The estimation of actual crop evapotranspiration (ETc) from any given land cover or crop type is important for irrigation water management and agricultural water consumption analysis. The main parameter used for such estimations is the crop coefficient (Kc). Spectral reflectance indices, such as the normalized difference vegetation index (NDVI) and the crop coefficient of a specific crop or pasture canopy are important indicators of 'vigour', namely the photosynthetic activity and rate of biomass accumulation. Measuring both parameters simultaneously, with a view to understanding how they interact, or for creating optical, surrogate indicators of Kc is very difficult because Kc itself is difficult to measure. In this study a portable enclosed chamber was used to measure ETc of a pasture and subsequently calculated Kc from reference evapotranspiration (ETo) data derived from a nearby automatic weather station (AWS). Calibration of the chamber confirms the suitability of the device to measure the amount of water vapour produced by local plant evapotranspiration, producing a calibration factor (C) close to 1 (C=1.02, R2=0.87). The coincident NDVI values were measured using a portable active optical sensor. In a test involving a pasture (Festuca arundinacea var. Dovey) at two different stages of growth in two consecutive growing seasons, the NDVI and crop coefficients were observed to be strongly correlated (R2=0.80 and 0.77, respectively). A polynomial regression (R2=0.84) was found to be the best fit for the combined, multi-temporal Kc-NDVI relationship. The main advantages of this method include the suitability of operating at a smaller scale (<1 m2), in real time and repeatability.

Monitoring pasture growth rate is an important component of managing grazing livestock production systems. In this study we demonstrate a pasture growth rate (PGR) model, initially designed for very large scale satellite imagery, can be operated at a scale of metres when incorporating in-situ sensor data. A light use efficiency (LUE)-based PGR model was combined with in-situ measurements from proximal weather (temperature), plant (fAPAR) and soil (relative moisture) sensors to calculate the growth rate of a tall fescue pasture. When incorporating in-situ measurements of temperature and moisture index, the model provided an accuracy (RMSE) of 1.68 kg/ha.day (R² = 0.96, p-value ≈ 0).

Pasture biomass estimation from normalized difference vegetation index (NDVI) using ground, air or space borne sensors is becoming more widely used in precision agriculture. Proximal active optical sensors (AOS) have the potential to eliminate the confounding effects of path radiance and target illumination conditions typically encountered using passive sensors. Any algorithm that infers the green fraction of pasture from NDVI must factor in plant morphology and live/dead plant ratio, irrespective of the senor used. Moreover, livestock grazing affects the morphology of pastures so the veracity of instrument calibration procedures applied under 'protected plot' conditions is questionable if the sensor is subsequently deploy as a 'calibrated sensor' into grazed fields. In this research we have simulated pasture grazing on establish plots of Tall fescue ('Festuca arundinacea') in a heavy clay (vertosol) soil and examined the effect of such grazing on the temporal NDVI values as derived using a Crop CircleTM sensor. Five plots with different soil moisture condition were maintained in the study period. Time domain reflectometer (TDR) was used to monitor volumetric soil moisture content (%) and NDVI measurements were taken on a daily basis. Following a grazing event (facilitated by uniformly mowing the grass to a height of 6 cm), biomass samples were collected on 3rd, 4th and 5th day along with coincident measures of the NDVI. For those plots with low soil moisture level (< ~37% of the full profile), the NDVI progressively decreased up to 2 or 3 days following the 'grazing' event, despite the plot biomass increasing due to regrowth. The NDVI values did not 'recover' until approximately 4 days after the 'grazing' event. However, for those plots of moderate to high soil moisture (>~37%) the NDVI-time curves monotonically increased with biomass re-growth immediately following 'grazing'. This has important ramifications for those intending to use NDVI as the basis for pasture assessment, particularly in situations involving short-term grazing rotation.