Aspectos Procedimentales y la Certificación para acreditar el Origen

Scenarios. This update report evaluates two scenarios—baseline and high-yield. The baseline scenario assumes a continuation of the USDA 10- year baseline forecast for the major food and forage crops plus a 10-year extension to 2030. The USDA projections are based on specific assumptions about macroeconomic conditions, policy, weather, and international developments, with no domestic or external shocks to global agricultural markets (USDA- OCE/WAOB, 2010). It is a USDA long-term scenario for the agricultural sector based on a continuation of current policies and programs. Changes in any of the key fundamental assumptions underlying the baseline, such as economic growth, population, trade projections, or biofuels policy, will affect the projections. It is intended as a reference or business-as- usual case.

Over the 20-year simulation period, the average annual corn yield increase is slightly more than 1%. The baseline scenario, as implemented in this update, assumes a mix of tillage with a trend toward no-till and reduced tillage cultivation over the simulation period (see Table 4.4). Corn yield and tillage are two

largest single source of currently available biomass residue. Energy crop yields in the baseline scenario assume an annual increase of 1% that reflects learning or experience in planting energy crops and limited gains attained through breeding and selection of better varieties and clones.

In contrast, the high-yield scenario assumes higher corn yields and a much larger fraction of crop acres in reduced and no-till cultivation. The projected increase in corn yield averages almost 2% annually over the 20- year simulation period. The energy crop productivity increases are modeled at three levels—2%, 3%, and 4% annually. These gains are due not only to experience in planting energy crops, but also to more aggressive implementation of breeding and selection programs. Only a baseline scenario is assumed for forest biomass, as these residues are contingent on the demand for pulpwood and sawlogs with future projections based on RPA projections of timber harvests.

As discussed, the baseline scenario and underlying assumptions used in this resource assessment are generally conservative and essentially reflect a continuation of current trends with respect to commodity crop yields, planted acres, and current and projected demand for pulpwood and sawlogs.

assumptions about yield growth and the mix of tillage. In combination with market price, yield is the key determinant of resource availability, and tillage affects how much crop residue can be sustainably removed. Yield. Annual energy crop yields assumed for the baseline scenario vary considerably, ranging from 2 to 9.5 dry tons per acre for perennial grasses, 3.5 to 6 dry tons per acre for woody crops, and 6 to 9 dry tons per acre for annual energy crops (Tables 5.3 and 5.4). These baseline yields for perennial grasses and woody crops are well within observed test plot yields (See Section 5.1) and for specific crops (e.g., switchgrass).68 The baseline results for 2030 at a $60 per dry ton farmgate price (1% annual yield growth for plantings after year 2014) show a national average perennial grass harvested yield of 6 dry tons per acre, slightly less for woody crops, and 6.8 dry tons per acre for the annual energy crop. Results for the high-yield scenario in 2030—assuming the same farmgate price and a 3% annual yield growth—have perennial grass harvested yields increasing to a national average of 7.7 dry tons per acre, the same for woody crops, and 8.5 dry tons per acre for the annual energy crop. These yields are a national average based on harvested acres of energy crops in 2030.

Tillage. A number of key modeling assumptions involve tillage. The baseline assumes a combination of conventional, reduced, and no-till cultivation (see Table 4.4). Over the simulation period, a small fraction of corn acres shift into reduced and no-till. These tillage changes are relatively restrained, as about one-third of corn acres will still be in conventional tillage by 2030 and will be restricted from residue collection. Under the high-yield scenario, a much larger fraction of acres are assumed to shift from conventional tillage to no- till. The tillage proportions assumed in the high-yield scenario recognize that some corn acres will never shift from conventional tillage owing to farmers’ resistance to change; the potential for disease and weed control problems; and soil wetness issues in some situations. By comparison, the high-yield scenario in the 2005

BTS assumed 100% no-till.

Management practices and input costs. No attempt was made to conduct sensitivity analysis on management practices and input costs as the intent is to understand the resource potential, which is largely driven by yield and, in the case of crop residues, by tillage restrictions in addition to crop yield. For example, a reduction in crop residue collection costs owing to technology improvement will tend to shift supply curves down, thus making residue collection more profitable at lower farmgate prices. However, this modeled reduction in costs will not substantially change the reported quantities at the higher simulated prices.

Time of implementation. Throughout this report, currently used and unused resources, such as crop and forest residues, are reported for 2012 and for selected years through 2030. For energy crops, simulation modeling of these prospective resources is assumed to begin in 2014 with initial results reported in 2017. The 2017 results do not include woody crops because of the 4- and 8-year cutting cycles or rotation lengths. As noted in Chapter 5, year 2014 is perhaps the earliest time when seeds and other planting materials will be readily available, assuming it will take 3 years to scale- up nursery operations. Results of model simulations show delays in the 2014 start date will shift estimated supply curves in time.

Energy crop demand for resources. Perennial grasses and woody crops generally require less fertilizer, pesticides, and fossil fuel than the commodity crops they displace—with the exception of the annual energy crops, which require about the same level of inputs. However, perennial grasses and woody crops are more intensive than pasture, requiring more fertilizer and pesticides, especially during crop establishment. Modeling of land-use change. Land-use change is principally affected by the presence of simulated markets (and prices) for energy crops. To be sure, some land-use change is associated with crop residue collection, but this amount is much less than the displacement of commodity cropland and pastureland

68 _{For example, average annual yields for switchgrass ranged from about 4 to 10 dry tons per acre, with most locations having an average}

between 5.5 and 8 dry tons per acre (McLaughlin and Kszos, 2005; BRDI, 2008). For woody crops, annual yields have been generally 5 dry tons per acre in most locations with the exception of the Pacific Northwest and subtropics (eucalyptus) where they have been higher.

by energy crops. Land-use change is modeled by POLYSYS, which allocates land to competing crops based on net returns. If model results show a given commodity crop in a particular county displaced by an energy crop, then the energy crop is more profitable. In the case of pasture, energy crop returns must be greater than the rental value of the pastureland plus additional ‘intensification’ costs to make up for lost forage. A key assumption in this analysis is that for every acre of pasture converted to energy crops, an additional acre of pasture is intensified to make up for lost forage. Because sufficient rainfall is needed, the analysis limits the conversion of pastureland to energy crops to counties situated east of the 100th Meridian and in the Pacific Northwest.

POLYSYS modeling includes 250 million acres planted to the eight major crops, 61 million acres of land in hay production, and 140 million acres of cropland pasture and non-irrigated, permanent pasture. This land base is assumed constant throughout the modeling period. The analysis does not account for any competition and potential losses (or gains) of land to other major land uses, such as the conversion of pastureland to urban uses and the conversion of forestland to cropland. The analysis does not include land currently enrolled in the CRP69 _{or land that might become available as} contracts expire. This update (as well as the USDA projections) assumes that there are approximately 32 million acres currently enrolled in the CRP throughout the simulation period. The analysis does not consider any scenarios where high biomass prices provide strong financial incentives for growers to withdraw from the CRP, give up annual rental payments, and convert land into energy crop production. Further, the analysis does not consider any policy changes to the CRP that will allow the harvesting of energy crops. Finally, the CRP is designed to reduce soil erosion and provide other benefits (e.g., create wildlife habitat, reduce sedimentation, improve water quality, prevent excess crop production, and provide a stable source of income for farmers). Removing land from the CRP has the potential to reduce wildlife habitat and increase the delivery of sediment, nutrients, and pesticides to water bodies (BRDI, 2008). Although it is recognized

that the conversion of some CRP land to energy crops can occur without any adverse environmental impacts, especially if sensitive areas are removed from consideration, the analysis of the CRP for either energy crop production or crop and forage production is not considered in this update.

Environmental sustainability. The primary crop residues, on both cropland and forestlands, explicitly consider resource sustainability with potential collection quantities that are only available after all restrictions are satisfied. This includes meeting soil erosion restrictions due to water and wind and maintaining soil carbon levels for crop residue removal. The forest residue analysis removes steep, wet, and roadless sites and restricts residue removal based on slope considerations. These slope restrictions consider erosion, soil nutrients, biodiversity, soil-organic carbon, and LTSP. For energy crops, sustainability is assumed practiced as implemented through BMPs, and crop budgets reflect these considerations. Displacement of commodity crops by perennial grasses and woody crops should improve environmental sustainability because they require smaller amounts of fertilizers and pesticides and stabilize soils. Once established, perennial grasses and woody crops require little maintenance. These crops can provide more habitat diversity and depending on how planted provide riparian buffers and offer opportunities to capture runoff of nutrients. For annual energy crops, planting is assumed limited to non-erosive cropland, considered part of a multi- crop rotation, and grown using BMPs so as not to impose any additional impacts to local and regional ecosystems.

Roundwood markets. In Section 3.1.2 there is a discussion of an underlying assumption that unmerchantable biomass components of forest stands are uneconomic, unless they are removed during the harvest of commercial roundwood. The analysis includes an upper biomass availability level that is associated with the roundwood harvest level for each state. The restriction is only an approximation due to the fact that wood is transferred among states to processing facilities and is based on 2006 data and the

2005 RPA projections, which are subject to change under different economic conditions. The assumption removes a significant amount of biomass from the

assessment—about 7 million dry tons annually for the United States. More importantly, almost half the states lose 50% or more of the potential thinning biomass because of this restriction.

6.3.3 Factors Affecting Potential