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The present review discusses not only advances in coconut tissue culture and associated biotechnological interventions but also future research directions toward the resilience of this important palm crop. Coconut (Cocos nucifera L.) is commonly known as the 'tree of life'. Every component of the palm can be used to produce items of value and many can be converted into industrial products. Coconut cultivation faces a number of acute problems that reduce its productivity and competitiveness. These problems include various biotic and abiotic challenges as well as an unstable market for its traditional oil-based products. Around 10 million small-holder farmers cultivate coconut palms worldwide on c. 12 million hectares of land, and many more people own a few coconut palms that contribute to their livelihoods. Inefficiency in the production of seedlings for replanting remains an issue; however, tissue culture and other biotechnological interventions are expected to provide pragmatic solutions. Over the past 60 years, much research has been directed towards developing and improving protocols for (i) embryo culture; (ii) clonal propagation via somatic embryogenesis; (iii) homozygote production via anther culture; (iv) germplasm conservation via cryopreservation; and (v) genetic transformation. Recently other advances have revealed possible new ways to improve these protocols. Although effective embryo culture and cryopreservation are now possible, the limited frequency of conversion of somatic embryos to ex vitro seedlings still prevents the large-scale clonal propagation of coconut. This review illustrates how our knowledge of tissue culture and associated biotechnological interventions in coconut has so far developed. Further improvement of protocols and their application to a wider range of germplasm will continue to open up new horizons for the collection, conservation, breeding and productivity of coconut.

The idea that simulation models of agricultural production can serve as tools for farmers remains a compelling idea even after 3 decades of mostly disappointing development efforts. This paper is the first in a series that reports on 17 years of systems research that used models differently from the Decision Support System idea that has dominated the field. The starting point of FARMSCAPE (Farmers', Advisers', Researchers', Monitoring, Simulation, Communication And Performance Evaluation) was finding whether farmers could value simulation when conditions for appreciation were improved by (a) specifying the simulator for individual paddocks in question and (b) delivering customised simulation to decision makers as a supporting service rather than software as a decision support product. The first aim of the program has been to learn how to effectively intervene in farm management practice using complex, abstract models of croplands, specified with local soil, climate, and management data. The second aim has been to learn how a resulting service that farmers value can be delivered cost effectively by a third party.This first paper deals with an aspect of the first aim, i.e. valued decision support intervention. In the terms used by Checkland (1981), the activities that served this systems practice aim were guided by 'what we thought we were doing' in intervening in farmers' practice, i.e. our systems thinking. This first paper concerns FARMSCAPE systems thinking and how it evolved over 17 years as we learned successively through discovery of a new concept or representation in the literature to overcome limitations of the then-current conceptual framework. Subsequent papers deal with customising scientific monitoring and simulation for farmers, communication as engagement in situations of practice, understanding decision support intervention as facilitation of personal knowledge construction, and piloting commercial delivery of a simulationbased service to farmers and their advisers.

Crop simulation models relevant to real-world agriculture have been a rationale for model development over many years. However, as crop models are generally developed and tested against experimental data and with large systematic gaps often reported between experimental and farmer yields, the relevance of simulated yields to the commercial yields of field crops may be questioned. This is the third paper in a series which describes a substantial effort to deliver model-based decision support to Australian farmers. First, the performance of the cropping systems simulator, APSIM, in simulating commercial crop yields is reported across a range of field crops and agricultural regions. Second, how APSIM is used in gaining farmer credibility for their planning and decision making is described using actual case studies.Information was collated on APSIM performance in simulating the yields of over 700 commercial crops of barley, canola, chickpea, cotton, maize, mungbean, sorghum, sugarcane, and wheat monitored over the period 1992 to 2007 in all cropping regions of Australia. This evidence indicated that APSIM can predict the performance of commercial crops at a level close to that reported for its performance against experimental yields. Importantly, an essential requirement for simulating commercial yields across the Australian dryland cropping regions is to accurately describe the resources available to the crop being simulated, particularly soil water and nitrogen.Five case studies of using APSIM with farmers are described in order to demonstrate how model credibility was gained in the context of each circumstance. The proposed process for creating mutual understanding and credibility involved dealing with immediate questions of the involved farmers, contextualising the simulations to the specific situation in question, providing simulation outputs in an iterative process, and together reviewing the ensuing seasonal results against provided simulations.This paper is distinct from many other reports testing the performance and utility of cropping systems models. Here, the measured yields are from commercial crops not experimental plots and the described applications were from real-life situations identified by farmers. A key conclusion, from 17 years of effort, is the proven ability of APSIM to simulate yields from commercial crops provided soil properties are well characterised. Thus, the ambition of models being relevant to real-world agriculture is indeed attainable, at least in situations where biotic stresses are manageable.