The Concepts and quantification of yield gap using boundary lines. A review

A - Papers appearing in refereed journals

Miti, C., Milne, A. E., Giller, K. E. and Lark, R. M. 2024. The Concepts and quantification of yield gap using boundary lines. A review. Field Crops Research. 311 (1 May), p. 109365.

AuthorsMiti, C., Milne, A. E., Giller, K. E. and Lark, R. M.

Context: The potential yield of crops is not usually realised on farms, the yield gap is an obstacle to global food security. Methods are needed to diagnose yield gaps and to select interventions. One method is the boundary line model (BL) in which the upper bound of a plot of yield against a potentially limiting factor is viewed as the most efficient response to that factor and anything below it has a yield gap caused by inefficiency of other factors. If many factors are studied, the cause of the yield gap can be identified (yield gap analysis, YGA). Though BL is agronomically interpretable, its estimation and statistical inference are not straightforward and there is no standard method to fit the BL to data.
Objective: We review the different methods used to fit the BL, their strengths and weaknesses, interpretation, factors influencing the choice of method and its impact on YGA.
Methods: We searched for articles that used BL for YGA, using the Boolean “Boundary*” AND “Yield gap*” in the Web of Science.
Results: Methods used to fit BL include heuristic methods (visual, Binning, BOLIDES and quantile regression) and statistical methods (Makowski quantile regression, censored
bivariate model and stochastic frontier analysis). In contrast to heuristic methods, which in practice require ad hoc decisions such as the quantile value in the quantile regression method, statistical methods are typically objective, repeatable and offer a consistent basis to quantify parameter uncertainty. Nonetheless, most studies utilise heuristic methods (87% of the articles reviewed), which are easier to use. The BL is usually interpreted in terms of the law of the minimum or law of optimum to explain yield gaps. Although these models are useful, their interpretation holds only if the modelled upper limit represents a boundary and not just a particular realization of the upper tail of the distribution of yield. Therefore,
exploratory and inferential analysis tools that inform boundary characteristics in data are required if BL is to be useful for YGA.
Conclusions and implications: Statistical methods to fit BL models consistently and repeatably, with quantified uncertainty and evidence that there is a boundary limiting the observed yields, are required if BL methods are to be used for YGA. Practical and conceptual obstacles to the use of statistical methods are required. Bayesian methods should also be explored to extend further the capacity to interpret uncertainty of BL models.

KeywordsPotential yield; Attainable yield; Yield gap; Boundary line
Year of Publication2024
JournalField Crops Research
Journal citation311 (1 May), p. 109365
Digital Object Identifier (DOI)
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeGrowing Health (WP3) - bio-inspired solutions for healthier agroecosystems: Discovery landscapes
Growing Health [ISP]
Publisher's version
Output statusPublished
Publication dates
Online13 Apr 2024
Publication process dates
Accepted01 Apr 2024

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