Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts

We tested the capacity of several published multispectral indices to estimate the nitrogen nutrition of wheat canopies grown under different levels of water supply and plant density and derived a simple canopy reflectance index that is greatly independent of those factors. Planar domain geometry was used to account for mixed signals from the canopy and soil when the ground cover was low. A nitrogen stress index was developed, which adjusts shoot %N for plant biomass and area, thereby accounting for environmental conditions that affect growth, such as crop water status. The canopy chlorophyll content index (CCCi) and the modified spectral ratio planar index (mSRPi) could explain 68 and 69% of the observed variability in the nitrogen nutrition of the crop as early as Zadoks 33, irrespective of water status or ground cover. The CCCi was derived from the combination of 3 wavebands 670, 720 and 790 nm, and the mSRPi from 445, 705 and 750 nm, together with broader bands in the NIR and RED. The potential for their spatial application over large fields/paddocks is discussed.

Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI).

Varying the spatial distribution of applied nitrogen (N) fertilizer to match demand in crops has been shown to increase profits in Australia. Better matching the timing of N inputs to plant requirements has been shown to improve nitrogen use efficiency and crop yields and could reduce nitrous oxide emissions from broad acre grains. Farmers in the wheat production area of south eastern Australia are increasingly splitting N application with the second timing applied at stem elongation (Zadoks 30). Spectral indices have shown the ability to detect crop canopy N status but a robust method using a consistent calibration that functions across seasons has been lacking. One spectral index, the canopy chlorophyll content index (CCCI) designed to detect canopy N using three wavebands along the "red edge" of the spectrum was combined with the canopy nitrogen index (CNI), which was developed to normalize for crop biomass and correct for the N dilution effect of crop canopies. The CCCI-CNI index approach was applied to a 3-year study to develop a single calibration derived from a wheat crop sown in research plots near Horsham, Victoria, Australia. The index was able to predict canopy N (g m-2) from Zadoks 14-37 with an r2 of 0.97 and RMSE of 0.65 g N m-2 when dry weight biomass by area was also considered. We suggest that measures of N estimated from remote methods use N per unit area as the metric and that reference directly to canopy %N is not an appropriate method for estimating plant concentration without first accounting for the N dilution effect. This approach provides a link to crop development rather than creating a purely numerical relationship. The sole biophysical input, biomass, is challenging to quantify robustly via spectral methods. Combining remote sensing with crop modelling could provide a robust method for estimating biomass and therefore a method to estimate canopy N remotely. Future research will explore this and the use of active and passive sensor technologies for use in precision farming for targeted N management.

Patch selection by cattle can be quantified using satellite imagery and GPS in extensive, semi-arid savannas.

Patch selection by grazing animals is difficult to quantify, particularly in large, extensive paddocks like those in northern Australia. However, advances in high resolution satellite imagery now allow identification of patch types over an entire paddock which combined with GPS collars to capture positional data, can give an accurate and comprehensive picture of landscape use by cattle.

Geographic and Phenotypic Variation in Heartwood and Essential-Oil Characters in Natural Populations of Santalum austrocaledonicum in Vanuatu.

Phenotypic variation in heartwood and essential-oil characters of Santalum austrocaledonicum was assessed across eleven populations on seven islands of Vanuatu Trees differed significantly in their percentage heartwood cross-sectional area and this varied independently of stem diameter The concentrations of the four major essential-oil constituents (alpha-santalol, beta-santalol. (Z)-beta-curcumen-12-ol, and cis-nuciferol) of alcohol-extracted heartwood exhibited at least tenfold and continuous tree-to-tree variation Commercially important components alpha- and beta-santalol found in individual trees ranged from 0 8-47% and 0-24 1%, respectively, across all populations, and significant (P < 0 05) differences for each were found between Individual populations. The Erromango population was unique in that the mean concentrations of its monocyclic ((Z)-beta-curcumen-12-ol and cis-nuciferol) sesquiterpenes exceeded those of Its bi- and tricyclic (alpha- and beta-santalol) sesquiterpenes Heartwood colour varied between trees and spanned 65 colour categories, but no identifiable relationships were found between heartwood colour and alpha- and beta-santalol, although a weak relationship was evident between colour saturation and total oil concentration These results indicate that the heartwood colour is not a reliable predictive trait for oil quality The results of this study highlight the knowledge gaps in fundamental understanding of heartwood biology in Santalum genus The intraspecific variation in heartwood cross-sectional area. oil concentration. and oil quality traits is of considerable importance to the domestication of sandalwood and present opportunities for the development of highly superior S austrocaledonicum cultivars that conform to the industry's International Standards used for S album.

Improving productivity in mixed-species plantations.

A workshop held in Ballina (NSW, Australia) during 5–7 August 2005 in association with the XXII IUFRO World Congress, sought to quantify the potential of mixtures to sustain and enhance the growth and productivity, soil fertility, tree and stand health, wood quality, and economics of polyculture plantations. Participants were specifically asked to address whether demonstrable productivity gains in mixed-species plantations, compared with monoculture plantations, could make mixtures a commercially attractive option. We specifically sought to attract presentations addressing the operational challenges of making mixed-species plantations practical and successful, and attracted representatives from several industrial plantation agencies.

Ground Cover Monitoring in the Fitzroy Basin

Ground Cover Monitoring in the Fitzroy Basin.

Hardwood Drying Length Evaluation

Evaluating the length of time required to dry hardwood timber using vacuum drying compared to conventional drying facilities and technology.

FWPA Cullity Fellowship AR Redman

FWPA Cullity Fellowship research program - Characterisation of wood properties and transverse anatomy for vacuum drying modelling of commercially important Australian hardwood species.

PN08.2047 Vacuum Drying

Research the viability of vacuum drying Australian commercially important hardwood species.

Computational Modelling of the Vacuum Drying of Australian Hardwoods

Computational Modelling of the Vacuum Drying of Australian Hardwoods.

Smart technologies for peanut industry

Commercialisation and adoption of remote sensing and GIS technologies for improved production forecasting, productivity, quality and paddock- to- plate tracking within the Australian Peanut Industry.

Productivity and marketing enhancement for peanut in Papua New Guinea and Australia

Enhance productivity of peanuts in Papua New Guinea and Australia. Also the application of remote sensing technologies to enhance profitability in peanut systems.

Estimating winter crop area across seasons and regions using time-sequential MODIS imagery.

The wheat grain industry is Australia's second largest agricultural export commodity. There is an increasing demand for accurate, objective and near real-time crop production information by industry. The advent of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite platform has augmented the capability of satellite-based applications to capture reflectance over large areas at acceptable pixel scale, cost and accuracy. The use of multi-temporal MODIS-enhanced vegetation index (EVI) imagery to determine crop area was investigated in this article. Here the rigour of the harmonic analysis of time-series (HANTS) and early-season metric approaches was assessed when extrapolating over the entire Queensland (QLD) cropping region for the 2005 and 2006 seasons. Early-season crop area estimates, at least 4 months before harvest, produced high accuracy at pixel and regional scales with percent errors of -8.6% and -26% for the 2005 and 2006 seasons, respectively. In discriminating among crops at pixel and regional scale, the HANTS approach showed high accuracy. The errors for specific area estimates for wheat, barley and chickpea were 9.9%, -5.2% and 10.9% (for 2005) and -2.8%, -78% and 64% (for 2006), respectively. Area estimates of total winter crop, wheat, barley and chickpea resulted in coefficient of determination (R(2)) values of 0.92, 0.89, 0.82 and 0.52, when contrasted against the actual shire-scale data. A significantly high coefficient of determination (0.87) was achieved for total winter crop area estimates in August across all shires for the 2006 season. Furthermore, the HANTS approach showed high accuracy in discriminating cropping area from non-cropping area and highlighted the need for accurate and up-to-date land use maps. The extrapolability of these approaches to determine total and specific winter crop area estimates, well before flowering, showed good utility across larger areas and seasons. Hence, it is envisaged that this technology might be transferable to different regions across Australia.