Abstract

Precise agricultural statistics are necessary to track productivity and design sound agricultural policies. Yet, in settings where multi-cropping is prevalent, even crop yield—perhaps the most common productivity metric—can be challenging to measure. In a survey of the literature on crop yield in low-income settings, we find that scholars specify how they estimate the area denominator used to measure yield in under 10% of cases. Using household survey data from Tanzania, we consider four alternative methods of allocating land area on multi-cropped plots, ranging from treatment of the entire plot as the yield denominator to increasingly precise approaches that account for the space taken up by other crops. We then explore the implications of this measurement decision for analyses of yield, focusing on one staple crop that is often grown on its own (rice) and one that is frequently found on mixed plots and in intercropped arrangements (maize). A majority (64%) of cultivated plots contain more than one crop, and average yield estimates vary with different methods of calculating area planted—particularly for maize. Importantly, the choice among area methods influences which of these two crops is found to be more calorie-productive per hectare. This choice also influences the statistically significant correlates of crop yield, such that the benefits of intercropping and including legumes on a maize plot are only evident when using an area measure that accounts for mixed cropping arrangements. We conclude that the literature would benefit from greater clarity regarding how yield is measured across studies.

Highlights

  • Precise agricultural statistics are necessary to measure and track productivity, allocate scarce resources effectively, and design policies and investments aimed at agricultural sector development in low-income countries

  • Gaps remain in our understanding of how to use household survey data to generate accurate agricultural statistics, with limited attention given to the implications of different choices around how some common variables are constructed

  • This paper is motivated by the question of whether construction choices for a very common metric of productivity—crop yield—could affect empirical analyses and thereby have policy and investment implications (Anderson et al 2015)

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Summary

Introduction

Precise agricultural statistics are necessary to measure and track productivity, allocate scarce resources effectively, and design policies and investments aimed at agricultural sector development in low-income countries. To address what had been a decline in the quantity and quality of agricultural statistics in low-income countries (and in Africa) (FAO 2010), the Living Standards Measurement Study Integrated Surveys on Agriculture (LSMS-ISA) were launched in 2008 (Carletto et al, 2010). These nationally representative household data sets gather detailed information on agricultural production at the plot level.

Literature survey
Variables and empirical approach
Method
Descriptive results
Econometric results
Conclusion
Findings
Compliance with ethical standards
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