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How accurate are yield estimates from crop cuts? Evidence from smallholder maize farms in Ethiopia

Creator: Kosmowski, F.
Creator: Chamberlin, J.
Creator: Hailemariam Ayalew
Creator: Sida T.S.
Creator: Abay, K.A.
Creator: Craufurd, P.
Year: 2021
URI: https://hdl.handle.net/10883/21587
Language: English
Publisher: Elsevier
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
Type: Article
Country focus: Ethiopia
Place of Publication: United Kingdom
Volume: 102
DOI: 10.1016/j.foodpol.2021.102122
Keywords: Agricultural Systems
Keywords: Measurement Errors
Keywords: Farm Survey Data
Keywords: Sampling Methods
Description: Agricultural statistics and applied analyses have benefitted from moving from farmer estimates of yield to crop cut based estimates, now regarded as a gold standard. However, in practice, crop cuts and other sample-based protocols vary widely in the details of their implementations and little empirical work has documented how alternative yield estimation methods perform. Here, we undertake a well-measured experiment of multiple yield estimation methods on 237 smallholder maize plots in Amhara region, Ethiopia. We compare yield from a full plot harvest with farmer assessments and with estimates from a variety of field sampling protocols: W-walk, transect, random quadrant, random octant, center quadrant, and 3 diagonal quadrants. We find that protocol choices are important: alternative protocols vary considerably in their accuracy relative to the whole plot, with absolute mean errors ranging from 23 (farmer estimates) to 10.6 (random octant). Furthermore, while most methods approximate the sample mean reasonably well, the divergence of individual measures from true plot-level values can be considerable. We find that randomly positioned quadrants outperform systematic sampling schemes: the random octant had the best accuracy and was the most cost-effective. The nature of bias is non-classical: bias is correlated with plot size as well as with plot management characteristics. In summary, our results advocate that even “gold standard” crop cut measures should be interpreted cautiously, and more empirical work should be carried out to validate and extend our conclusions.
Agrovoc: FARMING SYSTEMS
Agrovoc: CROP PRODUCTION
Agrovoc: CROP YIELD
Agrovoc: FARM SURVEYS
Agrovoc: MEASUREMENT
Agrovoc: SAMPLING
Related Datasets: https://www.sciencedirect.com/science/article/pii/S0306919221001019?via%3Dihub#s0080
ISSN: 0306-9192
Journal: Food Policy
Article number: 102122


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  • Socioeconomics
    Including topics such as farming systems, markets, impact & targeting, innovations, and GIS
  • Sustainable Intensification
    Sustainable intensification agriculture including topics on cropping systems, agronomy, soil, mechanization, precision agriculture, etc.

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