Research Project: Advancing Water Management and Conservation in Irrigated Arid Lands

Location: Water Management and Conservation Research

2021 Annual Report

Objectives
The long-term objectives of this project are to develop decision support tools and sensing and computing technologies to support improved crop water use efficiency for irrigated agriculture in arid lands. Objective 1: Develop and integrate models, tools, and strategies to optimize water and nutrient use efficiencies under sufficient, reduced, and variable-rate irrigation strategies in arid environments. Subobjective 1A: Quantify cotton physiological development, fiber yield, fiber quality, and water use responses to variable irrigation rate and timing. Subobjective 1B: Develop end-user irrigation scheduling models for cotton and other crops. Subobjective 1C: Develop nitrogen fertilizer scheduling strategies and tools for cotton. Objective 2: Use remote and proximal sensing at regional and field scales for crop and water management and use proximal sensing for high throughput phenotyping for heat and drought tolerant cultivars. Subobjective 2A: Develop and evaluate airborne and satellite-based remote sensing methods to estimate crop evapotranspiration, ETc, at irrigation district scale. Subobjective 2B: Develop and evaluate airborne and drone-based remote sensing methods to estimate crop evapotranspiration, ETc, at field scale. Subobjective 2C: Develop and evaluate ground-based proximal sensing methods that identify crop heat and drought stress at field scale. Objective 3: Develop and evaluate crop simulation models as tools to synthesize “big” data from agricultural field studies and analyze alternative strategies for crop and water management. Subobjective 3A: Evaluate and improve Cotton2K and DSSAT-CSM models for simulation of cotton physiology, water use, and nutrient use in response to water and nutrient deficit. Subobjective 3B: Develop crop simulation modeling methodologies to analyze potential water savings and production impacts of variable-rate and deficit irrigation practices. Subobjective 3C: Develop methodologies to guide crop simulation and irrigation scheduling models using “big data” from remote and proximal sensing and crop and soil mapping equipment. Objective 4: Develop concepts, technologies, and software tools for the hydraulic analysis of surface irrigation systems. Subobjective 4A: Develop software for the hydraulic analysis of irrigation systems. Subobjective 4B: Model irrigation-induced soil erosion. Subobjective 4C: Develop field technologies for improved surface irrigation management.

Approach
Objective 1 Goal 1A: Conduct cotton field experiments using a new VRI system on a lateral move overhead sprinkler to deliver precise irrigation treatments to cotton. Goal 1B: Develop improved irrigation scheduling models and software that account for spatial water application and crop water use in irrigation management and provide guayule growers with new irrigation scheduling tools. Goal 1C: Develop improved N management scheduling models and software that will help optimize the N fertilizer application rate guidelines for cotton under lateral move overhead sprinkler and subsurface drip irrigation. Objective 2 Goal 2A: Reduce ETc estimation uncertainty and bias at irrigation district scales by integrating sensing technologies with weather-based approaches. Goal 2B: Develop a field-scale decision support system for mapping ETc using drone platforms. Goal 2C: Demonstrate that field-based high-throughput plant phenotyping with proximal sensors could be an effective approach for crop breeding. Objective 3 Goal 3A: Conduct evaluations of the Cotton2K and DSSAT-CSM cotton simulation models and identify options for model improvement. Goal 3B: Conduct simulation analyses to assess effects of variable-rate irrigation management practices on crop production and water use. Goal 3C: Develop mathematical approaches for integrating remote and proximal sensing data with irrigation scheduling models and crop simulation models. Objective 4 Goal 4A: Enhance the functionality of the WinSRFR software package by improving the design procedures to account for flow-depth dependent infiltration, developing procedures for furrow systems with return flow, and developing procedures for predicting the transverse distribution of infiltrated water in a furrow cross section based on soil textural properties. Goal 4B: Development and testing of a process-based model of sediment transport coupled to surface irrigation flow. Goal 4C: Develop a process for evaluating the field-level seasonal performance of an irrigation system and developing field technologies for acquiring irrigation evaluation data reliably and inexpensively.

Progress Report
In support of Sub-objective 1B, crop coefficients (Kc) were developed for cotton grown under subsurface drip irrigation using data from experiments conducted at Maricopa in 2016, 2017, and 2018. The study also developed calibration procedures to adjust cotton evapotranspiration (ET) for deficit irrigation management. The web-based irrigation scheduling tool (WINDS II), hosted by the University of Arizona partner in Tucson, Arizona, is currently being calibrated for cotton using field data collected by ARS in 2007 at Maricopa, Arizona. Currently, on-going studies of the perennial natural rubber crop, guayule, are being conducted in Maricopa and Eloy, Arizona, to develop irrigation scheduling methods for re-growing guayule following initial two-year harvests that were made in 2020. In support of Sub-objectives 2A and 2B, a methodology was developed that combines time-series vegetation indices derived from 5-day observations with Sentinel 2 images to create daily evapotranspiration estimates. The estimates were validated for wheat, lettuce, cotton, spinach, and melons using ground-based eddy covariance observations. A second method using satellite data and crop classification maps provide by the United States Bureau of Reclamation for the Yuma-Wellton irrigation districts has created a way to revise and improve FAO-56 crop duration values for vegetables, small grains, and alfalfa. Using drone data we created functions to transform vegetation indices to fractional cover for vegetables. Additional work is underway to validate crop coefficients for brassica and citrus. For Sub-objective 2C, under collaboration with faculty at Arkansas State, we developed a calibration routine for thermal sensors used on drones for phenomics and evapotranspiration. In support of Sub-objective 3A, techniques to integrate soil water content data with the Cropping System Model (CSM) were developed using field data from cotton field experiments conducted at Maricopa in 2016, 2017, and 2018. Several statistical and computational approaches for combining soil water content measurements with the model were developed and evaluated. Yield simulation results were compared among model simulations that did and did not incorporate soil water content measurements. Results showed improvements of simulated cotton yield for some, but not all, of the tested cases. Further efforts are needed to understand best practices for incorporating soil water content measurements with simulation models for purposes of improved water budgeting, yield forecasting, and irrigation scheduling. In support of Sub-objective 3C, research progressed on the development of a computational approach (Kalman smoothing) to model daily cotton canopy cover from weekly field imagery collected from a small unmanned aircraft system (sUAS). During the 2019 and 2020 cotton field seasons, the sUAS was flown weekly to image a 3-hectare area under cotton production. Cotton canopy coverage was estimated in 6 meter x 6 meter zones using image processing to separate plant and soil areas in the images. As additional coverage estimates were obtained on a weekly basis, a mathematical model was fit to the data, which enabled canopy coverage estimation both between imaging dates and into the future. A smoothing technique was developed to obtain best canopy coverage estimates from measured and modeled data. The canopy coverage estimation technique was used for estimating and predicting crop water use, which has potential value for improving irrigation management decisions while considering both spatial and temporal variation in crop growth. In support of Sub-objective 4A, research was conducted to characterize the transverse distribution of infiltrated water in furrow irrigation systems as a function of soil texture and flow variables. Conventional hydraulic analyses of such systems treat furrow infiltration as a one-dimensional flow process. Therefore, it is assumed that the infiltrated water distributes uniformly across the furrow spacing, independently of the geometry of the infiltrating surface and the water application depth. However, since the flow process is two-dimensional and the infiltrated water is subject to rapid redistribution for a limited time, it can potentially produce a wetting bulb that is substantially longer in the vertical than in the horizontal direction, or vice-versa. The non-uniformity of the wetting bulb needs to be considered when determining how much water is stored in the root zone following an irrigation and how much is lost below the root zone. It also needs to be considered when calculating infiltration itself, as the wetting bulb of neighboring furrows can interfere with each other and reduce the infiltration rate. The study consisted of a large number of simulations conducted with a two-dimensional porous-media flow model and a particular soil hydraulic model (the Van-Genuchten-Mualem model). Wetting bulbs were computed for different soil textures and hydraulic conditions. A technique called method-of-moments was used to describe the evolution of the wetting bulb with time and with infiltrated volume per unit length. This technique, used in one previous study on the subject, calculates the statistical moments of the ellipsoid produced by the wetting bulb. The moments can be related to the fraction of water contained at some distance from the centroid of the ellipse. Although the moment calculations are relatively simple, substantial effort was required for developing the software tools needed to handle different furrow geometries and process the simulation outputs. As expected, results showed differences in the moments, and therefore, the shape of the wetting bulb for different soil textures. Heavier soils produce bulbs that are more elongated in the horizontal than the vertical direction while lighter soils produce bulbs that are longer in the vertical direction, but the differences are not substantial. The shape of the wetting bulb is well defined by the end of the infiltration period and changes little during the redistribution period, as long as the bulbs of neighboring furrows do not interfere with each other. Bulb interference helps to promote a more uniform water distribution across the furrow spacing, at least for typical furrow spacings. On the other hand, bulb interference does reduce infiltration rates. Most affected are soils with high silt content but the effect is not large, for typical furrow spacings. Using data from various soils, relationships were developed for a typical furrow geometry and initial and boundary conditions that predict the second moment (i.e., the standard deviation) of the water distribution as a function of sand-silt-clay content and the volume of infiltrated water. This relationship can be used to determine when interference can be expected for a given furrow spacing. Overall, results suggest that non-uniformity of infiltration in furrow systems may be only of concern when irrigation every-other- furrow, which is a practice used by some irrigators.

Accomplishments
1. Cotton petiole nitrate sufficiency/deficiency guidelines for irrigated cotton in the Desert Southwest revised. Petiole nitrate-nitrogen (NO3-N) sampling and testing remains a popular in-season nitrogen (N) management practice in the Western United States for cotton (Gossypium hirsutum). ARS researchers at Maricopa, Arizona, in collaboration with scientists at the University of Arizona, revised and updated cotton petiole nitrate sufficiency/deficiency guidelines for irrigated cotton in Arizona, which dated back to 1984. Additionally, a phone application (app) was developed that easily allows growers and consultants to use the new guidelines to manage in-season N for cotton. Users input their planting date and petiole nitrate data including the date of analysis into the app, if the levels are deficient, a N fertilizer recommendation is returned. The revised guidelines and new phone app will lead to more profitable N management practices for southwestern cotton growers and a reduction in over-fertilization and related export of N to the environment.

2. Active optical sensor vegetation indices accurately estimate in-season cotton nitrogen status. The use of active optical sensors (AOS) in nitrogen (N) management of row crops continues to grow and several commercial AOS are now available. Typically, canopy reflectance in red and near infrared (NIR) bands are used to calculate the normalized difference vegetation index (NDVI), but recently, commercially available AOS include a third, red-edge band that allows the calculation of additional vegetation indices (VIs). ARS researchers at Maricopa, Arizona, conducted five site-years of N management studies in Maricopa, Arizona, to evaluate 12 vegetation indices for the ability to assess in-season nitrogen status in cotton. Two vegetation indices that use red-edge reflectance, the normalized difference red edge index (NDRE) and the chlorophyll index using red edge (CIRE) detected N before any other vegetation indices. Use of NDRE or CIRE to guide in-season N management in cotton can save producers N fertilizer without hurting yields and reduce the amount of N being released into the environment.

3. Evaluating water use calculations in the Cropping System Model (CSM). Many different methods exist for calculating water use in crop production. Comparing the accuracy among the methods can lead to improved techniques for water management and conservation. ARS researchers at Maricopa, Arizona, compared six methods for calculating crop water use in the CSM, a popular crop simulation tool. The evaluations were based on measurements from a cotton field near Bushland, Texas. One calculation method performed statistically better than the other five. The results are guiding CSM developers toward better methods for calculating crop water use. More than 2,000 download requests are received for the CSM annually, and its listserv reaches 10,600 email addresses worldwide. The CSM is used primarily for agricultural research, and uses for policy decisions, yield forecasting, and on-farm management decisions are increasing.

4. Updates and advances to the FAO56 crop water requirements method. The Food and Agriculture Organization guidelines on crop evapotranspiration estimation (FAO56), first proposed by Allen et al. (1998), are now universal standard methodologies used in agricultural water management. The publication has been cited well over 30,000 times in English literature. However, during the 20+ years since it was published, there have been numerous requests by the agricultural community to clarify various FAO56 procedures and to update the crop coefficient data tabulated in the paper. ARS researchers at Maricopa, Arizona, in collaboration with an international group, provided updated or new crop coefficient data for nearly 100 vegetable, herb, specialty, forage, and field crops, including lettuce, bell pepper, quinoa, cotton, wheat, canola, sugar cane, soybean, rice, and sorghum. A research scientist at Maricopa, Arizona, also served as guest editor to oversee the 2021 publication of a special issue, comprising over 20 papers, in Agricultural Water Management devoted to updating FAO56. The special issue responds to the need for incorporating in the FAO56 methods the results and practices of innovative science and technology, such as data handling, better use of the available research, and use of newer tools, including, remote sensing. The information provided is expected to improve the accuracy of crop water requirement calculations, thus supporting precision agriculture in terms of irrigation applications to crops.

Review Publications
Pereira, L.S., Parades, P., Hunsaker, D.J., Lopez-Urrea, R., Mohammadi Shad, Z. 2020. Standard single and basal crop coefficients for field crops. Updates and advances to the FAO56 crop water requirements method. Agricultural Water Management. 243. Article 106466. https://doi.org/10.1016/j.agwat.2020.106466.
Bronson, K.F., French, A.N., Conley, M.M., Barnes, E.M. 2021. Use of an ultrasonic sensor for plant height estimation in irrigated cotton. Agronomy Journal. 113(2):2175-2183. https://doi.org/10.1002/agj2.20552.
Hunsaker, D.J., Bronson, K.F. 2021. FAO56 crop and water stress coefficients for cotton using subsurface drip irrigation in an arid US climate. Agricultural Water Management. 252. Article 106881. https://doi.org/10.1016/j.agwat.2021.106881.
Pereira, L., Parades, P., Hunsaker, D.J., Lopez-Urrea, R., Jovanovic, N. 2021. Updates and advances to the FAO56 crop water requirements method. Agricultural Water Management. 248. Article 106697. https://doi.org/10.1016/j.agwat.2020.106697.
Barnes, E.M., Campbell, B.T., Vellidis, G., Porter, W., Payero, J., Leib, B., Sui, R., Fisher, D.K., Anapalli, S.S., Colaizzi, P.D., Bordovsky, J., Porter, D., Ale, S., Mahan, J.R., Taghvaeian, S., Thorp, K.R. 2020. Forty years of increasing cotton’s water productivity and why the trend will continue. Applied Engineering in Agriculture. 36(4):457-478. https://doi.org/10.13031/aea.13911.
Thompson, A.L., Thorp, K.R., Conley, M.M., Roybal, M.D., Moller Jr, D.C., Long, J.C. 2020. A data workflow to support plant breeding decisions from a terrestrial field-based high-throughput plant phenotyping system. Plant Methods. 16. Article 97. https://doi.org/10.1186/s13007-020-00639-9.
Thorp, K.R., DeJonge, K.C., Marek, G.W., Evett, S.R. 2020. Comparison of evapotranspiration methods in the DSSAT Cropping System Model: I. Global sensitivity analysis. Computers and Electronics in Agriculture. 177. Article 105679. https://doi.org/10.1016/j.compag.2020.105679.
Thorp, K.R., Marek, G.W., Dejonge, K.C., Evett, S.R. 2020. Comparison of evapotranspiration methods in the DSSAT Cropping System Model: II. Algorithm performance. Computers and Electronics in Agriculture. 177. Article 105679. https://doi.org/10.1016/j.compag.2020.105679.
Ayankojo, I.T., Thorp, K.R., Morgan, K.T., Kothari, K., Ale, S. 2020. Assessing the impacts of future climate on cotton production in the Arizona low desert. Transactions of the ASABE. 63(4):1087-1098. https://doi.org/10.13031/trans.13731.
Bronson, K.F., Norton, E., Silvertooth, J. 2021. Revising petiole nitrate sufficiency/deficiency guidelines for irrigated cotton in the desert southwest. Soil Science Society of America Journal. 85(3):893-902. https://doi.org/10.1002/saj2.20213.
Bronson, K.F., Hunsaker, D.J., El-Shikha, D., Rockholt, S.M., Williams, C.F., Rasutis, D., Soratan, K., Venterea, R.T. 2021. Nitrous oxide emissions, N uptake, biomass, and rubber yield in N-fertilized, surface-irrigated guayule. Industrial Crops and Products. 167. Article 113561. https://doi.org/10.1016/j.indcrop.2021.113561.
Pereira, L.S., Parades, P., Lopez-Urrea, R., Hunsaker, D.J., Mota, M., Mohammadi Shad, Z. 2020. Standard single and basal crop coefficients for vegetable crops, an update of FAO56 crop water requirements approach. Agricultural Water Management. 243. Article 106196. https://doi.org/10.1016/j.agwat.2020.106196.
Fisher, J.B., Lee, B., Purdy, A.J., Halverson, G.H., Dohlen, M.B., Cawse-Nicholson, K., Wang, A., Anderson, R.G., Aragon, B., Arain, M., Baldocchi, D.D., Baker, J.M., Barral, H., Bernacchi, C.J., Bernhofer, C., Biraud, S.C., Bohrer, G., Brunsell, N., Cappelaere, B., Castro-Contreras, S., Chun, J., Conrad, B.J., Cremonese, E., Demarty, J., Desai, A.R., Ligne, A.D., Foltýnová, L., Goulden, M.L., Griffis, T.J., Grunwald, T., Johnson, M.S., Kang, M., Kelbe, D., Kowalska, N., Lim, J., Mainassara, I., McCabe, M.F., Missik, J.E., Mohanty, B.P., Moore, C.E., Morillas, L., Morrison, R., Munger, J., Posse, G., Richardson, A.D., Russell, E.S., Ryu, Y., Sanchez-Azofeifa, A., Schmidt, M., Schwartz, E., Sharp, I., Šigut, L., Tang, Y., Hulley, G., Anderson, M.C., Hain, C., French, A.N., Wood, E., Hook, S. 2020. ECOSTRESS: NASA’s next generation mission to measure evapotranspiration from the International Space Station. Water Resources Research. 56(4). Article e2019WR026058. https://doi.org/10.1029/2019WR026058.
Lentz, R.D., Bautista, E., Koehn, A.C., Sojka, R.E. 2020. Infiltration and soil water distribution in irrigation furrows treated with polyacrylamide. Transactions of the ASABE. 63(5):1451-1464. https://doi.org/10.13031/trans.13939.
Bronson, K.F. 2021. Optimal internal nitrogen use efficiency for irrigated cotton in the Southwestern United States. Agronomy Journal. 113(3):2821-2831. https://doi.org/10.1002/agj2.20674.
Elshikha, D.M., Waller, P.M., Hunsaker, D.J., Dierig, D., Wang, S., Cruz, V.V., Thorp, K.R., Katterman, M.E., Bronson, K.F., Wall, G.W. 2021. Growth, water use, and crop coefficients of direct-seeded guayule with furrow and subsurface drip irrigation in Arizona. Industrial Crops and Products. 170. Article 113819. https://doi.org/10.1016/j.indcrop.2021.113819.