Development of Future Precipitation Forecasting Program Using Probability Forecast and Climate Change Scenario

Jang, Dongwoo; Park, Hyoseon; Choi, Jin-Tak

doi:10.1016/j.proeng.2016.07.564

Cited by 2 publications

(2 citation statements)

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“…Jang, Park, and Choi () reported that there could be an increase in the precipitation amount in August and September, and larger variability of precipitation amount, which could increase the possibility of contradictory precipitation phenomena such as drought and heavy rain in certain regions of the country. These scenario could affect early growing season water requirement of maize and subsequently the DMY.…”

Section: Discussionmentioning

confidence: 99%

Detecting dry matter yield trend of whole crop maize considering the climatic factors in the Republic of Korea

et al. 2018

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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.

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Section: Discussionmentioning

confidence: 99%

Detecting dry matter yield trend of whole crop maize considering the climatic factors in the Republic of Korea

et al. 2018

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“…This is in agreement with Chemere et al [14], who reported a similar effect of precipitation on the DMY of whole crop maize in the Republic of Korea. Furthermore, Bae et al [30] and Jang et al [37] have indicated that there could be a contradictory precipitation phenomenon that causes severe drought during the early growing season, and increased precipitation during August and September. In an attempt to estimate the effect of previous year's precipitation on the subsequent year's soil moisture profile, we have estimated the precipitation trend during the cultivation of SSH.…”

Section: Discussionmentioning

confidence: 99%

Detecting Long-Term Dry Matter Yield Trend of Sorghum-Sudangrass Hybrid and Climatic Factors Using Time Series Analysis in the Republic of Korea

et al. 2018

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Despite the gradual increase in livestock feed demands, the supply faces enormous challenges due to extreme climatic conditions. As the presence of these climatic condition has the potential to affect the yield of sorghum-sudangrass hybrid (SSH), understanding the yield variation in relation to the climatic conditions provides the ability to come up with proper mitigation strategies. This study was designed to detect the effect of climatic factors on the long-term dry matter yield (DMY) trend of SSH using time series analysis in the Republic of Korea. The collected data consisted of DMY, seeding-harvesting dates, the location where the cultivation took place, cultivars, and climatic factors related to cultivation of SSH. Based on the assumption of normality, the final data set (n = 420) was generated after outliers had been removed using Box-plot analysis. To evaluate the seasonality of DMY, an augmented Dickey Fuller (ADF) test and a correlogram of Autocorrelation Function (ACF) were used. Prior to detecting the effect of climatic factors on the DMY trend, the Autoregressive Integrated Moving Average (ARIMA) model was fitted to non-seasonal DMY series, and ARIMA (2, 1, 1) was found to be the optimal model to describe the long-term DMY trend of SSH. ARIMA with climatic factors (ARIMAX) detected significance (p < 0.05) of Seeding-Harvesting Precipitation Amount (SHPA) and Seeding-Harvesting Accumulated Temperature (SHAMT) on DMY trend. This does not mean that the average temperature and duration of exposure to sunshine do not affect the growth and development of SSH. The result underlines the impact of the precipitation model as a major factor for the seasonality of long-term DMY of SSH in the Republic of Korea.

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Development of Future Precipitation Forecasting Program Using Probability Forecast and Climate Change Scenario

Cited by 2 publications

References 1 publication

Detecting dry matter yield trend of whole crop maize considering the climatic factors in the Republic of Korea

Detecting dry matter yield trend of whole crop maize considering the climatic factors in the Republic of Korea

Detecting Long-Term Dry Matter Yield Trend of Sorghum-Sudangrass Hybrid and Climatic Factors Using Time Series Analysis in the Republic of Korea

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