High-output forage systems for meeting beef markets – Phase 2

This project investigated the relative profitability of six forage options for backgrounding or finishing cattle in the Fitzroy River catchment of Queensland. Data was collected at 24 forage sites on commercial properties over 2011-2014. Whole-farm economic case studies were developed with five co-operators. The factors affecting profitability were further investigated through constructed forage scenarios. This work has provided a better understanding of the expected forage, animal and economic performance from key forage options under commercial management conditions. Under current market and cost conditions, perennial legume-grass pastures, particularly leucaena-grass, had a significant advantage over perennial grass pasture and annual forages in terms of profitability. However, legume-grass pastures were not as profitable as grain cropping when grain cropping was a feasible alternative. Annual forages were unable to add economic value to the beef enterprise due to their higher average growing costs when compared to perennial forages. Existing models could not accurately predict forage and animal production from annual forage crops. A prototype decision support tool was developed. A producer guide to forage use, and gross margin spreadsheets for forages grown in three sub-regions of the Fitzroy River catchment, have been developed and will support informed decision making with regard to forage use

An integrated framework for predicting the risk of experiencing temperature conditions that may trigger late-maturity alpha-amylase in wheat across Australia

Late-maturity alpha-amylase (LMA) is a key concern for Australia’s wheat industry because affected grain may not meet receival standards or market specifications, resulting in significant economic losses for producers and industry. The risk of LMA incidence across Australia’s wheatbelt is not well understood; therefore, a predictive model was developed to help to characterise likely LMA incidence. Preliminary development work is presented here based on diagnostic simulations for estimating the likelihood of experiencing environmental conditions similar to a potential triggering criterion currently used to phenotype wheat lines in a semi-controlled environment. Simulation inputs included crop phenology and long-term weather data (1901–2016) for >1750 stations across Australia’s wheatbelt. Frequency estimates for the likelihood of target conditions on a yearly basis were derived from scenarios using either: (i) weather-driven sowing dates each year and three reference maturity types, mimicking traditional cropping practices; or (ii) monthly fixed sowing dates for each year. Putative-risk ‘footprint’ maps were then generated at regional shire scale to highlight regions with a low (66%) likelihood of experiencing temperatures similar to a cool-shock regime occurring in the field. Results suggested low risks for wheat regions across Queensland and relatively low risks for most regions across New South Wales, except for earlier planting with quick-maturing varieties. However, for fixed sowing dates of 1 May and 1 June and varying maturity types, the combined footprints for moderate-risk and high-risk categories ranged from 34% to 99% of the broad wheat region for South Australia, from 12% to 97% for Victoria, and from 9% to 59% for Western Australia. A further research component aims to conduct a field validation to improve quantification of the range of LMA triggering conditions; this would improve the predictive LMA framework and could assist industry with future decision-making based on a quantifiable LMA field risk

A cross-scale analysis to understand and quantify the effects of photosynthetic enhancement on crop growth and yield across environments

Abstract Photosynthetic manipulation provides new opportunities for enhancing crop yield. However, understanding and quantifying the importance of individual and multiple manipulations on the seasonal biomass growth and yield performance of target crops across variable production environments is limited. Using a state-of-the-art cross-scale model in the APSIM platform we predicted the impact of altering photosynthesis on the enzyme-limited (Ac) and electron transport-limited (Aj) rates, seasonal dynamics in canopy photosynthesis, biomass growth, and yield formation via large multiyear-by-location crop growth simulations. A broad list of promising strategies to improve photosynthesis for C3 wheat and C4 sorghum were simulated. In the top decile of seasonal outcomes, yield gains were predicted to be modest, ranging between 0% and 8%, depending on the manipulation and crop type. We report how photosynthetic enhancement can affect the timing and severity of water and nitrogen stress on the growing crop, resulting in nonintuitive seasonal crop dynamics and yield outcomes. We predicted that strategies enhancing Ac alone generate more consistent but smaller yield gains across all water and nitrogen environments, Aj enhancement alone generates larger gains but is undesirable in more marginal environments. Large increases in both Ac and Aj generate the highest gains across all environments. Yield outcomes of the tested manipulation strategies were predicted and compared for realistic Australian wheat and sorghum production. This study uniquely unpacks complex cross-scale interactions between photosynthesis and seasonal crop dynamics and improves understanding and quantification of the potential impact of photosynthesis traits (or lack of it) for crop improvement research

Developing and testing an analysis framework for long-term fertiliser decisions: Deep-P Calculator

Grain growers in Queensland and northern New South Wales are facing a new challenge in crop nutrition management: phosphorus (P) depletion of many soils below 10 cm. Research has indicated potential yield benefits from replenishing P in sub-surface layers (referred to as a ‘deep-P’ application). However, it was unknown if amelioration had economic merit. The fundamental question of deep-P placement is ‘how much P (what rate) should be applied and how often (at what frequency)?’ A program of close consultation was implemented, with leading scientists, fertiliser industry researchers, advisors and growers all contributing knowledge. Focus groups, a literature review and case studies were conducted. The iterative process implemented by this project effectively utilised various extension techniques to engage industry and leading researchers. This knowledge was used to develop a framework to produce a web-based tool that answers the fundamental question of deep-P fertiliser placement, ‘how much P and how often?

Lead time and skill of Australian wheat yield forecasts based on ENSO-analogue or GCM-derived seasonal climate forecasts – A comparative analysis

Foresight of crop yield is fundamental to producers and industry to better manage climate risks and mitigate ebbs and troughs in crop production. Rain-fed grain production in Australia is highly volatile and producers and industry are progressively confronted with projected uncertainties due to climate variability and change, input costs and market prices. Thus, having advance knowledge of the likely impact of the coming season's climate on crop yield and production is critical for decisions across the supply chain. Here we explore and analyse the lead time and skill of a wheat yield forecasting system using a biophysical crop yield simulation model connected to either a statistical ENSO-analogue climate forecasting system or a dynamic general circulation model (GCM) derived climate forecasting system. The comparative skill was investigated for 16 wheat producing districts (shires) of the broad Australian winter cropping region, each containing 9–35 irregularly-spaced simulation points associated with climate stations. Both the ENSO-analogue and GCM-derived systems produced reliable wheat yield forecasts with the GCM-based approach having general improved skill, and particularly during the early months of the season (March to May) before sowing. The shift in the forecast yield distributions relative to the climatology-based yield distribution were dependent on location and time in the season, with the GCM-derived forecast shifts more widespread and earlier in the season. Overall, the GCM-based climate/crop forecasting system showed a significant improvement in lead time (greater than two months before the normal planting time of wheat), across the Australian wheat belt. This result demonstrates an avenue for improved efficacy in future commodity forecasting frameworks via likely enhanced relevance and utility to industry associated with the use of GCM-derived approaches

CropGen: A novel tool for optimising sorghum crop design

Sorghum is an important food and feed crop globally. Strategies to optimise its yield performance are needed to help meet rising demands in changing future environments. Typically, sorghum is grown as a dryland crop, with water availability being a major limitation for yield. Here we present a new tool, CropGen, for optimising sorghum design and yield performance. CropGen connects the APSIM sorghum model with an evolutionary optimisation algorithm to enable iterative exploration of genotype × agronomy combinations to identify optimal strategies based on a number of objectives, including grain yield, water use, and crop failure risk over diverse production environments. Initial model testing was undertaken using single year simulations both with and without irrigation to explore tillering × maturity combinations and quantify their yield and water use. In the irrigated scenario, optimal yields were achieved with high tillering and long maturity sorghum designs, whereas lower tillering and shorter maturity designs were identified for the non-irrigated scenario. These findings are consistent with known effects of water availability, crop growth, and yield interactions. CropGen was then applied to optimise tillering × maturity designs for Dalby, a key sorghum production region in Australia, by quantifying the yield, water use, and crop failure risks. The nuanced understanding of crop performance over diverse environments generated by CropGen is key in identifying optimal strategies for guiding research and plant breeding efforts to maximise crop improvement

Yield trends under varying environmental conditions for sorghum and wheat across Australia

Globally as well as nationally, food production is being exposed to increased climatic and market volatility. The trend in sorghum yield in Australia has been consistent and positive over the last 30 years, while yield trends globally for other cereals like wheat, maize and rice have slowed. Australia is of interest not only as a major exporter in world markets, but also because considerable research effort has been focused on developing crops and practices that help to reduce the risks of yield losses under drought conditions. This study examines sorghum and wheat yield trends over the previous three to four decades in Australia after realistically accounting for the effects of year-to-year climate variability. We quantified the yield trends within three distinct types of crop stress environments (i.e. DRY: ENVT1, MODERATE: ENVT2& WET: ENVT3). Overall trends in sorghum yields were 2.1% per year (44 kg/ha/year), which was nearly double that found for wheat (1.2% per year; 21 kg/ha/year). However, in dry environments, relative yield trends for sorghum were 3.6 times those for wheat, whereas in wet environments trends were similar. Likely technology and environmental factors underpinning these trends are discussed