Despite the increase of publications focusing on the consequences of extreme weather events (EWE) for the agricultural sector, a specific review of EWE related to agriculture is missing. This work aimed at assessing the interrelation between EWE and agriculture through a systematic quantitative review of current scientific literature. The review analysed 19 major cropping systems (cereals, legumes, viticulture, horticulture and pastures) across five continents. Documents were extracted from the Scopus database and examined with a text mining tool to appraise the trend of publications across the years, the specific EWE-related issues examined and the research gaps addressed. The results highlighted that food security and economic losses due to the EWE represent a major interest of the scientific community. Implementation of remote sensing and imagery techniques for monitoring and detecting the effects of EWE is still underdeveloped. Large research gaps still lie in the areas concerning the effects of EWE on major cash crops (grapevine and tomato) and the agronomic dynamics of EWE in developing countries. Current knowledge on the physiological dynamics regulating the responses of main crops to EWE appears to be well established, while more research is urgently needed in the fields of mitigation measures and governance systems.

Heatwaves are common in many viticultural regions of Australia. We evaluated the potential of satellite-based remote sensing to detect the effects of high temperatures on grapevines in a South Australian vineyard over the 2016–2017 and 2017–2018 seasons. The study involved: (i) comparing the normalized difference vegetation index (NDVI) from medium- and high-resolution satellite images; (ii) determining correlations between environmental conditions and vegetation indices (Vis); and (iii) identifying VIs that best indicate heatwave effects. Pearson’s correlation and Bland–Altman testing showed a significant agreement between the NDVI of high- and medium-resolution imagery (R = 0.74, estimated difference −0.093). The band and the VI most sensitive to changes in environmental conditions were 705 nm and enhanced vegetation index (EVI), both of which correlated with relative humidity (R = 0.65 and R = 0.62, respectively). Conversely, SWIR (short wave infrared, 1610 nm) exhibited a negative correlation with growing degree days (R = −0.64). The analysis of heat stress showed that green and red edge bands—the chlorophyll absorption ratio index (CARI) and transformed chlorophyll absorption ratio index (TCARI)—were negatively correlated with thermal environmental parameters such as air and soil temperature and growing degree days (GDDs). The red and red edge bands—the soil-adjusted vegetation index (SAVI) and CARI2—were correlated with relative humidity. To the best of our knowledge, this is the first study demonstrating the effectiveness of using medium-resolution imagery for the detection of heat stress on grapevines in irrigated vineyards.

Early detection of within‐field yield variability for high‐value commodity crops, such as cotton (Gossypium spp.), offers growers potential to improve decision‐making, optimize yields, and increase profits. Over recent years, publicly available datasets have become increasingly available and at a resolution where within‐field yield prediction is possible. However, the viability of using these datasets with machine learning to predict within‐field cotton lint yield at key growth stages are largely unknown. This study was conducted on two cotton fields, located near Mungindi, New South Wales, Australia. Three years of yield data, soil, elevation, rainfall, and Landsat imagery were collected from each field. A total of 12 models were created using: (a) two machine learning algorithms: random forest (RF) and gradient boosting machines (GBM); (b) three growth stages: squaring, flowering, and boll‐fill; and (c) two different amounts of variables: all variables and the optimal variables determined by a recursive feature elimination (RFE). Results showed a strong agreement between predicted and observed yields at flowering and boll‐fill when more information was available. At flowering and boll‐fill, root mean square error (RMSE) ranged between 0.15 and 0.20 t ha⁻¹ and Lin's concordance correlation coefficient (LCCC) ranged between 0.50 and 0.66, with RF providing superior results in most cases. Models created using the optimal variables determined by the RFE provided similar results compared to using all variables, allowing greater model accuracy and resolution with targeted sampling. Overall, these findings indicate significant potential of publicly available datasets to predict within‐field cotton yield and guide decision‐making in‐season.