Automating Inconsistency Discover and Historical Investigation of External Influences on Livestock Population Data

A mathematical model of the dynamics of Mongolian livestock populations

This study implements the Tsukamoto Fuzzy Logic method to determine the sheep livestock population based on gender and age categories in Aceh Tamiang Regency. The population data of male and female sheep are categorized into three age groups: young, adolescent, and adult. The processes of fuzzification, fuzzy inference, and defuzzification are used to model the data and produce more accurate population predictions. Fuzzy rules are applied to consider the combination of membership levels of each age category and gender. The analysis results indicate that the sheep livestock population in Aceh Tamiang Regency is 5553,69007. The Tsukamoto Fuzzy Logic method has proven effective in handling uncertainty and variability in livestock population data, providing flexibility in complex data-driven decision-making. This study makes a significant contribution to the utilization of fuzzy logic methods for planning and managing livestock populations and can serve as a reference for policymakers in the livestock sector.

Economic losses occurring due to brucellosis in Indian livestock populations

The traditional livestock industry in Inner Mongolia has evolved rapidly in response to social and economic transformations during recent decades, resulting in substantial impacts on the rural economy and livelihoods of pastoralists. Improved understanding of these changes and potential drivers may help foster strategies to sustain the pastoral system of this region. Using long-term climate, social-economic, and livestock (cattle, horses, sheep, and goats) population data from 1970 to 2010, we analyzed the dynamics of the livestock industry and main driving factors in the Xilinhot region—a central part of the Inner Mongolia Grassland. Our results show that the total livestock population increased dramatically in the past four decades, especially during 1987–2010. Livestock composition also changed substantially, with increasing sheep, goat, and cattle populations but a decreasing horse population. Pastoral population growth and land use policy were the primary drivers for livestock dynamics during 1970–2010. Livestock structure became differentiated progressively with changes in land use policy. Also, climate factors had an important influence on livestock production. The current study suggests that sustainable animal husbandry in this region requires government policies that promote ecological urbanization, livestock production efficiency, incentive systems for grassland conservation, and collective action and cooperation for enhancing social capital and resilience.

The evaluation of forensic characteristics and the phylogenetic analysis of the Ong Be language-speaking population based on Y-STR

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BackgroundThis paper describes a combined heuristic and hidden Markov model (HMM) method to accurately impute missing genotypes in livestock datasets. Genomic selection in breeding programs requires high-density genotyping of many individuals, making algorithms that economically generate this information crucial. There are two common classes of imputation methods, heuristic methods and probabilistic methods, the latter being largely based on hidden Markov models. Heuristic methods are robust, but fail to impute markers in regions where the thresholds of heuristic rules are not met, or the pedigree is inconsistent. Hidden Markov models are probabilistic methods which typically do not require specific family structures or pedigree information, making them very flexible, but they are computationally expensive and, in some cases, less accurate.ResultsWe implemented a new hybrid imputation method that combined heuristic and HMM methods, AlphaImpute and MaCH, and compared the computation time and imputation accuracy of the three methods. AlphaImpute was the fastest, followed by the hybrid method and then the HMM. The computation time of the hybrid method and the HMM increased linearly with the number of iterations used in the hidden Markov model, however, the computation time of the hybrid method increased almost linearly and that of the HMM quadratically with the number of template haplotypes. The hybrid method was the most accurate imputation method for low-density panels when pedigree information was missing, especially if minor allele frequency was also low. The accuracy of the hybrid method and the HMM increased with the number of template haplotypes. The imputation accuracy of all three methods increased with the marker density of the low-density panels. Excluding the pedigree information reduced imputation accuracy for the hybrid method and AlphaImpute. Finally, the imputation accuracy of the three methods decreased with decreasing minor allele frequency.ConclusionsThe hybrid heuristic and probabilistic imputation method is able to impute all markers for all individuals in a population, as the HMM. The hybrid method is usually more accurate and never significantly less accurate than a purely heuristic method or a purely probabilistic method and is faster than a standard probabilistic method.

Technical note: PaGELL v.1.5: A flexible parametric program for the Bayesian analysis of longevity data within the context of animal breeding

BackgroundGenotyping by sequencing (GBS) still has problems with missing genotypes. Imputation is important for using GBS for genomic predictions, especially for low depths, due to the large number of missing genotypes. Minor allele frequency (MAF) is widely used as a marker data editing criteria for genomic predictions. In this study, three imputation methods (Beagle, IMPUTE2 and FImpute software) based on four MAF editing criteria were investigated with regard to imputation accuracy of missing genotypes and accuracy of genomic predictions, based on simulated data of livestock population.ResultsFour MAFs (no MAF limit, MAF ≥ 0.001, MAF ≥ 0.01 and MAF ≥ 0.03) were used for editing marker data before imputation. Beagle, IMPUTE2 and FImpute software were applied to impute the original GBS. Additionally, IMPUTE2 also imputed the expected genotype dosage after genotype correction (GcIM). The reliability of genomic predictions was calculated using GBS and imputed GBS data. The results showed that imputation accuracies were the same for the three imputation methods, except for the data of sequencing read depth (depth) = 2, where FImpute had a slightly lower imputation accuracy than Beagle and IMPUTE2. GcIM was observed to be the best for all of the imputations at depth = 4, 5 and 10, but the worst for depth = 2. For genomic prediction, retaining more SNPs with no MAF limit resulted in higher reliability. As the depth increased to 10, the prediction reliabilities approached those using true genotypes in the GBS loci. Beagle and IMPUTE2 had the largest increases in prediction reliability of 5 percentage points, and FImpute gained 3 percentage points at depth = 2. The best prediction was observed at depth = 4, 5 and 10 using GcIM, but the worst prediction was also observed using GcIM at depth = 2.ConclusionsThe current study showed that imputation accuracies were relatively low for GBS with low depths and high for GBS with high depths. Imputation resulted in larger gains in the reliability of genomic predictions for GBS with lower depths. These results suggest that the application of IMPUTE2, based on a corrected GBS (GcIM) to improve genomic predictions for higher depths, and FImpute software could be a good alternative for routine imputation.

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Comparison of genotype imputation strategies using a combined reference panel for chicken population

In the State of Michigan, with both a strong agricultural base and an abundance of surface waters, there is a general concern as to the production, use, and fate of nutrients. In 1993 commercial fertilizer use in Michigan totaled more than 254 000 Mg of nitrogen, 105 000 Mg of phosphate, and 213 000 Mg of potash. Total cost was more than $207 million. In addition, analysis of recent statistical and census data suggests that statewide nutrient production from livestock manure totaled about 64 000 Mg of nitrogen, 37 000 Mg of phosphate and 52 000 Mg of potash. The potential impact on the environment of this quantity of nutrient production and application requires an understanding of the areal distribution of nutrient availability from manure generation, balanced with the potential for beneficial utilization in crop production. Extrapolation of nutrient production from livestock population data, combined with crop yields, harvested cropland acreage and typical nutrient uptake estimates for the state’s primary crops, indicates that manure could provide about 19% of the nitrogen, 37% of the phosphorus, and 25% of the potassium needs of the state. The nutrient balances in this study have been conducted at the county level; nutrients of interest are nitrogen (N), phosphate (P 2 O 5 ), and potash (K 2 O). The livestock populations considered were non-grazing cattle, horses, hogs and pigs, sheep and lambs, laying hens and broilers. Field crops included were alfalfa, barley, corn for grain, corn for silage, dry beans, oats, potatoes, rye, soybeans, sugarbeets, and wheat. Data analysis indicates that most areas of the state could utilize the nutrients in manure produced in-county to partially offset the amount of commercial fertilizer necessary to meet typical crop requirements. Few counties in the state produce more nutrients than could be used in crop fertilization, but the results show that the potential for pollution from excess nutrients exists in a number of lakeshore counties, especially when fertilizer is considered. A gross nutrient balance for manure nutrients plus fertilizer less crop uptake shows the state to be in deficit for nitrogen but in significant excess for phosphate and potash.

Across the world, social-ecological rangeland systems have been transformed from a preindustrial extensive status to intensive exploitation, often leading to long-term livestock population booms, overgrazing, and rangeland degradation. To understand the regulatory mechanisms involved in such historical social-ecological transformations, we collected population data on the native sheep of the last nomadic county in the Inner Mongolia Autonomous Region (1961-2005). We detected changes in internal feedbacks (e.g., density-dependent effects) and external disturbance (e.g., winter harshness, rainfall, harvest) between the extensive and intensive management periods using regression models of sheep population growth rate and counterfactual analyses. We found that, in the extensive period, sheep populations were regulated during harsh winters by climate, while they were regulated during mild winters by negative density dependence. In the intensive period, the negative feedback of density dependence was removed through the provision of additional forage and shelter, and only winter climate and growing season rainfall regulated sheep populations. Counterfactual analyses also confirmed the irreplaceable role of density-dependence in maintaining a sustainable rangeland ecosystem. Although herders attempted to adapt to the removal of negative feedbacks by improving livestock harvest, overgrazing and grassland degradation remain a challenge in this system. We conclude that internal feedbacks within social-ecological systems should be carefully anticipated and accounted for when managing rangelands for sustainability.

Monitoring of Spatio-temporal vegetation dynamic is one of the most important indicators used to track environmental quality. Spatial and temporal dynamics of Normalized Difference Vegetation Index (NDVI) values are the most useful and reliable technique to analyze the general vegetation dynamics at the regional level. Thus, this study analyzed the spatial pattern and temporal trend of NDVI values of naturally vegetated areas in the Madi Watershed of Nepal relating to its geomorphic and built-up density factors in 2000, 2010,and 2020. Lands at images were used to derive NDVI values and SRTM DEM and the topographic map were used to derive geomorphic factors and built-up density. This study excluded cultivated land, built-up area, water body, cliff, snow-cover area, glacier, and glacier moraine areas to derive only naturally vegetated areas. Human population data was collected from the population censuses. Field observation and information collected from the field to verify ground reality in 15 different watersheds. This study revealed that there is a significant increase in natural vegetation in all parts of the watershed. However, there is the highest rate of increase in vegetation in lower plain areas, where the population density is the highest. The overall increase in natural vegetation is because of the decreasing human and livestock population, changes in lifestyle, etc. The highest increase in natural vegetation in lower plain areas is because of the alternative use of cooking energy and building materials in urban and accessible areas, decreasing the number of livestock. Thus, it can be concluded that there is a significant impact of population dynamics on vegetation in the Madi Watershed of Nepal.