Saad A. Al-Hamed

Tractor performance spreadsheet for radial tires

Abstract Lotus-compatible templates were revised from the Zoz (1987) spreadsheets. Performance of two-wheel-drive (2WD) and four-wheel-drive/mechanical-front-wheel-drive (4WD/MFWD) tractors were predicted on agricultural soils for radial tractor tires. Comparisons between the predicted and experimental results of 41 tractors using radial tractor tires showed fair agreement. The performance parameters used in the comparisons were wheel slip, drawbar pull and tractive efficiency. The percentage differences were calculated and the acceptable range was set at 20% difference. For wheel slip, 22 out of 41 tractors had percentage differences that fell within the acceptable range. The drawbar pull comparison showed that 35 out of the 41 tractors had percent differences within the acceptable range. The tractive efficiency comparison showed that 27 out of 33 tractors had percentage differences that fell within the 20% range. The predicted drawbar pull and tractive efficiency were higher than the experimental values in most cases of MFWD tractors. Because some input values for the spreadsheet such as available power, cone index and travel speed were not given in the source of data, the predicted results of tractor performance were not in close agreement to experimental results.

A program for predicting tractor performance in Visual C

Abstract A tractor performance program that predicts the performance of two-wheel-drive (2WD) and four-wheel-drive/mechanical-front-wheel-drive (4WD/MFWD) tractors on agricultural soils for both bias-ply and radial tires was developed to meet the user requirements for both educational and research programs. The program was written in Visual C++ programming language as a new method of predicting 2WD and 4WD/MFWD tractor performance. The program provides an intuitive user interface by linking databases such as tractor specifications, tire data (front, bias-ply, and radial), and traction equation coefficients to predict the performance of a selected tractor and model. The program has been proven to be user friendly and efficient.

Effects of Tire Size and Inflation Pressure on Tractive Performance

The dynamic traction ratio and tractive efficiency were compared for a tractor equipped with three different size rear tires (18.4R42, 18.4R46, and 12.4R54) each operating at three different inflation pressures 55 kPa (8 psi), 83 kPa (12 psi), and 124 kPa (18 psi) and on two different surfaces (wheat stubble and plowed wheat stubble). In general, lower drive tire inflation pressures increased tractive performance. On a firm soil and at a given inflation pressure, the larger diameter tires perform better than the smaller diameter tires. On the soft soil surface, the smaller diameter tire had the lower performance of the three different size tires with an interaction with the two larger diameter tires.

A setup of mobile robotic unit for fruit harvesting

The description of a mobile robotic unit for fruit harvesting was illustrated in this paper. The setup of the system was developed to harvest date palm fruit which is the most common fruit in Saudi Arabia. The system was based on readymade industrial robotic arm.

Prediction of Potato Yield Based on Energy Inputs using Artificial Neural Networks and C-sharp under Saudi Arabia Conditions

The objective of this study was to explore the relation between energy inputs and potato yield using artificial neural network (ANN) under Saudi Arabia conditions. Additionally, the extracted weights from ANN model were formulated using C-sharp language to develop interactive application for friendly and easy use. For this purpose, the energy use pattern was determined by collection data from two sources, the first source was actual field experiments in three sites belong to Riyadh region, Saudi Arabia and the second source from growers by using a face to face questionnaire method. The results indicated that total energy consumption and yield of potato production were different based on production pattern. In this study, for field experiment data, average the energy indices covering energy efficiency, specific energy and energy productivity were calculated at 2.25, 1.60 MJ/kg and 0.63 kg/MJ, respectively. For predicting of potato yield based on energy inputs, artificial neural network (ANN) with standard back propagation algorithm was employed. The results illustrated the ANN model with 6-1522-1 architecture that had the best condition to the prediction of potato yield. With respect to ANN model, R2, mean absolute error and mean relative error were computed as 0.704, 2.36 ton/ha and 5.59%, respectively in testing stage. Moreover, contribution analysis was applied after training process of the ANN model. The results disclosed the water irrigation energy which had the highest contribution (24.75%) to potato yield among all inputs (machinery energy, diesel fuel energy, labor energy, chemicals energy and seeds energy). The developed C-sharp interactive application was tested and it can estimate potato yield. Soil and agronomy researchers, framers and agricultural engineers can use the developed C-sharp interactive application to explore the input variables that have more potential to increase potato yield on a farm. It is user-friendly and could be run on Windows desktop without C-sharp environment.

Effect of Three Tillage Implements on Potato Yield and Water Use Efficiency

The objective of this study was to evaluate the effect of tillage implements on potato yield and water use efficiency. The study was conducted at a private farm at Al-Kharj Governorate, Saudi Arabia during 2015 in randomize complete block design with three replications. Three primary tillage implements commonly used in Saudi Arabia for seedbed preparation with different configurations: disk harrow, chisel plow and moldboard plow were used. The potato variety used was Spunta. Centre pivot system was used to provide irrigation water. The statistical analysis indicated that tillage implement had significant effect on yield and water use efficiency. The highest yield of 37.19 t/ha was observed during plowing with moldboard plow and the lowest (32.33 t/ha) was observed during plowing with disk harrow. The highest water use efficiency of 7.91 kg/m was observed during plowing with moldboard plow and the lowest (6.88 kg/m) was observed during plowing with disk harrow. The results could be helpful to develop comprehensive technology to increase potato yield and water use efficiency in semi-arid region like Saudi Arabia. Original Research Article Al-Hamed et al.; AJEA, 12(3): 1-6, 2016; Article no.AJEA.24950 2

Effect of soil and water characteristics on yield and properties of ‘Spunta’ potatoes

Soil texture and its characteristics besides water characteristics can play an eminent role in potato (Solanum tuberosum L.) production. Therefore, a soil and water quality index (SWQI, %) was derived to investigate the effect of combination of soil and water characteristics (sodium adsorption ratio for water and soil, electric conductivity of water and soil, pH for water and soil, organic matter in the soil, and soil texture index) on yield and properties of ‘Spunta’ potatoes produced under center pivot irrigation system. This index was formed studying separately the effect of irrigation water quality and soil texture on yield, water use efficiency, tuber modulus of elasticity, and tuber shape index of ‘Spunta’ potatoes. Field results demonstrated that the lowest potato yield was approximately 34.12 t ha-1 at SWQI 79.63%, and the highest potato yield was 37.79 t ha-1 at SWQI of 30.93%. The lowest water use efficiency was approximately 6.09 kg m-3 at SWQI 30.93%, and the highest water use efficiency was 6.83 kg m-3 at SWQI 79.63%. The lowest tuber modulus of elasticity was approximately 3.98 N mm-1 at SWQI 21.7%, and the highest tuber modulus of elasticity was 4.74 N mm-1 at SWQI 79.63%. Finally, the tuber shape index was approximately 342% at SWQI 79.63%, 418% at SWQI 21.72%, and 403% at SWQI 30.93%, which belongs to long and very long shapes. The soil and water quality index could be a useful tool to get relationships among water and soil characteristics, yield, and other properties of the potato crop.