Karunaratne, Asha Sajeewani
Modelling the response of Bambara groundnut (Vigna subterranea (L.) Verdc) for abiotic stress.
PhD thesis, University of Nottingham.
Bambara groundnut (Vigna subterranea (L.) Verdc) is an indigenous legume that is still cultivated in subsistence agricultural systems in sub-Saharan Africa, despite the lack of any major research effort until recently. The crop is cultivated from local landraces as there are no true varieties of the species bred for specific traits. The variable and hostile climates in the region mean that annual yields of most rainfed crops including bambara groundnut are far below their agronomic or genetic potential. The lack of quantitative information on the eco-physiological responses of the crop to various abiotic factors has resulted in poor decision making on crop management practices especially in relation to sowing date and the selection of appropriate landraces for different locations. Modelling of bambara groundnut was initiated previously but there is still insufficient understanding of how growth and developmental processes can be simulated under abiotic stress and different photoperiods. The aim of this study was to develop a crop simulation model for bambara groundnut to predict growth, development and yield under drought, heat and cold stress and different daylengths.
The present model (BAMGRO) is an adaptation of the established CROPGRO and previous bambara groundnut models; BAMnut and BAMFOOD project model. It uses climate data, landrace specific parameters and soil characteristics and runs on a daily time-step to determine the canopy development, biomass production and yield of a landrace in a specific environment. The parameters of the model have been determined with glasshouses data (TCRU, University of Nottingham) and published information. BAMGRO is capable of describing differences between landraces, and the influence of drought, temperature and photoperiod using a simplified approach.
The present modelling approaches with BAMGRO model provide useful predictive information on canopy development, biomass production and yield formation of bambara groundnut landraces under contrasting environments. Two contrasting landraces; Uniswa Red (Swaziland) and S19-3 (Namibia) were used in the present study to evaluate the growth and yield performances under drought, heat and cold stress.
BAMGRO has been primarily validated against independent data sets of two years glasshouse for two contrasting landraces; Uniswa Red and S19-3 grown under two temperatures (23 ± 5 0C, 33 ± 5 0C) with drought. Further, it was validated for field data in Botswana with two sowing dates (January 18, February 1) during the 2007 season and for Swaziland for three landraces; Uniswa Red, DipC, OM1. The model achieves a good fit between observed and predicted data for LAI (Nash and Sutcliffe (N-S), 0.78-0.98; Mean Absolute Error, ± 0.14-0.57) for tested four landraces. Pod yield simulation was correlated well with measured values especially for Uniswa Red and S19-3 (N-S 0.73-0.87; Mean Absolute Error ± 16 g m-2) while it was poor for DipC and OM1 (N-S, 0.46-0.50; Mean Absolute Error, ± 15.6-17.7 g m-2). Further, the comparison of simulated and measured data of TDM reported lower correlation compared to LAI and yield. (N-S, 0.59-0.79; Mean Absolute Error ± 48-100 g m-2) indicating overall underestimation. The performance of the BAMGRO-soil water module was tested by validating the available soil moisture and results indicating that it over estimated for upper layers while deeper layers showed lower prediction.
The possible reasons for the discrepancies in measured and simulated data are differences in quality and quantity of solar radiation in UK summer and Semi-arid Africa, intra-landrace variability and poor calibration of soil water module. Four potential applications of BAMGRO and three future developments are presented in this thesis.
Thesis (University of Nottingham only)
||S Agriculture > SB Plant culture
||UK Campuses > Faculty of Science > School of Biosciences
||09 Dec 2009 13:11
||13 Sep 2016 13:41
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