Location: Sustainable Water Management Research
2020 Annual Report
Objectives
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.
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.
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.
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.
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.
2. Develop conservation management practices to improve water management and enhance environmental sustainability.
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.
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.
2.3. Study impact of tillage radish cover crops on runoff water quantity and quality and crop production.
3. Develop integrated conservation management cropping systems that improve soil health, water availability, water quality, and productivity.
4. Develop integrated irrigation and crop management systems that increase profitability, conserve water, and protect water quality in the Mid-South.
5. Assess the profitability and risks associated with integrated production agriculture and conservation systems in the Mid-South.
6. Assess surface and subsurface hydrology, surface runoff, and contaminant transport in conservation crop production systems at plot and field scales.
7. Improve knowledge and understanding of the hydrological and climate variability processes governing the movement, storage, quantity and quality of water in the Lower Mississippi River Basin (LMRB), and develop tools/technologies to enhance the sustainability of water resources for agriculture.
8. Utilize UAS and multi-scale geospatial technologies to assess and improve the long-term sustainability of water resources in agroecosystems.
Approach
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. 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. Please refer to related docs for 6001-13000-001/002-00D for remaining approach.
Progress Report
Field test results in the previous 3 seasons with corn and soybean crops were analyzed and published. Based on the results in previous tests, new variable rate irrigation (VRI) maps were created based on soil electrical conductivity and used in the 2020 season. The VRI center pivot was upgraded with a new AgSense device for VRI irrigation control. For a large-scale test in VRI management, we planted soybean crops in both Field A and Field B. TDR (time domain reflectometer) soil moisture sensors were installed in the fields for monitoring soil water status for irrigation scheduling. VRI irrigation events were triggered using sensor-measured soil water content. Soybean grain yields in various VRI management zones and rainfed zones were obtained using a yield monitor equipped on the combine and GIS (geographic information system) software. Combined with the data from the past three years, crop yield and irrigation water-use efficiency in VRI management and rainfed zones in 2020 will be analyzed and reported.
An irrigation scheduling model was developed previously based on weather data and a soil-water balance method. In a further effort to reduce user input requirements, a similar model was developed which downloads weather data automatically from internet-based weather-data services. The limited spatial coverage of these services may provide acceptable temperature measurements, but highly variable rainfall patterns make the usefulness of rainfall measurements questionable when applied at the individual farm scale. The ability of the user to input irrigation applications, and to replace downloaded rainfall with locally measured rainfall if desired, is under development.
The corn-soybean rotation, beginning with corn, was established in an 80-ac field with furrow irrigation facilities in the research farm in 2017. In 2018, the experiment was rotated to soybean, in 2019 the crop was rotated back to corn, and in 2020 the crop rotated back to soybean under conservation tillage management. Eddy covariance (EC) measuring systems redesigned to monitor both water (evapotranspiration, ET) and CO2 fluxes and land-surface energy balance monitoring systems were reestablished in the full, half, and rainfed irrigation fields planted to soybean.
Poor water drainage from the fields due to excessive rain and floods were noticed during the crop growth period in all the four-years (2017, 2018, 2019, 2020), making irrigation unnecessary on a routine basis, as envisaged in the project design. However, at one or two occasions per year, when plant-available water in the soil fell below 65% of soil-specific maximum plant-available water, irrigations were provided at 100 (full irrigations in which all furrows were irrigated), 50 (half irrigations in which, every second furrow was irrigated), and 0 (rainfed with no added irrigation) % of the irrigation demands.
Crop growth and development responses of soybean to the applied irrigation were collected. Methods for computing ET using both eddy covariance and residual energy balance approaches were applied to soybean and developed ET data. Four journal articles based on this were published, and another one communicated to a journal.
The Eddy Covariance (EC) and Residual Energy Balance (REB) based soybean ET data collected during 2016-2019 were used to calibrate and improve crop growth simulations of the agricultural system model RZWQM2. The calibrated model was further integrated with climate-change scenarios for the Mississipps Delta region.
Sensors for measuring canopy temperatures (Tc), normalized difference vegetation index (NDVI), and photochemical reflectance indices (PRI) were installed in the soybean fields that were maintained at 100, 50, and 0% irrigation levels. The Tc, NDVI, and PRI data are being continuously monitored during the current crop season. This year also there were no significant drought (water stress) periods, while there were a few flood events and associated crop-stand loss and instrument failures due to storms. This again rendered it impossible to test water-stress indices with the 2019-2020 data as well.
Open-source electronic hardware and inexpensive electronic sensors used previously to develop ground-based static and mobile monitoring systems were evaluated for their suitability in unmanned aerial vehicles (UAV)-based monitoring. Microcontroller-based circuits incorporating multispectral reflectance, thermal, and lidar distance sensors, along with a GPS receiver, were attached to a UAV platform and used to generate field maps of the different sensor characteristics. Initial results suggest that some sensors may not be sensitive enough to operate at higher altitudes above the plant canopy and may be more suitable for use on ground-based vehicles for whole-field mapping.
Using the EC towers and REB systems, collection of ET (evapotranspiration) and REB data were continued at multiple locations in the Mississippi Delta. Methane fluxes from a continuous-flood rice field were monitored in 2019 and continued in 2020. The EC systems installed in Stoneville and Arcola, Mississippi were maintained and system damages due to lightning strike were repaired for continued operation. Three EC and REB systems were installed inside fields with various crops measuring the ET and REB for estimating water stress of crops under different irrigation levels. ET and REB data collected in previous seasons were processed.
Cover crop (tillage radish) was planted in the plots in fall of 2019. However, due to the late planting caused by delayed cotton harvesting, the tillage radish did not grow well, resulting in no significant biomass production in the 2020 season. Cotton (variety: ST 4990B3XF) was planted in the 2020 season and field managed as described in the experimental design. Runoff water monitoring systems were maintained for continuous data collection from the 12 experimental plots. Runoff water samples were collected for water quality analysis. Twelve lysimeters were installed in the plots, and underground water samples were collected from the lysimeters. Soil moisture in each plot was continuously measured using soil water content sensors. Cotton was harvested using a cotton picker and seed-cotton and lint samples were collected.
The old water-sampling plots at the site in Stoneville, Mississippi, known as 21-Guns were removed, the ground releveled, plots delineated, and cotton planted. Biomass, soil, and water sampling is being conducted, and new autosamplers and soil moisture and temperature probes have been installed.
An experiment was established in two 100-ha paddy fields and the study is being conducted in collaboration with the farmer. Rice crops were planted under both alternate wet and dry (AWD) and continuous flood (CF) systems. EC systems with sensors for measuring water, CO2, and CH4 were installed in the middle of the AWD and CF fields. Soil samples were collected for soil texture and characterizing the soil for water-holding capacities and available nutrients. First-year data on crop growth, development/phenology, and grain yield were collected. One of the methane sensors failed twice in rainstorms, rendering us unable to collect methane flux data for about a one-month period. However, efforts were made to procure and replace the sensor as soon as it was damaged. A new methane analyzer was procured and installed.
Soybean crops were established under conventional and no-tillage soil management in farm-size silt loam soils. Soil water, soybean phenology, soil C, and N content data under no-tillage with residue retention (NT) and conventional tillage (CT) were collected. Air and soil temperatures, net solar radiation, wind speed, relative humidity of the air, and soybean canopy temperature were also collected. Weather and crop management data were used to calibrate the Root Zone Water Quality Model (RZWQM) for simulations of the NT and CT experiments. Long-term climate data (1960-2020) at Stoneville were collected for integration with the calibrated model for simulating production risk associated with the two soil tillage systems in the region.
In the study to assess surface and subsurface hydrology, surface runoff, and contaminant transport in conservation crop production systems, data from the cotton years have been analyzed and written up to be submitted for publication. The data from the corn years is being analyzed currently.
The analyses of the routine and seasonal water sample grab data and storm event data from the tailwater recovery system known as Mason have been completed. The data from the cotton years have been analyzed and written up to be submitted for publication. The data from the corn years is being analyzed currently.
A UAS (unmanned aircraft system)-based sensing system has been designed for water management research. The system consists of a drone, and cameras in visible, infrared, and thermal bands. Equipment for the UAS-based remote sensing platform has been purchased, including three drones, three cameras, a high-performance computer and software for image acquisition and processing and data analysis.
Accomplishments
1. Variable rate irrigation (VRI) for water saving. Variable rate irrigation (VRI) can site-specifically apply irrigation water at variable rates within the field to account for temporal and spatial variabilities in soil and plant characteristics. ARS researchers in Stoneville, Mississippi, working with collaborators in an ARS multi-location project, developed and evaluated VRI technologies for irrigation management. VRI prescriptions were created based on soil properties, plant water stress, and soil water content. Desired amounts of water were applied site-specifically to meet plant water needs. This VRI technology significantly saved irrigation water, and adoption of this technology could improve sustainability of water resources for agriculture. This accomplishment received three national-level awards: 2020 Excellence in Technology Transfer Award from Federal Laboratory Consortium (FLC), 2020 Technology Focus Award from FLC, and 2020 Vanguard Award from the Irrigation Association.
2. Climate change. Accurate information on crop-ecosystem water-use efficiencies is essential for developing environmentally and economically sustainable water-management practices that also help account for CO2, the most abundant of the greenhouse gases, exchange rates from cropping systems. Using the eddy covariance and residual energy balance methods, ARS researchers in Stoneville, Mississippi, quantified water and CO2 fluxes and water-use efficiencies in corn, soybean, and cotton crops under a humid climate in the Mississippi Delta. Results have been published and could be used for irrigation scheduling and adopting crop mixtures that are environmentally and economically sustainable, conserving limited water resources in the region.
Review Publications
Anapalli, S.S., Fisher, D.K., Rao, S.P., Reddy, K.N. 2020. Qunatifying evapotranpsiration and crop coefficients for cotton (Gossypium hirsutum L.) using an eddy covariance approach. Agricultural Water Management. 233:106091. https://doi.org/10.1016/j.agwat.2020.106091.
Anapalli, S.S., Fisher, D.K., Reddy, K.N., Rajan, N., Pinnamaneni, S.R. 2019. Modeling evapotranspiration for irrigation water management in a humid climate. Agricultural Water Management. 225:105731. https://doi.org/10.1016/j.agwat.2019.105731.
Falconnier, G.N., Coreels, M., Boote, K.J., Affholder, F., Adam, M., Ruane, A.C., Ahuja, L.R., Anapalli, S.S., Baron, C., Basso, B., Baudron, F., Bertuzzi, P., Timlin, D.J. 2020. Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa. Global Change Biology. 1-48. https://doi.org/10.1111/gcb.15261.
Fisher, D.K., Fletcher, R.S., Anapalli, S.S. 2019. Evolving open-source technologies offer options for remote sensing and monitoring in agriculture. Advances in Internet of Things. 10:1-10. https://doi.org/10.4236/ait.2020.101001.
Fletcher, R.S., Fisher, D.K. 2019. Spatial analysis of soybean plant height and plant canopy temperature measured with on-the-go tractor mounted sensors. Agricultural Sciences. 10/1486-1496.
Li, M., Sui, R., Yangyang, M., Yan, H. 2019. A real-time fuzzy decision support system for alfalfa irrigation. Computers and Electronics in Agriculture. 163:104870.
Singh, S., Nouri, A., Singh, S., Anapalli, S.S., Lee, S., Arelli, P.R., Jagadamma, S. 2019. Soil organic carbon and aggregation in response to thirty-nine years of tillage management in the southeastern U.S. Soil and Tillage Research. 197:1-9. https://doi.org/10.1016/j.still.2019.104523.
Sui, R. 2019. Use of pressured-air for cotton lint cleaning. Journal of Agricultural Science. Vol. 12, No. 1; 2020. https://doi.org/10.5539/jas.v12n1p31.
Sui, R., Vories, E.D. 2020. Comparison of sensor-based and weather-based irrigation scheduling. Applied Engineering in Agriculture. 36(3):375-386. https://doi.org/10.13031/aea.13678.
