Thunen Institute for Climate Smart Agriculture

Braunschweig, Germany

Thunen Institute for Climate Smart Agriculture

Braunschweig, Germany
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Sjostrom M.,Lund University | Zhao M.,University of Maryland University College | Archibald S.,South African Council for Scientific and Industrial Research | Arneth A.,Karlsruhe Institute of Technology | And 15 more authors.
Remote Sensing of Environment | Year: 2013

MOD17A2 provides operational gross primary production (GPP) data globally at 1km spatial resolution and 8-day temporal resolution. MOD17A2 estimates GPP according to the light use efficiency (LUE) concept assuming a fixed maximum rate of carbon assimilation per unit photosynthetically active radiation absorbed by the vegetation (εmax). Minimum temperature and vapor pressure deficit derived from meteorological data down-regulate εmax and constrain carbon assimilation. This data is useful for regional to global studies of the terrestrial carbon budget, climate change and natural resources. In this study we evaluated the MOD17A2 product and its driver data by using in situ measurements of meteorology and eddy covariance GPP for 12 African sites. MOD17A2 agreed well with eddy covariance GPP for wet sites. Overall, seasonality was well captured but MOD17A2 GPP was underestimated for the dry sites located in the Sahel region. Replacing the meteorological driver data derived from coarse resolution reanalysis data with tower measurements reduced MOD17A2 GPP uncertainties, however, the underestimations at the dry sites persisted. Inferred εmax calculated from tower data was higher than the εmax prescribed in MOD17A2. This, in addition to uncertainties in fraction of absorbed photosynthetically active radiation (FAPAR) explains some of the underestimations. The results suggest that improved quality of driver data, but primarily a readjustment of the parameters in the biome parameter look-up table (BPLUT) may be needed to better estimate GPP for African ecosystems in MOD17A2. © 2012 Elsevier Inc.

Tiemeyer B.,Thunen Institute for Climate Smart Agriculture | Kahle P.,University of Rostock
Biogeosciences | Year: 2014

Nitrate-nitrogen (NO3-N) as well as dissolved organic carbon (DOC) and nitrogen (DON) concentrations and losses were studied for three and two years, respectively, in a small catchment dominated by a degraded peatland used as intensive grassland. Concentrations in the shallow groundwater were spatially and temporally very variable, with NO3-N being the most dynamic component (7.3 ± 12.5 mg Lg-1) and ranging from 0 to 79.4 mg Lg-1. Average NO3-N concentrations of 10.3 ± 5.4 mg Lg-1 (0 to 25.5 mg Lg-1) in the ditch draining the catchment and annual NO3-N losses of 19, 35 and 26 kg hag-1 confirmed drained peatlands as an important source of diffuse N pollution. The highest NO3-N losses occurred during the wettest year. Resulting from concentration of 2.4 ± 0.8 mg Lg-1 (0.7 to 6.2 mg Lg-1), DON added a further 4.5 to 6.4 kg hag-1 to the N losses and thus formed a relevant (15%) component of the total N losses. Ditch DOC concentrations of 24.9 ± 5.9 mg Lg-1 (13.1 to 47.7 mg Lg-1) resulted in DOC losses of 66 kg hag-1 in the wet year of 2006/2007 and 39 kg hag-1 in the dry year of 2007/2008. Ditch DOC concentration were lower than the groundwater DOC concentration of 50.6 ± 15.2 mg Lg-1 (14.9 to 88.5 mg Lg -1). Both DOC and N concentrations were governed by hydrological conditions, but NO3-N reacted much faster and clearer on rising discharge rates than DOC, which tended to be higher under drier conditions. In the third year of the study, the superposition of a very wet summer and land use changes from grassland to arable land in a part of the catchment suggests that, under re-wetting conditions with a high groundwater table in summer, NO 3-N would diminish quickly, while DOC would remain on a similar level. Further intensification of the land use, on the other hand, would increase N losses to receiving water bodies.© Author(s) 2014. CC Attribution 3.0 License.

Bauwe A.,University of Rostock | Tiemeyer B.,Thunen Institute for Climate Smart Agriculture | Kahle P.,University of Rostock | Lennartz B.,University of Rostock
Journal of Hydrology | Year: 2015

Nitrate is one of the most important sources of pollution for surface waters in tile-drained agricultural areas. In order to develop appropriate management strategies to reduce nitrate losses, it is crucial to first understand the underlying hydrological processes. In this study, we used Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to analyze 212 discharge events between 2004 and 2011 across three spatial scales (68 events at the collector drain, 72 at the ditch, and 72 at the brook) to identify the controlling factors for hydrograph response characteristics and their influence on nitrate concentration patterns. Our results showed that the 212 hydrological events can be classified into six different types: summer events (28%), snow-dominated events (10%), events controlled by rainfall duration (16%), rainfall totals (8%), dry antecedent conditions (10%), and events controlled by wet antecedent conditions (14%). The relatively large number of unclassified events (15%) demonstrated the difficulty in separating event types due to mutually influencing variables. NO3-N concentrations showed a remarkably consistent pattern during the discharge events regardless of event type, with minima at the beginning, increasing concentrations at the rising limb, and maxima around peak discharge. However, the level of NO3-N concentrations varied notably among the event types. The highest average NO3-N concentrations were found for events controlled by rainfall totals (NO3-N=17.1mg/l), events controlled by wet antecedent conditions (NO3-N=17.1mg/l), and snowmelt (NO3-N=15.2mg/l). Average maximum NO3-N concentrations were significantly lower during summer events (NO3-N=10.2mg/l) and events controlled by dry antecedent conditions (NO3-N=11.7mg/l). The results have furthermore shown that similar hydrological and biogeochemical processes determine the hydrograph and NO3-N response on storm events at various spatial scales. The management of tile-drained agricultural land to reduce NO3-N losses should focus explicitly on flow events and, more specifically, active management should preferably be conducted in the winter season for discharge events after snowmelt, after heavy rain storms and when the soil moisture conditions are wet. © 2015 Elsevier B.V.

Vos C.,Thunen Institute for Climate Smart Agriculture | Don A.,Thunen Institute for Climate Smart Agriculture | Prietz R.,Thunen Institute for Climate Smart Agriculture | Heidkamp A.,Thunen Institute for Climate Smart Agriculture | Freibauer A.,Thunen Institute for Climate Smart Agriculture
Geoderma | Year: 2016

Texture is one of the most important and most frequently measured parameters in soil science. It is common knowledge among field experienced soil scientists that soil texture can be well estimated in the field manually with so called "texture-by-feel". However, no systematic evaluation exists that assessed the precision and accuracy of field based texture estimates as compared to the common, but time consuming, standard laboratory methods. In the course of the German Agricultural Soil Inventory, the texture of 3896 soil samples from 728 soil pits was estimated manually in the field and measured in the laboratory using standard sedimentation techniques. The field based estimations of the sand, silt and clay content showed a relative deviation from the measurements of only 3.8, 11.5 and 15.5%, respectively. The absolute uncertainty of field texture was 23, 32 and 17gkg-1 for sand, silt and clay, respectively. A large fraction (57-72%) of deviations between field and laboratory derived texture estimates was due to the laboratory measurement uncertainty, and due to the fact that only texture classes were estimated in the field and not mass fractions. Our findings indicate that for most purposes it is sufficient to estimate the soil texture manually "by feel" instead of conducting expensive particle size analyses in the laboratory. © 2016 Elsevier B.V.

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