Capurro, Maria Cristina, authorAndales, Allan A., advisorHam, Jay M., advisorComas, Louise, committee memberChávez, José L., committee member2023-06-012025-05-262023https://hdl.handle.net/10217/236665Modern agriculture is facing a scenario of decreased water availability and sustainability concerns. Accurate estimation of crop transpiration is crucial to improve agricultural water management. However, transpiration estimation is challenging due to the difficulty in modeling canopy conductance (gc). There is currently no standardized approach for the calculation of gc. Additionally, direct measurements of gc and transpiration at the field scale are difficult. There are few commercially available sensors that measure transpiration, and those available are expensive. For gc, most of the equipment available use manual or indirect approaches. Meanwhile, during the past years there has been a dramatic development of sensor technology and communications. The expansion of low-cost circuit boards and 3D printing development and the Internet of Things (IoT) has led to a decrease in the cost of sensors and facilitated data acquisition and fabrication of research-grade instruments. The purpose of this study was to develop low-cost tools to measure actual crop transpiration and gc and contribute to the improvement of water use estimation in irrigated fields. We developed two types of IoT plant-based devices using 3D-printing and low-cost electronics and sensors: a sap flow gauge (SFG) and an artificial reference surface (ARS) system. We developed a new theory for a heat pulse method for calculation of transpiration rate that was coupled with a new type of sap flow gauge. The gauge is easy to build and adaptable to a range of stem sizes. We calibrated and validated the sensors in maize (Zea mays L.) plants in the greenhouse and tested them in a well-watered maize field in two locations in northern Colorado, for two years. The sap flow sensors calibration coefficient and standard deviation (SD) was 1.28 g/h ± 0.2, used to convert heat velocity to transpiration flow. A higher calibration coefficient was found in 2019 when a longer heating time was applied, confirming that the coefficient takes into account wounding effects on the plant. The data collected allowed the calculation of the maize transpiration every 15 minutes and showed that they were in good agreement with estimated transpiration from plants on weighing scales from greenhouse studies, from field measurements with commercially available sap flow gauges and with estimations with the Penman-Monteith (PM) approach in field conditions. Daily transpiration from SFGs compared with measured values in the greenhouse had a root mean squared error (RMSE) of 15.4% and a mean absolute error (MAE) of 12.1% of the mean T value in 2020. In 2019 the RMSE was 12.4% and the MAE was 10.2% from the mean T value. In field conditions, when SFGs were compared to daily transpiration estimates using the PM approach, the RMSE and MAE were 0.70 mm and 0.56 mm, a 13.2% and 11% error, respectively. When compared to commercially available SFGs, the RMSE and MAE were 0.66 mm and 0.54 mm (12.4% and 10.2% from the mean T value), respectively. In 2020, daily transpiration estimation in the field with the developed SFGs had a precision of ±1.04 mm SD and when compared to the PM approach had an RMSE and MAE of 0.62 mm and 0.48 mm, respectively (both were within 10% of mean transpiration). Results also showed that error in estimations decrease with additional sensors deployed in the field. More than 4 sensors should be deployed in the field to obtain estimations of corn transpiration with less than 20% error. The required number of gauges varies according to the accuracy desired for transpiration measurements. The ability of the sap flow sensors to measure plant transpiration directly make them powerful tools for multiple applications. They can capture the effects of the environment and characteristics of the plant. Therefore, they are valuable for assessing the partitioning of total crop evapotranspiration (ETc) into plant transpiration and soil evaporation. They can be used for local basal crop coefficient estimations and for fine-tuning local irrigation applications. They can also provide valuable information for ground-truthing complex multi-layer models and simulations that aim to estimate actual transpiration from the field. The use of the sap flow sensors for site-specific basal crop coefficients (Kcb) derivation was tested. Locally derived Kcb values from Trout and DeJonge (2018) for maize was verified using our low-cost sap flow sensors for a period of 22 days in 2020 and for a period of 17 days in 2019. The period was divided into mid-season, beginning of late-season and end late-season. Mean Kcb values from SFGs for mid-season and beginning of late season agreed with those from Trout and DeJonge (2018). Locally derived Kcb was 1.05 and 0.82, while Kcb from SFGs were 1.08 and 1.06 for mid-season during 2019 and 2020 and 0.82 for beginning of late season in 2020. However, end of late-period Kcb from sap flow data in 2020 was higher than the tabulated value (0.62 vs 0.4). This was probably due to the fixed end-of-cycle-date for the maize growing season in contrast to variable growing degree days. This approach is especially useful for low budget and rapid evaluations. We described the advantages of using Kcb curves for estimation of crop water requirements and highlight the benefits of using sap flow gauges for its derivation. The second device was an ARS that consisted of a plastic hemispherical surface that allowed monitoring of dry leaf temperature. This temperature was used to estimate actual maize transpiration, gc and to detect and monitor water stress in field conditions. The hemispherical ARS closely mimicked the temperature of non-transpiring leaves (R2=0.99) in a field study conducted in 2020 at Fort Collins, CO. Actual maize transpiration was adequately estimated for a 14-day period using the thermal approach with the ARS temperature. Comparisons with transpiration calculated from SFGs and the ASCE standardized tall reference ET (ASCE, 2005) with local basal crop coefficient (Kcb) values for maize showed a root mean square error (RMSE) of 0.61 mm and margin of error (ME) of 0.53 mm, representing a 12% and a 10% error of the method in relation to the Penman-Monteith approach and a RMSE and ME of 0.78 mm and 0.73 mm in relation to actual maize transpiration from SFGs. Differences from the total transpiration were within 7.5%. Absolute hourly values of gc were also calculated with this approach during the daytime, showing a pattern and values similar to the conductance derived from the SFGs for a 6-day period. However, underestimations were observed at the beginning and end of the day. When mean mid-day values of gc were compared to sap flow measurements, the MAE and RMSE were 0.51 mm/s. and 0.72 mm/s, representing 8.1% and 11.6% error, respectively. The method was also tested in a deficit-irrigated maize field, showing a reduction in transpiration and in gc due to soil water depletion and demonstrating the sensitivity of the method to detect water stress in the field. However, transpiration values were severely underestimated due to the similarity of the temperatures from the ARS and canopy. Changing the color of the ARS might reduce these errors. Results suggest that a darker color should be used. A simple thermal method for water stress detection was also tested. Its strong correlation with gc (R2=0.7) demonstrated that it could be a method to detect the onset and development of plant water stress in the field. The use of the dry ARS could be a practical approach for maize transpiration, gc and water stress estimation since it requires less weather data. Its simplicity of fabrication, implementation, and low requirements for maintenance during the season are also valuable advantages of the method. We were able to develop versatile low-cost IoT tools for real time monitoring of crop transpiration and gc. These tools can be used for multiple purposes and have the potential to improve our ability to estimate maize crop water requirements.born digitaldoctoral dissertationsengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.canopy conductanceplant transpirationsensorsmaizebasal crop coefficientsap flowTextEmbargo Expires: 05/26/2025