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.

‘How to’ guide for measuring fan performance and efficiency in meat chicken sheds

This ‘how to’ guide provides readers with method to measure fan performance and energy efficiency of fans installed in meat chicken sheds. These methods are also useful for identifying fans that are under-performing or require maintenance. For more information about fan energy efficiency, a complementary report is available on the RIRDC website ‘Review of fan efficiency in meat chicken sheds’ (RIRDC Publication No. 15/018). A spreadsheet was also developed under this project for comparing and ranking fans against others in terms of energy efficiency, air flow and costs (‘Tunnel Ventilation Fan Comparison Spreadsheet’), and is available on the RIRDC website.

A promising and simple method to quantify soil/manure mixing on beef feedlot pens

On beef cattle feed pen surfaces, fresh and decayed manure is mixed with base rock or soil (base). Quantifying this mixing has beneficial applications for aspects including nutrient and greenhouse gas budgeting. However, no practical methods exist to quantify mixing. We investigated if measuring element concentrations in: (A) fresh manure, (B) base material, and (C) pen manure offers a promising method to quantify manure/base mixing on pen surfaces. Using three operational beef feedlots as study sites, we targeted carbon (C), and silicon (Si), which are the two most abundant and easily measurable organic and inorganic elements. Our results revealed that C concentrations were strongly (>15 times) and significantly (P < 0.05) higher whereas Si concentrations strongly (>10 times) and significantly (P < 0.01) lower in fresh manure than base material at all three sites. These relative concentrations were not significantly impacted by manure decay, as determined by an 18-week incubation experiment. This suggested that both of these elements are suitable markers for quantifying base/manure mixing on pens. However, due to the chemical change of manure during decay, C was shown to be an imprecise marker of base/manure mixing. By contrast, using Si to estimate base/manure mixing was largely unaffected by manure decay. These findings were confirmed by measuring C and Si concentrations in stockpiled pen surface manure from one of the sites. Using Si concentrations is a promising approach to quantify base/manure mixing on feed pens given that this element is abundantly concentrated in soils and rocks.

Comparison of firmness meters for measuring ‘Hass’ avocado fruit firmness

The temporal passage of fruit through the supply chain and the selection of consumable fruit by shoppers depend primarily upon fruit firmness. Traditionally, fruit firmness measuring methods, like Effegi and conical probes, are relatively inefficient and destructive. Simple, accurate and non-damaging methods of measuring fruit firmness are ideally required to help assure eating quality to the consumer without fruit wastage. The firmness of 'Hass' avocado fruit at a range of ripening stages was measured with the various different firmness measuring techniques of the Sinclair iQ Firmness Tester (SIQFT), the Electronic Firmometer (EF), the Analogue Firmness Meter (AFM) and hand squeezing. Measurements were made by each method at different points on the same fruit. Destructive bruise assessment was performed 48 h later, thereby allowing sufficient time for fruit to express any bruising resulting from the act of firmness measurements. Non-linear relationships were determined between fruit firmness values measured with the different techniques. The adjusted R2 for the relationship between the SIQFT and the EF was 91.6%. For the SIQFT and the AFM, the adjusted R2 was 73.7%. It was 77.7% for the SIQFT and hand squeezing. A significantly (P<0.05) high incidence of bruising was associated with firmness assessment by the EF as compared with either the SIQFT or the AFM. Among the methods compared, the SIQFT was non-damaging compared with the EF and relatively efficient for measuring the firmness. This instrument merits consideration as a quality control tool of choice in 'Hass' avocado supply chains.