.png)
Climate change in recent years has caused a reorganization of protection strategies, especially for those crops where management of many diseases and insects is required. Due to alternating frosts and droughts, and the occurrence of heavy and poorly distributed rainfall, it becomes essential to anticipate the onset of diseases and insect infestations in order to respond quickly and limit possible damage. In recent years, interest in Decision Support Systems (DSS) has also increased due to regulations (Directive 2009/128/C) regarding the reduction of chemical inputs in integrated pest management. To this must be added all the other commitments that Europe and beyond impose on us in terms of environmental sustainability such as: Green Deal, Agenda 2030, Pesticide Package and Farm to Fork strategies. All this is happening in a context in which the Utilized Agricultural Area (UAA) and the number of molecules for the management of pathogens and phytophagous diseases are decreasing, air pollution, soil erosion as well as the number and severity of diseases and insects are increasing due to the non-rational use of plant protection products and globalization; not to mention the increase in world population that will lead all actors in the agri-food system to have to increase the quantity and quality of food in the near future.
A plant disease model is a simplification of a real system consisting of the pathogen/phytophage agent, the host plant, and the environment. Models are used in epidemiology to describe, understand, predict and compare epidemics and their components. Practical help for growers comes from disease prediction, which is an estimation process in future, past, present or unknown situations.
General climate data such as minimum, average and maximum temperature and relative humidity, daily and monthly precipitation and number of rainy days, wind speed and duration, dew point, and hours of sunshine are required as inputs for the development of forecast models. Depending on the complexity of the data, historical climate data (e.g., es. on base 30 years) are also used. In addition, data on crops and pests (pathogens and/or phytophages) may also be requested. For example, the following parameters may be used: for crop - leaf moisture, plant density, phenological stages, maximum leaf area index, vapor pressure deficit, net irradiance above and below the canopy, susceptible phenological stages; for fungal pathogens - the rate of spore release, maturation and germination, their number on leaves, the amount of primary and secondary inoculum, and the rate of colonization. For insects (es. L. botrana on grapevine) - flight periods of male individuals (detected with pheromone traps) and distribution of male catches between generations.
The areas of application in agriculture are diverse, from crop growth and development to crop productivity, water balance, and defense against biotic and abiotic adversities.
As the quality and computing power offered by computers improve day by day, models are increasingly being incorporated into DSS, which are tools that help users in tactical and operational decision making in crop protection to better plan applications with plant protection products in defense strategies. The models can also be part of local disease warning systems (e.g.: regional phytosanitary observatories, etc...). The use of forecasting for crop protection against fungal diseases and pests is not so new (es. rule of 3-10 for grapevine blight and the rule of 10 for tomato blight).
The main advantages of DSS are the ability to analyze different scenarios, to better understand physical and biological processes, to perform manipulations on the real system in order to test hypotheses about its operation, to evaluate the effect of any external interventions in order to change the behavior of the system, to facilitate data exchange, and to enable better use of data and resources. In contrast, critical aspects consist of use that is often limited by software package development and the need to test and validate procedures in different years.
In the context of integrated crop protection, the use of predictive models is a valuable tool that allows the identification of the "useful time window" within which a treatment, can exert maximum effectiveness and efficiency in controlling the target pathogen/phytophage. All this leads to a more rational and efficient decision-making methodology and thus minimizes the number of applications and inputs introduced into the system. The predictive models of xFarm Lab are conceived, designed, calibrated and validated to provide farmers and entire supply chains with an additional technical tool that does not simply complement the others already available (es. agronomic, biological, physical, chemical) but coordinates them all to increase the efficiency and effectiveness of protection strategies and cultivation techniques in order to increase the quality and quantity of production.
This concept is as valid for farmers with small plots as it is for agribusiness chains that have the opportunity, on a large scale, to amplify the positive effect from rational and efficient resource management.
The primary objective of xFarm Lab is to support the entire agribusiness system through digitization and technologies applied to agriculture in order to safeguard the sustainability of production processes in both agronomic, economic and social terms.
Dubey, P.K., Singh, G.S., Abhilash, P.C. (2019). Adaptive agricultural practices: Building resilience in a changing climate. Springer.
Pertot, I.; Caffi, T.; Rossi, V.; Mugnai, L.; Hoffmann, C.; Grando, M.S.; Gary, C.; Lafond, D.; Duso, C.; Thiery, D.; Mazzoni, V.; Anfora, G. (2016). A critical review of plant protection tools for reducing pesticide use on grapevine and new perspectives for the implementation of IPM in viticulture. Crop Protection, (), S0261219416303489-.
Rossi, V., Joshua, S., Caffi, T. (2010). Modelling plant diseases for decision making in crop protection. In Precision crop protection-the challenge and use of heterogeneity (pp. 241-258). Springer, Dordrecht.
.svg)
