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NEWS

nasapower: NASA POWER Global Meteorology, Surface Solar Energy and Climatology Data Client

International users of APSIM can now enjoy easy access to APSIM metrological files (.met files) from NASA POWER via a new R package developed by USQ’s Associate Professor of Field Crops Pathology Dr Adam Sparks called nasapower now available on CRAN.  One of the functions in this new package takes the NASA POWER agroclimatology data and reformats it into an APSIM .met file for use in APSIM simulations.  Information and documentation for the new package can be found at https://ropensci.github.io/nasapower/.

Thursday, 18 October 2018/Author: Sarah Cleary/Number of views (4)/Comments (0)/
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Updated Instructions for Submission of Improvements to APSIM for Review

As a user of APSIM, you may access and modify all source code. As per the conditions agreed to on download, all modifications to APSIM must be submitted to the AI and be subject to the Reference Panel evaluation process before inclusion into the official APSIM release. Updated instructions for submitting Improvements to APSIM for review by the APSIM Initiative Reference Panel can be found here

It can be useful to notify the APSIM Initiative that you are planning on making an improvement to APSIM. This can often lead to others suggesting ideas or collaborators to work with. To do this you need to create an issue in GitHub with a description of what you intend doing. This is for APSIM 7.x and APSIM Next Generation.  Any queries, please email apsim@csiro.au.

Tuesday, 9 October 2018/Author: Sarah Cleary/Number of views (16)/Comments (0)/
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APSIM Initiative Development Plan

The APSIM Initiative Development Plan is now available: http://www.apsim.info/AboutUs/DevelopmentPlan.aspx

The plan outlines work that is currently being undertaken by APSIM Initiative Members and partners.  It covers work that is being carried out this year as well as planned or aspirational work in the near future. 

If you’re interested in more information, co-developing or partnering in any of this work, please email apsim@csiro.au.

Monday, 24 September 2018/Author: Sarah Cleary/Number of views (43)/Comments (0)/
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APSIM Initiative Strategic Plan

The APSIM Initiative Strategic Plan is now available.  The plan summarises the Mission and Vision of the APSIM Initiative whilst clearly outlining the purpose and objectives for APSIM. 

The APSIM Initiative welcomes feedback from our stakeholders.  It can be found at http://www.apsim.info/AboutUs/StrategicPlan.aspx

Wednesday, 5 September 2018/Author: Sarah Cleary/Number of views (73)/Comments (0)/
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APSIM Downloads for 17/18

 

For the 2017/18 year there were 2103 non-commercial licenced users registered (an 11% increase on the previous year). This resulted in some 3775 downloads of APSIM (all versions as some users download older versions or use multiple versions).

APSIM is being used in 116 countries around the world, around the same number as the previous year. In Australia there are 482 users (up from 462 the previous year), with 157 in Queensland, an increase of 51 users. In New Zealand there were 46 users.

Monday, 30 July 2018/Author: Sarah Cleary/Number of views (318)/Comments (0)/
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FEATURES

Integrating modelling and phenotyping approaches to identify and screen complex traits: transpiration efficiency in cereals

Following advances in genetics, genomics, and phenotyping, trait selection in breeding is limited by our ability to understand interactions within the plant and with the environment, and to identify traits of most relevance to the target population of environments. We propose an integrated approach that combines insights from crop modelling, physiology, genetics, and breeding to characterize traits valuable for yield gain in the target population of environments, develop relevant high-throughput phenotyping platforms, and identify genetic controls and their value in production environments. This paper uses transpiration efficiency (biomass produced per unit of water used) as an example of a complex trait of interest to illustrate how the approach can guide modelling, phenotyping, and selection in a breeding programme. We believe that this approach, by integrating insights from diverse disciplines, can increase the resource use efficiency of breeding programmes for improving yield gains in target populations of environments.

Set-up of a large lysimeter system with a general view of an experiment with sorghum (short and intermediate plants in the picture) and maize (tall plants) (A) and with wheat (B); the watering system (C, D)

Full article can be found here: https://academic.oup.com/jxb/article-abstract/69/13/3181/4883180

Tuesday, 25 September 2018/Author: Sarah Cleary/Number of views (58)/Comments (0)/
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A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter.

The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. 

Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3%lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost.

Overview of the main factors influencing the economic optimum nitrogen fertilizer (EONR) rate and their interactions. Soil organic matter (SOM).

https://www.frontiersin.org/articles/10.3389/fpls.2018.00436/full

Thursday, 13 September 2018/Author: Sarah Cleary/Number of views (210)/Comments (0)/
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Using grassland models to determine sound mitigation practices while quantifying the uncertainties

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.

 

Monday, 18 June 2018/Author: Sarah Cleary/Number of views (426)/Comments (0)/
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Lateral spread affects nitrogen leaching from urine patches

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

Monday, 30 April 2018/Author: Sarah Cleary/Number of views (533)/Comments (0)/
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Predicting optimum crop designs using crop models and seasonal climate forecasts

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.



Thursday, 22 February 2018/Author: Dean Holzworth/Number of views (670)/Comments (0)/
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