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Closing the yield gap is essential for global food security and for farmers who face increasing costs of production. Recent work showed that Australia’s wheat growers are achieving about half their water-limited yield. While quantifying the yield gap is a necessary first step towards closing them, the next step is to understand which factors constrain rainfed grain growers from achieving their water-limited yields. Here we conducted in silico experiments over 15 years at 50 weather stations to ascertain the impact on grain yield of suboptimal practices against the ‘best management practice’ rules that were used to calculate the benchmark water-limited yields. Average national losses per suboptimal practice were: the average N fertiliser application rate – 40%; conventional tillage – 33%; suboptimal weed control during the summer fallow – 26%; low seedling density – 12%; and a two week delay in sowing – 7%. Combining two suboptimal practices does not necessarily lead to an additive effect on yield. Other factors that contribute to the yield gap include biotic stresses such as plant diseases, insects and other pests, in-crop weeds and extreme weather events (e.g. floods, strong winds and hail). In addition to calculating the impact of causes of the yield gap we investigated the opportunity to lift the water-limited yield by adopting an emergent new management practice of sowing on an optimised site specific date that is earlier than the traditional sowing window as described for the currently accepted best practice. We found that this emergent practice, matched with slower maturing varieties and additional N inputs as required, has the potential to increase wheat yields nationally by 30%. Frost and heat stress accounted for losses of 16% to 26% depending on the stress function used. Allowing for the impact of frost and heat stress reduced the yield potential of both the current and emergent water-limited yields yet it did not reduce the advantage of the emergent practice.

Resource shortages, driven by climatic, institutional and social changes in many regions of Asia, combined with growing imperatives to increase food production whilst ensuring environmental sustainability, are driving research into modified agricultural practices. Well-tested cropping systems models that capture interactions between soil water and nutrient dynamics, crop growth, climate and farmer management can assist in the evaluation of such new agricultural practices. One such cropping systems model is the Agricultural Production Systems Simulator (APSIM). We evaluated APSIM’s ability to simulate the performance of cropping systems in Asia from several perspectives: crop phenology, production, water use, soil dynamics (water and organic carbon) and crop CO 2 response, as well as its ability to simulate cropping sequences without reset of soil variables. The evaluation was conducted over a diverse range of environments (12 countries, numerous soils), crops and management practices throughout the region. APSIM’s performance was statistically assessed against assembled replicated experimental datasets. Once properly parameterised, the model performed well in simulating the diversity of cropping systems to which it was applied with RMSEs generally less than observed experimental standard deviations (indicating robust model performance), and with particular strength in simulation of multi-crop sequences. Input parameter estimation challenges were encountered, and although ‘work-arounds’ were developed and described, in some cases these actually represent model deficiencies which need to be addressed. Desirable future improvements have been identified to better position APSIM as a useful tool for Asian cropping systems research into the future. These include aspects related to harsh environments (high temperatures, diffuse light conditions, salinity, and submergence), conservation agriculture, greenhouse gas emissions, as well as aspects more specific to Southern Asia and low input systems (such as deficiencies in soil micro-nutrients).

Closing the gap between yields currently achieved on farms and those that can be achieved with best practice and current technology in a given environment is a key strategy for increasing grain production on existing cropland. In this study, the yield gap of rainfed wheat in Australia was derived by a data rich (DR) analysis method made possible by the high density of data available in the Australian grain zone. At the national scale, the analysis revealed that Australia’s 15 year (1996–2010) average actual wheat yield (Ya) was 1.7 t/ha, while the simulated water-limited yield potential (Yw) was 3.5 t/ha; thus the yield gap (Yg = Yw-Ya) was 1.8 t/ha; and the relative yield (Y % = 100 x Ya/Yw) was 49%. The fifteen year average Ya and Yw values for each of the 245 statistical local areas (SLAs) in Australia’s wheat zone were strongly and positively correlated (Ya = 0.456 x Yw + 0.15; R 2 = 0.65; RMSE = 0.291; p 2 = 0.95; RMSE = 0.087; p

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 1.1 km 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 4.12 Mg ha −1 with an average of 2.00 Mg ha −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 1.09 M tonnes to 1.65 M tonnes. 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.

While the amount of nitrogen (N) contributed to agricultural soils by above ground legume biomass can be over 300 kg N/ha when grain is not harvested, its availability to subsequent sugarcane crops is uncertain. In Australia it is generally accepted that inorganic N fertiliser applied to the sugarcane (Saccharum officinarum L.) crop in the first year (termed the plant crop) following a ‘good’ legume fallow crop that is not harvested can be substantially reduced, or even eliminated. However, current recommendations do not consider the carry over of legume N to crops beyond the plant crop (termed ratoons), for which standard N fertiliser rates are considered necessary. Based on a simple field experiment extending the duration of a soybean (Glycine max L.) fallow and two sugarcane crops (plant and first ratoon), cropping system simulation was used to provide a first estimate of how long and how much soybean N remains available for uptake by sugarcane following a soybean break crop. The soybean and sugarcane rotation plot was carried out at Mossman, Queensland over 3 years during which crop yield, plant N, and total soil C and mineral N were measured. The soybean variety Leichhardt produced 9 t/ha (±0.7) above ground dry weight, containing 301 kg N/ha (±36) which contributed to the soil N stores. After independently developing parameter values for Leichhardt, model simulations were run. The model explained 91% of the variation observed in soil mineral N to a depth of 1.5 m. Additional legume and sugarcane rotation simulations were run for the Burdekin and Bundaberg sugarcane regions. Across all three study sites the simulations suggested that legume N was available to the sugarcane crop up to the fourth ratoon. Acknowledging the limitations of this initial exploratory study, it is hypothesized that potential reductions in fertiliser application rate could be up to approximately 100% in the first ratoon, and 60%, 25% and 10% in the subsequent ratoons. These findings require further validation but suggest a potential economic and environmental win–win outcome from refining N management practices in sugarcane-legume rotation cropping systems in Australia and other countries.