Developing a fuzzy logic model for predicting soil inltration rate based on soil texture properties

The prediction of the soil infiltration rate is advantageous in hydrological design, watershed management, irrigation, and other agricultural studies. Various techniques have been widely used for this with the aim of developing more accurate models; however, the improvement of the prediction accuracy is still an acute problem faced by decision makers in many areas. In this paper, an intelligent model based on a fuzzy logic system (FLS) was developed to obtain a more accurate predictive model for the soil infiltration rate than that generated by conventional methods. The input variables that were considered in the fuzzy model included the silt and clay contents. The developed fuzzy model was tested against both the observed data and multiple linear regression (MLR). The comparison of the developed fuzzy model and MLR model indicated that the fuzzy model can simulate the infiltration process quite well. The coefficient of determination, root mean square error, mean absolute error, model efficiency, and overall index of the fuzzy model were 0.953, 1.53, 1.28, 0.953, and 0.954, respectively. The corresponding MLR model values were 0.913, 2.37, 1.92, 0.913, and 0.914, respectively. The sensitivity results indicated that the clay content is the most influential factor when the FLS-based modelling approach is used for predicting the soil infiltration rate.Keywords: FLS, infiltration rate, MLR, modelling, sensitivity analysi

Levering proteomic analysis of Pseudomonas fluorescens mediated resistance responses in tomato during pathogenicity of Fusarium oxysporum f. sp. oxysporum

The tomato, one of the world’s most extensively cultivated and consumed vegetable crops is negatively impacted by various pathogens. This study aimed to observe the differentially expressed proteins in tomato samples in plant–pathogen-biocontrol interactions. The fungal pathogen associated with wilted plants were isolated and identified based on its morphological and molecular characteristics. Fourteen strains of Pseudomonas fluorescens from agricultural soils were identified and described using biochemical assays, molecular analyses, and screening for antagonistic ability against the Fusarium wilt pathogen. Results demonstrated that the potential of P. fluorescens (TPf12) positively influenced the expression of antagonism against tomato wilt disease. A total of 14 proteins expressed differently were revealed in the 2D-PAGE-MS investigation. Proteins such as nucleoside diphosphate kinase, phenylalanine ammonia-lyase, protein kinase family protein, Ser/Thr protein kinase-like are unchanged in FOL pathogen interaction, but up-regulated in FOL + TPf12 treated roots, and lipid transfer-like protein, and phenylalanine ammonia-lyase were down-regulated in FOL infested roots and upregulated in FOL + TPf12 treated tomato roots. Phenylalanine ammonia-lyase protein expression is commonly found in TPf12 bioenriched roots, and FOL + TPf12 treated roots, indicating its role in response to the application of TPf12 in tomato. A GC–MS analysis was performed to detect the bioactive metabolites synthesized by TPf12. Molecular docking investigations were conducted using the maestro’s GLIDE docking module of the Schrodinger Software program. Among the secondary metabolites, Cyclohexanepropanoic acid, 2-oxo-, methyl ester (CAS), and 3-o-(4-o-Beta-D-Galactopyranosyl-Beta-D-Galactopyraosyl)-2-Acetylamino-2-Deoxy-D-Galactose were shown to be top-ranked with a least docking score against each differently expressed proteins. The profiled molecules expressed differently due to plant-pathogen-biocontrol interactions may be directly or incidentally involved in the wilt disease resistance of tomato plants

Suitability of waste water for irrigation in Saudi Arabia : analysis of public perceptions and quantitative microbial risk assessment

Wastewater reuse for irrigated practice is un alternative solution in which food production can be improved especially in the arid-region where freshwater resources are often limited However; the potential public health risk associated with wastewater reuse remain a major concern. as well as public perceptions towards wasteater. This research was conducted in two main agricultural cities within the Saudi ArabiaEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Developing a fuzzy logic model for predicting soil infiltration rate based on soil texture properties

The prediction of the soil infiltration rate is advantageous in hydrological design, watershed management, irrigation, and other agricultural studies. Various techniques have been widely used for this with the aim of developing more accurate models; however, the improvement of the prediction accuracy is still an acute problem faced by decision makers in many areas. In this paper, an intelligent model based on a fuzzy logic system (FLS) was developed to obtain a more accurate predictive model for the soil infiltration rate than that generated by conventional methods. The input variables that were considered in the fuzzy model included the silt and clay contents. The developed fuzzy model was tested against both the observed data and multiple linear regression (MLR). The comparison of the developed fuzzy model and MLR model indicated that the fuzzy model can simulate the infiltration process quite well. The coefficient of determination, root mean square error, mean absolute error, model efficiency, and overall index of the fuzzy model were 0.953, 1.53, 1.28, 0.953, and 0.954, respectively. The corresponding MLR model values were 0.913, 2.37, 1.92, 0.913, and 0.914, respectively. The sensitivity results indicated that the clay content is the most influential factor when the FLS-based modelling approach is used for predicting the soil infiltration rate

Growth, Yield, Quality and Insect-Pests in Sugarcane (Saccharum officinarum) as Affected by Differential Regimes of Irrigation and Potash under Stressed Conditions

Land productivity and quality were negatively impacted by both unbalanced fertilization and water-stressed conditions, which has arisen as an important topic of research. In the semi-arid tropics, sugarcane is the main source of sugar and ethanol; however, no potash (K) dose is recommended for the deficient sites in the region, which are further responsible for lower recovery. As a result, in order to standardize the K dose for deficient sites, present experiments carried out during plant (2019–2020) and ratoon (2020–2021) seasons. The statistical design was a split-plot design with main plot treatments comprised of I1 (irrigated) and I2 (stressed) treatments followed by K1, K2, K3, and K4 plots fertilized with 0, 40, 80, and 120 kg K2O ha−1 in subplots. Germination was reported to be 13.7, 25.0 and 32.3% higher during plant and 6.2, 17.3 and 24.4% higher during ratoon season in K2, K3, and K4 plots, respectively. Tiller’s cane−1 was recorded to be significantly affected by potash levels at 241 days after planting (DAP) and 261 and 326 days after harvesting (DAH). Periodic chlorophyll content of the sugarcane leaves was reported not to be affected by irrigation treatments except at 355 DAP and 324 and 357 DAH, where respected values were reported to be 2.06% in the plant season and 1.55 and 2.54% higher in the ratoon season in I1 plots, respectively. During plant season purity and extraction after the 10th month, respective values were reported to be 1.5% lower and 4.03% higher under I1 plots, while only Brix (%) was reported as significant and 2.42% higher in I1 plots during plant season after the 12th month. The incidence of early shoot borer (Chilo infuscatellus) and stalk borer (Chilo auricilius) was reported to be significantly higher under stressed conditions (30.4 and 21.5% lower in I1 plots) during the plant season, while early shoot borer (Chilo infuscatellus), stalk borer (Chilo auricilius) and top (Scirpophaga excerptalis) incidences were significantly lower in I1 plots to the tune of 19.6, 22 and 9.73% as compared to the I2 plots during the ratoon season. The application of 80 kg K2O ha−1 resulted in significantly higher cane yield and decreased insect-pest occurrence. Even though 120 kg K2O ha−1 promoted different plant and ratoon sugarcane characteristics, they were all statistically equivalent. In I1 plots, benefits increased from K2 to K3 plots by 26.7% during plant and 155% during ratoon seasons but decreased from K3 to K4 plots by 21.0% during plant and 26.1% ratoon seasons. In I2 plots, however, benefits from K2 to K3 plots were reported to be 72.7% during plant and 76.5% during ratoon seasons, which was reduced to 10.5% during plant and 16.7% during ratoon seasons in K4 plots. Results of a two-year study on plant and ratoon canes revealed that 80 kg K2O ha−1 at deficient sites significantly improved the performance of both plant and ratoon canes yields, sugar yields, reduced the insect-pests’ incidence, and finally the benefits of the cane farmers under both irrigation regimes

Interactive Effects of Nitrogen Application and Irrigation on Water Use, Growth and Tuber Yield of Potato under Subsurface Drip Irrigation

Potatoes are a high-value crop with a shallow root system and high fertilizer requirements. The primary emphasis in potato production is minimizing nitrogen-leaching losses from the shallow root zone through fertigation. Therefore, a field experiment was conducted for two consecutive years, 2018–2019 2019–2020 to assess the effect of nitrogen and irrigation amount and frequency on tuber yield, water balance components and water productivity of potatoes under surface and subsurface drip irrigation. The experiment was laid out in a split-plot design with three nitrogen levels (187.5 kg N ha−1 (N1), 150 kg N ha−1 (N2) and 112.5 kg N ha−1 (N3)) in main plots and six irrigation levels in the subsurface (drip lines were laid at 20 cm depth) and one surface drip in subplots. Irrigation scheduling was based on 100% of cumulative pan evaporation at an alternate (I1) and two-day interval (I2), 80% of cumulative pan evaporation at an alternate (I3) and two-day interval (I4), 60% of cumulative pan evaporation at an alternate (I5) and two-day interval (I6) and 80% of cumulative pan evaporation at alternate days with surface drip (I7). Our results showed that potato transpiration was higher in N1 and N2 compared to N3, while soil evaporation was higher in N3 over N1 and N2. Irrigation regimes I5 and I6 had lower transpiration than I1, I2, I3 and I7, while I7 had more soil evaporation than I1, I2 and I3. Leaf area index (LAI), dry matter accumulation (DMA), root mass density (RMD) and tuber yield in N1 and N2 were at par but significantly higher than N3. The LAI and DMA were statistically at par in I1, I2 and I3 but significantly higher than recommended irrigation (I7). Tuber yield was statistically at par in I1, I2, I3 and I7 but I3 and I7 saved 20% irrigation water compared to I1 and I2. On the other hand, real water productivity (WPET) under N1 and N2 were comparable in I3 and I4 but significantly higher than recommended practice (I7) as pooled evapotranspiration (ET) and soil evaporation (E) in I7 were 19.5 and 20.6 mm higher, respectively, than in I3. Among interactive treatment combinations, N1I1, N1I2, N1I3, N1I7, N2I1, N2I2 and N2I3 recorded the highest tuber yields without any significant differences among them. Treatment N2I3 saved 20% nitrogen and irrigation water compared to all other combinations. Water productivity in N1 and N2 was comparable in I3 and I4 but significantly higher than recommended practice (I7)

Hybrid river stage forecasting based on machine learning with empirical mode decomposition

The river stage is certainly an important indicator of how the water level fluctuates overtime. Continuous control of the water stage can help build an early warning indicator of floods along rivers and streams. Hence, forecasting river stages up to several days in advance is very important and constitutes a challenging task. Over the past few decades, the use of machine learning paradigm to investigate complex hydrological systems has gained significant importance, and forecasting river stage is one of the promising areas of investigations. Traditional in situ measurements, which are sometime restricted by the existing of several handicaps especially in terms of regular access to any points alongside the streams and rivers, can be overpassed by the use of modeling approaches. For more accurate forecasting of river stages, we suggest a new modeling framework based on machine learning. A hybrid forecasting approach was developed by combining machine learning techniques, namely random forest regression (RFR), bootstrap aggregating (Bagging), adaptive boosting (AdaBoost), and artificial neural network (ANN), with empirical mode decomposition (EMD) to provide a robust forecasting model. The singles models were first applied using only the river stage data without preprocessing, and in the following step, the data were decomposed into several intrinsic mode functions (IMF), which were then used as new input variables. According to the obtained results, the proposed models showed improved results compared to the standard RFR without EMD for which, the error performances metrics were drastically reduced, and the correlation index was increased remarkably and great changes in models’ performances have taken place. The RFR_EMD, Bagging_EMD, and AdaBoost_EMD were less accurate than the ANN_EMD model, which had higher R≈0.974, NSE≈0.949, RMSE≈0.330 and MAE≈0.175 values. While the RFR_EMD and the Bagging_EMD were relatively equal and exhibited the same accuracies higher than the AdaBoost_EMD, the superiority of the ANN_EMD was obvious. The proposed model shows the potential for combining signal decomposition with machine learning, which can serve as a basis for new insights into river stage forecasting.Validerad;2024;Nivå 2;2024-03-07 (hanlid);Funder: Deanship of Scientific Research, King Saud University;Full text license: CC BY</p

Automated Model Selection Using Bayesian Optimization and the Asynchronous Successive Halving Algorithm for Predicting Daily Minimum and Maximum Temperatures

This study addresses the crucial role of temperature forecasting, particularly in agricultural contexts, where daily maximum (Tmax) and minimum (Tmin) temperatures significantly impact crop growth and irrigation planning. While machine learning (ML) models offer a promising avenue for temperature forecasts, the challenge lies in efficiently training multiple models and optimizing their parameters. This research addresses a research gap by proposing advanced ML algorithms for multi-step-ahead Tmax and Tmin forecasting across various weather stations in Bangladesh. The study employs Bayesian optimization and the asynchronous successive halving algorithm (ASHA) to automatically select top-performing ML models by tuning hyperparameters. While both the Bayesian and ASHA optimizations yield satisfactory results, ASHA requires less computational time for convergence. Notably, different top-performing models emerge for Tmax and Tmin across various forecast horizons. The evaluation metrics on the test dataset confirm higher accuracy, efficiency coefficients, and agreement indices, along with lower error values for both Tmax and Tmin forecasts at different weather stations. Notably, the forecasting accuracy decreases with longer horizons, emphasizing the superiority of one-step-ahead predictions. The automated model selection approach using Bayesian and ASHA optimization algorithms proves promising for enhancing the precision of multi-step-ahead temperature forecasting, with potential applications in diverse geographical locations

Seasonal Changes Modulate the Rhizosphere of Desert Plant Species

Arid and semi-arid ecosystems are categorized as having degraded soils due to the limited availability of water and nutrients. The perennial shrubs in these regions have developed different ecological and physiological adaptations to cope with harsh conditions. The plant species vary in the chemical profile of their root exudates, which can induce variability in the microbial community in the rhizosphere. The present research has been conducted (i) to investigate the variation in composition, diversity, and structure of rhizosphere’s bacterial community of desert plants; (ii) to identify plant-specific effects on the rhizosphere microbial community structure; and (iii) to determine the influence of soil moisture on the rhizosphere’s microbial community and soil biological properties under stressful conditions. Ten desert plant species from the Cholistan desert were selected as test specimens. Bacterial communities from the rhizosphere of 10 plants of each species were explored. Soil samples were collected during monsoon (June–August) and dry months (March–May). Microbial community structure analyses were carried out through 16S rRNA sequencing by targeting V3 and V4 regions. Among tested plant species, the rhizosphere of Leptadenia pyrotechnica (S6 vs. S16), Aerva javanica (Burm. f.) Juss. ex Schult (S9 vs. S19), and Vachellia jacquemontii (Benth.) (S10 vs. S20) had greater microbial diversity in both seasons. Higher levels of microbial communities were found during monsoon season. Furthermore, Gammaproteobacteria were abundant in the rhizospheres of all studied plants during the monsoon season. In contrast, the rhizosphere was abundant with unidentified_Actinobacteria during the dry season. The rhizospheric soil was further analyzed for biological properties. The maximum microbial biomass carbon (165 mg kg–1) and microbial biomass nitrogen (6.7 mg kg–1) were found in the rhizosphere of Vachellia jacquemontii (Benth.) Benth during monsoon season. However, a minimum of microbial biomass carbon (119 mg kg–1) and microbial biomass nitrogen (4.2 mg kg–1) were found in the rhizosphere of Cleome pallida Kotschy during dry seasons. The diversified microbial community structure and biological properties enable desert plants to cope with adverse climate conditions