Assessing Allometric Models to Predict Vegetative Growth of Yams in Different Environments

Abstract

Yams are a neglected crop, grown mostly in West Africa by resource‐poor farmers. Little is known about the physiology of the crop, and researchers lack practical and efficient tools to conduct growth analysis. The objective of this study was to develop allometric models able to predict yam leaf area and leaf and stem dry mass with acceptable accuracy. The models were calibrated using a data set comprising 10 cultivars belonging to the two main species (Dioscorea alata L. and D. rotundata Poir.) grown at two locations in Guadeloupe (Lesser Antilles) and two locations in Benin (West Africa). The best models were selected based on Akaike's information criteria and validated against independent data sets. A power regression was best for predicting leaf area from leaf measurements while linear relationships were sufficient to predict the relationship between crop leaf area and leaf and stem mass. The use of species‐specific models for the estimation of leaf and stem mass significantly improved the models’ performance. Models predicting yam leaf area and leaf mass proved to be reliable and accurate (no significant deviation and adjusted R2 > 0.95). For stem mass, overestimation always occurred during validation (9%). To overcome this discrepancy, a methodology was proposed that allows the user to calibrate the model by tailoring the sampling size to obtain the required precision. The use of the selected models provides a nondestructive and reliable alternative to estimate leaf area and leaf and stem biomass for different cultivars and sites.