Farming digital data: Even when the cows come home

Increasingly, multinational brands, manufacturers, and retail customers want to demonstrate where individual components of their supply chain have come from, and they have been made sustainably using a triple-bottom-line approach (i.e., social well-being, environmental health, and a just economy). One example is the need for farmers to demonstrate they are transforming their operations into climate-smart landscapes, decarbonising their operations (i.e., minimising inputs) and supply chains to contribute to global net zero, while at the same time being financially sustainable. Others include reduction in inputs including but not limited to precision application of fertilisers (e.g., nitrogen and phosphorus), ameliorants to overcome soil acidity (e.g., lime) and water for irrigation. In the first instance, this requires information on various soil ‘conditions’ including the ‘capacity’ of soil to be improved in terms of its soil ‘capability’. In this presentation we demonstrate how we develop digital soil maps (DSM) of soil ‘capacity’ including but not limited to i) physical (mineral surface area [MSA]), ii) biological (carbon [C] and nitrogen [N]), iii) chemical (cation exchange capacity [CEC], and P-sorption [P]), and iv) hydrological (permanent wilting point [PWP], field capacity [FC], plant available water [PAW]), on the Lincoln University Dairy Farm. In this regard, the DSM are developed using digital data collected using either remote (i.e., LiDAR) or proximal sensed (i.e., gamma-ray spectrometry and electromagnetic (EM) induction) data. In this presentation, we show how the individual DSM are stored online (ArcGIS web app) and the rationale for ‘Farming digital data’ described (ArcGIS Story Map). The final DSMs are described in terms of how knowledge of the heterogeneity of different soil ‘capacity’ enables a farmer to understand how the ‘capability’ of soil can be improved, respectively, and in terms of; i) where best to invest in soil organic carbon sequestration initiatives (MSA), ii) how to monitor carbon dioxide/nitrous oxide emissions and microbial population (C:N ratio), iii) more precisely apply fertilisers (e.g., N and P) and ameliorants (e.g., lime and gypsum), and iv) improve water use efficiency with variable rate irrigation (PAW). Brief insights into how these DSMs underpin the development of a Digital Agriculture framework are also presented ‘Even when the cows come home’.