Types of Research
- (-) Remove LSMS & LSMS-ISA filter LSMS & LSMS-ISA
- (-) Remove 2018 filter 2018
- (-) Remove 2008 filter 2008
- (-) Remove Agricultural Productivity, Yield, & Constraints filter Agricultural Productivity, Yield, & Constraints
- (-) Remove Education & Training filter Education & Training
- (-) Remove Global & Regional Public Goods filter Global & Regional Public Goods
- (-) Remove East Africa Region and Selected Countries filter East Africa Region and Selected Countries
- (-) Remove Rural Populations filter Rural Populations
- (-) Remove Health filter Health
- (-) Remove Smallholder Farmers filter Smallholder Farmers
- (-) Remove 2011 filter 2011
Precise agricultural statistics are necessary to track productivity and design sound agricultural policies. Yet, in settings where intercropping is prevalent, even crop yield can be challenging to measure. In a systematic survey of the literature on crop yield in low-income settings, we find that scholars specify how they estimate the yield denominator in under 10% of cases. Using household survey data from Tanzania, we consider four alternative methods of allocating land area on plots that contain multiple crops, and explore the implications of this measurement decision for analyses of maize and rice yield. We find that 64% of cultivated plots contain more than one crop, and average yield estimates vary with different methods of calculating area planted. This pattern is more pronounced for maize, which is more likely than rice to share a plot with other crops. The choice among area methods influences which of these two staple crops is found to be more calorie-productive per ha, as well as the extent to which fertilizer is expected to be profitable for maize production. Given that construction decisions can influence the results of analysis, we conclude that the literature would benefit from greater clarity regarding how yield is measured across studies.
This brief explores agricultural data for Tanzania from the LSMS-ISA and Farmer First household surveys. We first present the differences in the LSMS and Farmer First survey design and in basic descriptives from the two data sources. We then present the results of our initial LSMS data analysis using the 2008/2009 wave of the Tanzania National Panel Survey (TZNPS), focusing on the agricultural data, before presenting our analysis of farmer aspirations and of gender differences using the Farmer First data.