Frost Risk Management in Chickpea Using a Modelling Approach

Post-flowering frosts cause appreciable losses to the Australian chickpea industry. The Northern Grains Region (NGR) of Australia, which accounts for nearly 95% of chickpea production in Australia, is frequently subjected to such events. The objective of this study was to map frost risk in chickpea in the NGR and develop strategies to minimise the impacts of such risk. The Agricultural Production System Simulator (APSIM) modelling framework was used to determine spatial and temporal trends in post-flowering frost risk. The NGR could be divided into six broad sub-regions, each delineating locations with similar frost risk. The risk was nearly two to three times greater in the Southern Downs and Darling Downs sub-regions as compared to the Central Queensland Highlands, Dawson Callide, New South Wales, and Northern New South Wales–Western Downs sub-regions. There was an increasing trend in the frequency of frost events in the Southern Downs and New South Wales sub-regions, and a decreasing trend in the Central Queensland Highlands and Dawson Callide sub-regions, consistent with the changing climate of the NGR. In each sub-region, frost risk declined with delayed sowings, but such sowings resulted in simulation of reduced water limited yield potential (unfrosted) as well. The model output was also used to compute 10, 30, 50, and 70% probabilities of the last day of experiencing −3 to 2 °C minimum temperatures and identify the earliest possible sowings that would avoid such temperatures after flowering. Choosing the earliest sowing times with a 30% frost risk could help increase overall yields in environments with high frost risk. Simulations involving genotype x environment x management interactions suggested additional opportunities to minimise frost losses through the adoption of particular cultivars of differing phenology and the use of different agronomy in various environments of the NGR. The study indicates that there is considerable variation in frost risk across the NGR and that manipulating flowering times either through time of sowing or cultivar choice could assist in minimising yield losses in chickpea due to frost

Inheritance of photo-sensitivity in pigeonpea

Pigeonpea [Cajanus cajan (L.) Millsp.] is a short-day legume species and the late maturing genotypes are more photosensitive than early types. To generate information about the inheritance of photo-sensitivity, this study was conducted under natural and artificially extended (16 h) photo-periods using F1, F2 and BC1F1 generations. Under natural photo-period, F1 hybrids showed partial dominance of earliness; while in F2, a normal distribution that was skewed towards earliness was observed. In contrast under extended photo-period, the spread of F2 data was wide with discontinuities recorded at day 70, 82 and 103. Chisquare tests, when applied to F2 and BC1F1 data, suggested that three dominant genes (PS3, PS2 and PS1) controlled the expression of photo-sensitivity. These genes were found operating in a hierarchical order with PS2 and PS1 genes failing to express in the presence of PS3 gene. Similarly in the absence of PS3 gene, PS2 expressed but it masked the expression of PS1. Further, PS1 gene expressed only when both PS3 and PS2 were in recessive homozygous state. Hence, the proposed genetic model for photosensitivity in pigeonpea is PS3 > PS2 > PS1 and photoinsensitive genotype being a triple recessive (ps3ps3ps2 ps2ps1ps1)

Relationships of frequencies of extreme low temperatures with grain yield of some Australian commercial chickpea cultivars

In this study, we examined the relationships between extremes of low temperatures and chickpea yield in 12 field experiments conducted at six sites in the subtropical environment of southeast Queensland (SEQ) from 2014 to 2019. Three commercial chickpea cultivars, PBA-Boundary, PBA-HatTrick and PBA-Seamer, were grown in all the experiments. Cultivars PBA-Pistol, PBA-Monarch and Kyabra were also included in three of these experiments conducted in 2015. In these experiments, the crop experienced a total of 8 to 41 frosts (minimum temperature <  = 0 °C), 2 to 41 pre-flowering frosts, 2 to 19 frosts during the critical period, 0 to 13 frosts and 2 to 71 low-temperature days (< = 15 °C) after flowering. The mean yield, which varied from 1 to 3 t/ha, was negatively related to post-flowering frosts (r =  − 0.74, p < 0.01) and low-temperature days (r =  − 0.76, p < 0.01), and positively related to pre-flowering frosts (r = 0.67, p < 0.05). Each post-flowering frost was associated with a 5% decrease and a low-temperature day with a 1% decrease in yield. The cultivar × site interaction was significant only in the three experiments with six commercial cultivars. This interaction was most likely due to an increase in the sensitivity range with additional cultivars, as indicated by frost damage scores and their relationships with yield. The results imply that extreme low-temperature events after flowering could negatively impact chickpea yield in SEQ and similar subtropical environments. Overcoming these effects through management and breeding should increase and stabilise chickpea yield

Peanut agronomy experiments with five varieties in the Bundaberg and Kingaroy regions in the 2021-22 season

This report summarises the results of the peanut agronomy experiments conducted at Bundaberg and Kingaroy during the 2021-22 season as part of the above project. The experiments were laid out in a split-plot design with three replications at each location. Five peanut varieties, including Holt, Alloway, Kairi, Wheeler and P85-p112-151 (P85), were assigned to main plots, and four plant populations, 6, 12, 18, and 24 plants per m2, were assigned to subplots. All varieties were runner types except Wheeler, which represented a 'Virginia' type. Planting was done by the precision planting 20/20® and vSet® electronic seed metering system. The experiments were irrigated using irrigation scheduling software Aquaman via the web-based 'Yield Prophet'

Can partial reduction of shoot biomass during early vegetative phase of chickpea save subsoil water for reproductive and pod filling?

The present study investigated if partial reduction of shoot dry matter during early vegetative growth phase of chickpea crop (cv. PBA Seamer) saves sub-soil water for reproductive growth and grain filling of the crop grown at 9 diverse environments. The environments were created by a combination of 3 sites (Emerald, Hermitage and Kingaroy), 3 planting windows (environments 1, 2, 3 at each site) with and without supplementary irrigation. The effects of environments on canopy management (partial reduction in shoot dry matter vs control) and irrigation treatments on the water uptake by roots, crop growth and yield performance and yield components were investigated. Crops in the planting windows (EN 1, 2, 3) experienced variable environments at each site. Days to 50% flowering and crop maturity reduced progressively from EN 1 to EN 3 at the three sites. The environment had significant effect on shoot biomass, yield and HI at the three sites (P  0.5 in EN 2 at Emerald. There was a trend for an increase in HI from EN 1 to EN 3 at all sites. The response to Irr, computed as the difference in peak shoot biomass and yield between the Irr and RF treatments, was the highest at Hermitage and the least at Emerald site. Vapour pressure deficit during reproductive phase accounted for the majority of variation in shoot biomass response to irrigation (r2 =0.66, P < 0.001) for total dry matter and (r2 =0.46, P < 0.01) for yield. The environments had a significant effect on radiation use efficiency and water use efficiency and the yield components including hundred seed weight

Physiological and Agronomic Strategies to Increase Mungbean Yield in Climatically Variable Environments of Northern Australia

Mungbean (Vigna radiata (L.) Wilczek) in Australia has been transformed from a niche opportunistic crop into a major summer cropping option for dryland growers in the summer-dominant rainfall regions of Queensland and New South Wales. This transformation followed stepwise genetic improvements in both grain yields and disease resistance. For example, more recent cultivars such as &lsquo;Crystal&rsquo;, &lsquo;Satin II&rsquo;, and &lsquo;Jade-AU&lsquo; have provided up to a 20% yield advantage over initial introductions. Improved agronomic management to enable mechanised management and cultivation in narrow (&lt;50 cm) rows has further promised to increase yields. Nevertheless, average yields achieved by growers for their mungbean crops remain less than 1 t/ha, and are much more variable than other broad acre crops. Further increases in yield and crop resilience in mungbean are vital. In this review, opportunities to improve mungbean productivity have been analysed at four key levels including phenology, leaf area development, dry matter accumulation, and its partitioning into grain yield. Improving the prediction of phenology in mungbean may provide further scope for genetic improvements that better match crop duration to the characteristics of target environments. There is also scope to improve grain yields by increasing dry matter production through the development of more efficient leaf canopies. This may introduce additional production risks as dry matter production depends on the amount of available water, which varies considerably within and across growing regions in Australia. Improving crop yields by exploiting G &times; E &times; M interactions related to cultivar photo-thermal sensitivities and make better use of available water in these variable environments is likely to be a less risky strategy. Improved characterisation of growing environments using modelling approaches could also better define and identify the risks of major abiotic constraints. This would assist in optimising breeding and management strategies to increase grain yield and crop resilience in mungbean for the benefit of growers and the industry

Potential productivity and water requirements of maize-peanut rotations in Australian semi-arid tropical environments - A crop simulation study

The growing demand for maize (Zea mays L.) in intensive livestock and other industries has opened up fresh opportunities for further expansion of the maize industry in Australia, which could be targeted in relatively water rich semi-arid tropical (SAT) regions of the country. This crop simulation study assessed the potential productivity and water requirements of maize peanut (Arachis hypogaea L.) rotations for the SAT climatic zone of Australia using the Agricultural Production Systems Simulator (APSIM) model. APSIM was configured to simulate maize (Pioneer hybrid 3153) either in the dry (May–October) or wet season (November–April) and peanut (cv. Conder) in the following season for three soils found at Katherine (14.48°S, 132.25°E) from 1957 to 2008. The simulated mean total yield potential of the dry season maize and wet season peanut (DMWP) rotation (15–19.2 t/ha) was about 28% greater than the wet season maize–dry season peanut (WMDP) rotation because of the higher yield potential of maize in the dry season compared to in the wet season. These high yields in the DMWP rotation have been achieved commercially. The overall simulated irrigation water requirement for both rotations, which varied from 11.5 to 13.8 ML/ha on different soils, was similar. The DMWP rotation had 21% higher water use efficiency. Similar yield and water use efficiency advantages of the DMWP rotation were apparent for eight other agriculturally important locations in the Northern Territory, Western Australia and Queensland. The simulations for Katherine also suggested that the irrigation requirement of the two rotations could increase by 17.5% in El-Nino years compared to La-Nina years for only a small gain in yield, which has implications for climate change scenarios

Chapter 11 - Peanut

Peanut is presently grown in over 26 Mha worldwide with a total production of over 45 Mt. About 80% of the world peanut production comes from rainfed regions of the semiarid tropics, where the yields are generally low and variable due to erratic water deficits and elevated temperature. Peanut generally responds favourably to water deficit applied from emergence to start of flowering, resulting in increased pod yields. Sensitivity to water deficit increases progressively during the reproductive phase. Biomass accumulation in peanut is usually proportional to the amount of water transpired by the plant. Researchers have demonstrated variation in transpiration efficiency (TE) between peanut genotypes with similar transpiration (T). Tis chapter describes genotypic, environmental, and management factors affecting T and TE. This chapter describes major nutrient deficiencies that affect productivity and seed quality production in many regions of the world. With increasing drought frequency in semiarid tropics, there is interest in breeding shorter duration cultivars. However, much of the short-duration germplasm available in the global gene banks has low yield in favourable environments, and poor seed quality and foliar disease resistance. Major introgression effort to incorporate all these traits into adapted early-maturing genotypes has been largely successful over the past decade. In 1950’s peanut has been described as ‘the unpredictable legume’ because of its unpredictable responses to inputs. However, the physiological principles developed in the past decades have been successfully applied in peanut crop models making the crop performance predictable across diverse environments

Risks of yield loss due to variation in optimum density for different maize genotypes under variable environmental conditions

Understanding the nature of complex genotype-by-environment-by-management interactions is crucial to identify risks and opportunities for increasing maize yield and profitability in rainfed production systems. The objectives of this study were to (i) define the conditions where hybrids of different maturity and plant densities are viable options in terms of improving productivity, and (ii) quantify the risk levels associated with different genotype-by-management combinations in relation to target environments. Responses to plant density were analysed on field experimentation with different genotypes representing early, medium and late maturity types and 2, 4 and 6 plant/m2 plant densities at three major or potential dryland maize production environments in Queensland, Australia. Agricultural Production Systems sIMulator (APSIM)-Maize module was employed to simulate yield responses and compute the cumulative probability distribution. APSIM simulations suggested that the risk of expecting a yield level less than 2 t/ha increased up to about 17 and 27% for quick and late maturing types, respectively, when density increased to 10 plants/m2 in marginal environments such as Emerald. In relatively better environments, however the risk increased only up to 10% for late hybrids, and 7% for a quick hybrid at 10 plants/m2. In both high and low potential environments, choice of hybrids and plant densities should be based on seasonal weather forecasts to minimize risks and maximize opportunities for higher yields