The main objective of this work was to study the feasibility of using the green red vegetation index (GRVI) and the red edge ratio (RE/R) obtained from UAS imagery for monitoring the effects of soil water deficit and for predicting fibre quality in a surface-irrigated cotton crop. The performance of these indices to track the effects of water stress on cotton was compared to that of the normalised difference vegetation index (NDVI) and crop water stress index (CWSI). The study was conducted during two consecutive seasons on a commercial farm where three irrigation frequencies and two nitrogen rates were being tested. High-resolution multispectral images of the site were acquired on four dates in 2017 and six dates in 2018, encompassing a range of matric potential values. Leaf stomatal conductance was also measured at the image acquisition times. At harvest, lint yield and fibre quality (micronaire) were determined for each treatment. Results showed that within each year, the N rates tested (> 180 kg N ha⁻¹) did not have a statistically significant effect on the spectral indices. Larger intervals between irrigations in the less frequently irrigated treatments led to an increase (p < 0.05) in the CWSI and a reduction (p < 0.05) in the GRVI, RE/R, and to a lesser extent in the NDVI. A statistically significant and good correlation was observed between the GRVI and RE/R with soil matric potential and stomatal conductance at specific dates. The GRVI and RE/R were in accordance with the soil and plant water status when plants experienced a mild level of water stress. In most of the cases, the GRVI and RE/R displayed long-term effects of the water stress on plants, thus hampering their use for determinations of the actual soil and plant water status. The NDVI was a better predictor of lint yield than the GRVI and RE/R. However, both GRVI and RE/R correlated well (p < 0.01) with micronaire in both years of study and were better predictors of micronaire than the NDVI. This research presents the GRVI and RE/R as good predictors of fibre quality with potential to be used from satellite platforms. This would provide cotton producers the possibility of designing specific harvesting plans in the case that large fibre quality variability was expected to avoid discount prices. Further research is needed to evaluate the capability of these indices obtained from satellite platforms and to study whether these results obtained for cotton can be extrapolated to other crops.

The present work assessed the usefulness of a set of spectral indices obtained from an unmanned aerial system (UAS) for tracking spatial and temporal variability of nitrogen (N) status as well as for predicting lint yield in a commercial cotton (Gossypium hirsutum L.) farm. Organic, inorganic and a combination of both types of fertilizers were used to provide a range of eight N rates from 0 to 340 kg N ha⁻¹. Multi-spectral images (reflectance in the blue, green, red, red edge and near infrared bands) were acquired on seven days throughout the season, from 62 to 169 days after sowing (DAS), and data were used to compute structure- and chlorophyll-sensitive vegetation indices (VIs). Above-ground plant biomass was sampled at first flower, first cracked boll and maturity and total plant N concentration (N%) and N uptake determined. Lint yield was determined at harvest and the relationships with the VIs explored. Results showed that differences in plant N% and N uptake between treatments increased as the season progressed. Early in the season, when fertilizer applications can still have an effect on lint yield, the simplified canopy chlorophyll content index (SCCCI) was the index that best explained the variation in N uptake and plant N% between treatments. Around first cracked boll and maturity, the linear regression obtained for the relationships between the VIs and both plant N% and N uptake was statistically significant, with the highest r² values obtained at maturity. The normalized difference red edge (NDRE) index, and SCCCI were generally the indices that best distinguished the treatments according to the N uptake and total plant N%. Treatments with the highest N rates (from 307 to 340 kg N ha⁻¹) had lower normalized difference vegetation index (NDVI) than treatments with 0 and 130 kg N ha⁻¹ at the first measurement day (62 DAS), suggesting that factors other than fertilization N rate affected plant growth at this early stage of the crop. This fact affected the earliest date at which the structure-sensitive indices NDVI and the visible atmospherically resistant index (VARI) enabled yield prediction (97 DAS). A statistically significant linear regression was obtained for the relationships between SCCCI and NDRE with lint yield at 83 DAS. Overall, this study shows the practicality of using an UAS to monitor the spatial and temporal variability of cotton N status in commercial farms. It also illustrates the challenges of using multi-spectral information for fertilization recommendation in cotton at early stages of the crop.

Integrated rice–fish culture is a competitive alternative to rice monoculture for environmental sustainability and food productivity. Compared to rice monoculture, rearing fish in rice field ecosystems could increase food (rice and fish) production from this coculture. Moreover, the water productivity of rice–fish coculture is considerably higher than that of rice monoculture, because of double cropping. Despite these benefits, rice–fish coculture has not yet been broadly practiced. One of the potential challenges for the wider adoption of rice–fish coculture is water management. There are two forms of water involved in rice–fish cultivation: (1) blue water–surface and groundwater, and (2) green water–soil water from rainfall. The aim of this article is to focus on key factors determining the adoption of rice–fish cultivation through the effective utilization of blue–green water. We suggest that the efficient application of blue and green water in rice–fish coculture could help confronting water scarcity, reducing water footprint, and increasing water productivity.

Irrigated rice is the largest user of precious global water reserves. Adoption of water-saving irrigation practices is limited by the associated increased labor demand compared to flooded rice cultivation. Automated gravity surface irrigation systems have shown the potential to deliver significant labor savings in traditional flooded rice; however, widespread adoption does not seem apparent. Furthermore, previously designed systems have not been capable of irrigation control during both ponded and non-ponded periods. This study aimed to evaluate the performance of an automated irrigation system for rice with features not previously developed, provide direction for future systems and analyze the opportunity cost (the value of other on- or off-farm activities that could be conducted with that time) of time associated with automated irrigation. The automated irrigation system was found to successfully control 23–31 flush-irrigation events per bay per season in a 9-bay border-check aerobic rice field for 2 seasons. In addition, successful water control was achieved in a traditional drill-sown field with 4 flush irrigations followed by 15 weeks of permanent flooding. Labor savings of 82–88% during the flush-irrigation events and 57% during the ponding period were achieved with automation when compared to manual irrigation. However, the opportunity cost of the saved time was found to comprise the greatest benefit. Changing the analysis from using a flat “cash” cost of time to using opportunity cost of time reduced the payback period from seven to four years at the traditional ponded-rice site. In the more labor-intensive aerobic rice site, the payback period was reduced from three years to one year when accounting for the opportunity cost of time as opposed to only the direct costs. Whilst the payback period is site-dependent and cultivation method-dependent, these case studies demonstrate that automated gravity surface irrigation can enable novel water-saving practices in rice and provide substantial economic benefits.

Irrigated agriculture requires high reliability from water delivery networks and high flows to satisfy demand at seasonal peak times. Aquatic vegetation in irrigation channels are a major impediment to this, constraining flow rates. This work investigates the use of remote sensing from unmanned aerial vehicles (UAVs) and satellite platforms to monitor and classify vegetation, with a view to using this data to implement targeted weed control strategies and assessing the effectiveness of these control strategies. The images are processed in Google Earth Engine (GEE), including co-registration, atmospheric correction, band statistic calculation, clustering and classification. A combination of unsupervised and supervised classification methods is used to allow semi-automatic training of a new classifier for each new image, improving robustness and efficiency. The accuracy of classification algorithms with various band combinations and spatial resolutions is investigated. With three classes (water, land and weed), good accuracy (typical validation kappa >0.9) was achieved with classification and regression tree (CART) classifier; red, green, blue and near-infrared (RGBN) bands; and resolutions better than 1 m. A demonstration of using a time-series of UAV images over a number of irrigation channel stretches to monitor weed areas after application of mechanical and chemical control is given. The classification method is also applied to high-resolution satellite images, demonstrating scalability of developed techniques to detect weed areas across very large irrigation networks.

Declining water availability is pressing rice growers to adopt water-saving irrigation practices such as aerobic rice to maintain profitability per megalitre (ML) of water input. Irrigators require well-defined irrigation thresholds to initiate irrigation to maximise water productivity. Such thresholds do not exist for temperate rice regions. Adopting a strategy that has been reported to succeed in non-temperate environments may fail in temperate climates, and therefore, needs investigation. This study aimed to investigate, in a temperate Australian environment, the effect of increasing soil moisture deficit during the rice vegetative period on crop physiological development, grain yield and water productivity. The study was conducted in a commercial farm using a randomised complete block design in the 2020/21 and 2021/22 growing seasons. Automated gravity surface irrigation technologies were adopted to enable high-frequency irrigation. Extending soil moisture deficit beyond 15 kPa was found to significantly delay panicle initiation by at least 13–14 days, exposing rice to cold temperatures in Year 1 during the cold-sensitive early pollen microspore period. This reduced yield by up to 55% (4.5 t/ha) compared to the 15 kPa treatment that was not impacted by cold sterility. In the absence of cold sterility, irrigated water productivity and total water productivity ranged between 1.02 and 1.61 t/ML, and 0.84 and 0.93 t/ML, respectively. The highest yields (8.1 and 7.5 t/ha) were achieved irrigating at a soil tension of 15 kPa in growing seasons 2020/21 and 2021/22. This research demonstrates that sound water productivity can be achieved with aerobic rice cultivation in temperate climates, providing cold temperatures during early pollen microspore are avoided. The quantification of the delay in crop development caused by increasing soil moisture deficit provides rice farmers greater confidence in determining the irrigation strategy and timing of pre-emergent irrigation in regions at risk of cold sterility. However, due to the high labour demand associated with aerobic rice, the adoption of aerobic rice at a commercial scale in this Australian environment is unlikely without adopting automated irrigation technology.