Cucurbit production plays an important role in Australia’s horticultural industry, but growers face a range of complex challenges that threaten the long-term sustainability of the sector.

One of the key challenges is pollination, a critical aspect of vegetable cultivation, which typically relies on insects to ensure effective fruit development, high yields and fruit quality. Cucurbits are monoecious plants, producing distinct male and female flowers on the same plant, which makes them particularly reliant on insect visitation for cross pollination.

Timely and accurate assessment of rice productivity, encompassing grain yield, aboveground biomass (AGB), and harvest index (HI), is essential for harvest planning, supply chain coordination, and food security. This study evaluates the feasibility of predicting all three productivity components using satellite and weather time series data while examining trade-offs between forecast accuracy and operational lead time. Five machine learning models (CatBoost, Gaussian Process Regression (GPR), Random Forest, Ridge regression, and TabPFN) were compared across six in-season prediction windows (December to May) using Sentinel-2 vegetation indices (Normalized Difference Vegetation Index (NDVI), Chlorophyll Index Red Edge 2 (CIRE2), Land Surface Water Index (LSWI)), weather variables (minimum and maximum temperature and radiation), and agronomic records from 256 commercial and experimental rice fields in southern New South Wales, Australia, over four growing seasons (2022–2025) using leave-one-year-out cross-validation. Rolling in-season forecasts were evaluated across December–May; March was selected for further analysis as a practical window that balances accuracy and timeliness for decision-making, with minimal additional error reduction in later months closer to harvest. TabPFN had the lowest RMSE for yield prediction (RMSE = 1.85 t ha⁻¹, 𝑟=0.72), Ridge had the lowest RMSE for AGB (RMSE = 3.05 t ha⁻¹, 𝑟=0.77), while tree-based models yielded the lowest RMSE for derived HI (RMSE ≈ 0.07). HI prediction showed weak regional relationships, with direct prediction yielding |𝑟|≈0.24 and derived HI (predicted yield divided by predicted AGB) showing |𝑟|≈0. Although strong correlations (𝑟>0.9) between HI and vegetation indices were observed within individual site-seasons, consistent with other studies, these relationships were highly variable across site-seasons, reflecting the difficulty of inferring HI from canopy reflectance when biotic and/or abiotic stresses decouple AGB accumulation from grain filling. Both direct and derived HI approaches yielded comparable errors, indicating that satellite and weather data lack information content for regional-scale HI prediction. These findings support satellite-based yield and AGB forecasting for operational use.

Intercultural competence (IC) is increasingly essential in higher education due to globalisation and growing classroom diversity. While much IC research centres on international students, perspectives of academic staff remain underexplored. This qualitative study addresses that gap by examining how Australian lecturers perceive and enact IC when teaching culturally and linguistically diverse (CaLD) students. Using Deardorff's (2004, 2006) process model of IC and semi-structured interviews with six academic staff from three Australian universities, it investigates how the staff understand and apply IC, and the institutional support they receive to develop IC. Findings show that IC development largely stems from personal and professional experience, rather than formal training. Participants reported increased awareness of diverse student needs and communicative styles but noted limited institutional guidance, structured reflection, or targeted professional development. The study highlights a set of staff attributes conducive to IC development and characteristics of structured programmes to support staff professional development.

Purpose – This study aims to explore the intricate relationship between housing prices and transaction volumes in Tehran, a city with diverse socioeconomic and regional characteristics. This research addresses a critical gap in understanding the role of local spatial factors, which previous studies have often overlooked, focusing instead on macroeconomic variables.

Design/methodology/approach – Using a data set of housing transactions of Metropolitan Tehran from 2010 to 2020 sourced from secondary data, this study uses generalized linear mixed models and spatial clustering techniques. These methods enable an examination of geographical clustering and the effects of local contextual variables on the dynamics between housing prices and transaction volumes.

Findings – Results indicate significant spatial heterogeneity within Tehran’s housing market. Higher prices and transaction volumes are concentrated in the northern and western regions, influenced by factors such as employment rates, rental housing supply and the physical attributes of the housing stock. The findings suggest that macroeconomic policies alone are insufficient to address housing challenges in Tehran; targeted, localized interventions are necessary.

Research limitations/implications – This study’s reliance on secondary data and its focus on a single urban environment may limit the generalizability of the findings. Further research incorporating a wider range of local and macro variables could strengthen the applicability of the results across different contexts.

Practical implications – This study underscores the need for region-specific housing policies that consider local economic, social and spatial conditions. Policymakers could improve housing affordability and accessibility in Tehran by implementing tailored strategies to address the distinct needs of different districts.

Originality/value – This study offers a novel perspective by integrating spatial and contextual factors in housing market analysis, providing insights that challenge the traditional macroeconomic focus. The use of advanced statistical and spatial analysis techniques contributes to a deeper understanding of urban housing market dynamics.