## 5 Conclusions  
In this paper, we analyzed forage crop productivity and changes in suitable cultivation areas based on climate change in Republic of Korea. The dry matter data of IRG and forage with labels of region and year were collected through literature reviews and experimental reports from the Korea Institute of Animal Science and Technology. In addition, we developed a climate data crawler, through which climate data were crawled from the website of the Korea Meteorological Administration, that gathered climate data according to the region and year labels. Subsequently, the collected data were preprocessed to establish a foundational dataset database. By applying various regression models, predictive models that could predict future production were compared and analyzed, and major climate factors affecting production were determined. As a result of the production prediction model, the Lasso model with the highest determination coefficient, R2, was selected to derive climate factors that greatly affected each forage crop. Precipitation in October (PREOCT), the minimum temperature in January (MINJTAN), and the growing degree days from January to April (GDDFJTA) were identified as key climate factors with the greatest impact on Italian ryegrass production. The climate factors that had the greatest impact on grass production were the drought days (DDAYS), the sum of precipitation days in August (PREAUG), the growing degree days from January to December (GDDTOTAL), the maximum temperature in July (MAXJUL), and the maximum temperature in August (MAXAUG). Figure 9: Electromagnetic climate map applying the predictive model of forage crop productivity in Republic of Korea (2022). Based on the results of the analyses, electromagnetic climate maps were constructed for suitable cultivation areas and production prediction. In Republic of Korea, the ratio of optimal areas was high in the southern region and the ratio of suitable areas was high in the central region. In the case of the mid-northern mountainous area, the ratio of possible and low-production areas was relatively high. Therefore, mid-northern and mountainous regions are likely to be advantageous in terms of productivity to use domestically developed IRGs with high cold resistance. Grasses are categorized as C3 crops. Their economic lifespan is often shortened due to high temperatures in Korea. Given that the optimal temperature range is between 20 \({}^{\circ}\)C and 25 \({}^{\circ}\)C, the optimal and suitable areas are primarily concentrated in the mid-mountainous area of the mid-northern region and the mid-mountainous area of Jeju Island. In the case of the central and southern regions, there are many possible and low-production areas. Thus, it seems necessary to introduce high-temperature-resistant varieties or C4 grasses that can replace northern grasses in the future. Our contribution is that we have built a database related to feed crops, which exists rarely. In addition, the research results can be used for livestock farms to establish policies to prepare against climate change. The limitation of our study is that it excluded various factors such as soil, fertilizer, breed, and so on, which affect the production of feed crops, and that it was carried out to only focus on analyzing climate data with impacts on feed crop production. The value of R\({}^{2}\), the determination coefficient, was considered to be not much higher due to these limitations. In future work, we will collect feed crop data and climate data to predict suitable cultivation areas, so as to build an electronic climate map, and publish it for farming guidance for livestock farms. Conceptualization, S.H., J.-S.J. and M.S. ; methodology, S.H. and J.-S.J. ; investigation, J.K. and B.K. ; validation, B.K. and S.H. ; writing--original draft, J.-S.J. and M.S. ; writing--review and editing, J.K. and M.S. ; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.