Performing Department
(N/A)
Non Technical Summary
Potato is the most widely consumed vegetable in the U.S. and an important staple worldwide. While cultivated potato is a cool-season crop, increased demand has led to an expansion of its production into warmer areas, such as the southeastern U.S., making the region an essential supplier of potatoes during the winter/spring. However, the potato physiology is critically affected by high temperatures, leading to losses of up to 45% of the potential yield. This project features a unique international partnership to access new germplasm and leverage elite clones selected for heat-tolerance from three breeding programs (UF, International Potato Center and Federal University of Lavras). Moreover, we propose to develop, integrate, and evaluate genomic prediction with crop growth models, as a tool to capture genotype-by-environment interactions and guide breeding decisions. These integrated models have been successfully demonstrated in row crops, but here, we propose to adapt the methods for polyploid species by incorporating non-additive effects and marker-dosage into the prediction model. To accomplish these goals, we propose to create and evaluate a multi-parental heat-tolerant breeding population (Objective 1), improve the existing crop growth models to better model heat-stressed environments and heat tolerant genotypes (Objective 2), and integrate the crop growth and genomic prediction models aiming at improving prediction accuracy (Objective 3). Models will be validated across environments and using the heat tolerant population developed in this project. The project brings together a transdisciplinary team that will train six students/post-docs and the germplasm will be made available to other public breeders. ?
Animal Health Component
80%
Research Effort Categories
Basic
(N/A)
Applied
80%
Developmental
20%
Goals / Objectives
Our long-term goal is to supply southeastern U.S. potato growers with adapted and higher-yielding varieties, as well as applied solutions to mitigate the effects of heat stresses on potato yield. To advance towards this goal, we have established an international partnership that will combine heat-tolerant elite germplasm from three different countries, as well as expertise in potato breeding, potato production, crop growth models, and whole genome prediction. The overall objective of this proposal is to combine crop growth modeling and whole genome prediction as a tool to determine the impact of heat on potato yields effectively, complemented by a breeding-oriented objective to accelerate the development of heat-tolerant elite varieties adapted to the southeastern.
Project Methods
To achieve these goals, we propose the following objectives:1) Development and multi-environment evaluation of a multi-parental heat tolerant breeding population. The University of Florida's potato breeding program has established a partnership to access elite genotypes from well-established public breeding programs in Peru and Brazil. In objective 1, we establish multi-parental populations using elite x elite parents. The population will be extensively phenotyped for a variety of traits, including yield and quality under high-heat environments, and whole genome prediction models will be calibrated to serve as the baseline comparison for our model development. We hypothesize that elite cultivars adapted to the southeastern U.S. can be derived from this population, and we expect that WGP models developed here will be an important resource for the breeding programs.2) Crop growth model improvement and model calibration using heat-tolerant cultivars. While crop growth models have been previously calibrated for well-established potato cultivars, our preliminary results indicate that the available crop models cannot properly predict the physiological effects under high heat conditions. Furthermore, model calibration is currently not available for heat-tolerant cultivars, which are known to respond differently to the effects of high heat. In this objective, we will improve the currently available Decision Support System for Agrotechnology Transfer (DSSAT), Simulation of Underground Bulking Storage Organs (SUBSTOR-Potato) model and calibrate the model parameters for the elite genotypes selected from the three partner institutions. We expect to incorporate the outcomes of this objective into the open-source DSSAT software.3) Develop and integrate the WGP-CGM methods. In this last objective, we will create prediction models that integrate the Crop Growth Model and Whole Genome Prediction. CGM parameters will be predicted using approximate Bayesian computation, and prediction accuracy will be validated by leave-one-environment-out cross-validation. We hypothesize that prediction accuracies of CGM-WGP will be more accurate than standard WGP models, and we expect this model to be implemented routinely to select elite materials in our breeding program.