Co-design of crop monitoring-modeling systems via identifiability analysis
Year: 2025
Type: article
Abstract: Context: Crop yield increases are lagging those required to avoid major mid-century food security disruptions. Skilled models are needed to predict environment-specific cropping outcomes for applications ranging from accelerating breeding program gain rates via efficient selection to sustainable, in-field production decision-making that incorporate genetic × environment × management interactions. In an attractive vision, genotype-specific ecophysiological crop model parameters are inferred by genomic prediction. However, these parameters are often unidentifiable from typically observed data, also known as the equifinality problem. This is because wide parameter value ranges generate identical predictions so alternative estimates are indistinguishable and, thus, unusable in genomic prediction training. Objective: Given a model with parameters, a prescribed data scheme and goodness-of-fit objective function this paper introduces a method to measure identifiability and semi-automate its improvement. Methods: Identifiability is innovatively quantified as the objective function’s Gaussian curvature at the parameter optimum. Higher values indicate a more easily searchable, bowl-like objective function response surface. Lower values imply a flatness that prevents unique parameter value identification. A simplified, seven-parameter wheat model is analyzed, revealing two nearly unidentifiable parameters. Three analyses are presented: (1) increasing observation numbers, (2) adding a new observation type, and (3) automatically determining two parameters whose direct field measurement would maximally increase Gaussian curvature. Results and conclusions: Two nearly unidentifiable parameters – Thermal time to end of leaf expansion (TTL) and thermal time to maturity (TTM) – were revealed. Analyses (2) and (3) drastically improved identifiability. After adding the new observation type (physiological maturity), Gaussian curvature increased by a factor of 7600. Likewise, when we fixed the values of the two parameters, the Gaussian curvature increased by a factor of 2 . 69 × 1 0 11 . Surprisingly, however, increasing observation numbers lowered identifiability for reasons related to the Nash–Sutcliffe Efficiency objective function variant used. Relatedly, because the method is formally objective function agnostic, it can also ameliorate any non-uniqueness in model-based production decision aids that optimize economic objectives while integrating a full suite of in-field interactions. Significance: The demonstrated ability to maximize identifiability by optimizing both the data taken and model structure prior to actual fieldwork points toward a co-design process in which the model and its supporting monitoring system are created simultaneously.
Source: Agricultural Systems
Authors: Pratishtha Poudel, Stephen M. Welch, Phillip D. Alderman, Xiaomao Lin
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
Estimated APC paid: $4,000
OpenAlex ID: W7114769329