Theses and Dissertations

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Open Access
Alfalfa reference crop evapotranspiration in Colorado and its use for irrigation scheduling
(Colorado State University. Libraries, 2015) Aljrbi, Abdulkariem Mukhtar, author; Davis, Jessica G., advisor; Andales, Allan A., advisor; Qian, Yaling, committee member; Hansen, Neil, committee member
The goal of irrigation scheduling is efficient use of water such that water is applied to the field for optimal crop production. Previous studies have optimized irrigation scheduling using different models to manage sprinkler irrigation. This research evaluated approaches for obtaining alfalfa reference evapotranspiration (ETr) and its use in a new irrigation scheduling model for a furrow irrigation system. The objectives of this research were to: 1) Compare seasonal trends of daily ETr from the American Society of Civil Engineers Standardized Penman-Monteith (ASCE-SPM) equation and the Penman-Kimberly (PK) equation along a climatic gradient in Colorado, 2) Verify the agreement between calculated ETr from the ASCE-SPM equation and measured ETr from a lysimeter during the 2010 season for the Arkansas Valley of Colorado and correct the lysimeter ETr for alfalfa overgrowth, and 3) Test the ASCE-SPM ETr along with a locally adapted Kcr curve for corn in an irrigation scheduling spreadsheet tool for simulating the daily soil water deficit of furrow irrigated corn in northeast Colorado. The two reference ET equations were compared using R2, Root-Mean-Square Error (RMSE), Relative Error (RE), and index of agreement (d). The R2 values ranged from 0.93 to 0.99; d ranged from 0.98 to 0.99, RMSE ranged from 0.29 to 0.75 mm/d, and RE ranged from -6.35 to 1.91 %. In a comparison of the ASCE-SPM and PK equations at the Fort Collins and Rogers Mesa sites in 2011, differences were observed between the energy balance and aerodynamic terms of each equation. The energy budget calculated by the ASCE-SPM was generally 28% lower than the energy budget calculated by the PK equation at both locations for 2011. On the other hand, the aerodynamic term calculated by the ASCE-SPM equation was from 27 - 28 % higher than the aerodynamic term calculated from PK during most of 2011 at both locations. The second objective of this research compared alfalfa ET measured with a lysimeter in the center of a 4.06 ha furrow irrigated field at the Colorado State University Arkansas Valley Research Center in Rocky Ford, CO to the calculated values from the ASCE-SPM equation in periods of reference conditions in 2010. Four days were selected when alfalfa in the lysimeter was 50 - 55 cm tall, unstressed, completely covering the ground, but with its canopy extending beyond the outer walls of the lysimeter. On these dates, hourly lysimeter ETr was 0.08 to 0.11 mm/h higher than ASCE-SPM ETr. The theoretical surface area of the lysimeter was 9.181 m², while the observed effective canopy area was up to 12.461 m² due to overgrowth. Surface area corrections for the overgrowth increased the index of agreement (d) between hourly lysimeter ETr and ASCE-SPM ETr from the 0.96 - 0.98 range to the 0.99 - 1.0 range. These results showed that it is important to use the correct effective canopy area when computing ETr from a weighing lysimeter. The CIS model for calculating water deficit under a furrow irrigation system with the addition of some data from field measurements such as soil moisture content, gross irrigation, climate data, and plant height and leaf area index generated good results. The water deficit under corn was simulated at the Limited Irrigation Research Farm (LIRF) located near Greeley, Colorado during the years 2010, 2011 and 2012. Daily corn crop ET (ETc) calculated from daily ASCE-SPM ETr and a locally-derived crop coefficient curve (Kcr) were used by the CIS for daily soil water deficit calculations via water balance. This data was used to test a furrow irrigation system via the CIS model and to simulate the field irrigation by predicting the time and the amount of water for the next irrigation. The results showed good agreement between calculated and measured deficits where index of agreement (d) ranged from 0.5 to 0.99 for most years of this study, specifically when measurements of soil water content (SWC) were inserted bi-weekly or monthly. The RMSE did not exceed 2.54 mm when using SWC once per season in 2011, while bi-weekly measurements recorded d to be 0.96 in 2010, 0.99 in 2011 and 0.70 in 2012. Also, the CIS showed that irrigation water usage could be reduced by 30 to 50% through use of CIS.
Open Access
Alfalfa water use under deficit irrigation for farm savings
(Colorado State University. Libraries, 2022) Sitterson, Jan, author; Andales, Allan A., advisor; Mooney, Daniel F., committee member; Brummer, Joe E., committee member
Colorado water law allows for water rights to be leased between agriculture and municipality users. Decreasing the consumptive use (CU) of agricultural land while maintaining profits and yields will allow farmers to lease their water rights for revenue. Deficit irrigation is a water-saving approach to avoid the complete dry up of irrigated farmland while providing profitable yields and monetary gains from water transfers. To maximize water savings, efficient irrigation systems such as subsurface drip irrigation (SDI) are used to prevent water losses from soil evaporation. This study evaluated the feasibility of using SDI with deficit irrigation practices to grow alfalfa (Medicago Sativa L.) at production scale in northeast Colorado (2018 – 2022). Alfalfa was found to have good potential for decreasing CU due to its drought tolerance, multiple harvests per season, and improved quality of hay with less irrigation water. The Water Irrigation Scheduler for Efficient Application (WISE) model was also found to be a useful tool for estimating CU of deficit irrigated alfalfa and the regrowth phases after multiple harvests in a growing season. Mid-season corrections of the soil water deficit in WISE improved the accuracy of modeled CU. Overall the water savings from deficit irrigation at low, medium, and high irrigation levels with an SDI system can be profitable when prices for leasing water exceed hay prices per unit area of production.
Open Access
Enhancement of agricultural systems models for limited irrigated cropping systems research
(Colorado State University. Libraries, 2013) Anapalli, Saseendran S., author; Andales, Allan A., advisor; Ham, Jay M., advisor; Ahuja, Lajpat R., committee member; Ma Liwang, committee member; Chávez, José L., committee member
To view the abstract, please see the full text of the document.
Open Access
Evaluating the ASCE standardized Penman-Monteith equation and developing crop coefficients of alfalfa using a weighing lysimeter in southeast Colorado
(Colorado State University. Libraries, 2011) Al Wahaibi, Hamdan Salem, author; Andales, Allan A., advisor; Hansen, Neil, committee member; Trout, Thomas, committee member; Chávez, José, committee member
To view the abstract, please see the full text of the document.
Open Access
Evapotranspiration-based irrigation scheduling tools for use in eastern Colorado
(Colorado State University. Libraries, 2013) Gleason, David Jamin, author; Andales, Allan A., advisor; Chavez, Jose L., committee member; Bauder, Troy A., committee member
Accurate evapotranspiration (ET) information can be used to improve irrigation water management in eastern Colorado. Crop ET information can be used to help an irrigation manager make decisions on when to initiate irrigation and to determine how much water should be applied. ET information can be obtained through the use of specialized equipment, estimated using models, or obtained from sources such as Colorado Agricultural Meteorological Network (CoAgMet) (http://climate.colostate.edu/~coagmet/). This study has one main focus, the testing of tools for use in ET-based irrigation scheduling. The purpose of the first part of this study was to develop and test two irrigation scheduling tools, one for use with annual crops (Colorado Irrigation Scheduler: Annual (CIS-A)) and the other for use with forage crops (Colorado Irrigation Scheduler: Forage (CIS-F)). The tools use ET information calculated using the ASCE Standardized Reference ET equation to track the daily soil water balance in a crop's root zone and make recommendations on irrigation timings and amount of water to be applied. The second part of this study tested the accuracy of a Model E atmometer (ETgage Company, Loveland, CO, USA) in providing estimates of reference ET in southeastern Colorado. In the first part of the study the CIS-A was tested at two sites (north and south) during the 2010 - 2012 growing season in a corn (Zea mays L.) field located near Greeley, Colorado. The results of the study indicated that the performance of the tool was acceptable based on the relatively small magnitude of errors in the estimated deficits compared to total available water (TAW) in the soil profile. RMSE was at most 15.3% of TAW, as was the case in 2012 at the north site, and was as low as 8.6% of TAW in 2011 at the north site. The CIS-A tended to overestimate the observed deficit during all years of the study and across all sites (relative error, RE = 13.58% and mean bias error MBE = -3.41 mm). Overall average error indicated that the CIS-A was within 15.92 mm (root mean square error, RMSE) and 12.61 mm (mean absolute error, MAE) of the observed deficit for the entire study. Satisfactory performance of the CIS-A was observed in all years and across all sites with the exception of 2012 at the north site. In 2012 the performance of the CIS-A was less than acceptable (RMSE = 22.89 mm, MBE = -12.86 mm, MAE = 18.01 mm, and RE = 30.85%). Evaluations of the CIS-F during the 2010 and 2011 growing seasons showed mixed results. The CIS-F was tested on two weighing lysimeters in two different alfalfa (Medicago sativa L.) fields located at the Arkansas Valley Research Center (AVRC) near Rocky Ford, Colorado. During both years of the study the CIS-F tended to overestimate the observed deficit. The CIS-F performed best in 2011(RMSE = 22.02 mm, MBE = -16.95 mm, MAE = 17.65 mm, and RE = 18.73%) with a RMSE within 6.6% of TAW. In 2010 poorer results were obtained (RMSE = 38.21 mm, MBE = -32.84 mm, MAE = 32.94 mm, and RE = 34.11%). However, in 2010 RMSE was still within 11.5% of TAW. Upon further analysis it was found that much of the error encountered during the evaluation of the CIS-F occurred early in each growing season. It was determined that during this period, crop ET (ETc) estimated using the scheduler was higher than lysimeter measured ET. The difference between lysimeter measured ET and ETc estimated using the ASCE (2005) hourly guidelines for a tall crop and crop coefficients developed using data from the lysimeters was determined to be the major source of the error experienced during both growing seasons. ETc was found to be significantly higher than lysimeter measured ET during the initial part of the alfalfa growing season causing the CIS-F to estimate a deficit greater than what was observed. The objective of the second part of this study was to determine if an ETgage Model E atmometer, equipped with a canvas #54 cover, could be used to effectively estimate alfalfa reference ET. The ASCE Standardized Alfalfa Reference ET Equation (ASCE ETrs) was used as the standard for comparison of atmometer ET values to determine atmometer performance. Four years of alfalfa ET, as determined by an atmometer (ETgage), were compared to ASCE ETrs. Daily as well as 2, 3, 5, and 7 day sums of daily ETgage and ASCE ETrs were compared using simple least-squares linear regression. Coefficients of determination (R2) between daily ETgage and ASCE ETrs for all years were greater than or equal to 0.80. Throughout the study, the atmometer tended to underestimate ASCE ETrs. Average seasonal underestimation of ASCE ETrs measured by the atmometer ranged from 9.06% to 18.9%. Root Mean Square Error (RMSE) and Mean Bias Error (MBE) ranged from 1.14 to 1.82 mm d-1 and -0.66 to -1.51 mm d-1, respectively. The atmometer underestimated daily ASCE ETrs 88% of the time, with an average underestimation of 1.30 mm d-1. Under estimation of ASCE ETrs measured by the atmometer occurred most often on days when mean daily horizontal wind speeds were greater than 2 m s-1 or when mean daily air temperatures were below 20 °C. The atmometer performed best when the alfalfa was at reference condition. Localized calibration equations for reference and non-reference conditions with a temperature correction were developed to improve accuracy, with average magnitude of MBE reduced from -0.97 mm d-1 to 0.13 mm d-1.
Open Access
Improving accuracy for sugar beet and developing an iOS app to increase functionality of a Colorado irrigation scheduler
(Colorado State University. Libraries, 2014) Bartlett, Andrew Charles, author; Andales, Allan A., advisor; Bauder, Troy, committee member; Arabi, Mazdak, committee member
Modeling actual crop water usage allows for improved information-based decision making and ultimately more effective use of water allocations within irrigated agriculture. Evapotranspiration (ET), a dynamic process of water loss through the soil surface (evaporation) and plant stomata (transpiration), is the main component of consumptive water use. Scheduling agricultural irrigation events is an effective tactic to minimize crop stress while avoiding unnecessary irrigation. A cloud based irrigation scheduling tool (WISE - Water Irrigation Scheduling for Efficient Application) which applies the soil water balance (SWB) approach, has been developed on the eRAMS (Environmental Risk Assessment Management System) platform. Actual crop water usage (ETc) is the main cause of the depletion of soil moisture, thus ETc is one of the most important variables within the SWB. Multiple ET equations have been developed as a function of a handful of meteorological measurements including the equation used in this thesis, the 2005 American Society of Civil Engineers (ASCE-EWRI) Standardized Reference equation. The alfalfa based reference evapotranspiration (ETr) models the water loss via ET for a 0.5 m tall, well watered alfalfa stand. In order to model sugar beet water use, an empirically derived crop coefficient (Kcr) curve is applied to the alfalfa reference (ETc = ETr x Kcr). Region specific sugar beet crop coefficient values are available; however, these values have not yet been adjusted for the semi-arid climate of Northeastern Colorado. The first objective of this thesis was to modify the sugar beet Kcr curve for the semi-arid climate of Northeastern Colorado to increase the accuracy of sugar beet scheduling within WISE. By using the soil water balance and observing plant growth and water uptake rates, it was discovered that the original coefficient was drastically overestimating ETc. Shortening the full canopy stage by delaying the initial point (cutoff 2) from 33% to 43% maturity and reducing the length until senescence from 83% to 69% maturity reduced predicted water use to an acceptable value. After comparing actual soil water deficits (D) with modeled values for both the original and adjusted Kcr over two growing seasons, it was found that the relative error (RE) of daily D over all fields was decreased from RE values ranging from 11% - 300% down to RE values ranging from 0% - 265%. Large errors were caused by uncertainties in soil properties, effects of hail damage on actual leaf area and ETc, spatial variability in precipitation or irrigation, and differences in field micro-climate and measured weather station data. The second objective of this thesis was to describe the development and purpose of an iPhone and iPad application that was created to add mobile functionality to the WISE tool. This app allows users to view their field's current soil moisture profile, previous day's weather, upload irrigation and precipitation amounts, and calculate gross irrigation amounts as a function of flow rate, length of application, and acreage. The new sugar beet Kcr curve and the iOS app can lead to more effective irrigation scheduling in agriculture within Colorado.
Embargo
Innovative tools for maize water use assessment
(Colorado State University. Libraries, 2023) Capurro, Maria Cristina, author; Andales, Allan A., advisor; Ham, Jay M., advisor; Comas, Louise, committee member; Chávez, José L., committee member
Modern 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.

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