Reducing dairy data inconsistency through Regional Modeling Approach (RMA): a case from North-Western part of Bangladesh

Abstract

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.

Bang. J. Anim. Sci. 2020. 49 (2): 128-141