Modelling and monitoring for strategic yield gap diagnosis in the South African sugar belt
South African Sugarcane Research Institute, Private Bag X02, Mount Edgecombe 4300, South Africa; Department of Plant Production, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa; School of Agriculture, Earth and Environmental Sciences, University of Kwazulu-Natal, Private Bag X01, Scottsville 3209, South Africa
This paper revisits the diagnostic use of industry-wide sugarcane (Saccharum sp. hybrid) modelling and monitoring in South Africa for gaining a better understanding of production trends and the strategies required to address temporal and spatial yield variation.Such reviews have been conducted annually since 2008, by comparing the ratio of actual to simulated (potential) average sugarcane yields for 14 sugar mills with that of preceding seasons (since 1980). Actual yields are determined from total amount of cane crushed at the mill and the estimated area harvested as determined from mill records and grower surveys. Potential yields are determined by using the Canesim model with daily weather data for 48 homogenous agro-climatic zones. Widening yield gaps in some key producing regions and significant differences between regions indicated the need to investigate the impact of non-climatic factors such as pests, diseases, and sub-optimal agronomic management, even though this analysis is still qualitative and incomplete, and not fully objective. Factors that were highlighted as likely causes of suboptimal production were damaging effect of a new pest (sugarcane thrips), inadequate nutrition and inadequate replanting, apparently linked to unfavourable socio-economic conditions; even more so for small-scale growers than for large-scale growers. In addition to providing a service that is valued by the industry, the annual reviews have contributed to strengthening co-operation between researchers of distinct disciplines as well as between researchers and canegrowers, and to help identify priorities for further research. The quality of the analysis could be further improved by more accurate and timely estimates of the area harvested, improved resolution of yield data and extended surveys of pests, diseases and other yield limiting or reducing factors. © 2012 Elsevier B.V.
agricultural management; agricultural modeling; agroecology; agronomy; crop production; crop yield; farmers knowledge; harvesting; limiting factor; monitoring; pest damage; research work; socioeconomic impact; spatial variation; sugar cane; temporal variation; South Africa; Saccharum; Saccharum sp.; Thysanoptera