Refine
Document Type
- Conference Proceeding (12)
- Article (4)
- Part of a Book (1)
Is part of the Bibliography
- yes (17)
Keywords
- Ackernutzungspotential (1)
- C-Sequestration (1)
- CH4 (1)
- CO2 (1)
- Convolutional neural networks (CNNs) (1)
- Klimawandel (1)
- Landkreis Osnabrück (1)
- Landwirtschaft (1)
- Moor (1)
- N2O (1)
Institute
- Fakultät AuL (17)
Comparison of variable liming strategies in organic farming systems using online pH-measurements
(2011)
In organic farming, soil pH is one of the most important soil characteristics affecting nutrient availability, soil microbial activity and plant growth. Using the soil pH mapping sensor system Veris MSP, detailed information on in-field variability of soil pH can be obtained enabling spatial variable lime application. Scenario calculations for an organically managed field in Germany reveal that compared with the standard farm practice (i.e. uniform liming rate) variable lime application does not lead to higher costs while soil pH is optimized in different field zones resulting in increased crop productivity. Using two different lime qualities increases liming costs moderately but gives farmers the chance to increase pH quickly in extreme low pH areas.
pH-Wert online messen
(2011)
Semi-natural grasslands (SNGs) are an essential part of European cultural landscapes. They are an important habitat for many animal and plant species and offer a variety of ecological functions. Diverse plant communities have evolved over time depending on environmental and management factors in grasslands. These different plant communities offer multiple ecosystem services and also have an effect on the forage value of fodder for domestic livestock. However, with increasing intensification in agriculture and the loss of SNGs, the biodiversity of grasslands continues to decline. In this paper, we present a method to spatially classify plant communities in grasslands in order to identify and map plant communities and weed species that occur in a semi-natural meadow. For this, high-resolution multispectral remote sensing data were captured by an unmanned aerial vehicle (UAV) in regular intervals and classified by a convolutional neural network (CNN). As the study area, a heterogeneous semi-natural hay meadow with first- and second-growth vegetation was chosen. Botanical relevés of fixed plots were used as ground truth and independent test data. Accuracies up to 88% on these independent test data were achieved, showing the great potential of the usage of CNNs for plant community mapping in high-resolution UAV data for ecological and agricultural applications.