A DECISION SUPPORT SYSTEM FOR SUGARCANE VARIETY SELECTION IN SOUTH AFRICA BASED ON GENOTYPE-BY-ENVIRONMENT ANALYSES

SUMMARY The objective of this study was to develop a basic variety selection decision support system (DSS) based on industry legalities, varietal characteristics and structured genotype-by-environment (G × E) analyses. Trial data extracted from a variety trial database at the South African Sugarcane Research Institute (SASRI) were categorized into different regions, harvest ages (12, 18, 24 months) and harvest seasons (early, mid, late season harvests). Restricted maximum likelihood analyses were conducted regionally to determine varietal adaptability to different harvest ages and seasons. Highly significant variety × harvest age and variety × season interactions allowed for the appropriate categorization of varieties. Varietal adaptability to different yield potential conditions was determined using the sites regression technique, and varietal adaptability was interpreted from the slope of the regression curves. The analysed data were used to create simplistic ‘yes/no’ spreadsheets, which were housed within a relational database. A web interface linked to the database allows users to specify characteristics of their production environment. The system then selects appropriate varieties that conform to specified criteria and eliminates non-compliers in a stepwise approach. The system was subsequently validated against expert extension specialist opinion and acceptable performance was observed.

[1]  S. Ramburan,et al.  Variety evaluation in the South African sugarcane industry: an overview. , 2007 .

[2]  Pongola-SASEX Pongola,et al.  YIELD AND QUALITY DIFFERENCES BETWEEN IRRIGATED SUGARCANE VARIETIES , 2009 .

[3]  José Crossa,et al.  Statistical analyses of multilocation trials , 1990 .

[4]  Hugh G. Gauch,et al.  Statistical analysis of regional yield trials: AMMI analysis of factorial designs. , 1992 .

[5]  R. A. Lawes,et al.  Applications of industry information in sugarcane production systems , 2005 .

[6]  H. D. Patterson,et al.  Recovery of inter-block information when block sizes are unequal , 1971 .

[7]  Allan Leck Jensen,et al.  Building a web-based information system for variety selection in field crops—objectives and results , 2001 .

[8]  M. van den Berg,et al.  Crop growth models for decision support in the South African sugarcane industry. , 2005 .

[9]  Kaye E. Basford,et al.  Analysis of multi-environment trials - an historical perspective. , 1996 .

[10]  K. W. Finlay,et al.  The analysis of adaptation in a plant-breeding programme , 1963 .

[11]  Manjit S. Kang,et al.  GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists , 2002 .

[12]  Graeme L. Hammer,et al.  Plant Adaptation and Crop Improvement , 1996 .

[13]  S. Ma’ali.  Additive mean effects and multiplicative interaction analysis of maize yield trials in South Africa , 2008 .

[14]  J. Shine,et al.  The effect of genotype, environment and time of harvest on sugarcane yields in Florida, USA , 2006 .

[15]  K. Pillay.  Adoption of new sugarcane varieties by the non-miller-planters in Mauritius: the importance of on-farm trials , 1999, Experimental Agriculture.

[16]  R. Parfitt.  Release of sugarcane varieties in South Africa. , 2005 .