A yield prediction model for Italian ryegrass (IRG) was constructed based on climatic data by locations in South Korea using a general linear model. The sample size of the final dataset was 312 during 25 years. The forage crop and climatic data were collected from the reports of two national research projects on forage crops and Korean meteorological administration, respectively. Five optimal climatic variables were selected through the stepwise multiple regression analysis with dry matter yield (DMY) as the response variable. Subsequently, three climatic variables were selected after considering the interpretability of the five variables. The three selected climatic variables were spring accumulated temperature, mean temperature in January and spring rainfall days. Then, the yield prediction model was constructed based on these three climatic variables using general linear model with the cultivated locations as dummy variables. The model constructed in this research could explain 73.6% of variation in DMY of IRG. The goodness‐of‐fit of the model was tested through residual diagnostics and 10‐fold cross‐validation. For climatic variables, the high partial eta squared value of spring accumulated temperature and spring rainfall may reflect the growth characteristics that spring is the main growing period for IRG and IRG has strong waterlogging tolerance and weak drought tolerance. The results may also support the possibility to sow IRG in the subsequent spring if autumnal seeding was missed in South Korea.

Italian Ryegrass (IRG), which is known as high yielding and the highest quality winter annual forage crop, is grown in mid-south area in Korea. This study aims to analyze the cause-and-effect relationship between IRG yield and climate variables such as temperature and precipitation by using IRG data and climate data of Korea Weather Bureau. From path analysis of structural equation model under multivariate normality, we found that there was a weather effect on IRG yield that the winter grass IRG yield was directly affected by spring temperature and indirectly affected by spring rainfall. These results showed that IRG can be sown in early spring in the area where it is hard to prepare for winter due to low temperature. This paper can contribute to increase IRG yield by showing the cause-and-effect relationship and this study can be extended to various structural equation models for other crops.

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The objective of this study was to detect the historical dry matter yield (DMY) trend and to evaluate the effects of heavy rainfall events on the observed DMY trend of whole crop maize (WCM, Zea mays L.) using time-series analysis in Suwon, Republic of Korea. The climatic variables corresponding to the seeding to harvesting period, including the growing degree days, mean temperature, etc., of WCM along with the DMY data (n = 543) during 1982–2011, were used in the analysis. The DMY trend was detected using Autoregressive Integrated Moving Average with the explanatory variables (ARIMAX) form of time-series trend analysis. The optimal DMY model was found to be ARIMAX (1, 1, 1), indicating that the DMY trend follows the mean DMY of the preceding one year and the residual of the preceding one year with an integration level of 1. Furthermore, the SHGDD and SHHR were determined to be the main variables responsible for the observed trend in the DMY of WCM. During heavy rainfall events, the DMY was found to be decreasing by 4745.27 kg/ha (p < 0.01). Our analysis also revealed that both the intensity and frequency of heavy rainfall events have been increasing since 2005. The forecasted DMY indicates the potential decrease, which is expected to be 11,607 kg/ha by 2045. This study provided us evidence for the correlation between the DMY and heavy rainfall events that opens the way to provide solutions for challenges that summer forage crops face in the Republic of Korea.

This study was aimed to detect the dry matter yield (DMY) trend of whole crop maize (WCM) considering the climatic factors responsible for growth and development of WCM using time series analysis in the Republic of Korea. The dataset consisted of DMY and climatic factors responsible for WCM yield from 1982 to 2011. The stationarity of the DMY was detected using augmented Dickey–Fuller (ADF) test, whereas the parameters of Autoregressive (AR) and Moving average (MA) were estimated from correlogram of Autocorrelation function (ACF) and partial ACF (PACF). The stationary DMY data was fitted to AR Integrated MA (ARIMA), and based on model selection criterion, ARIMA (2, 0, 1) was detected as the optimal model to describe the DMY trend of WCM. The DMY trend followed the mean of the preceding 2 years and residual of preceding 1 year. ARIMA with exogenous variables (ARIMAX) detected Seeding‐Harvesting Growing Degree Days (SHGDD, °C), Seeding‐Harvesting Rainfall Amount (SHRFA, mm), and Seeding‐Harvesting Rainfall Days (SHRFD, days) as major climatic factors responsible for the DMY trend of WCM. Furthermore, the amount and timing of rainfall found to be an important factor for the observed DMY trend. The fluctuation in the DMY trend implies the need to come up with a holistic approach that include new varieties development and improved agronomic management system to overcome the expected challenge from climate variability.