Saudi Arabia’s topographic features have great significance and impact on the diversity of physical environments for plant growth and agricultural activities. Cultivated land is at a premium in Saudi Arabia, and soil evaluation is increasingly important. Thus, the intended purpose of this investigation was to determine both the genesis and soil properties to improve the management of arid soil, which is represented by Al-Aflaj Oasis, during tillage operations. The parameters of this research output were the soil’s chemical and physical properties. Data were collected from field experiments by drilling and evaluating soil profiles using soil sampling tools. This study classified the soil of Al Aflaj Oasis, which is a part of the Najd Plateau. It analyzed the soil profile, the failure to provide nutrients for agricultural production, and the impact of spring floods, modern equipment, fertilizer management, and irrigation methods on agricultural prospects. Topographic and geological maps provided the origin of the soils in the area. The morphological description included measurements and characterization of soil horizons and boundaries, moisture status, soil texture, construction, cohesion, estimation of calcium carbonate, and other morphological phenomena. Laboratory analysis measured the soil particle size, soluble salts, calcium carbonate, organic material, electrical conductivity, and percentages of silt, clay, and sand. The soil was deep, with a coarse texture characterized as sandy to sandy clay; the gravel content ranged from 19.70 to 62.50%, with a cohesive structure at the bottom of the soil profile and slight cohesion at the surface. The soil had low organic matter content, and a hard layer of calcium existed at a depth of 100 cm. The soil was classified as arable land within a subgroup of Typic Haplocalcids. Chemical analysis showed low salinity, slight alkalinity, and high calcium carbonate (22–64%). The soil underwent a historical transformation. To enhance agricultural potential, the chemical and physical properties need adjustment by introducing organic matter, intensive deep cultivation, diversification of agricultural fertilizers, and careful irrigation management. Since recent cultivation has been limited to a depth of 100 cm, the hard calcium carbonate layer should be considered carefully. Future crop cultivation should include deep plowing (e.g., chisel and furrow plows) to fragment the solid soil structure and facilitate suitable farming practices, and the growth of plants in the lands affected by the ancient overflows of the Al Aflaj springs, such as the Al Aflaj Oasis, can be made productive and consistent with other agricultural areas.

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.

The effect of (NH₄)₂SO₄:Ca(NO₃)₂ ratios applied by fertigation on nutritional status, fruit set, yield, and marketable yield of pomegranate trees cv. Wonderful was evaluated. The trees were provided with five nutrient solutions with the same total nitrogen level (200 units/ha) but with different (NH₄)₂SO₄:Ca(NO₃)₂ ratios (100:0, 90:10, 80:20, 70:30, and 60:40). Increasing the (NH₄)₂SO₄ ratio from 60 to 100% significantly reduced the nitrogen (N), magnesium (Mg), calcium (Ca), and potassium (K) concentrations while significantly increasing P and Fe concentrations in pomegranate leaves. The highest (NH₄)₂SO₄ proportion (100%) induced a reduction in both chlorophyll content and dry matter values in pomegranate leaves. The maximum fruit set (33.65% and 31.40%) and the minimum fruit drop (6.74% and 6.25%) were recorded at the applied ratio of 60% of (NH₄)₂SO₄:40% of Ca(NO₃)₂. The applied proportion of 70% of (NH₄)₂SO₄:30% of Ca(NO₃)₂ provided the minimum fruit sunburn (9.54% and 9.74%) and fruit cracking (6.45% and 5.64%), maximum yield (33.62 and 33.00 kg/tree), and marketable yield (27.41 and 27.93 kg/tree) in the 2019 and 2020 seasons, respectively. Our results provide valuable information about the effects of partial replacement of nitrogen fertilizer from (NH₄)₂SO₄ with nitrogen fertilizer from Ca(NO₃)₂ on the growth characteristics of pomegranate trees cv. Wonderful.

As artificial neural networks (ANNs) have been shown to be precise and reliable in supporting the field of artificial intelligence technology, agricultural scientists have focused on employing ANN for agricultural applications. The ANN can be an effective alternative for evaluating agricultural operations. The intended aim of this investigation was to employ both ANN and multiple linear regression (MLR) to develop a model for determining the rear wheel slip of an agricultural tractor in two-wheel drive (2WD) mode during plowing operations. The output parameter of the models was tractor rear wheel slip. The training data were collected from filed experiments using chisel, moldboard, and disk plows. The plows were operated under different conditions of soil texture, plowing depth, soil moisture content, and plowing speed. All data were acquired during field experiments in two soil textures (clay and clay loam textures). The training dataset was comprised of 319 data points, while 65 data points were employed to test both ANN and MLR models estimation capability. The ANN model with a backpropagation training algorithm was created using the commercial Qnet2000 software by changing its topology and related parameters. The best ANN model possessed a topology of 7-20-1. The estimated tractor rear wheel slip using the testing dataset displayed strong agreement with measured tractor rear wheel slip with the coefficient of determination (R²) value of 0.9977. The results definitely illustrated that the ANN model was capable of defining the correlation between the inputs and rear wheel slip. The outcomes suggest that the established ANN model is trustworthy in predicting the tractor rear wheel slip for an agricultural tractor in 2WD mode during the tillage process compared to MLR models. This study provides a useful tool for management of tillage implements during field operations.

Unsaturated soil hydraulic conductivity is a main parameter in agricultural and environmental studies, necessary for predicting and managing water and solute transport in soils. This parameter is difficult to measure in agricultural fields; thus, a simple and practical estimation method would be preferable, and quantitative methods (analytical and numerical) to predict the field parameters should be developed. Field experiments were conducted to collect water quality data to model the unsaturated hydraulic conductivity of a sandy loam soil. A mini disk infiltrometer (MDI) was used to measure soil infiltration rate. Input variables included electrical conductivity and the sodium adsorption ratio of irrigation water. Suction rate (pressure head), soil bulk density, and soil moisture content acted as inputs, with unsaturated soil hydraulic conductivity as output. The performance of Gaussian process regression (GPR) was analysed, with multiple linear regression (LR) and multi-layer perceptron (MLP) models used for comparison. Three performance criteria were compared: correlation coefficient (r), root mean square error (RMSE), and mean absolute error (MAE). The simulations employed the Waikato environment for knowledge analysis (WEKA) open source tool. The results indicate that the GPR with Pearson VII function-based universal kernel (PUK kernel), cache size 250007, Omega 1.0 and Sigma 1.0 performs better than other kernels when evaluating test split data, with a correlation coefficient of 0.9646. The RMSEs for GPR (PUK kernel), MLP, and LR were 1.16 × 10−04, 1.87 × 10−04, and 2.22 × 10−04 cm·s−1, respectively. Predictive data mining algorithms (DMA) enable an estimate of unknown values based on patterns in a database. Therefore, the present methodology can be put to use in predictive tools to manage water and solute transport in soils, as the GPR model provides much greater accuracy than the LR and MLP models in predicting the unsaturated hydraulic conductivity of a sandy loam soil.

The advantage of a sprinkler irrigation method is that it saves up to 50% of water consumption during the application of water, as compared to any other surface irrigation system. To assess the behavior of a sprinkler irrigation method, wind drift and evaporation losses (WDEL) are often employed as important parameters. The predictive capacities of four previous mathematical empirical models and two data mining algorithms, namely, reduced-error pruning tree (REPTree) and artificial neural network (ANN) models, were employed to evaluate the impact of the operating parameters of a sprinkler irrigation method on WDEL. The inputs to the REPTree and ANN models were the working pressure, vapor pressure deficit, air temperature, wind speed, nozzle diameter, and air relative humidity. In the experimental field, for data collection, a solid set of sprinklers and collectors positioned per ASAE standards was employed. Promising results showed remarkable performance for one of the mathematical empirical models tested, with a confidence index value of 0.829. Meanwhile, the REPTree and ANN models presented smaller errors for testing data set and are qualified for use given their confidence index values of 0.956 and 0.964, respectively. The REPTree and ANN algorithms were classified as optimal models, indicating that the use of mathematical experimental models alone is inadequate in operational situations involving the nozzle diameter, working pressure, and other variables.