Qualitative crop condition survey reveals spatiotemporal production patterns and allows early yield prediction

Begueriá, Santiago ; Maneta, Marco P.

Resumen: Large-scale continuous crop monitoring systems (CMS) are key to detect and manage agricultural production anomalies. Current CMS exploit meteorological and crop growth models, and satellite imagery, but have underutilized legacy sources of information such as operational crop expert surveys with long and uninterrupted records. We argue that crop expert assessments, despite their subjective and categorical nature, capture the complexities of assessing the "status" of a crop better than any model or remote sensing retrieval. This is because crop rating data naturally encapsulates the broad expert knowledge of many individual surveyors spread throughout the country, constituting a sophisticated network of "people as sensors" that provide consistent and accurate information on crop progress. We analyze data from the US Department of Agriculture (USDA) Crop Progress and Condition (CPC) survey between 1987 and 2019 for four major crops across the US, and show how to transform the original qualitative data into a continuous, probabilistic variable better suited to quantitative analysis. Although the CPC reflects the subjective perception of many surveyors at different locations, the underlying models that describe the reported crop status are statistically robust and maintain similar characteristics across different crops, exhibit long-term stability, and have nation-wide validity. We discuss the origin and interpretation of existing spatial and temporal biases in the survey data. Finally, we propose a quantitative Crop Condition Index based on the CPC survey and demonstrate how this index can be used to monitor crop status and provide earlier and more precise predictions of crop yields than official USDA forecasts released midseason.

Idioma: Inglés

DOI: 10.1073/pnas.1917774117

Año: 2020

Publicado en: Proceedings of the National Academy of Sciences 117, 31 (2020), 18317-18323
ISSN: 0027-8424

Factor impacto JCR: 11.205 (2020)

Categ. JCR: MULTIDISCIPLINARY SCIENCES rank: 8 / 73 = 0.11 (2020) - Q1 - T1

Factor impacto SCIMAGO: 5.011 - Multidisciplinary (Q1)

Financiación: info:eu-repo/grantAgreement/ES/MINECO/CGL2017-83866-C3-3-R

Tipo y forma: Artículo (Versión definitiva)