## 2 Data Collection and Preprocessing  
In this section, the research methodology is described, and the process of building a basic database, including data collection, are presented. Our research methodology is described below. The goal of our study is to predict forage crop production and build an electronic climate map according to climate change. A framework of the research methodology process is shown in Figure 2. The flow process is as follows: (1) the collection of pasture/fodder crop production data, (2) the collection of climate data according to the year and region of the collected production data, (3) the selection of the most influential climate elements in response to the production data, (4) the creation of various regression models and a comparison analysis of the results, (5) the establishment of a production prediction model with climate factors that affect each feed crop, and (6) the construction of an electronic climate map, accordingly. This can serve as basis for the movement of suitable cultivation areas for feed crops due to climate change in the Korean Peninsula, and is planned to be used for farming guidance for livestock farmers in Republic of Korea. To analyze the impact of climate on grasses and forage crops resulting from climate change, to predict production, and to figure out shifts in the suitable cultivation areas, it is necessary to analyze forage crop production data and climate data. While the IRG data and forage data were collected through literature reviews and experimental reports from the Korea Institute of Animal Science and Technology, for climate data collection, we developed a climate data crawler using Python 3.10.7. This study was conducted in collaboration with the Korea Institute of Animal Science and Technology. To assess the vulnerability of forage crops to climate change, production data, with labels of the region and year as referencing labels, as well as the climate data of the corresponding region and year could be crawled. The climate data crawler implemented using Python carries out to crawl the climate data obtained from the Korea Meteorological Administration's Automatic Weather Station (AWS) and synoptic weather observation system, the Automated Surface Observing System (ASOS). The procedures to collect the climate data were as follows: First, the type of climate data and the date of the climate data to be collected were selected from the Start menu of the climate data crawler. After that, a region in the 'Configuration' tab was selected and the 'Start' button for crawling was clicked. Climate data for the chosen region and year were then collected. The data were saved as a CSV file. The collected climate data and forage crop production data were merged to establish a basic dataset, which was necessary for the analysis. Recently, there has been a growing interest in Italian ryegrass as a forage crop that can be cultivated in Republic of Korea [3; 13]. Italian ryegrass (IRG) is a representative winter forage crop and can be harvested from late April to May of the following year after sowing rice in Republic of Korea. In particular, it is resistant to injury via moisture, so it is possible to cultivate paddy fields after harvesting rice [3]. Italian ryegrass has an excellent livestock palatability, a high crude protein content, and a high digestible nutrient content. Thus, it is very valuable as a feed substitute for assorted feed. Its quality of silage is also very good due to its high sugar content compared to feeding rice straw [9; 14]. In the case of dairy cows, while feeding Italian ryegrass can lead to increasing milk production, higher milk fat, and higher milk protein content, it has been proven to consequently improve milk quality [15; 16; 17]. However, in recent times, due to abnormal climate conditions, the suitable cultivation areas of Italian ryegrass (IRG) are shifting and the pace of these changes is accelerating [18; 19; 20].