The government of Egypt encouraged the agricultural sector to enhance its strategic directions to achieve higher rate of agricultural output growth through different ways. One of these ways is enhancing possibilities of the testing laboratories overseen by Ministry of Agriculture and Land Reclamation by taking accredited steps acording to ISO 17025. However, ISO 17025 states testing laboratories shall have and shall apply procedures for estimating uncertainty of measurement. In order to improve the comparability of the testing results from different testing laboratories and give a reliable result on the basis of a specific standard, it is essential to evaluate and analyze the uncertainties related to measuring devices errors and test procedure.The testing laboratories of agricultural machines play important role to verify declarations/claims of the manufacturer/applicant for performance characteristics of machines that are in or ready for commercial production. The objectives of this study are to evaluate sources of uncertainty in measuring seed rate and to illustrate the effect of simulated forward speed and seeder opening on seeding rate. Simulated forward speed and seeder opening had significant effect on seeding rate. Increasing simulated forward speed resulted in increasing the seeding rate (g/s). The discussion and results presented focus on uncertainty related to seeding rate with unit ofg/s and their estimation based on Type A and Type B methods. The Type A uncertainty was affected by treatments and had average value of 0.247 g/s. Meanwhile, the Type B uncertainty was affected by sensitivity coefficients and measuring instruments. The results are reported at k =2 for approximately 95% confidence level and the expanded uncertainty had average value of ± 0.633 g/s. The results showed that the measurement uncertainty in seeding rate was mainly caused by the adjustments of seed drill before test, and the instruments contributed a little to the measurement uncertainty of seeding rate. This finding implies that if uncertainty estimates are included with measured data sets and adequately communicated to researchers and decision makers, then optimal monitoring agricultural machine design will result.