Abstract
Many perennial crops are cultivated in large plantation estates by agro-industrial companies. Some of the attributes of perennial crops, like annual variation in yields and time lag from planting to initial yield, create complex challenges in developing land utilisation strategies in plantations. This work develops a mathematical programming model to determine the optimal maturity (age) of the different plantations needed to meet the demand with reduced environmental impacts. The model also determines the corresponding planting period for new plantations, accounting for the yield profile of the perennial crops. Piecewise linearisation technique is used to model the yield profile, thus reducing the model to mixed integer linear programming. The optimisation is carried under two approaches – total cost and discounted carbon value (DCV). The total cost approach aims to determine the planting strategy that results in minimising the capital and operations cost at plantations. The DCV approach aims to delay the peak carbon emissions, thereby reducing the intensity of climate change effects and also buying time for mitigation and adaptive measures. The model developed in this work is illustrated with an oil palm plantation case study, which showed that though the total cost is the same for both the approaches, carbon emissions are 3.28% lower in the DCV result compared to the cost approach.
| Original language | English |
|---|---|
| Article number | 128526 |
| Journal | Journal of Cleaner Production |
| Volume | 318 |
| Early online date | 4 Aug 2021 |
| DOIs | |
| Publication status | Published - 10 Oct 2021 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 13 Climate Action
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SDG 15 Life on Land
Keywords
- Carbon emissions
- Discounted carbon value
- Land use change
- Mathematical optimisation
- Perennial crops
- Yield profile
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
- General Environmental Science
- Strategy and Management
- Industrial and Manufacturing Engineering
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