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Related Experiment Video

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High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.
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Multiyear Maize Management Dataset collected in Chiapas, Mexico.

Rodrigo G Trevisan1, Nicolas F Martin1, Simon Fonteyne2

  • 1Department of Crop Sciences, University of Illinois at Urbana-Champaign, 1102 Goodwin Ave. Urbana, IL, USA.

Data in Brief
|March 4, 2022
PubMed
Summary

This study compiles extensive maize management data from smallholder farms in Chiapas, Mexico. The dataset aids in understanding and optimizing farming practices for better environmental and economic outcomes.

Keywords:
Explanatory machine learningSmallholdersSustainable intensificationTropical agriculture

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Area of Science:

  • Agricultural Science
  • Agronomy
  • Data Science

Background:

  • Maize (Zea mays L.) management in tropical smallholder systems presents complex challenges.
  • Balancing environmental and economic outcomes of farming practices is crucial for sustainability.

Purpose of the Study:

  • To compile and present an extensive dataset on maize management practices in Chiapas, Mexico.
  • To facilitate analytical approaches for understanding spatial and temporal variability in crop management decisions.
  • To provide a foundation for explaining model-generated predictions in agriculture.

Main Methods:

  • Data collected from CIMMYT's knowledge hub in Chiapas, Mexico, over 7 years (2012-2018).
  • Dataset includes field variables (yield, cultivars, fertilization, tillage) and environmental data (soil properties, weather).
  • Observations from 4585 fields were analyzed.

Main Results:

  • A comprehensive dataset on maize cultivation under diverse management practices was established.
  • The data captures the spatial and temporal variability of on-farm operations and outcomes.
  • Facilitates data-driven analysis for optimizing agricultural decision-making.

Conclusions:

  • The compiled dataset is a valuable resource for smallholder maize farmers and researchers.
  • It enables advanced analytical approaches to improve agricultural management strategies.
  • Serves as a model for Big Data initiatives in agriculture globally.