Multi-scale analysis of carbon stocks and process indicators in the agro-ecosystem of Canterbury, New Zealand
Increasing interest in characterising carbon dynamics in the agricultural landscape has been driven by the clear need for greenhouse gas mitigation strategies in response to global climate change. However, little quantitative information is available about carbon stocks and processes in agro-ecosystems. Better understanding of carbon stocks and processes is of particular importance in New Zealand, where farmland occupies large areas across the country and ongoing change in farming practices, land management and intensification are affecting carbon stocks and processes and ultimately, agricultural sustainability. This study aimed to quantify and understand variation in above- and below-ground carbon stocks and processes in shelterbelts and paddocks at multiple scales within the agro-ecosystem of Canterbury, New Zealand. In an initial pilot study across four farms showed that variability in above- and below-ground carbon stocks among-farms was smaller than between shelterbelt and paddocks. Using this knowledge to determine appropriate sampling intensity, I quantified carbon stocks and processes within shelterbelts across 34 farms, focussing particularly on understanding how within-shelterbelt-scale (e.g. soil moisture, pH), shelterbelt-scale (e.g. shelterbelt type, age, and tree biomass), farm-scale (e.g. land use) and landscape-scale (e.g. soil type) factors was related to variability of these variables. Total above- and below-ground carbon pools in shelterbelts were similar for exotic and native shelterbelts (152.6 ± 131 t C ha-1 and 110.8 ± 128 t C ha-1, respectively [mean ± standard errors]), although native shelterbelts were on average half the age of exotic shelterbelts (16 and 27 years, respectively). The above-ground biomass carbon pool represented about 62% and soil carbon pool 33% in both native and exotic shelterbelts. Most variability in carbon stocks was explained at the shelterbelt scale by shelterbelt type, age and size, whereas farm scale played a minor role and soil type was unimportant. Carbon cycle processes varied primarily at the shelterbelt and paddock level. Leaf litter decomposition and microbial activity were twice as high and invertebrates were more active in native shelterbelts compared to exotic shelterbelts, and variability in these indicators were best explained by within-shelterbelt factors (e.g., soil moisture, pH, organic and labile carbon) and shelterbelt characteristics (age, biomass, and vegetation type). Therefore, above-ground shelterbelt-scale factors drive carbon cycle process indicator rates and biological activity in the Canterbury agro-ecosystem, rather than land use or landscape factors. These field data were used in GIS-based spatial carbon model which expored and evaluated a number of agricultural land use, cover and management scenarios and their impact on soil carbon stocks over the enxt 10 years (2014 – 2024) in combination with quantifying the amount of shelterbelt area needed to offset the potential soil carbon loss. While the model default settings predicted an increase in soil carbon stocks, the literature based alternative scenarios, commonly predicted a decrease of 10%, 6%, 34% in soil carbon stocks in the alternative dairy, sheep and beef and arable scenarios. This study shows that native shelterbelts in agro-ecosystems have considerable potential for increasing carbon pools and enhancing carbon cycle process, particularly as they age through time. Contrary to current assessments, this study was not able to provide definate evidence on the effect of further intensification and land clearing on carbon stocks in Canterbury. Future studies should include long-term investigation of carbon stocks and processes across a wider variety of shelterbelt and farm types.
carbon cycle, GIS, agriculture, variability, carbon stock, landscape modelling, variance partitioning, Canterbury, agroecology, native plants, shelterbelt, scale, ANZSRC::050102 Ecosystem Function, ANZSRC::070301 Agro-ecosystem Function and Prediction, ANZSRC::050204 Environmental Impact Assessment
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