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.
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.
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.
The objective of this study was to evaluate the nutritive value of polished rice
(PR) vs unpolished rice (UPR) as a potential feedstuff for sheep in order to use
as a replacer to corn in sheep diet, and as well as to present the application
in the formulation of cattle diet. Six corriedale ewe were randomly assigned to
each treatment. UPR and PR were provided as a dietary treatment together with
timothy grass as a basal diet in a crossover design for two period with 15-d
duration for each period. The ratio of experimental and basal feeds were
33.3% and 66.7%, respectively. The differences in the total
digestible nutrient (TDN) contents between sheep and cattle was determined
according to the references. The number of data collected sheep and cattle was 9
and 17, respectively. The PR showed higher nutrients digestibility than UPR.
Similarly, higher TDN content was observed PR than UPR (p
< 0.05). As a result, the replacement of corn in the formulate feed with
UPR and PR feed rice could be possible with the ratio of 91.2% and
100.0%, respectively. The result of comparation the TDN contents of UPR
and PR in sheep and cattle, the PR has no difference in the nutritive value
which suggests the applicability of the results of sheep to cattle. On the other
hand, UPR has known to have different nutritive value between sheep and cattle,
so caution should be taken when preparing formula feeds for cattle.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.