Optimization of solar grain drying in a continuous flow dryer

The optimum method of using a solar air collector combined with a grain drying system was investigated. Two types of solar collectors, integrated into a roof are considered; one uncovered (flow under absorber) and one covered (flow over absorber). A computer model was developed to minimize the cost of drying and in doing so optimize the geometry of the collector and the specific rate of air flow through the collector. The ratio of the cost of drying in a solar-supplemented system to the cost of drying in a conventional system was minimized. This ratio represents the maximum possible savings for the given drying conditions. Sensitivity analyses were carried out for drying air temperatures in the range 40 to 90°C and for grain initial moisture contents of 16 to 35% d.b.(dry basis). The effect of daily drying times, the length of the drying season and the specific cost of collector and fuel was also investigated. Two types of farm management systems were considered for various drying conditions: use of the dryer to operate for a fixed number of hours per day and, alternatively, drying a given quantity of grain per day. The results show that there is an optimum number of hours of operation per day, optimum drying temperature and optimum initial moisture content for any combination of the other variables. The most critical factors affecting annual savings are the length of the drying season and the specific cost of the collector. The optimum geometry of the collector and the specific flow rate are also functions of these factors

Assessment of the Value of Long Range Weather Forecasts in Wheat Harvest Management

Quality losses from weather damaged wheat cost the Australian wheat industry on average around $A30M annually. These losses are higher in the north-eastern region than in other regions of the Australian wheat belt because of spring and summer dominant rainfall coinciding with harvest. If a wet season can be predicted, growers could reduce grain losses by strategies such as early harvesting, contract harvesting, additional grain drying and harvesting at a faster rate. In north-eastern Australia, long-range rainfall forecasting is possible by the analysis of sea surface temperatures and air-pressure differences between Tahiti (17·S 150·w) and Darwin (12·S 131·E) as identified by the Southern Oscillation Index (SOI). Using monthly SOI and rainfall data since 1890, this paper examines the relationship between the SOI and rainfall during the harvest period in north-eastern Australia. A simulation model of wheat harvesting and drying developed earlier was used to investigate the value of seasonal forecasts as a decision aid in harvest management. The results show that in north-eastern Australia, the status of the SOI as early as May gives a reasonable indication of above- or below-average rainfall in the following spring and summer (October-December). This information could enable growers to minimize grain quality damage by altering their management practices according to seasonal variations in rainfall. This could involve harvesting of high-moisture grain and drying it or, the use of contract harvesting when the seasonal outlook for rainfall is high. The value of such information to the producer is predicted to be around$A12ha per year through improvements in grain quality and reduced losses

Rainfall and streamflow response to El Niño Southern Oscillation: a case study in a semiarid catchment, Australia.

This paper aims to compare the shift in frequency distribution and skill of seasonal climate forecasting of both streamflow and rainfall in eastern Australia based on the Southern Oscillation Index (SOI) Phase system. Recent advances in seasonal forecasting of climate variables have highlighted opportunities for improving decision making in natural resources management. Forecasting of rainfall probabilities for different regions in Australia is available, but the use of similar forecasts for water resource supply has not been developed. The use of streamflow forecasts may provide better information for decision-making in irrigation supply and flow management for improved ecological outcomes. To examine the relative efficacy of seasonal forecasting of streamflow and rainfall, the shift in probability distributions and the forecast skill were evaluated using the Wilcoxon rank-sum test and the linear error in probability space (LEPS) skill score, respectively, at three river gauging stations in the Border Rivers Catchment of the Murray-Darling Basin in eastern Australia. A comparison of rainfall and streamflow distributions confirms higher statistical significance in the shift of streamflow distribution than that in rainfall distribution. Moreover, streamflow distribution showed greater skill of forecasting with 0-3 month lead time, compared to rainfall distribution

Risk management strategies using seasonal climate forecasting in irrigated cotton production: a tale of stochastic dominance.

Decision-making in agriculture is carried out in an uncertain environment with farmers often seeking information to reduce risk. As a result of the extreme variability of rainfall and stream-flows in north-eastern Australia, water supplies for irrigated agriculture are a limiting factor and a source of risk. The present study examined the use of seasonal climate forecasting (SCF) when calculating planting areas for irrigated cotton in the northern Murray Darling Basin. Results show that minimising risk by adjusting plant areas in response to SCF can lead to significant gains in gross margin returns. However, how farmers respond to SCF is dependent on several other factors including irrigators’ attitude towards risk

Context evaluation: A profile of irrigator climate knowledge, needs and practices in the northern Murray-Darling Basin to aid development of climate-based decision support tools and information and dissemination of research

Understanding client needs, knowledge and practices offers a means of ensuring research outputs match intended audience requirements. This paper shows the initial impact of context evaluation on the development of a suite of decision support tools and information to help irrigators better manage their water resources under different climatic conditions. The context evaluation study involved a survey of ~170 irrigators in the northern MurrayÐDarling Basin in Australia. It sought to clarify how they make cropping area and water management decisions and their levels of understanding and use of climate information. We found irrigators consult widely on cropping decisions and those with large areas commonly apply the Southern Oscillation Index to property decisions. Respondents demonstrated a reasonably good understanding of climate phenomena in an Southern Oscillation Index knowledge test. Two-thirds use seasonal climate outlook information, but only 20% are very confident to apply climate information to decisions. More than half would find a decision support system (comprising tools and information) useful for cropping decisions. Almost 75% would change their crop area, and 43% their crop type, if given advance information about water availability up to 4 months ahead of irrigation season. About 70% have access to a computer and half to the internet, but two-thirds consider their personal computing skill is only nil or basic. Twenty-three percent of respondents expressed interest in working directly with the research team to interact regarding their requirements, indicating the potential for future participative research activities such as collaborative, on-farm research. The context evaluation facilitated formation of a focus group that cooperated to assess research findings and incorporate improvements to the projectÕs set of decision support tools. The evaluation was a new experience for the researchers and, albeit an arms-length consultation process, it has broadened our knowledge about our target audience and their preferred ways to access research findings