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NITREOS (Nitrogen Fertilization, Irrigation and Crop Growth Monitoring using Earth Observation Systems) is a farm management information system (FMIS) for organic and conventional agriculture which aims in enabling farmers to tackle crop abiotic stresses and control important growing parameters to ensure crop health and optimal yields. NITREOS employs a user friendly, webbased platform that integrates satellite remote sensing data, numerical weather predictions and agronomic models, and offers a suite of farm management advisory services to address the needs of smallholder farmers, agricultural cooperatives and agricultural consultants. This paper provides an analysis of different methodologies employed in the nitrogen fertilization service of NITREOS. The methods are based on the determination of the Nitrogen Fertilization Optimization Algorithm for cotton, maize and wheat crops. Available agro-meteorological data on two distinct agricultural regions were used for the calibration and validation of the recommended Nitrogen rates.

BEACON is a market-led project that couples cutting edge Earth Observation (EO) technology with weather intelligence and blockchain to deliver a toolbox for the Agricultural Insurance (AgI) sector with timely cost-efficient and actionable insights for the agri-insurance industry. BEACON enables insurance companies to exploit the untapped market potential of AgI, while contributing to the redefinition of existing AgI products and services. The Damage Assessment Calculator of BEACON employs remote sensing techniques in order to improve the quality and cost-effectiveness of agri-insurance by: i) increasing the objectivity of the experts field inspections; ii) reducing the cost of field visits and iii) increasing farmers' confidence in the estimation results, given the significant economic impact of erroneous estimation. This paper provides an analysis of different type of EO data and remote sensing techniques implemented in the operational workflow of BEACON that can be used by AgI companies to provide safe and reliable results on storms, floods, wildfires and droughts damage on crops.

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Food and feed production must be increased or maintained in order to meet the demands of the earth's population. Under this scenario, the question that arises is how to address the demand for agricultural products given that the pressures on land use have already increased. In addition, it is obvious that climate change will have a serious negative impact and threaten the productivity and sustainability of food production systems. Therefore, understanding and predicting the outcome of crop production, while considering adaptation and sustainability, is essential. The need for information on decision making at all levels, from crop management to adaptation strategies, is constantly increasing and methods for providing such information are urgently needed in a relatively short period of time. Thus arises the need to use effective data, such as satellite and meteorological data, but also operational tools, to assess crop yields over local, regional, national, and global scales. In this work, three modeling approaches built on a fusion of satellite-derived vegetation indices, agro-meteorological indicators, and crop phenology are tested and evaluated in terms of data intensiveness for the prediction of wheat yields in large scale applications. The obtained results indicated that medium input data intensity methods are effective tools for yield assessments. The methods, namely, a semi-empirical regression model, a machine learning regression model, and a process-based model, provided high to moderate accuracies by fully relying on freely available datasets as sources of input data. The findings are comparable with those reported in the literature for detailed field experiments, thereby introducing a promising framework that can support operational platforms for dynamic yield forecasting, operating at the administrative or regional unit scale.