Remote sensing of nitrogen and water stress in wheat

Nitrogen (N) is the largest agricultural input in many Australian cropping systems and applying the right amount of N in the right place at the right physiological stage is a significant challenge for wheat growers. Optimizing N uptake could reduce input costs and minimize potential off-site movement. Since N uptake is dependent on soil and plant water status, ideally, N should be applied only to areas within paddocks with sufficient plant available water. To quantify N and water stress, spectral and thermal crop stress detection methods were explored using hyperspectral, multispectral and thermal remote sensing data collected at a research field site in Victoria, Australia. Wheat was grown over two seasons with two levels of water inputs (rainfall/irrigation) and either four levels (in 2004; 0, 17, 39 and 163 kg/ha) or two levels (in 2005; 0 and 39 kg/ha N) of nitrogen. The Canopy Chlorophyll Content Index (CCCI) and modified Spectral Ratio planar index (mSRpi), two indices designed to measure canopy-level N, were calculated from canopy-level hyperspectral data in 2005. They accounted for 76% and 74% of the variability of crop N status, respectively, just prior to stem elongation (Zadoks 24). The Normalised Difference Red Edge (NDRE) index and CCCI, calculated from airborne multispectral imagery, accounted for 41% and 37% of variability in crop N status, respectively. Greater scatter in the airborne data was attributable to the difference in scale of the ground and aerial measurements (i.e., small area plant samples against whole-plot means from imagery). Nevertheless, the analysis demonstrated that canopy-level theory can be transferred to airborne data, which could ultimately be of more use to growers. Thermal imagery showed that mean plot temperatures of rainfed treatments were 2.7 °C warmer than irrigated treatments (P < 0.001) at full cover. For partially vegetated fields, the two-Dimensional Crop Water Stress Index (2D CWSI) was calculated using the Vegetation Index-Temperature (VIT) trapezoid method to reduce the contribution of soil background to image temperature. Results showed rainfed plots were consistently more stressed than irrigated plots. Future work is needed to improve the ability of the CCCI and VIT methods to detect N and water stress and apply both indices simultaneously at the paddock scale to test whether N can be targeted based on water status. Use of these technologies has significant potential for maximising the spatial and temporal efficiency of N applications for wheat growers. ‘Ground–breaking Stuff’- Proceedings of the 13th Australian Society of Agronomy Conference, 10-14 September 2006, Perth, Western Australia.

The limit to wheat water-use efficiency in eastern Australia. I. Gradients in the radiation environment and atmospheric demand

In the wheatbelt of eastern Australia, rainfall shifts from winter dominated in the south (South Australia, Victoria) to summer dominated in the north (northern New South Wales, southern Queensland). The seasonality of rainfall, together with frost risk, drives the choice of cultivar and sowing date, resulting in a flowering time between October in the south and August in the north. In eastern Australia, crops are therefore exposed to contrasting climatic conditions during the critical period around flowering, which may affect yield potential, and the efficiency in the use of water (WUE) and radiation (RUE). In this work we analysed empirical and simulated data, to identify key climatic drivers of potential water- and radiation-use efficiency, derive a simple climatic index of environmental potentiality, and provide an example of how a simple climatic index could be used to quantify the spatial and temporal variability in resource-use efficiency and potential yield in eastern Australia. Around anthesis, from Horsham to Emerald, median vapour pressure deficit (VPD) increased from 0.92 to 1.28 kPa, average temperature increased from 12.9 to 15.2°C, and the fraction of diffuse radiation (FDR) decreased from 0.61 to 0.41. These spatial gradients in climatic drivers accounted for significant gradients in modelled efficiencies: median transpiration WUE (WUEB/T) increased southwards at a rate of 2.6% per degree latitude and median RUE increased southwards at a rate of 1.1% per degree latitude. Modelled and empirical data confirmed previously established relationships between WUEB/T and VPD, and between RUE and photosynthetically active radiation (PAR) and FDR. Our analysis also revealed a non-causal inverse relationship between VPD and radiation-use efficiency, and a previously unnoticed causal positive relationship between FDR and water-use efficiency. Grain yield (range 1-7 t/ha) measured in field experiments across South Australia, New South Wales, and Queensland (n = 55) was unrelated to the photothermal quotient (Pq = PAR/T) around anthesis, but was significantly associated (r2 = 0.41, P < 0.0001) with newly developed climatic index: a normalised photothermal quotient (NPq = Pq . FDR/VPD). This highlights the importance of diffuse radiation and vapour pressure deficit as sources of variation in yield in eastern Australia. Specific experiments designed to uncouple VPD and FDR and more mechanistic crop models might be required to further disentangle the relationships between efficiencies and climate drivers.

Reassessing spatial and temporal dynamics of kangaroo populations

Long-running datasets from aerial surveys of kangaroos (Macropus giganteus, Macropus [uliginosus, Macropus robustus and Macropus rufus) across Queensland, New South Wales and South Australia have been analysed, seeking better predictors of rates of increase which would allow aerial surveys to be undertaken less frequently than annually. Early models of changes in kangaroo numbers in response to rainfall had shown great promise, but much variability. We used normalised difference vegetation index (NDVI) instead, reasoning that changes in pasture condition would provide a better predictor than rainfall. However, except at a fine scale, NDVI proved no better; although two linked periods of rainfall proved useful predictors of rates of increase, this was only in some areas for some species. The good correlations reported in earlier studies were a consequence of data dominated by large droughtinduced adult mortality, whereas over a longer time frame and where changes between years are less dramatic, juvenile survival has the strongest influence on dynamics. Further, harvesting, density dependence and competition with domestic stock are additional and important influences and it is now clear that kangaroo movement has a greater influence on population dynamics than had been assumed. Accordingly, previous conclusions about kangaroo populations as simple systems driven by rainfall need to be reassessed. Examination of this large dataset has permitted descriptions of shifts in distribution of three species across eastern Australia, changes in dispersion in response to rainfall, and an evaluation of using harvest statistics as an index of density and harvest rate. These results have been combined into a risk assessment and decision theory framework to identify optimal monitoring strategies.

Comparison of compost and a sandy loam as turf underlay materials on salt-affected parkland

Turfgrasses range from extremely salt sensitive to highly salt tolerant. However, the selection of a salt tolerant turf is not a 'silver bullet' solution to successful turf growth on salt-affected parklands. Interactions between factors such as cultivar, construction practices, establishment, and maintenance can be complex and should not be considered in isolation of one another. Taking this holistic approach, a study investigating cultivar evaluation for salt-affected sites also included a comparison of topsoil materials as turf underlay, as well as pre-treatment of the sod. The turf species and cultivars used in the study were: Cynodon dactylon, cultivar 'Oz Tuff (I) '; Paspalum vaginatum, cultivars 'Sea Isle 1 (I) ' and 'Velvetene (I) '; Zoysia matrella cultivar 'A-1 (I) '; and Zoysia japonica, cultivar 'Empire (I) '. The two underlay materials were compost (100%) or a sandy clay topsoil each applied above a coastal sand profile to a depth of 10 cm. Rooting depth or root dry weight did not significantly differ among turf cultivars. Compost profile treatment had significantly greater root mass than the topsoil among all turf cultivars. This higher root production was reflected by improved quality of all turf at the final evaluation. Turfgrass grown on compost had a higher normalised difference vegetation index (NDVI), regardless of whether full sod or bare-rooted turfgrass was used. The use of a quality underlay was paramount to the successful growth of the turf cultivars investigated. While each cultivar had superior performance in sub-optimal conditions, the key to success was the selection of the right species and cultivar for each situation combined with proper establishment and maintenance of each turf grass.

Phenotyping novel stay-green traits to capture genetic variation in senescence dynamics

Stay-green plants retain green leaves longer after anthesis and can have improved yield, particularly under water limitation. As senescence is a dynamic process, genotypes with different senescence patterns may exhibit similar final normalised difference vegetative index (NDVI). By monitoring NDVI from as early as awn emergence to maturity, we demonstrate that analysing senescence dynamics improves insight into genotypic stay-green variation. A senescence evaluation tool was developed to fit a logistic function to NDVI data and used to analyse data from three environments for a wheat (Triticum aestivum L.) population whose lines contrast for stay-green. Key stay-green traits were estimated including, maximum NDVI, senescence rate and a trait integrating NDVI variation after anthesis, as well as the timing from anthesis to onset, midpoint and conclusion of senescence. The integrative trait and the timing to onset and mid-senescence exhibited high positive correlations with yield and a high heritability in the three studied environments. Senescence rate was correlated with yield in some environments, whereas maximum NDVI was associated with yield in a drought-stressed environment. Where resources preclude frequent measurements, we found that NDVI measurements may be restricted to the period of rapid senescence, but caution is required when dealing with lines of different phenology. In contrast, regular monitoring during the whole period after flowering allows the estimation of senescence dynamics traits that may be reliably compared across genotypes and environments. We anticipate that selection for stay-green traits will enhance genetic progress towards high-yielding, stay-green germplasm.

Estimation and evaluation of multi-decadal fire severity patterns using Landsat sensors

Retrospective identification of fire severity can improve our understanding of fire behaviour and ecological responses. However, burnt area records for many ecosystems are non-existent or incomplete, and those that are documented rarely include fire severity data. Retrospective analysis using satellite remote sensing data captured over extended periods can provide better estimates of fire history. This study aimed to assess the relationship between the Landsat differenced normalised burn ratio (dNBR) and field measured geometrically structured composite burn index (GeoCBI) for retrospective analysis of fire severity over a 23 year period in sclerophyll woodland and heath ecosystems. Further, we assessed for reduced dNBR fire severity classification accuracies associated with vegetation regrowth at increasing time between ignition and image capture. This was achieved by assessing four Landsat images captured at increasing time since ignition of the most recent burnt area. We found significant linear GeoCBI–dNBR relationships (R2 = 0.81 and 0.71) for data collected across ecosystems and for Eucalyptus racemosa ecosystems, respectively. Non-significant and weak linear relationships were observed for heath and Melaleuca quinquenervia ecosystems, suggesting that GeoCBI–dNBR was not appropriate for fire severity classification in specific ecosystems. Therefore, retrospective fire severity was classified across ecosystems. Landsat images captured within ~ 30 days after fire events were minimally affected by post burn vegetation regrowth.