A wide variety of multilateral and bilateral agencies, private sector firms, and African governments have a need for high quality, reliable data on agricultural productivity. This paper identifies numerous situations where poor data lead to incorrect estimates of African land and labor productivity. The paper argues that better coordination of macro, meso, and micro data collection, reporting, and analysis efforts can lower costs and improve our ability to monitor trends and to quantify determinants of agricultural productivity. Seven key points are made in the discussion: (1) Missing or poorly measured variables used in the numerator (output) or denominator (land and labor, for example) are biasing productivity ratios; (2) In most cases, these errors underestimate levels of agricultural productivity in Africa and distort trends; (3) Micro data are an important source of information for identifying the existence and magnitude of these errors in macro and meso data; (4) Information from micro data can improve estimates of productivity ratios when macro data are not available and too costly to collect; (5) Detailed micro data sets are the best source of information on the farm-level determinants of agricultural productivity; this information contributes to the development of productivity-enhancing policies and technologies; (6) Micro data play an important role in identifying the appropriate variables to monitor in macro and meso series; (7) Only consistently high-quality macro data in unbroken time series can provide adequate information about productivity trends and the contribution of policy and technological change to national agricultural productivity over time. From these conclusions it becomes evident that improving the data used to monitor and analyze agricultural productivity requires much greater cross-fertilization of detailed micro studies and broad macro-data collection and reporting efforts. As data collection and analysis costs are high, researchers and statistical services need to ensure the maximum complementarity possible among different types of surveys and data. This requires coordination among donors, government agencies, and research institutes that fund, collect, and analyze agricultural data.