Precision and Digital Farming, Forecast models and Decision support systems (DSS)
Precision and digital farming methods and techniques have changed agricultural systems worldwide significantly in the last few years and will continue to change agricultural practices.
The European Commission has repeatedly emphasized the need for greater innovation, digitalisation and new technologies in the European agriculture. The use of precision agriculture is a sustainable practice to achieve the ambitious goals of the European Green Deal, the Common Agricultural Policy (CAP) for 2021-2027 and the Farm to Fork Strategy to foster competitive and sustainable farming.
Digital and Precision farming tools, such as in-field sensors, drones and satellites, as well as smartphones or tablets, help farmers in their day-to-day work. Data from sensors and satellite images allow the creation of precise 3-D maps from numerous observations and measurements (e.g. crop yield, terrain features/topography, organic matter content, moisture levels, nitrogen levels, etc). Such data are basic input data for Decision Support Systems (DSS), which collect, combine and analyse the data and provide data-driven support and information for precise monitoring and the management of crop conditions at all levels of agricultural production, focusing on the optimization of production and quality including the use of fertilisers, biostimulants and plant protection products and full-scale implementation of integrated pest management (IPM).
Smart farming solutions include the use of robotics, such as weeding robots to reduce herbicide inputs, robots to help picking in greenhouses under hot and difficult working conditions, ground robots for vineyard monitoring and protection (e.g. green pruning and bunch-tip thinning, precise spraying of chemicals and water, plant monitoring and protection) or swarm robotics/drones with on-board camera to monitor and map occurrence of pests and diseases in the field.
Weather-based forecast models are based on the complex interaction between a plant host, the pathogen and their environment. Forecast models can be used to predict the first appearance of an agricultural or horticultural disease or pest, simulate population dynamics of insects, calculate infection pressure for diseases, simulate the ontogenesis of the crop and calculate crop-loss relations.
Forecasting models can be integrated in Decision support systems (DSS) by combining disease monitoring data, use of decision thresholds and predictive models. The DSS is an important tool to estimate or forecast the disease/pest risk, the necessity for pesticide treatments, the optimal timing for pesticide treatments and the recommendation of appropriate pesticides. The results of DSS are distributed to the farmers via warning services and via the internet for guidance on pest management. Necessary input data for simulation models are meteorological data (current and forecast weather data, e.g. relative humidity, air temperature, leaf wetness duration, rainfall etc.) and assessed field-site specific data (e.g. susceptibility of the crop, planting date and ontogenesis). With regards to efficacy study plans and reports, the detailed and correct reporting of the respective data and information is critical. The accuracy of simulation models can be improved by using Geographic Information Systems (GIS) using interpolated meteorological data. Combined with radar measured precipitation data, spatial risk maps of areas of maximum risk of disease outbreak, infection pressure or pest appearances can be displayed.
Forecast models and Decision Support Systems (DSS) are widely used in EU or are even mandatory, depending on country and IPM-rules applied.
By precisely measuring the conditions within a field through precision farming tools and adapting the strategy proposed by a DSS, the effectiveness of pesticides and fertilizers can be increased and products can be used more selectively and resource-efficiently, resulting in a more sustainable agriculture.
Integrated model-based decision support systems can improve the effectiveness of pest control, while minimizing economic costs, environmental impacts and yield losses. It is also important for successful implementation of long-term integrated pest management (IPM) strategies as requested by Directive 2009/128/EC concerning the sustainable use of pesticides and the National Action Plans as well as the various requirements of EUs Green Deal or Farm to Fork Strategy.