Hydrological Trend Analysis Integrated with Landscape Analysis at the Watershed Scale (Case Study: Langat Basin, Malaysia)

In this study, the trends of water and sediment data collected from three hydrometer stations over the past 25 years of development in the state of Selangor, Peninsular Malaysia, were analyzed using the Mann–Kendall and Pettitt’s tests. Landscape metrics for establishing the relationship between land use changes and trends of hydrological time series were calculated. The hydrologic trends were also studied in terms of rainfall variations and man-made features. Results indicated upward trends in water discharge at the Hulu Langat sub-basin and sediment load at the Semenyih sub-basin. These increasing trends were mainly caused by rapid changes in land use. Upward trends of hydrological series at the Hulu Langat sub-basin matched its rainfall pattern. At the Lui sub-basin, however, trends of hydrological series and variations in rainfall and land use were not statistically significant

Application of SWAT for impact assessment of land use/cover change and best management practices : a review.

Globally, the quantification and evaluation of land use and cover changes on the hydrological status of river basins is a main concern. There is an urgent need for technologies and models that can quantify the impact of land use change and management practices in an organized manner. Approach: Soil and Water Assessment Tool (SWAT) integrated with Geographic Information System (GIS) has great potential in current estimation, future prediction and proper decision making in terrestrial ecosystems. This review discusses the current utilization of SWAT in impact assessment of land use/cover change and best management practices. Results: Deployment of SWAT and land use/cover simulation models for impact assessment improves accuracy, reduces costs, and allows the simulation of a wide variety of conservation practices at watershed scale. Conclusion: This review demonstrates the synergistic role of SWAT and GIS technologies in improving watershed management

Evaluating system of rice intensification using a modified transplanter: A smart farming solution toward sustainability of paddy fields in Malaysia

This paper presents the study reports on evaluating a new transplanting operation by taking into accounts the interactions between soil, plant, and machine in line with the System of Rice Intensification (SRI) practices. The objective was to modify planting claw (kuku-kambing) of a paddy transplanter in compliance with SRI guidelines to determine the best planting spacing (S), seed rate (G) and planting pattern that results in a maximum number of seedling, tillers per hill, and yield. Two separate experiments were carried out in two different paddy fields, one to determine the best planting spacing (S=4 levels: s1=0.16 m×0.3 m, s2= 0.18 m×0.3 m, s3=0.21 m×0.3 m, and s4=0.24 m×0.3 m) for a specific planting pattern (row mat or scattered planting pattern), and the other to determine the best combination of spacing with seed rate treatments (G=2 levels: g1=75 g/tray, and g2= 240 g/tray). Main SRI management practices such as soil characteristics of the sites, planting depth, missing hill, hill population, the number of seedling per hill, and yield components were evaluated. Results of two-way analysis of variance with three replications showed that spacing, planting pattern and seed rate affected the number of one-seedling in all experiment. It was also observed that the increase in spacing resulted in more tillers and more panicle per plant, however hill population and sterility ratio increased with the decrease in spacing. While the maximum number of panicles were resulted from scattered planting at s4=0.24 m×0.3 m spacing with the seed rate of g1=75 g/tray, the maximum number of one seedling were observed at s4=0.16 m×0.3 m. The highest and lowest yields were obtained from 75 g seeds per tray scattered and 70 g seeds per tray scattered treatment respectively. For all treatments, the result clearly indicates an increase in yield with an increase in spacing.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Fundamental Research on Unmanned Aerial Vehicles to Support Precision Agriculture in Oil Palm Plantations

Unmanned aerial vehicles carrying multimodal sensors for precision agriculture (PA) applications face adaptation challenges to satisfy reliability, accuracy, and timeliness. Unlike ground platforms, UAV/drones are subjected to additional considerations such as payload, flight time, stabilization, autonomous missions, and external disturbances. For instance, in oil palm plantations (OPP), accruing high resolution images to generate multidimensional maps necessitates lower altitude mission flights with greater stability. This chapter addresses various UAV-based smart farming and PA solutions for OPP including health assessment and disease detection, pest monitoring, yield estimation, creation of virtual plantations, and dynamic Web-mapping. Stabilization of UAVs was discussed as one of the key factors for acquiring high quality aerial images. For this purpose, a case study was presented on stabilizing a fixed-wing Osprey drone crop surveillance that can be adapted as a remote sensing research platform. The objective was to design three controllers (including PID, LQR with full state feedback, and LQR plus observer) to improve the automatic flight mission. Dynamic equations were decoupled into lateral and longitudinal directions, where the longitudinal dynamics were modeled as a fourth order two-inputs-two-outputs system. State variables were defined as velocity, angle of attack, pitch rate, and pitch angle, all assumed to be available to the controller. A special case was considered in which only velocity and pitch rate were measurable. The control objective was to stabilize the system for a velocity step input of 10m/s. The performance of noise effects, model error, and complementary sensitivity was analyzed

Development of a Field Robot Platform for Mechanical Weed Control in Greenhouse Cultivation of Cucumber

A prototype robot that moves on a monorail along the greenhouse for weed elimination between cucumber plants was designed and developed. The robot benefits from three arrays of ultrasonic sensors for weed detection and a PIC18 F4550-E/P microcontroller board for processing. The feedback from the sensors activates a robotic arm, which moves inside the rows of the cucumber plants for cutting the weeds using rotating blades. Several experiments were carried out inside a greenhouse to find the best combination of arm motor (AM) speed, blade rotation (BR) speed, and blade design. We assigned three BR speeds of 3500, 2500, and 1500 rpm, and two AM speed of 10 and 30 rpm to three blade designs of S-shape, triangular shape, and circular shape. Results indicated that different types of blades, different BR speed, and different AM speed had significant effects (P < 0.05) on the percentage of weeds cut (PWC); however, no significant interaction effects were observed. The comparison between the interaction effect of the factors (three blade designs, three BR speeds, and two AM speeds) showed that maximum mean PWC was equal to 78.2% with standard deviation of 3.9% and was achieved with the S-shape blade when the BR speed was 3500 rpm, and the AM speed was 10 rpm. Using this setting, the maximum PWC that the robot achieved in a random experiment was 95%. The lowest mean PWC was observed with the triangular-shaped blade (mean of 50.39% and SD = 1.86), which resulted from BR speed of 1500 rpm and AM speed of 30 rpm. This study can contribute to the commercialization of a reliable and affordable robot for automated weed control in greenhouse cultivation of cucumber

An agricultural investment map based on geographic information system and multi-criteria method.

The study aimed to produce an investment classification map, which shows the potential areas of investment in agriculture in Sinnar, Sudan. The spatial multi-criteria analysis was used to rank and display potential locations, while the analytical hierarchy process method was used to compute the priority weights of each criterion. The study attempted to explore the utilization of Geographic Information System (GIS) to map the potential investment areas, therefore, it did not cover a comprehensive analysis of all factors that influence investment in agriculture. In addition, the analysis was limited to criteria that had spatial reference. The investment criteria for spatial analysis were defined from the guidelines provided by the Ministry of Investment, Sudan. Even with the shortcomings of the data, it was found that the results obtained were very encouraging and provided clear indicative areas for agricultural investment in Sinnar. Government agencies can use GIS to access information regarding the potential areas of investment, and minimize investment risks. On the other hand, the economic development organizations will now have the ability to benefit from the Geographic Information System (GIS) solutions by leveraging on this technology to attract and retain business from worldwide sources. Thus, the model will serve as a decision support tool for investors and decision makers at various levels

KINEROS2 application for land use/cover change impact analysis at the Hulu Langat Basin, Malaysia

The impacts of land use/cover changes (LUCC) on a developed basin in Malaysia were evaluated. Three storm events in different intensities and durations were required for KINEROS2 (K2) calibration and LUCC impact analysis. K2 validation was performed using three other rainfall events. Calibration results showed excellent and very good fittings for runoff and sediment simulations based on the aggregated measure. Validation results demonstrated that the K2 is reliable for runoff modelling, while K2 application for sediment simulation was only valid for the period 1984-1997. LUCC impacts analysis revealed that direct runoff and sediment discharge increased with the progress of urban development and unmanaged agricultural activities. These observations were supported by the NDVI, landscape and hydrological trend analyses

Accuracy of GeoWEPP in estimating sediment load and runoff from a tropical watershed.

GeoWEPP, an integration of WEPP and TOPAZ within a GIS interface, was used to predict sediment load and runoff at the Lui Watershed, Selangor, Malaysia. Input files for land cover, slope, climate, soil, and management were generated within GeoWEPP and WEPP interfaces and topographic data comprising 1017 hillslopes were parameterized using TOPAZ algorithm. CLImate GENerator (CLIGEN) was used to estimate stochastic climatic parameters. Soil properties such as frequency of soil particles, CEC, OC, and rock fragment were utilised to define a reasonable range of soil parameters. A management file was generated using EPIC algorithm for different land use types and for all hillslopes during the simulation period 1996 - 2008. The results showed an over-estimation of sediment load and an underestimation of runoff compared to measured data. This work shows that GeoWEPP is able to predict runoff more accurately than sediment load

Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran

Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data

Scientific Irrigation Scheduling for Sustainable Production in Olive Groves

The present study aimed at investigating scientific irrigation scheduling (SIS) for the sustainable production of olive groves. The SIS allows farmers to schedule water rotation in their fields to abate crop water stress and maximize yields, which could be achieved through the precise monitoring of soil moisture. For this purpose, the study used three kinds of soil moisture sensors, including tensiometer sensors, irrometer sensors, and gypsum blocks for precise measurement of the soil moisture. These soil moisture sensors were calibrated by performing experiments in the field and laboratory at Barani Agricultural Research Institute, Chakwal in 2018 and 2019. The calibration curves were obtained by performing gravimetric analysis at 0.3 and 0.6 m depths, thereby equations were developed using regression analysis. The coefficient of determination (R2 ) at 0.3 and 0.6 m depth for tensiometer, irrometer, and gypsum blocks was found to be equal to 0.98, 0.98; 0.75, 0.89; and 0.82, and 0.95, respectively. After that, a drip irrigation system was installed with the calibrated soil moisture sensors at 0.3 and 0.6 m depth to schedule irrigation for production of olive groves as compared to conventional farmer practice, thereby soil moisture profiles of these sensors were obtained to investigate the SIS. The results showed that the irrometer sensor performed as expected and contributed to the irrigation water savings between 17% and 25% in 2018 and 2019, respectively, by reducing the number of irrigations as compared toother soil moisture sensors and farmer practices. Additionally, olive yield efficiencies of 8% and 9%were observed by the tensiometer in 2018 and 2019, respectively. The outcome of the study suggests that an effective method in providing sustainable production of olive groves and enhancing yield efficiency