WORKING PAPER Sensor datasets report from the value-proposition Ag-plot testing of automated field data collection and monitoring tools in Guatemala - 2023 Oscar H. Estrada Vargas, o.estrada@cgiar.org, Alliance Bioversity-CIAT Daniel Ricardo Jimenez, d.jimenez@cgiar.org, Alliance Bioversity-CIAT Gresia Dalal Ramos, g.d.ramos@cgiar.org, Alliance Bioversity-CIAT Jesus David Martinez, j.d.martinez@cgiar.org, Alliance Bioversity-CIAT Jelle Filip Van Loon, j.vanloon@cgiar.org, CIMMYT Abstract As part of the Digital Innovation initiative, the Alliance Bioversity International -CIAT and CIMMYT, established a digital plot at the San Carlos University, CUNORI, in Chiquimula, Guatemala. A set of meteorological, soil and crop data have been collected since July 2023. The information comes in that first stage from two strategically located weather stations and a soil device that measures soil moisture, salinity, and temperature. This data will be essential to understand environmental conditions and the behavior of climate, soil, and plants in the region, and enable informed crop management decisions for data-driven agronomy. Date: 12/12/2023 // Work Package: 4 // Partner: CUNORI University This publication has been prepared as an output of CGIAR Research Initiative on Digital Innovation, which researches pathways to accelerate the transformation towards sustainable and inclusive agrifood systems by generating research-based evidence and innovative digital solutions. This publication has not been independently peer-reviewed. Any opinions expressed here belong to the author(s) and are not necessarily representative of or endorsed by CGIAR. In line with principles defined in CGIAR's Open and FAIR Data Assets Policy, this publication is available under a CC BY 4.0 license. © The copyright of this publication is held by IFPRI, in which the Initiative lead resides. We thank all funders who supported this research through their contributions to CGIAR Trust Fund. Contents Introduction 2 Acurite weather station 2 Arable weather station 4 Soil moisture, salinity, and temperature sensor 5 Precipitation vs soil moisture 6 Attachments 7 Next steps 8 References 8 1 Sensor databases from digital plot Chiquimula, Guatemala - 2023 Introduction As part of the CGIAR Digital Innovation initiative activities, of which the Alliance Bioversity International - CIAT and CIMMYT are part, a digital plot was established with a maize crop at the San Carlos University, CUNORI headquarters, located in Chiquimula, Guatemala, constituting a space where agricultural tradition and cutting-edge technologies converge. This plot aims to promote the inclusion of digital technologies and their advantages for the agricultural sector, offering all local stakeholders (farmers, researchers, and academy, among others) the opportunity to learn about technologies that applied to agriculture can help improve productivity and sustainability of farming systems. In this aspect, climate and soil humidity, salinity and temperature sensors have been installed to measure key variables that have a direct impact on crop development. Throughout this report, an exploratory summary of the provisional information collected during the first months of the activity is presented, as well as the clean databases from each sensor and a first analysis of their usability. Acurite weather station The Acurite weather station records precipitation, external and internal temperature, external and internal relative humidity, dew point and wind speed, among other variables. The station recorded data from 11 July to 3 December (cut-off day) with 12-minute intervals between readings, which were resampled to obtain daily information (see Figure 1). Figure 1. Acurite weather station data. Figure 2. Acurite weather station data boxplots. Figure 2 shows the boxplots with the data distribution, it seems a normal range for temperature and relative humidity. However, the station shows data loss in several days and anomalous precipitation records, which indicates technical problems with the console that records the data, especially after a data extraction was performed, as noted by the field staff. The failure’s origin could not be identified so far; therefore, the use of this station is not recommended. Arable weather station If properly configured, this weather station can record a wide variety of variables in addition to the typical ones such as precipitation, temperature, and solar radiation, for example evapotranspiration, GDD, NDVI, dew point, among many others, in hourly and daily format. This station recorded data from 14 July to 30 November (cut-off date) and the data was selected in daily format intervals as shown in Figure 3. Figure 3. Arable weather station data. Figure 4. Arable weather station data boxplots. Figure 4 shows the distribution of the recorded variables, normal ranges are observed for all of them. No outliers or missing data were observed; therefore, this station is the chosen one to obtain the climatic information due to its reliability. Soil moisture, salinity, and temperature sensor This sensor records hourly soil moisture (%), ionic content (VIC), and temperature (°C) at depths of 20, 40, 60, 80, 100 and 120 cm, and kept records from 16 August to 30 November (cut-off date). Hourly data were resampled to obtain daily indicators. Figure 6 shows the behavior of soil moisture, soil ionic content and soil temperature variables at 6 different soil depth levels, with consistency between the data at each level. This sensor provides valuable information to know the soil conditions, which, when crossed with climate and crop management data, allows to define management plans that optimize the use of resources and productivity (Florentino, 2006). Figure 5. Soil sensor humidity, ionic content, and temperature at different soil depths Precipitation vs soil moisture A graph of the rainfall recorded by the Arable station was plotted against the soil moisture information from the soil sensor at depths of 20 and 40 cm. In figure 6, the correspondence between the two variables is clearly observed from the date on which both variables were registered. Figure 6. Precipitation vs soil moisture at two different soil depths Attachments The adjusted provisional databases with daily information for the stations and sensors of the digital plot are attached in this GitHub link: GitHub - CIAT-DAPA/di_digital_plot Acurite weather station: Acurite_230711_231203.csv Arable weather station: Arable_230714_231130.csv Humidity, salinity, and temperature sensor: Soil_230816_231130.csv Once more information is available and other sensors become operational, the databases will be updated and expanded. Next steps One of the next steps, once the maize crop cycle is over, is to cross-reference the sensor data with the crop management data to identify critical points to generate specific management practices to improve productivity and sustainability. Another step to follow involves putting into operation other sensors, among them, 4 electronic lysimeters installed in the digital plot and beginning to analyze the information to identify the water requirements of crops such as maize and beans (Pineda-Castro et al., 2023). At the current stage, it is challenging to definitively determine the optimal sensor choice considering practicality, cost-effectiveness, and reliability. Furthermore, the recommendation of sensors must also consider socio-economic and environmental factors. In our next reporting cycle, we anticipate being able to provide more precise conclusions in this regard. References Florentino, A. (2006). Métodos para medir el contenido de agua en el suelo. Venesuelos, 14(1), 48-70. Pineda-Castro, D., Diaz, H., Soto, J., & Urban, M. O. (2023). LysipheN: A gravimetric IoT device for Near Real-time High-Frequency Crop Phenotyping: a case study on common beans. 2 image1.png image2.png image3.png image4.png image5.png image6.png image7.png image8.png