Pest management in Queensland strawberries: Reality bites, and growers' perspectives change!

Prior to the 1980s, arthropod pest control in Queensland strawberries was based entirely on calendar sprays of insecticides (mainly endosulfan, triclorfon, dimethoate and carbaryl) and a miticide (dicofol). These chemicals were applied frequently and spider mite outbreaks occurred every season. The concept of integrated pest management (IPM) had not been introduced to growers, and the suggestion that an alternative to the standard chemical pest control recipe might be available, was ignored. Circumstances changed when the predatory mite, Phytoseiulus persimilis Athios-Henriot, became available commercially in Australia, providing the opportunity to manage spider mites, the major pests of strawberries, with an effective biological agent. Trials conducted on commercial farms in the early 1980s indicated that a revolution in strawberry pest management was at hand, but the industry generally remained sceptical and afraid to adopt the new strategy. Lessons are learnt from disasters and the consequent monetary loss that ensues, and in 1993, such an event relating to ineffective spider mite control, spawned the revolution we had to have. Farm-oriented research and evolving grower perspectives have resulted in the acceptance of biological control of spider mites using Phytoseiulus persimilis and the 'pest in first' technique, and it now forms the basis of an IPM system that is used on more than 80% of the Queensland strawberry crop.

Sorghum Crop Modeling and Its Utility in Agronomy and Breeding

Crop models are simplified mathematical representations of the interacting biological and environmental components of the dynamic soil–plant–environment system. Sorghum crop modeling has evolved in parallel with crop modeling capability in general, since its origins in the 1960s and 1970s. Here we briefly review the trajectory in sorghum crop modeling leading to the development of advanced models. We then (i) overview the structure and function of the sorghum model in the Agricultural Production System sIMulator (APSIM) to exemplify advanced modeling concepts that suit both agronomic and breeding applications, (ii) review an example of use of sorghum modeling in supporting agronomic management decisions, (iii) review an example of the use of sorghum modeling in plant breeding, and (iv) consider implications for future roles of sorghum crop modeling. Modeling and simulation provide an avenue to explore consequences of crop management decision options in situations confronted with risks associated with seasonal climate uncertainties. Here we consider the possibility of manipulating planting configuration and density in sorghum as a means to manipulate the productivity–risk trade-off. A simulation analysis of decision options is presented and avenues for its use with decision-makers discussed. Modeling and simulation also provide opportunities to improve breeding efficiency by either dissecting complex traits to more amenable targets for genetics and breeding, or by trait evaluation via phenotypic prediction in target production regions to help prioritize effort and assess breeding strategies. Here we consider studies on the stay-green trait in sorghum, which confers yield advantage in water-limited situations, to exemplify both aspects. The possible future roles of sorghum modeling in agronomy and breeding are discussed as are opportunities related to their synergistic interaction. The potential to add significant value to the revolution in plant breeding associated with genomic technologies is identified as the new modeling frontier.