# Challenges and Recommendations for Improved Identification of Low ILUC-Risk Agricultural Biomass
### Dynamic Yield Baselines  
Feedstock production must surpass the baseline estimate of \"business-as-usual\" yield in order to be certified as low ILUC-risk. Constructing this baseline is, as with all counterfactuals, non-trivial, especially since localised data may not be available and weather variability can make it hard to discern long-term trends. Nevertheless, the low ILUC-risk texts stipulate the methodology for constructing a \"dynamic yield baseline\" for both annual and perennial crops and make provisions for multi-year rotation cycles and the adaptation of new crops into existing cycles. Note that the word \"dynamic\" here means that the value of the yield baseline changes in time, based on historical production trends. A \"static\" baseline would be fixed at a constant yield value. Examples of static baselines for low ILUC-risk frameworks are provided by ICAO's CORSIA and the RSB (see Section 3.3). In this paper, we consider the simplest agricultural model (from the perspective of the RED II methodology): annual main crops in a one-year rotation on the same plot, with a stable off-season land use. For this case, constructing the yield baseline for a given crop on a given plot of land happens in two steps: first, a farm-specific initialisation point, and then a universal extrapolation [14]. 1. Initialisation point. The crop yield on the plot of land under consideration is averaged over (at least) the previous three years. Extrapolation. From this initialisation point, future yields are linearly extrapolated using the global average yield growth for that crop, based on the last 10 years (at least) of data. For the initialisation step, the Implementing Regulation [14] provides guidance in case accurate data at the farm level are missing, or if one of the years is considered non-representative. These provisions and their flexibilities are worthy of further discussion; however, in this paper we focus on the slope of the baseline (from the second step), the calculation of which has far-reaching consequences for how much crop production will be certifiable as low ILUC-risk. Global Yield Trends  
Historical records show that, over decadal timescales, many crops experience yield growth that can be well described by a linear trendline. This is observed at global, national, and regional scales, and emerges from the combination of climatic and ecological evolution as well as various local drivers, including expanding access to new crop varieties, harvesting machinery, irrigation technology, pest control, and soil nutrient management techniques. The Implementing Regulation methodology recognises that, in order to ensure that agricultural production certified as low ILUC-risk is genuinely additional, it must go beyond these long-term background trends. Specifically, applicants for low ILUC-risk status must compare their yields against a dynamic baseline based on the \"global average yield slope\", that is, the gradient of the straight line fit to global annual yields recorded in the FAOstat database (global annual yield, measured in units of t/ha/yr, means the total crop production (in tonnes, t) divided by the total harvest area (in hectares, ha), for a given year). Yield slopes for selected major crops such as wheat are calculated and tabulated in the Implementing Regulation (namely, for barley, maize, oil palm fruit, rapeseed, soybeans, sugar beet, sugar cane, sunflower seed, and wheat). For crops not included in this table, the yield slope must be calculated afresh using the most recent FAOstat data spanning at least 10 consecutive years. See Annex C in the Supplementary Material for an updated list of crops and yield slopes. The low ILUC-risk dynamic yield baseline is constructed to follow such linearised global trends for each crop. The Commission's rationale for this may have come from a sense that, as ILUC is a global market-mediated phenomenon, it is global rather than local trends in productivity that are relevant to assessments of additionality.