DATA FORCE

https://drive.google.com/drive/folders/1EdF8T-1ZgSWlQtPVaN5BvQPg4aVVqEbA?usp=sharing

https://drive.google.com/file/d/1YOpfUZs92u9Si-jlDFg96rXnIcj1ji8i/view?usp=sharing

Through our APP, farmers will submit information regarding their crops (type, sowing date, and location) for being compared with Food and Agricultural Organization (FAO) data (stages, length, and water requirements for each specific area and crop), and precipitation forecasting databases. The farmers will obtain alert outputs for each of their crop stages (such as the image below), indicating if expected precipitations will fall into the required optimum range. In that way, they will be able to take action either, by monitoring crops, or implementing Irrigation or drainage systems. As result, farmers will be able to maximize productivity and minimize losses in their crops leading us to achieve the "Food Security and Sustainable Agriculture" SDG.

The APP requires sowing date, geographic location, and type of crop as input data, this information is contrasted with precipitation forecasts from ESA satellites and the optimum levels of precipitation for the chosen crop (according to region and maturity of the crop according to FAO data). With that information, the APP generates alerts on expected precipitation levels out of the optimum range for each stage of the crop or close to the limits, and some suggestions respecting possible actions to take. This early alert system leads to implementing preventive controls for the benefit of the crops.

Additionally, the application aims to be a tool that every person could access and understand the information presented, thus the application is simple and shows the alerts with explanations and suggestions as a way to support people with different education levels, to motivate them to use it and improve their productivity performance, avoiding adverse conditions.

Our solution has also scalability elements like 1) Obtaining real information (precipitation and production) to smooth future predictions for specific productive units, 2) Suggesting better crops for specific areas, for investing optimizations or soil regeneration purposes, 3) Suggesting the best time to sow specific crops, 4) Customizing dashboards for productive units, and 5) Using other kinds of data forecasting like weather, wind, CO2 level, etc. to implement preventive controls.

See APP´s Mockup in the following link.

https://drive.google.com/file/d/13KzJInFOlL6PGfOzY8QZ6cVspOR4H7Ps/view?usp=sharing

See GitHub for data analysis in the following link.

https://github.com/lorenzeypc/NASA

We are using the Matlab software to visualize data extracted from ERA5 hourly data on single levels from 1959 to present. Precipitation, winds and temperature data for the Olympus Mountain region in different years are obtained and contrasted with high resolution data obtained from the OLYMPEX project measurements to provide graphical outputs of the monthly multiannual climatology and validating, thus allowing to use the forecasted information from ESA weather systems to acquire precipitation levels in a region and be compared with the acceptable levels of precipitation for the crops

As a team, this experience has been a unique step in our lives. Solving a problem using our creativity and skills was challenging. The way in which each of the members found their role in the project was an interesting experience. As a team, we are sure that after this challenge, our mindset will change towards innovation, productivity, and problem-solving. In carrying out the project we learned to better understand the cooperativity, and thus together we were able to complete this project. As a group, we use an approach oriented to the collection and processing of information. This was literally an experience "from another planet", we thank NASA for providing us with this space in which we learned so much, the Débora Arango library for lending us its facilities, thanks to the UGAC corporation for encouraging science and knowledge, and finally each one of our members, Lina, Sebastián, Tatiana, Juan David, and Antonio.

1. Houze, R. A., McMurdie, L. A., Petersen, W. A., Schwall Er, M. R., Baccus, W., Lundquist, J. D., Mass, C. F., Nijssen, B., Rutledge, S. A., Hudak, D. R., Tanelli, S., Mace, G. G., Poellot, M. R., Lettenmaier, D. P., Zagrodnik, J. P., Rowe, A. K., DeHart, J. C., Madaus, L. E., & Barnes, H. C. (2017). The olympic mountains experiment (Olympex). Bulletin of the American Meteorological Society, 98(10), 2167–2188. https://doi.org/10.1175/BAMS-D-16-0182.1
2. Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers, D., Soci, C., Villaume, S., Bidlot, J. R., Haimberger, L., Woollen, J., Buontempo, C., & Thépaut, J. N. (2021). The ERA5 global reanalysis: Preliminary extension to 1950. Quarterly Journal of the Royal Meteorological Society, 147(741), 4186–4227. https://doi.org/10.1002/QJ.4174
3. US EPA. (n.d.). Climate Impacts on Agriculture and Food Supply | Climate Change Impacts. Retrieved October 1, 2022, from https://climatechange.chicago.gov/climate-impacts/climate-impacts-agriculture-and-food-supply
4. FAO. (2022). Crop Information | Land & Water | Food and Agriculture Organization of the United Nations . https://www.fao.org/land-water/databases-and-software/crop-information/en/
5. United Nations. (2015). Food security and nutrition and sustainable agriculture | Department of Economic and Social Affairs. https://sdgs.un.org/topics/food-security-and-nutrition-and-sustainable-agriculture

Agriculture, water, precipitation, food security, crops, zero hungry, NASA, FAO, ESA.