Data-Driven Classifiers for Predicting Grass Growth in Northern Ireland: A Case Study

Abstract

There are increasing pressures to combat climate change and improve sustainable land management. The agriculture industry is one of the most challenging areas for these changes, especially in Northern Ireland, as agriculture is one of the larger industries. Research has been carried out across the island of Ireland into methods of improving farm efficiency in multiple areas of farming, including livestock health, machinery improvements, and crop growth. Research has been carried out in this study into grass growth in the dairy farming sector, specifically within Northern Ireland. Grass growth prediction aims to inform farmers and policy makers in their decision-making process regarding sustainable land management in agriculture. The present work focuses on analysing and evaluating how data-driven classifiers can be used for grass growth prediction using the data related to soil content, weather, grass quality components etc. Four classifiers, namely Decision Trees, Random Forest, Naïve Bayes, and Neural Networks, are chosen for this purpose. Classification results based on a real-world data set are analysed and compared to evaluate and illustrate the performance and robustness of the classifiers. The results indicate that it is difficult to declare a single classifier with the highest performance and robustness. Nevertheless, it indicates that tree classification methods are better suited to the data to be studied, as opposed to probabilistic methods and weighted methods, e.g., the naïve Bayes classifier obtained a predictive performance of 78% when classifying spring seasonal grass growth data.

Phenotypic Variation from Waterlogging in Multiple Perennial Ryegrass Varieties under Climate Change Conditions

“…The growth chamber climatological baselines were constructed from the last thirty years of meteorological data (1989 – 2018) collated from the meteorological station located at Cork Airport and publicly accessible via the Irish Meteorological Services (Met Éireann). The entire experiment simulated conditions from May to September but only climatological conditions replicating two months of optimal pasture growth (June and July) (McHugh et al, 2020) were used to investigate perennial ryegrass responses to waterlogging ( Table 1 ). Four common internationally grown varieties of perennial ryegrass were used for the experiment: Aberchoice, Abergain, Carraig and Dunluce (European Commission, 2019).…”

Phenotypic variation from waterlogging in multiple perennial ryegrass varieties under climate change conditions

“…The growth chamber climatological baselines (ambient conditions) were constructed from the last thirty years of meteorological data (1989–2018) collated from the meteorological station located at Cork Airport and publicly accessible via the Irish Meteorological Services (Met Éireann). The entire experiment simulated conditions from May to September but only climatological conditions replicating two months of optimal pasture growth (June and July) ( McHugh et al, 2020 ) were used to investigate perennial ryegrass responses to waterlogging ( Table 1 ).…”

Estimation of Harvest Time of Forage Sorghum (Sorghum Bicolor) CV. Samurai-2 Using Decision Tree Algorithm

“…According to Sitanggang & Ismail (2011), two widely used decision tree algorithms are Quinlan's ID3 and C4.5, which is an extension of ID3 and CART (Classification and Regression Tree), respectively. Decision trees have been employed in several agricultural and animal husbandry studies (Maxwell et al, 2018;Klompenburg et al, 2020;McHugh et al, 2020). According to Larose (2014), the two leading algorithms for building decision trees are the CART and C4.5.…”