The current proposal aims to benefit farmers by improving the rotational value of field pea, a legume cover crop. Therefore, this proposal has three major objectives:
1. Determine the extent of variation of beneficial crop rotational traits (e.g. nitrogen fixation, nutrient mobilization, organic matter deposition) within multiple field pea accessions.
2. Characterize the effect of field pea rotational trait variation on a subsequently grown crop under Northeastern U.S. conditions.
3 Characterize the variability and functional activity of the shifted microbiome communities in the soil/rhizosphere between field pea accessions.
The purpose of this project is to improve the rotational value of field pea (also known as Canadian pea, Pisum sativum var. arvense (L.)) a legume cover crop. In the United States, the acreage of land being cover cropped has rapidly increased over the past five years (CTIC, SARE, & ASTA, 2016). This is due to legume cover crops ability to increase soil fertility and subsequent crop yields at lower costs and environmental risks than fertilizers (Crews and Peoples, 2004; Reckling et al. 2016). Generally, legumes can fix 50 to 150 pounds per acre (lbs/acre) of Nitrogen (N) due to their symbiotic relationship with nitrogen-fixing bacteria (Clark, 2015). In addition to reincorporating N into the soil, legume cover crops can prevent soil erosion, attract and support beneficial insects, mobilize minerals and limiting nutrients, increase soil organic matter, and most importantly, increase the diversity and functional activity of the soil microbiome, which is directly linked with crop health (Berendsen et al. 2012; Clark, 2015; Sare, 2017). Cover crops manipulate microbial soil communities by releasing exudates into the soil to recruit beneficial microorganisms that suppress and protect against pathogens, promote plant growth, and alter plant metabolism (Chavarria et al. 2016; Jacoby et al., 2017; Brennan & Acosta-Martinez, 2017). Therefore, to maintain crop health and increase production, it is crucial for farmers to incorporate legumes into their rotations.
Despite the numerous benefits of cover crops, no single species currently meets all of the needs (fixing nitrogen, weed and pathogen suppression, promotion of microbial communities, etc.) of farmers. Some legume cover crops have poor establishment rates, and some face disease or other challenges. Additionally, non-legume cover crops, like cereals and forage grasses, provide erosion control, suppress weeds, and add organic matter to the soil, more efficiently than legumes (Clark, 2015). Consequently, farmers use mixtures or “cocktails” of multiple legume and non-legume cover crops to fulfill their needs (Clark, 2015). However, this method requires more time and management leading to increased costs (Clark, 2015). Therefore, there is a clear need to breed for the improvement of rotational value (how well the crop benefits a subsequently grown crop) of legume cover crops to meet farmer’s diverse set of goals at lowered costs to increase farm profitability.
Ultimately, the improvement of the rotational value of legume cover crops will further enhance soil quality, soil microbial diversity, and functional activity, which will increase the productivity of fields, ensuring farm profitability and food security. Additionally, increased soil fertility and pathogen suppression will reduce the need for fertilizers and other agricultural inputs, lessening the amount of chemicals in agricultural runoff and dampening the detrimental impacts of runoff on the environment. In all, the rotational improvement of legume cover crops will benefit farmers, improve agriculture sustainability, and help conserve environmental health.
Field Site: The experiment will take place at the University of Vermont’s’ Horticulture Research and Education Center located at 65 Green Mountain Drive, South Burlington, VT 05403 (Appendix, Figure 1).
Field pea material: 21 field pea accessions will be used in this experiment (Appendix, Figure 2A). All accessions were requested from the USDA NPGS and then amplified in Burlington, Vermont. Nine of the accessions: PI 577142, W6 3674, W6 3675, W6 26154, W6 26157, W6 26159, W6 26160, W6 26161, are wild material from Nepal (3) and Georgia (6) respectively. The other accessions are cultivated lines originating in the United States. The use of wild material that has not undergone the genetic bottlenecks of domestication and breeding will provide more genetic variation to this experiment.
Objective 1: Determine the extent of variation of beneficial crop rotational traits (e.g. nitrogen fixation, nutrient mobilization, organic matter deposition) within multiple field pea accessions.
Experiment 1: Testing for variation of rotational traits: nitrogen fixation, nutrient and mineral mobilization, and organic matter deposition.
To test for variation of rotational traits, 21 field pea accessions and two controls (no cover crop and no cover crop with fertilizer) will be grown or administered in a randomized block design in one continuous field (Appendix, Figure 2). There will be four replicates of each accession and control, for a total of 92 2m2 plots. Before the field pea is planted, “pre-planting” soil core samples approximately 30 cm (or 12 inches) deep will be taken from the direct center of each plot using a 5-inch diameter hand-held auger. Once soil samples have been collected, 50 field peas will be hand sown in each plot and will be grown for 40 to 60 days depending on sowing date. 50 plants in each plot will simulate the recommended field pea density of 180 lbs/ac (NDSU, 2002, Stepanovic, 2017). Two days before field pea harvesting, “post-planting” soil samples will be taken using the same methods as listed previously. Soil samples will only be collected from the center of plots to avoid edge effects of neighboring plots. All soil samples (182 soil samples) will be sent to the University of Vermont Agricultural and Environmental Testing Laboratory where they will test for pH, organic matter, available nitrate, phosphorus, potassium, aluminum, boron, calcium, copper, iron, magnesium, manganese, sulfur, and zinc.
A two-way repeated measures analysis of variance (ANOVA) test with a Tukey’s honestly significant difference (HSD) post hoc test will be used to analyze the measurements reported by the testing laboratory. The two factors used in the analysis will be accession and block. The analysis will determine if there is variation in rotational traits in field pea and if this variation is due to genetic variation or placement in the field.
Objective 2. Characterize the effect of field pea rotational trait variation on a subsequently grown crop under Northeastern U.S. conditions.
Experiment 2: How is the subsequently grown corn affected by the genetic variation of the previously planted field pea?
To test how the variation of rotational traits in field pea affects a subsequently grown crop, a nutrient-intensive (“heavy feeder”) subsequent crop of corn will be sown. Due to the late planting in June, the early sweet corn variety “Sugar Buns” will be used. “Sugar Buns” is an early maturing corn that is harvestable 70 days after sowing. The corn will be hand planted according to manufacturer’s specification, of 2 seeds a foot (30.48 cm) in rows 36 inches (91.44 cm) apart. Fertilizer will be added to the previously specified “no cover crop with fertilizer” control plots according to the recommendations from the UVM soil testing laboratory. Thirty-five to forty days after sowing, chlorophyll content will be recorded using a SPAD 502 Plus Chlorophyll Meter. The youngest fully developed leaf of the three plants closest to the center of each plot will be sampled. Once the corn is ready to be harvested, plant height, dry aboveground biomass, and yield will be measured for the same three plants. Plants closest to the direct center of the plot will only be sampled to avoid edge effects from neighboring plots.
A two-way repeated measures ANOVA test with a Tukey’s HSD post hoc test will be used to analyze chlorophyll content, plant height, dry aboveground biomass, and yield, with accession and block as the two factors. The analysis will determine if there is variation in corn performance and if this variation is due to genetic variation of the previously planted field pea or the location of the corn in the field.
Objective 3: Characterize the variability and functional activity of the shifted microbiome communities in the soil/rhizosphere between field pea accessions.
Experiment 3: Extracting microbial DNA from soil samples, sequencing 16s rRNA, and analyzing the sequences for diversity and functional activity.
To test for microbial community shifts in the soil, microbial DNA will be extracted from pre-planting soil samples and rhizosphere samples. A small portion, ~10 g, of the pre-planting soil samples will be separated and stored at -80C. The rhizosphere is the soil still attached to the roots after the plant has been uprooted. Rhizosphere microbial samples will be collected two days before the field pea is harvested. The center-most field pea plant in each plot will be uprooted and the soil still clinging to the roots will be removed and collected in the field and stored at -80C. The center most field pea plant will only be sampled to avoid edge effect of neighboring plots.184 DNA samples (92 pre-planting, 92 rhizosphere samples) will be extracted from all soil samples using the QIAGEN DNeasy PowerSoil Kit.
DNA extracts will be sent to LC Science, a biotechnology sequencing company. Samples will be processed for DNA library preparation in order to sequence 16s rRNA. Specifically, Phusion polymerase will be used to amplify the 16s rRNA region of the DNA samples for 25-25 PCR cycles. After a single cycle of PCR, sequencing adapters and barcodes will be added to each sample before further amplification. After amplification, an Illumina cBot system will be used to generate clusters for sequencing using a next generation MiSeq sequencer. Sequencing data will be “cleaned” by trimming the barcodes and adapters, merging paired ends reads into single sequence tags, excluding tags with more than 5% ambiguous bases, and lastly excluding tags with more than 20% of low-quality bases (a Phred Score of < 10). The clean data, which is usually 300-400 bp in length, will then be analyzed for operational taxonomic units (OTU) (analogous to species identification for microbial groups) using CD-HIT, a nucleotide clustering and comparison program. To determine an OTU, the sequence similarity has to be greater than 97% within clusters. In addition to OTUs, LC Science will provide, species accumulation curves, alpha diversity, Shannon index (H), Simpson index, Chao1 index, rank-abundance curves, beta diversity, and a principal coordinate analysis (PCA). These analyses will provide a measure of microbial diversity and species abundances for each submitted soil sample. In addition to diversity analysis, LC Science will determine the taxa of the OTUs, by mapping each OTUs’ representative tag in the Ribosomal Database Project (RDP, version 11.3), Greengeens Database, and the NCBI 16S Microbial Database. The entire process from the samples being sent to bioinformatic analysis will take approximately 6 to 8 weeks.
In addition to the above analysis, LC Science will also provide a FASTA file containing the 16S rRNA sequences of each soil sample. These sequences will be analyzed for predicted functional activity using PICRUSt, a bioinformatic software package that predicts metagenome functional content from 16s rRNA (Langille et al., 2013). Shannon’s diversity index (H) which accounts for abundance and evenness will be calculated for functional activity for both soil samples (pre-planting and rhizosphere samples).
A two-way repeated measures ANOVA test with a Tukey’s HSD post hoc test will be used to analyze the effect of field pea genetic variation on the calculated H of microorganism diversity and predicted functional activity diversity. Once again accession and block will serve as the two factors. The analysis will determine if there is variation in the microbiome shifts and predicted functional activity and whether these shifts are the result of the genetic variation of the field pea or due to location in the field.