Periodic Reporting for period 2 - RUC-APS (Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems)
Período documentado: 2018-10-03 hasta 2022-04-02
Currently in INIA in Chile, in collaboration with partners from CNR-ISPA, ULIV, BDI and FEDCOVA, new tomato rootstocks with high nutrient use efficiency (NUE) and tolerance to drought and saline stress conditions. Moreover, UNLP, is currently testing Horticultures crops from UE countries , in specific: arugula (Diplotaxis tenuifolia), top of rapa (Brassica rapa subsp. sylvestris var. esculenta), colored cauliflowers (Brassica oleracea. var. botrytis), as well as certain local italian radiccios (Cichorium intybus var. foliosum), and parsnip (Pastinaca sativa) and tendersteam from UK. Different types of tomatoes and peppers from Italy and Spain too. Aligned to this, ALSIA team, is currently working on two plant species to establish the first varietal behavior models: Peach and Strawberry, These plants were chosen due to their economic importance in Basilicata region. In fact, Basilicata is the first producer of Strawberry in Italy and it is cultivated on about 1,000 hectares while peaches production is made on roughly 6,000 hectares.Therefore the partner AINIA, technological center ,has collaborated with BDI, in the elaboration and development of an action plan with which to know with greater precision, the best moment for the harvesting of the pumpkin. In order to ensure a higher quality and durability of this vegetable, researchers from AINIA have analyzed Data Analytics (Artificial Intelligence applied to data analysis) data on cultivation and preservation of pumpkins of the Red Kuri variety. Within this, it is possible to determine the moment of harvest of pumpkins with technology 4.0. It has been shown an example of how Big Data can be applied to identify market trends and provide complementary qualitative information in order to understand the market. Taking into account the aim of ALSIA of technology transference, it has explained to ALSIA team that BIG DATA can be applied to do technological surveillance in a structured way.
The objectives of the project RUC-APS are performing healthy and running smoothly. Moreover, and as part of the continuous research and innovation process, a set of high-level questions have been produced in order to gather the relevant information for each objective. These are established in the three domains: Agriculture, Supply Chain and ICT, which addressed the key generic information and that are relevant to be collected, especially to get a proper understanding from each agribusiness dimension in terms of: SO1: To support the varietal behaviour in a variety of agro-climatic zones in terms of cost and production cycle; SO2: To optimise the agronomic management by understanding the incorporation of specialized machinery in the processes of sowing, transplanting and fertilizing; SO3: To analyse and refine quality standards to ensure the safety of the final agriculture based product under high risk and uncertain conditions; SO4: To evaluate the environmental conditions for enhancing soil management technologies and planting management; SO5: To model and optimise smarts and innovative collaborative production-transport planning solutions of horticulture products across the full value chain structure; SO6: To generate horizontal and vertical knowledge exchange mechanisms across the agriculture value chain.
This is explained in the following video links: https://www.youtube.com/watch?v=cPcQvM0bra4#action=share https://www.youtube.com/watch?v=p8iEOSZV2vQ#action=share http://www.youtube.com/watch?v=vugnVmgeiOs#action=share
The objectives of the project RUC-APS are performing healthy and running smoothly. Moreover, and as part of the continuous research and innovation process, a set of high-level questions have been produced in order to gather the relevant information for each objective. These are established in the three domains: Agriculture, Supply Chain and ICT, which addressed the key generic information and that are relevant to be collected, especially to get a proper understanding from each agribusiness dimension in terms of: SO1: To support the varietal behaviour in a variety of agro-climatic zones in terms of cost and production cycle; SO2: To optimise the agronomic management by understanding the incorporation of specialized machinery in the processes of sowing, transplanting and fertilizing; SO3: To analyse and refine quality standards to ensure the safety of the final agriculture based product under high risk and uncertain conditions; SO4: To evaluate the environmental conditions for enhancing soil management technologies and planting management; SO5: To model and optimise smarts and innovative collaborative production-transport planning solutions of horticulture products across the full value chain structure; SO6: To generate horizontal and vertical knowledge exchange mechanisms across the agriculture value chain.
This is explained in the following video links: https://www.youtube.com/watch?v=cPcQvM0bra4#action=share https://www.youtube.com/watch?v=p8iEOSZV2vQ#action=share http://www.youtube.com/watch?v=vugnVmgeiOs#action=share
ALSIA: Study water management for irrigation in Spain comparing it to how water is managed in Basilicata region. Several possibilities to improve the WUE were identified and will be further developed during future secondments; Introduce and understand new selected genotypes, reintroduction and valorisation of ancient local vegetable biodiversity. AINIA: Interview with representatives of the association of artichokes producers in La Plata area; - Focus Group to identify agri-food critical decision areas in Italy. BDI: Development of Benchmark activities (The UK example, The Spanish example, The Italian example, The Polish example). CNR-ISPA: Identify Local (Italian) germoplasm of tomato, carrots and broccoli are under agronomic and commercial evaluation in Argentina (Serviverde) in collaboration with CNR ISPA. FEDACOVA: Study the possibility of taking advantage of the distribution networks already created by each company to distribute their product in the country of destiny. INIA: Initial mapping process of the Chilean horticulture value chain, Regulation and Policy about pesticide residue in food. ULIV: Agribusiness process modelling by using several tool and modelling techniques. LEAF: Knowledge Exchange activities including the transfer of British and Spanish farming practises, and technology used in each country throughout the production (Farming) stage of the food supply chain. UNLP: Analysis of key Agriculture Scenarios; Understanding of key worldwide Scenarios: brief literature review about definitions and usages. UPV: identification of the different sources of uncertainty and risks have been made based on the UPV secondments and the revision of existing literature. Some proposals to classify these uncertainties and risks have been made. UL: Establish the first assumptions to describe Scenarios; Identify Main Scenario for Agriculture challenges once dealing with high uncertainties. UT1C: The GRUS system has been already implemented. Several experimentations have been conducted using the system. We define several scenarios of decision making in agriculture and one of them was more particularly studied. UoP: Studied specific value chains from the mini-projects put forward by relevant industrial partners and adjust the knowledge management research within the mini-projects’ context.
Mathematical modelling processes have been considered in RUC-APS. These new advanced mathematical models for the planting and harvesting of several crops from different regions such as Brittany (France) and Basilicata (Italy) that take into account the production cycle of planting, growing, harvesting, are being developed with the aim of achieving different objectives: maximize profits, minimize costs, maximize quality, minimize risks, etc. Therefore, in order to support the vertical and horizontal collaboration the ZACHMAN approach is going to be considered to gather the systemic view for the main agribusiness value chain process, especially in terms of the development of innovation and technology transfer. In addition to this, tthe main issues with IT interoperability in Agrifood supply chains have been identified, such as: Lack of database interoperability (differences in data models, in data formats); Automation of IT tasks (quite completely absent); Lack of knowledge from the data (They don’t use the knowledge wealth they have in their database and in their data); Use of IT resources from University (They don’t use the possible cooperation with University to improve the Interoperability issues). With this, the project will develop key Ontologies for the agriculture domain body of knowledge and to formalises an interoperability map between enterprise systems in agriculture domain