Guangnan Chen

An assessment of greenhouse gas emissions from the Australian vegetables industry

Recently, partly due to the increasing carbon consciousness in the electorates and partly due to the imminent introduction of the Australian Governments Carbon Pollution Reduction Scheme (CPRS), estimating carbon footprints is becoming increasingly necessary in agriculture. By taking data from several sources, this study estimates the national greenhouse gas (GHG) emissions from a variety of farm inputs, for the 23 key vegetables crops grown in Australia. For the 121,122 ha of land occupied by vegetable farms, there are 1.1 MtCO2e GHG emissions or 9.2 tCO2e ha− 1. In total, 65 % of total GHG emissions from the vegetable industry are due to electricity use for irrigation and post-harvest on-farm activities, 17 % from soil N2O emissions due to N fertiliser use, 10 % from agrochemicals, 7 % through fossils fuels and 1 % from on-farm machinery. The top four vegetables (by area), potatoes, lettuce, tomatoes and broccoli account for 29.1 %, 7.9 %, 5.9 % and 7.2 % of total GHG emissions from vegetables, respectively. However, the ratio of GHG emissions between the highest and lowest-emitting crops per hectare and per tonne, are different. Therefore, care must be exercised in carbon footprint labelling vegetable products to ensure that the labels reflect carbon emissions on a per tonnage basis.

Physical properties of cumin and caraway seeds

Abstract Physical properties of cumin and caraway seeds were measured and compared at constant moisture content of 7.5% w.b. The average thousand mass of grain, mean length, mean width, mean thickness, equivalent diameter, geometric mean diameter, surface area, volume, sphericity, aspect ratio, true density, bulk density and porosity were measured for cumin and caraway. There are significant differences (p<0.01) in most physical properties of cumin and caraway, except porosity and sphericity

Visible and shortwave near infrared spectroscopy for predicting sugar content of sugarcane based on a cross-sectional scanning method

The need for a reliable in-field quality measurement in the sugarcane industry is growing as the quality of sugarcane could vary significantly across the field. However, current monitoring systems in this industry only monitor crop yield and do not have the ability to measure the product quality. Thus, the potential of the visible/shortwave near infrared (vis/SW-NIR) spectroscopic technique as a low-cost alternative to predict sugar content from sugarcane stalks was investigated. Two hundred and ninety-two internode samples were extracted from three different sugarcane varieties to assess the ability of this technique. Each sample was cut into four sections and the spectra collected from the cross-sectional surface of each section were later correlated with its sugar content (°Brix). Partial least square (PLS) models were developed using calibration samples. The best model predicted samples in a prediction set had a coefficient of determination (r2) of 0.87 and root means square error of prediction (RMSEP) of 1.45°Brix. The value of the ratio of the standard deviation to the standard error of prediction (RPD) was 2. The variations of °Brix and prediction accuracy along the individual internode were 8.7 and 13%, respectively. These results indicated the vis/SW-NIR spectroscopy could be applied to predict °Brix values from sugarcane stalks based on a cross-sectional scanning method.

In-field measurement and sampling technologies for monitoring quality in the sugarcane industry: a review

Reliable in-field quality measurement and sampling techniques are needed in the sugarcane industry to accommodate spatial variability in crop quality during harvesting. Existing in-field monitoring systems only monitor the crop yield and do not have the ability to measure product quality. This is a serious limitation for the industry in dealing with a significant quality variation across a field. Conventional technologies for measuring sugarcane quality in a laboratory have severe limitations for field use because they require complex sample preparation procedures especially to have clarified juice samples for each measurement. This review focuses on the use of current and new emerging precision agricultural sensing technologies for measuring product quality and describes their potential application and limitation for field use in the sugarcane industry. Optical spectroscopy is among the most promising technologies for measuring sugarcane quality on a harvester. The key considerations for development of a measurement method and sampling mechanism in the field are also discussed.

An Assessment of Direct on-Farm Energy Use for High Value Grain Crops Grown under Different Farming Practices in Australia

I use energy cost share to characterize the role of energy in the economy. Specifically, I use an estimate of monetary expenditures for primary energy on an annualized basis for forty-four countries from 1978 to 2010 for natural gas, coal, petroleum, and electricity. I show that global energy cost share is significantly correlated to a one-year lag in the change in gross domestic product as well as measures of total factor productivity. Given the historical reduction in the relative cost of energy (including food and fodder for animate power) since the start of the Industrial Revolution, combined with a global energy cost share estimate, I conclude that the turn of the 21st Century represents the time period with the cheapest energy in the history of human civilization (to date). This potential historical nadir for energy expenditures around 2000 has important ramifications for strategies to solve future social, economic, and environmental problems such as reducing annual emissions of greenhouse gases (GHGs). Rapidly decreasing annual GHG emissions while internalizing their costs into the economy might feedback to increase energy expenditures to such a degree as to prevent economic growth during that transition.

Application of visible and shortwave near infrared spectrometer to predict sugarcane quality from different sample forms

Spectroscopic methods have been proposed to predict sugarcane quality in the field. There are different sample forms could be used to predict sugar content using spectroscopic methods; raw juice (RJ), clear juice (CJ), fibrated samples (FS), stalk cross sectional surface (SCS) and stalk skin (SS). Thus, this study was conducted to identify the optimum sample form for predicting quality using a low-cost and portable spectrometer. A total of 100 samples from each sample form were scanned using a visible-shortwave near infrared (Vis/SWNIR) spectrometer. The experiment was conducted under the same experimental setup and all data were treated using the same statistical methods. All spectral data were calibrated against brix value. The coefficient of determination (R2) for SCS, FS, CJ, SS and RJ were 0.88, 0.86, 0.84, 0.84, 0.81 and 0.80, respectively. The study found that a Vis/SWNIR spectrometer could be used to predict sugar content from all sample forms. The stalk samples scanned on cross sectional surface was found to be the optimum sample form for quality prediction using a Vis/SWNIR spectrometer.

Human energy expenditure in lowland rice cultivation in Malaysia

A study was undertaken to evaluate the human energy consumption of various field operations involved in lowland rice cultivation in Malaysia. Based on recorded average heart rates, fertilizing was found to be the most strenuous operation, with an average heart rate of 138 beats min(-1). There were no significant differences in the average heart rates of the subjects among the individual tasks within the first plowing, second plowing, and harvesting operations, with the average heart rates for these three tasks being 116, 106, and 106 beats min(-1), respectively. The corresponding energy expenditures were 3.90, 3.43, and 3.35 kcal min(-1). Loading the seed into the blower tank and broadcasting the seed were the most critical tasks for the seed broadcasting operation, with average heart rates of 124 and 136 beats min(-1), respectively. The highest energy expenditure of 418.38 kcal ha(-1) was observed for seed broadcasting, and the lowest energy expenditure of 127.96 kcal ha(-1) was for second plowing. The total seasonal human energy expenditure for rice cultivation was estimated to be 5810.71 kcal ha(-1), 55.7% of which was spent on pesticide spraying. Although the sample size in this study was relatively small, the results indicated that human energy expenditure per unit area (kcal ha(-1)) was positively linked to the average heart rate of the subjects and negatively linked to the field capacity. Thus, mechanization of certain tasks could decrease worker physical effort and fatigue and increase production.

Towards a faster and broader application of biochar: appropriate marketing mechanisms

Biochar derived from organic wastes and incorporated in soils offers multiple environmental and financial benefits. This approach tackles several critical global issues including waste management, renewable energy production, greenhouse gas mitigation, soil degradation prevention, food security, reduced water pollution from agrochemicals, and water quality and quantity enhancement. But farm level production and use of biochar is not yet viable, largely due to financial and technical constraints. Thus, an incentive mechanism for farmers is crucial for its successful adoption. This paper analyses both the current provisions and the marketing mechanisms of the Clean Development Mechanism (CDM), one of the three market‐based mechanisms of the Kyoto Protocol which links developed and developing countries in achieving global emission targets, and identifies appropriate marketing vehicle for biochar projects.

Evaluating On-Farm Energy Performance in Agriculture

Abstract On-farm energy efficiency is becoming increasingly important in the context of rising energy costs and concerns over greenhouse gas (GHG) emissions. Energy audits are a crucial part of the overall energy and environmental management process. In this paper, the results of six on-farm energy audits are presented. It is shown that energy uses for these farms varied significantly, ranging from 1.29 GJ/ha for dryland sorghum production to 43.06 GJ/ha for avocado production. In some cases, nearly 80% of operational energy was expended on water harvesting and irrigation. The research shows that there is currently a strong need to develop standardised energy assessments and reporting protocols so that effective and widespread energy audits in agriculture can take place. It is further suggested that an energy audit in agriculture in the future may best start from irrigation, as it also consumes a considerable proportion of on-farm energy cost.

The potential of computer vision, optical backscattering parameters and artificial neural network modelling in monitoring the shrinkage of sweet potato (Ipomoea batatas L.) during drying

BACKGROUND Drying is a method used to preserve agricultural crops. During the drying of products with high moisture content, structural changes in shape, volume, area, density and porosity occur. These changes could affect the final quality of dried product and also the effective design of drying equipment. Therefore, this study investigated a novel approach in monitoring and predicting the shrinkage of sweet potato during drying. Drying experiments were conducted at temperatures of 50-70 °C and samples thicknesses of 2-6 mm. The volume and surface area obtained from camera vision, and the perimeter and illuminated area from backscattered optical images were analysed and used to evaluate the shrinkage of sweet potato during drying. RESULTS The relationship between dimensionless moisture content and shrinkage of sweet potato in terms of volume, surface area, perimeter and illuminated area was found to be linearly correlated. The results also demonstrated that the shrinkage of sweet potato based on computer vision and backscattered optical parameters is affected by the product thickness, drying temperature and drying time. A multilayer perceptron (MLP) artificial neural network with input layer containing three cells, two hidden layers (18 neurons), and five cells for output layer, was used to develop a model that can monitor, control and predict the shrinkage parameters and moisture content of sweet potato slices under different drying conditions. The developed ANN model satisfactorily predicted the shrinkage and dimensionless moisture content of sweet potato with correlation coefficient greater than 0.95. CONCLUSION Combined computer vision, laser light backscattering imaging and artificial neural network can be used as a non-destructive, rapid and easily adaptable technique for in-line monitoring, predicting and controlling the shrinkage and moisture changes of food and agricultural crops during drying.