Development and use of a framework for characterising computer models of silvoarable economics

A review of existing computer models of silvoarable¹ economics was undertaken for a project, entitled Silvoarable Agroforestry for Europe (SAFE), which aims to reduce uncertainty regarding the introduction and management of silvoarable systems in Europe. Because the published literature describing and comparing such models is sparse, a framework was developed and then used to characterise five computer models: POPMOD, ARBUSTRA, the Agroforestry Estate Model, WaNuLCAS, and the Agroforestry Calculator. Key characteristics described for each model were the background, systems modelled, objective of the economic analysis, economic viewpoint, spatial and temporal scales, generation and use of biophysical data, model platform and interface, and input requirements and outputs. Each of the models could produce a partial budget of the profitability of a silvoarable, arable, or forestry system at a one-hectare level using discounted cost–benefit analysis. Whilst the research models undertook the analysis from a viewpoint of a generic farmer, models developed for decision-support also included appraisals from the perspectives of tenants, share-croppers, and participants in a joint-venture. The two farm-scale models, ARBUSTRA and the Agroforestry Estate Model, could also be used to examine the feasibility of silvoarable systems on an existing business, and to determine the effects of heterogeneous land types and phased planting. The framework allows users to identify the pertinent issues for selecting or developing a particular model. The word ‘silvoarable’ is synonymous with the word ‘agrisilviculture’ described by Nair (An Introduction to Agroforestry, Kluwer Academic, 1983) and describes the same type of production system. An erratum to this article is available at .     

Modelling of planted legume fallows in Western Kenya. (II) Productivity and sustainability of simulated management strategies

Improved fallow is a technology that can help to raise agricultural productivity in systems of poor soil fertility and low financial capital. Models, once calibrated, can be used to investigate a range of improved fallow systems relatively quickly and at relatively low cost, helping to direct experimental research towards promising areas of interest. Six fallow crop rotations were simulated using the WaNuLCAS model in a bimodal rainfall setting in Kenya over a 10 year period: (A) alternating fallow and crop seasons, (B) one season fallow followed by three seasons crop, (C) one season fallow followed by four seasons crop, (D–F) 1–3 seasons fallow periods followed by 3–5 seasons crop. The strategies were tested using a number of fallow growth rates, soil clay contents, and rainfall amounts to determine the interaction of fallow rotation and biophysical variables on maize (Zea mays (L.)) yield and sustainability (organic matter, N₂ fixation, leaching). The best simulated fallow strategies doubled maize yield compared to continuous maize over a 10 year period. Across all biophysical treatments strategy A and B of no more than three consecutive cropping seasons and of one consecutive fallow season yielded the most maize. This was because fallow benefits were largely due to the immediate fallow soil fertility benefit (IFB) rather than the cumulative benefit (CFB). The difference in yield between the two strategies was through a balance between (1) their interaction with the biophysical variables affecting accumulation of organic matter, hence increasing soil fertility and (2) the extra intrinsic soil fertility used for maize productivity by the inclusion of more cropping seasons within the rotation. We propose the following conceptual framework to manage fallows for maximum maize yield: when environmental factors are strongly limiting to fallow and crop growth then fallow strategy A would be the best strategy to employ (less risk but more labour) and when factors are less limiting then strategy B would be the best to employ.