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4 spaces left in the APSIM Training Workshop

Please email apsim@csiro.au if you are interested in attending the APSIM Training Workshop  - 20th to 21st of February - being run at St Lucia, Brisbane.  Only 4 spaces left.  This will be only training workshop held in the first half of 2019. 
Wednesday, 30 January 2019/Author: Sarah Cleary/Number of views (538)/Comments (0)/
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Eucalyptus Model

A model for Eucalyptus plantations has been released in APSIM Next Generation. The current version of the model is suitable for sub-tropical genotypes (e.g. Eucalyptus grandis, E. urophylla, E. saligna, and their hybrids) grown in temperate to tropical environments, as it has been validated and tested on data from multiple sites in Australia and Brazil. This model is suitable for broad-scale industrial plantations, but with further calibration and testing could be suitable for different management settings (e.g. agroforestry applications), or for different genotypes (e.g. temperate plantation genotypes). Examples provided include N-fertilising, irrigation, weeds, harvesting and replanting. The APSIM framework provides a flexible basis on which to further develop this model for both commercial and non-commercial applications. For commercial access, please email apsim@csiro.au 
Thursday, 13 December 2018/Author: Sarah Cleary/Number of views (354)/Comments (0)/
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APSIM Training - 20th and 21st February 2019

The next APSIM Training Workshop will be held in Brisbane on the 20th and 21st of February. 

The 2 day course is aimed at providing training in the use of the Agricultural Production Systems sIMulator (APSIM) focusing on the user interface. It is very 'hands on' with a mix of short presentations and tutorials relevant to research activities.

The course has been designed for both beginners and more advanced users who have developed simulations and require specific technical assistance. It will focus on individual needs with tutors providing one-on-one assistance. Common issues will be summarised and presented as more formal group tutorials.

The course will be limited to a maximum of 12 participants and can be tailored to meet specific needs of individuals and groups. A minimum of 10 participants will be required to run the course.

COURSE REQUIREMENTS

To successfully undertake this course you will need to have:

  • A Laptop PC:
  • A licensed copy of APSIM version 7.10 installed on the laptop;
  • For advanced users, current simulations you are working on, or background data for building simulations;
  • Organised all travel and accommodation associated with your training.

If you would like to register or have any questions, please email apsim@csiro.au

Thursday, 29 November 2018/Author: Sarah Cleary/Number of views (402)/Comments (0)/
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A simple demonstration of connecting APSIM to optimisation techniques

The APSIM Initiative Reference Panel has provided two examples for connecting APSIM to optimisation techniques.  These serve to demonstrate contrasting approaches in connecting APSIM to optimisation software. 

Examples can be found here:

Optimisation Techniques

Monday, 5 November 2018/Author: Sarah Cleary/Number of views (510)/Comments (0)/
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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 (520)/Comments (0)/
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FEATURES

The Value of Tactical Adaptation to El Niño–Southern Oscillation for East Australian Wheat

El Niño–Southern Oscillation strongly influences rainfall and temperature patterns in Eastern Australia, with major impacts on frost, heat, and drought stresses, and potential consequences for wheat production. Wheat phenology is a key factor to adapt to the risk of frost, heat, and drought stresses in the Australian wheatbelt.

Zheng et al 2018 explored broad and specific options to adapt wheat cropping systems to El Niño–Southern Oscillation in Eastern Australia. More specifically, they investigated the value of adaptation options to the Southern Oscillation Index (SOI) phases ahead of the season (i.e., April forecast), when local wheat producers make their most crucial management decisions.

Crop model simulations were performed with APSIM for commercially-grown wheat varieties, as well as for virtual genotypes representing possible combinations of phenology alleles that are currently present in the Australian wheat germplasm pool. Different adaptation strategies were tested at the site level, across Eastern Australia, for a wide range of sowing dates and nitrogen applications over long-term historical weather records (1900–2016).

The results highlight that a fixed adaptation system, with genotype maturities, sowing time, and nitrogen application adapted to each location would greatly increase wheat productivity compared to sowing a mid-maturity genotype, mid-season, using current practices for nitrogen applications. Tactical adaptation of both genotype and management to the different SOI phases and to different levels of initial Plant Available Water (‘PAW & SOI adaptation’) resulted in further yield improvement. Site long-term increases in yield and gross margin were up to 1.15 t/ha and AU$ 223.0/ha for fixed adaptation (0.78 t/ha and AU$ 153/ha on average across the whole region), and up to an extra 0.26 t/ha and AU$ 63.9/ha for tactical adaptation.

For the whole eastern region, these results correspond to an annual AU$ 440 M increase for the fixed adaptation, and an extra AU$ 188 M for the PAW & SOI tactical adaptation.

 

The benefits of PAW & SOI tactical adaptation could be useful for growers to adjust farm management practices according to pre-sowing seasonal conditions and the seasonal climate forecast.

 



 

Increase in simulated yield (A) and gross margin (B) for tactical adaptation options compared to the fixed adaptation in each studied site and the whole Eastern wheatbelt. The three tactical adaptation options correspond to optimise long-term yield for either (i) low/medium/high pre-sowing plant available water (PAW), (ii) each class of Southern Oscillation Index (SOI), or (iii) each combination of PAW group and SOI class.

Tuesday, 12 March 2019/Author: Sarah Cleary/Number of views (154)/Comments (0)/
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Crop model improvement in APSIM: Using wheat as a case study

Crop systems models are used in a wide range of applications and to be fit for these purposes crop models should be:

• Accurate, proven by evaluations over a wide range of situations.

• Documented, so that critics and users can understand the technical detail and processes used in the current version of the model.

• Reliable, able to reproduce results on an ongoing basis. • Adaptable, to and easily updated by a wide range of users and,

• Safe from accidental or unapproved changes by users

 

As such the process of building and maintaining a crop model is a major undertaking and many crop models fail to continually meet these standards. A paper recently published by Brown et al (2018) details how APSIM Next Gen has been engendered to support an efficient workflow in the development and maintenance of crop models to help meet these standards. The paper describes how the user interface is central to facilitating the development cycle Test simulations are configures via an efficient user interface (UI) which also collates corresponding observations and presents a range of statistics and graphs. Models are configured visually in the UI so changes may be quickly made simulations re-run and consideration of the effect of changes quickly reviewed. As such more simulations and observations can be included and more approaches to model set up and parameterisation tried as part of the model development process facilitating better models and more robust testing.

 

Once the model is complete it is released via a continuous integration system and users can access newly released models and improvements to existing models simply by clicking the update button in APSIM Next Gen. The data and simulations used for developing the model are entered into version control as a test set and every time any of the models code is change these test are rerun to ensure model performance is maintained. Documentation is generated automatically by interrogating the source code and constructing documentation that accurately represents the current version of the model.

This paper is a recommended read for anyone who wants to understand the challenges that face the modern crop model developer and the efforts that APSIM developers undertake to ensure their crop models meet modern expectations.

Friday, 8 March 2019/Author: Sarah Cleary/Number of views (169)/Comments (0)/
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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 (707)/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 (829)/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 (1184)/Comments (0)/
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