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Box 2.2 Precision Agriculture

Precision Agriculture (PA) or precision farming is a management approach based on observation, measurement, and responses to spatial and temporal variability in crops, fields and animals. PA aims to adapt agricultural inputs such as fertilizers, pesticides, water, feed and veterinary medicine to the real-time needs of plants and animals as well as agricultural practices such as tillage, sowing and harvesting to spatial variability. For this purpose a wide range of digital technologies are used including GPS and remote sensing systems, new sensor technologies as well as drones and robots. Rather than applying the same amount of fertilisers, pesticides or water over an entire agricultural field, information on e.g. soil type, soil moisture, nutrient availability and plant health are collected. Decision support systems can analyses the data in order to provide the farmer with precise recommendations.

Therefore, PA can help producing more agricultural output (crop yields and animal performance) with less input (labour, fuel, agrochemicals, anti-biotics, feed) and thus optimize agricultural production in a resource and cost efficient way. At the same time, PA has the potential to reduce the environmental impact on soil and surface water contamination. With regard to the protection of water bodies and the reduction of water consumption, PA technologies can contribute as follows:

• Automatic machine guidance and section control of sprayers and fertiliser can help to keep fertilisers and pesticides at recommended distances from waterways.
• Automatic steering systems reduce field traffic and thus have the potential to reduce soil compaction, soil erosion and the runoff of surface water, sediments and fertilisers.
• Sensors, remote sensing data and geo-mapping can be used to evaluate soil and crop health and adapt input and farming practices to local conditions. Therefore, these technics reduce the input of fertilizer and pesticides, prevent compaction and erosion and thus reduce the risk of water pollution and sedimentation.
• Robots can help to optimise inputs (fertilisers, pesticides, insecticides) and reduce the impact on soils and water tables. In addition, robots are flexible and able to intervene only where they are needed. This minimizes soil compaction by heavy machines.

With precision irrigation, a precise amount of water can be applied to plants at precise times to optimize crop yield and water productivity. As a result, this technique leads to a reduction in water use. Water metering and measurement of water use can be considered as the basis for precision irrigation. PA can increase profitability for farmer due to increase yields with less input and labour force and furthermore provide farmers with information on the status of crops and animals to improve yield forecasts.

There might also be some disadvantages from the further expansion of PA, especially for small farmers. Compared to large farms, they often lack the investment capital or the knowledge to acquire PA technologies. This can lead to growing competitive pressure between small and large farms, which is expected to reduce the number of farms and increase farm size. Furthermore, the number of jobs on farm holdings is expected to decrease with human labour potentially being increasingly replaced by robots and computers. In some rural areas, the application of PA technologies is still hampered by a lack of suitable IT infrastructure.

Apart from these impacts, PA has a large potential to contribute to the sustainability of the agri-food sector under a growing demand for agricultural products and actively contribute to food security and food safety. PA can contribute to the transparency of the agricultural sector. Monitoring of crops and livestock will allow better predictions of agricultural product quality, making the food chain easier to monitor for producers, retailers and customers. Furthermore, the digitalisation of agriculture makes the environmental impacts more measurable and verifiable and support true cost accounting.

Source:EIP-AGRI, 2015