Exploring profit - Sustainability trade-offs in cropping systems using evolutionary algorithms

Models that implement the bio-physical components of agro-ecosystems are ideally suited for exploring sustainability issues in cropping systems. Sustainability may be represented as a number of objectives to be maximised or minimised. However, the full decision space of these objectives is usually very large and simplifications are necessary to safeguard computational feasibility. Different optimisation approaches have been proposed in the literature, usually based on mathematical programming techniques. Here, we present a search approach based on a multiobjective evaluation technique within an evolutionary algorithm (EA), linked to the APSIM cropping systems model. A simple case study addressing crop choice and sowing rules in North-East Australian cropping systems is used to illustrate the methodology. Sustainability of these systems is evaluated in terms of economic performance and resource use. Due to the limited size of this sample problem, the quality of the EA optimisation can be assessed by comparison to the full problem domain. Results demonstrate that the EA procedure, parameterised with generic parameters from the literature, converges to a useable solution set within a reasonable amount of time. Frontier ‘‘peels’’ or Pareto-optimal solutions as described by the multiobjective evaluation procedure provide useful information for discussion on trade-offs between conflicting objectives.

Fast FIR Algorithms for the Continuous Wavelet Transform From Constrained Least Squares

New algorithms for the continuous wavelet transform are developed that are easy to apply, each consisting of a single-pass finite impulse response (FIR) filter, and several times faster than the fastest existing algorithms. The single-pass filter, named WT-FIR-1, is made possible by applying constraint equations to least-squares estimation of filter coefficients, which removes the need for separate low-pass and high-pass filters. Non-dyadic two-scale relations are developed and it is shown that filters based on them can work more efficiently than dyadic ones. Example applications to the Mexican hat wavelet are presented.

Prevalence and potential fitness cost of weak phosphine resistance in Tribolium castaneum (Herbst) in eastern Australia

The prevalence of resistance to phosphine in the rust-red flour beetle, Tribolium castaneum, from eastern Australia was investigated, as well as the potential fitness cost of this type of resistance. Discriminating dose tests on 115 population samples collected from farms from 2006 to 2010 showed that populations containing insects with the weakly resistant phenotype are common in eastern Australia (65.2 of samples), although the frequency of resistant phenotypes within samples was typically low (median of 2.3). The population cage approach was used to investigate the possibility that carrying the alleles for weak resistance incurs a fitness cost. Hybridized populations were initiated using a resistant strain and either of two different susceptible strains. There was no evidence of a fitness cost based on the frequency of susceptible phenotypes in hybridized populations that were reared for seven generations without exposure to phosphine. This suggests that resistant alleles will tend to persist in field populations that have undergone selection even if selection pressure is removed. The prevalence of resistance is a warning that this species has been subject to considerable selection pressure and that effective resistance management practices are needed to address this problem. The resistance prevalence data also provide a basis against which to measure management success.