquaCrop for a South African Bambara Groundnut Landrace
Author(s) -
Mabhaudhi Tafadzwanashe,
Modi Albert T.,
Beletse Yacob G.
Publication year - 2014
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2013.0355
Subject(s) - canopy , agronomy , mathematics , biomass (ecology) , crop , leaf area index , mean squared error , yield (engineering) , field experiment , environmental science , biology , statistics , botany , materials science , metallurgy
The aim of this study was to parameterize and test the generic crop model AquaCrop for a local bambara groundnut [ Vigna subterranea (L.) Verdc] landrace. Such a model should be water driven and assist in the promotion of neglected and underutilized species as possible future crops under water‐limited conditions. AquaCrop was parameterized for a South African bambara groundnut landrace using data from controlled field and rain shelter experiments conducted during two seasons (2010/2011 and 2011/2012) at Pretoria, South Africa. Observed weather, soil physical, and measured crop parameters from optimum experiments conducted during 2010/2011 were used to develop respective climate, soil, and crop files in AquaCrop and to parameterize the model. Model parameterization for bambara groundnut showed a very good fit for canopy cover ( R 2 = 0.94, Willmott’s d index of agreement = 0.99, RMSE = 3.37%) and biomass ( R 2 = 0.96, d index = 0.99, RMSE = 1.29 Mg ha –1 ). The model also predicted final biomass (RMSE = 1.70 Mg ha –1 ) and yield (RMSE = 0.29 Mg ha –1 ) reasonably well. Model testing showed good fit for canopy cover under irrigated ( R 2 = 0.86, d index = 0.96, RMSE = 9.72%) and rainfed field conditions ( R 2 = 0.95, d index = 0.97, RMSE = 6.18%) compared with simulation of results from rain shelter experiments. The model simulated final biomass and yield of bambara groundnut very well under field conditions. The model’s performance under rainfed conditions make it particularly suited for extrapolation to marginal areas of agricultural production in South Africa and the region.