The race is on to improve the biomass of food and fuel crops to meet the demand of a growing population under climate change.
Extensive testing of crop genotypes is required in plant breeding programs to develop varieties that have improved performance in diverse environments. High-quality phenotypic (trait) data is needed to evaluate these genotypes. Remote sensing technologies such as low flying unmanned aerial vehicles (UAVs), proximal sensing, and satellite-based imagery provide non-intrusive, high-throughput monitoring of plant physiological characteristics.
A team led by Dr. Mitchell Tuinstra, Professor of Plant Breeding and Genetics at Purdue University, used the crop model, Agricultural Production Systems sIMulator (APSIM), to predict variation in the phenology of commercial sorghum under different environments to identify trait targets for plant breeding.
“The APSIM Cropping Systems Model is one of the best platforms for simulating sorghum plant growth and development and predicting crop performance in diverse production environments. APSIM enables simulations of plant, environment, and management interactions.”
First, the team needed parameterization and validation sorghum data. Each of 3 years, 18 sorghum hybrids were grown in replicated field trials. 6 sorghum were the shorter “grain” type with large panicles, and 12 were taller “forage” type that were either photoperiod-sensitive or photoperiod-insensitive. Ground reference data was collected manually (plant density, flowering date, final dry biomass and maximum height), and RGB images from an UAV were used to calculate canopy cover.
According to Tuinstra, “crop growth models that integrate remote sensing data offer an efficient approach to parameterize larger plant breeding populations”.
Data from the extensive field trails demonstrated that maximum plant height, final dry biomass, and radiation use efficiency (RUE) of photoperiod sensitive and insensitive forage sorghum hybrids tended to be higher than observed in grain sorghum. Also, photoperiod sensitive sorghum hybrids exhibited greater biomass production in longer growing environments.
APSIM was then used to explore differences in productivity among sorghum hybrids in multiple years and different regions. Two hybrids had the highest biomass yields at both locations, and this trend persisted over multiple years (see figure). These kinds of studies are promising for farmers, decision makers, and researchers, as they could provide longer-term information for strategic management decisions, without extensive yield trials.
The authors concluded that using remote sensing data is an efficient approach to parameterize larger plant breeding populations for crop growth models.
The “R Pipeline for Calculation of APSIM Parameters and Generating the XML File” is stored at the Purdue University Research Repository and includes the data processing pipeline, data for model input parameters and outputs comparisons, and R-codes for generating or processing central datasets.
The APSIM files used in the model calibration procedures are stored at the Purdue University Research Repository in “2018 West Lafayette Simulation of 18 Sorghum Hybrids“
The APSIM files used for model validations are stored at the Purdue University Research Repository in the “2015 West Lafayette Simulation of 18 Sorghum Hybrids” and “2017 West Lafayette Simulation of 18 Sorghum Hybrids.”
The APSIM files used for the scenario simulations are stored at the Purdue University Research Repository in the “Texas Simulation of Sorghum Hybrids Using Historical Weather Data ” and “West Lafayette Scenario Simulation of Sorghum Hybrids Using Historical Weather Data” using multi-year historical weather data of Bushland, TX, and West Lafayette, IN.