Prediction of the Italian Ryegrass (Lolium multiflorum Lam.) Yield via Climate Big Data and Geographic Information System in Republic of Korea

Kim, Moonju; Oh, Seung Min; Kim, Ji Yung; Lee, Bae Hun; Peng, Jinglun; Kim, Si Chul; Chemere, Befekadu; Nejad, Jalil Ghassemi; Kim, Kyeong Dae; Jo, Mu Hwan; Kim, Byong Wan; Sung, Kyung Il

doi:10.5333/kgfs.2017.37.2.145

Cited by 4 publications

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“…Because of the different variables contained in the metadata, the causal relationship of climatic variables affecting yield was identified by longitudinal structural equation modeling for winter forage crops (Kim, Jeon, Sung, & Kim, 2016; Kim, Sung, & Kim, 2014). Since cultivation experiments were carried out in various places, the yield prediction was modeled based on the cultivated location in order to ensure data homogeneity (Kim et al, 2017; Peng et al, 2017). In general, it is possible to select and combine information for constructing analytical data based on clarity of the research purpose in the analysis using metadata.…”

Section: Introductionmentioning

confidence: 99%

The optimum seeding and harvesting dates of sorghum–sudangrass hybrid (Sorghum bicolor L.) via optimum moving response surface methodology

Kim

Sung

2020

Grassland Science

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In general, determining suitable seeding and harvesting times is important in achieving high yields of forage crops in terms of both physiology and ecology. To determine the optimum point, the response surface methodology (RSM) is well known in agricultural science. This study aimed to establish optimum seeding and harvesting dates and to verify the variation of dry matter yield (DMY) for sorghum–sudangrass hybrid (SSH), by determining what we called the “optimum moving RSM” proposed to optimize the RSM in the metadata. The SSH metadata consist of individual experiments (n = 856) that were performed to check the optimum seeding and harvesting dates using classic RSM. However, the SSH metadata failed to produce a single effective equation to determine the optimum seeding and harvesting dates due to the different characteristics of the experimental design. Therefore, the optimum moving RSM was introduced. As a result, we found that the optimum seeding and harvesting dates were clear, based on a single quadratic function. The maximum DMY at the optimum seeding and harvesting dates (the 4th of May and 23rd of September, respectively) was 25,404.50 and 23,697.01 kg/ha, respectively. SSH yield decreases rapidly from 10 days after the optimum date, and seeding and harvesting should be performed within 5 days to ensure a stable yield. Furthermore, the optimum moving RSM is expected to be useful to establish the optimum point in the metadata.

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Section: Introductionmentioning

confidence: 99%

The optimum seeding and harvesting dates of sorghum–sudangrass hybrid (Sorghum bicolor L.) via optimum moving response surface methodology

Kim

Sung

2020

Grassland Science

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“…Kim et al [6,7] detected the casual relationship between climatic factors (growing days, temperature, and precipitation) and the yield of Italian ryegrass and whole crop barley in a natural eco-system. The predicted yield of Italian ryegrass was shown by mapping the grid layers of climatic variables on the main cultivated locations by using a geographic information system [8]. The effect of summer depression on the yield of a pasture-based forage production system has also been the area of focus in relation to trends [9].…”

Section: Introductionmentioning

confidence: 99%

Effect of Heavy Rainfall Events on the Dry Matter Yield Trend of Whole Crop Maize (Zea mays L.)

2019

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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.

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“…Furthermore, the yield trend was checked with the climate effects (Chemere, Kim, Peng, Kim, & Sung, ). Predicted dry matter yield of Italian ryegrass (IRG) was mapped by geographic information system as a result of big data analysis (Kim et al, ). These methods were concise and efficient to predict the forage yield; however, it was not effective to take the necessary information from climate big data because explanatory variables were not independent of each other.…”

Section: Introductionmentioning

confidence: 99%

Causality of climate and soil factors affecting whole crop rye (Secale cereale L.) yield as part of natural ecosystem structure via longitudinal structural equation model in the Republic of Korea

2019

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In this study, the causality of climate and soil factors affecting the whole crop rye yield was identified via structural equation model, such as the seasonal effect between autumn and next spring, the indirect effect of the precipitation and the relationship between soil physical and climate factors. To identify the causality under the part of natural ecosystem, the structure was modeled with climates (autumnal accumulated temperature, autumnal growing days, spring accumulated temperature, spring growing days, spring amount of precipitation, and spring days with precipitation) and soil physical properties (drainage classes, effective soil depth, slope, and gravel content). As a result, we could estimate various specific relationships: First, the seasonal effect of accumulated temperature and growing days between autumn and next spring was obvious for the yield. Second, the precipitation had indirectly influenced the yield through suitable temperature in next spring. Final, the soil physical factor was related to the only precipitation, which means the effect of soil factor was significant through the spring precipitation. The structure could be modeled with climate and soil physical factors as a part of natural ecosystem, which was performed at the first time in the Republic of Korea and will be helpful to extend the structure with various factors in the future study.

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Prediction of the Italian Ryegrass (Lolium multiflorum Lam.) Yield via Climate Big Data and Geographic Information System in Republic of Korea

Cited by 4 publications

References 0 publications

The optimum seeding and harvesting dates of sorghum–sudangrass hybrid (Sorghum bicolor L.) via optimum moving response surface methodology

The optimum seeding and harvesting dates of sorghum–sudangrass hybrid (Sorghum bicolor L.) via optimum moving response surface methodology

Effect of Heavy Rainfall Events on the Dry Matter Yield Trend of Whole Crop Maize (Zea mays L.)

Causality of climate and soil factors affecting whole crop rye (Secale cereale L.) yield as part of natural ecosystem structure via longitudinal structural equation model in the Republic of Korea

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