The future of organic grassland farming in mountainous regions of Central Europe

8 % of the total EU population lives in mountainous areas which represent a particularly important eco-region in Central Europe. Because of ecological, climatic and economic reasons an increasing pressure is imposed upon agriculture. Hence in large parts of European mountainous areas drastic changes in agricultural structure and land use systems can be observed in terms of emigration and land abandonment. Due to disadvantaged production conditions, and the high ecological sensitivity, organic farming is an important option for a sound regional development. In alpine regions organic farming does not proceed in a homogeneous way. One essential reason for that could be seen in the differences of national and regional “traditions and orientations” and supporting tools. For a positive development it is necessary to work on further integration of organic farming in regional development concepts (e.g. organic regions) and on the development of the “quality leadership” through cross-regional production, marketing and merchandising concepts. Undisputedly, further positive development of organic farming in mountainous regions depends on ongoing financial, research and advisory support

Ergebnisse zur saisonalen Low-Input Vollweidehaltung von Milchkühen im österreichischen Berggebiet

In a research project six dairy farms (5 organic, 1 low input) in mountainous regions of Austria were supervised in converting to a seasonal low-input dairy production system based on grazing. Within an observation period of three years (October 1st, 2004 – September 30th, 2007) a strict annual cycle in milk production and reproduction could be implemented on two farms only. In average a pasture proportion of 42 % (26–61 %) of the total feeding ration per year could be determined, depending on the farm specific conditions and the implementation level of this low input strategy. On four farms, which fed low amounts of supplemental feeds, a pasture proportion of 50 % of the total feeding ration was realized. With an input of only 470 kg DM concentrate (8 % of DM intake) per cow and year a milk performance of 5.837 kg with 4.1 % fat and 3.3 % protein was achieved. The results clearly indicate that the full grazing strategy with seasonal calving is feasible in Austria for animal health reasons. The project farms realized an average value of 0.29 Euro of payments free of direct charge per kg milk and 1.640 Euro per cow

Changing towards a seasonal low-input pastoral dairy production system in moutainous regions of Austria - results from pilot farms during reorganisation

To get informations on pastoral milk production in mountainous regions a research project with six pilot dairy farms (5 organic, 1 low input) was carried out. The farms were supervised during the reorganisation period leading to a seasonal milk production system. Within an observation period of three years a strict annual cycle in milk production and reproduction was implemented on two farms only. Depending on the farm specific conditions and the implementation level of the low-input strategy on average a pasture proportion of 42 % (26 to 61 %) of the total DM intake y-1 could be determined. On four farms, which fed low amounts of supplemental feed during the grazing period, a pasture proportion of 50 % of the total DM intake y-1 was realized in the last project year. With an input of only 470 kg DM concentrate (8 % of DM intake) cow-1 y-1 a milk performance of 5,542 kg with 4.02 % fat and 3.34 % protein was achieved. The results clearly indicate that the full grazing strategy with seasonal calving is feasible for animal health reasons in Austria. Despite the lower milk yield the data based on a federal extension program reveal lower marginal costs and higher production efficiency per unit milk for the four pilot farms in comparison to the average results of the organic and conventional farms

Methods to describe the botanical composition of vegetation in grassland research

In terms of botanical composition, grassland vegetation in experimental plots and field studies can be described by means of different parameters (plant density, cover, frequency or yield proportion). Each parameter describes different features, which under certain circumstances may be correlated one to each other to some extent, but are not fully equivalent. The choice of the parameter to be assessed depends therefore, in first instance, on the specific aim of the investigation. For the assessment of the chosen parameter, many methods are available that differ from each other in terms of subjectivity, precision, effort and requirement for technical equipment. The choice of method depends mainly on the required precision, the affordable effort and on the available resources

Hyperspectral-Based Classification of Managed Permanent Grassland with Multilayer Perceptrons: Influence of Spectral Band Count and Spectral Regions on Model Performance

Detailed knowledge of botanical composition is a key factor for the sustainable and site-specific management of permanent grassland and facilitates an improvement in the performance and efficiency of livestock feeding. Spectral-based data acquisition combined with machine learning has the potential to classify species groups and plant parts in permanent grassland with high accuracy. However, a disadvantage of this method is the fact that hyperspectral sensors with a wide spectral range and fine spectral and high spatial resolution are costly and create large amounts of data. Therefore, the question arises as to whether these parameters are necessary for accurate grassland classification. Thus, the use of sensors with lower spectral and spatial resolution and correspondingly lower data processing requirements could be a conceivable approach. Therefore, we investigated the classification performance with reduced predictor sets formed by different approaches in permanent grassland. For pixel-based classification, a cross-validated mean accuracy of 86.1% was reached using a multilayer perceptron (MLP) including all 191 available predictors, i.e., spectral bands. Using only 48 high-performing predictors, an accuracy of 80% could still be achieved. In particular, the spectral regions of 954 nm to 956 nm, 684 nm to 744 nm and 442 nm to 444 nm contributed most to the classification performance. These results provide a promising basis for future data acquisition and the analysis of grassland vegetation