A new spin on an old debate: Errors in farmer-reported production and their implications for inverse scale - Productivity relationship in Uganda

Gourlay, Sydney; Kilic, Talip; Lobell, David B.

doi:10.1016/j.jdeveco.2019.102376

Cited by 86 publications

(93 citation statements)

References 38 publications

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“…In addition, we found that self-reported yields are likely inaccurate as they were unable to detect the known relationship between later sowing and lower wheat yields (Erenstein and Laxmi, 2008). Previous studies in sub-Saharan Africa have similarly found that farmers' self-reported yields are inaccurate (Lobell et al, 2018;Gourlay et al, 2019;Wahab, 2019), though several of these studies found that farmers over-reported their yields by up to 200% (Lobell et al, 2018;Gourlay et al, 2019). There are several unintentional and intentional reasons why selfreported yields in our study may be inaccurate.…”

Section: Discussioncontrasting

confidence: 71%

“…Unintentional reasons include that farmers often report yields in local weight (quintal, mand) and area (katha, bhiga) units, and errors may arise from the conversion of non-standard units to standard units during reporting (Fermont and Benson, 2011). In addition, recall bias could contribute to inaccurate self-reported estimates as human memory is subject to memory constraints (Gourlay et al, 2019). However, we do not believe that recall bias likely played a large role in our study given that all farmers were interviewed shortly after harvesting their crop (within 3 weeks).…”

Section: Discussionmentioning

confidence: 98%

“…However, there are concerns regarding the reliability of self-reported data (Baumeister et al, 2007), with previous studies finding that farmers' estimations of yield are highly subjective and can be misleading (Carletto et al, 2015). This is because farmers may over-or under-estimate their yields due to intentional and unintentional reasons, including recall bias, a tendency to average productivity over several seasons or fields, and the desire to influence perceived status within a given community (Gourlay et al, 2019;Wahab, 2019). Several studies have used self-reported data to calibrate satellite yield estimation models, and these studies have found that such data led to reduced yield prediction accuracies compared to other types of calibration datasets, including crop cut data (Wahab, 2019).…”

Section: Introductionmentioning

confidence: 99%

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The Accuracy of Self-Reported Crop Yield Estimates and Their Ability to Train Remote Sensing Algorithms

Paliwal

Jain

2020

Front. Sustain. Food Syst.

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Despite recent advances in remote sensing, one of the major constraints that still remains is collecting the ground data needed to calibrate and validate remote sensing algorithms at large spatial and temporal scales. This is particularly challenging when mapping continuous variables such as yield, where calibration data often do not exist at the field-scale and are difficult to obtain through visual interpretation of high-resolution imagery. While crop cut estimates of crop yield are widely used to calibrate satellite yield estimation models, these data are time and cost intensive to collect. In this study, we examine the ability of self-reported yield estimates, which are much faster and easier to collect at large scales, to train satellite yield estimation models. We assess the accuracy of self-reported yield data and identify whether it is possible to increase self-reported accuracy by providing more information to farmers about the study design and potential benefits. Our results showed that farmers' self-reported crop yields were not accurate, and that self-reported crop yields led to inaccurate satellite yield estimation models when used for calibration. We also found that providing more information to farmers about the study design and benefits of satellite yield estimation did not improve self-reported accuracy. These results suggest that even though self-reported yield estimates may be a faster and lower cost way to collect field-level yield estimates, they likely are not an adequate data source to train satellite yield prediction models and should be used with caution.

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Section: Discussioncontrasting

confidence: 71%

Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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The Accuracy of Self-Reported Crop Yield Estimates and Their Ability to Train Remote Sensing Algorithms

Paliwal

Jain

2020

Front. Sustain. Food Syst.

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“…Yet, on-farm estimates are generally lower. A recent study of on-farm yield reports figures between 270 kg per acre and 995 kg per acre (Gourlay et al, 2019). There is also evidence that maize plots under female management are less productive than maize plots under male management.…”

Section: Study Contextmentioning

confidence: 99%

Women’s empowerment, agricultural extension, and digitalization: Disentangling information and role model effects in rural Uganda

Lecoutere¹,

Spielman²,

Campenhout³

2019

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established in 1975, provides researchbased policy solutions to sustainably reduce poverty and end hunger and malnutrition. IFPRI's strategic research aims to foster a climate-resilient and sustainable food supply; promote healthy diets and nutrition for all; build inclusive and efficient markets, trade systems, and food industries; transform agricultural and rural economies; and strengthen institutions and governance. Gender is integrated in all the Institute's work. Partnerships, communications, capacity strengthening, and data and knowledge management are essential components to translate IFPRI's research from action to impact. The Institute's regional and country programs play a critical role in responding to demand for food policy research and in delivering holistic support for country-led development. IFPRI collaborates with partners around the world.

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“…In the former, survey enumerators ask farmers a series of questions related to total grain harvest and cultivated area, and compute yields as the ratio of reported production to reported area. Among the many sources of errors in these estimates are the frequent use of non-standard measurement units by farmers (which then require conversion to a common unit), the various moisture levels at which grain is harvested, partial harvest before crop maturity for home consumption (e.g., green maize), a tendency to round off numbers, and unobserved incentives to farmers to make their land seem more or less productive [5,6]. Self-reported yields are particularly problematic in subsistence systems where most food is grown for home consumption and where accurate record keeping by farmers is not the norm.…”

Section: Introductionmentioning

confidence: 99%

Sight for Sorghums: Comparisons of Satellite- and Ground-Based Sorghum Yield Estimates in Mali

et al. 2019

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The advent of multiple satellite systems capable of resolving smallholder agricultural plots raises possibilities for significant advances in measuring and understanding agricultural productivity in smallholder systems. However, since only imperfect yield data are typically available for model training and validation, assessing the accuracy of satellite-based estimates remains a central challenge. Leveraging a survey experiment in Mali, this study uses plot-level sorghum yield estimates, based on farmer reporting and crop cutting, to construct and evaluate estimates from three satellite-based sensors. Consistent with prior work, the analysis indicates low correlation between the ground-based yield measures (r = 0.33). Satellite greenness, as measured by the growing season peak value of the green chlorophyll vegetation index from Sentinel-2, correlates much more strongly with crop cut (r = 0.48) than with self-reported (r = 0.22) yields. Given the inevitable limitations of ground-based measures, the paper reports the results from the regressions of self-reported, crop cut, and (crop cut-calibrated) satellite sorghum yields. The regression covariates explain more than twice as much variation in calibrated satellite yields (R2 = 0.25) compared to self-reported or crop cut yields, suggesting that a satellite-based approach anchored in crop cuts can be used to track sorghum yields as well or perhaps better than traditional measures. Finally, the paper gauges the sensitivity of yield predictions to the use of Sentinel-2 versus higher-resolution imagery from Planetscope and DigitalGlobe. All three sensors exhibit similar performance, suggesting little gains from finer resolutions in this system.

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A new spin on an old debate: Errors in farmer-reported production and their implications for inverse scale - Productivity relationship in Uganda

Cited by 86 publications

References 38 publications

The Accuracy of Self-Reported Crop Yield Estimates and Their Ability to Train Remote Sensing Algorithms

The Accuracy of Self-Reported Crop Yield Estimates and Their Ability to Train Remote Sensing Algorithms

Women’s empowerment, agricultural extension, and digitalization: Disentangling information and role model effects in rural Uganda

Sight for Sorghums: Comparisons of Satellite- and Ground-Based Sorghum Yield Estimates in Mali

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