Assessing the relationship between shire winter crop yield and seasonal variability of the MODIS NDVI and EVI images

Australian researchers have been developing robust yield estimation models, based mainly on the crop growth response to water availability during the crop season. However, knowledge of spatial distribution of yields within and across the production regions can be improved by the use of remote sensing techniques. Images of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, available since 1999, have the potential to contribute to crop yield estimation. The objective of this study was to analyse the relationship between winter crop yields and the spectral information available in MODIS vegetation index images at the shire level. The study was carried out in the Jondaryan and Pittsworth shires, Queensland , Australia . Five years (2000 to 2004) of 250m resolution, 16-day composite of MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images were used during the winter crop season (April to November). Seasonal variability of the profiles of the vegetation index images for each crop season using different regions of interest (cropping mask) were displayed and analysed. Correlation analysis between wheat and barley yield data and MODIS image values were also conducted. The results showed high seasonal variability in the NDVI and EVI profiles, and the EVI values were consistently lower than those of the NDVI. The highest image values were observed in 2003 (in contrast to 2004), and were associated with rainfall amount and distribution. The seasonal variability of the profiles was similar in both shires, with minimum values in June and maximum values at the end of August. NDVI and EVI images showed sensitivity to seasonal variability of the vegetation and exhibited good association (e.g. r = 0.84, r = 0.77) with winter crop yields.

Optical peanut yield monitor: Development and testing

An optical peanut yield monitor was developed, fabricated, and field-tested. The overall system includes an optical mass-flow sensor, a GPS receiver, and a data acquisition system. The concept for the mass-flow sensor is based on that of the cotton yield-monitor sensor developed previously by Thomasson and Sui (2000). A modified version of the sensor was designed to be specific to peanut mass-flow measurement. Field testing of the peanut yield monitor was conducted in Australia during the May 2003 harvest. After subsequent minor modifications, the system was more extensively tested in Mississippi in October of 2003 and November of 2004. Test results showed that the output of the peanut mass-flow sensor was very strongly correlated with the harvested load weight, and the system's performance was stable and reliable during the tests.

Use of optical density as a measure of Claviceps africana conidial suspension concentration

Sorghum ergot, caused by Claviceps africana, has remained a major disease problem in Australia since it was first recorded in 1996, and is the focus of a range of biological and integrated management research. Artificial inoculation using conidial suspensions is an important tool in this research. Ergot infection is greatly influenced by environmental factors, so it is important to reduce controllable sources of variation such as inoculum concentration. The use of optical density was tested as a method of quantifying conidial suspensions of C. africana, as an alternative to haemocytometer counts. This method was found to be accurate and time efficient, with possible applications in other disease systems.