Recipient Organization
NORTH CAROLINA STATE UNIV
(N/A)
RALEIGH,NC 27695
Performing Department
Crop & Soil Sciences
Non Technical Summary
Problem Statement:Cover cropping systems impact pest management, water balance, and the nitrogen management on farms in North Carolina across both organic and conventional farms. This project works to define those relationships and uses a combination of on-farm trials and experiment station trials to parameterize models to predict those relationships. We also use those models to drive decision support tools for growers that are becoming available as web apps. The project also includes substantial work on cover crop breeding to increase the adaptedness of major cover crop species to our region including winter pea, crimson clover, hairy vetch, and rye.Cover Crop BreedingLegume cover crops are essential to long-term sustainability of organic cropping systems because they fix nitrogen, improve soil health, suppress weeds, and provide resources for beneficial organisms such as pollinators. Unlike cash crops, cover crops have not been bred to optimize the traits that organic farmers need. This deficiency means that modest investments in germplasm improvement could yield large benefits. Based on our experience working with organic farmers, legume cover crop germplasm can be improved to address a multitude of critical problems they face. Our team of organic and cover crop experts from multiple universities, non-profit organizations, and governmental agencies will work in partnership with leading seed companies and farmers, to breed new varieties of hairy vetch (Vicia villosa), winter pea (Pisum sativum), and crimson clover (Trifolium incarnatum). This will be accomplished through traditional breeding, by developing genetic markers linked to agronomic traits of interest (winter hardiness, disease resistance, hard seed, and flower timing), and by defining species- and cultivar-specific regions of optimal performance across the US. These regionally adapted varieties will be tailored to organic cropping systems and create a foundation from which future public legume cover crop breeding programs can develop improved varieties. This project is unique because our network of research and farm sites ensures that our work is applicable across multiple regions, scales, and organic cropping systems.Rationale and Significance to Society:Despite the potentially transformative benefits of CC-based field crop production for increasing water and food security, adoption of CCs remains low in the US (<3% of arable acreage; USDA ERS, 2014). Barriers to CC adoption are complex and include management, economic (cost, yields, and net returns), intrinsic (climate, hydrology, soil), social (knowledge of benefits and risks; aversion to change), and regulatory factors (crop insurance policy disincentives, inadequate policy incentives) (Stockwell et al., 2013). Furthermore, despite a plethora of CC research, there is very little analysis and evaluation on how CC management affects performance (CC quantity and quality) on a regional scale. This severely limits understanding of fundamental interactions between intrinsic (climate and soil) and management factors that underlie benefits and risks of integrating CCs with CT, a requirement for providing growers economical and viable agronomic solutions that address water and food security. Such understanding needs to be both current and prospective to support rapid CC adoption and allow cropping systems to adapt and be resilient to future climate change.Cover crops are an essential component of organic cropping systems as they provide a multitude of crop production (e.g. fertility, improved soil health, and pest suppression) and environmental (e.g. increased soil carbon sequestration and reduced surface water pollution) benefits. Unlike synthetic inputs and management practices used by conventional farmers, cover crops are more variable and subject to decreased performance due to poor establishment, soil fertility status, poor drainage, extreme weather, and pests. However, consistency in cover crop performance, particularly that of legumes, is critical for organic farmers. The three largest concerns with underperforming legume cover crops in organic systems are 1) insufficient return of N for the cover crop input costs (seed, fuel, and labor), 2) poor yield of the following cash crop, and 3) the need to supplement with animal byproducts to meet cash crop N needs, which can lead to P loading in soils and increased production costs (transport and application time). Variations in legume performance are largely due to germplasm that is poorly adapted to the needs of organic farms. Relatively little research has been done on developing legume cover crop cultivars and few studies have evaluated their variation in performance across a regional gradient. Organic farmers are routinely advised to use cultivar selection as their first defense against pests and to select genetics adapted to their region. However, cultivar options for cover crops are miniscule compared to cash crops. Organic farmers need cover crops with traits tailored to their systems that will perform reliably across a range of environments.
Animal Health Component
100%
Research Effort Categories
Basic
0%
Applied
100%
Developmental
0%
Goals / Objectives
Below are the major goals for this project:1) Quantify how on-farm management influences CC biomass quantity/quality and subsequent effects on water dynamics across the Mid-Atlantic and Southeast US.2) Model soil water and CC decomposition to determine water availability for following cash crop and volume of water moving out of the profile.3) Quantify and simulate how soil and CC management under current and future climates influence corn and soybean yield (potential, stability and economics) in the mid-Atlantic and Southeast.4) Develop an outreach program, informed by our early adopter network of farmers that identifies and overcomes misconceptions about the cost and value of CCs.5) Provide science-based data supporting integration of CCs into federal crop insurance programs and increased farmer adoption of advanced risk management tools (CT and CCs).6) Use a national legume cover crop breeding network to:Improve and develop new hairy vetch, winter pea, and crimson clover cultivars;Screen advanced lines across the US to define cultivar-specific optimal performance regions. 7) Develop and publically release improved winter pea, hairy vetch, and crimson clover varieties.
Project Methods
Methods:Quantify how on-farm management influences CC biomass quantity/quality and subsequent effects on water dynamics across the Mid-Atlantic and Southeast US. Using our network of CC-based no-till farmer-collaborators (~70) throughout the mid-Atlantic and Southeast (PA, MD, DE, NC, SC, GA, and AL), we will establish on-farm evaluations of CC vs. no CC using a strip-plot design. No-CC strips will be established by either skipping an area in the field when planting or killing CCs with herbicides 2 wk after planting. Strips will vary in size (~18-36 m wide x length of field). There will be two sub-plots within each strip-plot to account for spatial variability. Sub-plots will be used to quantify CC quantity/quality, and subsequent effects on soil, water, and CC decomposition kinetics. Farmers will make all production decisions allowing us to fully capture the interaction between management, soil, and climate. Farmers will participate for four yearsgiving us >250 farm field site-years of data.Water dynamics. A custom-designed wireless sensor network based on low-cost open source hardware and software (Fig. 1) will be used to measure soil water and temperature. Our Gateway-Node wireless sensor network is based on an Arduino clone (MoteinoMEGA, LowPowerLab, Canton, MI) with an 868-915 MHz LoRa (Long Range) radio transmitter. The Moteino-MEGA has an ATMega1284P microcontroller with 128KB of internal flash memory, 16KB of RAM, 4KB EEPROM, and several digital and analog pins. Power is from a 12-V 7.5 amp hr battery charged by a 3-W 12-V solar panel. Nodes collect data from soil water sensors (SWS) and a soil temperature sensor every 30 minutes. Data is stored on a microSD card and is transmitted to the gateway. The gateway collects data from a SHT31-D temperature and humidity sensor and has a cellular phone module to facilitate remote data retrieval. Data can also be retrieved via a microSD card reader or by direct connection to a PC. Three Acclima 310S and one 315L TDR SWSs (Acclima Inc., Meridian, ID) provide volumetric SWC, temperature, permittivity, Bulk EC, and pore water data. Acclima's technology is more suitable for measurement of SWC across a range of soil types than other sensors and remains accurate even in a range of saline or salt-affected soils (Schwartz et al., 2015). The 310S sensor head can be glued to a PVC pipe facilitating soil retrieval. The 310S sensors are installed (vertically) between crop rows with waveguide mid-points located at 15, 45, and 75 cm providing data for 0-30, 30-60 and 60-90 cm soil depths. Temperature below one residue decomposition bag is measured by a DS18B20 transducer while SWC is measured by a 315L installed horizontally at 5 cm.CC biomass and quality. Just prior to termination, aboveground CC biomass will be measured by clipping CC in two 1 m2 quadrates in each sub-plot. Sub-plot CC fresh weight will be used for filling litter decomposition bags (see below). A CC subsample will be collected and used to correct field fresh weight to dry weight. Periodic samples (3) will also be taken to the lab for leaf area measurements. Samples dried at 60 °C will be ground and analyzed for C and N (Leco TruMac CN, St. Joseph, MI) and residue quality (cellulose, hemi-cellulose, and lignin) using near-infrared reflectance spectroscopy. Near-infrared reflectance spectroscopy is a fast, inexpensive, and accurate method for determining litter quality, which is of greater relevance for predicting residue decomposition kinetics (Parsons et al., 2011; Vavrova et al., 2008).Soil nitrogen dynamics. Soils will be collected in the fall at CC establishment and in the spring prior to CC termination and fertilizer applications at three depths (0-30, 30-60, and 60-100 cm) in the soil profile. Soil extracts (1 M KCl extraction) will be analyzed colorimetrically for nitrate (NO3) and ammonium (NH4). This data will serve as a baseline of N cycling in farmer fields and inform cover crop performance. Soil inorganic N values are also required for the crop yield modeling work. Finally, this information will also provide insights into how CC management and intrinsic factors affect N scavenging and losses. Bulk density measurements will be required to convert N concentrations to total content on a mass basis.CC decomposition kinetics. Mesh bags (1-mm diameter) will be used to determine aboveground CC biomass decomposition kinetics. Fresh shoot biomass collected above will be weighed into six litter bags (60 x 26 cm) in the field. One bag will be retained (time 0) for chemical analysis while the remaining five will be secured on the soil surface between corn rows. Bags will be retrieved at 2, 4, 7, 11, and 15 wk after termination to determine mass loss. Retrieved litter bags will be dried at 60°C for 7-10 d, weighed, and analyzed for C and N content. Weight loss on ignition for a subsample will be used to correct weights to ash-free basis. Biomass, C and N content, and residue quality will be used to model decomposition at each site.CC biomass sensors. The Noble Research Institute has developed a CC "sensor box", which employs a combination of sensors (i.e. ultrasonic, laser, and spectral) to rapidly assess CC biomass quality and quantity. This technology has been proven for select species (Pittman et al., 2015, 2016). Our team will calibrate the technology for CCs in this project as an alternative to destructive sampling; this work will facilitate remote sensing approaches to quantifying CC quality/quantity. Data fusion modeling approaches will be used to allow rapid, predictive estimation of CC biomass and nutritive values. Sensors mounted on a ground-based mobile platform will traverse trial areas and collect data with custom acquisition software. Field position data will be collected using a WAAS GPS. Crop height is measured using two single beam 660 nm time-of-flight laser distance sensors and an ultrasonic proximity sensor. In addition, NDVI will be estimated from an active spectral field radiometer (Crop Circle ACS 211, Holland Sci., Lincoln, NE) collecting spectral red edge and red reflectance data at a rate of 5 Hz.