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

dc.creatorKosmowski, F.
dc.creatorChamberlin, J.
dc.creatorAyalew, H.
dc.creatorSida T.S.
dc.creatorAbay, K.A.
dc.creatorCraufurd, P.
dc.date2021-07-30T00:15:15Z
dc.date2021-07-30T00:15:15Z
dc.date2021
dc.date.accessioned2023-07-17T20:07:52Z
dc.date.available2023-07-17T20:07:52Z
dc.identifierhttps://hdl.handle.net/10883/21587
dc.identifier10.1016/j.foodpol.2021.102122
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7513366
dc.descriptionAgricultural 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.
dc.languageEnglish
dc.publisherElsevier
dc.relationhttps://www.sciencedirect.com/science/article/pii/S0306919221001019?via%3Dihub#s0080
dc.rightsCIMMYT 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
dc.rightsOpen Access
dc.source102
dc.source0306-9192
dc.sourceFood Policy
dc.source102122
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectAgricultural Systems
dc.subjectMeasurement Errors
dc.subjectFarm Survey Data
dc.subjectSampling Methods
dc.subjectFARMING SYSTEMS
dc.subjectCROP PRODUCTION
dc.subjectCROP YIELD
dc.subjectFARM SURVEYS
dc.subjectMEASUREMENT
dc.subjectSAMPLING
dc.titleHow accurate are yield estimates from crop cuts? Evidence from smallholder maize farms in Ethiopia
dc.typeArticle
dc.typePublished Version
dc.coverageEthiopia
dc.coverageUnited Kingdom


Este ítem pertenece a la siguiente institución