The relationship between spatial variation of greenhouse gases intensity and agri-environmental variables in Oil Palm plantations

Oil palm (OP) plantations account for 1.7 % of global CO2 emissions. Numerous studies have focused primarily on greenhouse gas (GHG) emissions from peatlands, constituting 20% of total OP area in the two largest OP producing countries, Indonesia and Malaysia. Few studies have investigated the potential for reducing GHG emissions in OP plantations. Strategies to reduce emissions and sequester carbon must consider how different practices affect production and the environment. Understanding the spatial distribution of GHG intensity and how the environment affects GHG intensity is therefore key to sustainable oil palm production.

GHG intensity was used as a metric to map the potential for sustainable OP plantations. GHG intensity represents the GHG emissions / removals (ton C ha-1) per unit of oil palm yields (ton ha-1). The approach for analysing the change in GHG emissions/ removals, referred to as the IPCC tier 1 method, is based on changes in soil organic carbon due to C and N emissions in drained peatlands and the associated change in aboveground biomass due to land use change. Changes in GHG intensity were investigated spatially for a case study in an industrial OP plantation located in Riau Province, Indonesia, from 2015 to 2019. Linear regression was used to analyse the relationships between GHG intensity and agri-environmental variables including NDVI, NPP, GPP, evapotranspiration, soil moisture in the root zone, soil moisture in deeper layer, C and N emissions from organic soils, and soil organic carbon (SOC).

The results show that around 90% of the new oil palm plantations in 2019 were converted from timber plantation, swamp scrubland, and bare land in 2015. Consequently, biomass growth from land use change acted as a carbon sink in this period. However, drained organic soils contributed significantly to GHG emissions. The change in GHG intensity in OP plantation in this study varied spatially from emitting (0.19 to 4.10 Ton C eq Ton-1 yields) to removing the GHG (0.23 to 2.40 Ton C eq Ton-1 yields). Among the environmental variables, NDVI and soil moisture showed the strongest relationship with GHG emissions/ removals (R2 = 0.23,   p value = < 2.2e-16) and yields (R2 = 0.2   p value = < 2.2e-16) in OP plantations.

These initial findings are advantageous for spatially identifying potential OP plantations that remove or emit GHG. Understanding the relationship between GHG emissions/removals and yields to environment variables provides insight into monitoring and enhancing OP sustainability, both from production and environmental perspectives. Future work will examine non-linear approaches to better model this relationship.