Ⅰ. INTRODUCTION

Climate change has increasingly disrupted the stability of agricultural systems, with notable impacts on temperature and precipitation patterns worldwide. According to the Intergovernmental Panel on Climate Change (IPCC, 2021), the frequency and severity of extreme weather events such as droughts, heavy rainfall, and unseasonal temperature fluctuations have intensified, posing significant risks to crop production. Forage crops, which are often cultivated in open fields without protective infrastructure, are especially vulnerable to these climatic fluctuations (FAO, 2018). In particular, winter forage crops are directly affected by low-temperature stress during overwintering and drought stress during early spring regrowth (Chung et al., 2016).

Italian ryegrass (Lolium multiflorum Lam.), a cool-season annual forage crop, plays a critical role in ruminant feeding systems due to its high palatability and biomass productivity. Its growth and yield performance are highly sensitive to climatic conditions, particularly minimum winter temperatures and spring precipitation (Shin et al., 2012;Woo et al., 2021). In Korea, Italian ryegrass is widely cultivated in both central and southern regions, with regional variability in climate responses often resulting in uneven productivity and stability across sites (Jung et al., 2020).

Previous studies have evaluated the potential effects of long-term climate change scenarios on the spatial distribution of suitable cultivation zones for forage crops using agroclimatic zoning and simulation models (Park et al., 2019;Lee et al., 2022). However, empirical analyses based on actual yield data and observed weather anomalies remain scarce. Furthermore, there is a lack of studies that explicitly define extreme weather events using statistical criteria such as standard deviations from a reference period, and apply these definitions to assess the associated impacts on forage crop productivity.

Despite growing concerns about the impacts of climate anomalies on forage crop cultivation, empirical evaluations linking observed extreme climate events to changes in suitability zones and forage yield remain limited. In particular, few studies have quantified these impacts based on statistically defined thresholds and actual multi-year production data. Therefore, this study was conducted to assess the spatial and temporal shifts in suitable cultivation zones for Italian ryegrass and to quantify the effects of recent climate variability and extreme weather events on regional yield levels using observed agro-climatic and productivity data collected between 2006 and 2023.

Ⅱ. MATERIALS AND METHODS

1. Study area and period

This study was conducted in seven provinces of South Korea, excluding Gangwon-do, and the sites were grouped into central and southern regions. The central region included Gyeonggi-do, Chungcheongbuk-do, and Chungcheongnam-do, while the southern region comprised Jeollabuk-do, Jeollanam-do, Gyeongsangbuk-do, and Gyeongsangnam-do. The baseline climate period was set from 2006 to 2015, and the recent comparison period was 2021 to 2023.

2. Climate data collection and definition of extreme weather

Climatic variables were collected from the Korea Meteorological Administration (KMA), including January mean minimum temperature and total annual precipitation. To assess trends and anomalies, independent t-tests and linear regressions were performed. Extreme weather events were defined as values exceeding ±2 standard deviations from the baseline mean, consistent with previous studies assessing agro-climatic risks in crop production (Trnka et al., 2014). This statistical threshold helps identify rare but impactful anomalies that may severely affect forage crop growth and yield.

3. Classification of suitability zones and spatial analysis

Climatic suitability zones were classified based on January mean minimum temperature using the following thresholds: best suitable (>−5°C), suitable (−6°C to −9°C), possible (−10°C to −12°C), and low productivity (< −13°C).

This classification system follows the agro-climatic zoning method proposed by Jung et al. (2020), who demonstrated that January minimum temperature showed the highest correlation with Italian ryegrass productivity under Korean climatic conditions.

4. Dry Matter yield data and regional stratification

Dry matter yield (DMY, kg/ha) data were collected from 35 representative sites in 29 cities and counties across central and southern Korea, selected based on Italian ryegrass (IRG) cultivation areas exceeding 500 ha. Survey locations included Hwaseong, Icheon, and Anseong (Gyeonggi-do); Cheongju (Chungcheongbuk-do); Gongju, Boryeong, Buyeo, Seocheon, and Taean (Chungcheongnam-do); Jeongeup, Namwon, Gochang, and Buan (Jeollabuk-do); Naju, Damyang, Goheung, Boseong, Jangheung, Gangjin, Haenam, Yeongam, Hampyeong, Yeonggwang, Jangseong, and Wando (Jeollanam-do); Gyeongju and Gumi (Gyeongsangbuk-do); and Jinju, Gimhae, Miryang, Haman, Goseong, Hadong, Sancheong, and Ulsan (Gyeongsangnam-do). The data were obtained from field surveys conducted by the Rural Development Administration (RDA) during 2021–2023. Statistical differences in DMY between the baseline period (2006–2015) and the recent period (2021–2023) were analyzed using independent t-tests.

5. Statistical analysis of yield–climate relationships

Pearson correlation analysis and multiple linear regression were conducted to assess the relationships between climatic variables and forage yield. A total of six dataset (three years × two regions) were used for the regression analysis. All statistical analyses were performed using the SAS software package (ver. 9.4; SAS Institute Inc., Cary, NC, USA). A significance level of p<0.05 was adopted.

6. Mapping software

Climatic suitability classification and spatial visualization were conducted using ArcGIS 10.0 (Esri, Redlands, CA, USA).

Ⅲ. RESULTS AND DISCUSSION

1. Trends in climate change and extreme weather events

As shown in Table 1, the national average of January minimum temperatures increased by +0.27°C during the recent period (2021–2023) compared to the baseline (2006–2015), although the difference was not statistically significant (p = 0.111). In contrast, total annual precipitation showed a significant decline of 84.3 mm (p<0.001). A linear regression within the baseline period also indicated a significant downward trend of approximately −9.76 mm per year, suggesting progressive aridification in Korea’s agro-climatic conditions (IPCC, 2021;KMA, 2024).

This reduction in precipitation, coupled with localized warming, reflects increasing climate volatility, which may critically affect forage crop productivity. Italian ryegrass, in particular, is sensitive to overwintering conditions and spring moisture availability (Chung et al., 2016;FAO, 2018). The sharp decrease in spring rainfall in southern Korea in 2022, as reported by Choi et al. (2018) and observed in this study, led to a measurable yield decline.

2. Spatial and temporal changes in suitability zones

Temporal changes in the distribution of suitability zones, based on January minimum temperatures, are summarized in Table 2. The proportion of the best suitable zones declined from 30.9% during the baseline period to 23.9% in 2021, accompanied by an increase in marginal zones to 18.2%. These shifts are visualized in Fig. 1, which provides spatial maps of the suitability classification for each year.

A partial recovery was observed in 2023, with the best suitable zones increasing to 29.6%, illustrating year-to-year climatic variability. These findings are consistent with projections by Park et al. (2019), who predicted a northward shift of suitable zones under warming scenarios. Furthermore, Jung et al. (2020) emphasized the influence of regional temperature trends on heterogeneous spatial change patterns also evident in this study’s comparison between central and southern regions.

3. Regional yield variation

Regional differences in dry matter yield (DMY) are presented in Table 3. In the central region, yield increased significantly by 31.6%, from 7,656 ± 362 to 10,075 ± 377 kg/ha (p = 0.0002), likely due to improved winter survival and a longer growing season under warmer conditions (Shin et al., 2012;Kim et al., 2011). Conversely, the southern region exhibited a 9.4% reduction in yield, from 8,695 ± 187 to 7,881 ± 194 kg/ha (p = 0.0031), attributed to severe spring drought stress (Woo et al., 2021;Lee et al., 2022).

These contrasting regional responses emphasize the importance of site-specific climate adaptation strategies and underscore the need for regionally tailored cultivar selection and management approaches.

4. Correlation between climate variables and yield

Pearson correlation coefficients are shown in Table 4. A moderate positive correlation was found between annual precipitation and forage yield (r = 0.518), although not statistically significant (p = 0.292). Correlation between temperature and yield was negligible (r = 0.015, p = 0.977). While the small sample size (n = 6) limited statistical power, the observed trends align with Woo et al. (2021), who highlighted the sensitivity of forage yield to water availability.

According to Schlenker and Roberts (2009), the relationship between temperature and crop yield is nonlinear, with yields increasing up to a certain temperature threshold and declining sharply thereafter. In this study, the near-zero correlation observed for temperature may reflect offsetting regional effects: heat stress reducing yield in the southern region, while improved overwintering conditions increased yield in the central region.

5. Quantifying climatic influence on yield via regression

Multiple linear regression results are summarized in Table 5. The combined effects of annual precipitation and January minimum temperature explained 97.9% of the variation in dry matter yield (R^² = 0.979). Precipitation exhibited a stronger effect (β = 7.64, p = 0.071), nearing statistical significance, whereas temperature showed a non-significant effect (β = 528.67, p = 0.538). The residual plot confirmed good model fit, with no systematic patterns detected in residuals. However, it should be noted that the regression model was developed based on a limited dataset (n = 6), which raises concerns about potential overfitting. While the model explains 97.9% of the yield variation (R^² = 0.979), the small sample size may restrict the generalizability of the findings. Future studies with larger datasets are recommended to validate the robustness and transferability of the model.

These findings corroborate prior studies that emphasize precipitation as a dominant factor influencing forage crop productivity under climate stress (FAO, 2018;Woo et al., 2021). Specifically, Jung et al. (2020) identified precipitation-driven variability as particularly critical in the central inland regions of Korea.

A key strength of this study lies in its use of a statistical definition for extreme weather events exceeding ±2 standard deviations from the baseline and linking these anomalies directly to observed yield changes. Unlike previous studies that focused on long-term trends (Park et al., 2019;Woo et al., 2021), this research offers actionable insights into the immediate impacts of real-time climate anomalies.

Despite the small dataset, the results underscore the central role of water availability in forage crop resilience. Future work should expand data coverage and include additional climatic factors (e.g., soil moisture, solar radiation, wind speed, evapotranspiration) to enhance predictive power and improve adaptation strategies.

Ⅳ. CONCLUSIONS

This study quantitatively evaluated the effects of recent climate change and extreme weather events on the suitability distribution and dry matter yield of Italian ryegrass in Korea. A significant decrease in annual precipitation (−84.3 mm, p<0.001) was observed between the baseline period (2006– 2015) and the recent period (2021–2023), while changes in temperature were not statistically significant. These climatic shifts altered the spatial distribution of suitability zones, with a notable reduction in the best suitable areas in 2021. Regionally, dry matter yield increased by 31.6% in the central region due to improved overwintering conditions, whereas the southern region experienced a 9.4% yield decline associated with severe spring drought.

Pearson correlation and multiple regression analyses indicated that precipitation had a stronger influence on yield than temperature. The regression model exhibited a high explanatory power (R^² = 0.979). Notably, this study applied a statistical definition of extreme weather events, using thresholds exceeding ±2 standard deviations from the baseline mean, thereby enabling a quantitative linkage between climate anomalies and forage yield response. These findings underscore the importance of accounting for region-specific climate sensitivities in future forage crop research and cultivation planning.