<p>Cereal rye (<i>Secale cereale</i>, L., CR) is the most commonly utilized cover crop
species within the United States. Yet, the total land area planted to CR on an
annual basis remains relatively low despite its numerous proven environmental
benefits. The relatively low rates of CR adoption could be due to a dearth of
knowledge surrounding certain agronomic and economic components of CR adoption.
Currently, there exists knowledge gaps within the scientific literature
regarding CR performance, N cycling, and associated economic risk. <a>Thus, to address the above-mentioned knowledge gaps, three
individual studies were developed to: i) investigate the fate of scavenged CR
nitrogen (N) amongst soil N pools, ii) assess the suitability of
visible-spectrum vegetation indices (VIs) to predict CR biomass and nutrient
accumulation (BiNA), and iii) characterize the economic risk of CR adoption at
a regional scale over time.</a></p>
<p>In the first
study, <sup>15</sup>N, a stable isotope of N, was used in an aerobic incubation
to track the fate of CR root and shoot N among the soil microbial biomass,
inorganic, and organic N pools, as well as explore CR N bioavailability over a
simulated corn growing season. In this study, the C:N ratio of the shoot
residues was 16:1 and the roots was 31:1 and differences in residue quality affected the dynamics of CR N
release from each residue type. On average, 14% of whole plant CR N was
recovered in the soil inorganic N pool at the final sample date.
Correspondingly, at the final sampling date 53%, 33%, and less than 1% of whole plant CR N was
recovered as soil organic N, undecomposed residue, and as microbial biomass N,
respectively. Most CR N remained unavailable to plants during the first cash
crop growing season subsequent to termination. This knowledge could support the
advancement of N fertilizer management strategies for cropping systems
containing cereal rye.</p>
<p>In the second
study, a commercially available unmanned aerial vehicle (UAV) outfitted with a
standard RGB sensor was used to collect aerial imagery of growing CR from which
visible-spectrum VIs were computed. Computed VIs were then coupled with weather
and geographic data using linear multiple regression to produce prediction
models for CR biomass, carbon (C), N, phosphorus (P), potassium (K), and sulfur
(S). Five visible-spectrum VIs (Visible Atmospherically Resistant Index (VARI),
Green Leaf Index (GLI), Modified Green Red Vegetation Index (MGRVI), Red Green
Blue Vegetation Index (RGBVI), and Excess of Green (ExG)) were evaluated and
the results determined that MGRVI was the best predictor for CR biomass, C, K,
and S and that RGBVI was the best predictor for CR N and P. Furthermore, the
final prediction models for the VIs selected as the best predictors developed
in this study performed satisfactorily in the prediction of CR biomass, C, N,
P, K, and S producing adjusted R<sup>2</sup> values of 0.79, 0.79, 0.75, 0.81,
0.81, and 0.78, respectively. The results of this study have the potential to
aid producers in making informed decisions regarding CR and fertility
management. </p>
<p>In the final
study, agronomic data for corn and soybean cropping systems with and without CR
was collected from six states (Illinois, Indiana, Iowa, Minnesota, Missouri,
and Wisconsin) and used within a Monte-Carlo stochastic simulation to
characterize the economic risk of adopting CR at a regional scale over time.
The results of this study indicate that average net returns to CR are always
negative regardless of CR tenure primarily due to added costs and increased
variability in cash crop grain yields associated with CR adoption. Further, the
results demonstrate that the additional risk assumed by adopting CR is not adequately
compensated for with current CR adoption incentive programs and that the risk
premium necessary can be 1.7 to 15 times greater than existing incentive
payments. Knowledge gained from this study could be used to reimagine current
incentive programs to further promote adoption of CR.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/15081360 |
| Date | 30 July 2021 |
| Creators | Richard T Roth (11206164) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/A_CHARACTERIZATION_OF_CEREAL_RYE_COVER_CROP_PERFORMANCE_NITROGEN_CYCLING_AND_ASSOCIATED_ECONOMIC_RISK_WITHIN_REGENERATIVE_CROPPING_SYSTEMS/15081360 |
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