Recipient Organization
PURDUE UNIVERSITY
(N/A)
WEST LAFAYETTE,IN 47907
Performing Department
(N/A)
Non Technical Summary
The U.S. Midwest, a vital agricultural hub contributing significantly to the nation's economy, primarily relies on monoculture corn or simple corn-soybean rotations. While these systems are highly productive, their heavy dependence on chemical inputs, limited diversity, and winter fallow practices have led to adverse consequences including declining soil organic carbon (SOC) storage, deteriorating water quality, reduced biodiversity, and increased greenhouse gas emissions. This project aims to investigate whether incorporating kura clover, a perennial legume crop well-suited for the Midwest, can improve SOC storage and soil health by enhancing plant C inputs and microbial functions in corn production systems. Leveraging a long-term field trial in west-central Indiana, we will examine the effects of the kura clover living mulch system on (1) the quantity, quality, and vertical distribution of plant C inputs; (2) the size, structure, and functions (microbial C use efficiency) of soil microbial community; and (3) SOC storage, composition and soil health indicators. Field and lab observations combined with process-based modeling will be utilized to elucidate how plant C input traits and soil microbial attributes influence the variations in SOC quantity and composition. This project, led by an interdisciplinary team specializing in soil health, crop ecophysiology, microbial ecology, and agroecosystem modeling, directly addresses AFRI priorities (A1401 Soil Health) by unravelling soil biogeochemical processes linked to C sequestration under management practices and providing evidence for perennial kura clover living mulch systems as a viable strategy to enhance soil microbial functioning, C sequestration, and ecosystem services in the Midwest corn production systems.
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Goals / Objectives
The overall goal of this project is to investigate the effects of perennial kura clover mulching on plant-microbe associations and soil microbial functions related to C cycling and SOC accumulation in Midwestern corn production systems. Specific objectives include: 1.Evaluate the impact of kura clover mulching on the quantity, quality, and vertical distribution of plant C inputs and root characteristics compared to other annual corn cropping, and perennial switchgrass and restored, mixed prairie systems; 2.Determine the effect of kura clover mulching on the size, structure, and functions (CUE) of soil microbes compared to other systems; 3.Assess how kura clover mulching influences the quantity and composition of SOC, as well as soil physical, chemical, and biological health indicators; and 4. Evaluate different SOC models with both implicit and explicit microbial representation to identify a more suitable SOC modeling approach for Midwestern cropping systems.
Project Methods
This project will build upon the existing management systems and infrastructure and prior work of the long-term Water Quality Field Station (WQFS) field trial to support our comprehensive field and lab data collection. Within the scope of this project, we will select seven management systems (Table 1) for rigorous data collection and analysis to achieve research objectives. Five corn production systems include continuous corn (CC), corn/soy rotation (C/S), continuous corn with annual rye cover crop (CC+Rye), corn/soy rotation with annual rye cover crop (C/S+Rye), and continuous corn with kura living mulch (CC+Kura). CC and C/S stand for the dominant, status quo grain cropping systems in the Midwest. CC+Rye and C/S+Rye represent the predominant annual cover crop systems for regional farmers. Two perennial systems include switchgrass and restored, mixed prairie, representing typical bioenergy production or conservation-focused systems in the Midwest.Objective 1. We will evaluate how kura clover mulching impacts the quantity and quality of above and belowground biomass, and root characteristics. We plan to conduct data collection in Years 1 and 2 of this project. A PhD student of PD Rui's lab and a research technician of Co-PD Volenec's lab (Nicole De Armond) will conduct this work under Drs. Rui and Volenec's supervision. Obj. 1.1: Aboveground and belowground biomass, and C:N ratios.In Years 1 and 2, aboveground biomass within a quadrat (1 m2) will be cut after crop physiological maturity stage. The collected roots will be washed gently over a sieve to get rid of soil residues. After cleaning, roots will be subjected to drying in an oven set at 70°C for 72 hours. Corn yields will be measured using a plot combine at harvest. Aboveground biomass of annual rye cover crop will be measured just prior to cover crop termination in the spring. Three quadrats will be randomly placed in each field. Belowground biomass of rye will be collected by a soil core (10 cm diameter) as previously described. Above and belowground biomass of kura clover will be collected at two times: late spring/early summer (before herbicide application prior to corn planting), and late summer/early fall (during corn biomass collection). Structural and nonstructural carbohydrateswill be quantified. Obj. 1.2: Corn root characteristics. To evaluate rooting characteristics of corn, we will measure root length density (RLD), root average diameter (RAD), and fine root surface area (FRSA) during peak season following the method by Lazicki et al. (2016). Root cores (10 cm diameter) will be obtained from 0 - 15 and 15 - 30 cm depths. After collection, root cores will be put on ice and kept at 4°C. Roots will be elutriated, cleaned, and stored in 50% ethanol at 4°C. The RLD, RAD, and FRSA will be measured on an Epson 1680 scanner (Epson America Inc., Long Beach, CA, USA) utilizing WinRhizo root scanning software (WinRhizo, Regent Instruments, Québec, Canada).To determine root exudation, we will use a method modified from Phillips et al. (2008) and Brzostek et al. (2013). We will sample corn plants between V7 to V10 stage (after N sidressing application). Three corn plants will be carefully excavated from each plot by hand. After washing, the root system will be placed in a container to be filled with sterile glass beads to serve as a mechanical substrate. C and N free nutrient solution will then be added to buffer the root system. The roots will be incubated for 24 hours and exudates will be collected and stored at -20°C. The accumulated total non-particulate organic C will be analyzed using a TOC analyzer (Shimadzu Scientific Instruments). The rate of root C exudation will be normalized by the FRSA and the mass of each root system.Objective 2.To assess how kura clover mulching affects the size, structure, and functions (CUE) of soil microbes, we will conduct multiple soil sampling across the growing season in Years 1 and 2. We will combine lab analysis and in-situ observation to study soil microbial functions related to SOC formation.We will target spring (mid-late April) when cover crops are growing, early summer (mid-June) after planting, and peak season (late July) to conduct soil sampling for microbial analyses. Soil samples (0-15 and 15-30 cm) will be collected at each time. Ten cores will be taken within each plot using a zigzag sampling pattern (to equally sample across the corn row) and mixed to form a composite sample. Soil samples will be passed through a 2 mm sieve and subsampled into two portions. One subsample will be kept at 4? for microbial biomass and community structure (Phospholipid fatty acids: PLFA), microbial CUE, and biological soil health, and the other subsample will be air-dried and ground to fine powder. An in-situ isotope tracer experiment will be established using 13C-labeled glucose and phenol to study the assimilation of 13C into microbial biomass and mineral-associated SOC pool.?Objective 3. We will determine how the kura clover mulching affects SOC content and composition and soil physical, chemical, and biological health indicators. Sample and data collection will take place in Years 1 and 2 of this project. This work will be conducted by PhD student and undergraduate students (TBD) under the supervision of Drs. Rui and Brouder. Fresh and air-dried soils from Obj. 2 (peak-season sampling) will be utilized to perform SOC and soil health analyses.Obj. 3.1: Soil C content and composition. POM and MAOM. We will perform SOM fractionation using the method described in Bradford et al. (2008).Diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) will be used to examine the molecular structures/functional groups of SOC.We will also assess the relative contribution of the fungal and bacterial necromass to SOC accumulation, which are represented by ratios of amino sugars to SOC (Amelung et al., 2001; Liang et al., 2013). We will determine the content of detectable bulk soil amino sugars including glucosamine (GluN), which is predominantly derived from fungal cell walls and muramic acid (MurA), which is unique to bacteria, as described by Zhang and Amelung (1996).Obj. 3.2: Assessments of physical, chemical, and biological soil health. Biological, physical, and chemical soil health indicators will be determined for soil samples collected at the peak season in Years 1 and 2.Objective 4. Data collected in Objectives 1-3 and legacy data from 1998 to present from these long-term plots will be used to calibrate and validate two soil C models with implicit and explicit microbial representation at the field scale. This work will be conducted by a MS student (TBD) under the supervision of Co-PD Poudel. Obj. 4.1: Dynamic simulation modeling of SOC using process-based models. SOC will be simulated using two models: DAYCENT and MIMICS.The MIMICS model will be parameterized and calibrated with project data on microbial biomass and the legacy data as it has been mostly used to simulate forest soil carbon dynamics (Wieder et al., 2014). Both models will be initialized with a spin-up simulation to bring SOC stocks at a steady state before running the simulations for the experiments. The models will be validated with the soil and plant data collected in objectives 1-3 during the project period. The models will be evaluated and compared using goodness-of-fit metrics root mean squared error (RMSE), Willmott's agreement index (d), and Nash-Sutcliffe efficiency (NSE).Obj.4.2: Uncertainty analysis.Uncertainty analysis will be performed for both models with the goal of identifying the major sources of uncertainties: model inputs, model parameters and model structure. Two types of uncertainty analysis methods, namely i) error propagation methods such as Monte Carlo analysis (Kalos & Whitlock, 2009) and ii) empirically-based uncertainty estimators (Ogle et al., 2007), will be implemented.