Abstract
This manuscript aims to create large-scale calculations of agro-climatic factors from global climatic data with high granularity-climatic ERA5-Land dataset from the Copernicus Climate Change Service in particular. First, we analyze existing approaches used for agro-climatic factor calculation and formulate a frame for our calculations. Then we describe the design of our methods for calculation and visualization of certain agro-climatic factors. We then run two case studies. Firstly, the case study of Kojčice validates the uncertainty of input data by in-situ sensors. Then, the case study of the Pilsen region presents certain agro-climatic factors calculated for a representative point of the area and visualizes their time-variability in graphs. Maps represent a spatial distribution of the chosen factors for the Pilsen region. The calculated agro-climatic factors are frost dates, frost-free periods, growing degree units, heat stress units, number of growing days, number of optimal growing days, dates of fall nitrogen application, precipitation, evapotranspiration, and runoff sums together as water balance and solar radiation. The algorithms are usable anywhere in the world, especially in temperate and subtropical zones.
Highlights
There is a growing need in agriculture to analyze data and, following this, synthesize as much relevant information as possible to support decision making
As particular agro-climatic factors were described in the previous section, demand for relevant information about soil and air temperatures, precipitation, evapotranspiration, and sunlight was settled
The village lies in a similar climatic environment as the main area of interest (Pilsen region), and we were able to gain access to at least a year-long time series of climatic measurements—which turned to be a complicated task to gain the sensor data directly from the Pilsen region
Summary
There is a growing need in agriculture to analyze data and, following this, synthesize as much relevant information as possible to support decision making Such information is essential in current situations, where climate change is discussed. Agriculturally oriented IT experts work on the utilization of Earth Observation data (both multi and hyperspectral), climatic data, in-situ sensor data (connected to IoT), crowdsourced and linked data together with traditional geographic data. They search for innovative methods of data processing and analysis to enable evidence-based decision making in agriculture. Based on the knowledge of agro-climatic factors and their variability in time, a farmer can select appropriate crops, proper cultivation methods, and optimize field works
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