Recipient Organization
PENNSYLVANIA STATE UNIVERSITY
408 Old Main
UNIVERSITY PARK,PA 16802-1505
Performing Department
(N/A)
Non Technical Summary
The goal of our proposed research is to design cover crop mixtures that are more effective at providing an array of ecosystem services. Currently, our ability to design better performing cover crop mixtures is limited by a lack of understanding of the relationship between relative abundance of functionally diverse species in mixtures and their provisioning of ecosystem services, as well as how this is affected by environmental conditions and management. To address these knowledge gaps, we will: Obj. 1) Evaluate how initial seeding rate of three cover crop species interacts with environmental conditions to determine the final mixture composition; Obj. 2) Determine the cover crop mixture seeding rate that optimizes individual ecosystem service functions, as well as overall multifunctionality; Obj. 3) Evaluate how variation in both weather and management affect cover crop mixture composition and the associated ecosystem services provided by cover crop monocultures and mixtures across 16 years of PSU's cover crop cocktails experiment. We will address obj. 1 and 2 through a novel proportional replacement series design field experiment (consisting of mixtures varying in proportions of three functionally distinct cover crops) replicated across two sites over three years combined with diversity interaction models. Additionally, we will utilize 16 years of data compiled from PSU's cover crop cocktails experiment to evaluate how environmental variation influences mixture composition and the resulting ecosystem services. Results from the proposed research will aid farmers in designing cover crop mixtures that are farm-tuned to their desired ecosystem functions, farming operations, and climate.
Animal Health Component
60%
Research Effort Categories
Basic
30%
Applied
60%
Developmental
10%
Goals / Objectives
Our ability to design better performing cover crop mixtures is limited by a lack of understanding of: 1) the relationship between relative abundance of functionally diverse species in mixtures and their provisioning of ecosystem services, and 2) how environmental variation influences cover crop composition and ecosystem functions. To address these knowledge gaps, we propose three research objectives aimed at maximizing mixture multifunctionality:Evaluate how environmental conditions and seeding rate interact to influence the composition of three functionally diverse cover crop species in mixtures, as well as the composition of cover crop functional traits.Determine the cover crop mixture seeding rate that optimizes individual ecosystem functions, as well as overall multifunctionality, across environmental variation.Evaluate how variation in both weather and management affect cover crop mixture composition and the associated ecosystem functions performed by cover crop monocultures and mixtures across 16 years of the Cover Crop Cocktails long-term experiment.
Project Methods
Objective 1: We will use a replacement series design to establish mixtures varying in proportions of three functionally distinct classes of overwintering cover crop species (canola, crimson cover, and triticale), which will be repeated for three years. The experiment will consist of 13 levels of cover crop mixtures or monocultures varying in three functionally distinct species, established as a randomized complete block design with four complete replicates.Shoot Biomass & Shoot Mass Fraction: In the fall (prior to the first hard frost) and in spring (prior to cover crop termination) we will measure cover crop shoot biomass sorted to species in 2 small quadrats (0.5 m x 0.5 m) per plot by clipping, drying and weighing the biomass. Cover crop nutrient content (total N and P, C:N ratio): At both the fall and spring cover crop sampling timepoint, we will grind the dried shoot and root tissue, and submit for nutrient analysis of plant tissue to PSU's Agricultural Analytical Services Laboratory (AASL) by dry combustion (C and N), and acid digestion followed by ICP .Percent Ground Cover: Percentage ground cover by cover crops will be estimated using the Canopeo app within 0.5 m2 quadrats monthly at three locations in each plot.Flower timing and density: Two randomly located 0.25-m2 quadrats will be flagged in each plot prior to the onset of flowering. We will monitor quadrats wfor open cover crop flowers once per week and the total number of open blooms and number of blooming plants will be recorded for each quadrat from the onset of flowering until the day prior to termination of the cover crops.Edaphic properties. Prior to cover crop planting in the fall we will collect soil cores (2.5 cm diameter x 20 cm depth) within each plot, and submit these samples for a standard soil fertility test (pH, extractable P and K, and micronutrients). We will also analyze these soils for extractable inorganic N, as well as soil organic matter.Meteorological Conditions. A weather station at both experimental sites will provide meteorological data, including: daily minimum, maximum, and average temperature, as well as daily precipitation.We will use multivariate analyses for proportional data to model the final species composition (percent of each species in the final cover crop above- and belowground biomass) as a function of composition of each cover crop species in the initial seeding rate mixture, inorganic soil N, temperature (daily max, min, and average), and precipitation. We will use diversity-interaction (DI) models to evaluate the effect of initial cover crop mixture seeding proportions on cover crop functional traits (e.g. total biomass, C:N, etc.). A DI model is a multiple regression model with continuous predictors consisting of the proportions of each species or functional group in a mixture, and their interactions.Objective 2:Labile soil organic matter: We will measure C and N in particulate organic matter (POM > 53um), isolated by shaking with a dispersant (sodium hexametaphosphate) followed by sieving to remove fine particles (< 53 um) before C and N analysis by dry combustion.Soil water content. To monitor the effect of cover crop mixtures on soil volumetric water content to the subsequent maize crop, we will use a Campbell Scientific (CS, Logan, Utah, USA) HydroSense II handheld probe to measure volumetric content weekly in each plot from planting to late-August.Subsurface compaction. A penetrometer (Field Scout, SC900) will be used to quantify soil penetration resistance at 10 cm increments to a depth of 50 cm at two times annually.N supply to the crop: Within each plot, we will collect maize plants from 2 rows (5.0 m length) by severing the entire plant shoot just above the brace roots, and ears will be separated from the rest of the plantand analyzed for total N concentration by the Penn State Agricultural Analytical Services Laboratory.Nutrient retention: Immediately before cover crop planting, three 5-cm diameter anion exchange resin membranes will be buried in each plot just below the plow layer. Resin membranes are positively charged and thus attract anions. By using resins of a fixed area, we can calculate the flux of anions through a soil layer.Weed suppression: Weed biomass will be assessed as a point-in-time measure of weed suppressiveness in November and May in the cover crop treatments, and in August in the subsequent maize crop.Insect injury: In 5 random locations in each plot, we will sample a 1.5 m row segment between 7 to 10 days following plant emergence, when crops are most vulnerable to economic injury from early season pests.Floral Resources for pollinators: Cover crop bloom will be used as a metric of floral resources to pollinators. Across cover crop treatments, we will calculate two metrics of floral resources: 1) duration of flowering (total # days flowers present in the plot), and 2) the area under the curve (AUC) of the number of flowers at each timepoint over time.Maize grain yield: Maize grain yields will be measured from each plot using a plot combine. Data will include harvest weight and moisture content.Profitability: Profitability differences between cover crop treatments will be quantified by using a partial budget analysis, a standard technique for comparing changes in profitability resulting from a change in the production process. To do this, we will quantify the yield within each treatment and multiply by current maize prices, and subtract the cover crop seed price for each mixture.The measured values of each ecosystem service will be standardized to have a mean value of zero and a standard deviation of one within the same year. Therefore, a positive value always indicates greater than average provisioning, and the ecosystem services can be interpreted relative to one another regardless of the original units. Positive values indicate that the cover crop performed better than the average, while negative values indicate that the cover crop performed worse than the average. Multifunctionality will be calculated as the average of the standardized values for services exhibiting a significant response to cover crop treatment (P < 0.05, mixed-model ANOVA, R package lmer4).Objective 3:We will collate the following data available from the CCC experiment:Weather/Environmental Conditions during cover crop and maize growth: 1) daily precipitation; 2) temperature (daily average, daily minimum, and daily maximum); and 3) soil inorganic N measured when cover crops were planted. We will calculate growing degree day (GDD) accumulation (base 4 for cover crops, base 10 for maize crop); as well as fall cold hardiness accumulation (number of days temperature is less than 5°C, but greater than 0°C).Cover Crop Management: 1) Planting and termination dates of cover crops; 2) planting date of cash crop; 3) dates of tillage relative to cover crop termination and cash crop planting.Cover crop mixture composition: Aboveground biomass in every individual species in the cover crop mixtures in both the Fall and Spring sampling timepoints. To determine the community composition of each mixture, we will use the aboveground biomass of each species to calculate their percent abundance in a mixture.Ecosystem Services: The ecosystem services in the main CCC experiment were measured as described above in the ecosystem services portion of Objective 2.We will use structural equation modelling (SEM) to evaluate how variation in environmental variables and management affects cover crop mixture composition, and how that in turn affects ecosystem functions and multifunctionality.