Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture

pp 175-188

Open Access This content is freely available online to anyone, anywhere at any time.


Scaling Point and Plot Measurements of Greenhouse Gas Fluxes, Balances, and Intensities to Whole Farms and Landscapes

  • Todd S. RosenstockAffiliated withWorld Agroforestry Centre (ICRAF) Email author 
  • , Mariana C. RufinoAffiliated withCenter for International Forestry Research (CIFOR)
  • , Ngonidzashe ChirindaAffiliated withInternational Center for Tropical Agriculture (CIAT)
  • , Lenny van BusselAffiliated withWageningen University and Research Centre
  • , Pytrik ReidsmaAffiliated withWageningen University and Research Centre
  • , Klaus Butterbach-BahlAffiliated withInternational Livestock Research Institute (ILRI)Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU)


Measurements of nutrient stocks and greenhouse gas (GHG) fluxes are typically collected at very local scales (<1 to 30 m2) and then extrapolated to estimate impacts at larger spatial extents (farms, landscapes, or even countries). Translating point measurements to higher levels of aggregation is called scaling. Scaling fundamentally involves conversion of data through integration or interpolation and/or simplifying or nesting models. Model and data manipulation techniques to scale estimates are referred to as scaling methods.

In this chapter, we first discuss the necessity and underlying premise of scaling and scaling methods. Almost all cases of agricultural GHG emissions and carbon (C) stock change research relies on disaggregated data, either spatially or by farming activity, as a fundamental input of scaling. Therefore, we then assess the utility of using empirical and process-based models with disaggregated data, specifically concentrating on the opportunities and challenges for their application to diverse smallholder farming systems in tropical regions. We describe key advancements needed to improve the confidence in results from these scaling methods in the future.