#Ag systems model archive#
#Ag systems model iso#
ISO 14040-14044: this methodology specifies requirements and provides extensive and detailed guidelines for LCA.The assessment boundaries may range from “cradle” to “grave” or only for parts of this range or product’s life cycle. An LCA may be conducted in agriculture, for example, for food products or supply chain equipment. Initially developed for industrial operations, the LCA methodology has been expanded to a broader range of fields, including agriculture. An LCA framework can determine areas of most significant impact and compare reduction strategies for agricultural production systems. Vertical lines denote SEs for emission estimates based on the ΔEF model (Shcherbak et al., 2014).Ī life-cycle assessment (LCA) is a quantitative method for determining GHG emissions or removals and other environmental impacts and resource demands across a product’s entire supply chain. (B) Relative N2O emission reductions for the three models when N fertilizer rates are reduced by 50 kg ha−1 from four baseline N fertilization scenarios: 300, 200, 150, and 50 kg ha−1. ( 2001) model (0.001 N), and the ΔEF model for average upland grain crop emissions from this meta-analysis (0.001 N). (A) Comparison of N2O emission models for N fertilizer reduction scenarios: N2O emissions estimated by the IPCC Tier 1 (1% linear emission) model, the Hoben et al. Therefore, this analysis helped improve the EF provided by the IPCC Tier 1 method that assumes a linear relationship between N input and N 2O emissions (Fig. The statistical analysis revealed that the N 2O response to N inputs grew significantly faster than linear for synthetic fertilizers and most crop types. ( 2014), for example, performed a meta-analysis using 78 published studies (233 site-years) with at least three N-input levels to estimate N 2O emission factors (EFs).
They are also less flexible to handle combinations of variable management ( Olander & Haugen-Kozyra, 2011). However, they are not appropriated to capture the effects of spatial and temporal variability on GHG dynamics at finer scales. Empirical models are relatively easy and transparent to use. Regression analysis can be used to extrapolate existing research and data to develop empirical models that describe explicit GHG emissions and carbon sequestration factors as a function of agriculture activities. Jump to: Life-cycle assessment models | Process-based models | Proxy indicators
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