A 50% chance of exceedance suggests that in 50% of the previously modelled years, the crop would have exceeded the corresponding yield. This shows the projected yield for sorghum given each year’s daily weather data and the starting moisture.
#APSIM SORGHUM SOFTWARE#
Once the software has modelled the crop for each year a probability exceedance chart can be created. The cultivar used in the modelling was Buster and the soils were assumed to be a heavy black clay. The cropping cycle in these charts is based on a 1 st of October planting. The years from 1957 (better quality data from that date) to 2020 were modelled but the charts for the previous 100 years look very similar. Starting moisture was assumed to run evenly from the top of the soil profile ignoring the possibility of variable moisture in the top layers which is another aspect of planting risk not explored in this model. Įach crop was modelled under ideal circumstances with each year’s crop starting with plenty of nitrogen and no other limiting factors. The model ignores other factors such as heat blast, pest damage, certain soil constraints and other such aspects. The sorghum model in APSIM considers temperature, rainfall, radiation, soil water, frost impact on leaves and soil nitrogen. As with any model, it is important to understand what the model does and does not consider. This allows a user to simulate what would have happened in the development of a crop in any particular year given the weather assuming different levels of soil moisture. The APSIM model was developed to simulate many of the agricultural and plant processes that impact the growth and development of a crop. In this article I will explore the use of the APSIM modelling software to evaluate the risks associated with planting Sorghum at different soil moisture rates and the associated yield potential. A greater soil moisture profile lowers risk and increases the chances of a good return by allowing for reduced reliance of rainfall. Initial soil moisture plays a significant role in the planting decision risk. Many growers will use their hard-fought experience and intuition to select a course of action however an understanding of the risks and the corresponding returns are often hard to grasp. Determining whether to plant immediately on the moisture that is there now, delay until later in the season or leave the field to winter cropping is a significant decision. Nature Plants 5:380–388.The decision to plant can often be a difficult one. Quantifying impacts of enhancing photosynthesis on crop yield. Wu A, Hammer GL, Doherty A, von Caemmerer S, Farquhar GD. On the dynamic determinants of reproductive failure under drought in maize. Messina CD, Hammer GL, McLean G, Cooper M, van Oosterom EJ, Tardieu F, Chapman SC, Doherty A, Gho C. Sorghum: state of the art and future perspectives. Sorghum crop modelling and its utility in agronomy and breeding. Hammer G, McLean G, Doherty A, van Oosterom E, Chapman S. Environmental Modelling & Software 62:385–398. Plant modelling framework: software for building and running crop models on the APSIM platform. īrown HE, Huth NI, Holzworth DP, Teixeira EI, Zyskowski RF, Hargreaves JNG, Moot DJ. European Journal of Agronomy 100:141–150. Crop model improvement in APSIM: using wheat as a case study. Given the modularity of this framework, more detailed options for specific processes have also been developed and interfaced into this general framework for specific purposes (eg grain set and abortion – Messina et al (2019) leaf and canopy photosynthesis – Wu et al., (2019)).īrown HE, Huth N, Holzworth D. The approaches utilise daily weather data and detailed soil characterisation to simulate the soil-plant-atmosphere continuum at a daily time step and generate predictions of crop stage, leaf area, biomass and yield along with the dynamics of soil water and N content. The general framework in these crop models as exemplified in the attached document incorporates approaches to predicting crop phenology, canopy development, resource (light, water, N) capture and use efficiency, and arbitration rules for assimilate and N allocation and redistribution among organs. There are many other crop modules in APSIM that are now being developed in, or re-coded into, the Plant Modelling Framework (PMF) (Brown et al., 2014) for APSIM NextGen. All references in the attached document can be found in Hammer et al., 2019. A description of the wheat model is also available in Brown et al. This document provides a verbal description and diagrammatic overview of the algorithms underpinning simulation of the crop growth and development dynamics in APSIM using the sorghum crop model as an exemplar (for further detail see Hammer et al., 2019).