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The Irrigation and Drainage Service Concept
Published in Hector M. Malano, Paul J.M. Van Hofwegen, Management of Irrigation and Drainage Systems – A Service Approach, 2018
Hector M. Malano, Paul J.M. Van Hofwegen
The concept of irrigation scheduling is to apply water to the crop in the correct amount and at the proper time to maximise crop production and/or profit, while maintaining a reasonably high irrigation efficiency. Scheduling irrigation at the farm level will thus depend on the type of crops, soil and climate as discussed earlier, the irrigation technology and the farmer’s skills to apply it. To plan for an efficient use of water, irrigators require information on the actual irrigation needs and the time and amount of water available to them in terms of discharge, duration and frequency. Ideally, irrigators should have direct access to water so they can react adequately to changes in the soil moisture status, optimise their irrigation schedules and synchronise them with other on- and off-farm activities. Consequently, farmers would prefer a maximum flexibility in water delivery. However, in most irrigation schemes this flexibility is difficult to provide, and the amount of water available or the timing of supply is often constrained by the flow control infrastructure.
Advanced Tools for Irrigation Scheduling
Published in Guangnan Chen, Advances in Agricultural Machinery and Technologies, 2018
Susan A. O’Shaughnessy, Ruixiu Sui
In the late 1990s, Howell (1996) reported that few fundamentals governing irrigation scheduling have changed since the 1970s. The fundamentals of irrigation scheduling include determining the amount of water to replenish crop water use or evapotranspiration, i.e., water that has evaporated from the soil and plant surfaces and water transpired through the crop; and deciding when to irrigate so as to minimize yield loss due to water stress. Crop water needs vary depending on atmospheric demand, the type of crop, and the growth stage of the crop. The timing of the irrigation events and the amount of water to apply are critical. The main methods of scientific irrigation scheduling can be categorized as soil-based, plant-based, and weather-based (Henggeler et al., 2011). Early methods of soil water sensing began as early as the 1930s (Smith-Rose, 1933), as reported by Or and Wraith (2002). Weather-based irrigation scheduling began in the 1950s (Penman, 1952; Bayer, 1954; Pruitt and Jensen, 1955). Decades later, the same can be said of irrigation scheduling fundamentals, but changes in the delivery of the scheduling methods have occurred with the development of more robust plant and soil water sensors, the introduction of radio frequency (RF) telemetry for outdoor wireless sensor network systems, and advancements in software applications and algorithms used for models and to formulate decision support for irrigation management.
Drip Irrigation Scheduling Of Citrus Reticulata Blanco (Kinnow): Using Low Cost Plant Leaf Temperature Sensor
Published in Megh R. Goyal, Balram Panigrahi, Sudhindra N. Panda, Micro Irrigation Scheduling and Practices, 2017
M. Debnath, a. K. Mishra, n. Patel
Water is becoming a scarce commodity worldwide today. Low-cost automated drip irrigation system can be one of the best options to save irrigation water as well as increasing crop yield. Drip irrigation scheduling for shallow rooted crops can be conducted using soil moisture content measurement using available soil moisture sensors. However, irrigation scheduling for deep-rooted crops is a difficult task. Plant based measurements in case of deep rooted crops will be helpful for irrigation scheduling. Recently developed microcontroller based leaf-air temperature differential sensor circuitry for drip irrigation scheduling of Kinnow crop can also be used for various other deep rooted horticultural crops for irrigation scheduling thereby saving irrigation water and increasing crop yield. This study indicates that the developed low cost automated drip irrigation system based on plant leaf temperature sensor system can save 8.6% more water compared to manually operated drip system and 49.6% more water compared to basin irrigation system.
Towards precision irrigation management: A review of GIS, remote sensing and emerging technologies
Published in Cogent Engineering, 2022
Erion Bwambale, Zita Naangmenyele, Parfait Iradukunda, Komi Mensah Agboka, Eva A. Y. Houessou-Dossou, Daniel A. Akansake, Michael E. Bisa, Abdoul-Aziz H. Hamadou, Joseph Hakizayezu, Oluwaseun Elijah Onofua, Sylvester R. Chikabvumbwa
Irrigation Scheduling is the process of deciding when and how much to irrigate. Optimal irrigation scheduling is important to ensure that crop water requirements are met and also in saving water (Bwambale et al., 2022). GIS has been applied in irrigation scheduling. Rowshon et al. (2003)2003 developed a program using GIS for the spatial and temporal distribution of irrigation supply for Malaysia’s large-scale rice irrigation system. The system determined and delivered water periodically based on spatial and temporal demand. Fortes et al. (2005) used a GIS-based irrigation scheduling simulation model to support improved water use. The model implemented a 15–20-day time interval between irrigations. In addition, the model was useful in handling time series of weather data. Table 4 presents a summary of published literature that has used GIS for irrigation scheduling applications.
The water footprint of the EU: quantification, sustainability and relevance
Published in Water International, 2018
To increase water productivity for agricultural production and tackle water scarcity and water pollution, integrated land and water management is a necessity. This refers to the promotion of system-based farming practices that integrate land, water, nutrient, livestock and crop management within a river basin management approach. The following farm-level actions are relevant (amongst others) (Vanham & Bidoglio, 2013): Management of irrigation systems: choice of technology (e.g., drip), irrigation scheduling, deficit irrigation, use of soil moisture and canopy sensors.Soil moisture conservation: e.g., by conservation tillage that optimizes soil carbon content or mulching (organic residues, plastic films).Nutrient management: including on-farm nutrient cycling or precision agriculture, to increase yields and water productivity and reduce water pollution.Cropping strategies: including the avoidance of growing high-water-demand crops during summer (e.g., maize) when water availability is low; and growing drought-tolerant or low-water-demand crops.Optimal use of available water resources: including rain water harvesting, wastewater recycling and artificial groundwater recharge (natural water buffer for use during dry periods).Limit the intensification of livestock production to animal welfare constraints.Riparian buffers (Weissteiner, Bouraoui, & Aloe, 2013) or constructed wastewater treatment wetlands to control water quality.
Soil moisture estimation using novel bio-inspired soft computing approaches
Published in Engineering Applications of Computational Fluid Mechanics, 2022
Roozbeh Moazenzadeh, Babak Mohammadi, Mir Jafar Sadegh Safari, Kwok-wing Chau
Soil moisture plays a key role in irrigation scheduling as the goal of irrigation is to increase current soil moisture until reaching field capacity. Therefore, knowing SM content will be very helpful in determining irrigation depth and frequency, which are effective in optimal water consumption and prevention of water stress to plants, respectively. This work examined the capability of bio-inspired optimization algorithms in estimating SM over 2007–2008 in Turkey using meteorological variables as model inputs. Hybridization of all bio-inspired algorithms with ANFIS (ANFIS-WOA, ANFIS-KHA and ANFIS-FA) improved SM estimates in comparison with the base ANFIS model, although ANFIS-WOA performed best with an RMSE of 1.68. All models showed almost the same performance for measured moisture contents higher than 35%, so it can be concluded that the differences in performance among the optimization algorithms and their differences with the base ANFIS model in estimating SM has stemmed from the first two intervals, i.e. [15–25) and [25–35)%. In terms of model performance from the viewpoint of under- or over-estimation of SM, ANFIS-WOA in under-estimation set and ANFIS-KHA in over-estimation set outperformed the other models, with RMSEs of 1.44 and 1.94, respectively. All models except ANFIS had lower errors in under-estimation set compared to the over-estimation set. Limitations of the present study are twofold. The first is SM simulation in the form of AI models, which, similar to all modeling attempts, may be accompanied by uncertainties. The structure of the ANFIS model is complex, and the number and type of its membership functions can add to this complexity. Metaheuristic optimization algorithms also suffer such disadvantages as parameter setting, high computational complexity, getting trapped in local optimums, and high running time. The second limitation is the constraints that affect the structures defined in this study. For example, the authors tried to estimate SM – which is one of the hard-to-measure soil parameters and only measured in rare and special conditions – using the main meteorological parameters which are measured at all stations together with a single soil parameter (soil temperature) as the inputs of AI models. Obviously, the use of other readily available soil parameters along with the parameters used in this study can lead to a better understanding of SM dynamics and thus to better SM estimates. The basic idea of the present work can be evaluated more rigorously in further studies by employing other AI models and optimization algorithms under various climates, and using remote sensing indices that indirectly indicate the SM status and can help in making more accurate SM estimates across larger areas.