Greg Lyle

Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale

The long term archiving of both Landsat imagery and wheat yield mapping datasets sensed by precision agriculture technology has the potential through the development of statistical relationships to predict high resolution estimates of wheat yield over large areas for multiple seasons. Quantifying past yield performance over different growing seasons can inform agricultural management decisions ranging from fertilizer applications at the sub-paddock scale to changes in land use at a landscape scale. However, an understanding of the magnitude of prediction errors is needed. In this study, we examine the predictive wheat yield relationships developed from Normalised Difference Vegetation Index (NDVI) acquired Landsat imagery and combine-mounted yield monitors for three Western Australian farms over different growing seasons. We further analysed their predictive capability when these relationships are used to extrapolate yield from one farm to another. Over all seasons, the best predictions were achieved with imagery acquired in September. Of the five seasons reviewed, three showed very reasonable prediction accuracies, with the low and high rainfall years providing good predictions. Medium rainfall years showed the greatest variation in prediction accuracy with marginal to poor predictions resulting from narrow ranges of measured wheat yield and NDVI values. These results demonstrate the potential benefit of fusing together two high resolution datasets to create robust wheat yield prediction models over different growing seasons, the outputs of which can be used to inform agricultural decision making.

Simple models for managing complex social-ecological systems

Integrated modelling and assessment can facilitate exploration of complex social-ecological interactions and quantify trade-offs in regional policy, planning, and management options. However, there have been challenges in its acceptance and adoption for supporting decisions. Here we overcome this implementation gap through the development of an interactive online tool called the Landscape Futures Analysis Tool (LFAT) (http://www.lfat.org.au/). Identifying four high priority regional management issues; agricultural production, carbon sequestration, biodiversity conservation and weed management, we developed a series of simple models to explore them through a range of environmental and economic scenarios including climate change, carbon price, agricultural commodity price, and production costs. These models were implemented within the LFAT to allow users to select, query and explore combinations of key variables and examine their impact on each of the management issues through a range of interactive maps and summary statistics. We developed simple models to explore 4 key regional land management issues.Models were implemented in the interactive, online Landscape Futures Analysis Tool.Users can explore key uncertainties in productivity, prices, costs and global change.LFAT provides interactive maps and summary statistics to inform planning.LFAT helps bridge the implementation gap in land management and planning

Regional engagement and spatial modelling for natural resource management planning

Changing unsustainable natural resource use in agricultural landscapes is a complex social–ecological challenge that cannot be addressed through traditional reductionist science. More holistic and inclusive (or transdisciplinary) processes are needed. This paper describes a transdisciplinary project for natural resource management planning in two regions (Eyre Peninsula and South Australian Murray-Darling Basin) of southern Australia. With regional staff, we reviewed previous planning to gain an understanding of the processes used and to identify possible improvement in plan development and its operation. We then used an envisioning process to develop a value-rich narrative of regional aspirations to assist stakeholder engagement and inform the development of a land use management option assessment tool called the landscape futures analysis tool (LFAT). Finally, we undertook an assessment of the effectiveness of the process through semi-structured stakeholder interviews. The planning process review highlighted the opinion that the regional plans were not well informed by available science, that they lacked flexibility, and were only intermittently used after publication. The envisioning process identified shared values—generally described as a trust, language that is easily understood, wise use of resources, collaboration and inclusiveness. LFAT was designed to bring the best available science together in a form that would have use in planning, during community consultation and in assessing regional management operations. The LFAT provided spatially detailed but simple models of agricultural yields and incomes, plant biodiversity, weed distribution, and carbon sequestration associated with future combinations of climate, commodity and carbon prices, and costs of production. Stakeholders were impressed by the presentation and demonstration results of the software. While there was anecdotal evidence that the project provided learning opportunities and increased understanding of potential land use change associated with management options under global change, the direct evidence of influence in the updated regional plan was limited. This project had elements required for success in transdisciplinary research, but penetration seems limited. Contributing factors appear to be a complexity of climate effects with economic uncertainty, lack of having the project embedded in the plan revision process, limited continuity and capacity of end users and limited after project support and promotion. Strategies are required to minimise the controlling influence that these limitations can have.

Identifying the spatial and temporal variability of economic opportunity costs to promote the adoption of alternative land uses in grain growing agricultural areas: An Australian example

Grain growers face many future challenges requiring them to adapt their land uses to changing economic, social and environmental conditions. To understand where to make on ground changes without significant negative financial repercussions, high resolution information on income generation over time is required. We propose a methodology which utilises high resolution yield data collected with precision agriculture (PA) technology, gross margin financial analysis and a temporal standardisation technique to highlight the spatial and temporal consistency of farm income. On three neighbouring farms in Western Australia, we found non-linear relationships between income and area. Spatio-temporal analysis on one farm over varying seasons found that between 37 and 49% (1082-1433ha) of cropping area consistently produced above the selected income thresholds and 43-32% (936-1257ha) regularly produced below selected thresholds. Around 20% of area showed inconsistent temporal variation in income generation. Income estimated from these areas represents the income forgone if a land use change is undertaken (the economic opportunity cost) and the average costs varied spatially from

The second industrial transformation of Australian landscapes

190±114/ha to

Understanding the nested, multi-scale, spatial and hierarchical nature of future climate change adaptation decision making in agricultural regions: A narrative literature review

560±108/ha depending on what scenario was chosen. The interaction over space and time showed the clustering of areas with similar values at a resolution where growers make input decisions. This new evidence suggests that farm area could be managed with two strategies: (a) one that maximises grain output using PA management in temporally stable areas which generate moderate to high income returns and (b) one that proposes land use change in low and inconsistent income returning areas where the financial returns from an alternative land use may be comparable. The adoption of these strategies can help growers meet the demand for agricultural output and offer income diversity and adaptive capacity to deal with the future challenges to agricultural production.