Evaluating a Cassava Crop Growth Model by Optimizing Genotypic-Specific Parameters Using Multi-environment Trial Breeding Data

Pamelas Michelle Okoma ^1,2 Siraj Ismail Kayondo ³ Ismail Rabbi ⁴ Patricia Moreno Cadena ^5,6 Gerrit Hoogenboom ⁷ Jean-Luc Jannink ^8*

• ¹ Tuskegee University, Tuskegee, Alabama, United States
• ² Section of Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, New York, United States
• ³ International Institute of Tropical Agriculture (Tanzania), East African hub, Tanzania
• ⁴ International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
• ⁵ Alliance Bioversity International and CIAT, Montpellier, Languedoc-Roussillon, France
• ⁶ University of Kentucky, Lexington, Kentucky, United States
• ⁷ University of Florida, Gainesville, Florida, United States
• ⁸ Agricultural Research Service, United States Department of Agriculture, Washington D.C., United States

Cassava (Manihot esculenta Crantz) is a critical food security crop for sub-Saharan Africa. Efforts to improve cassava through breeding have expanded over the past decade. Crop growth models (CGM) are becoming common place in breeding efforts to expand the inference of evaluations of breeding germplasm to environments that have not been tested and to prepare for breeding for adaptation to future climates. We parameterized a CGM, the CROPGRO-MANIHOT-Cassava model in the DSSAT family of models, using data on 67 clones from the International Institute of Tropical Agriculture cassava breeding program evaluated from 2017 to 2020 and over eight locations in Nigeria using trial and error parameter adjustments and the General Likelihood Uncertainty Estimation method. Our objectives were to assess the feasibility of this large-scale calibration in the context of a cassava breeding program and to identify systematic biases of the model. For each cultivar we calculated the Pearson correlation between model prediction and observation across the environments, as well as root mean squared error and d statistics. As a result of calibration, the correlation coefficient increased from -0.03 to +0.08, the RMSE dropped from 21 t ha -1 to 5 t ha -1 while d increased from 0.23 to 0.44. We found that the model underestimated root yield in dry environments (low precipitation and high temperature) and overestimated root yield in wet environments (high precipitation and low temperature). Our experience suggests both that CGM calibration could become a routine component of the cassava breeding data analysis cycle and that there are opportunities for model improvement.