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ARS Home » Southeast Area » Stoneville, Mississippi » Sustainable Water Management Research » Research » Research Project #432474

Research Project: Development of Sustainable Water Management Technologies for Humid Regions

Location: Sustainable Water Management Research

2018 Annual Report

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.

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
Wireless monitoring systems developed previously were deployed to monitor soil-moisture status and meteorological variables in remote locations. The inexpensive, open-source microcontroller-based systems incorporated cellular communications devices to transmit data to the internet for convenient, real-time access to field data. Slightly modified monitoring systems were deployed in surface-irrigated fields to monitor the movement and location of advancing water during irrigation events. Real-time monitoring of field conditions was accomplished by accessing the internet-available data on smartphones. The cellular/internet monitoring system also provided a platform for monitoring atmospheric-stability for a project funded by the Mississippi Soybean Promotion Board to measure and report atmospheric conditions to determine whether conditions were suitable for aerial application of agricultural chemicals. Moisture-sensor data were compared to weather-based irrigation scheduling models and showed similar trends, providing options for scheduling appropriate timing of irrigation events. A corn-soybean rotation experiment, starting with corn, was established in an 80 acre field with furrow irrigation facilities in the Crop Production Systems Research Unit farm in 2017. In 2018, the experiment was rotated to soybean. Poor water drainage from the field due to inadequate land-leveling was noticed during the experiments in 2017 making it difficult to irrigate using the furrow irrigation method. So, to enable proper irrigations during 2018 season, the land was leveled back to 1% slope before planting. As the top layer of the soil had to be disturbed for the land leveling, the conservation tillage management could not be established in the field this year. Irrigations were at 100 (full), 50 (half), and 0 (rainfed) % of the irrigation demands in excess of the rainfall received. Eddy covariance (EC) measuring systems redesigned to monitor both water evapotranspiration (ET) and CO2 fluxes and land-surface energy balance were established in the full, half, and rainfed irrigation fields planted to soybean. Crop growth and development, and physiological responses of soybean to irrigations are being collected. Methods for computing ET using both eddy covariance and residual energy balance approach was developed, and three articles based on this were published and another one communicated to a journal. Application of this method for quantifying corn water requirements using the data collected in 2017 was presented at the 2017 Mississippi Water Resources Conference, April 11-12 at Jackson, Mississippi. An eddy covariance quantification of soybean evapotranspiration in the Mississippi Delta” at the 2017 American Society of Agronomy Annual Meetings, Tampa, Florida. Also presented ‘Quantifying crop water requirements in the Mississippi Delta using an energy balance approach.’ Irrigation show and education conference. Nov. 6-10, 2018, Orlando, Florida, and received feedbacks on this research. The normalized difference vegetation index (NDVI) and photochemical reflectance indices (PRI) sensors and Infrared thermometer sensors for measuring canopy temperatures (Tc) were installed in the soybean crop fields maintained under 100, 50, and 0% irrigation levels. 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 this crop season. Due to above-normal rainfall that was adequate enough for stress-free crop growth during the corn season in 2017, there were no significant water stress periods. For that reason, it was not possible to test water stress indices with the 2017 year data. Data collected during 2018 is being analyzed for water stress periods for testing waters stress indices. The data collected from the EC systems in 2017 were processed for ET and Kc. The results were presented in Mississippi Water Resources Conference. Three Eddy Covariance (EC) systems, which were previously installed in Stoneville, MS and Arcola, Mississippi, were partially damaged by severe weather. Although the damage caused gaps in the dataset, they were repaired and continuously collect year-2 data including water vapor, CO2, and CH4 gas fluxes emitted from the fields for monitoring long-term Mississippi Delta agroecosystem. Three EC and REB systems were setup in soybean field for estimating crop water stress with different irrigation levels. Another two EC systems were installed in cotton field to measure the ET as well. Calibration and maintenance for all EC and REB systems were performed. Runoff water from the experimental plots were monitored using the 12 runoff water monitoring systems installed last year. Runoff water samples were also collected for water quality analysis. Biomass of the cover crop in last year were prepared to determine the organic matter in soil and above ground. Soil samples of the plots were collected for soil properties before cotton was planted. Cotton was planted and field managed using recommended practices in the region. TDR 315 soil moisture sensors were installed in each plot to monitor the soil water status. Due to the unusually cold weather in the winter, cover crop, tiller radish did not grow well. There was very small amount of biomass produced by the cover crop in 2018 season.

1. The capabilities, flexibility, and low cost of the open source Arduino and similar platforms, coupled with new and inexpensive sensing devices, is gaining interest in the scientific community and has enabled the development of monitoring systems for a variety of applications related to agriculture, environmental monitoring, and irrigation. ARS scientists at Stoneville, Mississippi, developed low-cost sensing devices to measure plant height, canopy temperature, and canopy multi-spectral reflectance. The device was mounted onto an agricultural vehicle to collect georeferenced sensor measurements as the vehicle travelled through the field. Efforts were begun to improve GPS performance, with the goal of increasing spatial accuracy of the global positioning system (GPS) information collected to near that of expensive RTK instrumentation. These inexpensive sensing devices could enable researchers and producers collecting field data at a low cost.

Review Publications
Fisher, D.K., Huang, Y. 2017. Mobile open-source plant-canopy monitoring system. Modern Intrumentation. 6:1-13.
Fisher, D.K., Fletcher, R.S., Anapalli, S.S., Pringle III, H.C. 2017. Development of an open-source cloud-connected sensor-monitoring platform. Advances in Internet of Things. 8:1-11.
Sui, R., Byler, R.K., Delhom, C.D. 2017. Effect of nitrogen application rate on yield and quality in irrigated and rainfed cotton. Journal of Cotton Science. 21:113-121.
Wang, R., Thomasson, J.A., Cox, M.S., Sui, R., Marley-Hollingsworth, E.G. 2017. Cotton fiber quality prediction based on spatial variability in soils. Journal of Cotton Science. 21:220-228.
Sui, R. 2017. Irrigation scheduling using soil moisture sensors. Journal of Agricultural Science. 10(1):1-11.
Li, M., Liu, Y., Yan, H., Sui, R. 2018. Effects of irrigation amounts on alfalfa yield and quality with center pivot system. Transactions of the ASABE. 60(5):1633-1644.