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This foreword provides a summary of papers included in this special issue on biochar and fertiliser interactions for crop and pastures productivity. This special issue includes published papers on how biochar and fertiliser affect soil health and crop yields and overcome soil constraints such as acidity, salinity, low fertility and remediation of metal and pesticides contaminated soils. [ABSTRACT FROM AUTHOR]

Context: Most weed species can adversely impact agricultural productivity by competing for nutrients required by high-value crops. Manual weeding is not practical for large cropping areas. Many studies have been undertaken to develop automatic weed management systems for agricultural crops. In this process, one of the major tasks is to recognise the weeds from images. However, weed recognition is a challenging task. It is because weed and crop plants can be similar in colour, texture and shape which can be exacerbated further by the imaging conditions, geographic or weather conditions when the images are recorded. Advanced machine learning techniques can be used to recognise weeds from imagery. Aims: In this paper, we have investigated five state-of-the-art deep neural networks, namely VGG16, ResNet-50, Inception-V3, Inception-ResNet-v2 and MobileNetV2, and evaluated their performance for weed recognition. Methods: We have used several experimental settings and multiple dataset combinations. In particular, we constructed a large weed-crop dataset by combining several smaller datasets, mitigating class imbalance by data augmentation, and using this dataset in benchmarking the deep neural networks. We investigated the use of transfer learning techniques by preserving the pre-trained weights for extracting the features and fine-tuning them using the images of crop and weed datasets. Key results: We found that VGG16 performed better than others on small-scale datasets, while ResNet-50 performed better than other deep networks on the large combined dataset. Conclusions: This research shows that data augmentation and fine tuning techniques improve the performance of deep learning models for classifying crop and weed images. Implications: This research evaluates the performance of several deep learning models and offers directions for using the most appropriate models as well as highlights the need for a large scale benchmark weed dataset. Deep machine learning-based weed detection techniques can offer automatic and practical solutions for large cropping areas. In this work, we construct a benchmark dataset by combining several small datasets and thoroughly evaluate five state-of-the-art deep learning models to classify crop and weed datasets and thoroughly evaluate five state-of-the-art deep learning models to classify crop and weed. [ABSTRACT FROM AUTHOR]

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]

This study reviews published information on the tactical management decisions needed to maximise economic grain yield in winter-dominant rainfall regions of the Mediterranean type. Tactical decisions are defined as those relating to the period from immediately before sowing to harvest. Tactical management is the principal means by which farmers respond to changing environmental and short-term economic conditions as the season progresses. The review considers published evidence that underpins these decisions and relates to cereal crops (wheat, barley and oats), pulse crops (field pea, faba bean, chickpea and narrow-leaved lupin) and canola. The criteria used to guide management decisions during the season involve soil and tissue tests for nutrients, knowledge of weed numbers and resistance status in the current and previous seasons, weather conditions that favour disease development, and knowledge of thresholds and biology of insect pests that may warrant control measures. All of these decisions can be related to the timing of the opening rains and the length of the growing season; the crop, pasture or weeds present in the previous two seasons; the presence of pest- and disease-bearing crop residues; and the type of tillage in use. Most of these indicators require further refinement through research across environments, soil types, crop types and production systems. The likely interactions between tactical or short-term management decisions, longer term or strategic decisions, and genetic factors are discussed. The prevalent use of chemicals in the management of biotic factors that can impact the crops is noted, as is progress towards various systems of 'integrated' management of these threats to crop production. Most tactical decisions in rainfed cropping systems appear to be supported by adequate evidence, although some decisions are still based on practical experience and observations. Application of tactical management practices together with strategic management and use of improved genotypes provides the possibility of achieving rainfall-limited potential grain yield at a regional scale. The papers reviewed have been selected partly on the basis that the experimental treatments achieved the estimated potential grain yield. Where the potential grain yields are not being achieved in commercial crops, it remains unclear whether this is due to inadequate adoption of existing information or inadequate research to identify and address the underlying causes. We highlight the need to devise a simple decision aid to assist farmers and their advisers to respond to the variable seasonal conditions evident since the turn of the Century. Rainfed climate across the world have always been variable but the variability both within and between seasons has increased. Consequently, crop producers must adjust their management practices at sowing time and during the season to avoid crop failures and to utilise fully the rain that does fall. This review confirms that short-term or in-season decisions are major contributors to yield improvement, but that they need to be further supported by practical research. [ABSTRACT FROM AUTHOR]

Context. Computer-based crop simulation models are important tools for agricultural research and management. APSIM (Agricultural Production Systems sIMulator) is commonly used around the world but has not been widely validated in North America. Aims. The objective of this work was to evaluate the reliability of APSIM for simulating wheat production in California, with the aim of providing guidance for future field research aimed at model calibration and validation. Methods. Environmental and management data from state-wide wheat variety trials of common wheat (Triticum aestivum L.) were used to parameterise the APSIM-Wheat module (ver. 7.10 r4220). Simulated yield and protein data were compared with observed field trial results to test the reliability of APSIM simulations. Key results. The most reliable simulation of grain yield had a root-mean-square error of 1040 kg/ha and normalised root-mean-square error of 16% relative to actual field data. Preliminary calibration of the model for Californian wheat varieties did not improve simulation accuracy or precision. Conclusions. The accuracy or precision of the simulations was comparable to that of other tests of the APSIM-Wheat module in environments where it has not been previously calibrated but was considered too low to be reliable. The lack of reliability was due to the poor representation of local Californian wheat genotypes by existing APSIM cultivars, as well as possible lack of precision and accuracy of field data. Implications. APSIM could be a valuable tool for wheat research and management in California; however, further research is needed to generate suitable field data for model calibration and validation. [ABSTRACT FROM AUTHOR]

Context. Crop production is one of the agricultural sectors most vulnerable to climate change. In order to minimise risks, innovative technologies and management strategies are continually being developed. Early sowing is a strategy used in wheat production; however, with irregular rainfall patterns, false breaks can occur. Aims. We sought to determine the optimal volume of autumn-break rainfall for the start of season, and whether formation of a larger rhizosheath (i.e. the thick layer of soil adhering to roots) can buffer wheat seedlings fromfalse-break conditions, thereby keeping plants alive until the next rainfall. Methods. In glasshouse experiments, six varieties of common wheat (Triticum aestivumL.), two with contrasting rhizosheath-forming ability and the other four untested, were grown on two soil types (Kandosol and Vertosol) under simulated autumn-break rainfall and false-break durations. Rhizosheath size and plant establishment traits were measured. Key results. The ability to form large rhizosheaths explained ~80% of the variability in crop establishment under various scenarios of false-break conditions. Comparative growth performance of the cultivars forming the largest (cv. Flanker) and smallest (cv. Westonia) rhizosheaths showed that they were largely similar for the first 28 days of drought but differed significantly thereafter. Flanker was progressively better able to establish as false-break duration increased and showed significantly greater tiller and leaf production than Westonia. Conclusions. We demonstrate that genotype selection for formation of large rhizosheaths can help to reduce plant seedling death under false-break conditions. Implications. Although the amount of starting rainfall is critical, cultivars differ significantly with regard to how far they can develop without follow-up rain. Future research is suggested for a field-scale study of the potential for selection for larger rhizosheaths to improve wheat crop establishment under harsh conditions. [ABSTRACT FROM AUTHOR]

In order to develop a better understanding of some yield components, the response of three modern cotton (Gossypium hirsutum L.) cultivars to increasing nitrogen (N) fertiliser application was assessed for lint percentage (LP) and seed weight, as well as seed protein and oil concentrations. Application of N fertiliser led to decreased LP in all three cultivars (by 1.5 percentage points), even if the N-fertiliser rate was above the optimum rate. There was a strong negative correlation between LP and seed mass because seed mass is a component of LP. Seed protein concentration increased, and seed oil concentration decreased, with increasing N fertiliser application. Seed germination was improved in seeds with higher oil concentration; therefore, seed produced with high N fertiliser had reduced seed vigour. It was concluded that little scope exists for further lint-yield increase via LP. For cultivars with small seeds, an integrated system across seed production and crop agronomy can achieve satisfactory seed vigour in the field while also achieving high commercial yields. Future improvements in yield will come from alternative and more complex yield components such as growth habit. The response of modern cotton cultivars to increasing N-fertiliser application was assessed for lint percentage, seed mass, seed protein and oil concentrations, and seed vigour. Increasing N fertiliser decreased lint percentage by increasing seed mass, increased seed protein concentration, and decreased seed oil concentration. Seed germination was improved in seeds with higher oil concentration, meaning that seed produced with high N fertiliser had reduced seed vigour. It was concluded that little scope exists for further lint yield increase via lint percentage. [ABSTRACT FROM AUTHOR]

Context. Australian grain producers may need to report their farm greenhouse emissions. Accordingly, nitrogen fertiliser application strategies will need to include consideration of their environmental as well as economic impacts. Aims. We aim to identify the nitrogen application strategies suited to dryland cropping in Western Australia that are highly profitable and that generate lower emissions. Methods. Simulation modelling is used to examine the gross margins and emissions associated with four broadly different nitrogen strategies at 14 locations in the grainbelt of Western Australia for different frequencies of cropping. Key results. Strategies that generate high gross margins and moderate emissions often focus on maximising the gross margin of crop production, and apply a decile 5 view of unfolding seasonal conditions. A similarly useful strategy applies nitrogen in a fixed ratio where a tonne of expected cereal receives 45 units of nitrogen from various sources, and a tonne of expected canola receives 70 units of nitrogen from various sources. Where a farmer prefers to apply a constant rate of nitrogen, then exceedingly high or low rates of application should mostly be avoided, either for economic or for environmental reasons, with the better option at many locations being to apply 50 or 75 kg N/crop ha. Conclusions. A few preferred nitrogen application strategies are suggested to be applicable to dryland cropping in the study region. The strategies achieve high profits and generate moderate or low emissions. Implications. Selection of highly profitable and lower emission nitrogen application strategies across the study region can deliver sizeable economic and environmental benefits. [ABSTRACT FROM AUTHOR]

Context: Under natural field conditions, plants confront the co-occurrence of stresses. A comprehensive insight into combined-stress tolerance is requisite to developing stress resilience in cultivars of oats (Avena sativa L.) for saline–alkaline soil. Aims: This study was undertaken to characterise diverse oat genotypes for seedling growth attributes under two saline and alkaline concentrations and under combined saline–alkaline conditions. Methods: We screened 105 accessions of the genus Avena with the following treatments: control, reverse osmosis water; moderate salinity (50 mM); high salinity (100 mM); moderate alkalinity (15 mM); high alkalinity (30 mM); combined moderate salinity–alkalinity (50 mM + 15 mM); and combined high salinity–alkalinity (100 mM + 30 mM). For saline treatments, NaCl and Na2SO4 salts were used in equimolar concentrations, and for alkaline treatments, NaHCO3 and Na2CO3. Key results: Analysis of variance showed significant (P ≤ 0.001) variation among treatments and genotypes. Principal component analysis revealed 83.3% of the total genetic variation accounted for in the first two principal components. Correlation analysis showed a significant positive correlation between final germination percentage and seedling vigour index. Stress tolerance index identified tolerant and sensitive oat genotypes under high saline and alkaline stress, and multi-trait stability analysis confirmed the stability of performance of some genotypes under the imposed treatments. Conclusions: According to the stress tolerance index and multi-trait stability analysis, genotypes IG-20-477, OS-377, IG-20-798 and IG-20-575 were found suitable for high saline–alkaline stress. Implications: The identified tolerant oat genotypes can be used as donors for the development of stress-resilient oat cultivars, and for generating mapping populations in oat. Saline–alkaline soil is a global threat to crop production, and the combined effects of salinity and alkalinity may be greater than individual effects, with seed germination the most sensitive stage to these stresses. We evaluated oat germplasm under combined stress in order to identify and select lines with better seed germination and high vigour. The stress-tolerant lines identified could serve as base material for stress-tolerance breeding programs. [ABSTRACT FROM AUTHOR]

Climate change impacts crop production through the imposition of different abiotic and biotic stresses, and by altering the prevalence of pests and diseases. The wild relatives of crop plants exhibit enhanced tolerance to environmental stresses due to reduced severity of selection of agriculturally important traits. Wild bananas represent a largely untapped wealth of genetic diversity. Although some wild relatives of the banana crop have been screened for their tolerance to different biotic and abiotic stresses, many remain to be explored. The wild bananas show more hardiness and higher resilience to different stressors in comparison to their cultivated counterparts. They have been harnessed in banana improvement programmes to enhance stress tolerance and productivity. To utilise wild bananas for crop improvement, they need to be readily available to breeders. This warrants devising conservation strategies and the development and maintenance of centres from which different accessions can be procured. In this article, we have discussed some important biotic and abiotic stresses including banana wilt disease, Black Sigatoka disease, viral diseases, salt stress and drought stress where wild bananas are used for imparting tolerance. The conservation of wild bananas, related challenges and contemporary limitations related to their use for crop improvement has also been outlined. Bananas being most important food crop in the world and generally understudied, here, we present a comprehensive review of the use of wild relatives of banana and their related germplasm for the improvement of biotic and abiotic stress tolerance. Wild relatives of banana crop show resilience to climate change. To save the bananas for our future generations, wild bananas need to be conserved and maintained in specialised centres from which breeders can readily procure experimental accessions. Wild bananas have been screened for disease and environmental tolerance, with the aim of introgressing these traits in cultivated varieties. This study provides a comprehensive account of challenges and future prospects for use of crop wild relatives for banana improvement. [ABSTRACT FROM AUTHOR]

Faba bean (Vicia faba L.) is an important component of cropping systems in cool, arid environments. However, no review has specifically focused on the quantitative sensitivity of physiological processes in faba bean to low temperature and water deficits. The objective of this review was to examine published functional relationships between physiological activity and these environmental variables. Among faba bean genotypes, temperature generally resulted in a consistent linear response in plant ontogeny and leaf area development. By contrast, nitrogen fixation exhibited a sharp threshold response to temperature such that at temperatures below ~13.5–15°C faba bean had virtually no nitrogen fixation activity. This inability to fix nitrogen under cool temperatures is likely to be a major weakness for faba bean in cool-season production systems. Water deficit also had a large impact on the physiology of faba bean. Ontogeny was generally shortened when plants were subjected to drought, resulting in major yield decreases. Genotypic differences within faba bean have been identified for initiation of partial stomata closure at high soil-water content, resulting in possible soil-water conservation in the field. Also, differences among genotypes have been identified in the sensitivity of nitrogen fixation activity to water deficits. Finally, collectively the reviewed functional relationships have been applied to simulation analysis of the geospatial impact of irrigation regimes and of sowing date for faba bean production. These geospatial studies offered insights on options to improve faba bean management. Faba bean is an important species for crop production in cool, arid environments. This review examines the quantitative responses of plant physiological processes to temperature and soil-water deficit. These responses have been used in simulation analysis in geospatial assessments to examine management options for improving yield of faba bean. [ABSTRACT FROM AUTHOR]

Context: Crop production in Eastern Himalaya is constrained by combined effects of soil acidity and moisture deficit during post monsoon under a changing climate. Aim: This study aimed to identify potential buckwheat genotypes and stress mechanisms in hill environments of Meghalaya (India). Method: Field and microcosm experiments were conducted to study field performance, genotypic variability and physio-morphological efficiency in buckwheat. Key results: Substantial genetic variability for shoot length (30.3–110 cm), crop canopy distribution (3.0–7.5 number of primary branches), leaf area retention (15.7–60.2 cm2/two leaves), 1000 seed weight (16.2–34.7 g) and seed yield (0.088–1.31 metric tonne (MT)/ha) was observed. Mean grain yield of buckwheat genotypes was 0.446 MT/ha whereas IC13411, IC24298 and IC37305 produced significantly higher seed yield (1.31, 1.28 and 1.14 MT/ha, respectively) accounting to 1.93, 1.87 and 1.67 times higher than average yield (0.680 MT/ha). Genotypes like IC42416, EC323730 and EC218784 have lower crop yields (0.8, 1.02, 0.93 MT/ha respectively). Interestingly, few high yielders (IC13411 and IC24298) exhibited higher photosynthetic rate (46.1 and 32.3 μmol/m2.s), increased stomatal conductance (311.0 and 326.2 mmol/m2.s), leaf chlorophyll (2.47 and 2.55 mg/g fresh weight (FW)) and carotenoids (95 and 106.3 μg/g FW). Chlorophyll a and b ratio (3.3 and 3.0) and total chlorophyll to carotenoids (4.2 and 4.0) were higher in high yielders (IC13411 and IC24298) compared to other low yielders. Conclusions: Robust root systems, stomatal structure and higher cuticle wax ideally enhanced water use efficiency (WUE) and drought tolerance in high-yielding buckwheat genotypes. Implications: IC13411 and IC24298 are promising for enhanced productivity in the mountainous ecosystem of Meghalaya. Buckwheat is a potential but lesser known crop, with wide adaptability and multiple utility, cultivated under resource poor and marginal soils of mountainous Eastern Himalaya. Our results indicate significant genetic variability for physio-morphological traits responsible for moisture stress tolerance on hill slopes of Meghalaya. Identified high-yielding buckwheat genotypes (IC13411 and IC24298) with increased stress tolerance could be promising for enhanced crop productivity and food security under fragile mountainous ecosystem of Meghalaya (India). [ABSTRACT FROM AUTHOR]

Context: Investigating agronomic responses of dryland maize (Zea mays L.) systems under global change could provide important insights in designing climate-resilient cropping systems. Aims and methods: In this study, we integrated Agricultural Production Systems sIMulator (APSIM) with Representative Concentration Pathways 8.5 and 20 Global Climate Models to systematically: (1) calibrate and validate APSIM using large-field study conducted in East-Central Texas; (2) evaluate the impacts of climate change on maize productivity and risks; and (3) investigate the variations in growth stage lengths. Key results: Results indicated that APSIM simulated grain yield, biomass production, precipitation productivity (PP; kg ha−1 mm−1) and developmental stage transition agreed well with observation (NRMSE < 14.9%). Changes in temperature and precipitation shortened growing seasons and affected available water, resulting in widely varied yield and PP. Mean grain yield changed from −34.8 to +19.7%, mean PP were improved 9.2–36.5%. The grain production could be maintained at least the standard of 75% of historical in most cases, but with greater risks for achieving higher threshold (50% of baseline). Finally, simulations indicated shortened days (4–13 days) for reaching key developmental stages for maize. Conclusions and implications: The results advocate adoptions of management practice that incorporating early sowing, irrigations at sowing/VT stages, and selections of late-maturing cultivars for better sustainability and higher productivity. Global change greatly challenge the maize (Zea mays L.) systems, and crop modelling could provide comprehensive assessment on the impacts of climate change. We investigated the effects of projected future climate on maize yield, climate risks and growth stage transitions in the East-Central Texas (ECT), indicating that yield could maintain historical level in most cases with shorten seasons and higher risk. Extending maize growing season, choosing soil with low-sand level and supplement of irrigation in ECT have greater impact in moderating risk. [ABSTRACT FROM AUTHOR]

The increased incidence of extreme temperature events due to global climate change poses a major challenge for crop production. Ability to increase temperature tolerance through genetic improvement requires understanding of how crops and their wild relatives respond to extreme temperatures. We developed a high-throughput technique to evaluate tolerance to freezing stress (FS) and heat stress (HS) in wild, crop–wild hybrid and cultivated sunflower (Helianthus annuus L.). We also investigated whether trade-offs exist between stress tolerance and growth under benign conditions. Eleven experiments were performed under a combination of growth-chamber and field conditions. In growth-chamber experiments, FS and HS consisted of exposing acclimated plants at the 2–4-leaf stage to temperatures ranging from to –2.5°C to –4°C for 2–4 h and from 52°C to 54°C for 2–3 h. In the field, plants were grown for 32 days during midwinter (FS: average Tmean = 9.9°C and Tmin = 3.8°C) or for 10 days in a heat tent (HS: average Tmean = 30.1°C and Tmax = 43.3°C). We observed large differences in tolerance to FS and HS between wild and cultivated sunflower. Wild sunflower showed higher FS tolerance than cultivated in both growth-chamber and field experiments, whereas cultivated sunflower showed higher HS tolerance in growth-chamber experiments. No differences in HS tolerance were observed in the field. Crop–wild hybrids generally showed intermediate HS and FS tolerance. We found no evidence of a growth-tolerance trade-off, which suggests that tolerance might be introgressed into elite germplasm without growth penalties. The study reveals that wide genetic variation for the tolerance to extreme temperatures exists in the primary gene pool of sunflower. Increased incidence of extreme temperature events due to global climate change is a major challenge for crop production worldwide. We evaluated the response to extreme temperatures of cultivated sunflower and its wild ancestor, and observed wide genetic variation for tolerance to extreme temperatures within this species. Understanding how crops and their wild relatives respond to extreme temperatures will help breeders to develop new cultivated varieties adapted to future climate. [ABSTRACT FROM AUTHOR]

Crop models are important tools for assisting farmers and crop consultants to make decisions about fertilisation, irrigation and harvest management, because they allow users to understand productivity from the view of integrated sensitivities of basic plant physiological processes. The first objective of this study was to evaluate the performance of the CSM-CROPGRO-Perennial Forage model (PFM) to simulate regrowth of Urochloa brizantha (Hochst. ex A.Rich.) R.D.Webster cv. Marandu under varying irrigation and nitrogen levels. The second objective was to evaluate the water-balance module of the model under soil and climatic conditions in the Cerrado biome of central-eastern Brazil. The experimental data for model evaluation were obtained from a field experiment conducted during 2015, 2016 and 2017, and included herbage production, plant-part composition and plant nitrogen (N) concentration. The results suggest that the model can be used to simulate growth of Marandu palisade grass adequately under different managements of irrigation and N fertilisation. The findings indicate also that the agreement between simulations and field-observed soil moisture shows good performance of the water-balance module of CSM-CROPGRO-PFM. The most important parameterisation required by the model was the determination and calibration of inputs such as the stable soil carbon pool (SOM3) for N mineralisation, which affected the N response, and the soil water-holding characteristics, which affected the irrigation response. The default parameterisation (species, ecotype, cultivar) of cv. Marandu in CSM-CROPGRO-PFM was sufficient for adequate performance of the model for this new environment and new crop management. However, minor modifications of species parameters were helpful to account for winter-kill of foliage. Crop models are important tools for assisting farmers and crop consultants to make decisions about management practices. The performance of the CSM-CROPGRO-Perennial Forage model to simulate regrowth of Marandu grass was evaluated under soil and climatic conditions in the Cerrado biome of central-eastern Brazil. The results suggest that the model simulates forage growth adequately under different managements of irrigation and N fertilisation. [ABSTRACT FROM AUTHOR]

Lodging is the permanent displacement of plant shoots from an upright position and represents a major obstacle to reaching yield potential in bread wheat (Triticum aestivum L.). Breeding programs would benefit from the identification of lodging-related traits amenable to easy and rapid screening, even in the absence of lodging. However, no locally tested lodging model is available for the Pampas region of Argentina, and most lodging models are based on measurements before crop maturity. We adapted two existing models and generated a new one, using easily measurable traits at crop maturity in 24 cultivars (14 for model fit and 10 for model validation) grown in plot trials with no nutritional, water or disease restrictions in three crop seasons at Balcarce, Argentina. Of 17 traits evaluated, 16 showed differences between cultivars (P < 0.05), and in 11 of these traits, no genotype × environment interaction was detected (P > 0.05). Estimations of the safety factor against stem lodging, proposed by Crook et al., and the wind velocity that produces lodging, proposed by Berry et al., showed a high correlation with lodging score (R 2 = 0.60 and 0.72, respectively), but when the estimators were tested with another set of cultivars there was no association. A new empirical regression model was based on three traits measured at maturity: plant height, spike dry weight, and the inertia moment of the stem base (stem resistance to bending estimated from stem diameter and wall thickness). The model was then simplified by replacing the third trait with basal stem diameter, and it showed an even better fit (R 2 = 0.90). These models were satisfactorily validated by rank correlations with a different cultivar set. The regression model proposed in this study can easily be applied to the evaluation of commercial cultivars and may be used to screen breeding materials. Measurements at maturity are convenient and easy to combine with other traits of possible selective advantage. Key traits were identified to predict lodging susceptibility of wheat cultivars in the absence of lodging. A new regression model, based on plant height, spike dry weight and the inertia moment of the stem base was validated. The application of this model will be useful for evaluating commercial cultivars and breeding materials and discarding genotypes with high lodging susceptibility, even under conditions of no lodging. [ABSTRACT FROM AUTHOR]

Subsoil manuring is a practice that involves placing high rates of organic amendments in bands at the base of rip-lines that extend down into the subsoil to a depth of 30–40 cm, in order to ameliorate poorly structured clay subsoils. The objective of this study was to determine whether the increases in crop yield from this practice, which occurred at one site in the high-rainfall zone in Victoria from 2005 to 2007, would occur at other sites and in other seasons in south-western Victoria. On-farm field experiments were therefore carried out at three sites in south-western Victoria between 2009 and 2012 to measure the yield responses to subsoil manuring. The study found that subsoil manuring with poultry litter resulted in consistent and recurring increases in estimated grain yield at these sites, with responses occurring with consecutive crops. Increases in estimated grain yield continued for 4 years, with average increases in hand-harvested yield of 3.5 t ha–1 for wheat, 1.6 t ha–1 for canola and 2.3 t ha–1 for faba beans. The estimated increases in grain yield were frequently associated with the increased extraction of deep subsoil water after anthesis. A treatment involving deep-banded nitrogen (N) and phosphorus (P) fertilisers and additional in-crop N, which matched the N and P in the poultry litter, yielded less than the full rate of subsoil manuring in seven of the eight site-years. This suggests that yield responses from subsoil manuring were not solely due to the release of N and P from the deep-banded poultry litter. Subsoil manuring is a practice where a high rate of nutrient-rich organic amendment is placed in deep bands to ameliorate dense clay subsoils and to improve crop yields. On-farm field experiments at three sites in south-western Victoria with dense clay subsoils resulted in large increases in estimated grain yield that continued for 4 years, with average increases in hand-harvested yield of 3.5, 1.6 and 2.3 t ha–1 for wheat, canola and faba beans, respectively. The results highlight how subsoil manuring can increase crop productivity on soils with poorly structured subsoils in this region. [ABSTRACT FROM AUTHOR]

Barley (Hordeum vulgare L.) is often grown in sites with low rainfall and high temperature during grain filling. Because spike architecture is one of basic footprints of barley domestication, the importance of spikes in adaptation to different environments or abiotic stresses can be hypothesised. In order to compare different barley spike types in terms of kernel growth and yield components, we tested 15 two-row and 10 six-row winter genotypes in eight environments where terminal drought was simulated by defoliation at 7 days after heading (7 DAH). Control plants were grown intact. On average, two-row genotypes outyielded six-row genotypes by 17% under control conditions and 33% under simulated late drought. Observations of kernel dry weights from 7 DAH through to harvest maturity at 5-day intervals were regressed onto a measure of thermal time. After preliminary evaluation of four nonlinear (S-shaped) models for kernel dry-weight accumulation, the ordinary logistic model was deemed the most appropriate in most cases and was finally applied to all plant-growth curves. Four parameters were estimated from the logistic model. Whereas two earliness estimators (inflection point and thermal time needed to reach maximum kernel weight) were similar for the two barley types, maximum kernel weight (Ymax) and mean rate of kernel growth (RG) were higher (P < 0.05) in two-row than in six-row barleys. Differences in Ymax and RG among six-row barley genotypes were greater between control and defoliation treatments than between years, whereas among two-row barley genotypes, differences between years were greater, suggesting better stability of six-row types and better drought tolerance of two-row types in the tested barley set. [ABSTRACT FROM AUTHOR]

Oats likely emerged as part of the weedy grass assemblage in early wheat and barley crops. Some Avena species, such as A. fatua and A. sterilis, evolved into aggressive weeds, and the high interspecific competitive ability of cultivated oats (A. sativa) is valued agronomically to facilitate weed control in rotations. We tested the hypothesis, verified in many crops, that high yield of oats is related to low intraspecific competitive ability. Ten contemporary oat varieties, selected for grain yield and milling attributes, where grown in three environments. Response to competition was calculated as 100 x (Yb - Yc)/Yc, where Y is yield measured in border (b) and centre (c) rows. The same definition was used to calculate response to competition of yield components (biomass, harvest index, grain number, grain weight) and the components of grain number (panicle number and grains per panicle). Yield response to competition was affected by all three sources of variation, i.e. environment, variety and variety x environment interaction. The interaction demonstrates the plasticity of yield response to intraspecific competition; for example, the response to competition of variety Mortlock varied from 9% to 71% among environments. This plasticity in yield response to competition was partially related to variety-dependent responses to competition for biomass and harvest index, number of panicles and number of grains per panicle. We did not find the expected negative association between yield and variety-dependent response to competition. We discuss how this lack of correlation could relate to sampling issues, i.e. a limitation in the range of environments and varieties explored in this study, or reflect a legitimate feature of oat crops arising from early and contemporary selective pressures. [ABSTRACT FROM AUTHOR]

Growers in the wheatbelt of south-eastern Australia need increases in water-limited potential yield (PYw) in order to remain competitive in a changing climate and with declining terms of trade. In drought-prone regions, flowering time is a critical determinant of yield for wheat (Triticum aestivum L.). Flowering time is a function of the interaction between management (M, establishment date), genotype (G, development rate) and prevailing seasonal conditions. Faced with increasing farm size and declining autumn rainfall, growers are now sowing current fast-developing spring wheat cultivars too early. In order to widen the sowing window and ensure optimum flowering dates for maximum yield, new GxMstrategies need to be identified and implemented. This study examined the effect of manipulating genotype (winter vs spring wheat and long vs short coleoptile) and management (sowing date, fallow length and sowing depth) interventions on yield and flowering date in high-, medium- and low-rainfall zones in south-eastern Australia. Twelve strategies were simulated at nine sites over the period 1990-2016. At all sites, the highest yielding strategies involved winter wheats with long coleoptiles established on stored subsoil moisture from the previous rotation, and achieved a mean yield increase of 1200 kg/ha or 42% relative to the baseline strategy. The results show promise for winter wheats with long coleoptiles to widen the sowing window, remove the reliance on autumn rainfall for early establishment and thus stabilise flowering and maximise yield. This study predicts that GxM strategies that stabilise flowering may increase PYw. [ABSTRACT FROM AUTHOR]