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dc.contributor.authorNikoloski, Stevanche
dc.contributor.authorMurphy, Philip
dc.contributor.authorKocev, Dragi
dc.contributor.authorDžeroski, Sašo
dc.contributor.authorWall, David
dc.date.accessioned2020-08-04T11:45:50Z
dc.date.available2020-08-04T11:45:50Z
dc.date.issued2019-08-22
dc.identifier.citationNikoloski, S., Murphy, P., Kocev, D., Džeroski, S. and Wall, D. (2019). Using machine learning to estimate herbage production and nutrient uptake on Irish dairy farms. Journal of Dairy Science. 102(11),10639-10656. doi: https://dx.doi.org/10.3168/jds.2019-16575en_US
dc.identifier.issn0022-0302
dc.identifier.urihttp://hdl.handle.net/11019/2230
dc.descriptionpeer-revieweden_US
dc.description.abstractNutrient management on grazed grasslands is of critical importance to maintain productivity levels, as grass is the cheapest feed for ruminants and underpins these meat and milk production systems. Many attempts have been made to model the relationships between controllable (crop and soil fertility management) and noncontrollable influencing factors (weather, soil drainage) and nutrient/productivity levels. However, to the best of our knowledge not much research has been performed on modeling the interconnections between the influencing factors on one hand and nutrient uptake/herbage production on the other hand, by using data-driven modeling techniques. Our paper proposes to use predictive clustering trees (PCT) learned for building models on data from dairy farms in the Republic of Ireland. The PCT models show good accuracy in estimating herbage production and nutrient uptake. They are also interpretable and are found to embody knowledge that is in accordance with existing theoretical understanding of the task at hand. Moreover, if we combine more PCT into an ensemble of PCT (random forest of PCT), we can achieve improved accuracy of the estimates. In practical terms, the number of grazings, which is related proportionally with soil drainage class, is one of the most important factors that moderates the herbage production potential and nutrient uptake. Furthermore, we found the nutrient (N, P, and K) uptake and herbage nutrient concentration to be conservative in fields that had medium yield potential (11 t of dry matter per hectare on average), whereas nutrient uptake was more variable and potentially limiting in fields that had higher and lower herbage production. Our models also show that phosphorus is the most limiting nutrient for herbage production across the fields on these Irish dairy farms, followed by nitrogen and potassium.en_US
dc.language.isoenen_US
dc.publisherElsevier for American Dairy Science Associationen_US
dc.relation.ispartofseriesJournal of Dairy Science;
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectnutrient uptakeen_US
dc.subjectherbage productionen_US
dc.subjectpredictive clustering treesen_US
dc.subjectrandom foresten_US
dc.titleUsing machine learning to estimate herbage production and nutrient uptake on Irish dairy farmsen_US
dc.typeArticleen_US
dc.embargo.terms2020-08-22en_US
dc.identifier.doihttps://dx.doi.org/10.3168/jds.2019-16575
dc.contributor.sponsorTeagasc Walsh Fellowship Programmeen_US
dc.contributor.sponsorEuropean Unionen_US
dc.contributor.sponsorGrantNumber635201en_US


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