Adoption of new plant varieties has played a significant role in eradicating global hunger. Previous research has mainly focused on farmer adoption and impact of new crop varieties, although upstream adoption of technologies in plant breeding can generate substantial multiplier effects on downstream impacts. This study moves upstream in the innovation system to generate policy advice on adoption and transfer of accelerated rice breeding technologies. More specifically, we assess the determinants of global adoption of rapid generation advance (RGA) through a sample of 158 rice breeders operating in various research institutes worldwide. Moving upstream in the innovation system has important theoretical and empirical implications due to the smaller number of decision-making units in the adoption process and the increasing role of institutional and managerial factors that may overrule individual adoption motivations. We revisit multi-stage models and devise the most robust estimation method that can be used in this situation. To generate insights on the impact of individual versus institutional adopter characteristics on upstream technology adoption, we juxtapose the response curves of the determinants of RGA adoption in rice breeding among alternative adoption stages, levels of conditionality and model specifications. Our findings confirm the importance of institutional and managerial factors and suggest that adoption and transfer of breeding technologies require breeding institutes to provide an enabling environment in which breeders are encouraged to take risks and are given sufficient freedom to experiment with and implement new technologies.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1477-9552.12450

Revisiting multi-stage models for upstream technology adoption: Evidence from rapid generation advance in rice breeding

Soybean (Glycine max L.) is one of the four most important crops in the world. The creation of new commercial varieties is a long-term activity that requires from seven to eight years from the beginning of the cross design up to registration for commercialization of cultivars. Rapid generation advance (RGA) is a technique that consists of controlling external factors that affect plant growth with the aim of shortening the sowing to harvest cycle. In the present study, an optimized method is described that can accelerate soybean breeding by means of RGA using inexpensive facilities and that can be easily applied by breeders. Our breeding method uses a modified cold storage chamber fitted with fluorescent lamps delivering a 12/24 h light photoperiod, while temperature is set at 24 °C. This method allows development of up to 5 generations per year instead of the 1-2 generations currently possible under field or greenhouse conditions.

A simple and inexpensive procedure to more quickly obtain new varieties in soybean

Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage: "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.

Genomic prediction: progress and perspectives for rice improvement

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.

Bruno Ferraro

Papers

Economic impact of marker-assisted selection and rapid generation advance on breeding programs.