Moonju Kim

Analysis of Climate Effects on Italian Ryegrass Yield via Structural Equation Model

Abstract 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 betweenIRG yield and climate variables such as temperature and precipitation by using IRG data and climate dataof 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 affectedby spring temperature and indirectly affected by spring rainfall. These results showed that IRG can be sownin early spring in the area where it is hard to prepare for winter due to low temperature. This paper cancontribute to increase IRG yield by showing the cause-and-effect relationship and this study can be extendedto various structural equation models for other crops.Keywords: Italian Ryegrass, structural equation model, outlier, mahallanobis distance. 1. 서론 지중해가 원산지인이탈리안 라이그라스(Italian ryegrass: IRG)는 연중 온화하고 습윤한 지역의비옥한 토양에서잘자라는 작물이다. 우리나라에서는 보통 가을에 파종하여 이듬해 봄에 이용하는 대표적인월년생 사료작물로 유식물 활력이높아 파종이쉽고 정착이잘되며 어느 정도 내습성이있어 답리작으로도 적합하며, 짧은기간에 높은수량을낼 수있기 때문에 월동이가능한 지역에서는 선호도가 높은사료작물로 알려져 있다(Shin 등, 2012). 사료작물로써 잠재력이큰 IRG의재배기술을발전시키고 재배한계를 확대하기 위해서는 IRG 생육과 기후 특성에 대한 다양한 심층 연구가 필요하나 우리나라에서는 거의없는 실정이다. 또한 기후변화로 인한 농작물의생산량의증감 폭도 커지고 있는 시점에서작물에 대한 기상요소들의이해는 중요한 전략적 요소가 될수있다(Lee 등, 2013).다른 생물과 마찬가지로 IRG의생산량은기후의영향을많이받는다(Schlenker와 Roberts, 2009). 작물의수량과 다양한 기후변인사이의관계를 통계학적 기법으로 밝혀낸다면 재배기술의향상에 의한 생

Accuracy evaluation of the crop-weather yield predictive models of Italian ryegrass and forage rye using cross-validation

The objective of this study was to evaluate the accuracy of the yield predictive models of Italian ryegrass (IRG, Lolium multiflorum Lam.) and forage rye (FR, Secale cereale L.) reported in previous studies through K-fold cross-validation method. In previous studies, statistical models were constructed for dry matter yield prediction of IRG and FR using general linear model based on climatic data by locations in the Republic of Korea. The yield predictive model for IRG cultivated in the southern region of the Korean Peninsula and Jeju Island were DMY = 78.178AGD–254.622MTJ + 64.156SGD–76.954PAT150 + 4.711SAP + 1028.295 + Location and DMY =–8.044AAT + 18.640SDS–7.542SAT + 9.610SAP + 17282.191, respectively. The yield predictive model for FR was as follows: DMY = 20.999AGD + 163.705LTJ + 113.716SGD + 64.379PAT100–4964.728 + Location. However, accuracy evaluation was not performed in the previous research. In this study, the reported models and the data set used for model construction were investigated. Subsequently, K-fold cross-validation was performed to assess the accuracy of the models. The results showed that the yield predictive models fit to the data sets well, while the accuracy of these models was in the common level since the data sources might keep major variances in cultivars, climatic conditions, and cultivated locations. Therefore, models with better fitness and accuracy might be constructed based on a data set with smaller variance. Hence, the standardization of the crop cultivation experiments is very necessary to decrease the variance in the historical data used for future crop yield modeling.

A Yield Estimation Model of Forage Rye Based on Climate Data by Locations in South Korea Using General Linear Model

The objective of this study was to construct a forage rye (FR) dry matter yield (DMY) estimation model based on climate data by locations in South Korea. The data set (n = 549) during 29 years were used. Six optimal climatic variables were selected through stepwise multiple regression analysis with DMY as the response variable. Subsequently, via general linear model, the final model including the six climatic variables and cultivated locations as dummy variables was constructed as follows: DMY = 104.166SGD + 1.454AAT + 147.863MTJ + 59.183PAT150 4.693SRF + 45.106SRD 5230.001 + Location, where SGD was spring growing days, AAT was autumnal accumulated temperature, MTJ was mean temperature in January, PAT150 was period to accumulated temperature 150, SRF was spring rainfall, and SRD was spring rainfall days. The model constructed in this research could explain 24.4 % of the variations in DMY of FR. The homoscedasticity and the assumption that the mean of the residuals were equal to zero was satisfied. The goodness-of-fit of the model was proper based on most scatters of the predicted DMY values fell within the 95% confidence interval.

Detecting the Climate Factors related to Dry Matter Yield of Whole Crop Maize

The purpose of this research is to identify the significance of climate factors related to the significance of change of dry matter yield (DMY) of whole crop maize (WCM) by year through the exploratory data analysis. The data (124 varieties; n=993 in 7 provinces) was prepared after deletion and modification of the insufficient and repetitive data from the results (124 varieties; n=1027 in 7 provinces) of import adaptation experiment done by National Agricultural Cooperation Federation. WCM was classified into early-maturity (25 varieties, n=200), mid-maturity (40 varieties, n=409), late-maturity (27 varieties, n=234) and others (32 varieties, n=150) based on relative maturity and days to silking. For determining climate factors, 6 weather variables were generated using weather data. For detecting DMY and climate factors, SPSS21.0 was used for operating descriptive statistics and Shapiro-Wilk test. Mean DMY by year was classified into upper and lower groups, and a statistically significant difference in DMY was found between two groups (p 0.05). These results indicate that the SHAGDD, SHP and SHH are related to DMY of WCM, but the comparison of R 2 among three variables (SHAGDD, SHP and SHH) couldn’t be obtained which is needed to be done by regression analysis as well as the prediction model of DMY in the future study.

Bayesian structural equation modeling for analysis of climate effect on whole crop barley yield

Whole Crop Barley (WCB) is a representative self-sufficient winter annual forage crop, along with Italian Ryegrass (IRG), in Korea. In this study, we examined the path relationship between WCB yield and climate factors such as temperature, precipitation, and sunshine duration using a structural equation model. A Bayesian approach was considered to overcome the limitations of the small WCB sample size. As prior distribution of parameters in Bayesian method, standard normal distribution, the posterior result of structural equation model for WCB, and the posterior result of structural equation model for IRG (which is the most popular winter crop) were used. Also, Heywood case correction in prior distribution was considered to obtain the posterior distribution of parameters; in addition, the best prior to fit the characteristics of winter crops was identified. In our analysis, we found that the best prior was set by using the results of a structural equation model to IRG with Heywood case correction. This result can provide an alternative for research on forage crops that have hard to collect sample data.