The goal of the project was to supply growers with knowledge on how incorporation of machine vision technology can affect the wild blueberry crop, disease pressures, and the overall savings of select agrochemical inputs. A machine vision system was developed and mounted on a rear sprayer boom in front of the sprayer nozzles capable of targeting the agrochemical application on an as-needed basis. Results showed that plants that received the proper fungicide application were less prone to premature leaf drop resulting in larger stem diameters and higher bud counts and harvestable fruit yield. Fungicide application savings using the smart sprayer for spot-application was 12% as compared to a uniform application.

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.

The objective of this study was to evaluate the potential of using Multiplex 3, a hand-held canopy fluorescence sensor, to determine rice nitrogen (N) status at different growth stages. In 2013, a paddy rice field experiment with five N fertilizer treatments and two varieties was conducted in Northeast China. Field samples and fluorescence data were collected simultaneously at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. Four N status indicators, leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU) and N nutrition index (NNI), were determined. The preliminary results indicated that different N application rates significantly affected most of the fluorescence variables, especially the simple fluorescence ratios (SFR_G, SFR_R), flavonoid (FLAV), and N balance indices (NBI_G, NBI_R). These variables were highly correlated with N status indicators. More studies are needed to further evaluate the accuracy of rice N status diagnosis using fluorescence sensing at different growth stages.

The difficulties of transporting heavy mobile robots limit robotic experiments in agriculture. Virtual reality however, offers an alternative to conduct experiments in agriculture. This paper presents an application of virtual reality in a robot navigational experiment using SolidWorks and simulated into MATLAB. Trajectories were initiated using Probabilistic Roadmap and compared based on travel time, distance and tracking error, and the efficiency was calculated. The simulation results showed that the proposed method was able to conduct the navigational experiment inside the virtual environment. U-turn trajectory was chosen as the best trajectory for crop inspection with 82.7% efficiency.

Intramuscular connective tissue plays an important role in meat tenderness. Our objective was to compare the collagen/hydroxyproline content and X-ray computed tomographic (CT) connective tissue proportion of longissimus thoracis (LT) muscle in Hungarian Simmental bulls and cows. Cows had lower carcass weight (247 kg v. 295 kg), EU conformation score (3.5 v. 5.5) and fatness score (4.2 v. 5.9) than bulls (P<0.01). Bulls had higher LT area, but intramuscular fat content was similar for bulls and cows. On the other hand, bulls had lower CT intramuscular connective tissue proportion in LT compared with cows (0.41±0.21% v. 0.66±0.28%, P<0.01). The same tendency could be observed for the collagen content (0.50±0.21% v. 0.67±0.11%, P<0.01). Correlation between the CT connective tissue proportion of LT and collagen content was r=0.8. There was a weak positive correlation between slaughter age and CT connective tissue as well as collagen content of LT (r=0.3 to 0.4). In conclusion, intramuscular connective tissue proportion in LT increased with slaughter age and older cows had higher collagen and connective tissue proportion than bulls. Mixed CT scans can be used for the analysis of intramuscular connective tissue content.