cuts. Second, in light of the time and skills required, specialized production surveys relying on crop cutting tended to neglect broader socio-economic variables, making it difficult to analyze results as an outcome of rational decision-making by an agricultural household. A second method that has been used traditionally to provide production information relied on periodic reports from agricultural extension workers. The lack of a micro orientation and the potential interest of such agents to report favorable outcomes tend to limit the credibility of such estimates. On the other hand, remotely sensed data may be useful for aggregate assessment of crop conditions and expected harvest amounts but too crude to provide the micro-evidence needed to address the multitude of issues -from determinants of technology adoption, interactions between crops, and equity impacts of improving technology across and within householdsthat are of considerable interest to policy makers. Given different costs, strengths and weaknesses by each of these methods, collecting meaningful data in a cost-effective way will invariably entail trade-offs. Failure to understand or address these trade-offs can undermine the credibility of agricultural statistics and the ability to generate meaningful research and policy prescriptions.

In light of these constraints, multi-purpose household surveys have emerged as a key tool to provide agricultural production information as well as complementary information that would allow analysis of agricultural supply, rural welfare, and links to the non-farm economy<sup>1</sup> . It is well known that a number of factors that include inadequate training of enumerators, long lags since completion of the harvest and the implied recall error, control of plots by people other than the respondent, use of non-standard units of measurement, intercropping, and limited reliability of self-reported area measurements from respondents without actual cross-checking in the field, may limit the accuracy of information provided by such surveys. A combination of these factors can result in agricultural production data of poor quality with large measurement errors. Surprisingly little attention has been devoted to measuring the extent of such errors and ways in which it could be minimized, although there are some initial attempts at taking this more seriously, for example by examining recall error more systematically (Beegle _et. al._ 2011).

While a number of countries, including China, routinely use diaries to obtain what are considered high quality data on household expenditure, diaries have seldom been used for estimating crop production, despite evidence in other fields of the potential benefits they can bring. Diaries are used in both developed and developing countries for recording food consumption and/or expenditures in household surveys such as Household Budget Surveys (HBS) and the Living Standards Measurement Study, or LSMS (Grosh and Glewwe 2000). In developing countries, their use and ultimate appropriateness has been questioned on several grounds, including the low level of literacy of respondents (Bowling 2002) and the presumption

> 1 Most recently, in a departure from previous methodological guidelines, the Global Strategy for Agricultural Statistics, a multi-agency effort by the UN, World Bank, FAO and USDA, among others, advocated for integrating the collection of agricultural statistics with a multi-topic framework, as to allow for a better understanding of agricultural processes and outcomes within the broader household economy.

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