Abstract
Plant breeding for the generation of cultivars adapted to local conditions has been an important and strategic concern of developing countries with agriculture-based economies. Considering economic constraints, breeders must improve genetic gain to increase the delivery of better cultivars with lower costs, through the implementation of molecular breeding and rapid generation advance. The aim of this work is to assess the actual economic impact of the implementation of these technologies on genetic gain for yield, rice blast disease resistance, and grain amylose content in a conventional rice breeding program. This analysis is intended as a case study of public breeding programs in developing countries. To accomplish this objective, cost analyses and genetic gain estimations were performed for four rice breeding scenarios: conventional and marker-assisted selection, with and without rapid generation advance. These estimations were then used to develop a cost index reflecting the breeding efficiency. The most efficient method was found to depend on the objective trait considered. For yield, there are small variations in genetic gain, but in terms of costs, the application of technology increases the breeding efficiency. For rice blast resistance, marker-assisted selection is not an efficient option when not using rapid generation advance. Conversely, the efficiency of marker-assisted selection increases when using rapid generation advance. For grain amylose content, the greatest effect on genetic gain is obtained when using marker-assisted selection. Rapid generation advance always increases the breeding efficiency. The use of new technological tools is recommended in terms of the cost–benefit function.
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Acknowledgements
The authros thank José Paruelo for encouraging the writing of this manuscript, Gabriela Molina, Victoria Genta, Wanda Iriarte, Aldo Fregossi, Fernando Escalante, Mario Villalba, and Sebastian Martínez for their input to the costing analysis, and Pedro Blanco, Fernando Pérez de Vida, and Federico Molina for providing all the phenotypic information required for the calculation of genetic gain parameters.
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Funding was provided by Instituto Nacional de Investigación Agropecuaria (AZ_35).
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ESM_1. Description of simulated CB + RGA rice breeding scenario.
Breeding location (field or greenhouse), generation, selection unit (plant, family, line), number of selection units, experimental unit (crossing pod, breeding row, phytopathology row, laboratory analysis), and selection criteria for each breeding generation. AC, amylose content; Br, breeding row; Cp, crossing pod; LAAC, laboratory analysis amylose content; Phr, phytopathology row; RBDR, rice blast disease resistance; SSD, single seed descent; StBio, seedling tray biotechnology; StPh, seedling tray phytopathology. (EPS 487 kb)
ESM_2 Description of simulated MAS rice breeding scenario.
Breeding location (field or greenhouse), generation, selection unit (plant, family, line), number of selection units, experimental unit (crossing pod, breeding row, phytopathology row, laboratory analysis), and selection criteria for each breeding generation. AC, amylose content; Br, breeding row; Cp, crossing pod; RBDR, rice blast disease resistance; SNP, single-nucleotide polymorphism. (EPS 447 kb)
ESM_3. Description of simulated MAS + RGA rice breeding scenario.
Breeding location (field or greenhouse), generation, selection unit (plant, family, line), number of selection units, experimental unit (crossing pod, breeding row, phytopathology row, laboratory analysis), and selection criteria for each breeding generation. AC, amylose content; Br, breeding row; Bt, biotechnology; Cp, crossing pod; RBDR, rice blast disease resistance; SNP, single-nucleotide polymorphism; SSD, single seed descent; StBio, seedling tray biotechnology; StPh, seedling tray phytopathology. (EPS 465 kb)
ESM_4.
Intermediate variables necessary to calculate the intermediate product (NTEU) for cost analysis. (XLSX 105 kb)
ESM_5.
Definitions and concepts of cost analysis. (DOCX 17 kb)
ESM_6.
Basis of distribution of indirect costs. (XLSX 10 kb)
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Bonnecarrere, V., Rosas, J. & Ferraro, B. Economic impact of marker-assisted selection and rapid generation advance on breeding programs. Euphytica 215, 197 (2019). https://doi.org/10.1007/s10681-019-2529-8
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DOI: https://doi.org/10.1007/s10681-019-2529-8