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.
Highlights
Missing data is a major challenge for agricultural planning, reporting and research at the level of individual farms, and at regional, national, or international scales
TheThe sensitivity analysis shows shows that precipitation and maximum temperature temperature have the highest influence on relative humidity, followed by solar radiation, speed have the highest influence on relative humidity, followed by solar radiation, wind speed wind and minimum temperature (Table 1)
Despite the low influence of TMIN on relative humidity, the variable has a strong influence on wind speed (Wind), solar radiation (Solar) and TMAX
Summary
Missing data is a major challenge for agricultural planning, reporting and research at the level of individual farms, and at regional, national, or international scales. Several procedures have been employed in previous applications to deal with data gaps. The Agricultural Resource Management Survey in the USA uses conditional or national averages with or without outliers [2]. Data gaps have been filled by combining survey and satellite information [3], spatial interpolations [4], introduction of proxy variables [5], and, in the case of climate research, by using the regularized EM algorithm for Gaussian data [6], empirical orthogonal functions [4], grouping methods of data handling [7], and others. While the procedures described above are mostly suitable for dealing with the problem, their practical implementation in developing countries is often difficult due to a lack of qualified personnel and financial shortfalls [8,9,10].
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