通过区域建模方法(RMA)减少乳制品数据不一致:来自孟加拉国西北部的案例

M. Uddin, A. Akter, M. Tanzin, M. Sultana, A. Khaleduzzaman, Islam, Palash, BB Bruemmer
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引用次数: 0

摘要

在孟加拉国,奶牛养殖从以生计为导向向企业驱动的养殖体系转变,可能需要更深入地了解奶牛进一步发展的区域潜力方面的区域差异。然而,这需要在区域一级的奶牛场的详细数据。由于一方面数据相对非常稀少,另一方面,即使有数据,各种来源之间在数据准确性和精度方面也存在矛盾,因此将数据和外推的区域建模应用于国家数据,反之亦然,是确定可能的选择以提高数据可用性和质量的方法之一。考虑到这一点,本研究通过比较奶牛群结构及其在区域层面的产奶量来评估数据的不一致性,并提出一种验证工具,通过使用区域研究结果得出国家数据。使用了国际农场比较网络(IFCN)区域建模方法(RMA)以及当地开发的综合乳品研究网络(IDRN)农场模型。主要数据是从该国西北部的三个省(9个县)收集的。结果表明,在以奶牛总数为单位定义农场类型时,家庭农场的比例高于家庭和商业农场。杂交奶牛占本地奶牛的比例分别为74.6%和24.4%。然而,本地奶牛中泌乳奶牛的比例(48.8%)似乎高于杂交奶牛(34.2%)。所有地区的平均产奶量为4.49 lit/day/cow,而杂交品种的平均产奶量为6.23 lit/day/cow,当地为1.71 lit/day/cow。利用区域模型及其对平均产奶量、畜群组成、泌乳奶牛占总产奶量的系数,IDRN新模型估计的2019年产奶量比DLS低36.5%,2018年比DLS低33.5%。2018年和2019年,IDRN 1.0和2.0版本的模型差异分别降低了15.4%和18.3%。考虑到这些大数据集的规模密集的数据需求,以及需要确保来自多个地区的数据的可靠性,模型的建立、校准和验证是一项耗时且具有挑战性的任务。本研究的结论是,区域模型对于验证牛奶生产的区域份额和国家牛奶生产非常有用。然而,这项研究将建议使用标准来收集和验证数据,从而对其他区域进行进一步研究,最后包括全国所有区域。
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Reducing dairy data inconsistency through Regional Modeling Approach (RMA): a case from North-Western part of Bangladesh
In Bangladesh, the transformation of dairy farming from livelihood-oriented to enterprise-driven farming system might require deeper understanding on the regional differences in terms of regional potential for further dairy development. This, however, entails detailed data on dairy farm at regional level. Since the data are relatively very scarce in one hand and on the other hand, even available, are contradicting among various sources in terms of data accuracy and precision, the application of the regional modeling on the data and extrapolates to the national data and vice-versa is one of the ways to identify the possible options to improve the data availability and quality. Considering this, the current study was undertaken to assess the data inconsistency by comparing the dairy herd structure and its milk production at regional level and propose a validation tool to arrive at the national data by using the regional findings. The International Farm Comparison Network (IFCN) Regional Modeling Approach (RMA) along with the locally developed Integrated Dairy Research Network (IDRN) farm model was used. The primary data was collected from three divisions (9 districts) from the North-Western part of the country. The results revealed that proportion of household farm dominates over family and business farm while considering the total dairy cow as unit for defining the farm type. The share of the cross bred cows to the local cows is 74.6% and 24.4%, respectively. However, the proportion of lactating cows over dry cows and heifer seems to be higher in local cows (48.8%) than cross breed cows (34.2%). The average milk production for all regions is 4.49 lit/day/cow while that for cross breed is 6.23 lit and local 1.71 lit/day/cow. Using regional model and its coefficient on average milk production, herd composition, proportion of lactating cows on total milk production of DLS and IDRN revealed that IDRN new model estimates 36.5% lower milk than the DLS in 2019 and 33.5% lower in 2018. The IDRN version 1.0 and 2.0 model difference was found to 15.4% and 18.3% lower for 2018 and 2019, respectively. The model setup, calibration and validation are time-demanding and challenging tasks for these large set of data, given the scale intensive data requirements, and the need to ensure the reliability data from multiple regions. This study concludes that regional modeling is quite useful for validating the regional share of the milk production and national milk production. However, this study would recommend for using standardized for data collection, validation and thus conducting further study on the other regions and finally including all regions of the country.
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