Accuracy of agricultural data and implications for policy: Evidence from maize farmer recall surveys and crop cuts in the Guinea Savannah zone of Ghana

William Adzawla*, Edinam D. Setsoafia, Eugene D. Setsoafia, Solomon Amoabeng-Nimako, Williams K. Atakora, Prem D. Bindraban

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)


CONTEXT: The need for accurate data in policy design aimed at agricultural transformation cannot be overemphasized. Unfortunately, the relevance of agricultural research in addressing the needs of farmers has been questioned due to debates about appropriate methodologies and approaches for establishing research activities and, in other instances, poorly reasoned premises and paltry delineation, definition, and understanding of the system being studied. For a country like Ghana, where agricultural transformation is a prerequisite for its sustainable development, an understanding of the accuracy of farm data measurement is necessary. OBJECTIVE: The objective of this study is to estimate the yield measurement error and to analyze the sources of such measurement errors among the farmers of the Guinea Savannah zone of Ghana. METHODS: Two years' data for both farmer recall surveys and crop cuts were used. Descriptive statistics, regression and sensitivity analyses were done to achieve the objectives of the study. RESULTS AND CONCLUSIONS: On average, farmers' recall of maize yields (1544.6 kg/ha) was lower than the estimated crop cut yields (2593.9 kg/ha), although about 11.2% of the farmers recalled higher yields than their estimated crop cut yields. The estimated average percentage error in yield measurement between crop cuts and farmer surveys was 36.4%. These yield measurement errors are due to systematic biases, including those involving the recall of farm size, and the socioeconomic conditions of the farmers. Although a crop cut is costly, it has limited bias in providing a better measure of yield than farmer recall surveys. Irrespective of the method used, however, more attention should be given to potential sources of systematic bias in the design and data collection. Moreover, for proper interpretation, yield estimates from recall surveys and crop cuts should be properly interpreted as economic yield and biological yields, respectively. SIGNIFICANCE: This paper provided clarity on the differences in maize yield estimates in Ghana and provided measures on how to improve precision in yield data.

Original languageEnglish
Article number103817
JournalAgricultural Systems
Publication statusPublished - Feb 2024


  • Agricultural data
  • Crop cut
  • Data accuracy
  • Farmer recall
  • Maize yield
  • Measurement error


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