By 2050, the world’s population is projected to grow to 9.3 billion, increasing to 2 billion today. At the same time, FAO reports a potential growth of 70% in agriculture. It is necessary to cover the projected demand for food and other agricultural products. There is a need to increase production, but access to resources for growing crops is limited. It appears that there is an ongoing need to increase production. However, the resources stay limited, and it’s quite challenging to produce more with the same water, land and fertilizers availability.
According to Lance Donnie’s classification, agriculture is in its third stage of evolution, also called Ag 3.0. This stage involves using data as a basis for decision-making. The use of data obtained from external sources offers many benefits to both producers and consumers. Innovative technologies allow farmers to get various data on soil conditions, water, and minerals and use the ndvi map to facilitate scouting and improve field activities’ accuracy. Technology helps you make effective decisions at all stages of the production cycle, from planning to harvest.
Data Science in Agriculture
Data collection and analysis transform the farming. This innovation helps to make more productive solutions, react the changes in time and can make life significantly easier for all participants of agribusiness.
Opportunities for Data-Based Solutions
To make productive and rational decisions, farmers select and use a set of variables that need to be adjusted appropriately. It is necessary to grow a varied crop and make adequate fertilization, irrigation, and other procedures. Farmers can use multi-source data collection and analytics solutions to get an accurate picture of crop and soil health.
In addition to the localized data that farmers receive from sensors, weather data from external sources are also used. The growers can use a combination of these data to assess better and improve planning. It is also possible to determine the state of soil and plants using the data from spectroscopes. Spectroscopic data helps to automate agricultural processes, including irrigation.
Data-driven solutions are also the backbone of precision farming. Precision farming is all about reducing resource consumption and not wasting it, thus increasing sustainability. Precision farming practitioners can detect areas that need change and plant health due to data. It turns out that you do not need to treat the field with fertilizers or any chemicals uniformly. It is enough to treat those zones or individual plants that need it. Furthermore, it is a great way to save resources and reduce your environmental impact. And all this is possible thanks to data from external and internal sources.
The Smart Farming concept involves managing a farm using on-the-edge technologies. It is used to boost the quantity and enhance the quality of goods. The list of smart farming technologies includes specialized software and apps for mobile devices, sensing and scanning of soil and other parameters, the Internet of Things and GPS. Data analytics is also an integral part of smart farming. These tools allow farmers to monitor conditions on their farmland remotely and make decisions based on the collected data. The IoT is of particular value in the context of this practice. This technology includes a range of intelligent machines and sensors.
The equipment collects data from the fields and process it to respond on time to problems and threats to the crop. The data is transmitted to special software, which determines the issues and how to solve them. IoT technologies help to be more aware of crops and soil state, identify possible issues, and reduce the influence of the human factor. IoT technologies enable the automatization of various processes and security enhancement.
Data Science in Forestry
Forestry productivity has increased significantly over the decades thanks to technological innovation. The highest productivity is observed in Scandinavia. The first stage in the rapid development of the industry was the transition from horse transport to trucks. Then productivity increased with the invention of chainsaws. At this stage, the increase in productivity conditioned by the use of data and analytics.
In forestry, data and analytics can be used in several areas. The first use case is related to identifying improvements in production performance. Analytics can also improve harvest planning. The second method concerns the operation of forestry equipment. In this case, operators who do not use the equipment optimally and lose momentum are responsible for this.
Proper organization is an integral part of optimizing forestry activities, especially communication between the various participants in the process. For effective optimization, workers’ contracts must be negotiated, appropriate equipment reserved, and more.
Low efficiency can also be found in the sector’s value chain. It concerns the transportation of logs and the payment of increased contract costs due to the conservative determination of the size of the fleet. Also, underperformance often occurs due to the untimely start of the shift and the absence of a fixed lunch break. The process slows down due to the formation of a queue for loading and unloading.