Rapid monitoring of cropland primary productivity and shipping activity in Ukraine.

Ukraine is an important global exporter of grain, especially to several countries with vulnerable food systems. The war in Ukraine may disrupt global food supply by limiting the planting, growth, and harvest of crops, or disrupting grain supply logistics. We apply a novel statistical modelling approach to satellite images of cropland in Ukraine for fast inference and exploration of cropping patterns and their influences in challenging environments.

The shifting influence of future water and temperature stress on the optimal flowering period for wheat in Western Australia.

An optimal flowering period (OFP) minimises the long-term combined risk of extreme weather events on crop yield and exists in all environments. With climate change, the frequency, timing and intensity of these events are likely to change, which in turn may shift the OFP. It is important to explore how the OFP would change under a future climate.

Predicting Cereal Root Disease in Western Australia Using Soil DNA and Environmental Parameters.

Root diseases have long been prevalent in Australian grain-growing regions, and most management decisions to reduce the risk of yield loss need to be implemented before the crop is sown. The levels of pathogens that cause the major root diseases can be measured using DNA-based services such as PreDicta B. Although these pathogens are often studied individually, in the field they often occur as mixed populations and their combined effect on crop production is likely to vary across diverse cropping environments.

Comparing agglomerative clustering and three weed classification frameworks to assess the invasiveness of alien species across spatial scales.

To prioritize weed management at the catchment scale, information is required on the species present, their relatively frequency, abundance, and likely spread and impact. The objective of this study was to classify the invasiveness of alien species that have invaded the Upper Burdekin Catchment in Queensland, Australia, at three spatial scales. A combination of three published weed classification frameworks and multivariate techniques were employed to classify species based on their frequency and cover at a range of spatial scales.