Value chain stakeholder preferences are misaligned with economic weights derived from the bio-economic model: what is the effect on the ranking of candidates?

Abstract

While breeding values for key traits are often known by horticultural crop breeders, the definition of each trait’s economic component has remained a grey area. The lack of pertinent quantitative data to derive economic weights limits the capacity of breeders to capture the direction the industry wishes to take and select cultivars accordingly. Price signal (all sources of income and costs related to a trait) is a relevant source of data to determine the economic weight of traits through bio-economic modelling. However, if price signals for a trait are only partially captured, its weight may become distorted. Using industry stakeholder preferences to rank the trait improvements may address price signal gaps. Using Kern et al. (2021), choice analysis data, the difference between weights derived from bio-economic modelling and stakeholder preferences for yield (tree age 10-20), canopy width, and kernel recovery was 10, 0, and -16%, respectively. The ranking of 30 macadamia cultivars was based on two sets of weights. The candidates’ genetic values, yield (kg tree‑1) for tree age 0-8, kernel recovery (%) and canopy width (m), both at tree age 8, were used to determine the candidates’ profitability. Results showed a correlation (r=0.997) between cultivar rankings using weights derived through the bio-economic model and stakeholders’ preferences. This indicates that despite the wide range of profitability between candidates (from $ 10,000 to 92,000), integrating stakeholders’ preferences to the economic weights did not significantly alter the ranking of the 30 candidates. Further research must be done to evaluate the impact of large differences between weights derived from bio-economic modelling and stakeholder preferences on economic weight. The results of this research would confirm the necessity of investing in a formal and industry-approved bio-economic model, or to develop a time-efficient approach emphasizing stakeholders’ preferences to determine economic weights.