New 3D model predicts best planting practices
Jun. 29, 2017
The University of Illinois and the Partner Institute for Computational Biology in Shanghai developed this computer model to predict the yield of different crop cultivars in a multitude of planting conditions. Published in BioEnergy Research, the model depicts the growth of 3D plants, incorporating models of the biochemical and biophysical processes that underlie productivity.
Teaming up with the University of Sao Paulo in Brazil, they used the model to address a question for sugarcane producers: How much yield might be sacrificed to take advantage of a possible conservation planting technique?
“Current sugarcane harvesters cut a single row at a time, which is time-consuming and leads to damage of the crop stands,” said author Steve Long, Gutgsell Endowed Professor of Plant Biology and Crop Sciences at the Carl R. Woese Institute for Genomic Biology at U of I. “This could be solved if the crop was planted in double rows with gaps between the double rows. But plants in double rows will shade each other more, causing a potential loss of profitability.”
The model found that double-row spacing costs about 10% of productivity compared to traditional row spacing; however, this loss can be reduced to just 2% by choosing cultivars with more horizontal leaves planted in a north-south orientation.
“This model could be applied to other crops to predict optimal planting designs for specific environments,” said Yu Wang, a postdoctoral researcher at U of I who led the study. “It could also be used in reverse to predict the potential outcome for a field."
The authors predict this model will be especially useful when robotic planting becomes more commonplace, which will allow for many more planting permutations.
The paper, “Development of a Three-Dimensional Ray-Tracing Model of Sugarcane Canopy Photosynthesis and Its Application in Assessing Impacts of Varied Row Spacing,” is published by BioEnergy Research (DOI: 10.1007/s12155-017-9823-x). Co-authors include: Yu Wang, Qingfeng Song, Deepak Jaiswal, Amanda P. de Souza, and Xin-Guang Zhu. This research was supported by the IGB, Realizing Increased Photosynthetic Efficiency (RIPE) project, Energy Biosciences Institute, and the Chinese Academy of Sciences. RIPE is an international research project funded by the Bill & Melinda Gates Foundation to engineer plants to more efficiently turn the sun’s energy into food to sustainably increase worldwide food productivity.