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Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks

1
Research Unit Suitability and Global Change, Center for Earth System Research and Sustainability, Universität Hamburg, Grindelberg 5, 20144 Hamburg, Germany
2
School of Integrated Climate System Sciences, CLISAP, Grindelberg 5, 20144 Hamburg, Germany
3
Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro # 1, Tonantzintla, 72840 Puebla, Mexico
*
Author to whom correspondence should be addressed.
Received: 16 October 2017 / Revised: 22 December 2017 / Accepted: 5 January 2018 / Published: 10 January 2018
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Abstract

Climate data availability plays a key role in development processes of policies, services, and planning in the agricultural sector. However, data at the spatial or temporal resolution required is often lacking, or certain values are missing. In this work, we propose to use a Bayesian network approach to generate data for missing variables. As a case study, we use relative humidity, which is an important indicator of land suitability for coffee production. For the model, we first extracted climate data for the variables precipitation, maximum and minimum air temperature, wind speed, solar radiation and relative humidity from the surface reanalysis dataset Climate Forecast System Reanalysis. We then used machine learning algorithms to define the model structure and parameters from the relationships of the variables found in the dataset. Precipitation, maximum and minimum air temperature, wind speed, and solar radiation are then used as proxy variables to infer missing values for monthly relative humidity and relative humidity for the driest month. For this, we used both complete and incomplete initial data. In both scenarios of data availability, the comparison of estimated and measured values of relative humidity shows a high level of agreement. We conclude that using Bayesian Networks is a practical solution to estimate relative humidity for coffee agricultural planning. View Full-Text
Keywords: probabilistic modeling; machine learning; modeling climate information; graphical models; proxy climatic variables; land evaluation; Central America; Coffea arabica L. probabilistic modeling; machine learning; modeling climate information; graphical models; proxy climatic variables; land evaluation; Central America; Coffea arabica L.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

  • Externally hosted supplementary file 1
    Link: https://dev.hed.cc/?s=HR
    Description: Simplified online version of the model (English and Spanish)
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MDPI and ACS Style

Lara-Estrada, L.; Rasche, L.; Sucar, L.E.; Schneider, U.A. Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks. Land 2018, 7, 4.

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