Multiyear Maize management dataset collected in Chiapas, Mexico

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...

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Autores principales: Trevisan, Rodrigo G., Martin, Nicolas F., Fonteyne, Simon, Verhulst, Nele, Dorado Betancourt, Hugo Andres, Jiménez, Daniel, Gardeazábal Monsalve, Andrea
Formato: Journal Article
Lenguaje:Inglés
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://hdl.handle.net/10568/125880
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author Trevisan, Rodrigo G.
Martin, Nicolas F.
Fonteyne, Simon
Verhulst, Nele
Dorado Betancourt, Hugo Andres
Jiménez, Daniel
Gardeazábal Monsalve, Andrea
author_browse Dorado Betancourt, Hugo Andres
Fonteyne, Simon
Gardeazábal Monsalve, Andrea
Jiménez, Daniel
Martin, Nicolas F.
Trevisan, Rodrigo G.
Verhulst, Nele
author_facet Trevisan, Rodrigo G.
Martin, Nicolas F.
Fonteyne, Simon
Verhulst, Nele
Dorado Betancourt, Hugo Andres
Jiménez, Daniel
Gardeazábal Monsalve, Andrea
author_sort Trevisan, Rodrigo G.
collection Repository of Agricultural Research Outputs (CGSpace)
description 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.
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spelling CGSpace1258802025-11-11T19:02:19Z Multiyear Maize management dataset collected in Chiapas, Mexico Trevisan, Rodrigo G. Martin, Nicolas F. Fonteyne, Simon Verhulst, Nele Dorado Betancourt, Hugo Andres Jiménez, Daniel Gardeazábal Monsalve, Andrea smallholders tropical agriculture sustainable intensification machine learning crop management aprendizaje automático intensificación sostenible pequeños agricultores data papers 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. 2022-02 2022-12-12T16:13:29Z 2022-12-12T16:13:29Z Journal Article https://hdl.handle.net/10568/125880 en Open Access application/pdf Elsevier Trevisan, R.G.; Martin, N.F.; Fonteyne, S.; Verhulst, N.; Dorado Betancourt, H.A.; Jimenez, D.; Gardeazabal, A. (2022) Multiyear Maize management dataset collected in Chiapas, Mexico. Data in Brief 40: 107837. 7 p. ISSN: 2352-3409
spellingShingle smallholders
tropical agriculture
sustainable intensification
machine learning
crop management
aprendizaje automático
intensificación sostenible
pequeños agricultores
data papers
Trevisan, Rodrigo G.
Martin, Nicolas F.
Fonteyne, Simon
Verhulst, Nele
Dorado Betancourt, Hugo Andres
Jiménez, Daniel
Gardeazábal Monsalve, Andrea
Multiyear Maize management dataset collected in Chiapas, Mexico
title Multiyear Maize management dataset collected in Chiapas, Mexico
title_full Multiyear Maize management dataset collected in Chiapas, Mexico
title_fullStr Multiyear Maize management dataset collected in Chiapas, Mexico
title_full_unstemmed Multiyear Maize management dataset collected in Chiapas, Mexico
title_short Multiyear Maize management dataset collected in Chiapas, Mexico
title_sort multiyear maize management dataset collected in chiapas mexico
topic smallholders
tropical agriculture
sustainable intensification
machine learning
crop management
aprendizaje automático
intensificación sostenible
pequeños agricultores
data papers
url https://hdl.handle.net/10568/125880
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