Agriculture accounts for 10 to 12% of the World’s total greenhouse gas (GHG) emissions. Manure management alone is responsible for 13% of GHG emissions from the agricultural sector. During the last decade, Québec’s egg production systems have shifted from deep-pit housing systems to manure belt housing systems. The objective of this study was to measure and compare carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) emissions from three different cage layer housing systems: a deep liquid manure pit and a manure belt with natural or forced air drying. Deep liquid manure pit housing systems consist of “A” frame layer cages located over a closed pit containing the hens’ droppings to which water is added to facilitate removal by pumping. Manure belt techniques imply that manure drops on a belt beneath each row of battery cages where it is either dried naturally or by forced air until it is removed. The experiment was replicated with 360 hens reared into twelve independent bench-scale rooms during eight weeks (19–27 weeks of age). The natural and forced air manure belt systems reduced CO₂ (28.2 and 28.7 kg yr⁻¹ hen⁻¹, respectively), CH₄ (25.3 and 27.7 g yr⁻¹ hen⁻¹, respectively) and N₂O (2.60 and 2.48 g yr⁻¹ hen⁻¹, respectively) emissions by about 21, 16 and 9% in comparison with the deep-pit technique (36.0 kg CO₂ yr⁻¹ hen⁻¹, 31.6 g CH₄ yr⁻¹ hen⁻¹ and 2.78 g N₂O yr⁻¹ hen⁻¹). The shift to manure belt systems needs to be encouraged since this housing system significantly decreases the production of GHG.

Daily measurements of the water table depth are sometimes needed to evaluate the influence of seasonal water stress on Sphagnum recolonization in restored ombrotrophic peatlands. However, continuous water table measurements are often scarce due to high costs and, as a result, water table depth is more commonly measured manually bimonthly with daily logs in few reference wells. A literature review identified six potential methods to estimate daily water table depth with bimonthly records and daily measurements from a reference well. A new estimation method based on the time series decomposition (TSD) is also presented. TSD and the six identified methods were compared with the water table records of an experimental peatland site with controlled water table regime located in Eastern Canada. The TSD method was the best performing method (R² = 0.95, RMSE = 2.48 cm and the lowest AIC), followed by the general linear method (R² = 0.92, RMSE = 3.10 cm) and support vector machines method (R² = 0.91, RMSE = 3.24 cm). To estimate daily values, the TSD method, like the six traditional methods, requires daily data from a reference well. However, the TSD method does not require training nor parameter estimation. For the TSD method, changing the measurement frequency to weekly measurements decreases the RMSE by 16% (2.08 cm); monthly measurements increase the RMSE by 13% (2.80 cm).