We would like to highlight a new paper in which some significant improvements to the simulation of root depth are detailed.  These improvements have been reviewed and accepted as part of APSIM version 7.10. 

In summary, the highlights are as follows:

• A new excessive moisture stress factor was developed and added into APSIM-soybean.
• The addition of this factor was more important in simulating root growth than the exact parameterization.
• Vertical root growth is inhibited when volumetric soil moisture approaches 3% below saturation.
• Root depth and length distributions were affected by the depth to the water table.

The paper by Ebrahimi-Mollabashi et al can be found here: https://doi.org/10.1016/j.fcr.2019.03.014 

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.

• 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.

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

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