A financial and managerial evaluation of the impacts of adopting radio-frequency identification technology on a conservation agriculture farm in the Swartland.
Date
2024-12
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Stellenbosch : Stellenbosch University
Abstract
Global population growth and urbanisation have increased the demand for food significantly. The global population figure surpassed eight billion in 2022 and two billion more people are projected for 2050. More than half of the population lives in urban areas, a figure expected to rise to 70%, expanding urban areas by 1.2 billion square kilometres. This growth intensifies food insecurity and pressures agricultural resources, necessitating increased food production often encroaching on fragile ecological regions. This increased pressure on agriculture necessitates sustainable farming practices. Fragile ecosystems, like the Amazon and Sahel, are particularly at risk from expansion. Conservation agriculture (CA) is a sustainable solution that reduces greenhouse gas emissions, conserves resources and enhances soil health. It is based on three principles: minimal soil disturbance, maintaining crop residues and crop rotation. CA improves yields, reduces vulnerability to climate shocks and provides broader environmental benefits, like carbon sequestration and reducing water system pollution. CA reduces soil disturbances, while precision livestock farming (PLF) technologies are designed to assist producers in managing livestock in a more efficient and data-driven manner. The contributions of CA towards sustainable production are well-known and proven, while the impacts of PLF and livestock towards sustainable production are less known. This study implements trial data from Langgewens Research Farm and technical PLF data, specifically radio-frequency identification, to evaluate the financial viability of this technology within a CA system in the Swartland region. Agriculture production systems are complex, consisting of various interrelated components. A whole-farm budget model is developed within a systems framework to evaluate and compare two typical production systems aligned within CA principles. As a benchmark for comparison, the model was based on a typical farm in the relatively homogeneous farming region of Middle Swartland. Research began with trial data on a specific crop rotation system — System E — from Langgewens Research Farm. This data was adapted for use in financial analysis and integrated into the typical farm model. Additionally, technical radiofrequency identification (RFID) data on sheep production was incorporated to evaluate and compare the viability of PLF technology within a CA. This study highlights the substantial potential of RFID and PLF technologies in transforming modern agriculture. The detailed analysis and sensitivity evaluations clearly demonstrate that these advanced tools not only enhance gross income per hectare but also offer substantial benefits in terms of efficiency, animal health, overall farm management and profitability and risk mitigation. By integrating technologies within a CA system, farmers can make more informed decisions, optimise resource use and improve their environmental footprint.
Description
Thesis (MAgric) --Stellenbosch University, 2024.