Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks

Leonel Lara-Estrada, Livia Rasche, L. Enrique Sucar and Uwe Schneider ()
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Leonel Lara-Estrada: Research Unit Suitability and Global Change, Center for Earth System Research and Sustainability, Universität Hamburg, Grindelberg 5, 20144 Hamburg, Germany
Livia Rasche: Research Unit Suitability and Global Change, Center for Earth System Research and Sustainability, Universität Hamburg, Grindelberg 5, 20144 Hamburg, Germany
L. Enrique Sucar: Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro # 1, Tonantzintla, 72840 Puebla, Mexico

Land, 2018, vol. 7, issue 1, 1-13

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.

Keywords: probabilistic modeling; machine learning; modeling climate information; graphical models; proxy climatic variables; land evaluation; Central America; Coffea arabica L. (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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