Data synthesis for crop variety evaluation. A review

Abstract

Crop varieties should fulfill multiple requirements, including agronomic performance and product quality. Variety evaluations depend on data generated from field trials and sensory analyses, performed with different levels of participation from farmers and consumers. Such multi-faceted variety evaluation is expensive and time-consuming; hence, any use of these data should be optimized. Data synthesis can help to take advantage of existing and new data, combining data from different sources and combining it with expert knowledge to produce new information and understanding that supports decision-making. Data synthesis for crop variety evaluation can partly build on extant experiences and methods, but it also requires methodological innovation. We review the elements required to achieve data synthesis for crop variety evaluation, including (1) data types required for crop variety evaluation, (2) main challenges in data management and integration, (3) main global initiatives aiming to solve those challenges, (4) current statistical approaches to combine data for crop variety evaluation and (5) existing data synthesis methods used in evaluation of varieties to combine different datasets from multiple data sources. We conclude that currently available methods have the potential to overcome existing barriers to data synthesis and could set in motion a virtuous cycle that will encourage researchers to share data and collaborate on data-driven research.

The tricot citizen science approach applied to on-farm variety evaluation: methodological progress and perspectives

“…Firstly, Brown et al, (2020) have described how the Plackett-Luce model can be used to synthesize trial data from across different trials. Ongoing work is doing this with tricot trials (and other trials, after converting absolute values to ranks to deal with highly heterogeneous data).…”

Agronomic and genetic assessment of organic wheat performance in England: a field-scale cultivar evaluation with a network of farms

“…Climatic data and soil variables, particularly regarding soil mineral N availability and crop N status, would need to be captured at a better spatial resolution and finer temporal scale. As such, climatic indices can be both covariates to improve the accuracy of cultivar evaluation (Brown et al 2020) and explanatory variables of crop performance in organic cropping systems. Relationship between grain protein content and grain yield by environment (b): estimated marginal means and related standard errors by environment, obtained from a mixed model considering cultivar as a random term and, as fixed terms, farm, year and farm-by-year interaction.…”

“…In summary, as pointed out by Brown et al (2020), future developments in cultivar testing programmes should rely on the improved capacity of synthesising data from different sources, from environmental and genetic data to farmers' and consumer's evaluation. For example, citizen-science approaches have been adopted in participatory cultivar selection programmes across networks of farms wide enough to detect and generalise differential varietal response to climatic patterns (Van Etten et al 2019).…”

Studies of Genetic and Morphological Characteristics of Indonesian Melon (Cucumis melo L. ‘Hikapel’) Germplasm

“…Along with the uncontrolled and unpredictable global climate development, conventional farmers in Indonesia must face worsening production conditions [3]. Pest variety and resistance are also obstacles that can reduce the quality and quantity of products [4].…”