The horizontal and vertical forces acting on three chisel plows having different shank shapes were measured in a sandy loam soil. The shank shape of the first plow was curved, while those of the second and third plows were semi-straight and semi-curved, respectively. The effect of forward speeds and plowing depth upon the horizontal and vertical force measurements were investigated. Soil characteristics, chisel plow specifications and results of tillage experiments were reported. A significant increase in horizontal force (N/shank/cm2 ) was observed for all the three commercial chisel plows and was proportional to the increase in the forward speed. However, non-significant increase in vertical force (N/shank/cm2) was observed for all the three plows with an increase in the forward speed. The curved shank gave values of horizontal and vertical forces (N/shank/cm2) greater than that of the other shank shapes.
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 6-15-22-1 architecture that had the best condition to the prediction of potato yield. With respect to ANN model, R 2 , 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.
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.
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