Livestock agriculture accounts for ∼15% of global anthropogenic greenhouse gas (GHG) emissions. Recently, natural climate solutions (NCS) have been identified to mitigate farm‐scale GHG emissions. Nevertheless, their impacts are difficult to quantify due to farm spatial heterogeneity and effort required to measure changes in carbon stocks. Remote sensing (RS) models are difficult to parameterize for heterogeneous agricultural landscapes. Eddy covariance (EC) in combination with novel techniques that quantitatively match source area variations could help update such vegetation‐specific parameters while accounting for pronounced heterogeneity. We evaluate a plant physiological parameter, the maximum quantum yield (MQY), which is commonly used to calculate gross and net primary productivity in RS applications. RS models often rely on spatially invariable MQY, which leads to inconsistencies between RS and EC models. We evaluate if EC data improve RS models by updating crop specific MQYs to quantify agricultural GHG mitigation potentials. We assessed how farm harvest compared to annual sums of (a) RS without improvements, (b) EC results, and (c) EC‐RS models. We then estimated emissions to calculate the annual GHG balance, including mitigation through plant carbon uptake. Our results indicate that EC‐RS models significantly improved the prediction of crop yields. The EC model captures diurnal variations in carbon dynamics in contrast to RS models based on input limitations. A net zero GHG balance indicated that perennial vegetation mitigated over 60% of emissions while comprising 40% of the landscape. We conclude that the combination of RS and EC can improve the quantification of NCS in agroecosystems.

Dairy farms are predominantly carbon sources, due to high livestock emissions from enteric fermentation and manure. Integrated crop–livestock systems (ICLSs) have the potential to offset these greenhouse gas (GHG) emissions, as recycling products within the farm boundaries is prioritized. Here, we quantify seasonal and annual greenhouse gas budgets of an ICLS dairy farm in Wisconsin USA using satellite remote sensing to estimate vegetation net primary productivity (NPP) and Intergovernmental Panel on Climate Change (IPCC) guidelines to calculate farm emissions. Remotely sensed annual vegetation NPP correlated well with farm harvest NPP (R² = 0.9). As a whole, the farm was a large carbon sink, owing to natural vegetation carbon sinks and harvest products staying within the farm boundaries. Dairy cows accounted for 80% of all emissions as their feed intake dominated farm feed supply. Manure emissions (15%) were low because manure spreading was frequent throughout the year. In combination with soil conservation practices, ICLS farming provides a sustainable means of producing nutritionally valuable food while contributing to sequestration of atmospheric CO₂. Here, we introduce a simple and cost-efficient way to quantify whole-farm GHG budgets, which can be used by farmers to understand their carbon footprint, and therefore may encourage management strategies to improve agricultural sustainability.

Research evaluating the impact of silvopastoral systems on physical and biological properties of Amazonian soils is scarce. Thus, this study aimed to compare the soil carbon storage potential and physical and chemical soil properties of silvopastoral systems (SPS) and conventional pastoral systems (CPS) in the San Martin region of Peru. Using the Walkley and Black method, we analyzed soil organic matter at two different depths (0–15 cm and 15–30 cm). In addition, bulk density, soil moisture, total porosity, and mechanical resistance were measured in both systems. The highest (P < 0.05) carbon stocks were reported at 0–15 cm of depth with values of 31.4 Mg ha⁻¹ and 34.4 Mg ha⁻¹ for CPS and SPS, respectively. At 15–30 cm depth, the total carbon stock was higher for SPS, with 29.12 Mg ha⁻¹, than for CPS, which had 26.4 Mg ha⁻¹. Despite the absence of statistically significant differences, soil carbon stocks were higher in SPS. No significant differences in soil moisture were found between systems, although soil moisture was slightly greater in SPS than CPS (28 and 25%, respectively). The CPS had 59% of the total porosity, which was higher than the SPS. Mechanical resistance was lower in SPS (2.15 kg/cm²) than in CPS (2.33 kg/cm²) at 10 cm of depth. These results indicated that the SPS has the potential to store more carbon and improve physical and chemical traits in the soil than the CPS.

Duff, A. J., P. H. Zedler, J. A. Barzen, and D. L. Knuteson. 2017. The Capacity-Building Stewardship Model: assessment of an agricultural network as a mechanism for improving regional agroecosystem sustainability. Ecology and Society 22(1):45. https://doi.org/10.5751/ES-09146-220145