1. bookVolumen 26 (2022): Edición 1 (January 2022)
Detalles de la revista
Primera edición
26 Mar 2010
Calendario de la edición
2 veces al año
access type Acceso abierto

Feeding Management Optimization in Livestock Farms with Anaerobic Digestion Plant: A Discrete Stochastic Programming (DSP) Model

Publicado en línea: 15 Aug 2022
Volumen & Edición: Volumen 26 (2022) - Edición 1 (January 2022)
Páginas: 587 - 605
Detalles de la revista
Primera edición
26 Mar 2010
Calendario de la edición
2 veces al año

Biogas-based energy production has become a successful strategy for many livestock farms around the world. However, raw materials production is threatened by a growing uncertainty due to effects of climate change on crops cultivation. The aim of this paper is to propose a tool for the optimal design of the biogas mixture, considering respectively the nutritional needs of livestock and the parameters of the biogas process. Within a context of climate variability, a three-stage Discrete Stochastic Programming (DSP) model is applied in a dairy cattle farm with anaerobic digestion plant. This state-contingent approach (DSP model) considers, as uncertain parameters, the watering needs and the yields of forage and energetic crops. The DSP model is compared with equivalent models of expected values to verify the benefits derived from the explicit inclusion of climatic states. The results showed a remarkable improvement in the efficiency of feedstock management, reflecting in a significant reduction in farm costs (11.75 %) compared to the baseline scenario. Whereas, the comparison between the state-contingent approach and the expected value model, showed only slight benefits (0.02 %). This confirms that the DSP model’s ability to offer a better hedged solution increases when high climate variability affects crop yields and irrigation needs.


[1] Reisinger A., Clark H. How much do direct livestock emissions actually contribute to global warming? Global Change Boil. 2018:24(4):1749–1761. https://doi.org/10.1111/gcb.13975 Search in Google Scholar

[2] Salem Ali O. A. A., Verdini L., De Mastro G. Effect of different crop management systems on rainfed durum wheat greenhouse gas emissions and carbon footprint under Mediterranean conditions. J. Clean. Prod. 2016:140(2):608–621. https://doi.org/10.1016/j.jclepro.2016.04.135 Search in Google Scholar

[3] Intergovernmental Panel on Climate Change. Climate Change 2013 – The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 2014. Search in Google Scholar

[4] Gerber P. J., et al. Tackling climate change through livestock – A global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO): Rome, 2013. Search in Google Scholar

[5] Dono G., et al. Adapting to uncertainty associated with short-term climate variability changes in irrigated Mediterranean farming systems. Agric. Syst. 2013:117:1–12. https://doi.org/10.1016/j.agsy.2013.01.005 Search in Google Scholar

[6] Dono G., et al. An Integrated Assessment of the Impacts of Changing Climate Variability on Agricultural Productivity and Profitability in an Irrigated Mediterranean Catchment. Water Resour. Manag. 2013:27(10):3607–3622. https://doi.org/10.1007/s11269-013-0367-3 Search in Google Scholar

[7] Bernabucci U., et al. The effects of heat stress in Italian Holstein dairy cattle. J. Dairy Sci. 2014:97:471–486. https://doi.org/10.3168/jds.2013-6611 Search in Google Scholar

[8] Mittenzwei K., et al. Combined effects of climate change and policy uncertainty on the agricultural sector in Norway. Agric. Syst. 2017:153:118–126. https://doi.org/10.1016/j.agsy.2017.01.016 Search in Google Scholar

[9] Godfray H. C. J., et al. Food security: the challenge of feeding 9 billion people. Science 2010:327:812–818. https://doi.org/10.1126/science.1185383 Search in Google Scholar

[10] Food and Agriculture Organization (FAO). FAOSTAT Statistical Database of the United Nation Food and Agriculture Organization Statistical Division. Rome [Online]. [Accessed 11.03.2022]. Available: http://faostat.fao.org/site/339/default.aspx Search in Google Scholar

[11] European Commission. Communication from the Commission EUROPE 2020. A strategy for smart, sustainable and inclusive growth. Com (2010) 2020, Brussels, Commission of the European Communities, 2020. Search in Google Scholar

[12] Haas R., et al. Efficiency and effectiveness of promotion systems for electricity generation from renewable energy sources – Lessons from EU countries. Energy 2011:36(4):2186–2193. https://doi.org/10.1016/j.energy.2010.06.028 Search in Google Scholar

[13] Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the Promotion of the Use of Energy from Renewable Sources and Amending and Subsequently Repealing Directives 2001/77/EC and 2003/30/EC. Official Journal of the European Union 2009:L140/16. Search in Google Scholar

[14] Weiland P. Biogas production: current state and perspectives. Appl. Microbiol. Biotechnol. 2010:85:849–860. https://doi.org/10.1007/s00253-009-2246-7 Search in Google Scholar

[15] Khalid A., et al. The anaerobic digestion of solid organic waste. Waste Manag. 2011:31(8):1737–1744. https://doi.org/10.1016/j.wasman.2011.03.021 Search in Google Scholar

[16] Bacenetti J., et al. Mitigation strategies in the agro-food sector: The anaerobic digestion of tomato purée by-products. An Italian case study. Sci. Total Environ. 2015:526:88–97. https://doi.org/10.1016/j.scitotenv.2015.04.069 Search in Google Scholar

[17] Bacenetti J., et al. Agricultural anaerobic digestion plants: What LCA studies pointed out and what can be done to make them more environmentally sustainable. Appl. Energy. 2016:179:669–686. https://doi.org/10.1016/j.apenergy.2016.07.029 Search in Google Scholar

[18] Lijó L., et al. Life Cycle Assessment of electricity production in Italy from anaerobic co-digestion of pig slurry and energy crops. Renew. Energy. 2014:68:625–635. https://doi.org/10.1016/j.renene.2014.03.005 Search in Google Scholar

[19] Venanzi S., et al. Use of agricultural by-products in the development of an agro-energy chain: A case study from the Umbria region. Sci. Total Environ. 2018:627:494–505. https://doi.org/10.1016/j.scitotenv.2018.01.176 Search in Google Scholar

[20] Bacenetti J., et al. Anaerobic digestion of different feedstocks: impact on energetic and environmental balances of biogas process. Sci. Total Environ. 2013:463–464:541–551. https://doi.org/10.1016/j.scitotenv.2013.06.058 Search in Google Scholar

[21] Torquati B., et al. Environmental Sustainability and Economic Benefits of Dairy Farm Biogas Energy Production: A Case Study in Umbria. Sustainability 2014:6(10):6696–6713. https://doi.org/10.3390/su6106696 Search in Google Scholar

[22] Tilman D., et al. Agricultural sustainability and intensive production practices. Nature 2002:418(6898):671–677. https://doi.org/10.1038/nature01014 Search in Google Scholar

[23] Sauerbrei R., et al. Increased energy maize production reduces farmland bird diversity. GCB Bioenergy 2014:6(3):265–274. https://doi.org/10.1111/gcbb.12146 Search in Google Scholar

[24] Negri M., et al. Evaluation of methane production from maize silage by harvest of different plant portions. Biomass Bioenergy 2014:67:339–346. https://doi.org/10.1016/j.biombioe.2014.05.016 Search in Google Scholar

[25] Massé D. I., Talbot G., Gilbert Y. On farm biogas production: A method to reduce GHG emissions and develop more sustainable livestock operations. Anim Feed Sci Technol. 2011:166–167:436–445. https://doi.org/10.1016/j.anifeedsci.2011.04.075 Search in Google Scholar

[26] Lesteur M., et al. Alternative methods for determining anaerobic biodegradability: A review. Process Biochem. 2010:45(4):431–440. https://doi.org/10.1016/j.procbio.2009.11.018 Search in Google Scholar

[27] Alatriste-Mondragón F., et al. Anaerobic codigestion of municipal, farm, and industrial organic wastes: a survey of recent literature. Water Environ. Res. 2006:78(6):607–636. https://doi.org/10.2175/106143006X111673 Search in Google Scholar

[28] Ashekuzzaman S. M., Poulsen T. G. Optimizing feed composition for improved methane yield during anaerobic digestion of cow manure based waste mixtures. Bioresour. Technol. 2011:102(3):2213–2218. https://doi.org/10.1016/j.biortech.2010.09.118 Search in Google Scholar

[29] Ba B. H., Prins C., Prodhon C. Models for optimization and performance evaluation of biomass supply chains: An Operations Research perspective. Renew. Energy 2016:87:977–989. https://doi.org/10.1016/j.renene.2015.07.045 Search in Google Scholar

[30] Møller H. B., Sommer S. G., Ahring B. K. Methane productivity of manure, straw and solid fractions of manure. Biomass and Bioenergy 2004:26(5):485–495. https://doi.org/10.1016/j.biombioe.2003.08.008 Search in Google Scholar

[31] Wolf C., McLoone S., Bongards M. Biogas plant optimization using genetic algorithms and particle swarm optimization. Proceedings of the Signals and Systems Conference 2008:244:249. Search in Google Scholar

[32] Wei X, Kusiak A. Optimization of biogas production process in a wastewater treatment plant. Proceedings of the 2012 Industrial and Systems Engineering Research Conference 2012:1–9. Search in Google Scholar

[33] Qdais H. A., et al. Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm. Resources, Resour Conserv Recy. 2010:54(6):359–363. https://doi.org/10.1016/j.resconrec.2009.08.012 Search in Google Scholar

[34] Wang X., et al. Optimizing feeding composition and carbon-nitrogen ratios for improved methane yield during anaerobic co-digestion of dairy, chicken manure and wheat straw. Bioresour Technol. 2012:120:78–83. https://doi.org/10.1016/j.biortech.2012.06.058 Search in Google Scholar

[35] García-Gen S., Rodríguez J., Lema J. M. Optimisation of substrate blends in anaerobic co-digestion using adaptive linear programming. Bioresour. Technol. 2014:173:159–167. https://doi.org/10.1016/j.biortech.2014.09.089 Search in Google Scholar

[36] Alvarez J. A., Otero L., Lema J. M. A methodology for optimising feed composition for anaerobic co-digestion of agroindustrial wastes. Bioresour. Technol. 2010:101(4):1153–1158. https://doi.org/10.1016/j.biortech.2009.09.061 Search in Google Scholar

[37] Dono G., et al. Income impacts of climate change: irrigated farming in the Mediterranean and expected changes in probability of favorable and adverse weather conditions. German J. Agric. Econ. 2014:63(3):177–186. Search in Google Scholar

[38] Liu Y., Tao F. Probabilistic Change of Wheat Productivity and Water Use in China for Global Mean Temperature Changes of 1°, 2°, and 3° C. J Appl Meteorol Climatol. 2013:52(1):114–129. https://doi.org/10.1175/jamc-d-12-039.1 Search in Google Scholar

[39] Steidl J., et al. Expansion of an Existing Water Management Model for the Analysis of Opportunities and Impacts of Agricultural Irrigation under Climate Change Conditions. Water 2015:7(11):6351–6377. https://doi.org/10.3390/w7116351 Search in Google Scholar

[40] Crean J., et al. Representing climatic uncertainty in agricultural models – an application of state-contingent theory. Agric Resour Econ. 2013:57(3):359–378. https://doi.org/10.1111/1467-8489.12005 Search in Google Scholar

[41] Dono G., et al. Winners and losers from climate change in agriculture: Insights from a case study in the Mediterranean basin. Agricultural Systems 2016:147:65–75. https://doi.org/10.1016/j.agsy.2016.05.013 Search in Google Scholar

[42] McCarl B. A., Spreen T. H. Applied mathematical programming using algebraic systems. 1997 [Online]. [Accessed 11.03.2022]. Available: https://agecon2.tamu.edu/people/faculty/mccarl-bruce/books.htm Search in Google Scholar

[43] Calatrava J., Garrido A. Modelling water markets under uncertain water supply. Eur. Rev. Agric. Econ. 2005:32(2):119–142. https://doi.org/10.1093/eurrag/jbi006 Search in Google Scholar

[44] Cortignani R., Dono G. Agricultural policy and climate change: An integrated assessment of the impacts on an agricultural area of Southern Italy. Environ. Sci. Policy 2008:81:26–35. https://doi.org/10.1016/j.envsci.2017.12.003 Search in Google Scholar

[45] Dono G., et al. Winners and losers from climate change in agriculture: Insights from a case study in the Mediterranean basin. Agric. Syst. 2016:147:65–75. https://doi.org/10.1016/j.agsy.2016.05.013 Search in Google Scholar

[46] Flaten O., Lien G. Stochastic utility-efficient programming of organic dairy farms. Eur. J. Oper Res. 2007:181(3):1574–1583. https://doi.org/10.1016/j.ejor.2005.11.053 Search in Google Scholar

[47] Schievano A., et al. Predicting anaerobic biogasification potential of ingestates and digestates of a full-scale biogas plant using chemical and biological parameters. Bioresour Technol. 2008:99(17):8112–8117. https://doi.org/10.1016/j.biortech.2008.03.030 Search in Google Scholar

[48] Nasir I. M., Mohd Ghazi T. I., Omar R. Anaerobic digestion technology in livestock manure treatment for biogas production: A review. Eng. Life Sci. 2012:3:258–269. https://doi.org/10.1002/elsc.201100150 Search in Google Scholar

[49] Zhai N., et al. Effect of initial pH on anaerobic co-digestion of kitchen waste and cow manure. Waste Manag. 2015:38:126–131. https://doi.org/10.1016/j.wasman.2014.12.027 Search in Google Scholar

[50] Maragkaki A. E., et al. Pilot-scale anaerobic co-digestion of sewage sludge with agro-industrial by-products for increased biogas production of existing digesters at wastewater treatment plants. Waste Manag. 2017:59:362–370. https://doi.org/10.1016/j.wasman.2016.10.043 Search in Google Scholar

[51] Pezzolla D., et al. Optimization of solid-state anaerobic digestion through the percolate recirculation. Biomass Bioenergy 2017:96:112–118. https://doi.org/10.1016/j.biombioe.2016.11.012 Search in Google Scholar

[52] Di Domenica N., et al. Scenario generation for stochastic programming and simulation: a modelling perspective. IMA J. Manag. Math. 2007:20(1):1–38. https://doi.org/10.1093/imaman/dpm027 Search in Google Scholar

[53] Madansky A., Inequalities for Stochastic Linear Programming Problems. Management Sci. 1960:6(2):197–204. https://doi.org/10.1287/mnsc.6.2.197 Search in Google Scholar

[54] De Menna F., et al. Optimization of agricultural biogas supply chains using artichoke byproducts in existing plants. Agric. Syst. 2018:165:137–146. https://doi.org/10.1016/j.agsy.2018.06.008 Search in Google Scholar

[55] Pastare L., Romagnoli F. Life Cycle Cost Analysis of Biogas Production from Cerathophyllum demersum, Fucus vesiculosus and Ulva intestinalis in Latvian Conditions. Environ. Clim. Technol. 2019:23(2):258–271. https://doi.org/10.2478/rtuect-2019-0067 Search in Google Scholar

[56] Dzene I., Bodescu F. Evaluation of Biomass Availability for Biogas Production at Regional Level. Environ. Clim. Technol. 2010:3(3):54–62. https://doi.org/10.2478/v10145-009-0007-6 Search in Google Scholar

[57] Schievano A., D’Imporzano G., Adani F. Substituting energy crops with organic wastes and agro-industrial residues for biogas production. J. Environ. Manage. 2009:90(8):2537–2541. https://doi.org/10.1016/j.jenvman.2009.01.013 Search in Google Scholar

[58] Auburger S., et al. Economic optimization of feedstock mix for energy production with biogas technology in Germany with a special focus on sugar beets – Effects on greenhouse gas emissions and energy balances. Renew. Energy. 2016:89:1–11. https://doi.org/10.1016/j.renene.2015.11.042 Search in Google Scholar

Artículos recomendados de Trend MD

Planifique su conferencia remota con Sciendo