Final Report for GNE12-045
Project Information
Flooding is projected to increase in the New England region. This change in weather will effect agricultural productivity by reducing time a farmer can work the field due to soil saturation and causing potential loss of soil fertility. Cover crops may be used either as a pro-active or reparative method to alleviate degradation caused by flooding. In this study, three sites were located on flood-prone organic agriculture land and the following six cover crop treatments were assessed: rye to increase infiltration, lupine to increase available phosphorus, vetch to increase and store nitrogen, radish to increase infiltration, a mix of these four, and fallow treatment for comparison. Overall, the data indicates that the cover crop treatments examined in this study did have a significant impact on soil quality parameters, however the impact was not consistent across all sites at all measurement dates. The data also suggests that cover crop treatment did not have a statistically significant impact on quantity or quality of the indicator cash crop, corn. However, this study was conducted over one year and with poorly established cover crops. Planting the cover crops earlier to allow enough time for adequate growth may produce different results showing greater benefits of cover crops.
Introduction:
In recent years, the Northeast has experienced a high level of flooding incidents. The year of 2011 was a particularly hard growing season for Vermont farmers. Spring and autumn flooding delayed planting early in the growing season and destroyed crops and fields during a period of peak harvest. Flooding events are expected to become more intense and frequent as global climate change progresses. Rainfall for the Northeast has increased by 67% in the past forty years and the risk of flooding rises with it (Groisman et al., 2004). According to FEMA and the USDC, flooding is our nation’s most frequent and costliest natural disaster.
Some of the most productive agricultural land in the Northeast is in floodplains. Flooding of agricultural fields has several demonstrated effects on soil health such as loss of aggregate stability and the deposition of silt size particles which cause hard soil crusts and/or compaction. This in turn may affect seedbed preparation and root penetration and, thus, the establishment of crops. Fertility effects are encapsulated in the concept of “Flooded Soil Syndrome” or “Post-Flood Syndrome” which is caused by diminished phosphorus (P) availability as a result of the loss arbuscular mychorrizal fungi from soil (Unger 2009; Jasa, 2011). This occurs even though P is more available after flooding as iron oxides may release P during anoxia caused by saturated conditions. In addition, the potential of nitrogen (N) losses is also high. These problems and remedies for these problems have come into sharper focus after the large floods that struck Midwestern farming communities.
This project is likely to be important nationwide in areas where agricultural land is prone to flooding. Conducting these experiments in Vermont has the additional benefit of evaluating the efficacy of the cover crops under cold climate and short growing season conditions. For example, in Vermont, cold winters and snow melt produce a prolonged “mud season” during which flooding and nutrient losses are likely. Cover crops appropriate for this climate are likely to grow well in more favorable conditions.
Although some studies focus on assessing methods that address managing farm land with the added complication of flooding. Research in the Midwest suggests that cover crop blends may be a way to mitigate soil health degradations that occur in flood prone areas (Montana NRCS, 2012). Winter cover crop blends may act as nutrient scavengers that increase nutrient retention and some may fix N as well (Dabney et al, 2001; Blevins et al., 1990). Other cover crops encourage the reestablishment of arbuscular fungi after flooding episodes.
The purpose of this project was to show that a cover crop blend of winter rye, forage radish, hairy vetch, and lupine is suitable for rejuvenating soils damaged by flooding. Most cover crop components have the ability to maintain mycorrhizal communities with high levels of infectiousness (Thompson, 1987), which is often lost after flooding. Each component in the proposed blend has additional benefits for soil quality and crop production. Winter rye quickly establishes and can reduce erosion. Forage radish creates large holes that can alleviate compaction (White, 2011). Hairy vetch fixes atmospheric nitrogen into the soil. Lupine accesses P typically unavailable to plants (Braum and Helmke, 1995). Lupine’s ability to scavenge for tightly held P and its rapid decomposition rate after incorporation can help increase P availability to new crops after flooding. Individually, each cover crop can fulfill important functions, but in a cover crop blend, components may interact synergistically.
Three farmers from Arethusa Farm, Adam’s Berry Farm, and the Intervale Center identified three flood-prone areas currently cultivated. In the autumn of 2012, four replicates of six different cover crop treatments were planted at each of the three identified sites. In the late spring the cover crops were disced in at Arethusa Farm and following field preparation, corn was planted as an indicator crop. One replicate of the six treatments was planted in August at Adam’s Berry Farm as a demonstration for a field day in September. The Intervale Center site was abandoned after initial spring 2013 soil sampling due to a prolonged saturation event compromising further soil testing or crop cultivation.
Soil health was measured for compaction by penetrometer , soil fertility with KCl extraction for nitrogen and ammonium acetate for other relevant soil fertility elements, aggregate stability was measured with the Cornell Sprinkle Infiltrometer, OM by loss on ignition (LOI), and pH with a pH meter. Cover crop biomass samples were collected to assess crop cover and ease of establishment.
Sweet corn was planted as an indicator crop to assess the impact of cover crop treatment on corn quality. Yield of corn ears was assessed by weight and quality of corn silage was assessed by NIR. This component of the study was included to further demonstrate the potential impacts of cover crops. The practical use of cover crops may be negligible to a farmer if the cover crop improves soil quality without improving cash crop quantity or quality.
Works Cited
Blevins, R. L,. J. H. Herbek, and W. W. Frye. 1990. Legume Cover Crops as a Nitrogen Source for No-Till Corn and Grain Sorghum. Agronomy Journal 82:769-772.
Braum, S.M. and P.A. Helmke. 1995. White Lupin utilizes soil phosphorus that is unavailable to soybean. Pant and Soil 176: 95-100.
Dabney, S. M., Delgado, J. A. and Reeves, D. W.2001. Using winter cover crops to improve soil and water quality, Communications in Soil Science and Plant Analysis,32:7,1221 — 1250
Groisman, P.Ya., R.W. Knight, T.R. Karl, D.R. Easterling, B. Sun, and J.H. Lawrimore, 2004: Contemporary changes of the hydro-logical cycle over the contiguous United States, trends derived from in situ observations. Journal of
Hydrometeorology, 5, 64-85.
Jasa, P. 2011. Cover Crops for Soil Health.
http://flood.unl.edu/c/document_library/get_file?uuid=28d20c75-028c-426c-9afa-890cefe2f283&groupId=4571136&.pdf. Accessed on March 20, 2012.
NRCS, Montana. 2012. Cover Crops Rebuild Soil After Floods.
http://www.mt.nrcs.usda.gov/news/features/covercrops.html. Accessed March 22.
Thompson, J.P. 1987. Decline of VAM in long fallow disorder of field crops and its expression in phosphorus deficiency of sunflower. Australian J. of Agricultural Research 38:847-867.
Unger I.M., A. C. Kennedy, R.-M. Muzika. 2009. Flooding effects on soil microbial communities. Applied Soil Ecology 42: 1–818
USDC - Department of Commerce – National Oceanic and Atmospheric Administration. 2005. FREEZE/FROST DATA - CLIM20 supp no. 1.
White, C.M and R.R. Weil. 2011. Forage Radish Cover Crops Increase Soil Test Phosphorus Surrounding Radish Taproot Holes. Soil Sci. Soc. Am. J. 75: 121–130.
Sept/Oct 2012: Seeding cover crops. Count initial weed populations. Setting goals for the first brochure on flooding; Set up web page, Start blog.
Cover crops were seeded later than expected. This is a busy harvest time of year for farmers and my priority was not necessarily their priority. Furthermore, I did not have the knowledge of how long disced crops would need to decompose nor the skill to operate the machinery myself. Pictures of emergence can be seen at Arethusa Farm in Figures 1-6, Adam’s Berry Farm in Figures 7-12, and Intervale Center in Figures 13-18. Information on climate change and flooding effects on soil were collected. The webpage (www.floodedsoils.wordpress.org) was created and maintained on a weekly basis during the growing season. If a week was missed, two entries would be made the next or additional entries past initial end date. Compaction was measured by penetrometer at 6, 12, and 18 inch depths.
November 2012-March 2013: Cover crops visually assessed, lab analysis of soil samples, and results statistically analyzed. Brochure draft written.
2013 Ongoing: Soil and data analysis for active carbon, nitrogen, other nutrients, pH, bulk density, and aggregate stability. May 2013 Intervale Center nutrient analysis (other than N) was incomplete due to ICP equipment malfunction.
January-February 2013: Preparation for NOFA Winter Conference Intensive Workshop and present “Flooding, Soil Quality, and Cover Crops.”
April/May 2013: Cover crops terminated by tilling. Post flood soil fertility tests were collected and compaction measured. Cover crop biomass samples.
Pictures of regrowth and flooding can be seen at the Intervale Center in Figures 19-21. Cover crop regrowth can be seen at Adam’s Berry Farm in Figures 22-27. Cover crops were terminated later than anticipated. This was due to the time requirements necessary to collect all samples. Cover crops were disced in at Arethusa Farm and Adam’s Berry Farm. Cover crops were not tilled in at the Intervale Center due to prolonged and prohibitive length of soil saturation with water. Post flood fertility and soil physical tests were conducted at Adam’s Berry Farm and Arethusa Farm. Corn was planted at Arethusa Farm and cover crops for demonstration during the field day were planted at Adam’s Berry Farm. Preparation for NOFA Summer Conference did not occur. Instead, preparation was done for the field day and was a part of NOFA’s fall workshop series.
June 2013: June soil and leaf tests were delayed until August due to delayed discing of cover crops. Corn seeds were planted. The fact sheet was edited to reflect new finding in data analysis.
July/Aug 2013: Corn growth and deficiency symptoms in the corn crop were assessed.
Corn crop emergence at Arethusa Farm in July can be seen in Figure 39. Corn crop growth in August can be seen at Arethusa Farm in Figures 28 and 29. A picture of average weed emergence in corn crop growth at Arethusa Farm is in Figure 30. Soil samples were collected in August. Upon processing soils for lab analysis, it was discovered that a bag was mislabeled calling into question the accuracy of all soil samples. As a result, soil data from August 2013 at Arethusa Farm (corn crop) is not presented. Corn tissue was collected for analysis. Corn height was measured. Corn yield has been collected, dried, and weighed. Corn leaf samples have been dried and ground, and analyzed. Corn heights were measured. Adam’s Berry Farm was replanted with larger plots for cover crop demonstration (Figures 31-38). The field day was reschedule to be apart of the NOFA fall workshop series.
September 2013: Field Day
The field day had 15 participants and the brochure (Figure 46) was disseminated at this time.
October 2013: Biomass samples collected at Adam’s Berry Farm. Corn heights measured, ears harvested, and brix measurements were taken to test for sweetness of corn.
January 2014: Presented at Cornell’s Cooperative Extension’s Climate Change Session and University of Maine’s CCA Conference
February 2014: Poster presented "Flooding, Soil Quality, and Cover Crops: Two Case Studies" during NOFA’s winter conference and the No-Till and Cover Crop Symposium hosted by Middlebury Extension (Figure 47). Paper copies of the cover crop survey deployed in person at conferences and online via Middlebury Extension monthly electronic newsletter.
March 2014: Mycorrizae staining and scoring initiated and not completed due to difficulty of assessment of damaged roots from prolonged storage. Analysis of pH complete.
June 2014: Site visit to Adam’s Berry Farm (Figures 41-45). Samples not collected due to lack of statistical analysis of one repetition.
- Arethusa Farm Cover Crop Establishment October 2012
- Arethusa Farm Corn Crop August 2013
- Adam’s Berry Farm Cover Crop Demonstration Plots May 2014
- Adam’s Berry Farm Cover Crop Establishment October 2012
- Intervale Center Cover Crop Establishment October 2012
- Brochure for Field Day
- Intervale Center Cover Crop Regrowth Spring 2013
- Adam’s Berry Farm Cover Crop Regrowth Spring 2013
- Arethusa Farm Corn Crop July 2013
- Adam’s Berry Farm Cover Crop Demonstration Plots August 2013
- Two Case Studies Poster Winter 2013
Cooperators
Research
The work was carried out in “The Intervale,” an area near Burlington well-known for its organic farming community. Each of the three partnering organizations, Arethusa Farm, Adam’s Berry Farm, and the Intervale Center, are farms situated on a floodplain of the Winooski River. Each farm set aside 1/20 of an acre for this project as a 400 X 5 foot strip. Each strip contained 24 plots, four replicates of the four cover crops, a blend of all, and a control. The plot size for each of the cover crops was 5X15 feet. There were 72 plots (24 plots per strip, 1 strip per farm, 3 farms) of approximately 5 foot by 15 foot rectangles.
Work under Objective 1: Demonstrate the ability of cover crops to mitigate fertility and soil health problems associated with flooding.
All cover crops are nutrient scavengers that carry over nutrients from one year to the next. They are also mycorrhizal hosts. However, lupine and forge radish also have tap roots and thus the ability to remedy compaction. Both lupine and hairy vetch are nitrogen fixers. In addition, Lupine is also able to access tightly bound P by solubilizing iron oxides. Lupine store P in its biomass and release it again during decomposition.
In this experimental design, site location affected ability to complete methods. Arethusa Farm dried more quickly than the other sites and was able to be planted with the indicator crop following cover crop termination. The Intervale Center site was abandoned after May 2013 due to heavy water saturation from the two consecutive wettest months recorded in Vermont. Adam’s Berry Farm was not planted with corn due to the same heavy saturation and was instead replanted with cover crops in larger plots as a field day demonstration site.
Soil Sampling in Field Procedure and Laboratory Analysis
Soil samples were collected for soil fertility testing using a 1-inch diameter, 12-inch long Oakfield soil sampler to collect 5 soil samples from random locations in each plot. Soil samples were taken in the spring of 2013 (after flooding, after cover crop has been tilled, but before planting of indicator crop), and in the fall of 2013 (after indicator crop harvest but prior to planting cover crop). These samples were composited for each plot, placed in a cooler on ice until transport to lab. When necessary the samples were stored in the dark at 4oC in a walk-in refrigerator. In total, there was a projected 144 samples collected for nitrogen analysis by ion chromatograph and other nutrients by inductively couple argon plasma mass spectrometer (ICP) over the project year (72 plots*2 sampling dates). The May 2013 Intervale Center ICP results are not shown due to an equipment malfunction. The August 2013 Arethusa Farm ion chromatograph and ICP results are not shown due to a field sampling error that reduced accuracy of correctly labeled samples.
The fertility testing protocol followed recommended NE Soil Analysis procedures for nitrogen analysis by ion chromatograph and other nutrient analysis by ICP (Wolf and Beegle, 2011). Extraction methods followed procedures recommended by The Northeast Coordinating Committee for Soil Testing. Soil samples were dried at 55°C in a General Signal Blue M Electric oven until dry, ground with pestle and mortar, then sieved through 2 mm mesh. Soil was weighed on a Metler Toledo PL303 scale to 4.000 grams. Soil was placed on a rack of 12 Erlenmeyer flasks and wither Modified Morgan’s extract (ammonium acetate, pH 4.8 +/- 0.05) for nutrient analysis by ICP or KCL for nitrogen analysis by Lachet were added at a volume of 20 mL. The extracts were then shaken on an Eberbach reciprocal shaker for 15 minutes.
The extracts were filtered through 9m Ahlstrom Filter Paper into funnel tubes. Extracts were filtered a second time if the extract contained sediment. Filtered extracts were placed into ICP tubes to measure concentrations of macro and micro nutrients (available phosphorus, potassium, magnesium, aluminum, calcium, zinc, sulfur, manganese, boron, copper, iron, sodium) on the inductively couple argon plasma spectrometry (ICP) or in ion chromatograph tubes to measure nitrogen. To get average mg/kg of an element in the soil, the ICP or ion chromatograph result was multiplied by five (the dilution factor of the extract).
Soil samples for nitrogen were analyzed for all site in May 2013. Nutrients measured via ICP (P, K, Al, Ca, and Mg) were measured for Arethusa Farm in spring 2013 and Adam’s Berry Farm in spring 2013 and Fall 2013. Cation exchange capacity was calculated from the ICP analysis results using the following equation: (Ross and Ketterings, 20111). The denominator is the equivalent weight and is determined by dividing the atomic weight by number of valences.
Soil organic matter (SOM) was measured by loss on ignition (LOI), following the same standard procedure recommended by The Northeast Coordinating Committee for Soil Testing (Schutle and Hoskins, 2011). Ten grams of dry soil were placed in crucibles in a furnace for two hours at 110°C and weighed. Then heated at 375°C for two hours, cooled to 105°C, then weighed. Percent SOM was calculated with the following equation: .
A Mettler Toledo SevenEasy pH meter was used to measure pH. A 1:10 ratio of dry soil to water was used. A standard weight of 4.0 grams of soil was placed 40 mL of reverse osmosis (R.O.) water, stirred, and then measured after 10 minutes. Soil was dried, ground, and sieved through 2 mm mesh.
Active carbon was measured using the potassium permanganate method (Weil et al. 2003). Inorganic N was extracted with 2M KCl followed by a micro-sized colorimetric assay. The same method of soil N analysis was used for the August 2013 Arethusa presidedress nitrate test (data not shown due to field sampling error).
Compaction was measured in situ, using a cone penetrometer (Eijkelkamp, Giesbeek, The Netherlands). Five penetrometer measurements per plot were taken in all three sites in October 2012 after the harvest of the cash crop, at the Adam’s Berry Farm and Arethusa Farm in spring 2013, at Arethusa Farm in August 2013, and Adam’s Berry Farm in fall 2013.
To corroborate the soil strength data, soil density was measured from undisturbed 2-inches diameter, 4-inches long cores extracted with an AMS hammer corer at all three sites in spring 2013, at Arethusa Farm in August 2013, and Adam’s Berry Farm in October 2013 (Figures 48-52). To measure bulk density, the difference in top and bottom soil height from the top of the core was measured. Fresh and dry soil samples were weight. Dry weight is calculating by dividing the dry weight by the total volume of the soil.
Infiltration rates were measured with mini-infiltrometers in situ by measuring how much water leave the infiltrometer in 90 seconds (Decagon Devices, Pullman, Wa) and discontinued as no discernable difference between the treatments was found and the time-consuming nature of the test.
The Cornell Sprinkle Infiltrometer (Cornell University, Ithaca, NY) was used to measure aggregate stability for all sites in spring 2013, Arethusa Farm in August 2013, and Adam’s Berry Farm in October 2013 (Figures 53-56). Dry soil was ground and sieved using 2 mm mesh. The sample was then placed on 0.25mm sieves. Filter paper was placed inside a funnel and the sieve with soil was placed on top of the funnel. The funnel, filter, sieve, and soil were placed under the Cornell Sprinkle Infiltromter. Water was released from the Infiltrometer for 5 minutes. The funnel, etc were removed from under the Infiltrometer. The filter with ‘failed’ soil was dried and weight. The soil sample fraction that remained on the sieved was collected into trays and dried.
Work under Objective 2: Show impact of yield when using different cover crop varieties and blends.
Post-flood syndrome is often expressed as a P deficiency. However, after a flood P can be high and thus a different mechanism may cause the syndrome. Therefore, a uniform indicator crop was grown at Arethusa Farm to assess cover crop impact on crop quantity and quality. Sweet corn was chosen sweet corn for this purpose as it is a P demanding crop and deficiency symptoms are well documented for this crop and easily recognized. Sweet corn is also grown by the farmers in the Intervale as a part of their organic vegetable cash crop.
Plant heights were measured twice in the season, before tasseling in August 2013 and after tasseling in October 2013. In August, approximately 5 weeks after planting, five corn leaf samples from each plot were randomly collected from the third leaf down from the tassel. Samples were combined, dried, ground in a Wiley mill, and analyzed for corn silage quality using NIR in the UVM Grain’s Lab.
In October 2013, ears per plot were counted and weighed. Results are recorded in lbs/acre. Quality of the ear of corn was tested using Brix on three random location per ear from five randomly collect corn ears per plot.
Work under Objective 3: Assess biomass of cover crops
Cover crop samples were randomly collected from an area with represenative cover from each plot using a nine inch diameter circle. Cover crops were cut one inch above the ground in all three sites in spring 2013 and again at Adam’s Berry Farm in October 2013. Cover crop species and total weeds were separated, dried at 60 C, and weighed. Results are presented in lbs/acre. Total weeds were counted at all sites in the fall of 2012 and at the Intervale Center in May 2013.
Samples were taken for root biomass and mycorrhiza scoring in 6 inch by 6 inch soil samples. However, due to the prolonged storing, time consuming nature of the process, and poor root quality, accurate biomass or mycorrhiza scoring was impeded.
Data Analysis
All statistical calculations were performed using JMP Pro 11.0 (SAS Institute Inc.). Impact of cover crop treatment on soil quality, corn quality, or biomass quantity were assessed by comparing the means with each pair, Student’s t-test.
Works Cited
Ross, Donald S. and Quirine Ketterings. 2011. Recommended Soil Testing Procedures for the Northeastern United States.
Schulte, E. E. and Bruce Hoskins. 2011. Recommended Soil Testing Procedures for the Northeastern United States.
Weil, R. R., R. Islam Kandikar, M.A. Stine, J.B. Gruver, S. E.Samson-Liebig. 2003. Estimating active carbon for soil quality assessment: A simplified method for laboratory and field use. American Journal of Alternative Agriculture. 18:3 – 8.
Wolf, Ann and Douglas Beegle. 2011. Recommended Soil Testing Procedures for the Northeastern United States.
Treatment Effect on Soil Fertility
Spring 2013: Cover Crop
Overall, there was relatively little significant difference of soil fertility parameters among the cover crop treatments, with the exceptions of the statistically lowest Ca content in the mixed cover crop planting and the statistically lowest total N content in the lupine cover crop plantings. This lack of difference may be explained by poor cover crop establishment (Arethusa: Figures 1-6, Adam’s Berry Farm: Figures 7-12, Intervale Center: Figures 13-18). However, when inspecting the spring 2013 data by site, the results were more varied (Table 1, Table 2, and Table 3).
There was a statistically significant difference in total nitrogen content at Arethusa farm and the Intervale Center among the cover crop treatments. The vetch cover crop plots consistently had high N concentrations which may be due to N-fixation and subsequent release upon decomposition. Lupine cover crop plots had the highest phosphorus concentrations. This finding aligns with previous research suggesting that lupine has an ability to acidify the rhizosphere and liberate phosphorus that would be unavailable to other plants. Lupine cover crop plots also had the lowest aluminum concentrations and high calcium concentrations. Great quantities of calcium are able to displace aluminum from the soil particles. The mix cover crop plot had statistically higher aluminum concentrations which may be due to lower levels of calcium resulting in the ability for more aluminum to bond to soil particles. Furthermore, the mix cover crop plots produced high levels of biomass (Tables 13-16) which may have absorbed more calcium.
Statistically significant difference of Ca and CEC among cover crop treatments was only observed at Arethusa Farm. At Arethusa and Adam’s Berry farm, the fallow, lupine, and radish plots had the highest Ca and CEC content which may be due to poor cover crop establishment resulting in less plant uptake of Ca. Calcium is a cation itself and higher levels of available calcium would directly increase CEC. Only at Adam’s Berry Farm was there a statistically significant difference among cover crop treatment on organic matter, where the mix cover crop plot had the highest percent organic matter. The slightly higher organic matter in the mix cover crop treatment may be due to the greater biomass production of the mix cover crop decomposing (Table 14, Table 15, and Table 16).
There was no statistically difference among cover crop treatments of pH, K, or Mg in any of the sites in the spring of 2013. Acidity of soil is difficult to alter over one season of cover cropping. Potassium and magnesium are components of the CEC calculations. The statistical insignificance of these two cations among treatments suggests that calcium concentrations drive CEC measurements at these study sites.
Fall 2013: Cover Crop
Cover crop treatments had a statistically significant pH, Al, and percent organic matter measurements at Adam’s Berry Farm in the fall of 2013 (Table 4). Lupine, radish, rye, and vetch had the highest pH. However, effect of this difference is quite negligible as all pH measurements were above 6.0 and only differed by 0.2 or less. Despite the relatively high biomass production, aluminum and organic matter content was statistically lowest in the vetch cover crop plots (Table 14, Table 15, and Table 16).
Treatment Effect on Physical Soil Characteristics
Fall 2012: Cover Crop
In the fall of 2012, only compaction was measured for physical soil characteristics. There was no statistical difference among the cover crop treatments for compaction at any depth at any site after the initial cover crop planting in the fall of 2012 (Table 5, Table 6, Table 7). The lack of statistical difference of compaction among treatments may be due to sampling occurring relatively close to the plowing date. Plowing disturbs the soil and can result in homogenization of physical soil characteristics like compaction.
Spring 2013: Cover Crop
In May 2013, soil samples were collected for ‘top’ and ‘bottom’ bulk densities, 0-2 inch depth and 2-4 inch depth respectively (Figures 48-52). Compaction was measured with penetrometer readings at Arethusa Farm and Adam’s Berry Farm. Compaction was not measured at the Intervale Center due to saturated soil conditions causing skewed measurements (Figures 19-21). Aggregate stability was measured using standard laboratory procedures for the Cornell Sprinkle Infiltrometer (Figures 53-56). Spring 2013 results for Arethusa Farm are in Table 8, Intervale Center are in Table 9, and Adam’s Berry Farm are in Table 10.
There was a statistically significant difference of bulk density in the top two inches of samples taken at the Intervale Center and Adam’s Berry Farm. The fallow cover crop treatment had the statistically lowest top bulk density at the Intervale Center and the lupine cover crop treatment had the statistically lowest top bulk density at Adam’s Berry Farm. There was a statistically significant difference of bulk density at depths 2 – 4inches (bottom bulk density) only at Adam’s Berry Farm. The mix had the lowest bulk density of any of the treatments at Adam’s Berry Farm. There was no significant difference among the other treatments and highest bulk density measurement.
There was a significant difference in compaction only at 12 inches at Arethusa Farm which may correspond to the plow layer. The radish cover crop treatment had the lowest compaction measurement of any of the treatments at Arethusa Farm. Again, with no significant difference among treatments and the highest compaction measurement, there was little statistical change of compaction measurements at 12 inches. There was a significant difference in aggregate stability only at Arethusa Farm. Although the radish treatment had less compaction which may increase infiltration rates, it also had the lowest percent aggregate stability meaning that it may be more susceptible to erosion. The aggregate stability data indicates that there was no or negligible difference among the other cover crop treatments and erosion potential.
Summer 2013: Corn
Bulk density (for ‘top’ and ‘bottom’ bulk densities, 0-2 inch depth and 2-4 inch depth respectively), compaction with penetrometer, and aggregate stability by Cornell Sprinkle Infiltrometer were measured at Arethusa Farm, five weeks after planting corn, at nitrogen sidedress time, in August 2013 (Table 11). Many top bulk density samples were not able to be measured due to crumbling and there were not enough samples to complete statistical analysis which may be due to high levels of remaining cover crop residue reducing soil cohesiveness. There was a statistical difference among cover crop treatments in aggregate stability. The mix cover crop treatment had the lowest aggregate stability, but there was no statistical difference in other treatments and the treatment with the highest measurement. There was no statistical difference among the cover crop treatments for bottom bulk density or compaction at any depth in August 2013 at Arethusa Farm.
October 2013: Cover Crop
Bulk density (for ‘top’ and ‘bottom’ bulk densities, 0-2 inch depth and 2-4 inch depth respectively), compaction with penetrometer, and aggregate stability by Cornell Sprinkle Infiltrometer were measured at Adam’s Berry Farm in the larger cover crop plots in October 2013 (Table 12). There was a significant difference among cover crop treatments in all physical soil characteristics. Vetch had the highest ‘top’ and ‘bottom’ bulk density measurements.
Compaction at 6 inches in depth was highest in the mix and radish cover crop plots. This may be due to the radish root expanding horizontally and pushing the soil together, which would increase compaction around the root zone, but not below. Compaction at 12 and 18 inches in depth was highest in the rye cover crop plots. Vetch had the statistically lowest aggregate stability of all the plots at Adam’s Berry Farm in October 2013 but there was no statistical difference in other treatments and the treatment with the highest measurement.
Treatment Effect on Biomass
Spring 2013: Cover Crop
In May 2013, biomass samples were collected from an area measuring 9 inches in diameter, average results by treatment are found in Table 13, Table 14, and Table 15. Growth in the fallow plots may be due to straying seed from broadcasting, lack of distinction between grass species (rye cover crop vs. other grasses), or rye regrowth from previous planting, particularly at Adam’s Berry Farm. Radish growth did not occur at Arethusa Farm or Adam’s Berry Farm. Hard seed may have contributed to the radish grown at the Intervale Center. Overall, particular cover crop biomass was highest in plots were it was planted.
There was statistical difference among cover crop treatment in lbs/acre of vetch. Average vetch biomass was consistently highest in the vetch plots, particularly at Arethusa Farm. There was statistical difference among cover crop treatments of average rye biomass. Average rye biomass was highest in the mix cover crop treatment at Arethusa Farm and Adam’s Berry Farm. Average rye biomass was highest in the rye cover crop plots at the Intervale Center. There was a statistical difference among cover crop treatment of average lupine biomass at Arethusa Farm and the Intervale center. Average lupine biomass was consistently higher in the lupine cover crop treatment, particularly at the Intervale Center. There was a statistical difference at Arethusa Farm and Adam’s Berry Farm among treatments of non-cover crop biomass, labeled ‘other’ in the tables, and may otherwise be considered weeds. Average weed biomass was highest in the lupine cover crop plots at Arethusa Farm and radish cover crop plots at Adam’s Berry Farm.
There was a statistical difference among cover crop treatments at Arethusa Farm and Adam’s Berry Farm. Overall, rye, vetch, and mix cover crop plots had the highest average biomass yield perhaps due to their relatively quick growth and cold tolerance. The mix cover crop treatment had the highest average total biomass. There was a statistical difference among average percent of cover crop at all three sites. Average percent biomass was highest in the rye cover crop plots at Arethusa Farm and Adam’s Berry Farm. The lupine cover crop treatment had the average highest percent cover crop at the Intervale Center. In terms of weed suppression, the mix cover crop treatment had the fewest weeds, perhaps due to cover crop competition has evidenced by high biomass yield of the mix cover crop treatment.
October 2013: Cover Crop
In October 2013, biomass samples were collected at Adam’s Berry Farm from an area measuring nine inches in diameter, average results per treatment are found in Table 16. There was no statistical difference among of average biomass produced among the treatments. The lack of statistical difference of biomass among plots may be due to better cover crop establishment resulting in more even growth among the treatments.
Treatment Effect on Weed Populations
Fall 2012
In the fall 2012, weed populations were counted in a 9” diameter area at Arethusa Farm, Intervale Center, and Adam’s Berry Farm (Table 17). There was a statistical difference in weed populations among treatments at Arethusa Farm and Adam’s Berry Farm. Lupine cover crop plots had the highest average weed population at Arethusa Farm and vetch cover crop plot had the highest average weed populations at Adam’s Berry Farm. Both the lupine and vetch cover crop treatments had relatively little soil cover by cover crop allowing weed seeds more sunlight to germinate than other plots. The fallow plots may have had few weeds due to less soil disturbance by seeding machinery.
Spring 2013
Weed populations were counted at the Intervale Center in May 2013 (Table 17). There was no statistical difference of weed populations among treatments at the Intervale Center in May 2013. This may be due to a very wet spring which hindered cover crop growth, creating less competition for weeds to thrive. Although weed populations were not counted in the corn crop at Arethusa Farm, an image of average weed population in August can be seen in Figure 30.
Treatment Effect on Corn Quality
In 2013, corn measurements were taken on five plants in August and October, number of ears per plot were counted, ears weighed (reported in pounds per acre), and sugar content was measured via Brix (Table 18) (Figures 29 and 30). There was no significant difference of any of these corn quality parameters among the cover crop treatments.
In August 2013, five weeks after planting, leaf samples were collected and analyzed via NIR for corn silage quality (Table 19). Although lupine plots did have higher average concentrations of phosphorus, there was no significant uptake of phosphorus by corn. In fact, there was no significant difference of any of these corn quality parameters among the cover crop treatments.
Education & Outreach Activities and Participation Summary
Participation Summary:
Data has been presented at a field day in NOFA’s 2013 fall workshop series. Information about the impact of flooding on soil quality and study updates were published weekly throughout the fields season in a blog, www.floodedsoils.wordpress.org. A field day in September 2013 with 15 farmer and other professional participants was held showing demonstration cover crop plots and impact of cover crop treatment on sweet corn.
Presentations on cover crops in January 2014 was given at Cornell’s Cooperative Extension’s Climate Change Session of the 2014 Empire State Producers Expo in Syracuse, NY and at University of Maine’s CCA workshop in Plymouth, New Hampshire. In addition, “Flooding, Soil Quality, and Cover Crops: 2 Case Studies” poster (Figure 47), scrolling images on laptop, and brochure with data from this research was available during the NOFA winter conference in February. The poster was also included during the No-Till and Cover Crop Symposium hosted by Middlebury Extension in February 2014.
Project Outcomes
Farmer Adoption
After attending the field day, forage radish was planted for the first time at Adam’s Berry Farm.
Areas needing additional study
This study would have benefited from planting cover crops earlier to have better established plots. The immaturity of the plants decreased the impact the cover crops could have on soil and corn quality. This study does indicate that more research on planting date would yield valuable information. With that in mind, changing planting date at different locations with different soil types, in effect, planting in different micro-climates, would help inform agricultural planning with more beneficial farming practices. In addition, there have been relatively few field studies of the effect of 100 year flood events or repeated inundations. Understanding how soil will change with increasingly varying weather patterns will provide information to farmers on how to be adjust farm management to stay viable.