Resource use and growth in semi-arid agroforestry systems

The work reported here aimed to provide a comprehensive database of core information to support the development and validation of process-based models of resource capture and growth in the semi-arid overstorey agroforestry systems. Intensive field studies were carried out in Kenya over a 30 month period and the results obtained were combined with data from a previous project to produce a dataset spanning a 4.5 year period. This dataset was then used to verify output from the HyPAR model. Allometric procedures developed from the pipe model theory (Lott et al., 1998) were used to estimate tree growth non-destructively throughout the observation period. Significant differences in tree size between the sole (Td) and dispersed agroforestry (CTd) treatments were established during the first 130 days after planting, probably because of competition with the associated crops. The above-ground biomass and trunk length and taper characteristics of the CTd trees remained inferior to those of Td trees throughout the observation period, seriously undermining the economic potential of this agroforestry system. Comparison of output from the HyPAR model against the observed results provided information pertinent to future model development. The model proved to be insufficiently flexible for end-users wishing to simulate the growth of different crops during the same simulation cycle, or to use model output to aid management decisions such as the timing of pruning. The allometric procedures used by the model to estimate canopy size from trunk diameter at breast height also proved incapable of accounting for reductions in canopy size resulting from pruning. Estimates of tree height are rounded to the nearest metre within the model, representing a potentially serious loss of resolution when annual increments often do not exceed 2 m. In addition, the numerous parameters required by the model would force most end-users to rely heavily on published information, potentially undermining the reliability of simulations.

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