Environmental performances of Sardinian dairy sheep production systems at different input levels.

Although sheep milk production is a significant sector for the European Mediterranean countries, it shows serious competitiveness gaps. Minimizing the ecological impacts of dairy sheep farming systems could represent a key factor for farmers to bridging the gaps in competitiveness of such systems and also obtaining public incentives. However, scarce is the knowledge about the environmental performance of Mediterranean dairy sheep farms. The main objectives of this paper were (i) to compare the environmental impacts of sheep milk production from three dairy farms in Sardinia (Italy), characterized by different input levels, and (ii) to identify the hotspots for improving the environmental performances of each farm, by using a Life Cycle Assessment (LCA) approach. The LCA was conducted using two different assessment methods: Carbon Footprint-IPCC and ReCiPe end-point. The analysis, conducted "from cradle to gate", was based on the functional unit 1 kg of Fat and Protein Corrected Milk (FPCM). The observed trends of the environmental performances of the studied farming systems were similar for both evaluation methods. The GHG emissions revealed a little range of variation (from 2.0 to 2.3 kg CO2-eq per kg of FPCM) with differences between farming systems being not significant. The ReCiPe end-point analysis showed a larger range of values and environmental performances of the low-input farm were significantly different compared to the medium- and high-input farms. In general, enteric methane emissions, field operations, electricity and production of agricultural machineries were the most relevant processes in determining the overall environmental performances of farms. Future research will be dedicated to (i) explore and better define the environmental implications of the land use impact category in the Mediterranean sheep farming systems, and (ii) contribute to revising and improving the existing LCA dataset for Mediterranean farming systems.

An improved model for determining degree-day values from daily temperature data.

Although using hourly weather data offers the greatest accuracy for estimating growing degree-day values, daily maximum and minimum temperature data are often used to estimate these values by approximating the diurnal temperature trends. This paper presents a new empirical model for estimating the hourly mean temperature. The model describes the diurnal variation using a sine function from the minimum temperature at sunrise until the maximum temperature is reached, another sine function from the maximum temperature until sunset, and a square-root function from then until sunrise the next morning. The model was developed and calibrated using several years of hourly data obtained from five automated weather stations located in California and representing a wide range of climate conditions. The model was tested against an additional data-set at each location. The temperature model gave good results, the rootmean-square error being less than 2.0 degrees C for most years and locations. The comparison with published models from the literature showed that the model was superior to the other methods. Hourly temperatures from the model were used to calculate degree-day values. A comparison between degree-day estimates determined from the model and those obtained other selected methods is presented. The results showed that the model had the best accuracy in general regardless of the season.

Temperature data for phenological models.

In an arid environment, the effect of evaporation on energy balance can affect air temperature recordings and greatly impact on degree-day calculations. This is an important consideration when choosing a site or climate data for phenological models. To our knowledge, there is no literature showing the effect of the underlying surface and its fetch around a weather station on degree-day accumulations. In this paper, we present data to show that this is a serious consideration, and it can lead to dubious models. Microscale measurements of temperature and energy balance are presented to explain why the differences occur. For example, the effect of fetch of irrigated grass and wetting of bare soil around a weather station on diurnal temperature are reported. A 43-day experiment showed that temperature measured on the upwind edge of an irrigated grass area averaged 4% higher than temperatures recorded 200 m inside the grass field. When the single-triangle method was used with a 10 degrees C threshold and starting on May 19, the station on the upwind edge recorded 900 degree-days on June 28, whereas the interior station recorded 900 degree-days on July 1. Clearly, a difference in fetch can lead to big errors for large degree-day accumulations. Immediately after wetting, the temperature over a wet soil surface was similar to that measured over grass. However, the temperature over the soil increased more than that over the grass as the soil surface dried. Therefore, the observed difference between temperatures measured over bare soil and those over grass increases with longer periods between wettings. In most arid locations, measuring temperature over irrigated grass gives a lower mean annual temperature, resulting in lower annual cumulative degree-day values. This was verified by comparing measurements over grass with those over bare soil at several weather stations in a range of climates. To eliminate the effect of rainfall frequency, using temperature data collected only over irrigated grass, is recommended for long-term assessment of climate change effects on degree-day accumulation. In high evaporative conditions, a fetch of at least 100 m of grass is recommended. Our results clearly indicate that weather stations sited over bare soil have consistently higher degree-day accumulations. Therefore, especially in arid environments, phenology models based on temperature collected over bare soil are not transferable to those based on temperature recorded over irrigated grass. At a minimum, all degree-day-based phenology models reported in the literature should clearly describe the weather station site.