Precision Livestock Farming in Uzbekistan Through Evidence Synthesis and A Technology-Context Fit Framework

Abstract

This study assesses which precision and digital livestock technologies suit Uzbekistan, where household farms keep about nine in ten cattle, and in what order they should be introduced. It combines an integrative review of six recent sources with national statistics and policy documents, re-examines published efficiency and productivity estimates, and scores 14 technologies on six context-fit criteria. Uzbekistan's livestock sector operated at a meta-frontier efficiency of 0.672 in 2000-2023, close to its income-group mean of 0.684, but its total factor productivity fell 0.6% a year, the weakest result in the group. Reported gains from precision livestock farming are large but come almost entirely from intensive, high-income farms. Six technologies reach the top tier, all of them shared services: animal identification, veterinary surveillance, mobile advisory, breeding services, digital milk collection and satellite pasture monitoring. Wearable sensors and precision feeding suit commercial farms, while robotic milking and computer vision rank last. The ranking is robust to weigh (Spearman's ρ of 0.94 and 0.98). The study proposes a three-phase adoption plan with measurable gates.