Die zweite Chemnitzer Brennstoffzellenkonferenz wurde vom Innovationscluster HZwo und dem Fraunhofer-Institut für Werkzeugmaschinen und Umformtechnik IWU durchgeführt. Ausgewählte Fachbeiträge und Präsentationen werden in Form eines Tagungsbandes veröffentlicht. / The second fuel cell conference was initiated by the innovation cluster HZwo and the Fraunhofer Institute for Machine Tools and Forming Technology. Selected lectures and presentations are published in the conference proceedings.

<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, ¹⁵N, 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 R² 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>

<p> Soybean seed cost has dramatically increased in recent decades which has led to producer interest in lowering input cost through reductions in seeding rate. Fifty-eight seeding rate trials of soybean were conducted at field-scale in Indiana from 2010 to 2021 to update recommendations of seeding rates and plant population. The objectives were to determine the agronomic optimal seeding rate (AOSR) and plant population (AOPP) based on planting equipment, tillage practices, and planting date. Economic optimal seeding rate (EOSR) was also determined based on these field scenarios. Harvest AOPP was not influenced by planting equipment (~212,000 plants ha-1) or tillage (~239,000 plants ha-1), but AOSR varied. Soybean seeded with a row-crop planter optimized grain yield with 352,600 seeds ha-1; whereas, the grain drill required 75,200 more seeds ha-1. Soybean seeded into conventional tillage maximized grain yield at 380,400 seeds ha-1; whereas, under no-till conditions 41,400 more seeds ha-1 were required. Timely planting required 417,300 seeds ha-1 to optimize grain yield, which resulted in harvest AOPP of 216,700 plants ha-1. Conversely, late plantings required 102,800 fewer seeds ha-1 but 36,200 more plants ha-1 than timely planting. Depending on seed cost and soybean market price, seeding rates could be reduced 13,700 to 92,800 seeds ha-1 below AOSR to maximize profit.</p> <p>Secondly, digital imagery with high spatial resolution was collected with an unmanned aerial vehicle (UAV) to develop a simple and practical method to segment soybean from non-plant pixels. The best vegetation indices were selected to segment young soybean plants (VC to V6). Two field-scale trials of soybean were planted in 2020 with the agronomic trial design of two varieties x five seeding rates with three replications. The imagery was collected during this period as it coincides with the time for determining whether a soybean stand should be replanted. Five relative vegetative indices based on the red, green, and blue (RGB) imagery were evaluated: excess greenness index (ExG), excess redness index (ExR), green leaf index (GLI), normalized green-red difference index (NGRDI) and visible atmospheric resistance index (VARI). Both GLI and ExG were superior in overall accuracy compared to all other vegetative indices with very small soybean plants (VC to V1 growth stages). VARI and NGRDI had relatively poor overall accuracy at VC and V1, but had similar overall accuracy to GLI as soybean plants grew larger (V2 to V6 growth stages). Across all growth stages and locations, ExR performed the poorest. Moreover, GLI had consistent performance across the range of growth stages, suggesting its suitability for early soybean stand assessment methods.</p> <p>Six field-scale trials were established in 2020 and 2021 in Indiana with two varieties seeded from 123,000 to 618,000 seeds ha-1. Canopy cover was calculated using GLI to create binary segmentation of plant pixels and non-plant pixels. UAV-derived canopy cover measurements were correlated with plant population of soybean from VC to V4 and growing degree days (GDD) after planting. Yield potential (75, 80, 85, 90, 95, 100%) was correlated with canopy cover from VC to V4 and GDD after planting. Canopy cover of 2.1, 5.0, 8.9 and 13.8% by 150, 250, 350, and 450 GDD°C after planting, respectively, would maximize yield. Canopy cover for 75% yield potential was one-fourth as much as the 100% yield potential. Recommended threshold for replant decisions should be based on canopy cover to attain 95% yield potential. Field observations below a canopy cover of 1.8, 4.2, 7.4, and 11.5% canopy cover by 150, 250, 350, and 450 GDD°C after planting respectively, would consider replanting. </p>