Quantifying the costs of soil constraints to Australian agriculture: a case study of wheat in north-eastern Australia

Abstract

Soil salinity, sodicity, acidity and alkalinity, elemental toxicities, such as boron, chloride and aluminium, and compaction are important soil constraints to agricultural sustainability in many soils of Australia. There is considerable variation in the existing information on the costs of each of the soil constraints to Australian agriculture. Determination of the cost of soil constraints requires measuring the magnitude and causes of yield gap (Yg) between yield potential and actual yield. We propose a ‘hybrid approach’ consisting of determining the magnitude of Yg and the cause(s) of Yg for spatiotemporal representation of Yg that can be apportioned between management and soil constraint effects, thereby allowing a better estimate of the cost of mitigation of the constraints. The principles of this approach are demonstrated using a 2820-ha wheat-growing farm over a 10-year period to quantify the costs of the proportion of forfeited Yg due to soil constraints. Estimated Yg over the whole farm varied annually from 0.6 to 2.4Mgha–1, with an average of 1.4Mgha–1. A multiyear spatiotemporal analysis of remote sensing data identified that 44% of the farm was consistently poor performing, suggesting the potential presence of at least one soil constraint. The percentage decrease in productivity due to soil constraints varied annually from 5% to 24%, with an average estimated annual loss of wheat grain production of 182 Mg per year on 1069ha. With the 2015 season’s average wheat grain price (A$0.29kg–1), the estimated annual value of lost agricultural production due to soil constraints was estimated at A$52780 per year. For successful upscaling of the hybrid approach to regional or national scale, Australia has reliable data on the magnitude of Yg. The multiyear spatiotemporal analysis of remote sensing data would identify stable, consistently poor performing areas at a similar scale to Yg. Soil maps could then be used to identify the most-limiting soil constraints in the consistently poor performing areas. The spatial distribution of soil constraint at similar scale could be used to obtain the cost of lost production using soil constraint–grain yield models.