Identifying risk-efficient strategies using stochastic frontier analysis and simulation: An application to irrigated cropping in Australia

In irrigated cropping, as with any other industry, profit and risk are inter-dependent. An increase in profit would normally coincide with an increase in risk, and this means that risk can be traded for profit. It is desirable to manage a farm so that it achieves the maximum possible profit for the desired level of risk. This paper identifies risk-efficient cropping strategies that allocate land and water between crop enterprises for a case study of an irrigated farm in Southern Queensland, Australia. This is achieved by applying stochastic frontier analysis to the output of a simulation experiment. The simulation experiment involved changes to the levels of business risk by systematically varying the crop sowing rules in a bioeconomic model of the case study farm. This model utilises the multi-field capability of the process based Agricultural Production System Simulator (APSIM) and is parameterised using data collected from interviews with a collaborating farmer. We found sowing rules that increased the farm area sown to cotton caused the greatest increase in risk-efficiency. Increasing maize area also improved risk-efficiency but to a lesser extent than cotton. Sowing rules that increased the areas sown to wheat reduced the risk-efficiency of the farm business. Sowing rules were identified that had the potential to improve the expected farm profit by ca. $50,000 Annually, without significantly increasing risk. The concept of the shadow price of risk is discussed and an expression is derived from the estimated frontier equation that quantifies the trade-off between profit and risk.

Genetic diversity of the Australian National Mango Genebank

Assessment of genetic diversity is an essential component in germplasm characterisation and utilisation. In this study the genetic diversity of mango was determined among 254 Mangifera indica L. accessions and related Mangifera species originating from 12 diverse geographic areas using eleven known simple sequence repeat (SSR) markers from mango. A total of 133 alleles were detected, ranging from eight (LMMA12) to 16 (MIAC-5) alleles per locus with a mean value of 12.36 and an average polymorphism information content (PLC) of 0.72. The mean number of alleles (8.45) was highest in the South East Asian accessions (Indonesia/Malesia) and lowest in the accessions from the Philippines (2.55). Diversity analysis divided the accessions into four major nodes broadly representing their geographical origins. The genetic diversity of 'Kensington Pride' was confirmed as being very low and no parents for this cultivar were identified. No association could be established between SSR markers analysed and embryony. Ten synonymous accessions were identified with matching genetic identity with at least one other accession at all SSR loci examined. Twenty-two unique genotypes were identified for 50 trees previously assigned different accession names. The remaining accessions were genetically distinct from each other. This increased understanding of genetic diversity in the Australian National Mango Genebank will assist breeders to better select parents with the potential to contribute desired genes to the progeny and thus more rapidly deliver improved cultivars to industry to meet consumer demand. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.