• Journal of Animal Science and Technology
• /
• v.63 no.2
• /
• pp.305-318
• /
• 2021

Alfalfa (Medicago sativa L.) is an important forage legume grown in Kansas, USA and its productivity with cool-season grasses however is unknown. The objective of this study was to determine the dry matter yield (DMY) and forage nutritive value of alfalfa-grass mixtures compared to those of alfalfa and grasses grown in monoculture with and without nitrogen fertilization. Three different alfalfa varieties were planted (reduced-lignin alfalfa, Roundup Ready, and conventional alfalfa) and two kinds of cool-season grasses (smooth brome, Bromus inermis Leyss, and tall fescue, Festuca arundinacea Schreb) were planted as a monoculture or in alfalfa-grass mixtures. Nitrogen fertilizer (urea) was applied at green-up at a rate of 56 kg/ha and after the second cutting at a rate of 56 kg/ha in 2016 and 2017, respectively. and control treatments received no nitrogen. DMY was significantly higher in monoculture alfalfa and alfalfa-grass mixtures than in grass monocultures. Between alfalfa monoculture and alfalfa-grass mixtures, no significant differences in DMY were found. For all treatments, nitrogen application significantly increased DMY compared to the control. In 2016 and 2017, the low-lignin alfalfa monoculture had the lowest acid detergent fiber (ADF) and the grass monocultures had the highest ADF. In 2016 and 2017, neutral detergent fiber (NDF) in smooth bromegrass and tall fescue was higher than in other species treatments. A low-lignin alfalfa monoculture had significantly lower NDF concentration compared to alfalfa-grass mixtures. When averaged over 2016 and 2017, relative feed value (RFV) was highest in low-lignin alfalfa and lowest in the grass monocultures. In both years, nitrogen fertilizer application did not affect nutritive values.

• Animal Bioscience
• /
• v.34 no.4
• /
• pp.489-498
• /
• 2021

Objective: Milk production is one of the most desirable traits in livestock. Recently, the toll-like receptor (TLR) has been identified as a candidate gene for milk traits in cows. So far, there is no information concerning the contribution of this gene in milk traits in sheep. This study was designed to investigate the TLR 4 gene polymorphisms in Barki ewes in Egypt and then correlate that with milk traits in order to identify potential single nucleotide polymorphisms (SNPs) for these traits in sheep. Methods: A part of the ovine TLR 4 gene was amplified in Barki ewes, to identify the SNPs. Consequently; Barki ewes were genotyped using polymerase chain reaction-single strand conformation polymorphism protocol. These genotypes were correlated with milk traits, which were the daily milk yield (DMY), protein percentage (PP), fat percentage (FP), lactose percentage, and total solid percentage (TSP). Results: Age and parity of the ewe had a significant effect (p<0.05 or p<0.01) on DMY, FP, and TSP. The direct sequencing identified a missense mutation located in the coding sequence of the gene (rs592076818; c.1710C>A) and was predicted to change the amino acid sequence of the resulted protein (p.Asn570Lys). The association analyses suggested a significant effect (p<0.05) of the TLR genotype on the FP and PP, while the DMY tended to be influenced as well (p = 0.07). Interestingly, the presence of the G allele tended to increase the DMY (+40.5 g/d) and significantly (p<0.05 or p<0.01) decreased the FP (-1.11%), PP (-1.21%), and TSP (-7.98%). Conclusion: The results of this study suggested the toll-like receptor 4 (TLR4) as a candidate gene to improve milk traits in sheep worldwide, which will enhance the ability to understand the genetic architecture of genes underlying SNPs that affect such traits.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.37 no.2
• /
• pp.145-153
• /
• 2017

This study was aimed to find yield prediction model of Italian ryegrass using climate big data and geographic information. After that, mapping the predicted yield results using Geographic Information System (GIS) as follows; First, forage data were collected; second, the climate information, which was matched with forage data according to year and location, was gathered from the Korean Metrology Administration (KMA) as big data; third, the climate layers used for GIS were constructed; fourth, the yield prediction equation was estimated for the climate layers. Finally, the prediction model was evaluated in aspect of fitness and accuracy. As a result, the fitness of the model ($R^2$) was between 27% to 95% in relation to cultivated locations. In Suwon (n=321), the model was; DMY = 158.63AGD -8.82AAT +169.09SGD - 8.03SAT +184.59SRD -13,352.24 (DMY: Dry Matter Yield, AGD: Autumnal Growing Days, SGD: Spring Growing Days, SAT: Spring Accumulated Temperature, SRD: Spring Rainfall Days). Furthermore, DMY was predicted as $9,790{\pm}120$ (kg/ha) for the mean DMY(9,790 kg/ha). During mapping, the yield of inland areas were relatively greater than that of coastal areas except of Jeju Island, furthermore, northeastern areas, which was mountainous, had lain no cultivations due to weak cold tolerance. In this study, even though the yield prediction modeling and mapping were only performed in several particular locations limited to the data situation as a startup research in the Republic of Korea.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.17 no.3
• /
• pp.261-269
• /
• 2015

The purpose of this research is to identify the significance of climate factors related to the significance of change of dry matter yield (DMY) of whole crop maize (WCM) by year through the exploratory data analysis. The data (124 varieties; n=993 in 7 provinces) was prepared after deletion and modification of the insufficient and repetitive data from the results (124 varieties; n=1027 in 7 provinces) of import adaptation experiment done by National Agricultural Cooperation Federation. WCM was classified into early-maturity (25 varieties, n=200), mid-maturity (40 varieties, n=409), late-maturity (27 varieties, n=234) and others (32 varieties, n=150) based on relative maturity and days to silking. For determining climate factors, 6 weather variables were generated using weather data. For detecting DMY and climate factors, SPSS21.0 was used for operating descriptive statistics and Shapiro-Wilk test. Mean DMY by year was classified into upper and lower groups, and a statistically significant difference in DMY was found between two groups (p<0.05). To find the reasons of significant difference between two groups, after statistics analysis of the climate variables, it was found that Seeding-Harvesting Accumulated Growing Degree Days (SHAGDD), Seeding-Harvesting Precipitation (SHP) and Seeding-Harvesting Hour of sunshine (SHH) were significantly different between two groups (p<0.05), whereas Seeding-Harvesting number of Days with Precipitation (SHDP) had no significant effects on DMY (p>0.05). These results indicate that the SHAGDD, SHP and SHH are related to DMY of WCM, but the comparison of R2 among three variables (SHAGDD, SHP and SHH) couldn't be obtained which is needed to be done by regression analysis as well as the prediction model of DMY in the future study.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.38 no.1
• /
• pp.53-60
• /
• 2018

Yield prediction model for mixed pasture was developed with a shortage that the relationship between dry matter yield (DMY) and days of summer depression (DSD) was not properly reflected in the model in the previous research. Therefore, this study was designed to eliminate the data of the regions with distinctly different climatic conditions and then investigate their relationships DMY and DSD using the data in each region separately of regions with distinct climatic characteristics and classify the data based on regions for further analysis based on the previous mixed pasture prediction model. The data set used in the research kept 582 data points from 11 regions and 41 mixed pasture types. The relationship between DMY and DSD in each region were analyzed through scatter plot, correlation analysis and multiple regression analysis in each region separately. In the statistical analysis, DMY was taken as the response variable and 5 climatic variables including DSD were taken as explanatory variables. The results of scatter plot showed that negative correlations between DMY and DSD were observed in 7 out of 9 regions. Therefore, it was confirmed that analyzing the relationship between DMY and DSD based on each region is necessary and 5 regions were selected (Hwaseong, Suwon, Daejeon, Siheung and Gwangju) since the data size in these regions is large enough to perform the further statistical analysis based on large sample approximation theory. Correlation analysis showed that negative correlations were found between DMY and DSD in 3 (Hwaseong, Suwon and Siheung) out of the 5 regions, meanwhile the negative relationship in Hwaseong was confirmed through multiple regression analysis. Therefore, it was concluded that the interpretability of the yield prediction model for mixed pasture could be improved based on constructing the models using the data from each region separately instead of using the pooled data from different regions.

• Journal of Animal Science and Technology
• /
• v.47 no.3
• /
• pp.355-362
• /
• 2005

This study was carried out to obtain basic information on variety selection for the utilization of whole crop rices(WCR) at National Institute of Crop Science, RDA, in 2004. Fifteen varieties and elite line were evaluated on feed value such as dry matter yield(DMY), crude protein( CP), acid detergent fiber(ADF), neutral detergent fiber(NDF) and total digestible nutrients(TDN). The dry matter yields were ranged from 13.23 to 17.83 ton per ha, the highest yielding varieties were Sobibyeo(l6.98ton / ha) in Japonica type, SR22060 (17.83 ton / ha) in New plant type, Hangangchalbyeo(I7.66 ton / ha) in Tongil type. Suweon 468 showed the highest value in the RFV and TDN content among the varieties, and Suweon 468, Suweon 498, Suweon 490 and SR22058 were chosen to have the high feed values through cluster analysis. The dry weight(grain) was found to be positively related with percent of the ripened grain, 1,000 grain weight and CPo TDN content was found to be positively related with CP, but negatively related with NDF and ADF. RFV was found to be negatively related with plant height, NDF and ADF. The promising rice varieties for WCR were Suweon 468, Suweon 498, Suweon 490 and SR22058 on the basis of CP, TDN and DMY.

• Asian-Australasian Journal of Animal Sciences
• /
• v.15 no.5
• /
• pp.627-632
• /
• 2002

Genetic parameters for dairy performance traits were estimated, breeding values for the traits of all breeding sires and cows were predicted and the genetic trends were estimated using the breeding values in the Central Cattle Breeding Station (CCBS). A total of 3,801 records for Bangladeshi Local, 756 records for Red Sindhi and 959 records for Sahiwal covering the period from 1961 to 1997 were used in this analysis. Traits considered were total milk production per lactation (TLP), lactation length (LL) and daily milk yield (DMY). The genetic parameters were estimated by the REML using MTDFREML program. The breeding values were predicted by a best linear unbiased prediction (BLUP). In all sets of data, the genetic trends for the dairy performance traits were computed as averages of breeding values for cows born in the particular year. The estimates of heritability for TLP (0.26 and 0.27) and DMY (0.28 and 0.27) were moderate in Bangladeshi local and Red Sindhi breed, respectively. Furthermore, the heritability estimate for LL (0.24) was moderate in Red Sindhi. The estimates of heritabilities for all traits were low in Sahiwal. The repeatability estimate was high for TLP, moderate for LL and moderate to high for DMY. All variances estimated in Bangladeshi Local were low, comparing the respective values estimated in both Red Sindhi and Sahiwal. On the other hand, additive genetic variances for the three traits were estimated very low in Sahiwal. The genetic trends for the three dairy production traits have not been positive except for the recent trend in Bangladeshi Local.

• Asian-Australasian Journal of Animal Sciences
• /
• v.10 no.6
• /
• pp.581-586
• /
• 1997

The present study analysed the various productive and reproductive characteristics of 1/2 Pabna Milking Cows (PMC)-1/2 Sahiwal (S) and 1/4 PMC-3/4 S collected from the Pabna milkshed area at Baghabarighat, Sirajgonj, Bangladesh. The studied traits were birth weight (BWT), age at puberty (AP), number of services per conception (NSC), post partum heat period (PPHP), gestation period (GP), daily milk yield (DMY), lactational production (LP), lactation length (LL), fat percentage and solids-not-fat percentage (SNFP). Five individual Sahiwal sires were used for the upgrading of Pabna Milking Cows. The used data covered from 1987 to 1994. Least-squares analysis of variance showed that genetic group had a significant effect on BWT (P < 0.05), AP (p < 0.01), DMY (p < 0.01), LP (p < 0.001), LL (p < 0.05), FP (p < 0.05) and SNFP (p < 0.01). Genetic group had non-significant effect on NSC, GP and PPHP. The effect of sire was significant on BWT (p < 0.01), NSC (p < 0.01), LP (p < 0.05) and LL (p < 0.05). The AP, NSC, DMY, LP, LL and SNFP were higher in 1/2 PMC-1/2S cows; BWT and PPHP were higher in 1/4 PMC-3/4S but GP and FP were almost same in both genetic groups. From this study it may be concluded that production and use of 1/2 PMC-1/2S would seem more profitable for commercial milk production in the Bangladesh Milk Producers' Cooperative Union Limited (BMPCUL) area and at the same time emphasis should be given on rigorous sire selection.

• Journal of Animal Science and Technology
• /
• v.63 no.1
• /
• pp.77-90
• /
• 2021

This study aimed to assess the impact of abnormal climate events on the production of Italian ryegrass (IRG), such as autumn low-temperature, severe winter cold and spring droughts in the central inland, southern inland and southern coastal regions. Seasonal climatic variables, including temperature, precipitation, wind speed, relative humidity, and sunshine duration, were used to set the abnormal climate events using principal component analysis, and the abnormal climate events were distinguished from normal using Euclidean-distance cluster analysis. Furthermore, to estimate the impact caused by abnormal climate events, the dry matter yield (DMY) of IRG between abnormal and normal climate events was compared using a t-test with 5% significance level. As a result, the impact to the DMY of IRG by abnormal climate events in the central inland of Korea was significantly large in order of severe winter cold, spring drought, and autumn low-temperature. In the southern inland regions, severe winter cold was also the most serious abnormal event. These results indicate that the severe cold is critical to IRG in inland regions. Meanwhile, in the southern coastal regions, where severe cold weather is rare, the spring drought was the most serious abnormal climate event. In particular, since 2005, the frequency of spring droughts has tended to increase. In consideration of the trend and frequency of spring drought events, it is likely that drought becomes a NEW NORMAL during spring in Korea. This study was carried out to assess the impact of seasonal abnormal climate events on the DMY of IRG, and it can be helpful to make a guideline for its vulnerability.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.43 no.3
• /
• pp.190-198
• /
• 2023

This study was conducted to calculate the damage of Italian ryegrass (IRG) by abnormal climate using machine learning and present the damage through the map. The IRG data collected 1,384. The climate data was collected from the Korea Meteorological Administration Meteorological data open portal.The machine learning model called xDeepFM was used to detect IRG damage. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The calculation of damage was the difference between the Dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of IRG data (1986~2020). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization (WMO) standard. The DMY_normal was ranged from 5,678 to 15,188 kg/ha. The damage of IRG differed according to region and level of abnormal climate with abnormal temperature, precipitation, and wind speed from -1,380 to 1,176, -3 to 2,465, and -830 to 962 kg/ha, respectively. The maximum damage was 1,176 kg/ha when the abnormal temperature was -2 level (+1.04℃), 2,465 kg/ha when the abnormal precipitation was all level and 962 kg/ha when the abnormal wind speed was -2 level (+1.60 ㎧). The damage calculated through the WMO method was presented as an map using QGIS. There was some blank area because there was no climate data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).