ABSTRACT Draft and energy requirements are the most important factors in the activities of farm machinery management owing to their role in matching the tractor with implements for different tillage operations. This study's aim was to model the draft and energy requirements of a moldboard plow based on two novel variables. The first was the soil texture index (STI), which was formed from the clay, sand, and silt contents with a range of 0.03–0.84. The second variable was the field working index (FWI), formed by combining the plow width, plowing speed, soil bulk density, soil moisture content, plowing depth, and tractor power into one dimensionless variable, which had a range of 7.17–82.45. The coefficient of determination (R2) values obtained using a testing dataset were found out to be 0.9134 for energy and 0.8602 for draft requirements. For the draft and energy requirements of the testing data points, the mean absolute errors between the measured values and the values predicted using the artificial neural networks (ANN) model were 0.99 kN and 2.39 kW·h/ha, respectively. Based on comparisons with other results reported using multiple linear regression, it was clear that the predictions by the proposed ANN model were very satisfactory.

ABSTRACT Most important farm operations require a significant amount of energy, and this consumes a major portion of the farm's budget. Consequently, analyzing the fuel consumption of agricultural machinery for farm operations of different sizes makes it possible to predict fuel consumption to set an appropriate budget for energy. The main purpose of this study was to determine the ability of the k-nearest neighbors (KNN) algorithm to predict the fuel consumption of tractor–chisel plow systems correctly. A training-set design of 139 points of 173 data points obtained from the literature was utilized, and the remaining 34 data points were applied as a test set. The input parameters were tractor power, plowing width, depth and speed of plowing, soil percentages of sand, silt, and clay, initial soil moisture content, and initial soil bulk density. The predictive power of the KNN method was compared with that of multiple linear regression (MLR), and experimental data were used to determine the predictive power of both methods. The KNN method generated better results than the multiple linear regression method. The test dataset correlation coefficients were 0.817 for the KNN (k = 2) method and 0.422 for the multiple linear regression method. This study suggests that the KNN method with k = 2 (two nearest neighbors) is suitable for estimating the fuel consumption of tractor–chisel plow systems for input values within the studied range.

ABSTRACT 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.