Foliar pathogens result in significant losses in herbage and seed yields and regeneration capacity in annual clover pastures, the last leading to their rapid deterioration and lack of persistence. The most important pathogens include Kabatiella caulivora (clover scorch), Cercospora zebrina (cercospora), Uromyces trifolii-repentis (rust), Erysiphe trifoliorum (powdery mildew), and Leptosphaerulina trifolii (pepper spot). Several other foliar pathogens on annual clovers, in particular Phoma medicaginis (black stem and leaf spot), one or more Stemphylium spp. (stemphylium leaf spot), Pseudopeziza trifolii (common leaf spot), Stagonospora spp. (stagonospora leaf spot), Colletotrichum trifolii (anthracnose) and Sclerotinia trifoliorum (sclerotinia), occur widely and together contribute to reduce productivity in some localities. Severe attack by the most important pathogens (e.g. K. caulivora, U. trifolii-repentis, E. trifoliorum) not only greatly reduces winter–spring pasture production but frequently also coincides with the critical feed shortage across autumn–winter, leading to significantly decreased autumn–winter biomass production in regenerating stands. Approaches to disease control include a range of management strategies. Wider utilisation of cultural and fungicidal control strategies offers producers greater management flexibility, particularly in conjunction with deployment of cultivars with useful resistance. Host resistance offers the greatest potential for delivering the most cost-effective and long-term control. Many of these foliar pathogens co-occur, magnifying losses; this highlights the need for individual host genotypes with resistance to multiple pathogens and unique geographic locations such as Sardinia offer enormous scope to select such clovers. Future research opportunities and critical priorities to improve management of foliar pathogens in annual clover pastures across southern Australia include the need to: (i) define pathogen strain–race structures, particularly for K. caulivora and U. trifolii-repentis, and determine associated host resistances against specific strains–races to allow strategic deployment of host resistances; (ii) define relative resistances to major fungal foliar pathogens of all parental and near-release breeding genotypes and all commercial cultivars across important annual clover species; (iii) identify new sources of host resistance, particularly genotypes with cross-resistance to multiple pathogens, for breeders to utilise; (iv) identify and demonstrate the benefits to farmers of effective cultural (e.g. grazing, removal of infested residues) and fungicidal control options that allow greater management flexibility to reduce the impact of fungal foliar diseases; and (v) determine current incidences and impacts (losses and economic importance) of major fungal foliar diseases in the different agro-climatic regions across southern Australia. Failure to address these critical issues leaves livestock industries carrying the risks from release of new varieties of unknown susceptibilities to one or more of the major foliar diseases, and the risks from continued use of older varieties exposed to new pathogen races; with few if any flexible management options during periods of critical feed shortage; and without the basic information on current disease impacts that is needed to make sensible management and funding decisions.

Global temperatures are rising, and concerns about the response of agricultural production to climate change are increasing. Adaptation is a key factor that will shape the severity of impacts of future climate change on food production. Based on actual meteorological, soil and agricultural management data at site scale, the CERES-Rice model, combined with the Regional Climate Model (RCM)-PRECIS, was used to simulate both the effects of climate change on rice yields and the efficacy of adaptive options in Northeast China. The impact simulation showed that rice yield changes ranged from 0.1% to –44.9% (A2 scenario) and from –0.3% to –40.1% (B2 scenario) without considering CO₂ fertilisation effects. When considering CO₂ fertilisation effects, rice yield reductions induced by temperature increases were decreased at all sites. The CO₂ fertilisation effects may partly offset the negative impacts of climate change on rice yields. Adaptive option results revealed that the adverse impacts of climate change on rice yields could be mitigated by advancing the planting dates, transplanting mid–late-maturing rice cultivars to replace early-maturing ones, and breeding new rice cultivars with high thermal requirements. Our findings provide insight into the possible impacts of climate change on rice production, and we suggest which adaptive strategies could be used to cope with future climate change, thus providing evidence-based suggestions for government policy on adaptive strategies.

Highly alkaline soils (pH >9) may adversely affect agricultural crop productivity. At pH >9.2, aluminium (Al) phytotoxicity may further retard plant development. Most alkaline soils have little alkaline buffering capacity, making it feasible to use acid to lower soil pH to <9.2. Many methods of lowering soil pH have been trialled; however, little research has been done on their relative effectiveness and longevity. Methods trialled in this study as means of lowering soil pH were chemical additives (gypsum), organic additives (glucose, molasses, horse manure, green manure, humus) and leguminous plants. Gypsum was also used in conjunction with plants to determine any synergistic effects of combining treatments. All ameliorants trialled except humus and horse manure proved effective at lowering soil pH to <9.2. The reduction achieved with biological amendments was temporary, with pH returning to pre-amendment levels over the course of the study. Gypsum was most effective amendment for lowering soil pH and sustaining the lowered pH level. The use of plants to lower soil pH, in conjunction with gypsum to sustain the lowered pH, may be an effective and economic method of remediating Al phytotoxicity in alkaline soils.

Early crop vigour in canola, as in other crops, is likely to result in greater competition with weeds, more rapid canopy closure, improved nutrient acquisition, improved water-use efficiency, and, potentially, greater final grain yield. Laborious measurements of crop biomass over time can be replaced with newer remote-sensing technology to aid data acquisition. Normalised difference vegetation index (NDVI) is a surrogate for biomass accumulation that can be recorded rapidly and repeatedly with inexpensive equipment. In seven small-plot field experiments conducted over a 4-year period with diverse canola germplasm (n = 105), we have shown that NDVI measures are well correlated with final grain yield. We found NDVI values were most correlated with yield (r >0.7) if readings were taken when the crop had received 210–320 growing degree-days (usually the mid-vegetative phase of growth). It is suggested that canola breeders may use NDVI to objectively select for vigorous genotypes that are more likely to have higher grain yields.

Canola (Brassica napus L.) is an important break crop in Australian cropping systems but weeds are a major cost to production and herbicide-resistant weeds are spreading. The potential competitive ability of canola genotypes to both suppress weed growth and maintain grain yield and quality in the presence of weeds has not been determined in Australia. Two experiments examined the range in competitive ability of 16 B. napus genotypes against annual ryegrass (Lolium rigidum Gaud.) and volunteer wheat (Triticum aestivum L.) over two contrasting seasons. Weed biomass at flowering was generally reduced 50% more in the presence of the strongly competitive genotypes than the least competitive, and this has significant benefits for lower weed seed production and reduced seedbank replenishment. Suppression of weed growth was negatively correlated with crop biomass. Significant differences in grain yield of canola were recorded between weedy and weed-free plots, depending on crop genotype, presence of weeds and season. Crop yield tolerance (where 0% = no tolerance and 100% = complete tolerance) to ryegrass competition ranged from 0% (e.g. with CB-Argyle) to 30–40% (e.g. with the hybrids 46Y78 and Hyola-50) in the dry season of 2009. Yield tolerance was higher (50–100%) with the lower densities of volunteer wheat and in the 2010 season. The range between genotypes was similar for both conditions. The hybrids and AV-Garnet were higher yielding and more competitive than the triazine-tolerant cultivars. The ranking of genotypes for competitiveness was strongly influenced by seasonal conditions; some genotypes were consistently more competitive than others. Competitive crops are a low-cost tactic for integrated weed management to reduce dependence on herbicides and retard the spread of herbicide-resistant weeds.

Genotype × environment interactions (G × E) induce differential response of soybean (Glycine max (L.) Merr.) genotypes to variable environmental conditions with respect to seed composition, and this may hinder breeding progress. The objectives of this study were to estimate the contribution of genotype, environment and G × E to seed chemical composition variability, and to identify the most stable non-transgenic genotypes for several chemical components. Seeds from six non-transgenic soybean genotypes that were grown in 23 environments in Argentina (24–38°S) were analysed. Although environment was the most important source affecting variation for most of the analysed chemical components, genotype and G × E also had a significant effect (P < 0.001). Stable genotypes with superior performance across a wide range of environments were ALIM3.20 for protein, linolenic acid (Len), Len : linoleic acid (LA) ratio (Len/LA), δ-tocopherol (δT) and total isoflavones (TI); ALIM4.13 for protein, oleic acid, α-tocopherol (αT) and δT; ALIM3.14 for Len, αT and TI; Ac0124-1 for Len and Len/LA; and Ac0730-3 for αT. Non-transgenic genotypes with stable chemical profile across environments would perform well under a wide range of environmental conditions for any chemical compound. This study contributes knowledge for breeders to use these genotypes to broaden the genetic backgrounds of currently available commercial cultivars, or to design production strategies that employ the genotypes directly as raw material.

Less prevalent viruses of family Poaceae are usually excluded from the focus of interest, even though they represent a possible threat to agricultural production. We designed and validated a set of primer pairs suitable for detection and quantification of five RNA viruses, Lolium latent virus (LoLV), Oat necrosis mottle virus (ONMV), Ryegrass mosaic virus (RgMV), Soil-borne cereal mosaic virus (SBCMV), and Spartina mottle virus (SpMV), by means of one-step RT-qPCR based on SYBR Green I. These primers were used together with primers for Brome mosaic virus (BMV) and Wheat streak mosaic virus (WSMV) described elsewhere to screen grass and cereal samples from the Czech Republic. The results revealed a high prevalence of WSMV and RgMV, which pointed to possible local epidemics. We also make the first report of LoLV presence in the Czech Republic.

A semi-quantitative enzyme-linked immunosorbent assay (ELISA) for the detection of Rathayibacter toxicus in hay or pasture was used to estimate the degree of R. toxicus bacterial gall contamination in hay or pasture that was unsuitable for export but that may be suitable for feeding to domestic livestock. Based on experience of testing of fodder samples from pastures where livestock showed clinical signs, or outbreaks of annual ryegrass toxicity (ARGT), four relevant levels of bacterial gall contamination were selected. Several 1-kg samples of hay with no contamination by R. toxicus were spiked with these four different amounts of bacterial galls to provide five different risk categories considered relevant to the occurrence of ARGT. Extracts of the spiked samples were assayed to determine the range of ELISA results to be expected in each category. To validate these risk categories, all cases of ARGT diagnosed between 2000 and 2012 by the Animal Health Laboratories, Department of Agriculture and Food Western Australia, associated with the submission of suspected toxic fodder were allocated to the risk categories on the basis of the recorded results for fodder. In ∼15% and 79% of all cases, the fodder associated with outbreaks of ARGT fell into the ‘moderate risk’ and ‘high risk’ categories, respectively. The selected categories were considered to provide realistic estimations of the risk that fodder within them might cause ARGT if fed to livestock. This risk-level reporting has been adopted to enable informed decision making as to whether feeding a particular batch of hay or fodder to animals represents an acceptable risk.

Forage-based livestock systems are complex, and interactions among animals, plants and the environment exist at several levels of complexity, which can be evaluated using computer modelling. Despite the importance of grasslands for livestock production in Brazil, tools to assist producers to make decisions in forage–livestock systems are scarce. The objective of this research was to use the CROPGRO-Perennial Forage model to simulate the irrigated and rainfed growth of Marandu palisade grass (Brachiaria brizantha (A. Rich.) Stapf. cv. Marandu), the most widely grown forage in Brazil, by using parameters previously calibrated for the tall-growing cv. Xaraes of the same species, under non-limiting water conditions. The model was calibrated for the irrigated experiment and then tested against independent data of the rainfed experiment. Data used to calibrate the model included forage production, plant-part composition, leaf photosynthesis, leaf area index, specific leaf area, light interception and plant nitrogen (N) concentration from a field experiment conducted during 2011–13 in Piracicaba, SP, Brazil. Agronomic and morpho-physiological differences between the two grasses, such as maximum leaf photosynthesis, N concentration and temperature effect on growth rate, were considered in the calibration. Under rainfed conditions, the simulations using the Penman–Monteith FAO 56 method gave a more realistic water stress response than the Priestley and Taylor method. After model parameterisation, the mean simulated herbage yield was 4582 and 5249 kg ha^–1 for 28 days and 42 days irrigated, and 4158 and 4735 kg ha^–1 for 28 days and 42 days rainfed, respectively. The root-mean-square error ranged from 464 to 526 kg ha^–1 and the D-statistic from 0.907 to 0.962. The simulated/observed ratios ranged from 0.977 to 1.001. These results suggest that the CROPGRO-Perennial Forage model can be used to simulate growth of Marandu palisade grass adequately under irrigated and rainfed conditions.

The repeated use of glyphosate to control annual ryegrass along fence lines and crop margins has resulted in the evolution of resistance to this herbicide in populations of annual ryegrass (Lolium rigidum) in cropping regions of Australia. Field trials were conducted between 2009 and 2011 at four fence-line sites in South Australia to identify suitable herbicide treatments for controlling glyphosate-resistant annual ryegrass. Annual ryegrass populations growing at all four sites were found to have 12–24-fold resistance to glyphosate compared with a standard susceptible population in dose-response experiments. Glyphosate alone (1080 g ha^–1) did not effectively control glyphosate-resistant annual ryegrass at any location. Single applications of paraquat   diquat and paraquat   amitrole were effective where weed populations were low, with up to 99% reduction in seed-head production. Mixtures of paraquat   diquat   diuron, glufosinate ammonium   diuron and two applications of paraquat   diquat 14 days apart consistently provided high levels of control of glyphosate-resistant annual ryegrass at all locations, with >90% reduction in seed-head production. In 2011, glyphosate resistant individuals of annual ryegrass were identified up to 50 m inside the fields adjacent to the fence. Therefore, failure to control glyphosate-resistant annual ryegrass along crop margins risks movement of resistance into adjacent cropped fields.

Crop & Pasture Science, 2014, 65(5), 442–452. doi:10.1071/CP13397

The equation in the first column of Page 451 is incorrectly published as:

Sowing PAW was positively correlated with %stubble cover (P< 0.05); therefore, %stubble cover was substituted for PAW:

The correct equation should be:

Sowing PAW was positively correlated with %stubble cover (P < 0.05); therefore, %stubble cover was substituted for PAW:

The authors apologise for the error.