Effects of climate change on some main ... amazonaws com
Effects of climate change on some main compounds of milk in Iran Step 1: Homogenization and calibration of Protein of milk and yield of milk and observed temperature and ERA40 reanalysis in Iran Mohammad reza Marami Milani , Angelika Ploeger, Andreas Hense‚ Elham Rahmani Meteorological Institute, University of Bonn, Germany Department of Organic Food Quality and Food Culture, University of Kassel, Germany ([email protected]) Introduction The United Nations (UN) Convention on Climate Change defines climate change as “a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is, in addition to natural climate variability, observed over comparable time periods. However, the consequences of climate change for the food system, which comprises all the stages from “farm to fork” (mainly primary production, processing, transport, trading and consumption), have received less attention compared with other human and animal health and welfare issues. The information that is available in literature mainly focuses on the consequences of climate change on food security, defined by the World Health Organization as access to sufficient, safe and nutritious food. In general, the projected climate change generally assumed to have a negative impact on food security, especially in developing countries.
Objective and Materials 1) Investigation of changes in milk compounds in different areas of Iran in terms of qualitative and quantitative characteristics based on the variability of climate parameters and determination of climate change trend on the milk compounds. 2) Investigation of the effect of different time periods of climate change on milk related to obtained results based on the maintained propose. Based on the above-mentioned research topics, the following data sets will be analysed: (1) milk components (total fat, total protein) and milk yield in whole Iran (about 600 herd stations) based on existing data (max 15 years) and adjusted for the period of times that cow uses the natural pasture for feeding (springs & summers) and (2) climatic parameters (temperature) for whole Iran with 16 long term stations covering 42 years 1967-2008.
Investigation of relation between milk yield and protein by fitting a two dimensional mixture model with the ExpectationMaximization algorithm The cluster classification of milk yield and Protein has been investigated from 5 years data in 35 herd stations. Example: Khorasan province in Northeast of Iran
The average of Protein and yield of milk during summer and spring season in 2003-2007 for first class (blue color)
Methodology We will estimate the probability density and related moments of milk compounds such as protein and milk yield in spring and summer. Then we will fit regression models between milk compounds and some local climate parameters. If this leads to a significant and robust model, the generated climate data based on ECMWF Re-Analysis (ERA40) data in combination with climate model output will be used to generate milk data for future developments. This will allow a mapping of the positive and negative effects of climate change on the milk components, finding the optimum scenario for minimizing negative effects of climate change on the milk components, such as changing the food rationing of cow, enrichment of milk, etc.
Case Study Iran simplified climate map
Officially name : Islamic Republic of Iran Formerly name : known internationally as Persia
Caspian Mild, subtropical Mountains Desert and Semi-Desert, ....arid and semi arid
Both names are used interchangeably in cultural contexts Place : In central Eurasia and west Asia
Covariance between Milk yield and Protein in 35 herd stations during 5 years (2003 – 2007)
Correlation (shaded) and regression coefficient (contours) between bootstrapped anomalies of monthly temperature (whole year) for observed and ERA40 example for Khorasan province (Mashhad) The large scale represenative field of 2 meter temperature variability at Khorasan province (Mashhad)
Area : The 18th largest country in the world in terms of area at 1,648,195 km² Population : over 74 million
Statistical analysis on milk yield and Protein with EM algorithm expectation-maximization (EM) algorithm is a method for finding maximum likelihood estimates of parameters in statistical models, where the model depends on unobserved latent variables.
Correlation and regression coefficient Correlation and regression coefficient between bootstrapped anomalies of between bootstrapped anomalies of daily temperature in August (the daily temperature in February (the warmest) for observed and ERA40 : coldest) for observed and ERA40: example for Khorasan province example for Khorasan province (Mashhad) (Mashhad)
Conclusions EM of Milk yield parameter Example: Khorasan province in Northeast of Iran in July 2004
EM of Protein parameter Example: Khorasan province in Northeast of Iran in July 2004
The average of Milk yield and Protein in 35 herd stations during 5 years (2003 – 2007)
The results indicate a strongly non-normal probability density distribution of cow milk variability in Iran. On the climatic results, the station in the same place in north east of Iran, in Khorasan province which the centre called as Mashhad give a very pronounced structure elongated in East-West direction with a clear influence of the Caspian Sea and Alborz Mountains. The seasonality of the patterns show almost the same shaped with annual pattern. The summer pattern is much smaller in its spatial extend. Local temperature variability which has been shown to be an important driver for animal phenology (here across herd variability of milk compounds and yield) is well to very well represented in the ERA-40 data set even on a daily scale.
Objective and Materials 1) Investigation of changes in milk compounds in different areas of Iran in terms of qualitative and quantitative characteristics based on the variability of climate parameters and determination of climate change trend on the milk compounds. 2) Investigation of the effect of different time periods of climate change on milk related to obtained results based on the maintained propose. Based on the above-mentioned research topics, the following data sets will be analysed: (1) milk components (total fat, total protein) and milk yield in whole Iran (about 600 herd stations) based on existing data (max 15 years) and adjusted for the period of times that cow uses the natural pasture for feeding (springs & summers) and (2) climatic parameters (temperature) for whole Iran with 16 long term stations covering 42 years 1967-2008.
Investigation of relation between milk yield and protein by fitting a two dimensional mixture model with the ExpectationMaximization algorithm The cluster classification of milk yield and Protein has been investigated from 5 years data in 35 herd stations. Example: Khorasan province in Northeast of Iran
The average of Protein and yield of milk during summer and spring season in 2003-2007 for first class (blue color)
Methodology We will estimate the probability density and related moments of milk compounds such as protein and milk yield in spring and summer. Then we will fit regression models between milk compounds and some local climate parameters. If this leads to a significant and robust model, the generated climate data based on ECMWF Re-Analysis (ERA40) data in combination with climate model output will be used to generate milk data for future developments. This will allow a mapping of the positive and negative effects of climate change on the milk components, finding the optimum scenario for minimizing negative effects of climate change on the milk components, such as changing the food rationing of cow, enrichment of milk, etc.
Case Study Iran simplified climate map
Officially name : Islamic Republic of Iran Formerly name : known internationally as Persia
Caspian Mild, subtropical Mountains Desert and Semi-Desert, ....arid and semi arid
Both names are used interchangeably in cultural contexts Place : In central Eurasia and west Asia
Covariance between Milk yield and Protein in 35 herd stations during 5 years (2003 – 2007)
Correlation (shaded) and regression coefficient (contours) between bootstrapped anomalies of monthly temperature (whole year) for observed and ERA40 example for Khorasan province (Mashhad) The large scale represenative field of 2 meter temperature variability at Khorasan province (Mashhad)
Area : The 18th largest country in the world in terms of area at 1,648,195 km² Population : over 74 million
Statistical analysis on milk yield and Protein with EM algorithm expectation-maximization (EM) algorithm is a method for finding maximum likelihood estimates of parameters in statistical models, where the model depends on unobserved latent variables.
Correlation and regression coefficient Correlation and regression coefficient between bootstrapped anomalies of between bootstrapped anomalies of daily temperature in August (the daily temperature in February (the warmest) for observed and ERA40 : coldest) for observed and ERA40: example for Khorasan province example for Khorasan province (Mashhad) (Mashhad)
Conclusions EM of Milk yield parameter Example: Khorasan province in Northeast of Iran in July 2004
EM of Protein parameter Example: Khorasan province in Northeast of Iran in July 2004
The average of Milk yield and Protein in 35 herd stations during 5 years (2003 – 2007)
The results indicate a strongly non-normal probability density distribution of cow milk variability in Iran. On the climatic results, the station in the same place in north east of Iran, in Khorasan province which the centre called as Mashhad give a very pronounced structure elongated in East-West direction with a clear influence of the Caspian Sea and Alborz Mountains. The seasonality of the patterns show almost the same shaped with annual pattern. The summer pattern is much smaller in its spatial extend. Local temperature variability which has been shown to be an important driver for animal phenology (here across herd variability of milk compounds and yield) is well to very well represented in the ERA-40 data set even on a daily scale.
