## 1 Introduction  
In Ireland, the dominant agricultural land use is grasslands (91%) with croplands representing less than 10% of the agriculture land [1]. The predominance of grassland is largely due to the near year-round conditions favorable for grass growth, associated with the mild maritime climate experienced here, which results in lower input costs for dairy production over other agricultural systems. Consequently, crop production is typically confined to the lighter soils and warmer and drier conditions associated with the east and south-east of Ireland. The main tillage area is dominated by cereal crops Spring Barley (_Hordeum vulgare_ L.; hereafter referred to as SB) and Winter wheat (_Tritcium aestivum_ L.; WW). SB provides a valuable source for animal feed and the malting industries with a land area of 128,720 ha and an average yield of 7.2 t-ha\({}^{-1}\)[2] and WW is the most economically viable crop grown here with an area of 60,400 ha and an average yield of 9.5 t-ha\({}^{-1}\)[3]. Winter oilseed rape (_Brassica napus_ L.; WOSR) is a more marginal crop, covering approximately 8500 hectares, with an average yield of 4.0 t-ha\({}^{-1}\), and is typically grown as a break crop for WW. While the dominant contribution from agriculture to Ireland's natural greenhouse gas emissions (GHG) comes from the dairy sector, reflecting its dominance in terms of area and economic activity, croplands represent a relatively small GHG source, mainly due to activities associated with cultivation and fertilizer use [4; 5]. In spite of the relatively small impact to the overall national emissions, this sector has potential to make an important contribution to reducing agricultural emissions. While process-based crop models are potentially useful tools for quantifying both production and carbon budgets [6; 7; 8; 9], and how these relate to weather, climate and environmental conditions, there are challenges related to the application of these models at the regional level. These challenges are further compounded by a lack of readily available in situ data in order calibrate and evaluate these models. A number of crop models have been developed, which vary in terms of their driving mechanisms and complexity [10]. For example, GECROS [11] and SUCROS [12] are illustrative of carbon-focused models; radiation-based models include SAFY [13], SAFY-WB [14], STICS [15], CERES [16], and water focused models include AqYield [17] and Aquacrop [18]. This list is not exhaustive but represents some of the main crop models currently available for use, depending on application. In addition, these models are generally employed as research tools and require a level of expertise in order to employ them. Models can be further classified depending on whether they simulate a crop using a generic approach or those that are specific to certain crops. For example, Aquacrop is a generic crop growth model that utilizes crop specific parameters. In contrast, SuMoToRI [19] and LINTUL-BRASNAP [20] are specific to a single crop type, namely WOSR. While the more general crop models, such as STICS and Aquacrop, have found widespread use they are relatively complex models and require a number of parameters to be calibrated prior to application; therefore their suitability for use at the regional level or in potentially data sparse regions is challenging [21; 22]. As an alternative, simple semi-empirical models, such as the Simple Algorithm for Yield Estimate (SAFY) model [14], which require a reduced number of inputs and parameters, offer potential for more widespread use and application, can be readily applied, and are potentially suitable for scaling to the regional level. However, similar to all models, they require appropriate testing and evaluation. While a number of empirical and semi-empirical based models have been developed for specific crop types [23; 24; 25; 26], the development of a 'common' model framework which is readily adaptable has been missing, therefore limiting their widespread use in new locations and/or for different crop types.