Seasonal Broiler Growth Performance Prediction Based on Observational Study

Peijie Huang1; Piyuan Lin1; Shangwei Yan1; 2; Meiyan Xiao1

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Seasonal Broiler Growth Performance Prediction Based on Observational Study

机译：基于观测研究的季节性肉鸡生长性能预测

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—For the modern poultry breeding companies, it isworthwhile to predict the poultry growth performanceparameters to help future production and management.Most of the existing literature of broiler growthperformance is based on well designed experiment. However,for the farmers who carry on the large scale breeding all theyear round, it is impractical to adjust all the ambient factorsto suitable levels systematically like in the laboratory. In thiscase, it requires an observational study based on the massivehistorical data gradually cumulated in production. In thispaper, observational study is proposed for seasonal broilergrowth performance prediction. Systematic observationalstudy with statistical analysis and data mining technology isadopted including macro analysis, exploratory data analysis,and modeling. Case study using the broiler growth datasetof the most famous poultry raising company in China showsthe effectiveness of our approach.

机译：- 对于现代家禽养殖公司来说，它是为了预测家禽增长性能参数，以帮助未来的生产和管理。大多数人的肉鸡增长素材文献是基于精心设计的实验。然而，对于携带大规模育种的农民循环循环，可以在实验室系统地将所有环境因素适当的水平调整所有环境因子，这是不切实际的。在该类别中，它需要基于在生产中逐渐累积的Massivehistorical数据进行观察研究。在此纸纸中，提出了观察研究，用于季节性肉鸡的性能预测。具有统计分析和数据挖掘技术的系统性观测研究，包括宏观分析，探索性数据分析和建模。案例研究采用肉鸡增长数据集，在中国最着名的家禽筹集公司展示了我们方法的效果。

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《Journal of Computers》 |2012年第8期|共8页
作者
Peijie Huang1; Piyuan Lin1; Shangwei Yan1; 2; Meiyan Xiao1;
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机译：使用所有数据来改善季节性海面温度预测：基于组合的模型预测，具有不等观察长度
2. Seasonal observations and machine-learning-based spatial model predictions for the common raven (Corvus corax) in the urban, sub-arctic environment of Fairbanks, Alaska [J] . A. P. Baltensperger, T. C. Mullet, M. S. Schmid, Polar biology . 2013,第11期

机译：阿拉斯加费尔班克斯市区，亚北极环境中普通乌鸦（Corvus corax）的季节性观测和基于机器学习的空间模型预测
3. Satellite Sea Surface Salinity Observations Impact on El Nino/Southern Oscillation Predictions: Case Studies From the NASA GEOS Seasonal Forecast System [J] . Hackert Eric, Kovach Robin M., Molod A., Journal of Geophysical Research, C. Oceans: JGR . 2020,第4期

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4. Data Mining for Seasonal Influences in Broiler Breeding Based on Observational Study [C] . Peijie Huang, Piyuan Lin, Shangwei Yan, Information computing and applications . 2011

机译：基于观测研究的肉鸡育种季节性影响数据挖掘
5. Evaluation of extruded cereal grains, oil seeds and exogenous enzyme supplementation based on growth performance and nutrient digestibility studies in broilers. [D] . Menon, Anita. 2001

机译：根据肉鸡的生长性能和养分消化率研究评估挤压谷物，油料种子和外源酶的补充。
6. Dietary phytate has a greater anti-nutrient effect on feed conversion ratio compared to body weight gain and greater doses of phytase are required to alleviate this effect as evidenced by prediction equations on growth performance bone ash and phytate degradation in broilers [O] . C.L. Walk, S.V. Rama Rao 2020

机译：与体重增加和更大剂量的植酸酶相比膳食植物对进料转化比具有更大的抗营养效果以缓解这种效果如肉鸡的生长性能骨灰和植物降解的预测方程所证明
7. Seasonal observations and machine-learning-based spatial model predictions for the common raven (Corvus corax) in the urban, sub-arctic environment of Fairbanks, Alaska [O] . Baltensperger, A. P., Mullet, T. C., Schmid, M. S., 2017

机译：基于季节观测和基于机器学习的空间模型预测，用于在阿拉斯加费尔班克斯的城市，亚北极环境中的普通乌鸦（Corvus corax）

Seasonal Broiler Growth Performance Prediction Based on Observational Study

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