APSIM was one of several models included in the work recently published in Science of the Total Environment - “The use of biogeochemical models to evaluate mitigation of greenhouse gas emissions from managed grasslands” https://doi.org/10.1016/j.scitotenv.2018.06.020

Simulation models quantify the impacts on carbon (C) and nitrogen (N) cycling in grassland systems caused by changes in management practices. To support agricultural policies, it is however important to contrast the responses of alternative models, which can differ greatly in their treatment of key processes and in their response to management. We applied eight biogeochemical models at five grassland sites (in France, New Zealand, Switzerland, United Kingdom and United States) to compare the sensitivity of modelled C and N fluxes to changes in the density of grazing animals (from 100% to 50% of the original livestock densities), also in combination with decreasing N fertilization levels (reduced to zero from the initial levels). Simulated multi-model median values indicated that input reduction would lead to an increase in the C sink strength (negative net ecosystem C exchange) in intensive grazing systems: −64 ± 74 g C m⁻² yr⁻¹ (animal density reduction) and −81 ± 74 g C m⁻² yr⁻¹ (N and animal density reduction), against the baseline of−30.5±69.5 g C m⁻² yr⁻¹ (LSU [livestock units] ≥ 0.76 ha⁻¹ yr⁻¹). Simulations also indicated a strong effect of N fertilizer reduction on N fluxes, e.g. N2O-N emissions decreased from 0.34 ± 0.22 (baseline) to 0.1 ± 0.05 g N m⁻² yr⁻¹ (no N fertilization). Simulated decline in grazing intensity had only limited impact on the N balance. The simulated pattern of enteric methane emissions was dominated by high model-to-model variability. The reduction in simulated offtake (animal intake + cut biomass) led to a doubling in net primary production per animal (increased by 11.6 ± 8.1 t C LSU⁻¹ yr⁻¹ across sites). The highest N2O-N intensities (N₂O-N/offtake) were simulated at mown and extensively grazed arid sites. We show the possibility of using grassland models to determine sound mitigation practices while quantifying the uncertainties associated with the simulated outputs.

The paddocks in APSIM simulations can be used to model experiments with complex geometry as shown in this example from a recently-published paper (https://authors.elsevier.com/c/1WxqtB8ccgYiR  - available free until 15 June) 

Schematic representation of a urine patch and the simpler representation of the complex geometry used in the APSIM modelling. 

Nitrate leaching from urine deposited by grazing animals is a critical constraint for sustainable dairy farming in New Zealand. While considerable progress has been made to understand the fate of nitrogen (N) under urine patches, little consideration has been given to the spread of urinary N beyond the wetted area. In this study, we modelled the lateral spread of nitrogen from the wetted area of a urine patch to the soil outside the patch using a combination of two process-based models (HYDRUS and APSIM). The simulations provided insights on the extent and temporal pattern for the redistribution of N in the soil following a urine deposition and enabled investigating the effect of lateral spread of urinary N on plant growth and N leaching. The APSIM simulation, using an implementation of a dispersion-diffusion function, was tested against experimental data from a field experiment conducted in spring on a well-drained soil. Depending on the geometry considered for the dispersion-diffusion function (plate or cylindrical) the area-averaged N leaching decreased by 8 and 37% compared with simulations without lateral N spread; this was due to additional N uptake from pasture on the edge area. A sensitivity analysis showed that area-averaged pasture growth was not greatly affected by the value of the dispersion factor used in the model, whereas N leaching was very sensitive. Thus, the need to account for the edge effect may depend on the objective of the simulations. The modelling results also showed that considering lateral spread of urinary N was sufficient to describe the experimental data, but plant root uptake across urine patch zones may still be relevant in other conditions. Although further work is needed for improving accuracy, the simulated and experimental results demonstrate that accounting for the edge effect is important for determining N leaching from urine-affected areas.

Cichota, R., Vogeler, I., Snow, V., Shepherd, M., Mcauliffe, R., Welten, B., 2018. Lateral spread affects nitrogen leaching from urine patches. Sci. Total Environ. 635: 1392–1404. doi:10.1016/j.scitotenv.2018.04.005

Expected increases in food demand and the need to limit the incorporation of new lands into agriculture to curtail emissions, highlight the urgency to bridge productivity gaps, increase farmers profits and manage risks in dryland cropping. A way to bridge those gaps is to identify optimum combination of genetics (G), and agronomic managements (M) i.e. crop designs (GxM), for the prevailing and expected growing environment (E). Our understanding of crop stress physiology indicates that in hindsight, those optimum crop designs should be known, while the main problem is to predict relevant attributes of the E, at the time of sowing, so that optimum GxM combinations could be informed to farmers. In a recent article published in Nature’s Scientific Reports by UQ-QAAFI’s Farming Systems Research Group, A/Prof Daniel Rodriguez tested our capacity to inform that “hindsight”. The work involved linking the APSIM-sorghum model with a skilful seasonal climate forecasting system, to answer “What is the value of the skill in seasonal climate forecasting, to inform crop designs?” 

The article is open access and can be downloaded from http://rdcu.be/F7Yp.

The global area and farming systems to which APSIM can be applied with confidence to have recently expanded with a research article recently published in the European Journal of Agronomy. A validation analysis showed that APSIM was able to accurately simulate the forage yield and water productivity of a range of forage crops and also continuous forage crop rotations across the Argentinian Pampas. Forage crops that the model was tested for includes maize, soybean, wheat, oats, annual ryegrass and barley. Water productivity was simulated with a greater accuracy when considering the whole crop rotation rather than individual crops. Crop forage yield simulated with greater accuracy for crops harvest without regrown compared to crops harvested frequently and allowed to regrowth.

In a paper recently accepted for publication in Global Change Biology (published open access), APSIM has contributed to an assessment of the ability of simulation models to simultaneously predict yield and N₂O emissions.  The study included five variants of APSIM (two in the crop part of the study and three in the grasslands part).  In this study the modellers started with little site information (soil properties, weather data, management information) as Stage 1 and incrementally more data was supplied until in Stage 5 modellers had all available data.  Crop model outputs improved at Stage 3 when phenology information was made available but grassland model outputs were little affected by the availability of additional information.  As with other intercomparisons, the ensemble median performed better than any one model when considering multiple sites.  It was found that a small ensemble of three models outperformed the full ensemble.  This is the first study which has examined the effect of data availability of the performance of an ensemble and also the first examining both yield and N₂O emissions.