Abstract

Rainfed crops rely on two sources of water: stored soil water at sowing and seasonal rain. In strongly seasonal winter-rainfall environments, stored soil water at sowing is minor, and uncertain seasonal rainfall is a source of risk. In south-eastern Australia, under-fertilisation is a common outcome of nitrogen risk management with implications for wheat yield and mining of soil organic matter. Here we explore the use of carbon isotope composition (δ 13 C) to capture the effects of crop water status on grain yield in a context of nitrogen top dressing. In the sampled environments, crops receive at least 50% of seasonal rainfall by stem elongation, and at least 70% of seasonal rainfall by flowering. In a sample of 1518 plots, yield varied from 0.07 to 9.96 t ha -1 and correlated with δ 13 C measured with isotope ratio mass spectrometer (IRMS) at flowering (r = −0.76, p < 0.0001); this is consistent with the rainfall pattern and the physiology of the crop featuring a critical period for yield from 300 °Cd before to 100 °Cd after anthesis. In a sample of 135 plots, yield varied from 1.2 to 8.4 t ha -1 and correlated with δ 13 C measured with IRMS at stem elongation (r = −0.56, p < 0.0001). Yield response to nitrogen, defined as the difference between yield in fertilised crops (50–200 kg N ha -1 ) and unfertilised controls, correlated with δ 13 C measured with IRMS at stem elongation, except for late-sown crops. Mid-infrared spectroscopy (MIR) returned estimates of δ 13 C that agreed with δ 13 C measured with IRMS (calibration: R 2 = 0.82, RMSE = 0.53‰, n = 833; validation: R 2 = 0.70, RMSE = 0.75‰, n = 364). We conclude that a MIR based, high-throughput, affordable measurement of δ 13 C could be scaled to guide nitrogen management of wheat in winter-rainfall environments. • In a sample of 1518 plots, yield varied from 0.07 to 9.96 tha -1 and correlated with δ 13 C at flowering. • Yield response to nitrogen correlated with δ 13 C at stem elongation, except for late-sown crops. • Mid-infrared spectroscopy is a cost-effective, high-throughput approach to estimate δ 13 C for agronomic applications.

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