Multiyear Maize Management Dataset collected in Chiapas, Mexico

Rodrigo G. Trevisan, Nicolas F. Martin, Simon Fonteyne, Nele Verhulst, Hugo A. Dorado Betancourt, Daniel Jimenez, Andrea Gardeazabal

Research output: Contribution to journalArticlepeer-review

Abstract

For several decades, maize (Zea mays L.) management decisions in smallholder farming in tropical regions have been a puzzle. To best balance alternative management practices' environmental and economic outcomes, an extensive dataset was gathered through CIMMYT's knowledge hub in Chiapas, a state in southern Mexico. In a knowledge hub, farmers, with the support of farm advisors, compare conventional and improved agronomic practices side-by-side and install demonstration fields where they implement improved practices. In all these fields data on on-farm operations and results is collected. The dataset was assembled using field variables (yield, cultivars, fertilization and tillage practice), as well as environment variables from soil mapping (slope, elevation, soil texture, pH and organic matter concentration) and gridded weather datasets (precipitation, temperature, radiation and evapotranspiration). The dataset contains observations from 4585 fields and comprises a period of 7 years between 2012 and 2018. This dataset will facilitate analytical approaches to represent spatial and temporal variability of alternative crop management decisions based on observational data and explain model-generated predictions for maize in Chiapas, Mexico. In addition, this data can serve as an example for similar efforts in Big Data in Agriculture.

Original languageEnglish (US)
Article number107837
JournalData in Brief
Volume40
DOIs
StatePublished - Feb 2022

Keywords

  • Explanatory machine learning
  • Smallholders
  • Sustainable intensification
  • Tropical agriculture

ASJC Scopus subject areas

  • General

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