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The Australian dryland grain-cropping landscape occupies 60 Mha. The broader agricultural sector (farmers and agronomic advisors, grain handlers, commodity forecasters, input suppliers, insurance providers) required information at many spatial and temporal scales. Temporal scales included hindcasts, nowcasts and forecasts, at spatial scales ranging from sub-field to the continent. International crop-monitoring systems could not service the need of local industry for digital information on crop production estimates. Therefore, we combined a broad suite of satellite-based crop-mapping, crop-modelling and data-delivery techniques to create an integrated analytics system (Graincast™) that covers the Australian cropping landscape. In parallel with technical developments, a set of user requirements was identified through a human-centred design process, resulting in an end-product that delivered a viable crop-monitoring service to industry. This integrated analytics solution can now produce crop information at scale and on demand and can deliver the output via an application programming interface. The technology was designed to underpin digital agriculture developments for Australia. End-users are now using crop-monitoring data for operational purposes, and we argue that a vertically integrated data supply chain is required to develop crop-monitoring technology further. The Graincast™ suite of tools was developed to monitor crop productivity in near real-time across the Australian landscape. Four separate tools were created: the C-Crop model, which monitors crop yields with satellite; CropID, which monitors the crop species and areas of production; the Graincast™ app, which provides field-scale estimates of crop production and soil-water use; and Field Boundaries (ePaddocks), which identifies every cropped field across the landscape. The paper describes the design process, including data acquisition, machine learning, testing, validation, human-centred design and the delivery of outputs to end-users. [ABSTRACT FROM AUTHOR]

As the dominant form of urban agriculture (UA) in Australia, existing home food gardens potentially represent a significant resource in the context of future urban food security and sustainability. However, a severe lack of in-field data has hindered our understanding of the form and function of home food gardens which in turn may hinder innovation and improvement. We investigated the productivity, resource efficiency and potential financial savings of home food gardens in South Australia. A group of 34 citizen science participants measured and recorded inputs and outputs from their gardens. Inputs included time spent on various gardening activities, financial costs, and water use. Outputs included crop yields, from which retail value and nutritional content were then derived. The paper outlines a field-demonstrated, comprehensive methodology for continued and consistent data collection for all forms of UA. We found smaller gardens to be more intensive than larger gardens, requiring higher inputs, but also returning higher outputs per unit area. Both productivity and resource efficiency varied among the gardens, and labour requirements were significantly lower than previously estimated. Water use efficiency of the gardens were calculated and found to have comparable water use efficiency to commercial horticulture. Of the gardens involved, we calculated that 65% should break even in five or less years and save money. After applying a minimum wage almost one in five gardens were financially viable. The results represent the most comprehensive measurements on home food gardens to date, and allow practical, evidence-based recommendations for diversification, time saving and smart irrigation practices to improve garden productivity and enhance the viability of UA. [ABSTRACT FROM AUTHOR]

While precision agriculture (PA) technologies are widely used in cropping systems, these technologies have received less attention in mixed farming systems. Little is known about the nature, extent, and temporal stability of spatial variability of pastures in mixed farming systems and the feasibility of managing this variability. This paper describes a technique to create a Stability Index based on both crop grain yield and pasture total green dry matter (TGDM) production over time, using high resolution spatial data in two climatic zones of Australia. Four productivity zones were used to characterise the Stability Index: high and stable, high and unstable, low and stable, and low and unstable. Mapping the indices shows the location and size of the spatial and temporal features of each paddock. The features of the stability zones generally corresponded with soil texture classes. Testing the Stability Indices with a Kruskal–Wallis one-way ANOVA showed significantly different medians for high and low production categories for both grain yield and pasture TGDM (p < 0.01). Crop grain yield stability showed significant differences between medians. In pasture TGDM, the differences between stability medians were not significant, but the technique still separated medians into stable and unstable groupings. This production Stability Index has the potential to be used by farmers to manage spatial variability in mixed farming systems by identifying homogenous areas within a paddock for investigation/amelioration and can also separate out areas of either spatial and/or temporal instability for specific management strategies. [ABSTRACT FROM AUTHOR]

Context: Water is widely assumed to be the factor most limiting the growth of annual crops in rainfed environments, but this is rarely tested at sub-continental scale. Aims: Our study aimed to determine the key environmental and management variables influencing the yield of wheat and barley in the grain-production regions of southern Australia, using data from National Variety Trials. Methods: We used generalised additive models to determine the importance of climatic and management variables on wheat and barley grain yield. We determined the effects of the best one, two or three variables and their interactions. Key results: The aridity index, defined as the ratio of cumulative rainfall to potential evapotranspiration, was the single strongest determinant of grain yield for both crops. Model performance was further improved by separating the aridity index into pre-seasonal and seasonal components. Interestingly, other variables that might be expected to influence yield, such as nitrogen fertilisation and extreme temperatures, had relatively minor effects. A comparison between data collected over two 6-year periods showed that there had been yield gains and increased water-use efficiency with time, especially in wetter environments. Conclusions: Our findings illustrate the importance of water availability for grain production in this region and suggest opportunities for benchmarking and yield prediction through use of readily available climate data. Implications: Our study reinforces the importance of factors such as water-use efficiency and drought tolerance as goals for cultivar development and agronomic research in wheat and barley. It also highlights the potential of National Variety Trial data as a resource for understanding grain production systems and climate resilience. Further work could explore the value of additional variables and improved weather data. Farmers value simple rules of thumb for predicting crop yields. Using 12 years of National Variety Trial data, we discovered that variation in yield of wheat and barley across southern Australia is most strongly correlated with variation in aridity index, the ratio of seasonal rainfall to seasonal potential evapotranspiration. Our finding reinforces the importance of water as the primary driver dictating grain yield in southern Australia. [ABSTRACT FROM AUTHOR]

Context: Spring-sown forage brassicas are commonly used to fill feed gaps in high-rainfall temperate livestock systems, but they have wider potential as an autumn-sown forage in drier environments within Australia's crop–livestock zone. Aims: We modelled the production potential of autumn-sown forage brassicas grown in diverse environments and tested their ability to alter the frequency and magnitude of feed gaps. Methods: Long-term production potential was simulated in APSIM for four forage brassica genotypes, compared with forage wheat and dual-purpose canola across 22 diverse agro-climatic locations. For seven regions, the change in frequency and magnitude of forage deficits from adding forage brassicas to representative forage–livestock systems was predicted. Key results: Across locations, median yields of forage brassicas ranged from 7 to 19 t DM/ha, and their annual metabolisable-energy yield was higher than that of forage wheat at most sites and nearly always exceeded dual-purpose canola. Forage brassicas performed better than forage wheat in later-sowing events (late April to early May) and maintained growth and quality later into spring. At five of the seven regions, adding 15% of farm forage area to forage brassicas reduced the frequency and magnitude of feed deficits by 35–50% and 20–40%, respectively. However, they were less beneficial where winter–spring feed gaps are uncommon. Conclusions: We demonstrated that autumn-sown forage brassicas can be reliable and productive contributors to the feed base in drier environments and are a suitable alternative to forage cereals. Implications: Forage brassicas can help reduce feed gaps and improve livestock production in a range of production systems spanning Australia's crop–livestock zone. Long-term simulation modelling showed that autumn-sown forage brassicas can serve as a reliable and valuable forage source in drier environments across Australia's crop–livestock zone. They have a long grazing window and stable production across a range of sowing dates and environments. Integrating forage brassicas into the existing feed base can reduce the frequency and magnitude of on-farm feed deficits in livestock production systems. They can also allow for a safe increase in stocking rates and reduce the need for supplementary feed. [ABSTRACT FROM AUTHOR]

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Background and Aim A long-term trend in early ripening of winegrapes in southern Australia has been attributed to an increasing growing season temperature, soil drying linked to global warming and crop management. However, the 5 × 5-km cell size used used in the continental-scale model to derive soil properties was too coarse for the study vineyards of 0.2-16 ha. This paper aims to test the modelling conclusions using long records of annual rainfall for sites as close as possible to the experimental vineyards. Methods and Results If prolonged soil drying has occurred, it should be correlated with a decrease in annual rainfall, given that actual evapotranspiration should change little as the effect of increased atmospheric carbon dioxide concentration compensates for the effect of temperature increase on evaporation. Analysis of the longest, most complete Bureau of Meteorology records showed a highly significant decrease in rainfall (27 mm/decade) at Margaret River, but paradoxically no significant early ripening. Conversely, significant earlier ripening on the Mornington Peninsula was associated with a significant increase in rainfall (8.5 mm/decade). Conclusion Although growing season temperature and crop management to reduce yields may have contributed to earlier ripening, the case for an effect of prolonged soil drying was not supported. Significance of the Study The output of simulation models describing complex biological systems needs to be tested wherever possible against experimental data and observations. Also, the scale of modelling should be appropriate to the system being studied, especially for soil, which has marked spatial variability. [ABSTRACT FROM AUTHOR]

Availability of nitrogen and water are major constraints for crop yield, and their interactions are manyfold. Yield gap analysis in rainfed systems commonly uses water-limited yield potential (Y w ) as a benchmark; benchmarking against nitrogen-limited yield potential (Y N ) is less common. The aim of this study was to benchmark wheat yield against Y N in winter-rainfall regions of Australia. We established experiments, and sampled both farmers’ fields and National Variety Trials spanning wide ranges of soils, management practices, and water supply (seasonal rainfall + irrigation) from 153 to 759 mm in South Australia, and from 178 to 428 mm in Western Australia. We measured yield, quantified crop nitrogen nutrition index (NNI) at stem elongation, anthesis or both, derived boundary functions relating Y N and NNI, calculated yield gaps as the difference between Y N and actual yield, and explored the associations between yield gaps and environmental, crop and agronomic factors. In South Australia, NNI at anthesis ranged from 0.45 to 1.45 and yield from 0.9 to 8.9 t ha −1 ; in Western Australia NNI at stem elongation ranged from 0.27 to 1.16 and yield from 1.0 to 7.1 t ha −1 . Bi-linear boundary functions were fitted with a linear Y N – NNI phase up to a NNIx threshold (0.95 ± 0.134 in South Australia, 0.87 ± 0.145 in Western Australia), and a plateau reflecting yield potential (7.8 ± 0.38 t ha −1 in South Australia, 6.5 ± 0.52 t ha −1 in Western Australia). Similar bi-linear boundaries, with congruent NNIx (≈0.9), were found for grain number and shoot biomass at maturity. In South Australia, water supply explained 54% of the yield gap, which declined linearly from about 6 t ha −1 to zero with increasing water availability. Further, the yield gap correlated negatively with carbon isotope discrimination at anthesis, a direct measure of crop water status. In Western Australia, the direct association between yield gap and rainfall was weak, but there was an indirect agronomic link, where low seeding rate in low rainfall environments contributed to low biomass at stem elongation, which in turn explained a large part of the yield gap. In two out of five National Variety Trials, where the aim is comparing the yield of current and emerging varieties, crops were nitrogen deficient. This is a potential source of bias as some varieties were above and others below the NNIx threshold of nitrogen sufficiency. Supply of nitrogen in varietal comparisons needs attention. The approach advanced in this paper can be applied to benchmark yield against crop nitrogen status and identify causes of yield gaps, and for in-season quantification of crop nitrogen status to assist fertiliser decisions directly, or indirectly as a reference for spectral indices. [ABSTRACT FROM AUTHOR]

Context. The depth-to a constraint determines how much of the soil profile, and the water it contains, can be accessed by plant roots. Information describing the impacts of soil constraints on available water capacity (AWC) and yield is important for farm management, but is rarely considered in a spatial context. Aims and methods. The depth-to three yield-limiting constraints (sodicity, salinity, and alkalinity) was mapped across ~80 000 ha in northern New South Wales, Australia using machine learning and digital soil mapping techniques. Soil AWC was calculated using soil data and pedotransfer functions, and water use efficiency equations were used to determine potential yield loss due to the presence of soil constraints. From this, the most-limiting constraint to yield was mapped. Key results. One or more constraints were found to be present across 54% of the study area in the upper 1.2 m of the soil profile, overall reducing the AWC by ~50 mm and potential yield by an average of 1.1 t/ha for wheat and 0.8 bales/ha for cotton. Sodicity (Exchangeable Sodium Percentage > 15%) was identified as the most-limiting constraint to yield across the study area. Implications. The simplification of multiple sources of information into a single decision-making tool could prove valuable to growers and farm managers in managing soil constraints and understanding important interactions with available water and yield. [ABSTRACT FROM AUTHOR]

The question addressed in this paper is whether the agronomic efficiency of cropping sequences can be improved in the northern grain zone of Australia. Analysis of whole systems over multiple seasons is particularly important in cropping regions such as Australia's northern grain zone, where farmers have the option of growing a variety of winter and summer crops and where fallowing is required to store water to safeguard yields of following crops. The study is based on a detailed longitudinal survey of the inputs and yields of 94 farmers' fields over 7 seasons. The Agricultural Production Systems Simulator (APSIM) was used to benchmark the 94 cropping sequences and to determine if the productivity of these cropping sequences can be improved through changed management, particularly of nitrogen nutrition and timely sowing of individual crops. Comparison of surveyed and simulated yields were then used to determine the yield gap that can be explained by factors unaccounted for in APSIM, i.e. biotic stresses such as weeds, pests and diseases or inadequate operational management. Using a generalised N response curve, data from the simulation analysis were plotted relative to a normalised input-output relationship to determine their position relative to an efficiency frontier representing the simulated production function. This analysis was conducted separately for both individual crops and for whole sequences in individual fields. The analysis of 193 individual crops showed that the average yield of 1.90t/ha was 65% of the simulated water and nitrogen-limited yield potential of 2.92t/ha. Investigation of nitrogen unlimited and early sowing management options revealed that there was relatively little yield advantage (0.15t/ha) to be gained from these management improvements. The remaining gap between observed yields and what the simulations suggest may be attributed to factors such as weeds, pests and diseases, sub-optimal operations or extreme weather events. Three production criteria were used to compare cropping sequences: (1) dollar income, (2) metabolisable energy yield, and (3) crude protein yields. While the income from 36% of the individual crops in the study was found to be more than 80% of their production frontier values, only 29% of whole cropping sequences achieved this benchmark. Similar results were achieved when crops and crop sequences were evaluated in terms of their metabolisable energy and crude protein yields. It is concluded that in order to increase the agronomic efficiency of cropping in Australia's northern grain zone, attention should be focussed on the intensity and configuration of cropping sequences and on the management of fallows in addition to the management of individual crops. [ABSTRACT FROM AUTHOR]

There is considerable interest and value in identifying the gap between crop yields that have actually been achieved, and yields that could have potentially been achieved. A suite of methods currently exist to estimate the yield potential of a crop, but there are no approaches that predict the site- and season-specific yield potential using datasets that are readily available and easily accessible for farmers. The aim of this study was to fill this need and develop a novel approach to identify crop yield gaps through site- and season-specific models of crop yield potential. The study focused on cotton lint yield, with data from 14 different seasons and 68 different fields from a collection of large, irrigated cotton farms in eastern Australia. This abundance of yield data was then joined with other spatial and temporal datasets that describe yield, such as rainfall, temperature, soil, and management. A quantile random forest machine learning model was then used to model yield at 30 m resolution, where the 95th percentile predictions were treated as the yield potential. The yield gaps at a 30 m resolution were then estimated for all seasons and sites. The results were compared to a more traditional 'historical maximum yield' approach, where no data modelling and only empirical yield data was used to estimate the yield potential. This revealed that there was a general agreement between the two approaches, although the quantile machine learning approach is both site- and season-specific, not just site-specific. Overall, there is a great need for alternative approaches to estimate yield potential and yield gaps, as the approaches currently available possess many limitations. The approach developed in this study has the potential for wide-spread adoption in broadacre cropping systems, and if the causes of yield gaps are identified, could lead to the implementation of management strategies to close them. [ABSTRACT FROM AUTHOR]

Abstract: To feed a growing world population in the coming decades, agriculture must strive to reduce the gap between the yields that are currently achieved by farmers (Ya) and those potentially attainable in rainfed farming systems (Yw). The first step towards reducing yield gaps (Yg) is to obtain realistic estimates of their magnitude and their spatial and temporal variability. In this paper we describe a new yield gap assessment framework. The framework uses statistical yield and cropping area data, remotely sensed data, cropping system simulation and GIS mapping to calculate wheat yield gaps at scales from 1.1km cells to regional. The framework includes ad hoc on-ground testing of the calculated yield gaps. This framework was applied to wheat in the Wimmera region of Victoria, Australia. Estimated Yg over the whole Wimmera region varied annually from 0.63 to 4.12Mgha−1with an average of 2.00Mgha−1. Expressed as a relative yield (Y %) the range was 26.3–77.9% with an average of 52.7%. Similarly large spatial variability was described in a Wimmera yield gap map. Such maps can be used to show where efforts to bridge the yield gap are likely to have the biggest impacts. Bridging the exploitable yield gap in the Wimmera region by increasing average Y % to 80% would increase average annual wheat production from 1.09Mtonnes to 1.65Mtonnes. Model estimates of Yw and Yg were compared with data from crop yield contests, experimental variety trials, and on-farm water use and yields. These alternative approaches agreed well with the modelling results, indicating that the proposed framework provided a robust and widely applicable method of determining yield gaps. Its successful implementation requires that: (1) Ya as well as the area and geospatial distribution of wheat cropping are well defined; (2) there is a crop model with proven performance in the local agro-ecological zone; (3) daily weather and soil data (such as PAWC) required by crop models are available throughout the area; and (4) local agronomic best practice is well defined. [Copyright &y& Elsevier]

Abstract: All available data on arbuscular mycorrhizal fungi (AMF) in Australian extensive cropping systems are reviewed in an agronomic framework. In the southern temperate and northern subtropical zones, 80–85% and 60%, respectively, of cropped area is winter cereals. In the south, non-mycorrhizal winter break crops are common and crops are often rotated with low input pastures. In the north, continuous cropping with winter crops, summer crops and long (12–18 month) plant-free fallow is common. Only here does reduced colonization by AMF cause intermittent yield reductions, perhaps reflecting low phosphorus (P) soils, high crop growth rates and dry surface soil limiting P availability. Soil temperatures below 10°C may limit the contribution of AMF to southern crops. In both zones, wheat yields are generally enhanced following fallows and non-mycorrhizal break crops, even under P limitation. AMF are unlikely to aid control of root diseases or dryland salinity adaptation; their role in soil structure and carbon sequestration requires clarification. Organic crops are highly colonized, but invariably P deficient. Thus, little evidence supports consideration of AMF in farm management and many agronomic practices that underpin sustainable productivity reduce colonization. We hope this paper stimulates dialog between mycorrhizal researchers and agronomists to target high priority research. [Copyright &y& Elsevier]

Abstract: To date, industry and crop forecasters have had a good idea of the potential crop yield for a specific season, but early-season information on crop area for a shire or region has been mostly unavailable. The question of “how early and with what accuracy?” area estimates can be determined using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) imagery was investigated in this paper. The study was conducted for two shires in Queensland, Australia for the 2003 and 2004 seasons, and focused on deriving total winter crop area estimates (including wheat, barley and chickpea). A simple metric (), which measures the green-up rate of the crop canopy, was derived. Using the unsupervised k-means classification algorithm, the accumulated difference of two consecutive images (one month apart) for three EVI threshold cut-offs (, where , 500 and 750) at monthly intervals from April to October was calculated. July showed the highest pixel accuracy with percent correctly classified for all thresholds of 94% and 98% for 2003 and 2004, respectively. The differences in accuracy between the three cut-offs were minimal and the T500 threshold was selected as the preferred cut-off to avoid measuring too small or too large fluctuations in the differential EVI values. When compared to the aggregated shire data (surveyed) on crop area across shires and seasons, average percent differences for the for July and August ranged from −19% to 9%. To capture most of the variability in green-up within a region, the average of July and August was used for the early-season prediction of total winter crop area estimates. This resulted in high accuracy (R2=0.96; RMSE = 3157 ha) for predicting the total winter crop from 2000 to 2004 across both shires. This result indicated that this simple multi-temporal remote sensing approach could be used with confidence in early-season crop area prediction at least one to two months ahead of anthesis. [Copyright &y& Elsevier]

Abstract: The rice super cultivar ‘Koshihikari’, grown on 37% of the total rice area in Japan, has been a leading cultivar since 1979. It is also grown in the USA and Australia. However, its tendency to lodge makes harvest difficult and reduces grain quality, and the regularity of large-scale typhoons compromises its production nationally. The genetic improvement of Koshihikari against lodging has been an extremely important theme for the past 30 years in Japan. This paper describes the development of a semidwarf form of Koshihikari by the fixing of an allele of the Green Revolution semidwarf gene sd1 via eight recurrent backcrosses. The cultivar, with excellent flavor and high yield, was named ‘Hikarishinseiki’, which means “Bright New Century” (rice cultivar No. 12273; Ministry of Agriculture, Forestry and Fisheries of Japan: MAFF). Hikarishinseiki, with ≥99.8% of the Koshihikari genome, is about 20cm shorter than Koshihikari. DNA monitoring of Hikarishinseiki confirmed its genomic substitution into Koshihikari except for sd1 and a marker-based method to distinguish Koshihikari from Hikarishinseiki was developed. Its flavor (upper medium grade) and quality were the same as those of Koshihikari grown at 10 sites across Japan, but its yield was on average 3% higher (maximum 29% higher at Hyogo) than that of Koshihikari and it was highly resistant to lodging. Hikarishinseiki is the first semidwarf form of Koshihikari registered by MAFF. Some economical impact has been achieved by introducing Hikarishinseiki and it is already notified as a brand rice description in the rice-producing districts of Niigata, Shiga, Tottori, Okayama, Tokushima, and Kochi prefectures. [Copyright &y& Elsevier]

Sonchus oleraceus L. (Asteraceae), an annual species native to Eurasia and northern Africa, is among the most widely distributed plant species on Earth. In Australia, S. oleraceus, is a common weed in disturbed areas such as crop fields, pastures, gardens and roadsides. In agricultural settings, it can dominate fallows and cultivated fields where it uses stored soil moisture and reduces crop yield. This weed has also developed herbicide resistance, predicating the need for alternative management solutions. In this context, we undertook field surveys and preliminary host range studies in the native range of S. oleraceus to determine the feasibility of developing classical biological control solutions for Australia. Fifty-nine phytophagous arthropod species were recorded and nine pathogenic fungi were recovered from disease symptoms. Four arthropod species were selected for initial host-specificity testing based on information available in the literature. Preliminary host-specificity tests were also performed with representative isolate(s) of six of the pathogenic fungi. All these candidate agents were shown in the tests to affect key native species in Australia in the same subtribe as S. oleraceus (i.e. Sonchus hydrophilus and Actites megalocarpus). The results of our investigations suggest that classical biological control may not be a feasible option for the management of S. oleraceus in Australia, and that alternative integrated weed management tactics may need to be pursued to mitigate the impacts of this weed. [ABSTRACT FROM AUTHOR]

• NUE was lower in grazed than in grain-only crops fertilized soon after grazing. • The reduction in NUE was partly due to low N demand by recently grazed crops. • Crop-N demand was increased by waiting two weeks for regrowth. Dual-purpose crops are grazed during the vegetative phase by cattle, goats and sheep in many dryland farming systems. After grazing, these crops continue to grow until maturity when they are harvested for grain. The process of grazing depletes crop nitrogen (N) content. Yield and grain protein concentration may therefore be more N-limited in grazed than in grain-only crops. This paper reviews 14 field experiments in south-eastern Australia, 12 of which were on farms, to measure the responses to N fertilizer by wheat, barley or canola crops defoliated by mowing or grazing with sheep. In five experimental comparisons of defoliated and grain-only crops, the average nitrogen use efficiency (NUE) of fertilizer N applied soon after defoliation, as is typically practiced, was 18 %, while NUE of grain-only crops fertilized at the same time and N-rate was 41 %. Applying N fertilizer to defoliated crops at this stage was usually unprofitable. In one experiment comparing responses of grazed and grain-only crops to time of N application post-grazing, a delay of two weeks after grazing increased yield of grain-only crops and NUE of both grazed and grain-only crops, compared with application soon after grazing. Our explanation of these results is the low N demand by small, defoliated crops and increased N demand as they regrew after defoliation. Additional evidence for the N-demand explanation came from a positive relationship between NUE of grazed crops and the yield of grain-only crops. The results suggest that delaying N application for two weeks after grazing and retaining more biomass after grazing can increase N demand, yield and NUE of grazed crops. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Chickpeas (Cicer arietinum L.) are a high value crop for farmers, but price penalties will be imposed or grain rejected whenever the standards are not met by growers whose crops suffer grain defects in a particular season. Australian chickpeas are renowned for their high quality and are generally in high demand globally because of good farming practice and strict grain quality standards. However, small quantities of defective seed in grain loads can reduce the price paid to individual farmers, with significant financial impacts. Information is scarce on the types of defects causing price penalties and there is no information on the magnitude of those penalties. An online farmer survey was conducted to capture information on the types of grain defects, price penalties imposed and load rejections with respect to the delivery of their 2017 chickpea crop. Here we show that the cost to individual chickpea farmers affected by price penalties or load rejections ranged from AU$743 to $1 293 750. Furthermore, the total cost of seed defects was calculated to be $154.2 million in that season, equating to a revenue loss of 23.7% of gross value of production in Australia. Chickpea seed defects also contributed to additional costs including seed cleaning, further transport costs and harvest delays, with subsequent risk of yield losses and further quality defects. Too often, crop yields are the focus while seed quality is overlooked as an essential driver of farmer profitability. We demonstrate how important seed quality is to farmer profitability; if 'yield is King' then seed quality is certainly Queen. We suggest that farmers prioritise harvest of their chickpea crops ahead of harvest of cereal crops to minimise the risk of chickpea seed defects and seed loss, and to maximise profits from this higher value crop. Additional surveys over several seasons are warranted to refine information on the types of seed defects occurring in chickpea and their financial impacts on farmers, and they could be expanded to other crops and countries. We suggest that misclassification of seed defects needs further exploration, as does research into minimising the major causes of seed defects. Improvements to grain classification systems globally should be sought to provide better support for farmer profitability so that they can continue to feed the world. In addition to yield, seed quality is an important driver of farmer profitability. Our survey showed a revenue loss of 23% of gross value of production for chickpea farmers solely from grain quality penalties. In addition, some 'seed markings' were misclassified as 'mould', which has a lower threshold and harsher price penalties. Improvements in grain classification globally are needed to ensure that farmers are appropriately rewarded for good quality, remain financially viable, and continue to feed the expanding global population. [ABSTRACT FROM AUTHOR]

Sustaining diverse, yet productive crop sequences that integrate break crops such as canola (Brassica napus L.) remains a critical challenge for farming systems in low-rainfall cropping environments. Recent advances in canola productivity through early sowing, understanding of critical stress periods, hybrid cultivars and improved nitrogen (N) fertilisation offer promise under many conditions but require careful adaptation for risky, low-rainfall environments. A series of eight experiments was implemented over four growing seasons (2015–18) in the low-rainfall environments of southern Australia to test combinations of sowing date, cultivar selection and N-management strategies. Simulation modelling extended the field experiment results, enabling a simple, whole-farm profit–risk analysis across growing season deciles. The aim was to identify combinations of practices where the potential production and risk were understood, thereby assisting management decisions in low-rainfall cropping systems. Earlier sowing (April) was generally beneficial but only where seasonal conditions led to successful establishment, meaning that the best fit for canola in low-rainfall environments is as an opportunity crop. A hybrid cultivar (triazine tolerant) did not provide a yield advantage in an early experiment, but productivity increases were measured with a modern hybrid cultivar (Clearfield) in a later experiment. Profit-risk analysis suggested that a yield advantage of >20% over open-pollinated cultivars needs to be sustained across the full range of season deciles to generate economic advantage. Although there was relative insensitivity to the timing of N application, an adequate dose of N, either through fertiliser or legume crops, was critical to improve canola productivity. We conclude that opportunities exist to make significant gains in yield (by up to 110% compared with current standard practice) and profit–risk outcomes (~30% increased gross margins across all season types) for canola in low-rainfall environments by using a package of agronomic management decisions that includes early sowing on genuine establishment opportunities, hybrids that offer sustained yield benefits, and matching N dose from both fertiliser and legume crops to yield potential of the soil type and seasonal outlook. TOC Summary: The low-rainfall environment presents significant challenges to consistent and profitable canola production. Combinations of time of sowing, cultivar selection and nitrogen treatments were tested to identify management packages that are productive and profitable across a range of season types. The best performing packages identified offer yield gains of up to 110% combined with 30% gross-margin benefits across below-average to above-average growing seasons, indicative of strategies that also manage risk. [ABSTRACT FROM AUTHOR]

A positive Indian Ocean Dipole (IOD) in 2019 that reached the level of the strongest events occurred in 1994 and 1997 and caused disasters in countries around the Indian Ocean. Using a quasi real‐time ensemble seasonal prediction system based on the Scale Interaction Experiment‐Frontier climate model, its occurrence was predicted a few seasons ahead and the possible impacts were warned by overcoming the so‐called winter predictability barrier. The successful prediction of such a super event at long lead time may contribute to reducing the risks of socioeconomic losses by introducing suitable measures for adaptation. Here, we have investigated possible sources of the successful prediction by analyzing covariability of intermember anomalies defined as deviations from the mean in the ensemble reforecasts. Interestingly, it is found that the potential predictability of the 2019 super positive event is linked with the preexisting El Niño Modoki in the tropical Pacific. Plain Language Summary: The Indian Ocean Dipole (IOD) is an intrinsic air‐sea coupled climate phenomenon in the tropical Indian Ocean, of which name was introduced because of the dipolar structure of both oceanic and atmospheric anomalies along the equator. Just 20 years after the introduction of IOD, another such extreme IOD event happened to emerge in 2019 and reached a level which is similar to the precedent events such as 1994 and 1997 and caused disasters in countries around the Indian Ocean. For example, many parts of Australia were seriously affected by a series of massive bushfires owing to the drier than normal conditions, while crop yield in East Africa was threatened by a plague of locusts owing to the wetter condition there. Its occurrence was predicted a few seasons ahead by overcoming the so‐called winter predictability barrier by a numerical prediction system. The successful prediction of such a super event at long lead time may contribute to reducing the risks of socioeconomic losses by introducing suitable measures for adaptation. Key Points: A positive Indian Ocean Dipole in 2019 that reached the level of the strongest events occurred in 1994 and 1997Its occurrence was predicted a few seasons ahead by overcoming the so‐called winter predictability barrierIts potential predictability is linked with the preexisting El Niño Modoki in the tropical Pacific [ABSTRACT FROM AUTHOR]

Climate oscillations are periodically fluctuating oceanic and atmospheric phenomena, which are related to variations in weather patterns and crop yields worldwide. In terms of crop production, the most widespread impacts have been observed for the El Niño–Southern Oscillation (ENSO), which has been found to impact crop yields on all continents that produce crops, while two other climate oscillations – the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) – have been shown to especially impact crop production in Australia and Europe, respectively. In this study, we analyse the impacts of ENSO, IOD, and NAO on the growing conditions of maize, rice, soybean, and wheat at the global scale by utilising crop yield data from an ensemble of global gridded crop models simulated for a range of crop management scenarios. Our results show that, while accounting for their potential co-variation, climate oscillations are correlated with simulated crop yield variability to a wide extent (half of all maize and wheat harvested areas for ENSO) and in several important crop-producing areas, e.g. in North America (ENSO, wheat), Australia (IOD and ENSO, wheat), and northern South America (ENSO, soybean). Further, our analyses show that higher sensitivity to these oscillations can be observed for rainfed and fully fertilised scenarios, while the sensitivity tends to be lower if crops were to be fully irrigated. Since the development of ENSO, IOD, and NAO can potentially be forecasted well in advance, a better understanding about the relationship between crop production and these climate oscillations can improve the resilience of the global food system to climate-related shocks. [ABSTRACT FROM AUTHOR]

Fifty-six sites throughout the Wimmera region of Victoria were sampled to 1 m depth, and 1:5 extracts analysed for pH, conductivity and cation content. The relationship between conductivity measured in saturated extracts and 1:5 extracts at five sites was used to calculate cation concentrations expected if saturated extracts had been prepared. Soils were generally alkaline and cations at depth were dominated by sodium. The ratio of sodium to calcium varied from <2.6 (10% of samples) to >85.5 (10%) to a maximum of 247.1. It exceeded 12.8 in 40% of samples, and exceeded 45.6 in 20% of samples, levels which are likely to reduce root growth and ability of roots to exclude sodium. Higher ratios were associated with depth, pH and conductivity. The ratio of magnesium to calcium exceeded 1 in 80% of samples, 2 in 55% of samples, 3 in 30% of samples and the maximum measured was a ratio of 7.6, suggesting plant growth may be reduced. The issues of extrapolating from experiments in solution culture to soil, and errors likely in using 1:5 extracts are discussed. [ABSTRACT FROM AUTHOR]

We review yield effects caused by aphid feeding and associated virus injury to cereal, oilseed and pulse crops, and estimate the potential economic loss caused by aphids in Australia. Potential yield reduction due to aphids was determined through a survey of quantitative data from experiments that assessed aphids' effect on grain yield. In cereals, four aphids caused damage; on barley, feeding injury caused by R hopalosiphum padi + R hopalosiphum maidis was most damaging in terms of yield reduction (25.5%) with an economic loss of $19/ha. Barley yellow dwarf virus transmitted by R . padi + S itobion miscanthi was more damaging than direct feeding, causing a yield reduction of 39% and economic loss of $21/ha for wheat. On canola, beet western yellow virus transmitted by M yzus persicae caused the highest yield reduction of 34% and economic loss of $115/ha, although this was measured through artificial inoculations. Feeding injury was high in B revicoryne brassicae which caused an average yield reduction of 34% and associated economic loss of $88.5/ha, while L ipaphis erysimi and M . persicae had negligible economic effects but more data are needed. On pulses, the most economically damaging (unidentified) aphids feeding on lupins caused a yield reduction of 43% and economic loss of $24/ha. The aphids M . persicae + A phis craccivora + A cyrthosiphon kondoi reduced lupin yields by 13% and economic returns by $7.40/ha. On field peas, a 14% reduction in yield was caused by transmitted viruses such as pea seed-borne mosaic virus which caused economic losses of $20.50/ha. In total, feeding and virus injuries resulted in potential economic costs of $241 and $482 million/year, respectively. Although this review provides estimates of potential yield and economic losses due to aphids, few data were available for some crops, aphid species or regions (e.g. oats). Nevertheless, economic costs associated with aphids appear substantial. [ABSTRACT FROM AUTHOR]

A well tested agricultural systems model was used together with 114 years of historical climate data to study the performance of a dryland wheat–fallow system as impacted by climate variations and nitrogen input levels in southeast Australia, and to investigate the value of: (1) historical climate knowledge, (2) a perfect climate forecast, and (3) various forecasts of targeted variables. The potential value of historical climate records increases exponentially with the number of years of data. In order to confidently quantify the long term optimal nitrogen application rate at the study site at least 30 years of climate data are required. For nitrogen management only, the potential value of a perfect climate forecast is about $54/ha/year with a reduction of excess nitrogen application of 20 kg N/ha/year. The value of an ENSO based forecast system is $2/ha/year. Perfect forecasting of three or six categories of growing season rainfall would have a value of $10–12/ha/year. Perfect forecasts of three or six categories of simulated crop yield would bring about $33–34/ha/year. Choosing integrated variables as a forecasting target, for example crop yield derived from agricultural modelling, has the potential to significantly increase the value of forecasts. [ABSTRACT FROM AUTHOR]

The effect of culture age on yields, desiccation tolerance and resistance to ultraviolet radiation of Plectosporium alismatis, a potential mycoherbistat of aquatic weeds in Australian rice fields, was studied. P. alismatis was grown in a liquid basal medium supplemented with malt extract and sodium nitrate and harvested after 7, 14 or 21 days incubation. Although chlamydospore yields harvested from 14-day-old liquid cultures were significantly higher (29.2×105 chlamydospores mL-1) than chlamydospore yields harvested from 7-day-old liquid cultures (1.07×105 chlamydospores mL-1) or from 21-day-old liquid cultures, the germination of freshly-harvested chlamydospores from 7-day-old cultures (72.7%) was significantly reduced when propagules were grown for 14 days (55.3%). When exposed to UV-radiation, conidia and chlamydospores harvested from 14-day-old cultures germinated at a lower rate (<20%) than conidia and chlamydospores harvested from 7-day-old cultures (>40%). When conidia and chlamydospores were dried and subsequently exposed to UV, less than 30% of propagules harvested from 7-day-old cultures germinated, whereas less than 10% of propagules harvested from 14-day-old cultures germinated. A three-way analysis of variance including culture age, UV exposure and type of propagules confirmed that the culture age had more impact on the germination of fresh or dry propagules (P=0.00001 and P=0.0004, respectively) than the type of propagules considered (P=0.5). These results demonstrate that the culture age impacts significantly propagule yields and germination of P. alismatis conidia and chlamydospores, particularly after stress caused by dehydration and/or exposure to UV-B radiation. [ABSTRACT FROM AUTHOR]

The levels of Verticillium dahliae and Pratylenchus spp. were determined in soil and potato plants collected from 77 fields in potato-growing regions of Australia between 1996 and 1997. Verticillium dahliae was detected in soil from 81% of the fields and in plants at 80% of the fields. Densities in soil ranged from 1 to 19 colony forming units per gram of dry soil. Pratylenchus spp. were detected in soil from 87% of the fields and from potato roots at 92% of the fields. Pratylenchus crenatus and P. neglectus were detected in soil from 60 and 27% of fields, respectively, whereas P. coffeae was found in one field in New South Wales and P. penetrans in one field in New South Wales and another in South Australia. Nematode densities over all fields ranged from 1 to 19 nematodes per gram of dry soil. These results show that V. dahliae, P. crenatus and P. neglectus are widespread in potato soils throughout Australia and that V. dahliae densities are likely to be causing significant yield losses within the potato industry. Further research is required to determine the impact of Pratylenchus spp. on potato productivity in Australia. [ABSTRACT FROM AUTHOR]

In the eastern wheatbelt of Western Australia the yield of barley relative to wheat is influenced by soil type. Field trials studied detailed aspects of growth, development, yield and water use of a range of barley and wheat cultivars on 2 soil types at 2 locations to identify those factors that lead to the differential relative yields. Barley had greater grain yields than wheat on both fine and coarse textured soils. On both soil types barley had a greater number of mainstem leaves which appeared faster than those of wheat and this was associated with greater tillering (6.5 v. 3.5 shoots/plant), higher GAI and greater dry matter production (845 v. 804 g/ m²). The difference in yield between the two species was greater on the fine textured soil (15 v. 7 %). Barley also had greater harvest index than wheat (6--15 %), and this combined with greater dry matter production on the fine textured soil led to a larger yield advantage over wheat than occurred on the coarse textured soil. Water use efficiency was greater for barley than for wheat on both soils. The greater yield advantage of barley over wheat on the fine textured soil was the result of greater biomass production by barley and greater harvest index. Differences in pattern of water use, and water use efficiency of grain production were associated with greater barley yields but are not themselves considered to be the cause of relative yield differences across soil types. The possible implications of factors such as intrinsic nutrient supply on the 2 soil types in relation to observed yield differences are discussed. [ABSTRACT FROM AUTHOR]

Modern agricultural farming systems acidify the soil profile due to application of fertilisers with acidifying properties. In most parts of Australia, lime has been used to improve agricultural soil conditions and restore its productive potential. The observed response of crop yield to applied lime often varies with soil type, acidity profile and seasonal conditions, so it is difficult to specify the expected yield response in a given situation. We conducted a meta-analysis of 86 agricultural field trials from Western Australia (WA), New South Wales (NSW) and Victoria (VIC) where various rates of lime had been applied to the soil surface and crop yield (wheat, barley, canola, lupin or field pea) measured for a number of years after the initial application. Information from the meta-analysis was then paired with output from a crop simulation model, where the water-limited yield potential was estimated for both a neutral and acidified soil profile. The average increase in yield to applied lime across all locations and crops was 12%, but the response ranged from 0 to 185%. A trend was observed, where sites with topsoil pH (CaCl2) < 5 and subsoil pH < 4.5 had the greater benefit to liming. Soil type had little effect on the percentage yield increase. Overall, responses to applied lime were most likely when the yield of the trial site was at 50% of water-limited yield potential (or less), the quantity of lime applied was greater than 2.5 t ha−1 and the time since lime had been applied was greater than three years (with the maximum response occurring from four and sometimes up to eight years after liming). Therefore, soil pH measurements, combined with an assessment of actual yield relative to potential yield, provide the best guide to the response to surface applied lime and this response is likely to take more than four years to be realised. [ABSTRACT FROM AUTHOR]

The onus for monitoring crop growth from space is its ability to be applied anytime and anywhere, to produce crop yield estimates that are consistent at both the subfield scale for farming management strategies and the country level for national crop yield assessment. Historically, the requirements for satellites to successfully monitor crop growth and yield differed depending on the extent of the area being monitored. Diverging imaging capabilities can be reconciled by blending images from high-temporal-frequency (HTF) and high-spatial-resolution (HSR) sensors to produce images that possess both HTF and HSR characteristics across large areas. We evaluated the relative performance of Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat, and blended imagery for crop yield estimates (2009–2015) using a carbon-turnover yield model deployed across the Australian cropping area. Based on the fraction of missing Landsat observations, we further developed a parsimonious framework to inform when and where blending is beneficial for nationwide crop yield prediction at a finer scale (i.e., the 25-m pixel resolution). Landsat provided the best yield predictions when no observations were missing, which occurred in 17% of the cropping area of Australia. Blending was preferred when <42% of Landsat observations were missing, which occurred in 33% of the cropping area of Australia. MODIS produced a lower prediction error when ≥42% of the Landsat images were missing (~50% of the cropping area). By identifying when and where blending outperforms predictions from either Landsat or MODIS, the proposed framework enables more accurate monitoring of biophysical processes and yields, while keeping computational costs low. [ABSTRACT FROM AUTHOR]

The fact that the shelter created by windbreaks can have a significant, positive effect on crop production is supported by eight decades of research from many countries around the world. Although the concept of planting windbreaks to enhance crop production has general currency in Australia, the practice is not as wide as it could be. This review of the last decade of windbreak literature defines the research needed to encourage wider utilization of windbreak technology. After outlining the principal mechanisms behind the effect of shelter on temperate crops, the review discusses relevant literature of the past decade especially that from Australia. The main mechanisms discussed are: the protection of crops from physical damage; soil conservation; the direct augmentation of soil moisture; and the alteration of the crop energy balance and plant water relations. Also discussed are the elusiveness of the shelter effect, competition from windbreak trees, and the modeling of windbreak systems. Suggestions for future research in Australia include: quantifying the competition of various windbreak species and the effect of root pruning on both crop and tree; a model of crop energy and water relations at the tree-crop interface; an economic model and a farmer-oriented decision support tool. [ABSTRACT FROM AUTHOR]