Biofuel co-products as livestock feed - Opportunities and challenges

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Land use in Australia for biofuels and bio-energy: opportunities and challenges for livestock industries 459At the highest stocking rate (37.5 sheep/ha) wool productionin the sheltered paddocks was increased by 31 percentover the 5 years of the trial, and the live weights of thesheep in the sheltered paddocks at 15 and 30 sheep/hawere significantly higher than those in the unshelteredpaddocks, attributed to a combination of increased pastureproduction and a saving in metabolizable energy of thesheep in the sheltered paddocks.Following Lynch and Donnelly’s findings with artificialwindbreaks and high stocking rates on high-input pastures,Reid and Thompson set up a project to look at the effect ofnatural windbreaks, consisting of a combination of nativetrees and shrubs, on sheep grazing low-input modifiednative pastures (Reid and Thompson, 1999). Sheep in thewindbreak paddocks finished the year 13 percent heavier(P = 0.067) and cut 13 percent more wool per head (3.4vs 3.0 kg, P
460Biofuel co-products as livestock feed – Opportunities and challengessecurity and regional development are as strong as everand the industries continue to enjoy support through StateGovernment mandates and Federal Government exciserelief for biofuels and the Renewable Energy Target scheme(Department of Climate Change and Energy Efficiency,2011) for bio-energy. In addition, at this time it is theAustralian government’s declared intention to introducea pricing mechanism for carbon, which would providefurther incentive for the development of alternative energysources, including biofuels and bio-energy.In this section we consider how further development offirst- or second-generation biofuels and bio-energy affectthe livestock portion of mixed cropping-grazing farming inAustralia.Bryan, King and Wang (2010b) have considered thisquestion at a landscape scale. Using a mixed farming areaof South Australia and adjoining regions in Victoria, theymodelled four-year rotations for cropping (wheat-wheatlupins-wheat),mixed cropping-grazing (wheat-grazinglupins-grazing),continuous grazing (grazing-grazing-grazing-grazing)and biofuels (continuous wheat-canola rotationsfor the production of ethanol and biodiesel). The aimwas to assess the impact of establishing a first-generationbiofuels industry in the area and to quantify the trade-offsbetween biofuel, food (grain, meat) and fibre (wool) production(Bryan, King and Wang, 2010).To do this they used APSIM (Keating et al., 2003) to spatiallymodel production of food and biofuel under baseline,mild, moderate and severe climate change scenarios. Theeffect of introducing farm subsidies tied to the net greenhousegas (GHG) emissions abatement achieved by a switchto biofuels was calculated based on the GHG emissions andenergy cycle of the biofuels and food agriculture systems.Finally, they calculated economic returns with or withoutsubsidy, then applied a rational economic model of adoptionwhere farmers switch to biofuels agriculture where itis more profitable than food agriculture and continue withfood agriculture in all other areas.The modelling predicted that at baseline climate andno carbon subsidy, the take up of biofuels agriculture onthe economically viable areas would use 44 percent of thearable land in the modelled area, reducing sheep meatproduction by almost 60 percent and wool productionby 78 percent. As would be expected, with a subsidy ofAUD 30/tonne CO 2 -eq, the model predicted the use of arableland for biofuels agriculture rising to 54 percent, furtherreducing sheep meat and wool production. However, underthe severe climate change scenario with no carbon subsidy,the economically viable area for biofuels agriculture waspredicted to be just 10 percent of the arable land. Whileall productivity decreased at each climate change scenario,the percentage decrease in canola for biodiesel was almostdouble that of sheep.The approach is a useful one, but the model had severalfundamental problems: The model was based on the growing of biomass feedstocksfor first-generation biofuels only and did notexamine the case for second-generation biofuels. The carbon payments were made to farmers when noreduction in carbon emissions were achieved at the farmlevel, as the same high input crops were grown. Instead,the reduction in carbon emissions is achieved furtheralong the value chain at the point where biofuels replacefossil fuels. This lacks logic. As such, it is unlikely to be apolicy action in Australia under the current government. Rational economics are applied for the adoption offarming systems, which does not include risk or farmers’perception of risk. As stated earlier, the primary reasonfor mixed cropping-grazing systems is the reductionof risk though a balance of enterprises. The predictedrelative productivity decreases from the modelling couldbe interpreted to suggest that farmers may continue tocombine grazing with cropping for food or biofuels toreduce the risks associated with seasonal variations andclimate change, and to utilize the grazing phase of croppingrotations so necessary for re-building soil carbonand subsequent crop productivity.Modifying the approach to address these problems, andinclude feedstocks relevant to new-generation technologies,would be a very useful next step.In a similar piece of research, Bryan, King and Wang,(2010a) modelled, at a landscape scale, the planting ofwoody biomass (SRC eucalypt) over the same area of SouthAustralia and Victoria, again with spatial modelling ofagricultural production and woody biomass plantings underclimate change scenarios. A drawback of this model is theuse of plantations rather than integrating SRC eucalyptalleys into agricultural lands, which has the potential toprovide greater benefits in production and conservation.Economic returns were calculated based on three biomassprices (AUD 30, AUD 40 and AUD 50/t), biomass plantingand maintenance costs, and average agricultural prices andcosts, including those for sheep and wool. In addition, theeffects on dryland salinization, wind erosion and carbonemissions were estimated. Although the economic modelincluded sheep, results were given as agricultural productionwithout identifying changes in production from sheep.The relative value of biomass and agricultural productionvaried with the price assigned to biomass, climatechange scenario and area within the study region, so insome areas, even under moderate climate change, biomassbecame more profitable than agriculture. However, pasture,a predictor of sheep production, was found to be the leastsensitive to climate change.Overall, biomass tended to be more viable than agriculturein marginal agricultural areas. At the landscape scale, it
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BIOFUEL CO-PRODUCTS AS LIVESTOCK FE
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Recommended citationFAO. 2012. Biof
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vco-products to swine - Feeding cru
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viiCHAPTER 22Use of Pongamia glabra
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ixPrefaceHumans are faced with majo
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xiiCBMCBSCCDSCCKCDOCDSCFCFBCFRCGECI
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xivHCHOHClHCNHisH-JPKMHMCHPDDGHPDDG
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xviPPbPCVPDPEMPFADPhePJPKCPKEPKMPKO
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xviiiWBPWCGFWDGWDGSWDGSHWPCWTWWWNSK
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2Biofuel co-products as livestock f
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10Biofuel co-products as livestock
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35Chapter 3Impact of United States
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Impact of United States biofuels co
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101Chapter 6Hydrogen sulphide: synt
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Hydrogen sulphide in cattle fed co-
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155Chapter 8Utilization of crude gl
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Utilization of crude glycerin in be
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209Chapter 11Co-products from biofu
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Co-products from biofuel production
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275Chapter 15Sustainable and compet
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Sustainable and competitive use of
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291Chapter 16Scope for utilizing su
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Scope for utilizing sugar cane baga
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303Chapter 17Camelina sativa in pou
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Camelina sativa in poultry diets: o
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311Chapter 18Utilization of lipid c
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Utilization of lipid co-products of
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351Chapter 21Use of detoxified jatr
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Use of detoxified jatropha kernel m
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379Chapter 22Use of Pongamia glabra
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Use of Pongamia glabra (karanj) and
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403Chapter 23Co-products of the Uni
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Co-products of the United States bi
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523Contributing authorsSouheila Abb
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Contributing authors 525for the Fee
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Contributing authors 527Friederike
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Contributing authors 529Sustainable
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Contributing authors 531During the
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Contributing authors 533(Hons.) and
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