In this study a spatial and structural analysis of sheep enterprise gross margins and emissions in Western Australia's agricultural region is conducted. Specifically, self-replacing Merino sheep enterprises are examined in 14 agroecological zones using five years of farm level data (2019-2023). Emissions calculations are based on the Australian Sheep and Beef Greenhouse Accounting Framework. Results reveal significant variability in gross margins and emissions across agroecological zones and across farm performance groupings. During the study period sheep enterprises in the high rainfall and medium rainfall southern zones displayed higher gross margins ($243-295 per winter-grazed hectare) than enterprises in the northern and low rainfall zones ($106-148 per winter-grazed hectare), although significant temporal variation was evident due to volatility in climatic conditions and commodity prices. Total emissions per annum ranged from 462 kg CO2-e per winter-grazed hectare in low rainfall regions to 1,455 kg CO2-e in high rainfall regions, with the top 25% of farms generating 2,300 kg CO2-e per winter-grazed hectare. Emissions per kilogram of liveweight sold ranged from 5.6-6.6 kg CO2-e per kg of liveweight for meat whilst for wool, emissions were 20.1-23.4 kg CO2-e per kg of greasy wool, decreasing from northern to southern zones. Survey results reveal a greater potential for emissions reduction on farms in the high rainfall region. Each dollar of forgone gross margin on these farms generates more emission reductions than is possible for farms in other rainfall regions. However, the practical concern for all farms is that there is currently no strong commercial penalty or reward for altering emissions from sheep production. The current gross margins for sheep production, especially in the high rainfall region, signal that maintained or greater intensification of sheep production is worthwhile, even though emissions per hectare or emissions per kilogram of sheep or wool may increase.
Climate projections for southern Australia indicate an increased likelihood of years of consecutive droughts. How resilient are farm businesses and their farming systems to recover from episodic drought? This study assesses the farm economic and financial impacts of consecutive droughts in southwest Australia. Bioeconomic simulation modelling of farm businesses with different farming systems at three locations is used to examine the long-term financial consequences of consecutive droughts. Various key factors affecting those consequences are examined. Farm location, farming system and starting equity are found to be the main determinants of how quickly a farm business can financially recover from the impact of consecutive droughts. Initial low equity greatly reduces the likelihood or speed of recovery from consecutive droughts, especially in low rainfall locations where crop dominant farming systems are commonplace. Projected future climate is shown to affect farms differently, based on the farm's location and farming system. Farms in high rainfall locations are projected to benefit from future climate change and are more resilient and quicker to recover from consecutive droughts. Changes in sheep or grain prices, during and immediately after consecutive droughts, are shown to only marginally affect a farm's long-term financial performance whereas rates of gain in crop improvement more strongly support the farm's financial recovery.
Agriculture Victoria Research has conducted several traceability pilots with industry partners to address a major impediment to the adoption of digital traceability in agriculture: i.e., the inability of isolated systems to interact and 'talk' with each other. One pilot was a collaboration with Rubens Technologies, who market a hand-held fluorescence-reflectance sensor and analytics for the nondestructive measurement of internal quality in pome and stone fruit. The Rubens pilot demonstrated how traceability functionalities could be retrofitted into the sensor's associated mobile application and application programming interface services. Along with applying unique identifiers to carriers (tags) placed on orchard features and objects (trees, bins), these changes enabled the seamless ingestion of contextual information critical for early supply chain management into the orchard's data ecosystem. Ex ante benefit: cost analysis suggests that practice changes made possible by the adoption of the modified Rubens product are likely to add to an orchardist's wealth. Risks and returns were examined for two decision-making processes for a hypothetical 2-ha block of Royal Gala apples: (1) harvest timing, and (2) post-harvest storage. Extra costs were weighed up against ongoing annual benefits. For both decisions, benefits included labour savings from a more streamlined testing and data collection process. Considering only the labour savings, the combined net present value (NPV) estimated over 5 years with a 7 per cent p.a. real discount rate was $1,479/block. For the second decision, a higher crop value was also possible by increasing the sample size on which the storage decision was based. In this case, estimation of benefits required the use of Monte Carlo simulation technology, and the combined NPV rose to an average of $16,697/block. This figure masks a modest 25 per cent probability that benefits would exceed the costs. Annualised, the NPV with the larger sample size averaged $2,039/ha p.a., which exceeds the gross margin estimated from recent industry data of $1,942/ha. This seemingly large net benefit is equivalent to a modest 3 percentage point increase in Class 1 pack-out from the industry average to the industry maximum of 79 per cent. These comparisons confirm the importance of growers minimising downside marketing/price risk by targeting fruit quality at harvest and at the earliest stages of the supply chain.
The nitrogen (N) fertilizer used to help grow fully irrigated cotton in Australia adds, through several pathways, nitrous oxide (N2O) to the stock of nitrous oxide in the atmosphere and increases the global externality cost of the warming climate. The focus of this analysis is on the extra social benefits and the extra private costs and negative externality costs of using different quantities of N on land in NSW and QLD to grow cotton over a year, and over the coming 15 years, as compared with not growing cotton on that land and replacing the activity with another economic activity. Starting at the farm, a welfare economics framework including the concepts of response of crop yield to N fertiliser, private costs, externality costs, marginality, with-without counterfactuals, opportunity costs, crop rotations, discounting, probabilities, consumer surplus, producer surplus and net social benefit are used to estimate the size of the social benefits and costs of N used to grow irrigated cotton. In the case analysed, with an illustrative counterfactual, the externality cost of direct N2O emissions from growing cotton after counting for the counterfactual was $102/ha yielding a Benefit to Cost (B:C) ratio of 7.2:1. The net social benefit on the industry over 15 years at a 5 per cent real discount rate per annum in net present value terms was $5.6 billion with an annuity of $541 million. In the case analysed and with the probabilities assumed for the values that the key uncertain variables could take, with only direct N2O emissions counted as the negative externality of the N used, there would be a 90 per cent probability that the B:C ratio of N used to grow cotton was between 5.4:1 and 9.6:1. There would be 55 per cent chance the B: C ratio would be more than 7:1. There would be zero chance the B:C ratio would be under 4:1. A significant finding about the negative externality of the N2O emissions from the N applied to cotton was that $80 of the $116/ha externality cost from the N2O emissions came from the marginal 50 kg of N/ha that was used. If the response function is relatively flat around the typical level of N/ha that is used in a typical year, then the marginal units of N applied would be adding little extra cotton yield relative to the extra externality cost attributable to the N2O emissions. In this situation, there would be scope for small reductions in N/ha used to grow extra cotton to bring large reductions in the externality cost of the N2O emissions from N used to grow cotton.
Farm management occurs against a backdrop of weather-year variation. In Australian mixed enterprise farming systems, how important is it for farm optimisation models to capture this variation and the management tactics matched to that variation? This study compares two whole farm optimisation models of an Australian mixed enterprise farming system. One model represents weather-year variation and the short-term tactical management responses tailored to the unfolding weather-year conditions. The other model is a traditional deterministic steady state model that employs the key assumption that every year is an expected weather-year. Both models require the farm manager to select a profit-maximising suite of enterprises and activities relevant to either the expected weather-year or the suite of weather-years that typify weather-year variation where the farm is located. Comparison of the models' results reveals key differences in farm strategy, farm tactics and farm profit. The model that includes tactics aligned to the weather-year variation reveals that tactical decision-making increases expected farm profit by about 18 per cent.
