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- ItemEnvironmental Effects over the First 2½ Rotation Periods of a Fertilised Poplar Short Rotation Coppice(New York, NY : Springer, 2017-12-7) Kern, Jürgen; Germer, Sonja; Ammon, Christian; Balasus, Antje; Bischoff, Wolf-Anno; Schwarz, Andreas; Forstreuter, Manfred; Kaupenjohann, MartinA short rotation coppice (SRC) with poplar was established in a randomised fertilisation experiment on sandy loam soil in Potsdam (Northeast Germany). The main objective of this study was to assess if negative environmental effects as nitrogen leaching and greenhouse gas emissions are enhanced by mineral nitrogen (N) fertiliser applied to poplar at rates of 0, 50 and 75 kg N ha−1 year−1 and how these effects are influenced by tree age with increasing number of rotation periods and cycles of organic matter decomposition and tree growth after each harvesting event. Between 2008 and 2012, the leaching of nitrate (NO3 −) was monitored with self-integrating accumulators over 6-month periods and the emissions of the greenhouse gases (GHG) nitrous oxide (N2O) and carbon dioxide (CO2) were determined in closed gas chambers. During the first 4 years of the poplar SRC, most nitrogen was lost through NO3 − leaching from the main root zone; however, there was no significant relationship to the rate of N fertilisation. On average, 5.8 kg N ha−1 year−1 (13.0 kg CO2equ) was leached from the root zone. Nitrogen leaching rates decreased in the course of the 4-year study parallel to an increase of the fine root biomass and the degree of mycorrhization. In contrast to N leaching, the loss of nitrogen by N2O emissions from the soil was very low with an average of 0.61 kg N ha−1 year−1 (182 kg CO2equ) and were also not affected by N fertilisation over the whole study period. Real CO2 emissions from the poplar soil were two orders of magnitude higher ranging between 15,122 and 19,091 kg CO2 ha−1 year−1 and followed the rotation period with enhanced emission rates in the years of harvest. As key-factors for NO3 − leaching and N2O emissions, the time after planting and after harvest and the rotation period have been identified by a mixed effects model. © 2017, The Author(s).
- ItemMonitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery(Basel : MDPI, 2016) Schirrmann, Michael; Giebel, Antje; Gleiniger, Franziska; Pflanz, Michael; Lentschke, Jan; Dammer, Karl-HeinzMonitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned aerial vehicles (UAV) over an 11 ha field. Flight missions were conducted at approximately 50 m in altitude with a commercial copter and camera system—three missions were performed between booting and maturing of the wheat plants and one mission after tillage. Ultra-high resolution orthoimages of 1.2 cm·px−1 and surface models were generated for each mission from the standard red, green and blue (RGB) aerial images. The image variables were extracted from image tone and surface models, e.g., RGB ratios, crop coverage and plant height. During each mission, 20 plots within the wheat canopy with 1 × 1 m2 sample support were selected in the field, and the leaf area index, plant height, fresh and dry biomass and nitrogen concentrations were measured. From the generated UAV imagery, we were able to follow the changes in early senescence at the individual plant level in the wheat crops. Changes in the pattern of the wheat canopy varied drastically from one mission to the next, which supported the need for instantaneous observations, as delivered by UAV imagery. The correlations between the biophysical parameters and image variables were highly significant during each mission, and the regression models calculated with the principal components of the image variables yielded R2 values between 0.70 and 0.97. In contrast, the models of the nitrogen concentrations yielded low R2 values with the best model obtained at flowering (R2 = 0.65). The nitrogen nutrition index was calculated with an accuracy of 0.10 to 0.11 NNI for each mission. For all models, information about the surface models and image tone was important. We conclude that low-cost RGB UAV imagery will strongly aid farmers in observing biophysical characteristics, but it is limited for observing the nitrogen status within wheat crops.