Evaluating Experimental and Commercial Yields: An Analysis of a Simple Correction Technique for Sugarcane in South Africa

Abstract

The objective of this study was to compare historical experimental and commercial yield data for sugarcane in order to determine the general level of disparity and assess the possibility of deriving a correction factor to adjust trial yields to realistic commercial levels. Over the 29-year comparison period, trial yields (fresh stalk weight) of sugarcane were significantly higher (p < 0.001) than commercial yields by approximately 30 t ha −1 . Trial and commercial yield data were used to derive a correction factor of 0.7, which can be used to successfully correct trial yields to equivalent commercial levels at both the national and regional level; however, at the local (mill supply area) level different correction factors were derived. The degree of correlation between experimental and commercial yields was found to be greater in areas of higher yield potential, and it was also established that off-station trials were more representative of the commercial potential than on-station trials. The correction factor of 0.7 can therefore be used as a tool to adjust experimental sugarcane yields to indicate likely commercial levels, thereby improving the confidence of growers in taking up recommendations for the adoption of new varieties derived from trial yields.

Socio-ecological factors determine crop performance in agricultural systems

“…The predominance of crop performance evaluation based on field experiments under relatively controlled conditions has led to the dominance of univariate and bivariate methods in agricultural research, and ignores the nonlinear effects, feedbacks and interactions observed in many ecological and social-ecological systems 11 . In addition to the high costs associated with experiments where the approach is to keep as many variables as possible under control, the large difference between yields obtained in experimental studies and on-farm yields demonstrates the discrepancies between expected and real farm outcomes, which is particularly the case for less intensive farming systems 12,13 . This calls for alternative assessment methods that qualitatively and quantitatively integrate disparate causes of variation in crop performance, such as the temporal and spatial boundaries used in…”