A high resolution broad scale spatial indicator of grain growing profitability for natural resource planning

The balancing of sustainable agricultural production with environmental, social, cultural and community objectives has become an increasing priority worldwide. Political focus has been on the prevention of environmental degradation and improving biodiversity values under the uncertainty of the impacts of climate change on rural livelihoods. In Australia, dry land salinisation is a major cause of environmental degradation in grain growing regions: at a farm scale the adoptions of key environmental strategies, such as revegetation, need to be considered.This study proposes that the identification and quantification of the spatial variability of wheat grain yield within the cropping landscape may help guide this revegetation. We used precision agriculture technology to collect data at the sub-field scale in conjunction with satellite imagery at the regional scale to create a high resolution regional indicator of wheat yield. This indicator is used to identify the economic value of land at sub-field scale which then allows identification of areas of marginal cropping value. This information provides an indication of how much land can be devoted to revegetation and quantifies the economic trade-off needed for this substitution to take place.Results of this study demonstrate that 90% of the income generated within the area of interest was produced by 55–74% of the wheat growing area depending on the choice of cost price scenarios. Between 27% and 44% of the study area made a financial loss or marginal monetary return indicating that trade-offs providing increased environmental benefits may be possible with minimal income loss from a substantial magnitude of cropping area. Although further analysis at larger regions with longer time series is necessary, results presented here show that overall economic returns may be improved by the reassignment of land use in selected cropping areas.The study also suggests that feasibility analyses of land use change at farm and regional scales should be conducted with a spatial resolution that is fine enough to reflect the spatial variability observed from yield mapping. While this information will not be available on every farm, this study shows that it may be possible to predict yield variability from remotely sensed imagery, thus providing a means to circumvent this problem and to produce high resolution indicators at a regional extent.