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Seasonal climate forecasts (forecasts) aim to reduce climate-related productivity risk by helping farmers make decisions that minimise losses in poor years and maximise profits in good years. Most Australian forecast valuations have focused on fertiliser decisions to wheat operations, and few assessments have evaluated the benefit of incremental improvements of forecast skill. These gaps have limited our understanding of forecast value to the broader agriculture sector and the benefit of investments to improve forecast skill. To address these gaps, we consistently assessed forecast value for seven Australian case studies (southern grains, northern grains, southern beef, northern beef, lamb, cotton, and sugar). We implemented a three-stage methodology which consisted of engagement with industry practitioners; modelling production under different climatic and environmental conditions; and economic modelling to evaluate forecast value for eleven levels of forecast skill. Our results show that forecast value was often low and highly variable. Value was found to vary based on forecast attributes (forecast skill, resolution and state), industry application and prevailing conditions (environmental and market). This is the first Australian valuation study where the same methodological approach was applied across multiple industries, incremental improvements in skill were valued, and prevailing conditions were explicitly evaluated for impact on value.

Understanding the drivers of soil organic carbon (SOC) change over time and confidence to predict changes in SOC are essential to the development and long-term viability of SOC trading schemes. This study investigated temporal changes in total SOC, total nitrogen (N), and carbon (C) fractions (particulate organic carbon - POC, resistant organic carbon - ROC and humus organic carbon - HOC) over a 16-year period for four contrasting farming systems in a low rainfall environment (424 mm) at Condobolin, Australia. The farming systems were 1) conventional tillage mixed farming (CT); 2) reduced tillage mixed farming (RT); 3) continuous cropping (CC); and 4) perennial pasture (PP). The SOC dynamics were also modelled using APSIM C and N modules, to determine the accuracy of this model. Results are presented in the context of land managers participating in Australian climate change mitigation schemes. There was an increase in SOC for all farming systems over the first 12 years (total organic C, TOC% at 0–10 cm increased from 1.33% to 1.77%), which was predominately in the POC% fraction (POC% at 0–10 cm increased from 0.14% to 0.5%). Between 2012 and 2015, there was a decrease in SOC back to starting levels (TOC = 1.22% POC = 0.12% at 0–10 cm) in all systems. The PP system had higher TOC%, POC% and HOC% levels on average and higher SOC stocks to 30 cm depth at the final measurement in 2015 (PP = 30.43 t C ha−1; cropping systems = 23.71 t C ha−1), compared to the other farming systems. There was a decrease in TN% over time in all farming systems except PP. The average C:N increased from 14.1 in 1999 to 19.7 in 2012, after which time the SOC levels decreased and C:N dropped back to 15.8. The temporal change in SOC was not able to be represented by the AusFarm model. There are three important conclusions for policy development: 1) monitoring temporal changes in SOC over 12 years did not indicate long-term sequestration, required to assure “permanence” in SOC trading (i.e. 25–100 years) due to the susceptibility of POC to degradation; 2) without monitoring SOC in reference land uses (e.g. CT cropping system as a control in this experiment) it is not possible to determine the net carbon sequestration, and therefore the true climate change mitigation value; and 3) modelling SOC using AusFarm/APSIM, does not fully represent the temporal dynamics of SOC in this low rainfall environment.