Project : USDA ARS

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Research Project: Sustainable Intensification of Cropping Systems on Spatially Variable Landscapes and Soils

Location: Cropping Systems and Water Quality Research

2021 Annual Report

Objectives
Objective 1: Use a GxExM research approach to develop decision support tools for on-farm implementation of sustainable and resilient cropping systems. 1a: Develop knowledge to aid planting-time decision support for optimizing corn emergence on variable soils and landscapes. 1b: Improve decision support for variable-rate grain crop nitrogen management. 1c: Develop and evaluate new and improved soil health assessments. 1d: Develop and evaluate proximal sensing approaches to provide spatially-dense information important in soil management and soil health applications. Objective 2: Develop and evaluate sustainable and resilient cropping systems using a site-specific GxExM framework. 2a: Evaluate production and soil health of grain and perennial grass cropping systems on degraded claypan soil landscapes. 2b: Evaluate effects of cover crops and reduced tillage on soil health and crop productivity. 2c: Evaluate spatial aspects of sustainability in site-specific management systems.

Approach
In this project, our interdisciplinary team will address key knowledge and technology gaps limiting the development of site-specific management systems using a genetics by environment by management (GxExM) research approach. In the first objective we focus on developing new decision support tools and the underlying knowledge needed to facilitate improved, targeted crop management systems. Here we will conduct field studies to understand how to vary planting depth to optimize corn emergence and yield and investigate the effect of emergence date on crop modeling (1a). We will conduct multiple analyses of a previously collected dataset to develop decision support guidelines for in-season variable-rate nitrogen management in corn (1b) We will collaborate with ARS colleagues in Oregon in developing decision support technology for variable-rate nitrogen management in wheat (1b). We will develop new laboratory-based soil health assessments and evaluate them in field experiments (1c). We will develop and evaluate in the field new proximal soil sensing techniques to support soil health and other management decisions (1d). In the second objective we develop, apply, and evaluate innovative management systems that incorporate information about spatially variable soil resources. Many of the studies incorporate application and evaluation of the decision tools described above. In long-term field experiments, we will investigate the effect of cropping systems and landscape variability on soil health and crop production and profitability (2a). We will quantify differences in energy yield of bioenergy crops grown across variable landscapes (2a). Also in field experiments, we will investigate the effects of cover crops and reduced tillage on soil health and crop productivity (2b). We will use a model-based approach to spatially compare production between site-specific and whole field management and validate model results with measured field data (1c). We will conduct field research that uses crop sensor technology to evaluate soybean drought and flood tolerance (1c). Much of the research in the second objective supports, and is coordinated with the Central Mississippi River Basin Long-Term Agroecosystem Research (CMRB LTAR) project, which is part of another research project within this ARS unit. Specifically, decision tools and knowledge from this project will inform possible future changes to the aspirational cropping system design for the CMRB LTAR common experiment.

Progress Report
Progress under Objective 1: (1) A journal manuscript examining the effects of corn seeding depth and within-field soil variability on emergence and yield in claypan and alluvial soil fields was accepted. (2) In a second year of field and lab research, commercial planter sensor systems were evaluated to determine the accuracy, precision, and repeatability of seed zone soil property estimates and for making on-the-go planting decisions. (3) Two journal papers were submitted from a previously collected 49-site-year dataset. One used soil hydrologic grouping along with soil and weather properties to estimate corn nitrogen need, and the other was a paper describing the dataset, summarizing of key findings, discussing the strengths and weakness of the dataset, and providing the dataset as supplemental files. (4) Completed a manuscript showing that incremental nitrogen use efficency (iNUE) (i.e., the last units of nitrogen applied to reach optimal yield) is very low, often less than 10%. Additional analyses identified major factors in low iNUE and suggested improvements in maize nitrogen fertilizer management decision tools, and educational programs. (5) The successful “Yield Editor” software (over 12,000 downloads to date) was updated and enhanced to accommodate wheat protein mapping and development of variable-rate fertilizer prescription maps. Before the end of FY21, the software will be demonstrated to a group of producers to get their suggestions for improvements. (6) Soil health sampling of a rotation and cover crop experiment was completed in November 2020. Samples are currently in the laboratory for a full suite of in-house soil health analyses. A manuscript comparing two methods of quantifying microbial community structure was published. (7) Completed on-farm data collection [approximately 100 fields, from 25 producers, across 19 counties, in three states (Missouri, Iowa, South Dakota) evaluating corn tissue and yield response to phosphorus, potassium, and sulfur fertilization as impacted by soil fertility and soil health metrics. Profile soil sensor measurements were obtained on most fields for comparison. Lab work has been completed and data analysis is nearly complete. Progress under Objective 2: (1) Annual operations, including field work, data collection and validation have been completed for year eight of a planned 10-year analysis comparing grain and perennial grass cropping systems. (2) A soil dataset comprising multiple cropping systems in a long-term experiment has been analyzed for phospholipid fatty acid (PLFA) as a soil health indicator. Genomic DNA samples for the same experiment were not analyzed by the cooperator; therefore, the focus of the manuscript will be on PLFA community structure. (3) A field study assessing the effects of cover crops and tillage on soil health in cotton production systems continued. Soil samples were collected in spring 2021 and are in the laboratory for a full suite of in-house soil health analyses. (4) Soil sampling of an extended rotation field experiment was completed in November 2020 and samples are in the laboratory for a full suite of in-house soil health analyses. (5) The study examining drought tolerant soybean genotypes had to be abandoned, as the genotypes used were extremely sensitive to dicamba herbicide. Even after waiting until late June to plant the study in hopes of encountering less herbicide injury, the damage to the plants made the planned measurements meaningless. Given the high levels of dicamba use in the region, the planned approach cannot work.

Accomplishments
1. Soil health interpretation is improved by accounting for inherent site conditions. Soil health indicators are dynamic soil properties that are tied to climate and soil characteristics. A robust understanding of the spatial and temporal variation of soil health indicators is needed to improve soil health assessment and interpretation for producers. ARS scientists at Columbia, Missouri, and Lubbock, Texas, along with collaborators from multiple other institutions, evaluated the variability of multiple soil health indicators using data collected across several states. Results demonstrated that soil health indicators varied due to regional soil and climate conditions, and that laboratory methods for soil microbial community assessment were affected by regional differences in soils. In addition, more sensitive soil health indicators varied across years at the same sites, likely due to changes in weather conditions. These results highlight the need to account for regional site characteristics and to monitor changes in soil health over time so that soil testing labs can provide improved interpretations for producers.

2. Grain crop profitability varies by tillage and cover crop conservation practices. Many cropping systems in the Midwestern United States consist of an annual corn-soybean rotation characterized by tillage and/or high rates of agrichemicals. Before growers will adopt more conservation-based cropping practices that minimize soil erosion, improve soil health, and create resiliency to extreme weather events, these practices must be shown to be profitable. In a 19-year study examining the relationship between conservation practices and grain crop profitability for poorly-drained soils in the U.S. Midwest, ARS scientists at Columbia, Missouri, in collaboration with University of Missouri scientists, found no-tillage cropping systems provided $75 more return per acre per year than a system including tillage. Among no-tillage cropping systems that included crop rotation and cover crop differences, soybean net return was similar, but corn was most profitable in a system without cover crops and without wheat in the rotation. This was due to differences in corn yield primarily attributed to planting challenges and reduced plant population under the high plant-residue environment. These findings highlight the need for developing and implementing crop- specific conservation management practices that also maintain profitability.

3. Conservation management improves soil health. It is widely recognized that intensive cultivation leads to soil degradation and negative environmental impacts. However, most soil health assessments are conducted on small-scale field experiments that do not allow for broad inference or conclusions regarding the benefits of conservation management across soils and climate. In addition, the trajectory for recovery of soil function upon conversion back to native systems is unknown. ARS scientists at Columbia, Missouri, Ames, Iowa, and Lubbock, Texas, along with cooperators from multiple other institutions, conducted a meta-analysis of soil health data from 456 studies that spanned 49 states, finding that increased perennialization, increased crop rotational diversity, and reduced tillage improved soil health indicators and soil health scores. Results from an examination of prairie reconstruction over 22 years also confirmed that the recovery of soil function following agricultural degradation can take several decades. Producers and agency specialists will benefit from the comprehensive summary of the benefits of conservation management practices and from a better understanding of the long-term effects of soil degradation.

4. Improved nitrogen fertilizer management depends on soil and weather. Nitrogen (N) is the essential nutrient that most often limits corn yield, but it is difficult to know from one year to the next how much N fertilizer to apply due to uncertainty in how much the soil will supply and how much will be lost due to weather conditions during the growing season. Adding less N fertilizer than crops need reduces profits, but adding too much results in excess N that is subject to environmental loss and contributes to problems like algal blooms. ARS scientists at Columbia, Missouri, and scientists at eight different U.S. Midwest universities evaluated how site-specific soil and weather information could be used to improve tools used for making corn N management decisions across a wide range of U.S. Corn Belt growing conditions. They found that accuracy in predicting how much N fertilizer to apply was improved by 20 to 40% by using soil properties and weather information (e.g., evenness of rainfall, soil pH, soil carbon, soil bulk density). In other analyses, the effectiveness of applying N fertilizer into standing corn also depended on soil and weather. Optimal yields were only realized when timely precipitation (or irrigation) was available to incorporate the N into the soil. Incorporation was especially important for coarse textured soils with greater N loss potential. These results show how producers can improve their N fertilizer application decisions through consideration of site-specific soil and weather information.

5. Soil mineralization tests marginally improve estimates of corn yield and economically optimal nitrogen rate. Predicting yield and adjusting corn nitrogen recommendations based on soil health status has the potential to improve farmer profits and reduce environmental impacts from agriculture. ARS scientists at Columbia, Missouri, along with collaborators from multiple other institutions, evaluated the utility of mineralizable nitrogen and carbon assays for improved corn nitrogen management decisions in 49 fields across eight states in the Midwestern U.S. They found that 50% of the variation in economically optimal nitrogen rate was associated with mineralizable carbon variations in one year, but the relationship was not as strong in other years, likely due to the influence of weather. Furthermore, including mineralizable nitrogen with standard soil tests only marginally improved predictability of grain yield, nitrogen uptake, and the economically optimum nitrogen rate. These results benefit producers and consultants by demonstrating that mineralization assays are unlikely to substantially improve tools for nitrogen fertilizer recommendations.

6. Unmanned aerial vehicle (UAV) imagery provides highly accurate early corn stand evaluation. Corn seed sometimes does not germinate quickly nor emerge from the soil uniformly from one plant to the next, particularly when soils are cold. The resulting non-uniform stands require farmers to decide whether they should replant portions or all of a field. Automated tools are needed to help farmers to make these decisions by quickly evaluating their fields, which may total thousands of acres. ARS researchers at Columbia, Missouri, in collaboration with University of Missouri researchers, evaluated corn stands under various soil and crop management conditions shortly after emergence using UAV images. They developed a sophisticated modeling method that processed the UAV images in a way that live plants, soil, and dead plant residues could be discriminated, and estimated the number of plants per acre with an accuracy of 90% or better. The research also showed that unique corn plant features could be distinguished in UAV images within the first week of emergence, allowing relatively accurate determination of the exact day each individual plant emerged. As the process of capturing and analyzing UAV imagery for estimating corn emergence and stand count becomes more automated, farmers will be able to routinely utilize UAV scouting to identify fields or portions of fields that need replanting.

U.S. DEPARTMENT OF AGRICULTURE

Cropping Systems and Water Quality Research: Columbia, MO