A dairy farm greenhouse gas (GHG) model was applied in this study to compare the Intergovernmental Panel on Climate Change (IPCC) method and the life cycle assessment (LCA) procedure, which are the principal methods for quantifying the carbon footprint of dairy production. The objectives of this paper were to compare the auditing methods in estimating the carbon footprint of grass and confinement dairy systems and to assess the methods in estimating the footprint of grass-based dairy farms varying in cow genetic potential, stocking rate and level of concentrate feeding. The input data used to operate the model was based on published research studies. The results of the study showed that the IPCC and LCA methods ranked the carbon footprint of dairy systems differently. For example, the IPCC method found that the carbon footprint of the confinement dairy system was 8% lower than the grass system, but the LCA results show that the confinement system increased the carbon footprint by 16%. The comparison of grass-based dairy systems, differing in cow genotype, stocking rate and concentrate fed per cow also showed that the methods did not agree on the ranking of dairy systems carbon footprint. The re-ranking of dairy systems carbon footprint occurred because the IPCC method excludes emissions associated with imported goods, for example, concentrate. Thus, it is incorrect to consider only components of the dairy system relevant for policy reporting such as that used by IPCC when estimating the carbon footprint of dairy produce. Instead, holistic approaches, such as LCA, which consider on and off-farm GHG emissions should be used. Therefore, reform of the present policy framework is required to enable quantification of the impact of mitigation strategies on global emissions. The evaluation of the carbon footprint from grass-based systems differing in cow genotype also demonstrated that selecting cows solely for milk production will increase the carbon footprint of grass-based dairy systems relative to cows selected on a combination of traits, because of reduced cow fertility and thus higher emissions from replacement heifers.

An increasing number of farm machines nowadays implement precision agriculture technologies. Most of these operate through proximal sensing using optical sensors (i.e. NIR or Vis-NIR). Imaging techniques in this context have received minor consideration due to the complex analysis of the data but on the other side offer great flexibility. This study reports a preliminary pilot imaging multi-sensor retrofit system to be applied independently on a wide range of agricultural machines and able to test different monitoring or control image-based applications for precision agriculture. The process, based on RGB image, was tested for in-field discrimination of weeds in lettuce and broccoli crops. It works by discriminating and extracting single plants from the soil and weeds. However, to be truly implementable, the experimental code should be optimized in order to shorten the time needed for acquisition and processing.

Potential yield is one of the criteria used as an input to nitrogen (N) fertilizer management decisions when using SIX EASY STEPS (6ES), the fertilizer recommendation tool used in the Australian sugar industry. Most commonly, 6ES is implemented using a district yield potential (DYP). In this study, we use analysis of sugar mill and yield monitor data from the Herbert River cane growing district to demonstrate that yield is markedly spatially variable, with this variability following the same patterns from year to year. There would therefore be value in a more location specific consideration of potential yield and application of 6ES. Similar analyses could be readily conducted in other sugar producing regions with potentially important implications for fertilizer use efficiency and the minimization of nutrient accessions to the Great Barrier Reef.

Farm equipment, including sensors and mobile machinery, create increasing amounts of data, and data can also be gained from third-party services. In order to be able to fully take advantage of this a farmer needs to be able to gather, store, process, and share the data as needed. In this work we describe a prototype for open environment that can gather, combine, store, select, and share data from arbitrary sources and with external partners, as well as use the data in decision making and provide it as input for various services. The environment is built using the Service Oriented Architecture paradigm and is therefore not tied to any specific operating system or software framework. We have tested the environment on the farm scale in Finland. The system was found suitable to improve the work in all tested tasks.

This study aimed to compare the quality of beef from suckler bulls raised on a high-energy concentrate ration and slaughtered at different carcass weights (CW)/ages. In total, 42 spring-born, Charolais and Limousin-sired, weaned suckler bulls were provided with a finishing diet of ad libitum concentrates and grass silage until they reached target CW of 340, 380 and 420 kg. Intramuscular fat (IMF) content tended (P<0.06) to be higher for 420 kg CW than for 380 and 340 kg CW. Sensory tenderness was lower (P<0.001) for 420 kg CW than for 380 and 340 kg CW. Juiciness was higher (P<0.05) for 420 kg CW than for 380 kg CW. Flavour liking was higher (P<0.05) for 420 and 380 kg CW (which did not differ) than for 340 kg CW. Overall, an increase in CW resulted in a slight increase in IMF content which could be responsible for the increase in juiciness and flavour liking of the beef. An increase in CW led to a decrease in the tenderness of the beef even though the overall liking of the beef was not affected.