Detecting dry matter yield trend of whole crop maize considering the climatic factors in the Republic of Korea. (5th December 2018)

• Record Type: Journal Article
• Authors: Chemere, Befekadu; Kim, Moonju; Peng, Jinglun; Kim, Byongwan; Sung, Kyungil
• Is Part Of: Grassland science. Volume 65:Number 2(2019)
• Page Start: 116
• Page End: 124
• Publication Date: 2018-12-05
• Subjects: climatic factors -- dry matter yield trend -- time series analysis -- whole crop maize; Grasslands -- Periodicals; Grasslands -- Management -- Periodicals; 578.74
• DOI: 10.1111/grs.12220
• Languages: English
• ISSNs: 1744-6961

Abstract

Abstract: 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.