Estimating sugarcane crop yield using a spectral based model, a case study of Kakira sugarcane plantations.
Abstract
Sugarcane (Saccharum sp.) is one of the world's most significant crops due to its strategic position
and wide range of uses in everyday life as well as for industrial purposes targeting nutritional and
economic sustenance. Sugarcane accounts for around 60% of global sugar production, with sugar
beet accounting for the remaining 40% (Onwueme and Sinha, 1999). It’s a tropical crop that takes
between 8 and 12 months to mature. When the sugar concentration in matured cane reaches its
peak, it turns green, yellow, purple, or reddish (Onwueme and Sinha, 1993).
Approximately 110 nations currently produce sugar from either cane or beet, with eight producing
sugar from both cane and beet. Sugarcane contributes for about 80% of world sugar output on
average. The top 10 producing nations (India, Brazil, Thailand, China, the United States, Mexico,
Russia, Pakistan, France, and Australia) accounted for over 70% of world output during the
October/September season. For a long time, Brazil has been the world's greatest sugar-growing
and producing country at the time (Goyalde et al, 2009). It generated around 42 million metric tons
of sugar in the years 2021 to 2022. During that time, worldwide sugar output was estimated to be
over 179 million metric tons. In Uganda, the central government has recently encouraged farmers
to participate in commercial agricultural operations as part of its attempts to alleviate poverty and
create prosperity. Sugarcane cultivation has been highly preferred in areas near sugarcaneprocessing industries in Uganda. These are areas near Kinyara Sugar Works Ltd., Masindi,
Mayuge Sugar Ltd., Mayuge, Kaliro Sugar Ltd., Kaliro, and Kakira Sugar Works Ltd., Jinja. In
these areas sugarcane production is perceived to be more profitable and economically valuable
than other traditional crops (e.g., coffee, cotton) as well as plantation forests (Mwavu et al, 2018).
Agricultural statistics on crop production are useful on a local, national and international level.
Locally, they can be used in the selection of seed types, fertilizer and accessing equipment
reliability. At the national level, statistics on agricultural production are necessary for agricultural
policy making, decision making and estimating national income. For developing countries these
agricultural statistics are not readily available. Without addressing the plights of the agricultural
sector, which remains the ultimate reservoir of employment and economic growth for many lowand middle-income countries (Ivanic and Martin, 2018; Christiaensen and Martin, 2018)