Mungbean (Vigna radiata (L.) R.Wilczek.) is an important annual legume cultivated in subtropical regions for its high-protein grains. However, it is susceptible to low temperatures (<20°C) during germination and establishment, which results in substantial yield loss. Early growth stages are crucial for successful cultivation in cooler climates to enable an optimal sowing window and effective establishment. This study aimed to identify cold-tolerant mungbean genotypes adapted to low-temperature germination (<20°C), particularly in southern Australia during November-December. The effects of temperature (14, 17 and 20°C) and soil water availability (40 and 80% of field capacity) on the germination and emergence of mungbean genotype were investigated through three experiments. In Experiment 1, thirty-two genotypes were evaluated for germination at constant temperatures of 14, 17 and 20°C using germination paper towels in a controlled environment. Additionally, in Experiment 2 and 3 in controlled environment experiments using soil-filled pots were conducted to determine the effect of temperature under constant (14, 17, and 20°C) and a range of diurnal temperature regimes (10–18°C, 13–21°C, and 16–24°C), ensuring that the average temperature for each treatment remained at 14, 17, and 20°C respectively. These temperatures were tested in factorial combination with soil water status (40 and 80% of field capacity) on the germination and emergence of commercial varieties Jade-AU and Celera II-AU. Germination occurred at all tested temperatures, with the highest germination percentage observed at 20°C on paper towels. Genotypes Putland, Jade-AU, Bari Mung-3, Bari Mung-4, Satin II, and Bina Mung-8 showed no significant differences in germination rates among the 14, 17, and 20°C temperature treatments, with average germination percentages exceeding 80% in the paper-towel evaluation. The median germination rate observed was highly variable (2–16 days) across genotypes in response to temperature treatment. The estimated base temperature of Celera II-AU and Jade-AU was 8.6 and 9.8°C, respectively. Seedling emergence was faster and higher for Celera II-AU than Jade-AU across the diurnal and constant temperatures. The germination was observed at both diurnal and constant temperature treatments of 20, 17, and 14°C. However, no emergence was observed at a constant temperature of 14°C for varieties Jade-AU and Celera II-AU. These findings suggest that mungbean can be successfully sown in early spring of southern Australia if soil temperature is at least 17°C. This research provides valuable insights for future breeding programs, germination studies, and sowing date recommendations in temperate environments.

ABSTRACT Mungbean is predominantly cultivated within a subtropical climate, and its potential for cultivation in temperate regions as a summer crop has not been comprehensively assessed. This research investigates the influence of temperature and seasonal variation with sowing time on mungbean phenology, growth and yield at five locations within the temperate environment of southeastern Australia (Victoria). Five mungbean varieties (Jade‐AU, Celera II‐AU, Golden Dragon, Putland and Crystal) were evaluated across two times of sowing (TOS) (spring and early summer) over 2 years (2020–2021 and 2021–2022 summers) at sites in southwestern (Hamilton and Horsham), northeastern (Rutherglen) and northwestern (Ouyen and Woomelang) Victoria. These locations are characterised by winter‐dominant rainfall patterns with sporadic summer rainfall events, which can be significant. Annual rainfall varied across sites: low rainfall (< 350 mm, Ouyen and Woomelang), medium rainfall (350–450 mm, Horsham) and high rainfall (> 350 mm, Hamilton and Rutherglen). Early sowing coincided with lower soil temperatures, leading to delayed emergence and reduced establishment. However, TOS did not significantly influence grain yield, except at Rutherglen (TOS 1, 715 kg/ha; TOS 2, 1576 kg/ha). Grain yield was correlated with grain number and biomass, significantly contributing to variations in yield. The variety Jade‐AU exhibited superior performance in low and medium rainfall regions in Victoria, whereas Putland and Crystal varieties demonstrated optimal performance in high rainfall zones. Under dryland cropping conditions, northeastern Victoria (e.g., Rutherglen) may represent the most promising opportunity for mungbean cultivation. Nevertheless, when selecting varieties for summer mungbean cultivation in southern Australia, it is imperative to consider differences in days to first flowering, yield potential, heat stress tolerance and rainfall patterns.

Mungbean is grown as a summer crop in subtropical climates globally. The global demand for mungbean is increasing, and opportunities exist to expand production regions to more marginal environments, such as southern Australia, as an opportunistic summer crop to help meet the growing global demand. Mungbean has the potential to be an opportunistic summer crop when an appropriate sowing window coincides with sufficient soil water. This expansion from subtropical to temperate climates will pose challenges, including low temperatures, a longer day length and a low and variable water supply. To assess mungbean suitability to temperate, southern Australian summer rainfall patterns and soil water availability, we conducted field experiments applying a range of water treatments across four locations with contrasting rainfall patterns within the state of Victoria (in southern Australia) in 2020–2021 and 2021–2022. The water treatments were applied prior to sowing (60 mm), the vegetative stage (40 mm) and the reproductive stage (40 mm) in a factorial combination at each location. Two commercial cultivars, Celera II-AU and Jade-AU, were used. Water scarcity during flowering and the pod-filling stages were important factors constraining yield. Analysis of yield components showed that increasing water availability at critical growth stages, viz. the vegetative and reproductive stages, of mungbean was associated with increases in total biomass, HI and grain number in addition to increased water use and water use efficiency (WUE). Average WUEs ranged from 1.3 to 7.6 kg·ha−1·mm−1. The maximum potential WUE values were 6.4 and 5.1 kg·ha−1·mm−1 for Celera II-AU and Jade-AU across the sites, with the estimated soil evaporation values (x-intercept) of 83 and 74 mm, respectively. Nitrogen fixation was variable, with %Ndfa values ranging from 9.6 to 76.8%, and was significantly affected by soil water availability. This study emphasises the importance of water availability during the reproductive phase for mungbean yield. The high rainfall zones within Victoria have the potential to grow mungbean as an opportunistic summer crop.

Faba bean grain yield and quality traits were effectively predicted using in-season hyperspectral data and partial least squares regression (PLSR) models. The optimal window for pre-harvest trait prediction was from flowering to pod filling stages. Near-infrared (NIR) regions between 750 and 950 and 1000–1600 nm were key contributors to predicting grain quality traits. Canopy-level hyperspectral data predicts pre-harvest grain traits better than leaf and pod spectral data. Faba bean (Vicia faba L.) is a sustainable protein source, but in-season stresses such as heat, drought and diseases cause grain discolouration and shrivelling, leading to market downgrades. Grain quality assessments are only performed post-harvest, limiting growers’ ability to manage quality risks proactively on-farm. To address this limitation, this study explored the potential of in-season hyperspectral sensing as a non-destructive, data-driven tool for early grain quality assessment. This study aimed to assess faba bean grain yield and quality pre-harvest by identifying optimal reproductive growth stage(s) and spectral regions linked to target grain traits. Hyperspectral data were collected at five locations in Victoria, Australia across five critical reproductive growth stages: flowering (BBCH 65–69), podding (BBCH 70–79), pod fill (BBCH 80–82), pod maturity (BBCH 83–89), and crop senescence (BBCH 90–99). Partial least squares regression (PLSR) models were applied to canopy, leaf and pod level spectra to extract wavelength-trait relationships and identify predictive temporal windows for faba bean grain traits prediction prior to harvest. This approach enabled identification of both temporal (growth stage) and spectral (wavelength region) factors most informative for early trait prediction. Grain traits predicted include grain yield, harvest index, grain number, single grain weight, seed size index (SSI), grain protein content, seed coat brightness, redness and yellowness. Canopy-level spectra provided the most reliable predictions. Harvest index (R² = 0.71, d-index = 0.75) and GPC (R² = 0.73, d-index = 0.76) were predicted as early as the flowering stage. The podding stage was optimal for predicting single grain weight (R² = 0.91, d-index = 0.76), SSI (R² = 0.71, d-index = 0.74), seed coat redness (R² = 0.68, d-index = 0.77) and yellowness (R² = 0.61, d-index = 0.68). Near-infrared (NIR) regions, 750–950 and 1000–1800 nm, were most informative for predicting grain quality traits. These findings demonstrate the potential of integrating hyperspectral sensing with chemometric modelling to enable pre-harvest prediction of faba bean grain agronomic and quality traits. Hyperspectral sensing as a precision agriculture application can mitigate on-farm grain quality downgrade risks by supporting early, data-driven harvest management decisions that maximise growers’ profitability and sustainability.

This study presents an explanatory biophysical model developed and validated to simulate seed coat colour traits including CIE L*, a*, and b* changes over time for stored lentil cultivars PBA Hallmark, PBA Hurricane, PBA Bolt, and PBA Jumbo2 under diverse storage conditions. The model showed robust performance for all cultivars, with R2 values ≥ 0.89 and RMSE values ≤ 0.0019 for all seed coat colour traits. Laboratory validation at 35 °C demonstrated a high agreement (Lin’s Concordance Correlation Coefficient, CCC ≥ 0.82) between simulated and observed values of all colour traits for PBA Jumbo2 and strong agreement (CCC ≥ 0.81) for PBA Hallmark in brightness (CIE L*) and redness (CIE a*), but not in yellowness (CIE b*). At 15 °C, both cultivars exhibited moderate to weak agreement between simulated and observed values of all colour traits (CCC ≤ 0.47), as very little change was recorded in the observed values over the 360 days of storage. Bulk storage system validation for PBA Hallmark showed moderate performance (CCC ≥ 0.46) between simulated and observed values of all colour traits. Modelling to simulate changes in seed coat colour traits of lentils over time will equip growers and traders to make informed managerial decisions when storing lentils for long periods.

The biochemistry underlying seed coat darkening of lentil due to extended storage is limited. This study investigated the relationship between seed coat darkening over time during storage and changes in concentration of phenolic compounds (total phenolic compounds, total condensed tannins, proanthocyanidins and anthocyanins) in two red lentil cultivars (PBA Hallmark and PBA Jumbo2), stored at two grain moisture contents (10 and 14%, w/w) and two temperatures (4 and 35 °C) for 360 days. Seed coat darkening was only significant (p = 0.05) at high temperatures (35 °C) but not at low temperatures (4 °C), irrespective of grain moisture content and cultivar. The concentration of all phenolic compounds tested in this study reduced significantly (p = 0.05) throughout the study period, regardless of temperature and grain moisture treatments. The changes in seed coat brightness and redness followed a linear pattern, except for yellowness, where phenolic compounds initially reduced linearly and then remained constant thereafter. Darkening of seedcoat was only associated with the reduction in phenolic compounds tested in this study at 35 °C, and not at 4 °C. This suggests that seed coat darkening due to extended storage may not be directly linked to broad reductions in the groups of phenolic compounds or individual compounds assessed in this study. This information prompts further research to identify the actual biochemical processes that cause the darkening of seed coats during storage and assist in developing cultivars with stable seed coat colour by selecting and modifying such processes.

For broad-acre crops grown in Mediterranean-type environments, variation in lentil (Lens culinaris) yield and quality occurs due to seasonal abiotic and biotic stresses. Because grain quality affects the price paid to growers, in-season assessment of likely final quality using remote sensing technologies could limit economic losses by informing spatial management at harvest. For a survey of lentil crops grown in southern Australia, in 2019 and 2020, Moran's I analysis identified significant field spatial autocorrelation for the grain quality traits of grain protein concentration (GPC), grain size, and grain brightness (CIE L*, where CIE is International Commission on Illumination), indicating an opportunity for zoning at harvest. Partial least squares calibration models of observed grain quality and proximal reflectance spectra were successfully derived for grain weight (R2 = 0.80), GPC (R2 = 0.80), and CIE L* (R2 = 0.86). For late senescence, Sentinel-2 satellite canopy reflectance, grain size was best predicted (R2 = 0.79) and GPC was poorer (R2 = 0.42). Spatial maps of fields for grain size, informed by models, could be derived and determined that for the market critical threshold (38 mg), field area that exceeded this threshold ranged between 30% and 94%. Overall, we determined that sensing technologies had utility for mapping lentil grain quality across fields, providing a potential tool for growers to selectively harvest to achieve best aggregate price based on grain quality targets. Further calibration and validation with multiple years and locations is also needed to test model stability and application to varying environments. Variation in lentil grain quality occurs due to abiotic and biotic stresses in dryland cropping environments.Selectively harvesting fields could help growers achieve best aggregate price based on grain quality targets.Remote sensing technologies using canopy reflectance (CR) could inform spatial management of lentil at harvest.Partial Least Squares models of CR could reasonably describe quality traits of grain weight protein and grain brightness.Moran's I identified field spatial autocorrelation for grain quality traits, thus indicating an opportunity for harvest zoning.

In semi-arid cropping regions where rainfall is variable, stored soil water is important for reducing the impacts of dry periods; however, in-season evaporation limits yield potential. We tested the productivity benefits of protecting wheat and lentil crops from evaporation using inter-row polymer cover, when grown in southern Australia. For wheat, white polyvinyl chloride (PVCw) increased yield by 50% (561 kg/ha) and 11% (408 kg/ha) for decile 2 (2018) and 3 (2019) seasonal rainfall respectively; and for lentil, yield increases were 20% (443 kg/ha) in 2019. A sprayable alginate polymer increased wheat yield by 8% (310 kg/ha). Total water use for wheat in 2018 was reduced (17 mm) for PVCw and water use efficiency (WUE) significantly increased from 7 to 11 kg/ha/mm, whereas in 2019, WU was greater (47 mm) for PVCw, but WUE was equivalent to the control (similar to 15 kg/ha/mm). Colour of the PVC modified the light available to the crop and canopy temperature, where a highly reflective inter-row cover (albedo 0.44) increased wheat yield (similar to 487 kg/ha). For the future, formulations of highly reflective polymers applied in-season within dryland cropping enterprises are likely to provide production advantages, although logistical viability of application and potential environmental implications may define impediments to adoption.

Grains intended for human consumption or feedstock are typically high-value commodities that are marketed based on either their visual characteristics or compositional properties. The combination of visual traits, chemical composition and contaminants is generally referred to as grain quality. Currently, the market value of grain is quantified at the point of receival, using trading standards defined in terms of visual criteria of the bulk grain and chemical constituency. The risk for the grower is that grain prices can fluctuate throughout the year depending on world production, quality variation and market needs. The assessment of grain quality and market value on-farm, rather than post-farm gate, may identify high- and low-quality grain and inform a fair price for growers. The economic benefits include delivering grain that meets specifications maximizing the aggregate price, increasing traceability across the supply chain from grower to consumer and identifying greater suitability of differentiated products for high-value niche markets, such as high protein product ideal for plant-based proteins. This review focuses on developments that quantify grain quality with a range of spectral sensors in an on-farm setting. If the application of sensor technologies were expanded and adopted on-farm, growers could identify the impact and manage the harvesting operation to meet a range of quality targets and provide an economic advantage to the farming enterprise.

Lentil seed coat colour influences market value, whilst germination is associated with crop establishment and hydration capacity with optimal processing outcomes. Storing lentil grain assists growers in managing price fluctuations; however, exposure to oxygen at higher temperatures during extended storage degrades seed coat colour, germination, and hydration capacity. Depleting oxygen prevents such degradation in other crops; however, studies in lentil are limited. This study examined the effects of oxygen-depleted modified atmospheres and temperatures on seed coat colour, germination, and hydration capacity in two red lentil cultivars, PBA Hallmark and PBA Jumbo2, stored for 360 days. Small volumes of lentil grain were placed in aluminium laminated bags filled with nitrogen (N2), carbon dioxide (CO2), or air and stored at either 15 or 35 °C. At 35 °C in an air atmosphere, the lentil’s seed coat significantly (p = 0.05) darkened after 30 days of storage, whereas germination and hydration capacities decreased after 60 days regardless of cultivar. In contrast, N2 and CO2 atmospheres maintained initial seed coat colour, germination, and hydration capacities in both cultivars throughout the study period regardless of temperature. Storing lentil grain in an oxygen-depleted modified atmosphere may assist to maximise returns to grower and maintain key quality traits.

Storing lentil is a strategy used by growers to manage price volatility. However, studies investigating the impact of storage conditions on the market and end use properties of lentil are limited. This study examined the effects of storage temperature (4, 15, 25, and 35 °C) and grain moisture (10 and 14%, w/w) on traits related to market (seed coat colour), viability (germination capacity), and end use properties (hydration capacity, milling efficiency, and cooking quality) in four red lentil cultivars (PBA Bolt, PBA Hallmark, PBA Hurricane, PBA Jumbo2) over 360 days. Storing lentil at 14% moisture content and 35 °C significantly (p = 0.05) darkened seed coat after 30 days, caused complete loss of viability within 180 days and reduced cooking quality (cooked firmness) after 120 days across all tested cultivars. Storing lentil at 10% moisture content and 35 °C reduced hydration capacity after 30 days, and milling efficiency after 120 days across all cultivars tested. PBA Jumbo2 exhibited a higher rate of degradation in hydration capacity and cooking quality, and a lower rate of degradation in the other traits studied. Storing lentil at ≤15 °C prevented degradation of all quality traits. These findings will support improved lentil storage protocols to maintain quality and improve economic outcomes for the pulse industry.

This review focuses on developments that quantify grain quality with a range of spectral sensors in an on-farm setting. If the application of sensor technologies were expanded and adopted on-farm, growers could identify the impact and manage the harvesting operation to meet a range of quality targets and provide an economic advantage to the farming enterprise.

Lentil production in arable, Mediterranean-type climates is limited by acute high temperature (HT) commonly occurring during the reproductive stage. With changing climate and greater weather extremes, there is a need to increase the HT tolerance of lentil to sustain production, and global germplasm provides adaptation opportunities. The current study assessed 81 genotypes for HT tolerance from a range of global climatic zones. Field screening of germplasm was undertaken over two consecutive years (2014 and 2015), in southern Australia, using a late-sowing approach, which included a subset of 22 genotypes that were screened in both years. Partially shaded temperature treatments within a split-plot arrangement were used to generate two different HT profiles. Stress indices, i.e., the yield stability index (YSI), the stress tolerance index (STI), and a third proposed high-temperature tolerance index (HTTI), were applied to rank the HT tolerance of germplasm. In 2014, under field conditions associated with natural temperature ranges that were favorable for screening, the following five landraces with increased temperature tolerance were identified: AGG 73838, AGG 70118, AGG 70951, AGG 70156, and AGG 70549. Among the 10 commercial varieties tested, one variety (i.e., cv. Nipper) was observed to have HT tolerance. For the YSI, which had the greatest amount of consistency in response across the 2 years (11 of the 22 genotypes), there were two genotypes (AGG 71457 and Nipper) which maintained their yield stability. These results demonstrate the opportunity that germplasm provides to improve the adaptation of lentil to HT. Ultimately, the late-sowing approach is one possible methodology to integrate into contemporary breeding programs for improving adaptation of lentil within Mediterranean-type environments.

Intercropping using mixtures of dryland crop species for grain or seed production was investigated in southern Australia across a range of rainfall zones over three years. The objective was to understand the productivity and profitability of intercropping in extensive, high-input grain cropping systems. Previous research has shown large productivity benefits of mixtures; however, few farmers practice intercropping in Australia, and an analysis of profitability is needed to support future potential adoption. Experimental results showed strong mixture responses (in terms of yield, value and land equivalence), but not all were profitable compared to an equivalent share of monoculture crops (as measured by gross margins). The most promising mixtures were those containing high-value crops (canola) and legumes (field pea or faba bean) at the wetter sites where the additional gross margin over equivalent monoculture crops ranged from $12/ha to $576/ha. Mixtures containing highly competitive crops (wheat or barley) were generally unprofitable. Mixtures involving cereals were doubly disadvantaged by the aggressiveness of these lower-value crops in the mixtures we examined and the high grain separation costs post-harvest. Cost reduction in mixture systems involving high-value crops that are synergistic (grain legumes) should provide enduring opportunities for intercropping in southern Australia.

Climate change impacts to crop production are likely to be greatest in semi-arid regions already constrained by marginal growing conditions. The response of temperate grain crops (wheat, field pea and lentil) to elevated CO2 (eCO(2)) (550 mu mol mol(-1)) under semi-arid field conditions was studied over 11 years in the Australian Grains Free Air CO2 Enrichment (AGFACE) research program. This review synthesizes key outcomes and implications for crop adaptation in a semi-arid environment. Across all crops and environments, eCO(2) increased mean yields (16-58%) compared to current ambient (aCO(2)) concentrations. Wheat yields increased by 18% and 29% under rainfed and supplemental irrigation, respectively resulting in yield increases of 6.1 (aCO(2)) and 14.1 (eCO(2)) kgha(-1) mm(-1) of additional water. Wheat grain [N] declined (similar to 7%) under eCO(2) across cultivars, resulting in reduced grain protein and bread baking quality, and this was not reversed by additional fertilizer N. Of several tested crop traits favorable for dryland cropping of wheat under eCO(2), a transpiration efficiency trait increased yields under eCO(2) representing a path for adaptation in semi-arid environments. The rates and amounts of N-2 fixation in legumes were increased by eCO(2) but were greater under higher soil water content. Barley yellow dwarf virus incidence increased by 10.6% due to changes in epidemiology under eCO(2). Results from AGFACE suggest that maximizing the advantages of eCO(2) requires synergistic development of adapted management systems, innovative genetics and removing physiological bottlenecks. This systems approach will increase the potential to maintain agricultural production in new combinations of environments for longer than if changes are piecemeal.

Intercropping is considered by its advocates to be a sustainable, environmentally sound, and economically advantageous cropping system. Intercropping systems are complex, with non-uniform competition between the component species within the cropping cycle, typically leading to unequal relative yields making evaluation difficult. This paper is a review of the main existing metrics used in the scientific literature to assess intercropping systems. Their strengths and limitations are discussed. Robust metrics for characterising intercropping systems are proposed. A major limitation is that current metrics assume the same management level between intercropping and monocropping systems and do not consider differences in costs of production. Another drawback is that they assume the component crops in the mixture are of equal value. Moreover, in employing metrics, many studies have considered direct and private costs and benefits only, ignoring indirect and social costs and benefits of intercropping systems per se. Furthermore, production risk and growers’ risk preferences were often overlooked. In evaluating intercropping advantage using data from field trials, four metrics are recommended that collectively take into account all important differences in private costs and benefits between intercropping and monocropping systems, specifically the Land Equivalent Ratio, Yield Ratio, Value Ratio and Net Gross Margin.

Frost damage to broadacre crops can cause up to an 85% loss in productivity. Although growers have few options for crop protection from frost, a rapid method for assessing frost-induced sterility would allow for timely management decisions (e.g., cutting for hay and altering marketing strategies). Spectral mixture analysis (SMA) has shown success in mapping landscape components and was used with hyperspectral data collected on the canopy, heads, and leaves of wheat at different sites to determine if this could quantify frost damage. Spectral libraries were assembled from canopy components collected from local field sites to generate spectral libraries for SMA from which a series of fraction sets was derived. The frost (Fr) fraction was then used to estimate final yield as a means of measuring frost damage. The best-fitting Fr fractions to yield were derived from the same data set as the source Fr spectra, and these ranged over R2 = 0.58–0.75 at the canopy scale. It was clear that spectral signatures need to be collected at scale to assess frost damage. While Fr fractions were able to estimate yield there was no “universal” endmember set from which a Fr fraction could be derived. The normalized difference vegetation index (NDVI) was not able to estimate frost damage consistently. Future work requires determining whether there is a “universal” set of endmembers and a minimum set of targeted wavebands that could lead to multispectral instruments for frost assessment for use in ground and aerial sensors.

The stimulatory effect of elevated [CO2 ] (e[CO2 ]) on crop production in future climates is likely to be cancelled out by predicted increases in average temperatures. This effect may become stronger through more frequent and severe heat waves, which are predicted to increase in most climate change scenarios. Whilst the growth and yield response of some legumes grown under the interactive effect of e[CO2 ] and heat waves has been studied, little is known about how N2 fixation and overall N metabolism is affected by this combination. To address these knowledge gaps, two lentil genotypes were grown under ambient [CO2 ] (a[CO2 ], ~400 µmol·mol-1 ) and e[CO2 ] (~550 µmol·mol-1 ) in the Australian Grains Free Air CO2 Enrichment facility and exposed to a simulated heat wave (3-day periods of high temperatures ~40 °C) at flat pod stage. Nodulation and concentrations of water-soluble carbohydrates (WSC), total free amino acids, N and N2 fixation were assessed following the imposition of the heat wave until crop maturity. Elevated [CO2 ] stimulated N2 fixation so that total N2 fixation in e[CO2 ]-grown plants was always higher than in a[CO2 ], non-stressed control plants. Heat wave triggered a significant decrease in active nodules and WSC concentrations, but e[CO2 ] had the opposite effect. Leaf N remobilization and grain N improved under interaction of e[CO2 ] and heat wave. These results suggested that larger WSC pools and nodulation under e[CO2 ] can support post-heat wave recovery of N2 fixation. Elevated [CO2 ]-induced accelerated leaf N remobilisation might contribute to restore grain N concentration following a heat wave.

This chapter discusses the importance of the management of water supply within rainfed agricultural productions systems in already dry regions that face the prospect of further decreasing rainfall from climate change. It examines the interactive effects of water supply with high temperature and rising atmospheric CO2 concentrations on grain production and quality. The chapter explores the use of simple models of transpiration efficiency and radiation use efficiency and how phenotypic crop ideotypes might help achieve the breeding and agronomic management objectives of maximizing the available water resources to maintain crop productivity. It draws examples from Australia and Spain. The examples of adaptation strategies to drought and water conservation issues from Australia and Spain are representative of typical dry and semi-arid regions where cropping is a dominant and economically important activity. Water is essential for crop production and its shortage significantly limits the productive levels that can be achieved.

The potential impact of elevated atmospheric carbon dioxide concentration ([CO2]) and future climate predicted for 2050 on wheat marketing grades and grain value was evaluated for Victoria, Australia. This evaluation was based on measured grain yield and quality from the Australian Grains FACE program and commercial grain delivery data from Victoria for five seasons (2009–13). Extrapolation of relationships derived from field experimentation under elevated [CO2] to the Victorian wheat crop indicated that 34% of grain would be downgraded by one marketing grade (range 1–62% depending on season and region) because of reduced protein concentration; and that proportions of high-protein wheat grades would reduce and proportions of lower protein grades would increase, with the largest increase in the Australian Standard White (ASW1) grade. Simulation modelling with predicted 2050 [CO2] and future climate indicated reduced wheat yields compared with 2009–13 but higher and lower grain quality depending on region. The Mallee Region was most negatively affected by climate change, with a predicted 43% yield reduction and 43% of grain downgraded by one marketing grade. Using 2016 prices, the value of Victorian wheat grain was influenced mainly by production in the different scenarios, with quality changes in different scenarios having minimal impact on grain value.