Weather Forecasting in Agriculture: How to Save Crops from Mother Nature

Weather Forecasting in Agriculture: How to Save Crops from Mother Nature

Interannual climate variability has a large impact on agriculture, which is highly dependent on rainfall, sunlight, and temperature. Human-driven climate change has introduced a new complicating factor to food security, causing changes in climate variability. At high latitudes, a longer growing season may be beneficial for some growers, but in arid and semi-arid areas, water scarcity will be increasingly felt. Extreme events such as floods and droughts are expected to increase in frequency and intensity, affecting crop and livestock production.

Better understanding and forecasting of climate variability will help us address climate change. Reducing the vulnerability of various sectors such as biodiversity, forestry, and agriculture to natural climate change through increased awareness of policy choices, practices, and ag weather monitoring technologies will increase the climate resilience of these spheres. This article will focus on weather data importance specifically for modern farmers.

Short-term and Long-term Weather Forecasts

The agricultural sector needs accurate, reliable, and timely weather and climate information to make daily tactical decisions and for long-term planning. Seasonal climate outlooks are becoming an increasingly important tool for decision-making regarding, for example, the choice of crops and the timing of their planting, as well as the need to sell livestock in the event of an impending drought. In the long term, basic historical weather and agricultural data and future weather scenarios will be needed to make major decisions such as buying land, designing irrigation systems, switching to more drought-tolerant seeds or crops, or implementing systems to prevent or mitigate saltwater intrusion.

Opportunities Weather Monitoring Holds for Farmers

Weather monitoring capabilities can be divided into 3 large blocks. The first block is related to forecasts. The first task of meteorological monitoring is to refine the forecast for a specific location.

The second task is to ensure the correctness of carrying out specific operations at a given moment under given weather conditions. After all, even plant protection products applied at the wrong time, at the wrong temperature, or humidity can significantly harm the crop.

The third block is long-term analytics. This is when historical weather data on a specific location has been collected for years, and then, based on these data and other factors, the most optimal use of land is found.

Critical Technologies for Weather Monitoring

Today, precipitation, temperature, wind, air pressure, and humidity data can be collected into a single weather monitoring platform accessible from any mobile device. Different companies all over the world already offer farmers similar systems, and every year they become more advanced, convenient, and functional.

However, today there are three main technologies that are used in weather monitoring: intelligent IoT sensors for data collection and analysis, satellites and weather stations, as well as artificial intelligence and machine learning systems. Let's consider each of them separately.


IoT sensors installed in the fields form the basis of a unified weather monitoring system. Cloud computing platforms, where all the information from the sensors flows, processes the collected data so that at the output the farmer can receive alarms or notifications about potential weather threats that could affect crops.

In general, using IoT sensors, farmers can receive real-time information about the environment, soil conditions, and impending frost or rain, which makes it possible to significantly increase the final yield.

Satellite Data and Hardware Stations

Farmers can use satellite data, such as aerial imagery, to monitor weather and crops. Satellites can be used in two ways. First, as a data source for weather forecasting applications used by farmers. Secondly, as transmitters of data collected from agricultural weather stations installed in the fields. This method, of course, is more expensive in itself, so it is more profitable to turn to specialized companies for services.

In general, farmers can use satellites to access geospatial and meteorological data to prepare fields for abnormal or severe weather. In addition, they can be used to monitor global climate change and predict weather disasters such as fires and floods.

Most often, satellites are controlled by government organizations, so they are not always available for individual use. Nevertheless, they give a fairly accurate picture of the weather conditions in a given area.

Artificial Intelligence and Machine Learning

The application of artificial intelligence and machine learning to weather forecasting is the newest and most promising technological direction in agriculture. Like any other AI solution, weather forecasting requires huge amounts of data to build algorithms. This data can be obtained using connected sensors, satellites, and local hardware weather stations.

Of the minuses, it can be noted that the forecasts obtained in this way require a lot of computing power to process large amounts of data, and powerful storage is required to save this data in order to use it in the future.

It is also worth noting that the increase in the number of accurate data sources plays a major role in successful weather forecasting in this way. But by collecting information from satellites in orbit, meteorological stations on the surface, as well as from special sensors installed in the field, you can get the most accurate weather information. This method helps to recognize the consequences of the slightest changes in temperature and humidity, to identify potential weather threats based on changes in wind direction and other parameters.

Next Post »