Crop yield must increase to keep pace with growing global demand. Past increases in crop production have rarely been attributable to an individual innovation but have occurred when technologies and practices combine to form improved farming systems. Inevitably this has involved synergy between genotypic and management improvements. We argue that research focused on developing synergistic systems that overcome clear production constraints will accelerate increases in yield. This offers the opportunity to better focus and multiply the impact of discipline-focused research. Here we use the rainfed grain production systems of south-eastern Australia as a case study of how transformational change in water productivity can be achieved with research focused on genotype × management synergies. In this region, rainfall is low and variable and has declined since 1990. Despite this, growers have maintained yields by implementing synergistic systems combining innovations in (i) soil water conservation, (ii) crop diversity, (iii) earlier sowing, and (iv) matching nitrogen fertilizer to water-limited demand. Further increases are emerging from synergies between genetic improvements to deliver flowering time stability, adjusted sowing times, and potential dual-purpose use. Collaboration between agronomists, physiologists, and crop breeders has led to development of commercial genotypes with stable flowering time that are in early phases of testing and adoption.

Context: Mungbean (Vigna radiata (L.) Wilczek) is an important grain legume for food, feed, and green manure. Mungbean yield is highly variable due to fluctuating temperature and unpredictable rainfall. Aims: To improve yield stability, it is critical to utilise a model that can simulate mungbean phenology, biomass, and yield accurately. Methods: A thorough understanding of the physiological determinants of growth and yield is required to advance existing mungbean crop modelling capability. Currently, there is limited understanding of the physiological determinants of canopy and reproductive development and their variation in mungbean germplasm. Two experiments (controlled and field environments) were conducted at Gatton, Queensland, in 2018–19. Six Australian mungbean genotypes and one black gram (Vigna mungo L.) were grown under non-limiting conditions. Plant phenotypic traits (canopy development, time to first, 50% flowering, duration of flowering and podding, flower appearance, pod addition rates) were recorded. Key results: Genotypes M10403 and Satin II had significantly higher leaf appearance rate (LAR). Genotypes with a greater LAR had higher number of leaves but lower individual leaf area. Genotypes varied significantly in time to first and 50% flowering, with Onyx-AU (black gram) and Celera II-AU flowering earliest. Flowering and podding rates, and duration of these phenological phases varied among genotypes. Total plant leaf area (TPLA) approached its maximum at mid-podding stage. Implications: This study quantified the key phenotypic and physiological relationships associated with canopy and reproductive development, critical for the improvement of mungbean crop modelling required to accurately simulate growth and development and inform possible canopy constraints that are limiting mungbean productivity.

Context: Unmanned aerial vehicles (UAV) with red–green–blue (RGB) cameras are increasingly used as a monitoring tool in farming systems. This is the first field study in mungbean (Vigna radiata (L.) Wilzcek) using UAV and image analysis across multiple seasons. Aims: This study aims to validate the use of UAV imagery to assess growth parameters (biomass, leaf area, fractional light interception and radiation use efficiency) in mungbean across multiple seasons. Methods: Field experiments were conducted in summer 2018/19 and spring–summer 2019/20 for three sowing dates. Growth parameters were collected fortnightly to match UAV flights throughout crop development. Fractional vegetation cover (FVC) and computed vegetation indices: colour index of vegetation extraction (CIVE), green leaf index (GLI), excess green index (ExG), normalised green-red difference index (NGRDI) and visible atmospherically resistant index (VARI) were generated from UAV orthomosaic images. Key results: (1) Mungbean biomass can be accurately estimated at the pre-flowering stage using RGB imagery acquired with UAVs; (2) a more accurate relationship between the UAV-based RGB imagery and ground data was observed during pre-flowering compared to post-flowering stages in mungbean; (3) FVC strongly correlated with biomass (R2 = 0.79) during the pre-flowering stage; NGRDI (R2 = 0.86) showed a better ability to directly predict biomass across the three experiments in the pre-flowering stages. Conclusion: UAV-based RGB imagery is a promising technology to replace manual light interception measurements and predict biomass, particularly at earlier growth stages of mungbean. Implication: These findings can assist researchers in evaluating agronomic strategies and considering the necessary management practices for different seasonal conditions.

Understanding genotypic variability in tolerance to heat stress during flowering, a critical growth stage, and post-stress recovery remains limited in mungbean (Vigna radiata) genotypes. This study investigates the genetic variability in in vitro pollen viability, seed set, and grain yield among mungbean genotypes in response to transient high temperatures. Thirteen genotypes were evaluated in a glasshouse study, and four in a field study, subjected to high temperatures (around 40 °C/22 °C day/night) imposed midday during flowering. Across all genotypes, the pollen viability percentage significantly decreased from 70% to 30%, accompanied by reductions in the pod size and seed number per pod, and increases in unfertilized pods and unviable seeds. However, the seed yield per plant significantly increased for four genotypes (M12036, Celera-II AU, Crystal, and M11238/AGG325961), attributed to elevated shoot growth and pod numbers under high-temperature treatment in the glasshouse study. Conversely, Satin II, which exhibited the highest stress tolerance index, recorded a greater seed yield under optimum conditions compared to high temperatures. Similar genotypic variability in post-heat-stress recovery and rapid growth was observed in the field study. Under non-limiting water conditions, mungbean genotypes with a relatively more indeterminate growth habit mitigated the heat stress’s impact on their pollen viability by swiftly increasing their post-stress vegetative and reproductive growth. The physiological mechanisms underlying post-stress rapid growth in these genotypes warrant further investigation and consideration in future breeding trials and mitigation strategies.

Context Unmanned aerial vehicles (UAV) with red–green–blue (RGB) cameras are increasingly used as a monitoring tool in farming systems. This is the first field study in mungbean (Vigna radiata (L.) Wilzcek) using UAV and image analysis across multiple seasons. Aims This study aims to validate the use of UAV imagery to assess growth parameters (biomass, leaf area, fractional light interception and radiation use efficiency) in mungbean across multiple seasons. Methods Field experiments were conducted in summer 2018/19 and spring–summer 2019/20 for three sowing dates. Growth parameters were collected fortnightly to match UAV flights throughout crop development. Fractional vegetation cover (FVC) and computed vegetation indices: colour index of vegetation extraction (CIVE), green leaf index (GLI), excess green index (ExG), normalised green-red difference index (NGRDI) and visible atmospherically resistant index (VARI) were generated from UAV orthomosaic images. Key results (1) Mungbean biomass can be accurately estimated at the pre-flowering stage using RGB imagery acquired with UAVs; (2) a more accurate relationship between the UAV-based RGB imagery and ground data was observed during pre-flowering compared to post-flowering stages in mungbean; (3) FVC strongly correlated with biomass (R2 = 0.79) during the pre-flowering stage; NGRDI (R2 = 0.86) showed a better ability to directly predict biomass across the three experiments in the pre-flowering stages. Conclusion UAV-based RGB imagery is a promising technology to replace manual light interception measurements and predict biomass, particularly at earlier growth stages of mungbean. Implication These findings can assist researchers in evaluating agronomic strategies and considering the necessary management practices for different seasonal conditions.

Mungbean has become an important cash and legume rotation crop in the Australian Northern Grains region. Thus, it is necessary to narrow the gap between potential productivity and actual production by understanding the crop physiological attributes contributing to the acquisition of radiation, and it’s conversion into total biomass. A field experiment was conducted at Gatton, during 2018-19 summer season, growing two commercial varieties of mungbean; Jade-AU and Satin II under irrigated conditions. The varieties were planted at 0.5m and 1.0m row spacing and different plant densities. Weekly biomass cuts were taken from a square meter and separated into its components. The area of a green leaf sub-sample was used to compute the leaf area of the canopy. Weekly Ceptometer measurements were taken above and below the canopy at noon on clear, sunny days to obtain a measure of Fraction of radiation interception (F_i). Total shoot dry matter (TDM), pod dry mater (PDM) and leaf area index (LAI) was investigated. There was a significant effect of canopy density on F_i. There was no significant difference in total dry matter between the two varieties, however, there was a highly significant effect of canopy density on TDM. A highly significant variation in LAI amongst the two varieties and across canopy densities, with Satin II having a higher mean LAI compared to Jade-AU. There was significant effect of canopy density on PDM. Our data indicate variation in dry matter production across canopy densities and there is a need to examine varietal differences in radiation use efficiency which may provide better understanding of hot the captured radiation is utilized in biomass production in mungbean.