Weather forecasts to enhance an Irish grass growth model

Abstract

Grass growth models have retrospectively predicted grass growth in Ireland using weather observations. However, to predict future grass growth to aid farm management, weather forecasts are necessary inputs. The Moorepark St. Gilles grass growth model (MoSt GGM) is mechanistic and was developed to predict perennial ryegrass growth on any Irish farm. To date, it has used local farm information, (retrospective) weather data and management factors to predict daily paddock-level grass growth. Here, we include weather forecasts in the MoSt GGM and assess its performance through two studies: daily grass growth predictions at four nitrogen fertiliser application levels using weather forecasts up to ten days in advance were compared with those using weather observations; and the GGM predictions for an Irish dairy farm using observed and forecast weather were compared with on-farm grass growth observations from 2013 to 2016. In the first study, all weather inputs captured the rise in grass growth predictions with higher fertiliser application. Based on the Root Mean Squared Error (RMSE), European Centre for Medium-Range Weather Forecasts (ECMWF) forecasts outperformed a forecast based on climatological averages as GGM inputs up to six days in advance, and up to ten days in advance after bias correction. In the second study, ECMWF forecasts were the best weather forecast to predict grass growth since they captured weather variability well and did not require the local weather observations necessary for bias corrections. Weather forecasts are useful inputs to the MoSt GGM, and yield accurate weekly predictions that could aid management decisions.

Grassland Aboveground Biomass Estimation through Assimilating Remote Sensing Data into a Grass Simulation Model

“…It can help decision makers and landowners gain a timely understanding of grassland distribution, growth status and environmental dynamics compared with the same period from previous years, and facilitating the adoption of management measures, or be utilized as important information for agricultural insurance. The method implemented in this study mainly relies on meteorological data [40]. Therefore, it can be applied to other regions with well-calibrated processed-based models.…”

A Review of Precision Technologies for Optimising Pasture Measurement on Irish Grassland

“…Studies have utilised online DST databases to combine grassland management factors with measurement and meteorological data from local weather stations to forecast HM growth rates [128,129]. Romera et al [130] utilised an algorithm to continuously train a model to simulate growth factors between measurement dates on New Zealand dairy pastures.…”

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

“…In Ireland, research has developed a grass growth prediction model for dairy based farming [3,5]. The Moorepark St. Gilles Grass Growth Model, known as the MoSt GG model, is a descriptive model providing insight into grass growth at paddock levels in Ireland.…”

“…This study relates to the improvement of dairy farming efficiency by focusing on sustainable land management and examining grass growth which is one of the cheapest feed sources for livestock in NI [3]. Grass growth rates are variable across the year and depend on various factors, with some of the most influential factors being meteorological e.g., rainfall, solar radiation, and temperature.…”