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Testing soils for plant nutrient contents has long been a controversial topic in the research literature. Because of its widespread deficiency and finite natural resource, phosphorus (P) is possibly the most researched plant nutrient, but the most accurate method of assessment for predicting P deficiency and fertilizer requirement remains unclear. The studies reported in the above special issue of Crop and Pasture Science, entitled ‘Making better fertiliser decisions for cropping systems in Australia’, are an attempt to collate and interpret all the relevant research data in Australia to achieve this purpose. Having published several research papers on soil P testing, which impinge on the current studies, I would like to make the following comments on some of the papers reporting soil P testing for wheat. In an introductory methodology paper (pp. 435–441), Dyson and Conyers (2013) gave a detailed description of the biometrical methodology used in these studies. For fitting yield response curves to applied fertiliser rate experiments, the Mitscherlich and two quadratic equations were proposed. Although the authors suggest that the first equation is particularly appropriate for rate of P response curves, the text does not state which of these equations was used, and there is no indication of the relative goodness-of-fit of the used equation. One may question whether all these equations would give the same values of Y0 and Ymax and whether the same equation was used for all sets of data. Their methodology for calibrating relative yield against soil test value is extraordinarily complex (p. 437–439), whereas an exponential equation, as used by Holford et al. (1985) would more simply and adequately describe the relationship and give the variance accounted for by the soil test. No measure of goodness-of-fit is given by Dyson and Conyers (2013), and their fitted calibration curve for the Colwell test in Fig. 3 (p. 438) does not appear to be a good fit and would underestimate the soil test value at 90% of maximum yield. The inaccuracies of this curve and derived critical value (cv) are demonstrated by the fact that, of the 23 soils which had soil test values above the critical value (22mg/kg), 16 (or 70%) of them were responsive to fertiliser P! Incidentally, the Mitscherlich function is not conventionally used for soil test calibration curves, as claimed in Fig. 3, although in this case it would have given a better fit and a more realistic cv similar to that given by a logarithmic curve (~40mg/kg). However, the widespread distribution of points in Fig. 3 demonstrates the inability of the Colwell test to reliably differentiate between responsive and non-responsive soils. In the first soil P testing paper, Moody et al. (2013) list seven soil tests (Table 2, p. 463) which they consider reflect the soil P pools and processes controlling P availability in soils. They do not, however, differentiate between the first four tests (Colwell, BSES, Olsen, and Mehlich 3), which aim to give a measure of the quantity of available P, and the other three tests (CaCl2, DGT, and FeO) which measure other aspects of available P. The qualitative term, ‘P availability’, is not the same as ‘available P’ (a quantitative term), but it is used throughout these papers, whether or not ‘available P’ is meant. Although the Colwell test is the most widely used soil test in Australia, the other three quantity tests poorly represent the range of tests that have been researched in Australia. The alkaline Olsen and Colwell modification both use the same sodium bicarbonate extractant at pH 8.5 and therefore extract soil P from the same sources. The acidic BSES test was developed for use on sugarcane growing soils in a different soil and climatic environment to wheat, and the Mehlich 3 test has not been widely used in Australia. There is no discussion or justification for the use of these soil tests or the omission of other tests which have been shown to be more accurate in predicting yield response. For example, two acidic anionic extractants (lactate and fluoride) were found to much more accurately predict responses to fertiliser P than the Colwell test in four separate studies (256 experiments) on slightly acid to alkaline soils representing the central western plains, the central western slopes, and northwestern slopes of NSW (Holford and Cullis 1985a; Holford and Doyle 1992; Holford et al. 1985, 1988). Speirs et al. (2013), in the following paper (pp. 469–479), used 164 soil samples from south-eastern Australia to evaluate six of the previously mentioned soil tests (FeO-P was omitted). According to Table 4 (p. 473), DGT-P was the most accurate soil test accounting for 30% of the variance in relative yield

Agriculture is highly dependent on the social sustainability inherent in rural communities. Yet too often we focus on the economic and environmental drivers relating to agricultural production, ignoring the social and community aspects that make rural livelihood not only possible but also rewarding and nurturing. In this paper I focus on climate change as yet another factor associated with rural restructuring that defrays community wellbeing. I argue that attention to social factors and a stronger role for government in assisting communities will enable greater adaptation and enhance resilience in what are essentially very uncertain times.