Location: Sustainable Water Management Research
2017 Annual Report
Objectives
The goal of this research project is to develop novel water management technologies and irrigation scheduling techniques using sensor and measurement technologies to detect crop water status, and provide irrigation application guidelines for improving water-use efficiency in humid regions. To achieve this goal, the following objectives will be undertaken.
Objective 1: Quantify water requirements of cotton, corn, and soybean cropping systems and develop crop coefficients for irrigation scheduling in humid regions, and develop and evaluate irrigation scheduling and variable-rate irrigation technologies to improve water use efficiency in cotton, corn, and soybean.
Sub-objective 1.1. Develop sensor technologies and algorithms for variable rate irrigation (VRI) scheduling, prescription development, and automation, and quantify the impacts of VRI technology on water-use efficiency and crop yield.
Sub-objective 1. 2. Develop new and/or improved sensing technologies to automatically monitor crop responses, and develop improved irrigation scheduling methods based on weather data and numerical models incorporating internet-based data access to provide real-time information access.
Sub-objective 1.3. Predict the impacts of climate change and variability on production and water requirements in cropping systems in the Mississippi Delta to develop adaptation strategies for sustainable production.
Sub-objective 1.4. Quantify and evaluate water stress indices and crop physiological responses for irrigation scheduling to enhance water productivity under drought conditions in humid regions.
Objective 2: Develop conservation management practices to improve water management and enhance environmental sustainability.
Sub-objective 2.1. Develop and evaluate mobile remote sensing applications including ground- and UAV-based sensing systems to monitor crop conditions for managing irrigation water and nutrient applications.
Sub-objective 2. 2. Use eddy covariance (EC) and residual energy balance (REB) methods to determine ET and crop coefficients for irrigation scheduling, and monitor emission of CO2 and CH4 from agricultural fields for assessing the impact of climate change on agroecosystems in the Mississippi Delta.
Sub-objective 2.3. Study impact of tillage radish cover crops on runoff water quantity and quality and crop production.
Approach
To complete Objective 1, Variable rate irrigation (VRI) experiments will be conducted. Experiments will consist of two irrigation management treatments, VRI management and ISSCADA (Irrigation Scheduling and Supervisory Control and Data Acquisition System) management. Sensors will be used to detect soil water content. An algorithm to calculate crop water requirements will be developed using soil water content, soil electrical conductivity, yield, and crop water stress index. VRI events will be scheduled according to the VRI prescriptions. Crop yield and irrigation water efficiency in VRI treatment will be compared to that in ISSCADA treatment. Wireless electronic sensing and monitoring systems will be developed to measure properties of interest for agronomic, water-management, and irrigation-scheduling applications. Advance and distribution of irrigation water across the field will be monitored to improve uniformity and reduce runoff. Weather-based water-balance and crop models will be compared for use in scheduling irrigations. Smartphone apps will be developed to provide capabilities to configure system operating parameters and download data. Crops will be grown in fields equipped with eddy covariance (EC) system for measuring water vapor and CO2 fluxes, and instrumentation for monitoring ET using a residual energy balance (REB) approach. Relevant data will be collected and analyzed to predict impacts of climate change and variability on production and water requirements in cropping systems. Sensors to monitor canopy temperature and reflectance will be deployed and used to develop vegetation indices. Plant physiological and morphological responses will be monitored. Water stress indices based on canopy temperature, NDVI, PRI, ET, and soil water will be developed and related to the crop physiological responses. To complete Objective 2, four-row datalogging systems, measuring plant height, canopy temperature, canopy spectral reflectance, and GPS information, will be developed for mounting on the front of agricultural equipment. Unmanned aerial vehicles will be tested for suitability as mobile sensing platforms to detect problem areas in the field, assess vegetation and changes, and collect sensor measurements. Four EC systems consisting of CH4 analyzer, CO2/H2O analyzer, 3D sonic anemometer, and biomet system will be deployed in Mississippi Delta to monitor long-term agroecosystem and collect data for ET and crop coefficients estimates. We will participate in the Lower Mississippi River Basin (LMRB) Delta Flux Network to share the resources and data appropriate to the USDA-ARS Long-Term Agroecosystem Research (LTAR) project. Tillage radish cover crop will be applied in 12 large plots of cotton field. One storm water monitoring system will be installed in each plot to measure the runoff. The runoff samples will be collected and analyzed for water quality. Soil water content, soil properties, and cotton plant characteristics and yield will be determined. In comparison with conventional cultivation, effects of the cover crop on soil water content, runoff water quantity and quality, and cotton yield will be analyzed.
Progress Report
A randomized block design was implemented with 2 treatments variable rate irrigation (VRI) and irrigation scheduling and supervisory control (ISSCADA) in soybean and VRI and uniform rate irrigation (URI) in corn and 2 replications in one block for each field. ISSCADA system was installed and tested with embedded-computer controller, 10 infrared thermography (IRT) sensors across the management zones on the pivot lateral, and 2 stationary IRT sensors in the field. There are 6 and 10 soil moisture sensing locations selected in corn and soybean field, respectively. Soil moisture sensors were installed in each location at various depths underground to detect the soil water content in the rooting zone. Soil moisture data were collected and reported in each hour. Soil electrical conductivity (EC) and soil texture properties data were obtained. Data of canopy temperature and plant height were collected using a wireless data acquisition system (WDAQ) and the ISSCADA system. Plant water stress index was estimated using the soil moisture and canopy temperature data. A preliminary algorithm for VRI was initialized using soil EC, soil moisture, and plant canopy temperature. VRI prescription maps were created and irrigation events were scheduled.
Wireless monitoring systems were developed and deployed to measure and report soil-moisture status and meteorological variables from remote field locations. The inexpensive, open-source microcontroller-based devices used the cellular communications infrastructure to transmit field data to internet webpages for convenient access and viewing via web browser. Weather data were input to water-balance computer models to monitor crop growth and soil-water status for use in scheduling irrigations. Water-balance model output and moisture-sensor data were compared and showed similar trends, indicating that both methods provided consistent guidance for irrigation scheduling. The microcontroller/cellular monitoring system served as the basis for an atmospheric-stability monitoring project funded by the Mississippi Soybean Promotion Board to remotely measure and report atmospheric conditions and determine suitability for application of agricultural chemicals by aerial applicators.
A corn-soybean rotation experiment, with starting corn, was established in an 80 ac field with furrow irrigation facilities in the Crop Production Systems Research Unit farm. As the field was already tilled in 2016 after harvesting crops, conservation tillage management could not be established in the field this year, but this will be established the next season onwards. Irrigations were at 100, 50, and 0% of evapotranspiration demand. Difference in water treatments could not be established till milk stage of corn due to high rainfall. An eddy covariance (EC) measuring system redesigned to monitor both water (evapotranspiration, ET) and CO2 fluxes and land-surface energy balance was established in the full irrigation (40 ac) plot. As the plot sizes for the 50%, and 0% (rainfed) irrigation plots were not large enough to accommodate EC sensor installations, we established energy balance monitoring towers in these plots for quantifying ET employing a residual energy balance approach. Data on crop growth, development, and physiological responses to irrigations are being collected. Climate change scenarios (CC) for the MS Delta region based on the IPCC-AR5 climate models’ databases were developed. Algorithms for computing ET using REB approach was developed, and an article based on this was communicated to a Journal. Presentation of this method with preliminary results obtained at the 2017 Mississippi Water Resources Conference, April 11-12 at Jackson, Mississippi.
Sensors for measuring canopy temperatures (Tc) and NDVI (normalized difference vegetation index) and PRI (photochemical reflectance indices) were installed above the crop canopies in corn fields maintained at 100, 50, and 0% irrigation treatments. The sensors were installed on height adjustable towers and constantly positioned at 1m above the crop canopies. The Tc, NDVI, and PRI data are being continuously monitored and collected during the crop season.
A microcontroller-based instrument was developed and deployed to measure characteristics of the crop canopy for crop growth and yield estimation and crop-stress detection. The device consisted of inexpensive open-source components (microcontroller, sensors, GPS receiver, Bluetooth radio) incorporated into a self-contained unit easily mounted onto an agricultural vehicle to collect and display georeferenced sensor measurements as the vehicle travelled through the field. An Unmanned Aerial Vehicle (UAV) was tested to evaluate operational requirements and initial as-delivered sensing capabilities. Operational requirements included knowledge and skills required of the operator to program the vehicle and sensor (high-resolution camera) for autonomous flight and data collection, safely launch and retrieve the vehicle, and manage the vehicle responsibly under rules established for use of common air space. Evaluation of several image-processing software packages was conducted to determine capabilities, operational requirements, and ease of use, critical for acceptance by the agricultural community.
Four locations in the 12 plots were selected to monitor soil water status using soil moisture sensors. In each location, Four TDR-315 soil water content sensors were installed into soils in the depths of 6, 12, 18, and 24”. A data logger with solar power supply was employed in each sensing site to collect and store the soil water content data from the sensors. For accurate measurements, the TDR-315 sensors were calibrated with various Mississippi soils. 12 runoff water monitoring systems were purchased, installed, and tested in the field with cover crop. There is one system in each experimental plot. The amount of the runoff water from the plot was measured by the system. Runoff water samples with each rainfall event were collected by the system for water quality analysis as well. Biomass of the cover crop grown in 2016 was measured. Soil samples were collected for soil property analysis.
Installation and test of 4 Eddy covariance (EC) systems were completed, including 2 EC systems in Stoneville, Mississippi and the other 2 in Arcola, Mississippi. Two of these 4 EC system are consisted of a CH4 analyzer for measuring methane gas flux, CO2/H2O analyzer for measuring carbon dioxide and water vapor fluxes, 3D sonic anemometer for measuring wind speed, and biomet system to collect ancillary data while the other two systems have the same configuration except there is no CH4 analyzer. One EC system in Stoneville was established for long-term Mississippi Delta agroecosystem monitoring and assessment. It was setup in a location surrounded with various crops including corn, soybean, cotton, and switchgrass. The other EC system in Stoneville was installed in a 10-ha corn field with 100% irrigation. The 2 systems in Arcola were installed in the center of an 88-ha corn and 100-ha soybean field, respectively. Furrow irrigation was managed by the producers in these fields. All these 4 EC systems are collecting water vapor, CO2, and CH4 gas fluxes emitted from the fields.
Accomplishments
Review Publications
Anapalli, S.S., Fisher, D.K., Reddy, K.N., Pettigrew, W.T., Sui, R., Ahuja, L.R. 2016. Vulnerabilities and adapting irrigated and rainfed cotton to climate change in the lower Mississippi Delta Region. Journal of Climate. 4(55):1-20.
Anapalli, S.S., Pettigrew, W.T., Reddy, K.N., Ma, L., Fisher, D.K., Sui, R. 2016. Climate optimized planting windows for cotton in the lower Mississippi Delta region. Agronomy Journal. 6(4):1-15.
Huang, Y., Brand, H., Sui, R., Thomson, S.J., Furukawa, T., Ebelhar, M.W. 2017. Cotton yield estimation using very high-resolution digital images acquired on a low-cost small unmanned aerial vehicle. Transactions of the ASABE. 59(6):1563-1574.
Ma, L., Ahuja, L.R., Islam, A., Trout, T.J., Anapalli, S.S., Malone, R.W. 2016. Modeling yield and biomass responses of maize cultivars to climate change under full and deficit irrigation. Agricultural Water Management. 180:88-98.
Runkle, B., Rigby Jr, J.R., Reba, M.L., Anapalli, S.S., Bhattacharjee, J., Krauss, K.W., Liang, L., Locke, M.A., Novick, K., Sui, R., Suvocarev, K., White Jr, P.M. 2017. Delta-Flux: An eddy covariance network for a climate-smart lower Mississippi basin. Agricultural and Environmental Letters. 2:170003. https://doi.org/10.2134/ael2017.01.0003.
Schielack, V.P., Thomasson, J.A., Sui, R., Ge, Y. 2016. Harvester-based sensing system for cotton fiber-quality mapping. Journal of Cotton Science. 20:386-393.
Sui, R., Yan, H. 2017. Field study of variable rate irrigation management in humid climates. Journal of Irrigation and Drainage. doi:10.1002/ird.2111.
Anapalli, S.S., Ahuja, L.R., Gowda, P., Ma, L., Marek, G.W., Evett, S.R., Howell, T.A. 2016. Simulation of crop evapotranspiration and crop coefficient with data in weighing lysimeters. Agricultural Water Management. 177:274-283.
Gao, F., Feng, G.G., Ouyang, Y., Wang, H., Fisher, D.K., Adeli, A., Jenkins, J.N. 2017. Evaluation of reference crop evapotranspiration methods in arid, semi-arid and humid regions. Journal of the American Water Resources Association. doi:10.1111/1752-1688.12530.
Lammers, P., Huesemann, M., Boeing, W., Anderson, D.B., Arnold, R.G., Brown, L., Brown, J., Brown, J., Downes, C.M., Sui, R. 2017. Review of the cultivation program within the national alliance for advanced biofuels and bioproducts. Algal Research. 22:166-186.
Hansen, N.C., Allen, B.L., Anapalli, S.S., Blackshaw, R., Lyon, D., Machado, S. 2016. Dryland agriculture in North America. In: Farooq, M., Siddique, K.H.M., editors. Innovations in Dryland Agriculture. Cham, Switzerland: Springer. p. 415-441.
