A Machine Learning-Based Assessment of Maize Silage Dry Matter Losses by Net-Bags Buried in Farm Bunker Silos
Estimating the dry matter losses (DML) of whole-plant maize (WPM) silage is a priority for sustainable dairy and beef farming. The study aimed to assess this loss of nutrients by using net-bags ( n = 36) filled with freshly chopped WPM forage and buried in bunker silos of 12 Italian dairy farms for...
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MDPI AG
2022
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ftdoajarticles:oai:doaj.org/article:0200307f4fe7446c99f815261790638c 2023-05-15T16:01:23+02:00 A Machine Learning-Based Assessment of Maize Silage Dry Matter Losses by Net-Bags Buried in Farm Bunker Silos Severino Segato Giorgio Marchesini Luisa Magrin Barbara Contiero Igino Andrighetto Lorenzo Serva 2022-05-01T00:00:00Z https://doi.org/10.3390/agriculture12060785 https://doaj.org/article/0200307f4fe7446c99f815261790638c EN eng MDPI AG https://www.mdpi.com/2077-0472/12/6/785 https://doaj.org/toc/2077-0472 doi:10.3390/agriculture12060785 2077-0472 https://doaj.org/article/0200307f4fe7446c99f815261790638c Agriculture, Vol 12, Iss 785, p 785 (2022) maize silage porosity density dry matter loss bunker silo machine learning Agriculture (General) S1-972 article 2022 ftdoajarticles https://doi.org/10.3390/agriculture12060785 2022-12-30T21:36:28Z Estimating the dry matter losses (DML) of whole-plant maize (WPM) silage is a priority for sustainable dairy and beef farming. The study aimed to assess this loss of nutrients by using net-bags ( n = 36) filled with freshly chopped WPM forage and buried in bunker silos of 12 Italian dairy farms for an ensiling period of 275 days on average. The proximate composition of harvested WPM was submitted to mixed and polynomial regression models and a machine learning classification tree to estimate its ability to predict the WPM silage losses. Dry matter (DM), silage density, and porosity were also assessed. The WPM harvested at over 345 (g kg −1 ) and a DM density of less than 180 (kg of DM m −3 ) was related to DML values of over 7%. According to the results of the classification tree algorithm, the WPM harvested (g kg −1 DM) at aNDF higher than 373 and water-soluble carbohydrates lower than 104 preserves for the DML of maize silage. It is likely that the combination of these chemical variables determines the optimal maturity stage of WPM at harvest, allowing a biomass density and a fermentative pattern that limits the DML, especially during the ensiling period. Article in Journal/Newspaper DML Directory of Open Access Journals: DOAJ Articles Agriculture 12 6 785 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
maize silage porosity density dry matter loss bunker silo machine learning Agriculture (General) S1-972 |
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maize silage porosity density dry matter loss bunker silo machine learning Agriculture (General) S1-972 Severino Segato Giorgio Marchesini Luisa Magrin Barbara Contiero Igino Andrighetto Lorenzo Serva A Machine Learning-Based Assessment of Maize Silage Dry Matter Losses by Net-Bags Buried in Farm Bunker Silos |
topic_facet |
maize silage porosity density dry matter loss bunker silo machine learning Agriculture (General) S1-972 |
description |
Estimating the dry matter losses (DML) of whole-plant maize (WPM) silage is a priority for sustainable dairy and beef farming. The study aimed to assess this loss of nutrients by using net-bags ( n = 36) filled with freshly chopped WPM forage and buried in bunker silos of 12 Italian dairy farms for an ensiling period of 275 days on average. The proximate composition of harvested WPM was submitted to mixed and polynomial regression models and a machine learning classification tree to estimate its ability to predict the WPM silage losses. Dry matter (DM), silage density, and porosity were also assessed. The WPM harvested at over 345 (g kg −1 ) and a DM density of less than 180 (kg of DM m −3 ) was related to DML values of over 7%. According to the results of the classification tree algorithm, the WPM harvested (g kg −1 DM) at aNDF higher than 373 and water-soluble carbohydrates lower than 104 preserves for the DML of maize silage. It is likely that the combination of these chemical variables determines the optimal maturity stage of WPM at harvest, allowing a biomass density and a fermentative pattern that limits the DML, especially during the ensiling period. |
format |
Article in Journal/Newspaper |
author |
Severino Segato Giorgio Marchesini Luisa Magrin Barbara Contiero Igino Andrighetto Lorenzo Serva |
author_facet |
Severino Segato Giorgio Marchesini Luisa Magrin Barbara Contiero Igino Andrighetto Lorenzo Serva |
author_sort |
Severino Segato |
title |
A Machine Learning-Based Assessment of Maize Silage Dry Matter Losses by Net-Bags Buried in Farm Bunker Silos |
title_short |
A Machine Learning-Based Assessment of Maize Silage Dry Matter Losses by Net-Bags Buried in Farm Bunker Silos |
title_full |
A Machine Learning-Based Assessment of Maize Silage Dry Matter Losses by Net-Bags Buried in Farm Bunker Silos |
title_fullStr |
A Machine Learning-Based Assessment of Maize Silage Dry Matter Losses by Net-Bags Buried in Farm Bunker Silos |
title_full_unstemmed |
A Machine Learning-Based Assessment of Maize Silage Dry Matter Losses by Net-Bags Buried in Farm Bunker Silos |
title_sort |
machine learning-based assessment of maize silage dry matter losses by net-bags buried in farm bunker silos |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/agriculture12060785 https://doaj.org/article/0200307f4fe7446c99f815261790638c |
genre |
DML |
genre_facet |
DML |
op_source |
Agriculture, Vol 12, Iss 785, p 785 (2022) |
op_relation |
https://www.mdpi.com/2077-0472/12/6/785 https://doaj.org/toc/2077-0472 doi:10.3390/agriculture12060785 2077-0472 https://doaj.org/article/0200307f4fe7446c99f815261790638c |
op_doi |
https://doi.org/10.3390/agriculture12060785 |
container_title |
Agriculture |
container_volume |
12 |
container_issue |
6 |
container_start_page |
785 |
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1766397272554209280 |