Dana Porter

Subsurface Drip Irrigation: Status of the Technology in 2010

Subsurface drip irrigation (SDI), although a much smaller fraction of the microirrigated land area than sur- face drip irrigation, is growing at a much faster rate and is the subject of considerable research and educational efforts in the U.S. This article discusses the growth of SDI, highlights some of the research and extension efforts, and points out some of the challenges to SDI adoption and some of the future opportunities for SDI.

Cotton Response to Pre-plant Irrigation Level and Irrigation Capacity Using Spray, LEPA, and Subsurface Drip Irrigation

Wind incidence can affect microsprinkler water distribution. Evaluations in those conditions can be facilitated using simulations by computational models. The present work evaluates the performance of a ballistic model on simulating the wind effect on microsprinkler water distribution. Experimental tests were carried out using self-compensating microsprinklers, nozzle sizes 1.00 mm (gray), 1.10 mm (brown), 1.48 mm (orange), and 1.75 mm (yellow). The gray and brown nozzles used black swivels and the orange and yellow nozzles used blue swivels. The wind effect was artificially caused by fourteen 200 W fans. The computational simulations were realized using SIRIAS software, which is based on a ballistic model originally developed for sprinkler systems. The correlation coefficients (r) varied between 0.619 and 0.880, while the exactness coefficients (d) varied between 0.842 and 0.944. Swivels internal geometry influenced the results. The tested model presented a performance classified as very good for the black swivel nozzles and a regular performance for the blue swivels nozzles.

SENSITIVITY OF GRASS- AND ALFALFA-REFERENCE EVAPOTRANSPIRATION TO WEATHER STATION SENSOR ACCURACY

A sensitivity analysis was conducted to determine the relative effects of measurement errors in climate data input parameters on the accuracy of calculated reference crop evapotranspiration (ET) using the ASCE-EWRI Standardized Reference ET Equation. Data for the period of 1995 to 2008 from an automated weather station located at the USDA-ARS Conservation and Production Research Laboratory at Bushland, Texas were used for the analysis. Results indicated that grass (ETos) and alfalfa (ETrs) reference crop ET were most sensitive to measurement errors in wind speed and air temperature followed by incoming shortwave (solar) radiation, and that data sensitivity was greater during the mid-summer growing season in this semi-arid region. Given the highly advective conditions of the Texas High Plains and the relative sensitivity of ET calculations to errors in wind speed, special care is recommended in siting, sensor placement, and sensor maintenance for agriculturally-based ET weather stations.

Can Subsurface Drip Irrigation (SDI) be a Competitive Irrigation System in the Great Plains Region for Commodity Crops

Subsurface drip irrigation (SDI) as with all microirrigation systems is typically only used on crops with greater value. In the US Great Plains region, the typical irrigated crops are the cereal and oil seed crops and cotton. These crops have less economic revenue than typical microirrigated crops. This paper will present a case for how SDI can be economically competitive for the lesser value crops of the Great Plains. The case will have 5 sections: 1) How do Great Plains crops respond to SDI? 2) Are there special uses for SDI in the Great Plains? 3) How can SDI system costs be minimized without causing operational and maintenance problems? 4) Can SDI systems have a long life? 5) How does SDI compare economically to alternative irrigation systems?

Statistical learning algorithms for identifying contrasting tillage practices with Landsat Thematic Mapper data

Tillage management practices have a direct impact on water-holding capacity, evaporation, carbon sequestration and water quality. This study examines the feasibility of two statistical learning algorithms, namely the least square support vector machine (LSSVM) and relevance vector machine (RVM), for identifying two contrasting tillage management practices using remote-sensing data. LSSVM is firmly based on statistical learning theory, whereas RVM is a probabilistic model where the training takes place in a Bayesian framework. Input to the LSSVM and RVM algorithms were reflectance values at different bandwidths and indices derived from Landsat Thematic Mapper (TM) data. Ground-truth data for this study were collected from 72 commercial production fields in two counties located in the Texas High Plains of the south-central USA. Numerous LSSVM- and RVM-based tillage models were developed and evaluated for tillage classification accuracy. The percentage correct and kappa statistic were used for the evaluation. The results showed that the best LSSVM and RVM models included the use of TM band 5 or vegetation indices that involved TM band 5, indicating sensitivity of near-infrared reflectance of crop residue cover on the surface. This is consistent with other remote-sensing models reported in the literature. Overall classification accuracies of the best LSSVM and RVM models were 87.8 and 90.2%, respectively. The corresponding kappa statistics for those models were 0.75 and 0.80, respectively. Furthermore, comparison of the best LSSVM and RVM models with the published logistic regression-based tillage models developed with the same data indicated the superiority of the RVM model over LSSVM and logistic regression models in determining contrasting tillage practices with Landsat TM data.

Comparison of Subsurface Drip Irrigation Uniformity Designs on Cotton Production

Relaxing subsurface drip irrigation (SDI) uniformity design standards for systems irrigating cotton in semi-arid environments could reduce installation costs. A SDI system was installed and cotton production experiment conducted from 2001 to 2005 to evaluate good, acceptable, and poor irrigation uniformity (FV = 7.5, 15.7, and 37.1%) at moderate and near full irrigation levels (0.6BI and 1.0BI). Flow variation treatments were established by installing drip laterals in separate zones with lateral diameters of 22, 25, and 17 mm and pressurizing corresponding laterals at 83, 45, and 69 kPa in very good (VGD), acceptable (ACC), and poor (POOR) treatment areas. Cotton yield response tended to follow changes in emitter flow rate along the length of drip laterals, but not to the extent expected. Total cotton lint yield and total yield value within a treatment area were not significantly affected by irrigation system designs with flow rate variations (FV’s) between 7.5 and 37.1 during the four test years. Average total lint yields were in a narrow range between 1442 and 1490 kg ha-1 yr-1and average yield values were between 1681 and 1749

Technology Transfer: Promoting Irrigation Progress and Best Management Practices

ha-1 yr-1 among all treatments. Conditions other than SDI design, common to the Texas High Plains, impacted cotton yield variability.

Evaluation of Sensible Heat Flux and Evapotranspiration Estimates Using a Surface Layer Scintillometer and a Large Weighing Lysimeter

Educational efforts promoting irrigation best management practices are designed to increase adoption of these practices and increase public understanding of the importance of irrigation. They increase visibility and impact of the Ogallala Aquifer Program and promote affiliated research and extension programs to agricultural producers, consultants and water resources managers. Building upon existing programs and collaborations, successful programs are being expanded to accommodate additional audiences and applications. Improved quality, effectiveness and efficiency of educational programs are made possible through improved communication and complementary expertise of the collaborators. A variety of methods and media (electronic and print; modular educational packages; mass media; and traditional Extension field days and workshops) are being used to reach an expanding and diverse audience. Evaluation surveys and levels of participation in education events indicate that audience response has been very positive.

The ASCE Standardized Equation Based Bushland Reference ET Calculator

Accurate estimates of actual crop evapotranspiration (ET) are important for optimal irrigation water management, especially in arid and semi-arid regions. Common ET sensing methods include Bowen Ratio, Eddy Covariance (EC), and scintillometers. Large weighing lysimeters are considered the ultimate standard for measurement of ET, however, they are expensive to install and maintain. Although EC and scintillometers are less costly and relatively portable, EC has known energy balance closure discrepancies. Previous scintillometer studies used EC for ground-truthing, but no studies considered weighing lysimeters. In this study, a Surface Layer Scintillometer (SLS) was evaluated for accuracy in determining ET as well as sensible and latent heat fluxes, as compared to a large weighing lysimeter in Bushland, TX. The SLS was installed over irrigated grain sorghum (Sorghum bicolor (L.) Moench) for the period 29 July–17 August 2015 and over grain corn (Zea mays L.) for the period 23 June–2 October 2016. Results showed poor correlation for sensible heat flux, but much better correlation with ET, with r2 values of 0.83 and 0.87 for hourly and daily ET, respectively. The accuracy of the SLS was comparable to other ET sensing instruments with an RMSE of 0.13 mm·h−1 (31%) for hourly ET; however, summing hourly values to a daily time step reduced the ET error to 14% (0.75 mm·d−1). This level of accuracy indicates that potential exists for the SLS to be used in some water management applications. As few studies have been conducted to evaluate the SLS for ET estimation, or in combination with lysimetric data, further evaluations would be beneficial to investigate the applicability of the SLS in water resources management.

Irrigation in the Texas High Plains: a brief history and potential reductions in demand

Accurate daily reference evapotranspiration (ET) values are needed to estimate crop water demand for irrigation management and hydrologic modeling purposes. The Bushland Reference ET Calculator was developed by the USDA-ARS Conservation and Production Research Laboratory at Bushland, Texas for calculating hourly and daily grass and alfalfa reference ET. The user-friendly interface for the calculator was developed using .NET programming. The calculator uses the ASCE Standardized Reference Evapotranspiration (ET) Equation for calculating both grass and alfalfa reference ET at hourly and daily time steps. Users have the option of using a single set or time series weather data to calculate reference ET. Daily reference ET can be calculated either by summing the hourly ET values for a given day or by using daily averages of the climatic data. Although the Bushland Reference ET Calculator was designed and developed for use mainly by producers and crop consultants to manage irrigation scheduling, it can also be used in educational training, research, and other practical applications. This paper demonstrates use of the Bushland Reference ET Calculator that is available from the USDA-ARS Conservation and Production Research Laboratory web site to interested users at no cost.