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

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.

Recent advances in decision support for beef and dairy farming: modeling approaches and opportunities

“…Cecchini et al. (2022) and Rojas‐Downing et al. (2018) consider climate variability, while Worden and Hailu (2020) consider uncertainty in genomic selection and breeding.…”

“…Most of the whole farm optimization models are deterministic, with only three identified incorporating stochastic features (Rojas‐Downing et al., 2018; Worden and Hailu, 2020; Cecchini et al., 2022). Cecchini et al.…”

“…A frequent problem in this regard is how to balance onfarm land use with livestock production requirements for increasing economic performance (see Flaten et al, 2019a, and other studies occurring under both "crops, forage, pasture" and "feed" in Table 8). Less frequently, but worthy of mention, are problems on decisions about what type of farm to run (Mosnier et al, 2017;Diakité et al, 2019aDiakité et al, , 2019bDominati et al, 2019;Rendel et al, 2020;Kokemohr et al, 2022), biosecurity measures (Shrestha et al, 2018), the use of technology (Kuhn et al, 2019;Worden and Hailu, 2020;Kleftodimos et al, 2022), and how to dispose or make use of materials that represent an incidental side effect of primary production or byproducts (Kuhn et al, 2019;Taifouris and Martin, 2021;Cecchini et al, 2022;Kokemohr et al, 2022). Another topic worthy of mention is how to improve animal welfare through decisions relating to cow-calf contact, as exemplified by Asheim et al (2016) (see, e.g., Hansen et al, 2023, for further insight to the topic).…”

“…, Cecchini et al (2022), Pečnik and Žgajnar (2022), Kleftodimos et al (2022), Singh and Banerjie (2021), Taifouris and Martin (2021), Ouellet et al (2021), Bussoni et al (2021), Allison et al (2021), Bellingeri et al (2020)…”

“…Most of the whole farm optimization models are deterministic, with only three identified incorporating stochastic features (Rojas-Downing et al, 2018;Worden and Hailu, 2020;Cecchini et al, 2022). Cecchini et al (2022) and Rojas-Downing et al ( 2018) consider climate variability, while Worden and Hailu (2020) consider uncertainty in genomic selection and breeding.…”