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<p>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.</p>
</abstract>

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.

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

Department of Agricultural, Food and Environmental Sciences

Department of Civil and Environmental Engineering

Environmental and Climate Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Biogas-based energy production has become a successful strategy for many livestock farms around the world. However, raw materials production is threatened by a...

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

Published Online: Aug 15, 2022

Page range: 587 - 605

DOI: https://doi.org/10.2478/rtuect-2022-0045

Keywords
Biogas, by-products, climate uncertainty, livestock, stochastic programming

© 2022 Lucio Cecchini et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Published Online: Aug 15, 2022

Page range: 587 - 605

DOI: https://doi.org/10.2478/rtuect-2022-0045

KeywordsBiogas, by-products, climate uncertainty, livestock, stochastic programming

© 2022 Lucio Cecchini et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Biogas, by-products, climate uncertainty, livestock, stochastic programming