Yield and Nutritive Value of Spring-seeded Early and Late Maturity Italian Ryegrass (Lolium multiflorum Lam.)

Kim, Ki-Yong; Ji, Hee-Chung; Lee, Sanghoon; Lee, Ki-Won; Kim, Won‐Ho; Jung, Min-Woong; Seo, Sung; Choi, Gi-Jun

doi:10.5333/kgfs.2010.30.2.097

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…The growth and development of IRG is active during the spring, therefore normal yield of IRG is possible if seeding was done during May to June after thaw (early or middle March in South Korea) for earlymaturity and middle-maturity cultivators (Kim et al 2010). Especially, SAT and MTJ had no overlapping effects with other variables based on the results that the three correlation coefficients had no big changes.…”

Section: Resultsmentioning

confidence: 97%

“…This means the temperature and rainfall related variables in the next spring had important effects on the yield production of IRG. The growth and development of IRG is active during the spring, therefore normal yield of IRG is possible if seeding was done during May to June after thaw (early or middle March in South Korea) for earlymaturity and middle-maturity cultivators (Kim et al 2010). Therefore, the yield could be ensured if seeding was done in the next spring when the seeding period was missed in autumn (Seo 2009).…”

Section: Resultsmentioning

confidence: 99%

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Constructing Italian ryegrass yield prediction model based on climatic data by locations in South Korea

Peng

Kim

et al. 2017

Grassland Science

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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.

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Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

Constructing Italian ryegrass yield prediction model based on climatic data by locations in South Korea

Peng

Kim

et al. 2017

Grassland Science

View full text Add to dashboard Cite

show abstract

The current status of breeding research in Lolium genus

Nam,

Gwak,

Baek

et al. 2023

J. Crop Sci. Biotechnol.

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A Study on Analyses of the Production Data of Feed Crops and Vulnerability to Climate Impacts According to Climate Change in Republic of Korea

Shin,

Hwang,

Kim

et al. 2023

Applied Sciences

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According to the climate change scenario, climate change in the Korean Peninsula is expected to worsen due to extreme temperatures, with effects such as rising average temperatures, heat waves, and droughts. In Republic of Korea, which relies on foreign countries for the supply of forage crops, a decrease in the productivity of forage crops is expected to cause increased damage to the domestic livestock industry. In this paper, to solve the issue of climate vulnerability for forage crops, we performed a study to predict the productivity of forage crops in relation to climate change. We surveyed and compiled not only forage crop production data from various regions, but also experimental cultivation production data over several years from reports of the Korea Institute of Animal Science and Technology. Then, we crawled related climate data from the Korea Meteorological Administration. Therefore, we were able to construct a basic database for forage crop production data and related climate data. Using the database, a production prediction model was implemented, applying a multivariate regression analysis and deep learning regression. The key factors were determined as a result of analyzing the changes in forage crop production due to climate change. Using the prediction model, it could be possible to forecast the shifting locations of suitable cultivation areas. As a result of our study, we were able to construct electromagnetic climate maps for forage crops in Republic of Korea. It can be used to present region-specific agricultural insights and guidelines for cultivation technology for forage crops against climate change.

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Yield and Nutritive Value of Spring-seeded Early and Late Maturity Italian Ryegrass (Lolium multiflorum Lam.)

Cited by 8 publications

References 7 publications

Constructing Italian ryegrass yield prediction model based on climatic data by locations in South Korea

Constructing Italian ryegrass yield prediction model based on climatic data by locations in South Korea

The current status of breeding research in Lolium genus

A Study on Analyses of the Production Data of Feed Crops and Vulnerability to Climate Impacts According to Climate Change in Republic of Korea

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