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Improve Your Farm Efficiency with Data-Driven Decision-Making

We need to grow more nutritious food to feed everyone on the planet without hurting the environment. To put these in numbers, we need to grow more than 50 percent more food by 2050 to meet the demands of the ever-growing population. This is a complex challenge because the soils are not getting richer, the water quality and levels are decreasing, and we need to do all of it without hurting the planet. One of the most promising approaches to help address this problem is that of data-driven agriculture. For example, we could map every farm in the world and get information like soil moisture, temperature, and nutrient levels. With this type of data map, technologies like precision agriculture could help us produce more food efficiently without much negative impact on the planet. Also, every data in the food supply chain will benefit from using data and data-driven insights.
What is data-driven agriculture?
Data-driven agriculture is the process of maximizing the insights from data towards your ROI. So, the farmer uses data collected from sensors on the farms and satellites to make crucial and more informed decisions. However, this is easier said than done. Farming involves complex processes with lots of different variables involved. As a result, farmers have to evaluate many uncertainties before deciding. However, due to advances in precision agriculture, different types of data can be obtained from the farms. Data-driven farming involves finding how these different variables affect farm yields and the complexity of interactions between them. Now let us see how data is used to make farms more efficient.
How is data used in agriculture?
Crop monitoring
Crop monitoring is one of the most used applications of data in agriculture. For this to be possible, sensors are placed around the farm or on satellites. Data from these sensors will give you information such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). The ndvi formula calculates the difference between near-infrared and visible light reflectance from plants to generate a picture of the intensity of photosynthesis. The values range from -1.0 to 1.0. In a nutshell, NDVI is a measure of the crops’ health based on how they reflect light at specific frequencies. For example, an NDVI value of 0.1 indicates barren areas like snow, rocks, or sand. A Moderate value like 0.4 represents meadows or shrubs. Higher values from 0.6 show tropical or temperate forests.
Also, monitoring the health of crops will help the farm owners know when to apply pesticides on their farms.
Soil monitoring
Healthy soil is the foundation of good stewardship as a farmer. It needs to have stable aggregates. These are conglomerates of smaller particles that are glued together by the biological activity of the soil that allows air and water between the particles. If we don’t have many living organisms and nutrients in the soil, these aggregates break down, making the soil unproductive. That is why advancements in technology have brought about soil monitoring that helps scan the soil for nutrients and water, ensuring that it is suitable for agriculture. The sensors and IoT devices are placed in the ground, which measures soil moisture, temperature, and nutrients. This will allow farmers to know when and where to plant for maximum yield.
Weather forecasting
Farming and agriculture heavily depend on the weather. Some plants will do well and only produce under certain weather conditions. That is why farmers need to be able to predict the weather accurately. For this, they will need data from weather stations and satellites. This information will help them know when to plant. Also, they will know when to apply fertilizer so that heavy rains do not wash it away. Certain weather conditions attract pests, which will destroy crops. Pest and disease modeling incorporated with forecast guidance will help farmers determine when to apply pesticides on their farms.
As the world becomes more connected through IoT devices, farmers will have access to more data that will help them in their farm processes. Data collected from in and around the farm will give farmers a holistic view of their fields, enabling them to make informed and accurate decisions.

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