Farming Better, Not Harder: Lowering Cost With Better Monitoring

Vanderheyden, Lisa [UCL] Van den Broeck, Goedele [UCL]

Sustainable Food Systems Symposium 2024 Paper title: Scaling Up Soil Carbon Monitoring: The cost-effectiveness of Direct Measurement vs. a Model-Based Approach Keywords (maximum 5): result-based payment, cost-effectiveness, soil carbon accounting, soil carbon monitoring, remote sensing JEL code (maximum 3) : Q15, Q54, Q56 Introduction Carbon farming, involving the adoption of agricultural practices aiming at capturing carbon in soils, is a cornerstone of European climate strategies such as the Green Deal. Farmers are rewarded with carbon credits, which are purchased by companies on the Voluntary Carbon Market (VCM)(1,2). It may also lead to other benefits such as improved soil fertility and food security (3–5). To ensure carbon credit transparency and reliability, cost-effective, rigorously evaluated protocols for soil carbon monitoring are crucial (6–8). If soil organic carbon (SOC) is exclusively measured by physical soil sampling, this could be a potential barrier to the upscaling of carbon farming initiatives. The challenge stems from the high costs of collecting, storing, and analyzing soil samples over large areas (6,7). A viable alternative is to use a model-based approach that combines field measurements with remote sensing data. This could be a more cost-effective way to scale up the monitoring of SOC (6,9). However, such approaches are still in the research phase, and their estimates generally entail relatively high uncertainty (9,10). Additional research is needed to evaluate the cost-effectiveness of this approach compared to direct field measurements (6,7). Objective This study aims to fill this gap by carrying out a comprehensive cost-effectiveness analysis, comparing various approaches and identifying key elements and determining factors. This information is essential to enable decision-makers and researchers to make informed decisions and optimize processes for greater scalability. Method Our comparison of direct measurement and model-based approaches for carbon farming will be based on a previous study that has compared the cost-effectiveness of two direct SOC measurement methods (e.g. physical sample vs proxy sensing) in terms of their cost and accuracy (11), and on studies on a cost comparison of monitoring approaches for biodiversity conservation (12,13). First, to calculate the costs and understand the cost allocation, we will establish a cost framework that we evaluate using a hypothetical carbon farming scheme. The cost framework will account for various implementation costs, including sampling, monitoring, travel, and analysis costs. The hypothetical scheme will be designed based on the approved methodologies of existing standards (14,15). The cost allocation categories and parameters require technical expertise, which will be gathered through scientific literature (8,11,16) and expert consultation using an elicitation method. Second, accuracy will be determined based on experimental analysis. We will evaluate the accuracy of two monitoring methods by selecting fields—cropland and grassland—in distinct project study areas. Using direct field measurements and a process-based model informed by field data and remote sensing, we will estimate SOC. Comparative analysis will determine method uncertainties, calculating metrics like variance, 95th confidence intervals, and bias (10). Scenario analysis will be used to compare the cost-effectiveness of both approaches between implementing the scheme at farm level and landscape level (with all surrounding farms involved). Sensitivity analysis will be conducted to determine the relative cost and the impact of factors such as sampling size, monitoring frequency and scale. Findings Results will contrast the cost-effectiveness of direct and model-based methods at farm and landscape level. Expected results could discuss following hypotheses: • H1: Model-based approaches are potentially more cost-effective than direct soil measurements, but have a higher degree of uncertainty (6,7,9). The cost-effectiveness of both approaches is assessed based on the cost-accuracy ratio from a previous study (11). The direct measurement is at the nominator and model-based is at the denominator. A ratio above 1 will indicate that the model-based approach is more cost-effective. • H2: The high cost of direct measurement at large scale is explained by the high cost associated to data collection, storage, and analysis (7). By comparing cost allocations, we will determine whether direct measurement incurs higher costs for data collection, storage, and analysis at large scale compared to model-based approach. Figure 2 illustrates a fictive cost allocation for each approach. • H3: The high costs of field-based measurement at large scale can be attributed to the necessity of a more intensive sampling and increased labor requirements, as reported in a previous study (9). Sensitivity analysis will be employed to assess the influence of sampling size on cost-effectiveness, comparing cost per hectare across different sample sizes. A fictitious example (Figure 3) demonstrates that the direct and model-based approaches are comparable up to 50 plots, but the model-based method becomes more cost-effective beyond 100 plots. Other cost-influencing factors, such as the monitoring frequency, will also be analyzed. Figure 2: Fictitious results comparing aggregated cost of Direct Measurement and Model-based Approach at Farm LEVEL and landscape level (adapted from Markova-Nenova et al. (2023)). Figure 3: Present value of the costs for different sampling size for both direct measurement and model-based approach at farm scale and landscape level (adapted from Markova-Nenova et al. (2023)). Conclusion The results of this study will contribute to a better understanding on the costs for monitoring soil carbon sequestration, addressing critical assumptions, and paving the way for more effective scaling-up strategies. This knowledge could inform decision-making at various levels, the voluntary carbon market, and farmers: • Policymakers: By understanding the cost-effectiveness of these approaches, policymakers can make informed decisions about how to allocate resources and support carbon farming initiatives. • For market participants, such as carbon credit developers, this study offers evidence-based guidance on the design of carbon MRV guidelines and cost optimization strategies. By understanding the factors that influence the monitoring costs, carbon credit developers can make informed decisions about where to invest their resources to minimize costs and maximize the value of carbon credits.