Abstract
Nutrient 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.
Highlights
Grasslands make a significant contribution to food security through providing part of the feed requirements of ruminants used for meat and milk production
We used single-target regression predictive clustering trees (PCT) to estimate herbage production using the available S, E, and M variables to investigate the main drivers of herbage production on Irish dairy farms
We started with an interpretation of the tree that estimates the herbage production potential given the S, E, and M attributes, [i.e., herbage production = f (S, E, M), which means that the created trees construct the f function that outputs the predictions for herbage production and uses the S, E, and M attributes as an input] learned from the entire data set (Figure 1)
Summary
Grasslands make a significant contribution to food security through providing part of the feed requirements of ruminants used for meat and milk production. The utilization of grass by grazing should provide a sustainable basis for livestock production systems, as grazed grass is the cheapest source of nutrients for ruminants (O’Donovan et al, 2011). With feed cost accounting for more than 75% of the total variable costs on these livestock farms (Connolly et al, 2010), the production of sufficient grass for the grazing herd has a significant effect on farm profitability (Shalloo et al, 2004; Finneran et al, 2010). From 2013 to 2015, average levels of grass DM production on intensive dairy farms measuring grass in Ireland ranged from 8.0 to 18.5 t/ha (O’Leary et al, 2016). Grass production between and within farms can vary widely depending on several soil-, climate-, and managementrelated factors
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
