Progress report for GS22-262
Project Information
Florida agroecosystems are mostly established on coarse-textured soils and subject to a hot and humid climate that favors rapid soil organic matter decomposition, water percolation, and nutrient leaching. Cover crops can replace fallow periods and help protect and improve soil conditions, but the adoption of cover crops is limited because they are usually not profitable. Chickpea is a short-season, cool-season legume crop that could be used as an alternative as it fits the rotation gap. Moreover, chickpea can increase soil cover and increase crop residues while being more economically attractive as it can be harvested, serving as a dual-purpose (i.e., cash and cover) crop. Replacing a bare fallow with chickpea could also benefit soil biology, as the greater amount of crop residues returned to the system should stimulate microbially-driven carbon and nitrogen cycling. Greater microbial activity is critical to soil health because microbes decompose residues, stimulate plant growth, and increase nutrient cycling and availability. As the microbial community is responsive to management practices and provides information on microbially-driven nutrient cycling, microbial indicators could help assess agroecosystem sustainability and predict possible crop benefits, such as improved yield and/or nutrient use efficiency. This project aims to assess how replacing winter fallow periods with chickpea in row crop agroecosystems of the Southeast US affects soil microbes. More specifically, our goal is to quantify how chickpea, other cover crops and fallow affect soil carbon mineralization, enzyme activity, and gene expression, to determine the impacts of different winter crops on agroecosystem sustainability.
Original wording
- Evaluate how replacing a winter bare fallow with a legume cash crop will impact microbial activity, by comparing a novel legume cash crop for the area (chickpea) to common cover cropping options of rye (non-legume) and clover (legume). Our goal is to distinguish the effects of replacing fallow periods with a cash crop, with grains harvested and removed from the system (e.g., chickpea), from the effects of growing traditional cover crops where all the biomass is returned to the soil at termination (e.g., clover and rye).
- Quantify how three chickpea varieties, two cover crops and fallow affect different soil microbial indicators: soil C mineralization, enzyme activity, and gene expression.
- Determine if and how different winter management options (fallow, chickpea cash crop, rye cover crop, clover cover crop) affect the productivity of the subsequent crop in the rotation (corn). We also aim to determine if and how microbial indicators are correlated with crop growth and nutrition for all crops studied.
As of April 2023
The second objective had to be modified, due to issues in getting sufficiency chickpea varieties. As a result, in 2022-2023, we are testing how a chickpea commercial variety, two cover crops and fallow affect different soil microbial indicators: soil C mineralization, enzyme activity, and gene expression. Our hope is to add the other two chickpea varieties during the 2023-2024 season.
As of April 2024
We were unable to secure additional chickpea lines, hence the design remained the same in 2023-2024 as it was in 2022-2023, i.e., a chickpea commercial variety, two cover crops (rye, clover) and a fallow. Within these treatments, we measured different soil microbial indicators: soil C mineralization, enzyme activity, and gene expression. Moreover, we are assessing whether these indicators respond differently to winter crop treatments when the main cash crop (corn) is subject to different nitrogen fertilizer inputs (low and recommended rate).
Research
Experimental design
The experiment will be conducted for two years as a rotation system with corn, with three chickpea varieties selected based on their potential to fit the winter gap between summer row crops. For the first year of the rotation, chickpea, rye, and clover were planted at the Plant Science Research and Education Unit (PSREU) in Citra (FL) in 15 x 15 ft plots in December 2022. Four more plots were included as a fallow and kept weed-free through herbicide application to mimic growers’ standard management. Due to limited seed availability, the best performing chickpea varieties were not planted in 2022-2023, but will be planted in 2023-2024. Thus, we had a total of four treatments (a chickpea commercial variety, rye cover crop, clover cover crop, chemical fallow) set up as a randomized complete block design with four replications, for a total of 16 plots. In 2023-2024, we expect to plant the additional three chickpea varieties, resulting in a total of six treatments (three chickpea varieties, rye cover crop, clover cover crop, chemical fallow) set up as a randomized complete block design with four replications, for a total of 24 plots.
Rye was selected because it’s a common winter cover crop option used in North Florida. Clover was used to compare chickpea to a winter legume cover crop for which the biomass is incorporated into the soil rather than partially harvested (grains) like chickpea. Following chickpea harvest (planned for late April or early May in 2022-2023, ideally sooner in 2023-2024), all crops will be terminated, with the incorporation of post-harvest residues (chickpea) or the whole biomass (rye, clover) into the soil. Because the rye cover crop was ready for termination sooner due to earlier flowering relative to legumes, this crop was terminated mid-March in 2022-2023. A grain corn hybrid will be planted in late April to early May in 2022-2023 (ideally sooner in 2023-2024) in the same field to assess the effects of winter crops on soil microbes through the rotation system.
Chickpea, rye, clover, and corn aboveground biomass will be sampled at termination/harvest using two random 0.5 m quadrats in each plot. Samples will be dried at 65˚C and ground to determine nutrient concentration by digestion followed by quantification using ICP. Total C and N will be determined by combustion. Chickpea and corn yields will also be determined at harvest. Soil samples will be collected three times per year (six times total): before chickpea and cover crop planting, at termination, and after corn harvest, to measure changes in soil microbial indicators through the rotation system.
Ten cores per plot will be collected from the top 20 cm of soil, and samples will be split before processing: samples for gene expression and enzyme activity will be kept at -80˚C until processing whereas samples for soil C mineralization will be air-dried and sieved before processing.
Short-term C mineralization
Readily-available soil C will be measured based on the amount of CO2 released from soil by microbial activity during an incubation period after the rewetting of samples, as described in Stott et al. (2019). Two duplicates of mason jars will be set up for each sample. A 20 g subsample of air-dried soil will be placed in each mason jar, and a KOH trap assembly will be set in the jar. Each jar will contain 9 mL of KOH in the jar's trap and 7.5 mL of double-distilled water in a separate vial to maintain moisture. Samples will be incubated for 4 days at room temperature. After the incubation, an electrical conductivity (EC) meter will be used to analyze the samples in the KOH trap. As 9 mL of 0.5 M KOH can theoretically accommodate 99.025 mg CO2, and a fraction of that total trap capacity is absorbed during the incubation, CO2 released in each jar is computed as:
CO2 (mg/trap) = [(ECraw - ECsample)/(ECraw - ECsat)] x trap capacity(mg)
Where ECraw is the EC of fresh 0.5 M KOH, ECsample is the EC observed in the KOH trap for each sample, and ECsat is the electrical conductivity of 0.25 M K2CO3.
Enzyme assays
The enzymes β-glucosidase (C-cycle), N-acetyl-β-D-glucosaminidase (C and N cycles), phosphomonoesterase (P cycle), and arylsulfatase (S cycle) will be analyzed following a modified protocol based on Stott et al. (2019), where we will use samples stored at -80˚C instead of air-dried soils. Briefly, 0.5 g duplicates of each soil sample will be transferred to an Erlenmeyer flask, and an additional flask will be kept as a control. A start buffer (2 mL) and the enzyme substrate are added to each Erlenmeyer flask containing soil samples to start the incubations, then the flasks are incubated at 37°C for 1h, after which 0.5 mL CaCl2 and 2 mL of stop buffer solution is added to each flask to end the incubations. A control Erlenmeyer is also incubated (i.e., enzyme substrate with no soil). Samples are then filtered and analyzed through colorimetry, as the development of color is proportional to the amount of enzyme substrate processed by the enzyme.
N cycling gene expression
To reduce costs, samples to quantify N cycling gene abundance will be stored at -80°C until DNA extraction. DNA will be extracted from soils with Qiagen DNeasy PowerLyzer PowerSoil Kit, following the manufacturer’s instructions. The bacterial, fungal, and archaeal populations will be quantified using SYBR-based qPCR analysis. The relative abundances of selected functional genes quantified by qPCR will be determined for N-fixation (dinitrogen reductase, nifH), nitrification (archaeal and bacterial ammonia monooxygenase, amoA), and denitrification (nitrous oxide reductase, nosZ).
Data analysis
Factor analysis (FA) will be used to link soil microbial responses to other soil health indicators (factors) and the processes they describe. Then, the processes identified by FA will be used to evaluate the relationship between factors and response variables (e.g., biomass). FA uses covariance and correlation matrices to maximize correlations between factors and measured attributes, generating models that can predict response variables using microbial indicators. To determine how management practices alter microbial indicators and consequently crop production, an analysis of variance (ANOVA) will be conducted on the scores generated by the FA (Martin et al.,2022). All analyses will be conducted in R.
As of April 2024
Due to limited seed availability of the best performing chickpea varieties, the experiment was conducted in both years with a commercial chickpea variety rather than 3 chickpea varieties. In 2023-24, chickpea had low germination and extra seedlings were grown in a greenhouse and transplanted to the field in early January. Winter crops and corn management in the 2023-24 season will be like 2022-23, although reduced chickpea germination, growth delays due to transplanting, and maturity delays due to pest pressure may result in chickpea being terminated as a cover crop (i.e., without harvesting the grains) if plants are not mature by May, to allow corn planting in the right window.
Given the restrictions on the assessment of different chickpea varieties, the trial was set up as a randomized complete block design with split-plot restrictions in randomization and four replications, for a total of 16 main plots and 32 split-plots. Chickpea varieties and cover crops were set as the main plot during winter. Main plots were split into two plots receiving N rates of 18 kg ha-1 (i.e., only the starter fertilizer) and 270 kg ha-1 (i.e., current UF/IFAS recommendation) during the corn season (split in three applications) to evaluate if N rates affect microbial dynamics observed under different winter crops.
The method for short-term C mineralization was also modified: This is now measured with a 10 g sample of air-dried soil placed in a mason jar, then water is added to reach approximately 50% of the soil's field capacity (2.0 mL for our soil), the jars are sealed, and the CO2 concentrations are measured using an Infrared Gas Analyzer (IRGA) immediately after the addition of water (time 0) and after 24 hours of incubation. A standard curve is used with 2,000 to 10,000 ppm CO2, and CO2 released in each jar.
During this first year of the project, the different winter cover crops were planted and grown successfully, although harvest has yet to occur. Soil samples were collected and preserved for future analyses. As such, there are currently no results to share, but this will change for the next reporting period.
As of April 2024
Preliminary results from the first year of the project (i.e., 2022-23 season) are detailed below; data collection and analysis for the 2023-24 season is still ongoing. Analyses of enzyme activities have been delayed due to method optimization but will start soon.
For the winter crop phase of 2022-23, rye had the highest biomass among winter crops, followed by clover, and chickpea. Rye had less weed pressure compared to clover and chickpea, most likely due to higher biomass, faster growth, and quicker canopy closure. Chickpea had higher C mineralization than fallow after winter crop termination, with clover and rye being intermediate. Winter crop treatments generally had higher expression of N cycling genes (bacterial and archaeal amoA, nifH) relative to the fallow, especially legumes.
At corn harvest of 2022-23, C mineralization did not differ among winter crop treatments nor N rates. The expression of all genes was higher at corn harvest compared to winter crop termination, with no difference observed among winter crop treatments for the higher N rate. However, at the low N rate, winter crop treatments showed a trend of higher gene expression of amoA and nifH compared to the fallow.
In contrast to soil responses, winter crop treatments did not increase corn yields, grain N concentrations, and whole-plant N accumulation compared to fallow, although winter crops showed a trend of increasing corn yields relative to fallow at the lower N rate. The limited response of corn suggests that other benefits of replacing fallow periods, such as nutrient retention, improvements in soil health and/or microbial activity, may play a more pivotal role to the system’s overall sustainability than directly impacting yields via greater N availability.
Educational & Outreach Activities
Participation Summary:
As the project is currently completing its first year in terms of winter cropping, there is little information to share via education and outreach. As samples are processed and data analysis takes place, we will share results through outreach and education activities.
As of April 2024
In March 2024, we collaborated with the LS21-353 project ("Evaluating the Dual-Purpose of Chickpea: A Cash and Cover Crop for Agricultural Production Systems in the Southeast") by hosting and facilitating a grower assessment in our experimental trial, which involved both growers and scientists. Participants evaluated chickpea and cover crops based on criteria such as biomass production, canopy closure, and weed pressure in the field. After the field assessment, the participants discussed their impressions and addressed concerns around rotational fit and market viability. They identified the treatments that sounded more promising and provided insights on what they would like to see in terms of research being conducted in the future.
Project Outcomes
As of April 2024
As the project is currently undergoing its second year, which will end in Sept. 2024, project outcomes are limited to preliminary results from the 2022-2023 season. Once the second year of the experiment is completed, information on project outcomes will be further detailed.
As of April 2024
As the project is currently undergoing its second year, which will end in Sept. 2024, information on knowledge gain is limited to one year of data, which is insufficient to draw strong conclusions. As we are dealing with a novel crop to the Southeastern US, we already gained insights on chickpea management. Assuming patterns observed during the 2022-23 season hold for 2023-2024, we observed that microbial activity is higher with winter crops than in fallow periods, and this effect may continue during the cash crop phase when N fertilizer inputs are low. Once the second year of the experiment is completed, information on knowledge gained will be further detailed.
As of April 2024
As the project is currently undergoing its second year, which will end in Sept. 2024, it is too early to share recommendations. Once the second year of the experiment is completed, recommendations will be provided, assuming our dataset is complete and robust enough to do so.