Embedded energy in dairy stables

The calculation of the embedded energy (EE) of twenty barns shows that there is a considerable variation of EE per cow, where the lowest values were one fourth of the highest. Use of timber instead of concrete in walls had most effect to reduce the amount of EE. Cold barns can contribute to reduce the amount of EE, while the amount of EE is higher in free-stall than in tie-stall barns. While for an existing building the amount of EE is nearly fixed, calculating the anticipated amount for a new building can help to reduce energy use in agriculture and thus contribute to a more sustainable pro¬duction. Incorporating EE in planning new buildings should be of special importance for organic farming, since regulations demand more area per animal than in conventional farming. In addition to building new, renovation, extension as well as recycling of building materials should be considered. Planning new buildings should also include operational energy, as well as working conditions, animal welfare and economic considerations

Effect of Adding Moraine Soil or Shell Sand into Peat Soil on Soil Properties and Grass Yields in Western Norway

Cultivation and utilisation of peat soil leads to problems related to its high water content. The problems have become more pronounced with the increasing weight of agricultural machinery and more frequent harvesting. Increased particle density, reduced porosity and decreased potential plant-available water was found after incorporation of sand to peat in experiments conducted in the north of Norway (Sveistrup & Haraldsen, 1995). Peat soil has a weak soil skeleton, low bearing capacity, poor thermal properties and insufficient soil aeration. The objective of this study was to investigate the impact of added mineral material to peat soil to improve characteristics important for more optimal plant growth and management practices in the future

Use of near infrared reflectance spectroscopy to predict nitrogen uptake by winter wheat within fields with high variability in organic matter

In this study, the ability to predict N-uptake in winter wheat crops using NIR-spectroscopy on soil samples was evaluated. Soil samples were taken in unfertilized plots in one winter wheat field during three years (1997-1999) and in another winter wheat field nearby in one year (2000). Soil samples were analyzed for organic C content and their NIR-spectra. N-uptake was measured as total N-content in aboveground plant materials at harvest. Models calibrated to predict N-uptake were internally cross validated and validated across years and across fields. Cross-validated calibrations predicted N-uptake with an average error of 12.1 to 15.4 kg N ha-1. The standard deviation divided by this error (RPD) ranged between 1.9 and 2.5. In comparison, the corresponding calibrations based on organic C alone had an error from 11.7 to 28.2 kg N ha-1 and RPDs from 1.3 to 2.5. In three of four annual calibrations within a field, the NIR-based calibrations worked better than the organic C based calibrations. The prediction of N-uptake across years, but within a field, worked slightly better with an organic C based calibration than with a NIR based one, RPD = 1.9 and 1.7 respectively. Across fields, the corresponding difference was large in favour of the NIR-calibration, RPD = 2.5 for the NIR-calibration and 1.5 for the organic C calibration. It was concluded that NIR-spectroscopy integrates information about organic C with other relevant soil components and therefore has a good potential to predict complex functions of soils such as N-mineralization. A relatively good agreement of spectral relationships to parameters related to the N-mineralization of datasets across the world suggests that more general models can be calibrated

Near infrared reflectance spectroscopy for estimating soil characteristics valuable in the diagnosis of soil fertility

Soil fertility diagnostics rely not only upon measurement of available nutrients but also upon the soil’s ability to retain these nutrients. Near-infrared reflectance spectroscopy (NIRS) is a rapid and non-destructive analytical technique which allows to simultaneously estimate standard soil characteristics and does not require use of chemicals. Previous studies showed that NIRS could be used in local contexts to predict soil properties. The main goal of our research is to build a methodological framework for the use of NIRS at a more global scale. The specific goals of this study were (i) to identify the best spectra treatment and processing –LOCAL versus GLOBAL regression- methods, (ii) to compare NIRS performances to standard chemical protocols and (iii) to evaluate the ability of NIRS to predict soil total organic carbon (TOC), total Nitrogen (TN), clay content and cationic exchange capacity (CEC) for a wide range of soil conditions. We scanned 1,300 samples representative of main soil types of Wallonia under crop, grassland or forest. Various sample preparations were tested prior to NIRS measurements. The most appropriate options were selected according to ANOVA analysis and multiple means comparisons of the spectra principal components. Fifteen pre-treatments were applied to a calibration set and the prediction accuracy was evaluated for GLOBAL and LOCAL modified partial least square (MPLS) regression models. The LOCAL MPLS calibrations showed very encouraging results for all the studied characteristics. On average, for crop soil samples, the prediction coefficient of variation (CVp) was close to 15% for TOC content, 7% for TN content, and 10% for clay content and CEC. The comparisons of repeatability and reproducibility of both NIRS and standard methods showed that NIRS is as reliable as reference methods. Prediction accuracy and technique repeatability allow the use of NIRS within the framework of the soil fertility evaluation and its replacement of standard protocols. LOCAL MPLS can be applied within global datasets, such as the International global soil spectral library. However, the performance of LOCAL MPLS is linked to the number of similar spectra in the dataset and more standard measurements are needed to characterize the least widespread soils