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Assessment of Maize Silage Quality under Different Pre-Ensiling Conditions

Author

Listed:
  • Lorenzo Serva

    (Department of Animal Medicine, Production and Health, University of Padova, 35020 Legnaro, Italy)

  • Igino Andrighetto

    (Department of Animal Medicine, Production and Health, University of Padova, 35020 Legnaro, Italy)

  • Severino Segato

    (Department of Animal Medicine, Production and Health, University of Padova, 35020 Legnaro, Italy)

  • Giorgio Marchesini

    (Department of Animal Medicine, Production and Health, University of Padova, 35020 Legnaro, Italy)

  • Maria Chinello

    (KWS Italia S.P.A., 47122 Toscana, Italy)

  • Luisa Magrin

    (Department of Animal Medicine, Production and Health, University of Padova, 35020 Legnaro, Italy)

Abstract

Maize silage suffers from several factors that affect the final quality and, to some extent, pre-ensiled conditions that can be potentially tuned during harvesting. After assessing new indices for silage quality under lab-scale conditions, several trials have been conducted to find associations between fresh maize characteristics and silage features. Among the first, we included field input levels, FAO class, maturity stage, use of bacterial inoculants, sealing delay and chemical traits, whereas, among the latter, we assessed density and porosity, pH, fermentative profile, dry matter loss and aerobic stability. The trials were conducted using vacuum bags or mini silo buckets. More than 1500 maize samples harvested in Northeast Italy were analysed during the 2016–2022 period. Moreover, to evaluate silage aerobic stability, the fermentative profile and temperature were measured 14 days after the opening of the silo. The association between silage quality and aerobic stability was assessed, and a prognostic risk score was used to calculate the probability of aerobic instability. The dataset could provide baseline information to promote the continuous improvement of maize silage management from different botanical and crop fields, thus improving agronomic and animal farm resource allocation from a precision agriculture perspective.

Suggested Citation

  • Lorenzo Serva & Igino Andrighetto & Severino Segato & Giorgio Marchesini & Maria Chinello & Luisa Magrin, 2023. "Assessment of Maize Silage Quality under Different Pre-Ensiling Conditions," Data, MDPI, vol. 8(7), pages 1-8, July.
  • Handle: RePEc:gam:jdataj:v:8:y:2023:i:7:p:117-:d:1185332
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    References listed on IDEAS

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    1. Severino Segato & Giorgio Marchesini & Luisa Magrin & Barbara Contiero & Igino Andrighetto & Lorenzo Serva, 2022. "A Machine Learning-Based Assessment of Maize Silage Dry Matter Losses by Net-Bags Buried in Farm Bunker Silos," Agriculture, MDPI, vol. 12(6), pages 1-10, May.
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