Selection of an ideal mesh size for the cracking unit of a palm kernel processing plant

One of the main problems associated with cracking of palm nut is the mixture of small Tenera and the big Dura variety nuts. In general, low yield of about 40% is obtained from cracking the mixture of Dura-Tenera nuts. In cracking the mixture, most of the Tenera nuts are not cracked when a large sieve size is used. When the sieve size is small, the Dura kernels get broken, thereby affecting the quality of the processed kernel oil. Hence the objective of this work was to develop a model for selecting a suitable sieve size that can be used to crack the nut in order to increase productivity and also improve the quality of the palm kernel oil. Series of experiments carried out confirmed the need for separation of nuts before cracking and also the need to select an ideal sieve size for each type of nut. In conclusion, it was established that separating the two nut varieties before cracking led to a significant (p< 0.01) increase in cracking efficiency of up to about 90% thereby increasing the productivity by 40% and making an economic gain of GH¢ 1.07 (US$0.98, October, 2008) per every 50 kg nut processed

Performance evaluation of prototype mechanical cassava harvester in three agro-ecological zones in Ghana

Large-scale cassava harvesting, especially during the dry season, is a major constraint to its industrial demand and commercial production. Manual harvesting is slow and associated with drudgery and high root damage in the dry season. Research on mechanisation of cassava production is very low especially in the area of harvesting, and currently there exists no known functional mechanical cassava harvesters in Ghana. The main objective of the study was to test and evaluate mechanical cassava harvesting techniques in different agro-ecological zones in Ghana. Performance of two prototype mechanical harvesters (TEK MCH 2 and 6) was evaluated against manual harvesting methods for field capacity, efficiency and root damage using two cassava varieties, namely ‘Afisiafi’ and ‘Bankyehemaa’, on ridged and flat landforms. Results from field trials showed prototype harvesters weighing 268 – 310 kg can achieve optimum performance on ridged landforms. When harvested mechanically, tuber damage ranges from 16 per cent to 27 per cent for both ‘Afisiafi’ and ‘Bankyehemaa’. The mechanical harvester works best on dry fields with moisture content from one per cent to 17 per cent db containing minimum trash or weeds, and develops average drafts of 10.86 kN whilst penetrating depths from 13 to 40 cm. Optimum mechanical harvesting performance was achieved at tractor speeds of 5 – 8 km h-1, fuel consumption of 15 – 19 litres ha-1, and a field capacity of 2 h ha-1. After mechanical harvesting, the field is left ploughed with savings on fuel, time and production costs. It is, however, recommended to test the harvesters for wear and durability in major agro-ecological zones and through a wide range of soil moisture regimes in Ghana to support nationwide adoption

A meta-analysis of long-term effects of conservation agriculture on maize grain yield under rain-fed conditions

Conservation agriculture involves reduced tillage, permanent soil cover and crop rotations to enhance soil fertility and to supply food from a dwindling land resource. Recently, conservation agriculture has been promoted in Southern Africa, mainly for maize-based farming systems. However, maize yields under rain-fed conditions are often variable. There is therefore a need to identify factors that influence crop yield under conservation agriculture and rain-fed conditions. Here, we studied maize grain yield data from experiments lasting 5 years and more under rain-fed conditions. We assessed the effect of long-term tillage and residue retention on maize grain yield under contrasting soil textures, nitrogen input and climate. Yield variability was measured by stability analysis. Our results show an increase in maize yield over time with conservation agriculture practices that include rotation and high input use in low rainfall areas. But we observed no difference in system stability under those conditions. We observed a strong relationship between maize grain yield and annual rainfall. Our meta-analysis gave the following findings: (1) 92% of the data show that mulch cover in high rainfall areas leads to lower yields due to waterlogging; (2) 85% of data show that soil texture is important in the temporal development of conservation agriculture effects, improved yields are likely on well-drained soils; (3) 73% of the data show that conservation agriculture practices require high inputs especially N for improved yield; (4) 63% of data show that increased yields are obtained with rotation but calculations often do not include the variations in rainfall within and between seasons; (5) 56% of the data show that reduced tillage with no mulch cover leads to lower yields in semi-arid areas; and (6) when adequate fertiliser is available, rainfall is the most important determinant of yield in southern Africa. It is clear from our results that conservation agriculture needs to be targeted and adapted to specific biophysical conditions for improved impact

Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks

peer reviewedaudience: researcher, professionalVarious approaches exist to relate saturated hydraulic conductivity (Ks) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods, i.e.multiple linear regression and artificial neural networks, that use the entire grain-size distribution data as input for Ks prediction. Besides the predictive capacity of the methods, the uncertainty associated with the model predictions is also evaluated, since such information is important for stochastic groundwater flow and contaminant transport modelling. Artificial neural networks (ANNs) are combined with a generalized likelihood uncertainty estimation (GLUE) approach to predict Ks from grain-size data. The resulting GLUE-ANN hydraulic conductivity predictions and associated uncertainty estimates are compared with those obtained from the multiple linear regression models by a leave-one-out cross-validation. The GLUE-ANN ensemble prediction proved to be slightly better than multiple linear regression. The prediction uncertainty, however, was reduced by half an order of magnitude on average, and decreased at most by an order of magnitude. This demonstrates that the proposed method outperforms classical data-driven modelling techniques. Moreover, a comparison with methods from literature demonstrates the importance of site specific calibration. The dataset used for this purpose originates mainly from unconsolidated sandy sediments of the Neogene aquifer, northern Belgium. The proposed predictive models are developed for 173 grain-size -Ks pairs. Finally, an application with the optimized models is presented for a borehole lacking Ks data

Mouse models of neurodegenerative disease: preclinical imaging and neurovascular component.

Neurodegenerative diseases represent great challenges for basic science and clinical medicine because of their prevalence, pathologies, lack of mechanism-based treatments, and impacts on individuals. Translational research might contribute to the study of neurodegenerative diseases. The mouse has become a key model for studying disease mechanisms that might recapitulate in part some aspects of the corresponding human diseases. Neurode- generative disorders are very complicated and multifacto- rial. This has to be taken in account when testing drugs. Most of the drugs screening in mice are very di cult to be interpretated and often useless. Mouse models could be condiderated a ‘pathway models’, rather than as models for the whole complicated construct that makes a human disease. Non-invasive in vivo imaging in mice has gained increasing interest in preclinical research in the last years thanks to the availability of high-resolution single-photon emission computed tomography (SPECT), positron emission tomography (PET), high eld Magnetic resonance, Optical Imaging scanners and of highly speci c contrast agents. Behavioral test are useful tool to characterize di erent ani- mal models of neurodegenerative pathology. Furthermore, many authors have observed vascular pathological features associated to the di erent neurodegenerative disorders. Aim of this review is to focus on the di erent existing animal models of neurodegenerative disorders, describe behavioral tests and preclinical imaging techniques used for diagnose and describe the vascular pathological features associated to these diseases