Location: Genetics and Sustainable Agriculture Research
2022 Annual Report
Objectives
1. Develop more sustainable long-term soil health management systems for improved yields from humid, Southeast Agroecosystems.
1.1. Increase row crop yields in the upland soils of the South and Southeast by agronomic practices that improve soil physical and biological properties including application of organic- and inorganic-amendments and planting cover crops.
1.2. Develop soil water management strategies to increase the capture and storage of rain water in soil, minimize yield-robbing drought effects, and increase dryland and irrigated crop production in the South and Southeast.
1.3. Determine the environmental impact in soil, water, and air of proposed novel agronomic approaches on antibiotic resistance, emissions, and nutrient risks.
2. Develop improved decision support tools and technologies based on GxExM to optimize water use efficiency of rainfall and irrigation water for better yields from humid, Southeast Agroecosystems.
2.1. Develop techniques that utilize and integrate high resolution row crop canopy spectral images gathered during the growing season for in-season water management in cropping systems and fields characterized by high soil variability.
2.2. Implement databases, modeling tools, and decision-making paradigms for optimizing water management and crop yield.
3: Develop, optimize, and streamline a UAS operational system for agricultural research and pilot a regional ARS resource for improved data collection from UAS. The system will include: UAV selection process; UAV sensor selection process; flight planning; holistic software system to generate mosaics; handling potential PII information; and UAS-agronomic software that produces specific agronomic data from mosaics. The goals of this holistic system include data standardization and integration, development and release of new UAS technologies and capabilities, and increased ability for our UAS research to rapidly adapt to evolving technologies and policies. (NP216 C1: P1A; C4: P4A, P4B)
Approach
Several multi-year field plots will be established. These include a) cover crops for the major row cropping systems in then the southeast, b) planting various configurations of mixed cover crop species, c) cropping systems for land leveled fields, d) stabilizing dryland soybean production using cover crops and poultry litter, e) deep rooted cover crops and soil amendments and, f) cover crops and water use efficiency. From these field experiments we will measure effects on environmental quality, greenhouse gas emissions, and economics of each of the systems; environmental quality and antimicrobial resistance in each of the systems; contribution of soil organic matter to plant available water content in each of the systems; we will optimize yield by managing field variability, we will utilize high resolution thermal imaging to optimize irrigation management and we will model soil water requirements in each of the systems.
Progress Report
Sub objective 1.1 Exp. A: Buckwheat a winter sensitive crop and two legumes were planted as cover crops. Corn was planted in the spring without killing the winter-hardy cover crop. Early frost killed the buckwheat before attaining meaningful growth. In the spring, both vetch and crimson clover had excellent growth. The cover crop was killed after the corn emerged and after both cover crops reached flowering stage. Preliminary results show clear differences between the two cover crops vs the no cover crop. The practice of planting corn without killing the cover crop may be practical.
Exp B: Planting two cover crop species separately in alternating drill rows reduces the domination of one species when planted as a mixed stand. Growth of cotton reflected the effect of the competition when planted as a mixed stand. Cotton regardless of cover crop species grew excessively in the 2021. In the 2022, cotton was planted to measure the residual effect of the cover crops and poultry litter.
Exp C: Cool season cover crops, winter peas, cereal rye, and mixed cover crop (peas+ radish + rye), were planted after fall-applied poultry litter. One treatment to mimic current common farmers’ practice was included. Suction cup lysimeter collected leachate water samples after each rain event. To determine cover crop residue decomposition rate during cash crop growing season, In-situ litter bag procedure was used. Dry matter litter residue (100 grams) was inserted inside a mesh bag, incubated in the field, and collected to record mass reduction during corn and cotton growing seasons. At the time of litter bag collection, soil samples were collected under the litter bags and analyzed to determine the contribution of cover crop. Corn was planted green to allow cover crop and produce more biomass. Cotton was planted two weeks after termination of cover crops. After termination of cover crops, soil samples were collected and analyzed for pre-planting available soil Nitrogen for cash crops. Data loggers were installed in the plots and soil moisture and temperature were recorded during corn and cotton growing season. Performance indicators include plant height, vegetation water content, leaf area index, leaf chlorophyll, aboveground biomass and yield data were recorded every two weeks during corn and cotton growing season. Soil samples will be collected after harvest and soil health indicators will be measured.
Soil samples were collected after the termination of cover crops in Exp C on May 2022. In each plot, we have collected 3 undisturbed core soil sampling at depths of 0-5 and 5-10 centimeters (cm), and also disturbed soil samples at depths of 0-5, 5-10, 10-15 and 15-30 cm using soil auger. We randomly selected 8 native vegetation plots near the experiment site, and collected both 3 undisturbed and 3 disturbed soil samples at the same depths as the experiment plots. In total, we collected 640 undisturbed and 128 disturbed soil samples. Currently, we are measuring saturated hydraulic conductivity, soil aggregate size and stability, soil water retention curve, particle size distribution, bulk density, soil pH, electrical conductivity, total carbon, total nitrogen, extractable macro- and micro-nutrients.
Sub objective 1.2 EXP D involves five cover crop treatments and three fertility treatments is in its fifth year. Data collected in collaboration with Mississippi State University included cover crop biomass, soil chemical analysis, soybean leaf are index, chlorophyll index, and yield. Soybean fertilized with 2 ton/acre poultry litter grew much larger than soybean fertilized with four fertilizers (phosphorus, potash, sulfur, and zinc) based on soil test. Fertilizing soybean in this poor upland soil with 2 ton/acre poultry litter is superior to fertilizing with synthetic fertilizers. No soybean yield or growth advantage with any of the cover crops were shown in any of the five years. Poultry litter has consistently shown its benefits to soybean yield.
Exp. E. The 4th year study has been conducted in a marginal eroded upland soil in Pontotoc experiment station to test if integration of cover crop with animal and industrial byproducts improves soil health and corn yield. Cool season deep-rooted multispecies mixed cover crop including cereal rye, crimson clover and daikon radish were planted after harvesting corn in the fall of 2021. Suction pen lysimeters were deployed vertically and leachate volume and soil water storage following rain events were recorded. Leachate waters were collected during cover crop growing season from November 2021 to April 2022. Two weeks before planting corn the cover crop was chemically terminated. Before termination, above ground cover crop biomass was collected, and dry matter recorded. After planting corn, five litterbags containing 100 g cover crop biomass were left in the plots and were collected every three weeks during the corn-growing season and mass loss was recorded. Soil samples were collected beneath each litterbag for determination of inorganic Nitrogen (N) concentration as the function of cover crop residue decomposition. Data logger with soil moisture and temperature sensors were deployed to monitor soil moisture and temperature during corn growing season. At the eight-leaf stage, inorganic N fertilizer and poultry litter with/without gypsum and lignite were applied. Plant height and chlorophyll contents were measured every three weeks during corn growing season. At physiological maturity, corn plants were collected from 30 cm in row, dry matter recorded and analyzed for nutrient concentrations. At harvest, corn grain yield will be recorded. Soil physical and hydrological indicators will be measured after harvest.
Sub objective 1.3 Exp G, H & I: Greenhouse gas emissions were monitored. These efforts integrate the efforts of the USDA-ARS Genetics and Sustainable Agriculture and Mississippi State Geospatial Resources Institute teams. In addition to the ground infrared carbon dioxide flux measurements performed for 3 years, recent progress includes unmanned aerial vehicles (UAV) carbon dioxide (CO2) system development and application, as well as new simultaneous ground measurement of nitrous oxide and CO2 flux. These compliment soil property, soil biological, and crop data in addition to regular UAV flights using multispectral, thermal, and Lidar imaging.
Samples were collected from all experiments (B, C, E & others) including soil, water, and wildlife feces. Samples where culture and enzymatic analysis were necessary, were processed throughout the “maximize telework” time that personnel were available in the laboratory. This allowed us to triage those critical samples and extract and/or archive environmental Deoxyribose Nucleic Acid (DNA). E. coli and enterococci antimicrobial resistance isolates were collected from a collaborative study, while wildlife fecal samples were characterized for antimicrobial resistance genes and E. coli and enterococci in collaboration with Mississippi State (MSU) and Geospatial Resources Institute (GRI). Antibiotic resistance genes were characterized from vulture samples in collaboration with MSU scientists. Water samples were analyzed for the presence of E. coli, enterococci, and C. perfringens and archived. Soil samples were collected from experiments B, C, and E. The samples were analyzed for enzymatic activity and moisture content, while DNA was either extracted or the raw sample was archived for future extraction. New experiments were begun with MSU and GRI to deduce the spatial variability of soil health biological indicators. Unit experimental sites from Pontotoc and North Farm were visited and soil samples were collected and processed for enzymatic analysis, moisture content, water activity, and extracted for DNA. High throughput sequencing on DNA samples have been returned and preliminary bioinformatic analysis have been conducted.
Sub objective 2.1 Unmanned Aerial Systems (UAS) and Imaging: Work continue to establish multisource remote sensing analytics to enhance crop field monitoring at sites in Pontotoc, Mississippi and Mississippi State, Mississippi. A new system prototype with remote sensing big data of operational and analytic protocols was created in Microsoft Cloud-based high-performance computing platform and standalone workstations with selected software and tools from Environmental Systems Research Institute (ESRI). Images have been acquired from UAS in accordance with the imagery collected from satellite platforms for studies of cover crops and other agronomic treatment and measurements. UAS imaging has been routinely performed to cover the fields of experiment A, B, C, D, E and K. The imaging sensor used for experiment A, B and E is a digital red-green-blue (RGB) camera. The imaging sensor used for experiment D is a RGB and multispectral (green, red, red edge and near infrared) integrated camera. The imaging sensor used for experiment C and K is a multispectral and thermal integrated camera. The UAS data are for field analysis and scale up/down from the satellite data, especially from PlanetScope with relatively high spatial resolution (3 m) and revisit frequency (1/day).
Data management across multiple scientists and objectives have been conducted through maximizing ARS data management capabilities, thus benefitting the unit wide NPL 216 research project and ARS data integrity. This year, a data collection solution was the major focus. Three data collection engines have been defined through USDA-ARS Partnerships for Data Innovations (PDI) workbench: In the field, Farm Management Application (app) has been successfully deployed along with the Research Tool. Field Map associated with this app is in the development stage. A relational database for laboratory data collection was initiated. Site Scan is preliminary identified for a raw drone data collection system.
Accomplishments
1. A combination of remote sensing data is suggested for crop field monitoring. This investigation indicated that it can be beneficial if different sources of remote sensing data are integrated for crop field monitoring in the region of interest covering the experimental sites. ARS researchers in Mississippi State,Mississippi, identified that satellite imagery is good for observing the region of interest that covers the experiment sites for agroecosystem analysis. However, it may be limited by low revisit frequency, low spatial resolution and cloud covers. Unmanned Aerial Systems (UAS) has great potential for monitoring of individual crop fields, but it is also limited to image processing (radiometric and geometric) errors and cloud shadows even only for monitoring a small field. So, it is beneficial to compliment both data sources, imaging sensors on satellites and UAS, for complete agroecosystem monitoring and analysis.
2. A consumer grade digital camera can detect differences in corn growth. ARS scientists in Mississippi State, Mississippi, utilized a consumer-grade Unmanned Aerial Systems (UAS) with an RGB digital camera in corn field monitoring to detect corn growth differences. The system helps detect corn growth differences by using the normalized difference of photosynthetic vigor ratio, a vegetation index extracted from the RGB image data, even when visual identification, for example, among plots with and without cover crops is not able to be differentiated. Compared with control, Broiler Litter, and litter plus Flue Gas Desulfurization treatments had the largest positive effect on corn growth. The cover crops had a larger positive impact on the corn growth in control plots than the plots receiving fertilizer.
3. Conservation practices and their effects on soil biology are often region specific. For this reason, it is often difficult to suggest one conservation practice to all land managers. ARS researchers in Mississippi State, Mississippi, in collaboration with Mississippi State University researchers, determined that conservation practices such as minimal tillage and cover crop select for specific changes in the soil biology. Cover crop practices were found to select for increased bacterial diversity, based on a season, while no till practices decreased diversity. These changes as evidenced by sequencing of the soil microbiome indicate differences in biogeochemical outcomes in the soil, which ultimately impact how well a conservation practice performs in cropping systems, particularly based on regional differences. As the need for more resolution in soil health research increases, the use of soil microbiome sequencing will help identify practices more suited towards region-specific gains and help explain difficulties in implementing practices on a broad basis.
4. Poultry litter applied to fertilize cotton continues to supply potassium up to four years after ending poultry litter application. Poultry litter is a proven cotton fertilizer for the same season it is applied. It also has a carryover effect, but what component of the litter brings about this effect has not been well known. Nitrogen (N) is one element that gets carried over from year to year but its effect beyond the first year is small. In a recent study, ARS researchers in Mississippi State, Mississippi, found that potassium is a key element that carried over from year to year for up to four years to increase cotton lint yield above the normally fertilized cotton. They tested this after eliminating the effect of N by supplying all plots with equal amounts of N so that N is not limiting. The results show cotton planted in plots that received litter two to four years ago had greater leaf K concentration and produced more lint yield every year for three years than cotton planted in soil that received synthetic fertilizers. This shows the potassium from poultry litter carried over from year to year and met the potassium need of cotton. The results show that fertilizing cotton with poultry litter eliminates or reduces the need to apply potash fertilizers for cotton production long after stopping poultry litter applications.
5. Potassium (K) is required in large amounts by cotton for normal crop growth and fiber development. Shortage will result in poorer fiber quality and lowered yields. Nitrogen-based poultry litter applications to cotton adds substantial amount of K at the time of application. In an acidic upland soil marginal in organic matter, three years after termination of broiler litter, soil test K at 0-15 cm depth was greater by 38% in plots had received poultry litter than plots did not receive litter. However, cotton yield was not affected by K in residual poultry litter plots as compared to the plots did not receive litter. ARS researchers in Mississippi State, Mississippi, found out that addition of Flue Gas Desulurization (FGD) gypsum, CaSO4, to the residual poultry litter plots appeared to increase K in the soil solution as evidenced by greater cotton leaf K concentration. Cotton lint yield was greater in residual poultry litter plots received FGD gypsum than plots did not receive FGD gypsum, indicating Ca++ in FGD gypsum replaced K+ from the soil layers into the soil solution as the function of cation exchange.
6. Cover crops play important role in mediating N retention and supply to agroecosystems. Integrating cover crops into farming systems may contribute to meeting Nitrogen (N) demands of succeeding crops and reduce dependence on commercial fertilizer N inputs and environmental concerns. ARS researchers in Starkville, Mississippi, have discovered that the effectiveness and fertilizer value of cover crops for cash crops depends on the types, biomass yields and decomposition rate. The concentration of carbon and nitrogen in cover crop shoot and root biomass influences the rate of nitrogen release from decomposing cover crops. Results indicated that cereal rye had the slowest rate of mass loss and nitrogen release, and legumes include Australian peas and crimson clover released most of their nitrogen during early growth period of corn and cotton. The mixture of grass and legumes had intermediate rates of mass loss and release of nitrogen, most likely making it more synchronous with cash crops N demand under southeast agroecosystems.
7. Cover crop species or cash crops of corn and cotton did not affect carbon dioxide (CO2)flux from soil. Adoption of cover crops for soil health benefits has been slow in the humid southeastern United States. ARS researchers in Mississippi State, Mississippi, demonstrated in a long-term study, the effect of cover crop strategies in a cotton-corn rotation and the farmers’ best management practice of fall broiler litter application for sustainable performance and improved soil quality. Cover crops of radish, cereal rye, and winter peas have treatments of single, mixed, continuous, and rotating species. Measurements included soil CO2 flux and additional soil parameters (moisture, temperature, nitrogen and carbon compounds). Initial results for CO2 flux comparing 2020 and 2021 growing seasons indicate no significant differences in cover crop treatments or cash crops, but there are significant differences between measurements over time that are related to moisture and temperature. Conclusions are expected to yield recommendations for minimization of carbon loss for the 33° N, 88° W region with quantification of crop performance and cumulative CO2 flux for the respective growing seasons.
8. The use of poultry litter and cover crop increased soil water holding capacity. ARS researchers in Mississippi State, Mississippi, establish field studies to determine the effect of poultry litter, municipal biosolids, biochar, and cover crop on soybean yield, soil water holding capacity, plant water availability in soil, rain-water use efficiency and soil health: 1) Continuous application of poultry litter to a soil under a corn-soybean rotation improves soil total carbon by 17%, bulk density by 6%, and water infiltration by 44% and water retention by 11%; 2) Compared to the inorganic fertilizer treatments, poultry litter applications improved aggregate stability by 17%, saturated hydraulic connectivity were two to three times greater in the poultry litter treatments. The poultry litter addition increased field capacity of water and plant available water by 20%. In other words, soil was less compacted and could hold significantly more water because the litter allowed rainwater to soak into the ground more quickly; 3) Soybeans planted in the test fields produced better yields in the years following the addition of poultry litter. One year later, soybean yields were eight percent higher and three years later they were 11 percent higher than in fields treated with synthetic fertilizers. The study pointed to a clear value of poultry litter to both crop farmers and poultry farmers; and 4) Cover crops (CC) can increase soil organic matter by 15% and storage of rainwater in soils by 13% during the crop growing season within two months after terminating cover crop.
9. The use of the model RZWQM2 under long-term diverse weather conditions can assist in determining results difficult or impossible for field studies to ascertain. ARS researchers in Mississippi State, Mississippi, utilized the model to determine the following results: 1) planting cover crop (CC) reduced drainage deep percolation by 69 mm (11%), 53 mm (15%), and 51 mm (21%) and increased evapotranspiration by 79 mm (55%), 81 mm (57%), and 73 mm (56%) in wet, normal, and dry years, respectively; and 2) planting CC decreased surface evaporation by 38 mm (24%) for soybean growth periods. As compared with no CC scenario, model estimates indicated planting CC increased soybean yield by 4% (134 kg ha-1; approximately 2 bu acre-1) and improved soybean water use efficiency (WUE) by 9% (0.64 vs. 0.59 kg m-3). Long-term use of winter wheat CC, if managed similarly, can increase soil water storage and improve rain water use efficiency without sacrificing soybean growth. This research directly impacts 51% of the total soybean production in Mississippi state which is not irrigated (1.12 million acres with a value of $0.56 billion). With a 4% and 8% increase by cover crop and poultry litter in dryland yield and 5% decrease in costs, the profitability can be expected to rise by about $32 and $64 per acre. Beyond the economic impact, soil organic matter and soil health were also improved.
10. Optical sensors calibrated to relate to spring wheat yield and protein. ARS reseachers in Mississippi State, Mississippi, in collaboration with researchers at North Dakota State University, found that optical sensor calibrations were highly related to spring wheat yield (p<0.0001) and protein (p<0.0001) at the V5 growth stage. Ground optical sensor algorithms were tested for side-dress nitrogen status and yields in corn and explored for use with drone imagery. The practical application of improved in-season nitrogen side-dress will increase nitrogen use efficiency.
11. Geospatial proximally sensed soil maps. ARS reseachers in Mississippi State, Mississippi, in collaboration with researchers at North Dakota State University, used Laboratory soil analysis results to construct geospatial soil maps that are now being compared to the new technologies that provide geospatial proximally sensed data. The results of these comparisons will be used, by ARS researchers in Mississippi State, Mississippi, to develop calibrations for the proximal sensors (FarmLab & Topsoil mapper) and improve assessment of soil health status. Additionally, a special emphasis was placed on developing efficient spatial sampling approaches and proximal measurements to assess soil carbon levels. Efficient assessment of carbon is essential for the successful development of carbon sequestration markets, and engaging producers in these markets.
12. Weed identification via remote images. ARS reseachers in Mississippi State, Mississippi, in collaboration with researchers at North Dakota State University, collected over 1 million images using different sensors (Red, Green, Blue, multispectral, hyperspectral, thermal) on six different weed species and eight crop species in greenhouse and field studies. Also, ARS researchers in Mississippi State, Mississippi, created a third version of an autonomous platform (miniWeedbot) and used it to collect data. For sugarbeets, we used precision agriculture technologies to implement strip tillage and cover crops for waterhemp suppression and to improve soil conservation. Finally, for the first-time last season we were able to implement the whole workflow, from unmanned aerial system (UAS) data collection to field weed control, for site-specific weed management based using a commercial size sprayer.
13. Three software applications successfully deployed. ARS reseachers in Mississippi State, Mississippi, in collaboration with researchers at North Dakota State University, deployed three computer apps. FieldHub, to assist with field design of experiments for traditional, unreplicated, augmented and partially replicated experiments for plant breeding, forestry, and animal sciences. MrBean, developed to accurately predict the genetic potential of newer genotypes coming out of our breeding pipelines. Ag.Q.Hub, to generate reports for variety advancements.
14. Soil carbon dioxide emissions measured by unmanned aerial vehicles (UAV). ARS reseachers in Mississippi State, Mississippi, in collaboration with researchers Mississippi State University Geospatial Resources Institute (MSU-GRI), conducted regular ground measurements on soil carbon dioxide (CO2) emission with ancillary soil properties, including temperature and moisture during crop growing seasons. The team also developed a small unmanned aerial vehicle (UAV)-based system to monitor CO2 concentration along with meteorological parameters in the low-altitude atmosphere above cropping fields. The three-year ground measurements (Experiment E, 2019-2021) reveal that cover crops did not have significant effects on soil CO2 emissions while organic amendments consisting of broiler litter often increased soil CO2 emissions, with emission peaks observed between mid-June and mid-July. Test flights of the UAV-CO2 system show the UAV-based system was able to detect the difference in atmospheric CO2 concentration above cropping and bare soil systems when the flights were lower than 8-10 feet above crop top or soil surface. The results of the test flights ensure the feasibility of applying UAV-based systems to monitor greenhouse gas emissions in agroecosystems. Overall, our ground and UAV-based studies on greenhouse gas emissions will provide mechanistic understandings of how agricultural practices for Mississippi cropping systems affect greenhouse gas emissions, and advanced technologies to monitor agroecosystem greenhouse gas in less labor and time-consuming manners.
15. Unmanned aerial vehicle (UAV) based high-resolution soil moisture. ARS reseachers in Mississippi State, Mississippi, in collaboration with researchers at Mississippi State University, developed an unmanned aerial vehicle (UAV) based high-resolution soil moisture measurement technology for precision agriculture. The system utilizes signals of opportunity like global navigation satellite system (GNSS) transmissions that are already available and repurposes them together with multispectral camera and Lidar observations. We develop an artificial intelligence (AI) technique that learns to transform observations from the UAV into soil moisture values of the field. This way we can obtain the very critical soil moisture map of an agricultural field covering larger areas at only a few meter resolutions in a short amount of time with a single flight of the developed UAV, saving from costly, time-consuming, and labor intensive in-situ field observations. Our developed technology will also provide informed precision agriculture and irrigation decisions saving the precious resource of water.
Review Publications
Brooks, J.P., Durso, L.M., Ibekwe, A.M. 2021. Editorial: Exposure, risks, and drivers of the mobile antimicrobial resistance genes in the environment – a global perspective. Frontiers in Microbiology. 12:1-3. https://doi.org/10.3389/fmicb.2021.803282.
Li, Y., Tewolde, H., Miles, D.M., Munyon, J.W., Brooks, J.P., Feng, G.G., Yang, M., Zhang, F. 2021. Decomposition of poultry litter organic matter may be slowed by co-applied industrial and agricultural byproducts. Journal of Environmental Quality. 50:364-374. DOI: 10.1002/jeq2.20189.
Huang, Y., Zhao, X., Pan, Z., Reddy, K.N., Zhang, J. 2022. Hyperspectral plant sensing for differentiating Glyphosate-resistant and Glyphosate-susceptible Johnsongrass through machine learning. Pest Management Science. 78:2370-2377. https://doi.org/10.1002/ps.6864.
Yang, W., Feng, G.G., Miles, D.M., Gao, L., Jia, Y., Li, C., Qu, Z. 2020. Impact of biochar on greenhouse gas emissions and soil carbon sequestration in corn growth under drip irrigation with mulching. Science of the Total Environment. 729:138752. https://doi.org/10.1016/j.scitotenv.2020.138752.
Tewolde, H., Buehring, N., Way, T.R., Feng, G.G., He, Z., Sistani, K.R., Jenkins, J.N. 2021. Yield and nutrient removal of cotton-corn-soybean rotation systems fertilized with poultry litter. Agronomy Journal. 113:5483-5498. https://doi.10.1002/agj2.20857.
He, Z., Liu, Y., Kim, H.J., Tewolde, H., Zhang, H. 2022. Fourier transform infrared spectral features of plant biomass components during cotton organ development and their biological implications. Journal of Cotton Research. 5:11. https://doi.org/10.1186/s42397-022-00117-8.
