Relevant bibliographies by topics / Irrigation scheduling

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Irrigation scheduling'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Irrigation scheduling"

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Kruse, E. Gordon, James E. Ells, and Ann E. McSay. "Scheduling Irrigations for Carrots." HortScience 25, no. 6 (June 1990): 641–44. http://dx.doi.org/10.21273/hortsci.25.6.641.

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A 3-year irrigation scheduling study on carrots (Daucus carota L.) was conducted at the Colorado State Univ. Horticulture Research Center near Fort Collins to determine the irrigation schedule that produced the best combination of high water use efficiency and marketable yields with the least amount of water and fewest irrigations. This study used an irrigation scheduling program developed by the U.S. Department of Agriculture/Agricultural Research Service with crop coefficients calculated for carrots. Maximum carrot production and water use efficiency were obtained when the scheduling program simulated a 30-cm rooting depth at planting, increasing linearly to 60 cm in 75 days. Best yields and water use efficiency were attained by irrigating whenever 40% of the available water in the root zone had been depleted. The computer program for irrigation scheduling is available on diskette from the authors.
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Cremona, Maria Victoria, Hartmut Stützel, and Henning Kage. "Irrigation Scheduling of Kohlrabi (Brassica oleracea var. gongylodes) Using Crop Water Stress Index." HortScience 39, no. 2 (April 2004): 276–79. http://dx.doi.org/10.21273/hortsci.39.2.276.

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Two-year field experiments were carried out to evaluate the suitability of crop water stress index (CWSI) as a basis for irrigation scheduling of kohlrabi (Brassica oleracea L. var. gongylodes) by comparison with irrigation scheduling based on total soil water content (SWC). In the first year, irrigation scheduling when CWSI exceeded 0.3 resulted in more frequent water applications, but the total amount of irrigation water given was lower compared to irrigation when SWC fell below 70%. Kohlrabi tuber fresh weight at harvest was similar in both scheduling treatments, leading to 25% higher irrigation water use efficiency in the CWSI-scheduled plots. In the second year, three threshold levels, i.e., 0.2 and 80%, 0.4 and 60%, and 0.6 and 40% of CWSI and SWC, respectively, were investigated. At the level of highest water supply (CWSI = 0.2 and SWC = 80%), the total amount of water supplied was less in the CWSI but the number of irrigations was higher than in the SWC plots. The CWSI-based approach may be a method for irrigation scheduling of vegetables under temperate conditions. The higher irrigation frequency required would make this method particularly suitable in combination with irrigation system that allow frequent applications, i.e., in drip irrigation. To improve the method, a coupling with a soil water balance model seems promising.
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Butts, Christopher L., Ronald B. Sorensen, and Marshall C. Lamb. "Irrigator Pro: Progression of a Peanut Irrigation Scheduling Decision Support System." Applied Engineering in Agriculture 36, no. 5 (2020): 785–95. http://dx.doi.org/10.13031/aea.13909.

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HighlightsThe logic used in developing a decision support system for irrigating peanut based on max/min soil temperature is describedLogic to transform decision support system from peanut to irrigate corn and cotton with and without soil sensors.Progression of a decision support system from a desktop program to a web/mobile applicationAbstract. Irrigator Pro is a decision support tool for scheduling irrigation events in peanut. It was deployed in 1995 as a rule-based system using crop history, yield potential, soil type, in-season irrigation/rainfall and maximum/minimum soil temperature. As computing platforms have progressed from desktop personal computers to mobile web-based platforms, Irrigator Pro has been updated and is now deployed as a web-based program and an application for mobile devices. Irrigator Pro not only works for peanuts but has been modified to irrigate both corn and cotton. The irrigation decisions are now based on in-field soil water potential measurements in addition to the traditional checkbook with max/min soil temperatures. Users are individual growers, extension agents, and agronomic consultants. The objective of this manuscript is to document the initial development of Irrigator Pro as an expert system combining data and experiential knowledge and the progression from a checkbook-based decision support system to a hybrid system using observed weather data and soil moisture measurement. The background knowledge, equations, and thresholds for triggering irrigation recommendations are included. Keywords: Decision support system, Irrigation scheduling, Irrigator Pro, Mobile app, Peanut, Soil water potential.
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Ells, James E., E. Gordon Kruse, and Ann E. McSay. "IRRIGATION. SCHEDULING PROGRAM FOR ZUCCHINI SQUASH." HortScience 25, no. 9 (September 1990): 1072d—1072. http://dx.doi.org/10.21273/hortsci.25.9.1072d.

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An irrigation scheduling program has been developed for zucchini squash that produced high yields and high water use efficiency with, a minimum number of irrigations. The irrigation program is based upon a soil water balance model developed by the USDA. This irrigation program is available in diskette form and may be used with any IBM compatible personal computer provided wind run, temperature, solar radiation, humidity and precipitation data are available.
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McNiesh, C. M., N. C. Welch, and R. D. Nelson. "Trickle Irrigation Requirements for Strawberries in Coastal California." Journal of the American Society for Horticultural Science 110, no. 5 (September 1985): 714–18. http://dx.doi.org/10.21273/jashs.110.5.714.

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Abstract Controlled trickle irrigation applications in commercial strawberry [Fragaria xananassa Duch. ‘Heidi’] plantings in coastal California were used to estimate crop coefficients (Kc) relating applied water requirements to reference evapotranspiration (ETo) and Class A pan evaporation (Epan). The value of Kc increased with foliar growth during spring and leveled off at a relatively constant maximum through summer and early autumn. Since rates of ETo averaged 13% higher than Epan, lower Kc values were recommended for irrigation schedules based on ETo than for schedules based on Epan. The value of maximum Kc for ETo scheduling was fixed between 0.45 ≤ Kc ≤ 0.7, whereas the limits for Epan scheduling were 0.55 ≤ Kc ≤ 0.8. Upper Kc limits were established by scheduling small volume irrigations only as necessary to maintain favorable soil moisture conditions. Harvest results and soil matric potential measurements indicated that irrigations scheduled at the lower Kc limits could lead to production loss. An evaluation of grower irrigation practices showed that current applied water rates could be reduced significantly by scheduling irrigations with recommended Kc values.
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Pachore, Rahul Ashok, and Sachin Babaji Deore. "Study on Soil Moisture Depletion Pattern of Wheat Under Different IW/CPE Ratio." Journal of Agriculture Research and Technology 47, no. 02 (2022): 218–25. http://dx.doi.org/10.56228/jart.2022.47218.

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Wheat is one of the most important cereal crops and staple food of nearly 35 percent of the world population. In climatologically approaches, irrigation is scheduled on IW/CPE ratio. In IW/CPE approach, known amount of irrigation water is applied when cumulative pan evaporation reaches predetermined level. The experiment was conducted in randomized block design with irrigation scheduling on climatological approach i.e. on IW/CPE ratios of IW/CPE=0.6, IW/CPE=0.8, IW/CPE=1.0, IW/CPE=1.2 and control treatment with six irrigations at critical growth stages of wheat. Seasonal water requirement of wheat was found to be highest (570 mm) under irrigation scheduling at control treatment (I 4). Favorable soil moisture was maintained in the irrigation scheduling treatment of IW/CPE=1.2 (I 4) throughout the growing period and it was always maintained in allowable depletion regime. However, soil moisture was inadequate in irrigation scheduling at IW/CPE=0.6 (I 1). Highest water use efficiency was recorded in treatment I2 which may due to lowest water use, followed by I 3, I 4, I 1 and I 5.
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Sui, Ruixiu, and Earl D. Vories. "Comparison of Sensor-Based and Weather-Based Irrigation Scheduling." Applied Engineering in Agriculture 36, no. 3 (2020): 375–86. http://dx.doi.org/10.13031/aea.13678.

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HighlightsSensor-based irrigation scheduling methods (SBISM) were compared with computerized water balance scheduling.Number and time of irrigation events scheduled using the SBISM were often different from those predicted by the computerized method.The highly variable soils at the Missouri site complicated interpretation of the sensor values.Both SBISM and computerized water balance scheduling could be used for irrigation scheduling with close attention to soil texture and effective rainfall or irrigation.Abstract. Sensor-based irrigation scheduling methods (SBISM) measure soil moisture to allow scheduling of irrigation events based on the soil-water status. With rapid development of soil moisture sensors, more producers have become interested in SBISM, but interpretation of the sensor data is often difficult. Computer-based methods attempt to estimate soil water content and the Arkansas Irrigation Scheduler (AIS) is one example of a weather-based irrigation scheduling tool that has been used in the Mid-South for many years. To aid producers and consultants interested in learning more about irrigation scheduling, field studies were conducted for two years in Mississippi and a year in Missouri to compare SBISM with the AIS. Soil moisture sensors (Decagon GS-1, Acclima TDR-315, Watermark 200SS) were installed in multiple locations of a soybean field (Mississippi) and cotton field (Missouri). Soil water contents of the fields were measured hourly at multiple depths during the growing seasons. The AIS was installed on a computer to estimate soil water content and the required data were obtained from nearby weather stations at both locations and manually entered in the program. In Mississippi, numbers and times of the irrigation events triggered by the SBISM were compared with those that would have been scheduled by the AIS. Results showed the number and time of irrigation events scheduled using the SBISM were often different from those predicted by the AIS, especially during the 2018 growing season. The highly variable soils at the Missouri site complicated the interpretation of the sensor values. While all of the sites were within the Tiptonville silt loam map unit, some of the measurements appeared to come from sandier soils. The AIS assumed more water entered the soil than the sensors indicated from both irrigations and rainfalls less than 25 mm. While the irrigation amounts were based on the pivot sprinkler chart, previous testing had confirmed the accuracy of the charts. Furthermore, the difference varied among sites, especially for rainfall large enough to cause runoff. The recommendations based on the Watermark sensors agreed fairly well with the AIS in July after the data from the sandiest site was omitted; however, the later irrigations called for by the AIS were not indicated by the sensors. Both the sensor-based irrigation scheduling method and the AIS could be used as tools for irrigation management in the Mid-South region, but with careful attention to soil texture and the effective portion of rainfall or irrigation. Keywords: Irrigation scheduling, Soil moisture sensor, Soil water content, Water management.
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Ells, James E., E. Gordon Kruse, and Ann E. McSay. "Scheduling Irrigations for Cucumbers." HortScience 24, no. 3 (June 1989): 448–52. http://dx.doi.org/10.21273/hortsci.24.3.448.

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Abstract Cucumber (Cucumis sativus L.) irrigation scheduling was studied during the 4 years of 1983-1986. Tensiometers were used during the first year to determine when to irrigate, and the USDA irrigation scheduling program was used to determine the amount of water to apply. The data from the first year’s study indicated that the plants had not been stressed; therefore, the following year, estimates of the available water depletion were made with the USDA irrigation scheduling program, with tensiometers used only for comparison. After 4 years of study, we concluded that the best combination of high yield, high water use efficiency, and fewest number of irrigations was obtained if cucumbers were irrigated when the original scheduling program determined that 40% of the available water was depleted, applying only 70% of the water that the program indicated was required. This signaled that the program was overestimating the rate at which water was being depleted. Therefore, as a final step, a revised set of cucumber coefficients that approximated daily evapotranspiration (ET) more closely was determined. When using the revised coefficients, cucumbers should receive the exact amount of water called for by the irrigation program.
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Andrade, Manuel A., Susan A. O’Shaughnessy, and Steven R. Evett. "ARSPivot, A Sensor-Based Decision Support Software for Variable-Rate Irrigation Center Pivot Systems: Part A. Development." Transactions of the ASABE 63, no. 5 (2020): 1521–33. http://dx.doi.org/10.13031/trans.13907.

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HighlightsThe ARSPivot software seamlessly integrates site-specific irrigation scheduling methods with weather, plant, and soil water sensing systems in the operation of variable-rate irrigation (VRI) center pivot systems.ARSPivot embodies an Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system that incorporates site-specific irrigation scheduling methods and automates the collection and processing of data obtained from sensing systems supporting them.ARSPivot incorporates a friendly graphical user interface (GUI) that assists in the process of setting up a computerized representation of a coupled ISSCADA VRI center pivot system and simplifies the review of irrigation prescriptions automatically generated based on sensor feedback.ARSPivot’s GUI includes a geographic information system (GIS) that relates sensed data and imported GIS data to specific field control zones.Abstract. The commercial availability of variable-rate irrigation (VRI) systems gives farmers access to unprecedented control of the irrigation water applied to their fields. To take full advantage of these systems, their operations must integrate site-specific irrigation scheduling methods that in turn should be supported by a network of sensing systems. An Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system patented by scientists with the USDA-Agricultural Research Service (ARS) at Bushland, Texas, incorporates site-specific irrigation scheduling methods informed by weather, plant, and soil water sensing systems. This article introduces a software package, ARSPivot, developed to integrate the ISSCADA system into the operation of VRI center pivot systems. ARSPivot assists the operation and integration of a complex network of sensing systems, irrigation scheduling methods, and irrigation machinery to achieve this end. ARSPivot consists of two independent programs interacting through a client-server architecture. The client program is focused on automatically collecting and processing georeferenced data from sensing systems and communicating with a center pivot control panel, while the server program is focused on communicating with users through a friendly graphical user interface (GUI) involving a geographic information system (GIS). The GUI allows users to visualize and modify site-specific prescription maps automatically generated based on sensor-based irrigation scheduling methods, and to control and monitor the application of irrigation amounts specified in these recommended prescription maps using center pivots equipped for VRI zone control or VRI speed control. This article discusses the principles and design considerations followed in the development of ARSPivot and presents tools implemented in the software for the virtual design and physical operation of a coupled ISSCADA VRI center pivot system. This article also illustrates how the ISSCADA system and ARSPivot constitute a comprehensive sensor-based decision support system (DSS) for VRI management that is accessible to users without in-depth knowledge of sensing systems or irrigation scheduling methods. Keywords: Center pivot irrigation, Decision support system, Precision agriculture, Sensors, Site-specific irrigation scheduling, Software, Variable rate irrigation.n
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Anwar, Arif A., and Tonny T. de Vries. "Sequential Irrigation Scheduling Avoiding Night Irrigation." Journal of Irrigation and Drainage Engineering 143, no. 7 (July 2017): 04017012. http://dx.doi.org/10.1061/(asce)ir.1943-4774.0001178.

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Dissertations / Theses on the topic "Irrigation scheduling"

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Brown, Peter Derek. "Optimal irrigation scheduling." Thesis, University of Canterbury. Civil and Natural Resources Engineering, 2008. http://hdl.handle.net/10092/1255.

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An optimal stochastic multi-crop irrigation scheduling algorithm was developed which was able to incorporate complex farm system models, and constraints on daily and seasonal water use, with the objective of maximising farm profit. This scheduling method included a complex farm simulation model in the objective function, used decision variables to describe general management decisions, and used a custom heuristic method for optimisation. Existing optimal schedulers generally use stochastic dynamic programming which relies on time independence of all parameters except state variables, thereby requiring over-simplistic crop models. An alternative scheduling method was therefore proposed which allows for the inclusion of complex farm system models. Climate stochastic properties are modelled within the objective function through the simulation of several years of historical data. The decoupling of the optimiser from the objective function allows easy interchanging of farm model components. The custom heuristic method, definition of decision variables, and use of the Markov chain equation (relating an irrigation management strategy to mean water use) considerably increases optimisation efficiency. The custom heuristic method used simulated annealing with continuous variables. Two extensions to this method were the efficient incorporation of equality constraints and utilisation of population information. A case study comparison between the simulated annealing scheduler and scheduling using stochastic dynamic programming, using a simplistic crop model, showed that the two methods resulted in similar performance. This demonstrates the ability of the simulated annealing scheduler to produce close to optimal schedules. A second case study demonstrates the ability of the simulated annealing scheduler to incorporate complex farm system models by including the FarmWi$e model by CSIRO in the objective function. This case study indicates that under conditions of limited seasonal water, the simulated annealing scheduler increases pasture yield returns by an average of 10%, compared with scheduling irrigation using best management practice. Alternatively expressed, this corresponds to a 20-25% reduction in seasonal water use (given no change in yield return).
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Fox, Fred Andrew 1956. "Irrigation scheduling decision support." Diss., The University of Arizona, 1997. http://hdl.handle.net/10150/288770.

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Irrigation scheduling using the soil water balance approach has been recommended to irrigators for many years. Reasonably good results are normally obtained by researchers using carefully quantified inputs. Irrigators in production agriculture may estimate inputs and then question the validity of the method when the irrigation recommendations conflict with present irrigation schedules. By associating each input with an interval representing possible bias based on the way the input was estimated, and solving the irrigation scheduling model using the intervals as inputs, the output was associated with an interval representing possible bias. This method was also used to evaluate possible bias associated with growing degree day based crop coefficient curves developed from Arizona crop consumptive use measurements. For comparison purposes, roughly estimated inputs based on irrigation system type, soil type, area weather data and available crop coefficient curves were used as default intervals. Improved input intervals consisted of observed irrigation system performance, soil property measurements, local weather data and theoretical improvements in crop coefficient curves. For surface irrigation, field observation of plant stress and soil water content showed the greatest potential to improve irrigation date predictions. For buried drip under a row crop, accuracy of the predicted daily irrigation rate was most improved by a better estimate of irrigation efficacy.
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De, Vries Tonny Tessa. "Irrigation scheduling with integer programming." Thesis, University of Southampton, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273891.

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Scherer, T., D. Slack, J. Watson, and F. Fox. "Comparison of Three Irrigation Scheduling Methods and Evaluation of Irrigation Leaching Characteristics." College of Agriculture, University of Arizona (Tucson, AZ), 1989. http://hdl.handle.net/10150/204858.

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Three methods were used to schedule irrigations on replicated plots at the Maricopa Ag Center using DPL 90 cotton. The three methods were: a soil water balance model based on historic consumptive use curves, a soil water balance model based on the Modified Penman Equation and daily weather (AZMET), and infrared thermometry using the C.W.S.I. A potassium-bromide conservative tracer was applied at selected sites in the plots to evaluate leaching characteristics. The irrigation scheduling test was duplicated at the Safford Experiment Station and is presented in another report. Results from the 1988 data indicate that there was no significant difference in yield between the 3 methods. There was a significant difference in water applied; the historic consumptive-use curves was the lowest and the Penman equation method was the highest.
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Scherer, T., D. Slack, J. Watson, and F. Fox. "Comparison of Three Irrigation Scheduling Methods and Evaluation of Irrigation Leaching Characteristics." College of Agriculture, University of Arizona (Tucson, AZ), 1990. http://hdl.handle.net/10150/208305.

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Three methods were used to schedule irrigations during 1989 on replicated plots at the Maricopa Ag Center using DPL 90 cotton. This is a continuation of the research initiated in 1988 using the same field The three methods were; a soil water balance model based on historic consumptive use curves, a soil water balance model based on the Modified Penman Equation and daily weather (AZMET), and infrared thermometry using the C.W.S.I. A potassium- bromide conservative tracer was applied at selected sites in the plots to evaluate leaching characteristics. The irrigation scheduling test was again duplicated at the Safford Experiment Station and is presented in another report. Results from this years data indicate that there was no significant difference in yield among the three methods. However, as in 1988 there was a significant difference in water applied with historic consumptive use (ERIE) the lowest and the Penman equation method (CHECKBOOK) the highest.
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Scherer, Tom, Don Slack, Jack Watson, and Fred Fox. "Comparison of Three Irrigation Scheduling Methods and Evaluation of Irrigation Leaching Characteristics." College of Agriculture, University of Arizona (Tucson, AZ), 1991. http://hdl.handle.net/10150/208344.

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Three methods were used to schedule irrigations during the 1990 growing season on replicated plots at the Maricopa Ag Center using DPL 90 cotton. This is the final report of the research initiated in 1988. The three methods were: a soil water balance model based on historic consumptive use curves (ERIE), a soil water balance model (AZSCHED) based on the Modified Penman Equation and daily weather (AZMET), and infrared thermometry using the Crop Water Stress Index (CWSI). A potassium- bromide conservative tracer was applied at selected sites in the plots to evaluate leaching characteristics. The irrigation scheduling test was again duplicated at the Safford Experiment Station and is presented in another report. Results from this years data indicate that there was no significant difference in yield between the 3 methods. Also, there was no significant difference in the amount of applied irrigation water. The AZSCHED and ERIE methods will be developed into Extension educational tools and released for use by growers.
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Ahmed, M. "A plant analogue sensor for irrigation scheduling." Thesis, University of Newcastle Upon Tyne, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378303.

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Martin, Edward C. "Methods of Measuring for Irrigation Scheduling - WHEN." College of Agriculture, University of Arizona (Tucson, AZ), 2014. http://hdl.handle.net/10150/333138.

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Revised; Originally published: 2009
6 pp.
Proper irrigation management requires that growers assess their irrigation needs by taking measurements of various physical parameters. Some use sophisticated equipment while others use tried and true common sense approaches. Whichever method used, each has merits and limitations. In developing any irrigation management strategy, two questions are common: “When do I irrigate?” and “How much do I apply?” This bulletin deals with the WHEN.
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Martin, Edward. "Methods of Measuring for Irrigation Scheduling -- WHEN." College of Agriculture and Life Sciences, University of Arizona (Tucson, AZ), 2009. http://hdl.handle.net/10150/147005.

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Biggs, Niel, and Lee Clark. "Comparison of Irrigation Scheduling Methods on Wheat." College of Agriculture, University of Arizona (Tucson, AZ), 1986. http://hdl.handle.net/10150/200544.

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Several improved irrigation scheduling methods are available to farmers to reduce the amount of water used while not reducing crop yield. Each scheduling method has its own advantages and disadvantages. Because of the disadvantages, farmers have been slow in adopting some of the newer irrigation scheduling methods. This study compares two improved scheduling methods, the neutron hydro probe and a simplified bookkeeping method using a personal computer, with the irrigation practices normally used by a farm manager to grow wheat. In addition to the traditional parameters of applied water and yield, the time and difficulty associated with each method were evaluated.
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Books on the topic "Irrigation scheduling"

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Trimmer, Walter L. Irrigation scheduling. [Corvallis, Or.]: Oregon State University Extension Service, Washington State University Cooperative Extension, University of Idaho Cooperative Extension Service, and U.S. Dept. of Agriculture, 1986.

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Brouwer, C. Irrigation scheduling. Rome: Food and Agricultural Organization of the United Nations, 1989.

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Brouwer, C. J. Irrigation scheduling: A manual. Rome: Food and Agriculture Organization of the United Nations, 1989.

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Ontario. Ministry of Agriculture and Food. Irrigation scheduling for fruit crops. S.l: s.n, 1990.

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Goodwin, Ian. Irrigation of vineyards: A winegrape grower's guide to irrigation scheduling and regulated deficit irrigation. Tatura, Vic: Institute of Sustainable Irrigated Agriculture, 1995.

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India. Central Board of Irrigation and Power., ed. Irrigation scheduling with limited water supplies. New Delhi: Central Board of Irrigation and Power, 1991.

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Ley, Thomas W. Simple irrigation scheduling using pan evaporation. Pullman, [Wash.]: Cooperative Extension, College of Agriculture & Home Economics, Washington State University, 1987.

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1940-, Duke Harold R., Crookston Mark A, United States. Soil Conservation Service, United States. Agricultural Research Service, Colorado State University. Cooperative Extension Service, and High Plains Technical Coordinating Committee (U.S.), eds. Scheduling irrigations: A guide for improved irrigation water management through proper timing and amount of water application. Denver, Colo: Soil Conservation Service, 1987.

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Niederholzer, Franz. Simple irrigation scheduling using the "look and feel" method. [Corvallis, Or.?]: Oregon State University Extension Service., 1998.

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Camp, C. R. Scheduling irrigation for corn in the Southeast. [Washington, D.C.?]: U.S. Dept. of Agriculture, Agricultural Research Service, 1988.

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Book chapters on the topic "Irrigation scheduling"

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Whitmore, J. S. "Irrigation Scheduling." In Drought Management on Farmland, 199–207. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-015-9562-9_20.

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Heermann, Dale F. "Irrigation Scheduling." In Sustainability of Irrigated Agriculture, 233–49. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8700-6_14.

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Hill, Robert W. "Irrigation Scheduling." In Agronomy Monographs, 491–509. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 2015. http://dx.doi.org/10.2134/agronmonogr31.c21.

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Ray, Lala I. P., I. Suting, K. Siangshai, A. K. Singh, Ram Singh, and P. K. Bora. "Performance of Winter Vegetables Under Gravity-Fed Drip Irrigation System." In Micro Irrigation Scheduling and Practices, 3–22. Other titles: Innovations and challenges in micro irrigation ; [v. 7] Description: Waretown, NJ : Apple Academic Press, 2017. | Series: Innovations and challenges in micro irrigation ; [volume 7]: Apple Academic Press, 2017. http://dx.doi.org/10.1201/9781315207384-1.

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Khodke, U. M. "Water use efficiency of sorghum under drip irrigation." In Micro Irrigation Scheduling and Practices, 185–94. Other titles: Innovations and challenges in micro irrigation ; [v. 7] Description: Waretown, NJ : Apple Academic Press, 2017. | Series: Innovations and challenges in micro irrigation ; [volume 7]: Apple Academic Press, 2017. http://dx.doi.org/10.1201/9781315207384-10.

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Siddapur, A. D., B. S. Polisgowdar, M. Nemichandrappa, M. S. Ayyanagowder, U. Satishkumar, and A. Hugar. "Water use Efficiency for Marigold Flower (Tagetes Erecta L.) Under Furrow and Drip Irrigation Systems." In Micro Irrigation Scheduling and Practices, 195–206. Other titles: Innovations and challenges in micro irrigation ; [v. 7] Description: Waretown, NJ : Apple Academic Press, 2017. | Series: Innovations and challenges in micro irrigation ; [volume 7]: Apple Academic Press, 2017. http://dx.doi.org/10.1201/9781315207384-11.

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Behera, A., C. H. R. Subudhi, and B. Panigrahi. "Development of Software for Multi Crop Drip Irrigation Design." In Micro Irrigation Scheduling and Practices, 209–52. Other titles: Innovations and challenges in micro irrigation ; [v. 7] Description: Waretown, NJ : Apple Academic Press, 2017. | Series: Innovations and challenges in micro irrigation ; [volume 7]: Apple Academic Press, 2017. http://dx.doi.org/10.1201/9781315207384-12.

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Choudhari, K. "Planning, Layout and Design of Drip Irrigation System." In Micro Irrigation Scheduling and Practices, 253–97. Other titles: Innovations and challenges in micro irrigation ; [v. 7] Description: Waretown, NJ : Apple Academic Press, 2017. | Series: Innovations and challenges in micro irrigation ; [volume 7]: Apple Academic Press, 2017. http://dx.doi.org/10.1201/9781315207384-13.

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Mohanty, K. S., B. Panigrahi, and J. C. Paul. "Head loss in double inlet lateral of a drip irrigation system." In Micro Irrigation Scheduling and Practices, 299–341. Other titles: Innovations and challenges in micro irrigation ; [v. 7] Description: Waretown, NJ : Apple Academic Press, 2017. | Series: Innovations and challenges in micro irrigation ; [volume 7]: Apple Academic Press, 2017. http://dx.doi.org/10.1201/9781315207384-14.

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Chavan, S. V., B. S. Polisgowdar, A. B. Joshi, M. S. Ayyanagowder, U. Satishkumar, and V. B. Wali. "Fertigation in a Drip Irrigation System: Evaluation of Venturi Injectors and its Simulation Study." In Micro Irrigation Scheduling and Practices, 343–56. Other titles: Innovations and challenges in micro irrigation ; [v. 7] Description: Waretown, NJ : Apple Academic Press, 2017. | Series: Innovations and challenges in micro irrigation ; [volume 7]: Apple Academic Press, 2017. http://dx.doi.org/10.1201/9781315207384-15.

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Conference papers on the topic "Irrigation scheduling"

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"Irrigation Scheduling." In Irrigation Systems Management. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2021. http://dx.doi.org/10.13031/ism.2021.6.

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Klocke, Norman L., Loyd R. Stone, and Dale A. Bolton. "Irrigation Scheduling for Deficit Irrigation." In World Environmental and Water Resources Congress 2009. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41036(342)403.

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Koné, Bamory Ahmed Toru, Rima Grati, Bassem Bouaziz, and Khouloud Boukadi. "Computerized Irrigation Scheduling." In 2023 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA). IEEE, 2023. http://dx.doi.org/10.1109/aiccsa59173.2023.10479301.

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Holloway-Phillips, M. M., W. Peng, D. Smith, and A. Terhorst. "Adaptive scheduling in deficit irrigation – a model-data fusion approach." In SUSTAINABLE IRRIGATION 2008. Southampton, UK: WIT Press, 2008. http://dx.doi.org/10.2495/si080191.

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Montagu, K. D., and R. J. Stirzaker. "Why do two-thirds of Australian irrigators use no objective irrigation scheduling methods?" In SUSTAINABLE IRRIGATION 2008. Southampton, UK: WIT Press, 2008. http://dx.doi.org/10.2495/si080101.

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Rodrigues, D. F. B., and C. D. G. C. Almeida. "The Irrigation Scheduling IGdroid Tool." In II Inovagri International Meeting. Fortaleza, Ceará, Brasil: INOVAGRI/INCT-EI/INCTSal, 2014. http://dx.doi.org/10.12702/ii.inovagri.2014-a054.

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Brent Q Mecham. "Audit Based Landscape Irrigation Scheduling." In 5th National Decennial Irrigation Conference Proceedings, 5-8 December 2010, Phoenix Convention Center, Phoenix, Arizona USA. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2010. http://dx.doi.org/10.13031/2013.35895.

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"Smartphone Apps for Irrigation Scheduling." In 2015 ASABE / IA Irrigation Symposium: Emerging Technologies for Sustainable Irrigation - A Tribute to the Career of Terry Howell, Sr. Conference Proceedings. American Society of Agricultural and Biological Engineers, 2015. http://dx.doi.org/10.13031/irrig.20152143940.

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RICARDO ASCENCIOS TEMPLO, DAVID, KAREM BELENMEZA CAPCHA, JEISSON DOMINGO LLUEN MONTANO, and ROSA LISETH LLIQUE GALLARDO. "Hydraulic Calibration, Automation, Irrigation Scheduling of Sprinkler Irrigation System - Unalm." In 38th IAHR World Congress. The International Association for Hydro-Environment Engineering and Research (IAHR), 2019. http://dx.doi.org/10.3850/38wc092019-1842.

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Oswald, Jared K., and Hal D. Werner. "On-Line Irrigation Scheduling within the Belle Fourche Irrigation District." In World Environmental and Water Resources Congress 2009. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41036(342)410.

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Reports on the topic "Irrigation scheduling"

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Taber, Henry G. Tomato Irrigation Scheduling for Optimum Production. Ames: Iowa State University, Digital Repository, 2008. http://dx.doi.org/10.31274/farmprogressreports-180814-488.

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Cohen, Yehezkiel, Glenn Hoffman, Marcel Fuchs, Harry I. Nightingale, Samuel Moreshet, and Robert B. Hutmacher. Irrigation Scheduling of Orchards Bases on Direct Measurements of Transpiration. United States Department of Agriculture, February 1987. http://dx.doi.org/10.32747/1987.7566751.bard.

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Chou, Frederick. Optimal Real-Time Pump and Irrigation Scheduling for Center-Pivot Sprinkler Systems. Office of Scientific and Technical Information (OSTI), September 1988. http://dx.doi.org/10.2172/5425039.

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Dugas, W. A., Marcel Fuchs, Samuel Moreshet, Yehezkiel Cohen, and K. Peterson. Evaluation of Infrared Thermometry to Assess Cotton Water Use with Emphasis on Irrigation Scheduling. United States Department of Agriculture, August 1987. http://dx.doi.org/10.32747/1987.7568081.bard.

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Harrer, B. J., and A. J. Lezberg. Potential conservation opportunities from the use of improved irrigation scheduling in the Pacific Northwest region. Office of Scientific and Technical Information (OSTI), March 1985. http://dx.doi.org/10.2172/5912837.

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Alchanatis, Victor, Steven Evett, Shabtai Cohen, Yafit Cohen, Moshe Meron, Amos Naor, Terry Howell, Paul Colaizzi, Troy Peters, and Yehoshua Saranga. Improved analysis of thermally sensed crop water status and mapping spatial variability for site specific irrigation scheduling. United States Department of Agriculture, January 2008. http://dx.doi.org/10.32747/2008.7613873.bard.

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Dasberg, Shmuel, Jan W. Hopmans, Larry J. Schwankl, and Dani Or. Drip Irrigation Management by TDR Monitoring of Soil Water and Solute Distribution. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568095.bard.

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Drip irrigation has the potential of high water use efficiency, but actual water measurement is difficult because of the limited wetted volume. Two long-term experiments in orchards in Israel and in California and several field crop studies supported by this project have demonstrated the feasibility of precise monitoring of soil water distribution for drip irrigation in spite of the limited soil wetting. Time Domain Reflectometry (TDR) enables in situ measurement of soil water content of well defined small volumes. Several approaches were tried in monitoring the soil water balance in the field during drip irrigation. These also facilitated the estimation of water uptake: 1. The use of multilevel moisture probe TDR system. This approach proved to be of limited value because of the extremely small diameter of measurement. 2. The placement of 20 cm long TDR probes at predetermined distances from the drippers in citrus orchards. 3. Heavy instrumentation with neutron scattering access tubes and tensiometers of a single drip irrigated almond tree. 4. High resolution spatial and temporal measurements (0.1m x 0.1m grid) of water content by TDR in corn irrigated by surface and subsurface drip. The latter approach was accompanied by parametric modelling of water uptake intensity patterns by corn roots and superimposed with analytical solutions for water flow from point and line sources. All this lead to general and physically based suggestions for the placement of soil water sensors for scheduling drip irrigation.
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Tanny, Josef, Gabriel Katul, Shabtai Cohen, and Meir Teitel. Application of Turbulent Transport Techniques for Quantifying Whole Canopy Evapotranspiration in Large Agricultural Structures: Measurement and Theory. United States Department of Agriculture, January 2011. http://dx.doi.org/10.32747/2011.7592121.bard.

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Original objectives and revisions The original objectives of this research, as stated in the approved proposal were: 1. To establish guidelines for the use of turbulent transport techniques as accurate and reliable tool for continuous measurements of whole canopy ET and other scalar fluxes (e.g. heat and CO2) in large agricultural structures. 2. To conduct a detailed experimental study of flow patterns and turbulence characteristics in agricultural structures. 3. To derive theoretical models of air flow and scalar fluxes in agricultural structures that can guide the interpretation of TT measurements for a wide range of conditions. All the objectives have been successfully addressed within the project. The only modification was that the study focused on screenhouses only, while it was originally planned to study large greenhouses as well. This was decided due to the large amount of field and theoretical work required to meet the objectives within screenhouses. Background In agricultural structures such as screenhouses and greenhouses, evapotranspiration (ET) is currently measured using lysimeters or sap flow gauges. These measurements provide ET estimates at the single-plant scale that must then be extrapolated, often statistically or empirically, to the whole canopy for irrigation scheduling purposes. On the other hand, turbulent transport techniques, like the eddy covariance, have become the standard for measuring whole canopy evapotranspiration in the open, but their applicability to agricultural structures has not yet been established. The subject of this project is the application of turbulent transport techniques to estimate ET for irrigation scheduling within large agricultural structures. Major conclusions and achievements The major conclusions of this project are: (i) the eddy covariance technique is suitable for reliable measurements of scalar fluxes (e.g., evapotranspiration, sensible heat, CO2) in most types of large screenhouses under all climatic conditions tested. All studies resulted with fair energy balance closures; (ii) comparison between measurements and theory show that the model is capable in reliably predicting the turbulent flow characteristics and surface fluxes within screenhouses; (iii) flow characteristics within the screenhouse, like flux-variance similarity and turbulence intensity were valid for the application of the eddy covariance technique in screenhouses of relatively dilute screens used for moderate shading and wind breaking. In more dense screens, usually used for insect exclusions, development of turbulent conditions was marginal; (iv) installation of the sensors requires that the system’s footprint will be within the limits of the screenhouse under study, as is the case in the open. A footprint model available in the literature was found to be reliable in assessing the footprint under screenhouse conditions. Implications, both scientific and agricultural The study established for the first time, both experimentally and theoretically, the use of the eddy covariance technique for flux measurements within agricultural screenhouses. Such measurements, along with reliable theoretical models, will enable more accurate assessments of crop water use which may lead to improved crop water management and increased water use efficiency of screenhouse crops.
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