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1
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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Ells, James E., Ann E. McSay, and E. G. Kruse. "Irrigation Scheduling Programs for Cabbage and Zucchini Squash." HortTechnology 3, no. 4 (October 1993): 448–53. http://dx.doi.org/10.21273/horttech.3.4.448.
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Irrigation scheduling programs were developed for cabbage and zucchini squash that produced high yield and water-use efficiency with a minimum number of irrigations. The irrigation programs are based on a soil water balance model developed by the USDA. The procedure involved selecting irrigation programs developed for similar crops and using them as standards for cabbage and zucchini for three growing seasons. The treatments involved irrigation levels higher and lower than the standard. After the third year, the best treatment for each year was selected. Coefficients for the standard model then were adjusted by trial and error to produce a program that called for the same number of irrigations and the same amount of water as the best-performing treatment when using the same weather data. These revised programs for cabbage and zucchini squash are available on computer disks and may be used on any IBM compatible PC provided wind, temperature, solar radiation, humidity, and precipitation data are available,
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Humphreys, E., R. J. G. White, D. J. Smith, and D. C. Godwin. "Evaluation of strategies for increasing irrigation water productivity of maize in southern New South Wales using the MaizeMan model." Australian Journal of Experimental Agriculture 48, no. 3 (2008): 304. http://dx.doi.org/10.1071/ea06092.
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MaizeMan is Windows-based decision support software, derived from CERES Maize and SWAGMAN Destiny, which can be used for real-time irrigation scheduling or strategic analysis. Evaluation of MaizeMan for sprinkler and furrow-irrigated maize (Pioneer 3153) showed good predictive ability for yield, biomass, runoff and soil water depletion between sowing and harvest. MaizeMan simulations using 43 years of weather data from Griffith, New South Wales, suggested that the biggest influence on yield, irrigation requirement and irrigation water productivity is seasonal weather conditions. For example, yield of October-sown 3153 irrigated frequently to avoid soil water deficit varied from about 8 to 16 t/ha, while net irrigation and net irrigation water productivity varied from 7 to 11 ML/ha and 0.8 to 1.6 t/ML, respectively. The optimum sowing window for maximising yield and irrigation water productivity is wide, from late September to mid November. Delaying sowing beyond this may result in higher yield and irrigation water productivity; however, delayed maturity would lead to problems for grain drying and harvesting in winter and increased insect pressure. The simplest management strategy for maximising yield and irrigation water productivity is irrigation scheduling tailored to soil type. Irrigation scheduling can be assisted by real-time scheduling using MaizeMan, provided soil hydraulic properties are accurately characterised. One to two irrigations can also be saved by growing shorter duration hybrids, but the tradeoff is lower yield, while irrigation water productivity is maintained. Simulated sprinkler irrigation increased yield and net irrigation water productivity by small amounts (averages of 0.5 t/ha and 0.2 t/ML, respectively) relative to well-scheduled flood irrigation, through improved soil water and aeration status and reduced deep drainage loss.
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de Vries, Tonny T., and Arif A. Anwar. "Irrigation Scheduling Using Complex Machine Scheduling." Journal of Irrigation and Drainage Engineering 141, no. 5 (May 2015): 04014065. http://dx.doi.org/10.1061/(asce)ir.1943-4774.0000824.
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Hartz, T. K. "Drip-irrigation Scheduling for Fresh-market Tomato Production." HortScience 28, no. 1 (January 1993): 35–37. http://dx.doi.org/10.21273/hortsci.28.1.35.
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Drip-irrigation scheduling techniques for fresh-market tomato (Lycopersicon esculentum Mill.) production were compared in three growing seasons (1989-91). Three regimes were evaluated: EPK [reference evapotranspiration (ETo, corrected Penman) × programmed crop coefficients], ECC (ET0 × a crop coefficient based on estimated percent canopy coverage), and SMD (irrigation at 20% available soil moisture depletion). EPK coefficients ranged from 0.2 (crop establishment) to 1.1 (full canopy development). Percent canopy coverage was estimated from average canopy width ÷ row width. Irrigation in the SMD treatment was initiated at -24 kPa soil matric tension, with recharge limited to 80% of daily ET0. The EPK and ECC regimes gave similar fresh fruit yields and size distributions in all years. With the EPK scheduling technique, there was no difference in crop response between daily irrigation and irrigation three times per week. In all seasons, ECC scheduling resulted in less total water applied than EPK scheduling and averaged 76% of seasonal ET0 vs. 86% for EPK. Irrigating at 20% SMD required an average of only 64% of seasonal ET0; marketable yield was equal to the other scheduling techniques in 1989 and 1991, but showed a modest yield reduction in 1990. Using an SMD regime to schedule early season irrigation and an ECC system to guide application from mid-season to harvest may be the most appropriate approach for maximizing water-use efficiency and crop productivity.
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Simonne, Eric, and Doyle A. Smittle. "AN IRRIGATION SCHEDULING MODEL FOR TURNIP GREENS." HortScience 25, no. 9 (September 1990): 1068e—1068. http://dx.doi.org/10.21273/hortsci.25.9.1068e.
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An irrigation scheduling model for turnip greens (Brassica rapa L.) was developed and validated.. The irrigation scheduling model is represented by the equation: 12.7 (i-3) * 0.5 ASW = 0i-1 + Ei(0.365+0.00154i+0.00011i2) - R - I where crop age is i; effective root depth is 12.7 * (i-3) with a maximum of 300 mm; usable water (cm/cm of soil) is 0.5 ASW; deficit on the previous day is Di-1 evapotranspiration; is pan evaporation (Ei) times 0.365+0.0154i+0.00011i2; rainfall (R) and irrigation (I) are in millimeters. Yield measured as leaf weight, and quality analyzed in terms of color (Gardner XL20 cronameter L, a, b), leaf blade and blade: stem weight ratio were determined. Leaf yield and quality responses were affected by both irrigation and fertilizer rates. Yield increased quadratically as irrigation rates increased from 0 to 190% of the model rate. Maximum leaf yields were produced by irrigations at 100% of the model rate. Leaf quality parameters also tended to change quadratically with irrigation rates. Leaf yield and quality changed quadratically as nitrogen fertilizer rates increased from 80 to 120% of the median recommended N rate for Georgia.
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R., Rana Prathap, and Ratna Raju Ch. "Estimation of Crop Water Requirement and Determination of Irrigation Schedule for Tomato Crop by using CROPWAT 8.0 Model." Environment and Ecology 42, no. 1A (March 2024): 341–46. http://dx.doi.org/10.60151/envec/hjnw7239.
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A study was conducted to estimate the crop water requirement and also to determine the irrigation schedule of tomato crop using CROPWAT 8.0 model at College of Agricultural Engineering Madakasira, Andhra Pradesh. The experiment was carried out under two methods of irrigation scheduling (Traditional furrow irrigation method and CROPWAT irrigation scheduling method). The total volume of water applied to the crop under traditional furrow irrigation method and CROPWAT irrigation scheduling method was 73.188 m3 and 47.0611 m3 respectively. Hence 35.69% of water saved in CROPWAT irrigation scheduling method. It was observed that the yield of the tomato crop in CROPWAT irrigation scheduling method was 36.8% more than the traditional furrow irrigation method. The crop water use efficiency in CROPWAT irrigation method was 58.36% more than the traditional furrow irrigation method. Thus, the study concluded that it is capable for strategic planning on irrigation management and scheduling in the view of water saving technology.
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Al-haddad, Amer Hassan, and Alaa Ibraheem Badi Al-Safi. "Scheduling of Irrigation and Leaching Requirements." Journal of Engineering 21, no. 3 (March 1, 2015): 73–92. http://dx.doi.org/10.31026/j.eng.2015.03.05.
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Iraq depends mainly on Tigris and Euphrates Rivers to provide high percentage of agricultural water use for thousands years. At last years, Iraq is suffering from shortage in water resources due to global climate changes and unfair water politics of the neighboring countries, which affected the future of agriculture plans for irrigation, added to that the lack of developed systems of water management in the irrigation projects and improper allocation of irrigation water, which reduces water use efficiency and lead to losing irrigation water and decreasing in agricultural yield. This study aims at studying the usability of irrigation and leaching scheduling within the irrigating projects and putting a complete annual or seasonal irrigation program as a solution for the scarcity of irrigation water, the increase of irrigation efficiency, lessening the salinity in the projects and preparing an integral irrigation calendar through field measurements of soil physical properties and chemical for project selected and compared to the results of the irrigation scheduling and leaching with what is proposed by the designers. The process is accomplished by using a computer program which was designed by Water Resources Department at the University of Baghdad, with some modification to generalize it and made it applicable to various climatic zone and different soil types. Study area represented by large project located at the Tigris River, and this project was (Al-Amara) irrigation project. Sufficient samples of project's soil were collected so as to identify soil physical and chemical properties and the salinity of soil and water as well as identifying the agrarian cycles virtually applied to this project. Finally, a comparison was conducted between the calculated water quantities and the suggested ones by the designers. The research results showed that using this kind of scheduling (previously prepared irrigation and leaching scheduling) with its properties which made it applicable requires an intense care when using the plant distribution pattern, the agrarian cycle, its agrarian areas and agricultural intensity within all climatic regions. Also, it was found that this program was an instrumental tool for providing water if the plant distribution pattern was well-selected.
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King, Bradley A., David Dale Tarkalson, and David Bjorneberg. "Evaluation of Canopy Temperature Based Crop Water Stress Index for Deficit Irrigation Management of Sugar Beet in Semi-Arid Climate." Applied Engineering in Agriculture 40, no. 1 (2024): 95–110. http://dx.doi.org/10.13031/aea.15822.
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Highlights Sugar beet irrigation scheduling was based on daily average crop water stress index between 13:00 and 16:00 hours. Three crop water stress index thresholds, 0.2, 0.35, and 0.55, were evaluated for irrigation scheduling. Season evapotranspiration decreased and soil water extraction increased as crop water stress threshold increased. There was no significant difference in root or sucrose yield between full irrigation and 0.2 crop water stress index, while seasonal irrigation depths were reduced from133 to 185 mm. Abstract. Sugar beet is an economically important crop in the semi-arid Intermountain Western U.S., with seasonal water use ranging from 500 to 900 mm. Sugar beet is a deep-rooted crop (1.5-2 m) in unrestricted soil profiles that can utilize stored soil water to reduce seasonal irrigation requirements. Effective use of stored soil water below 0.6 m requires precise irrigation scheduling and knowledge of soil water availability below 0.6 m, which is usually unknown due to the labor and expense of soil water monitoring at deeper depths and uncertainty in effective rooting depth and soil water holding capacity. Deficit irrigation (DI) management of sugar beet using a thermal-based crop water stress index (CWSI) has the potential to overcome soil water monitoring limitations and facilitate the utilization of stored soil water to reduce seasonal irrigation requirements. The objective of the research summarized in this paper was to implement and evaluate the effect of automated DI scheduling of sugar beet using three daily average CWSI thresholds (0.2, 0.35, and 0.55) on seasonal irrigation requirement, crop evapotranspiration, seasonal soil water depletion, root yield, estimated recoverable sugar (ERS) yield, and water use efficiency compared to full irrigation. There were no significant differences in root and ERS yield between full irrigation and 0.2 CWSI DI treatment, while seasonal ET was significantly decreased, seasonal soil water extraction was significantly increased, and seasonal irrigation depths were reduced from 133 to 185 mm. Root and ERS yield water production functions were curvilinear with a downward concave. Root and ERS yield water use efficiencies were constant or increased slightly for crop evapotranspiration reductions up to 85% of full irrigation evapotranspiration. The results indicate that irrigating when the average daily CWSI sugar beet exceeds 0.2 is an effective means for mild deficit irrigation scheduling to reduce seasonal irrigation requirements with no significant effect on root and ERS yield. Keywords: Crop water stress index, Evapotranspiration, Irrigation, Irrigation scheduling, Root yield, Sucrose yield, Sugar beet.
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Kallestad, Jeffery C., Theodore W. Sammis, John G. Mexal, and John White. "Monitoring and Management of Pecan Orchard Irrigation: A Case Study." HortTechnology 16, no. 4 (January 2006): 667–73. http://dx.doi.org/10.21273/horttech.16.4.0667.
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Optimal pecan (Carya illinoiensis) production in the southwestern United States requires 1.9 to 2.5 m of irrigation per year depending on soil type. For many growers, scheduling flood irrigation is an inexact science. However, with more growers using computers in their businesses, and with soil moisture sensors and computerized data-collection devices becoming more inexpensive and accessible, there is potential to improve irrigation and water use efficiencies. In this project two low-cost soil monitoring instruments were introduced to a group of pecan producers. They were also given instruction on the use of Internet-based irrigation scheduling resources, and assistance in utilizing all of these tools to improve their irrigation scheduling and possibly yield. The objectives were to determine whether the technology would be adopted by the growers and to assess the performance of the sensors at the end of the season. Three out of the five growers in the project indicated they used either the granular matrix (GM) sensors or tensiometer to schedule irrigations, but compared to the climate-based irrigation scheduling model, all growers tended to irrigate later than the model's recommendation. Graphical analysis of time-series soil moisture content measured with the GM sensors showed a decrease in the rate of soil moisture extraction coincident with the model's recommended irrigation dates. These inflection points indicated the depletion of readily available soil moisture in the root zone. The findings support the accuracy of the climate-based model, and suggest that the model may be used to calibrate the sensors. Four of the five growers expressed interest in continued use of the tensiometer, but only one expressed a desire to use the GM sensor in the future. None of the participants expressed interest in using the climate-based irrigation scheduling model.
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Bhavitha, K., Md Latheef Pasha, V. Ramulu, T. Ram Prakash, P. Rajaiah, and P. Revathi. "Impact of AI based Irrigation Scheduling Approaches and Drip Irrigation Methods on Yield of Chilli (Capsicum annum L.) and Chemical Properties of Soil." International Journal of Environment and Climate Change 14, no. 7 (July 13, 2024): 540–47. http://dx.doi.org/10.9734/ijecc/2024/v14i74291.
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Aim: To assess the effect of AI based irrigation scheduling approaches and drip irrigation methods on soil chemical properties and yield in chilli. Study Design: The study employs drip irrigation methods as the main plots and irrigation scheduling approaches as the subplots. A split plot design was chosen as suitable design because the main plots (drip irrigation methods) need a bigger plot sizes and subplots (irrigation scheduling approaches) requires more precise results with smaller plot sizes. Place and Duration of Study: Water Technology Centre field, College Farm, College of Agriculture, Rajendranagar, Hyderabad during rabi 2022-23 (first year) and 2023-24 (second year). Methodology: The investigation consisted of two drip irrigation methods as main plots and four irrigation scheduling approaches as subplots with total of 8 treatment combinations replicated thrice. Data recorded on various parameters was subjected to scrutiny by ANOVA technique for split plot design concept. Results: Green (fresh) fruit and stalk yield was found to be significantly higher under subsurface drip (41859 and 5037 kg ha-1) among drip irrigation methods; whereas, among irrigation scheduling approaches, ET sensor based irrigation triggering resulted in significantly higher green (fresh) fruit and stalk yield (43139 and 5196 kg ha-1) followed by irrigation scheduling at 1.0 Epan by manual (control) (42235 and 5065 kg ha-1). The post-harvest soil chemical properties were found to be non-significantly influenced by drip irrigation methods and irrigation scheduling approaches. Conclusions: Subsurface drip and ET sensor based irrigation triggering resulted in higher fruit and stalk yield which might be recommended for conserving irrigation water and reducing labour use. Whereas, the drip irrigation methods and irrigation scheduling approaches did not exert any significant influence on chemical properties of post-harvest soil.
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Karthik, Elukur, and Rajesh Singh. "Effect of Irrigation Scheduling and Foliar Organic Nutrition on Yield and Economics of Summer Groundnut (Arachis hypogaea L.)." International Journal of Plant & Soil Science 35, no. 4 (March 8, 2023): 95–99. http://dx.doi.org/10.9734/ijpss/2023/v35i42804.
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A field experiment was conducted during summer season of 2022 at Crop Research Farm (CRF), Department of Agronomy, SHUATS, Prayagraj (UP) on soil with sandy loam in texture to investigate the effect of Irrigation Scheduling and Foliar Organic Nutrition on growth and yield of Zaid Groundnut. The treatments consist of three Irrigation Schedulings viz., I1: 3 Irrigations (25,45,70 DAS), I2: 2 Irrigations (25,45 DAS), I3: 2 Irrigations (25,70 DAS) and three Foliar Organic Nutrition Comprising of F1 – Panchagavya at 3%, F2 – Jeevamrutha at 3%, F3 – Panchamrutha at 3% whose effect is observed on Groundnut (var. Kadiri-6). The experiment was laid out in Randomized Block Design with Ten treatments replicated thrice. The treatment with application of 3 Irrigations (25,45,70 DAS) + panchagavya-3% recorded significantly higher number of pods per plant (20.33), number of kernels per pod (2.47), seed index (39.84 g), pod yield (2.85 t/ha), haulm yield (4.4 t/ha) gross returns (1,61,808.4 INR), net returns (1,08,262.20 INR) and B:C ratio (2.02) compared to other treatment combinations.
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Colimba-Limaico, Javier Ezcequiel, Sergio Zubelzu-Minguez, and Leonor Rodríguez-Sinobas. "Optimal Irrigation Scheduling for Greenhouse Tomato Crop (Solanum Lycopersicum L.) in Ecuador." Agronomy 12, no. 5 (April 24, 2022): 1020. http://dx.doi.org/10.3390/agronomy12051020.
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Tomato crop is grown worldwide and is considered a mass consumer product. In Ecuador, tomato growers face two major issues: water scarcity and water mismanagement, which cause a reduction in the framers’ gross income and ecosystem services. This paper is aimed at finding an optimal irrigation scheduling in greenhouse tomato crop to achieve a balance among production, fruit quality and water use efficiency. Thus, two experiments were settled. In the first experiment, four water doses (80, 100, 120 and 140% ETc) and two irrigation frequencies (one and two irrigations per day) were compared. The second experiment evaluated the two best water doses of the first one (100 and 120% ETc) and four irrigation frequencies (one and two irrigations per day, one irrigation every two days, one irrigation every three days). Each experiment monitored the variables for tomato production (plant height, stem diameter, fruits per plant, yield) and tomato quality (pH, total soluble solids, titratable acidity). The study concluded that water doses affected more than irrigation frequency to fruit quality and production. The dose of 100% ETc, applied in one irrigation per day, is suggested to obtain a balance between production, fruit quality and water use efficiency.
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Gordin, Leandro Candido, Ceres Duarte Guedes Cabral de Almeida, José Amilton Santos Júnior, Ênio Farias de França e. Silva, Alexsandro Claudio Dos Santos Almeida, and Girlayne Santana Noberto da Silva. "Irrigation scheduling techniques and irrigation frequency on capsicum growth and yield." DYNA 86, no. 211 (October 1, 2019): 42–48. http://dx.doi.org/10.15446/dyna.v86n211.77678.
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The present study aimed to evaluate different irrigation scheduling strategies on capsicum growth and yield inprotected environment. The experiment was carried out at the Northeastern of Brazil. Five irrigation scheduling techniques to define water depth (weighing lysimeter, Hargreaves-Samani equation, Piché evaporimeter, tensiometer and soil moisture sensor) andtwo application frequencies (F1-once a day and F2-alternating frequency) were tested. A completely randomized factorial design experiment was installed in a 5 x 2 factorial scheme, with eight replicates. It was observed that the variables stem diameter and leaf area index were influenced by the irrigation scheduling techniques, and treatments based on Hargreaves-Samani and lysimeter scheduling methods led to the lowest values. Fruit biometric parameters were significantly affected only by the Hargreaves-Samani treatment. It can be concluded that both irrigation scheduling techniques and frequencies influenced capsicum growth and yield. Furthermore, irrigation management techniques based on soil sensors caused the highest yields.
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Wabela, Kedrala, Ali Hammani, Taky Abdelilah, Sirak Tekleab, and Moha El-Ayachi. "Optimization of Irrigation Scheduling for Improved Irrigation Water Management in Bilate Watershed, Rift Valley, Ethiopia." Water 14, no. 23 (December 5, 2022): 3960. http://dx.doi.org/10.3390/w14233960.
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The availability of water for agricultural production is under threat from climate change and rising demands from various sectors. In this paper, a simulation-optimization model for optimizing the irrigation schedule in the Bilate watershed was developed, to save irrigation water and maximize the yield of deficit irrigation. The model integrated the Soil and Water Assessment Tool (SWAT) and an irrigation-scheduling optimization model. The SWAT model was used to simulate crop yield and evapotranspiration. The Jensen crop-water-production function was applied to solve potato and wheat irrigation-scheduling-optimization problems. Results showed that the model can be applied to manage the complicated simulation-optimization irrigation-scheduling problems for potato and wheat. The optimization result indicated that optimizing irrigation-scheduling based on moisture-stress-sensitivity levels can save up to 25.6% of irrigation water in the study area, with insignificant yield-reduction. Furthermore, optimizing deficit-irrigation-scheduling based on moisture-stress-sensitivity levels can maximize the yield of potato and wheat by up to 25% and 34%, respectively. The model developed in this study can provide technical support for effective irrigation-scheduling to save irrigation water and maximize yield production.
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Ivanova, Maria, and Zornitsa Popova. "Irrigation Scheduling of Maize Grown on a Vertisol Soil Under Changing Climate of Sofia’s Field." Acta Horticulturae et Regiotecturae 24, s1 (May 1, 2021): 1–7. http://dx.doi.org/10.2478/ahr-2021-0001.
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Abstract The purpose of this study is to evaluate the impact of climate uncertainties on maize irrigation requirements, grown on a Vertisol soil, Sofia’s field, Bulgaria. Through the validated WinIsareg model, four irrigation scheduling alternatives are simulated for the years of “very high“, “high“ and “average“ irrigation demands of past (1952–1984) and present (1970–2004) climate. Adaptation of irrigation scheduling to the present climate conditions during the “very dry“ years (P I ≤12%) consists of an extension of the irrigation season by 15–20 days and a need of additional irrigation relative to alternative 1 and two irrigation events at alternatives 2 and 3. During the past climate alternatives 2 and 3 led to savings of 30 mm of water, while up to the current climate conditions the three irrigations alternatives should provide 360 mm of irrigation water. To obtain maximum yields in “dry“ (P I = 12–30%) years, irrigation season should end by 05/09, as in the present climate, irrigation season has shifted about a week earlier for the three alternatives. In the “average“ (P I = 30–60%) years the adaptation consist in accurately determination of the last allowed date for irrigation.
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Smittle, Doyle A., and W. Lamar Dickens. "Water Budgets to Schedule Irrigation for Vegetables." HortTechnology 2, no. 1 (January 1992): 54–59. http://dx.doi.org/10.21273/horttech.2.1.54.
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Instrumented rainfall- and groundwater-protected irrigation shelters were used to establish relationships (daily crop factors) between pan evaporation and daily water use for several vegetables. Use of these daily crop factors (water use/pan evaporation) and pan evaporation data for scheduling irrigations are described. Snap bean (Phaseolus vulgaris L.) is used to illustrate irrigation scheduling by this method. A table of the model output with columnar headings of age, root depth, date, pan evaporation, crop factor, daily water use, cumulative water use, allowable water use, rainfall, and irrigation is presented. When irrigation was applied according to the model, soil water tension was held below 25 db at 6-inch (15-cm) soil depth. With varying irrigation rates under a line-source irrigation system, marketable pod yields were maximized at 100% of the model rate. Marketable yields of summer squash also were maximized when irrigation was applied at 100% of the model rate. Marketable yields of sweetpotato were not affected by irrigation rates ranging from 1% to 177% of the model rate.
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Yang, Yanxiang, Jiang Hu, Dana Porter, Thomas Marek, Kevin Heflin, and Hongxin Kong. "Deep Reinforcement Learning-Based Irrigation Scheduling." Transactions of the ASABE 63, no. 3 (2020): 549–56. http://dx.doi.org/10.13031/trans.13633.
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Highlights Deep reinforcement learning-based irrigation scheduling is proposed to determine the amount of irrigation required at each time step considering soil moisture level, evapotranspiration, forecast precipitation, and crop growth stage. The proposed methodology was compared with traditional irrigation scheduling approaches and some machine learning based scheduling approaches based on simulation. Abstract. Machine learning has been widely applied in many areas, with promising results and large potential. In this article, deep reinforcement learning-based irrigation scheduling is proposed. This approach can automate the irrigation process and can achieve highly precise water application that results in higher simulated net return. Using this approach, the irrigation controller can automatically determine the optimal or near-optimal water application amount. Traditional reinforcement learning can be superior to traditional periodic and threshold-based irrigation scheduling. However, traditional reinforcement learning fails to accurately represent a real-world irrigation environment due to its limited state space. Compared with traditional reinforcement learning, the deep reinforcement learning method can better model a real-world environment based on multi-dimensional observations. Simulations for various weather conditions and crop types show that the proposed deep reinforcement learning irrigation scheduling can increase net return. Keywords: Automated irrigation scheduling, Deep reinforcement learning, Machine learning.
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Kallestad, Jeffery C., John G. Mexal, Theodore W. Sammis, and Richard Heerema. "Development of a Simple Irrigation Scheduling Calendar for Mesilla Valley Pecan Growers." HortTechnology 18, no. 4 (January 2008): 714–25. http://dx.doi.org/10.21273/horttech.18.4.714.
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For farmers to accurately schedule future water delivery for irrigations, a prediction method based on time-series measurements of soil moisture depletion and climate-based indicators of evaporative demand is needed. Yet, numerous reports indicate that field instruments requiring high in-season labor input are not likely to be used by farmers. In New Mexico, pecan (Carya illinoensis) farmers in the Mesilla Valley have been reluctant to adopt new soil-based or climate-based irrigation scheduling technologies. In response to low adoption rates, we have developed a simple, practical irrigation scheduling tool specifically for flood-irrigated pecan production. The information presented in the tool was derived using 14 years of archived climate data and model-simulated consumptive water use. Using this device, farmers can estimate the time interval between their previous and the next irrigation for any date in the growing season, in a range of representative soil types. An accompanying metric for extending irrigation intervals based on field-scale rainfall accumulation was also developed. In modeled simulations, irrigations scheduled with the tool while using the rainfall rule were within 3 days of the model-predicted irrigation dates in silty clay loam and loam soil, and less than 2 days in sandy loam and sand soil. The simulations also indicated that irrigations scheduled with the tool resulted in less than 1% reduction in maximum annual consumptive water use, and the overall averaged soil moisture depletion was 45.14% with an 18.1% cv, relative to a target management allowable depletion of 45%. Our long-term objective is that farmers using this tool will better understand the relationships between seasonal climate variation and irrigation scheduling, and will seek real-time evapotranspiration information currently available from local internet resources.
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HusseinMamoun, Mamoun, and Said A. Shokry. "Irrigation Scheduling using WSN." International Journal of Computer Applications 88, no. 2 (February 14, 2014): 37–40. http://dx.doi.org/10.5120/15326-3643.
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Miyamoto, S. "SCHEDULING IRRIGATION FOR PECANS." Acta Horticulturae, no. 275 (July 1990): 513–22. http://dx.doi.org/10.17660/actahortic.1990.275.64.
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Hill, Robert W., and Richard G. Allen. "Simple Irrigation Scheduling Calendars." Journal of Irrigation and Drainage Engineering 122, no. 2 (March 1996): 107–11. http://dx.doi.org/10.1061/(asce)0733-9437(1996)122:2(107).
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O’Shaughnessy, Susan A., Manuel A. Andrade, Paul D. Colaizzi, Fekede Workneh, Charles M. Rush, Steven R. Evett, and Minyoung Kim. "Irrigation Management of Potatoes Using Sensor Feedback: Texas High Plains." Transactions of the ASABE 63, no. 5 (2020): 1259–76. http://dx.doi.org/10.13031/trans.13925.
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HighlightsPotatoes irrigated at 80% and 100% replenishment of soil water depletion to field capacity resulted in statistically similar tuber yields and irrigation water productivity.In the drier growing season, irrigation scheduling using sensor feedback resulted in fewer irrigations compared with the manual-control method.In the wetter growing season, irrigation scheduling using sensor feedback resulted in similar or better tuber yields compared with the manual-control method.Abstract. Few studies have investigated yield and crop water productivity of plant and soil water sensing feedback systems for site-specific irrigation management of a potato crop. In this two-year study (2018 and 2019), the irrigation scheduling supervisory control and data acquisition (ISSCADA) system developed by scientists at the USDA-ARS Conservation and Production Research Laboratory in Bushland, Texas, was used to manage a potato crop at three irrigation levels. The ISSCADA system used two different irrigation scheduling methods: (1) plant feedback and (2) a hybrid method that combines plant feedback with soil water sensing with a soil water depletion (SWD) threshold initially set at 50% and reduced to 35% in the second year. Tuber yield, crop water productivity (CWP), and irrigation water productivity (IWP) resulting from the two ISSCADA irrigation scheduling methods were compared with a manual-control method based on weekly neutron probe readings. The irrigation levels were 100%, 80%, and 60% (I100, I80, and I60) of full and were accomplished by either replenishment of SWD to field capacity or by the equivalent plant feedback or SWD thresholds of the ISSCADA system. In the second study year, the SWD threshold was reduced to 35%. Cumulative irrigation amounts for the ISSCADA treatment methods were significantly less compared with the manual-control method in the I100 levels for both years. This resulted in significantly smaller tuber yields and CWP in the first year of the study, a hot dry growing season. In the second year of the study, tuber yields and CWP were similar between irrigation scheduling methods, and IWP was significantly greater for the ISSCADA-plant feedback method. Considering the effect of irrigation treatment, the tuber yields, CWP, and IWP between the I100 and I80 levels were similar in both years, resulting in an average savings of 85 mm at the I80 level. Future studies are needed to investigate if the change in the SWD threshold could enable the ISSCADA-hybrid system to adjust to variable climatic conditions and successfully irrigate potatoes in this region. Keywords: Center pivot, Dynamic prescription maps, Plant feedback, Site-specific variable-rate irrigation, Soil water sensing feedback, Wireless sensor networks
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Stone, Ken, Eric D. Billman, Philip J. Bauer, and Gilbert Sigua. "Using NDVI for Variable Rate Cotton Irrigation Prescriptions." Applied Engineering in Agriculture 38, no. 5 (2022): 787–95. http://dx.doi.org/10.13031/aea.15071.
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HighlightsCrop coefficients (Kcb) were calculated using Normalized Difference Vegetative Indices (NDVI) and compared to the FAO-56 method.Cotton yields using NDVI-Kcb based irrigation scheduling to a uniform checkbook irrigation were compared.Irrigated cotton yields were not significantly different between irrigation methods but were significantly higher in years requiring higher volumes of irrigation water.Cotton fiber quality was not significantly different for the two irrigation methods or plant populations.Abstract. Irrigation timing is crucial for achieving high cotton yields and lint quality. This irrigation timing is more challenging in the southeastern U.S. Coastal Plain region due to its spatial variable sandy soils with low water and nutrient holding capacities and rainfall variability during the growing season. To address these challenges, we conducted a 2-year (2017 and 2018) study evaluating two irrigation scheduling methods under a variable rate irrigation system. The two irrigation methods were: (1) a uniform irrigation management based on weekly crop water usage, and (2) spatial crop coefficients derived from normalized difference vegetative indices (NDVI). We compared cotton yields and water use efficiency using the two irrigation scheduling methods at two different planting densities. The two plant populations were 5 and 11.5 plants m2 to provide different NDVI readings and water requirements. In 2017, there were no significant differences in cotton yields due to the adequate rainfall during the growing season that required only three irrigations events. The mean irrigation depth for the NDVI method was significantly lower than the uniform method (56 and 64 mm, respectively, LSD = 4.2). In 2018, there was lower rainfall during the growing season requiring eight irrigation events and the cotton yields in the two irrigation treatments were significantly higher than the rainfed treatment. Irrigation depths in 2018 were not significantly different for the two irrigation methods. Water use efficiencies were not significantly different for the two irrigation methods. The planting density had little impact on the cotton yields, irrigation depth, water use efficiency, or cotton fiber quality. These results indicate that the NDVI-derived crop coefficient values were as effective in prescribing irrigation applications as the uniform irrigation method for irrigation management. The NDVI-derived crop coefficient irrigation method appears to be a useful tool for managing irrigation and developing irrigation prescriptions. Keywords: Cotton, Irrigation scheduling, Normalized difference vegetation indices, Variable rate irrigation
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Igbadun, Henry E. "Irrigation Scheduling Impact Assessment MODel (ISIAMOD): A decision tool for irrigation scheduling." Indian Journal of Science and Technology 5, no. 8 (August 20, 2012): 1–10. http://dx.doi.org/10.17485/ijst/2012/v5i8.4.
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Chen, Qi, Gui, Gu, Ma, Zeng, Li, and Sima. "A Model-Based Real-Time Decision Support System for Irrigation Scheduling to Improve Water Productivity." Agronomy 9, no. 11 (October 27, 2019): 686. http://dx.doi.org/10.3390/agronomy9110686.
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A precisely timed irrigation schedule to match crop water demand is vital to improving water use efficiency in arid farmland. In this study, a real-time irrigation-scheduling infrastructure, Decision Support System for Irrigation Scheduling (DSSIS), based on water stresses predicted by an agro-hydrological model, was constructed and evaluated. The DSSIS employed the Root Zone Water Quality Model (RZWQM2) to predict crop water stresses and soil water content, which were used to trigger irrigation and calculate irrigation amount, respectively, along with forecasted rainfall. The new DSSIS was evaluated through a cotton field experiment in Xinjiang, China in 2016 and 2017. Three irrigation scheduling methods (DSSIS-based (D), soil moisture sensor-based (S), and conventional experience-based (E)), factorially combined with two irrigation rates (full irrigation (FI), and deficit irrigation (DI, 75% of FI)) were compared. The DSSIS significantly increased water productivity (WP) by 26% and 65.7%, compared to sensor-based and experience-based irrigation scheduling methods (p < 0.05), respectively. No significant difference was observed in WP between full and deficit irrigation treatments. In addition, the DSSIS showed economic advantage over sensor- and experience-based methods. Our results suggested that DSSIS is a promising tool for irrigation scheduling.
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Shahrokhnia, Mohammad Ali, and Ebrahim Zare. "Technical and economic study of irrigation scheduling devices on corn water productivity in a semi-arid region." Italian Journal of Agrometeorology, no. 1 (July 19, 2022): 13–22. http://dx.doi.org/10.36253/ijam-1513.
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It is essential to consider water allocation control and on-farm irrigation scheduling to increase water productivity in agriculture. There are several devices used for irrigation scheduling, however the best device with the most priority is not identified yet. In the present study, the effect of using several irrigation scheduling devices on increasing water productivity in a corn field was investigated. The devices were classified technically and economically using analytic hierarchy process. The experimental farm was located in a semi-arid region in Iran, which was managed by a farmer and irrigated with drip irrigation system. Six techniques for irrigation scheduling were studied including Penman-Monteith model (T2), infrared thermometer (T3), soil moisture meter (T4), tensiometer (T5), and gypsum block (T6). The irrigation scheduling treatments were compared with the conventional treatment adopted by the farmer (T1). Economic analysis was performed. The ease of use of the devices was also evaluated. Results showed for the irrigation scheduling treatments of T3 to T6, applied irrigation water was reduced by 11 to 26% compared to T1. The corn yield in irrigation scheduling treatments was not reduced significantly compared to T1. As a result, water productivity increased by 35% from 2.0 to 2.7 kg/m3. The best irrigation scheduling device in terms of water productivity was gypsum block. In regard to affordability and ease of use by farmers, the Penman-Monteith model had more priority. Considering all assessment criteria, tensiometer (T5) was given the first priority. The infrared thermometer (T3) and Penman-Monteith model (T2) were identified as the next priorities.
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Rahwa, M. K., and A. S. Bhanvadia. "Impact of Irrigation Scheduling and Nitrogen Management Through Drip Irrigation System on Nutrient Content and Uptake of Rabi Maize (Zea mays L.)." International Journal of Plant & Soil Science 35, no. 23 (December 21, 2023): 345–55. http://dx.doi.org/10.9734/ijpss/2023/v35i234249.
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The state of the soil's nutrients can be significantly impacted by improper irrigation and nitrogen application, which may accelerate nutrient losses and cause soil erosion and ground water pollution. A field experiment was conducted during consecutive two rabi seasons of the years 2021 and 2022 at Regional Research Station, Anand Agricultural University, Anand, Gujarat, India to study the “Impact of irrigation scheduling and nitrogen management through drip irrigation system on nutrient content and uptake of rabi maize (Zea mays L.)”. The soil of the experimental field was loamy sand in texture, with low organic carbon and available nitrogen, medium available phosphorus and high potassium with soil pH 8.21. The experiment was carried out in split plot design with four levels of irrigation scheduling based on Alternate Day Pan Evaporation Fraction (ADPEF) were considered in main plot viz., I1 : Irrigation scheduling at 0.8 ADPEF, I2 : Irrigation scheduling at 1.0 ADPEF, I3 : Irrigation scheduling at 1.2 ADPEF and I4 : Control (Flood irrigation) and three nitrogen management treatments viz. N1 : 100% RDN through inorganic fertilizer, N2 : 75% RDN through inorganic fertilizer + Bio NPK consortium, N3 : 50% RDN through inorganic fertilizer + Bio NPK consortium + 5 t/ha FYM were assigned in sub plots, comprised of 12 treatment combinations. Result of the experiment showed that irrigation scheduling at 1.2 ADPEF (I3) recorded significantly higher N content in grain and stover and it was remained at par with irrigation scheduling at 1.0 ADPEF (I2) during 2021, 2022 and pooled analysis. P and K content in grain and stover was found non-significant due to various irrigation scheduling treatments during individual years and on pooled basis. Irrigation scheduling at 1.2 ADPEF (I3) recorded significantly higher N, P uptake by grain and stover and K uptake by grain during 2021, 2022 and on pooled basis. K uptake by stover found significantly higher under irrigation scheduling at 1.2 ADPEF (I3) during 2021-22 and pooled basis. While K, uptake by stover found non-significant. Nitrogen management with 100% RDN through inorganic fertilizer recorded significantly the highest N content in grain and stover and pooled analysis. While, P and K content grain and stover found non- and pooled analysis. Application of 100% RDN through inorganic fertilizer found significantly higher N, P and K uptake by grain and stover and pooled basis. Interaction effect of irrigation scheduling and nitrogen management treatments was found significantly higher N uptake by grain and K uptake by stover (pooled basis) under irrigation scheduling at 1.2 ADPEF with 100% RDN through inorganic fertilizer (I3N1).
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Shaglouf, Mohamed M., Mostafa A. Benzaghta, Hassin AL. Makhlof, and Moftah A. Abusta. "Scheduling Drip Irrigation for Agricultural Crops using Intelligent Irrigation System." Journal of Misurata University for Agricultural Sciences, no. 01 (October 6, 2019): 244–55. http://dx.doi.org/10.36602/jmuas.2019.v01.01.19.
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The expansion of agriculture to provide the necessary food is related to the availability of water, but the limited availability of irrigation requires research on techniques to reduce water losses. This paper presents an application of a prototype design of microcontroller based on an intelligent irrigation system which will allow irrigation to take place in the areas. This method can be applied to the system of drip irrigation and its impact on the quantities of water used in irrigation as its application is part of the solution to the problem of water shortage suffered by Libya in addition to reducing the amount of water wasted while irrigating crops. In this study, a network of smart irrigation system was designed for a 5-hectare farm in AL-Sawawa area, located to the east, at about 20 km from Sirte city. The farm was divided into two parts, a vegetable crops section with an area of 3ha and the other section of 2 ha for olive trees. The intelligent irrigation system senses the moisture content of the soil and the temperature of the air through the sensors and turns on or off the water pumps using the relays to carry out this procedure. The main advantage of using this irrigation system is to minimize human intervention and ensure proper irrigation. The microcontroller serves as the main unit of the entire irrigation system, Photovoltaic cells are used to provide solar energy as an energy supply for the whole system. The system is controlled by the microcontroller; it obtains data from the sensors, it compares the data as pre-programmed, and the output signals activate the relays to operate the pumps to start the irrigation process.
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Hossain, MB, S. Yesmin, M. Maniruzzaman, and JC Biswas. "Irrigation Scheduling of Rice (Oryza sativa L.) Using CROPWAT Model in the Western Region of Bangladesh." Agriculturists 15, no. 1 (August 4, 2017): 19–27. http://dx.doi.org/10.3329/agric.v15i1.33425.
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Understanding of crop water requirement is essential for irrigation scheduling and selection of cropping pattern in any particular area. A study was conducted to estimate irrigation requirement and made irrigation scheduling of T. Aman (wet season) and Boro (dry season irrigated) rice in the western region of Bangladesh using CROPWAT model. Historical climate data from three weather stations in the region along with soil and crop data were used as input to FAO Penman-Monteith method to estimate reference evapotranspiration (ETo). Effective rainfall was calculated using USDA soil conservation method. The model estimated1408 mm annual ETo in the study area, of which the highest amounts of 175 mm was in April and the lowest (70 mm) in December. The average annual rainfall was 1592 mm of which 986 mm was effective for plant growth and development. The model estimated ETc of BRRI dhan49, which was 473 to 458 mm, depending on its transplanting dates from 15 July to 15 August. Rice transplanted on 15 July required no irrigation, whereas three supplemental irrigations amounting 279 mm were required for transplanting on 15 August. The CROPWAT model estimated seasonal irrigation water requirement of 1212 mm (12 spilt applications) for BRRIdhan28 transplanted on 15 January. This model has also a potentiality to make irrigation scheduling of other crops. The Agriculturists 2017; 15(1) 19-27
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Surve, Vaishali, Hitesh, H. H. Patel, and Payal. "Sensor Based Irrigation Management in Crop Production: A Review." Annual Research & Review in Biology 39, no. 4 (March 12, 2024): 1–4. http://dx.doi.org/10.9734/arrb/2024/v39i42068.
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Efficient water management in agriculture is crucial for sustainable crop production, especially in regions facing water scarcity. Sensor-based irrigation scheduling offers a promising solution by enabling precise and timely irrigation, optimizing water usage while maintaining or enhancing crop yields. This study was made in order to investigate the efficacy of sensor-based technologies in irrigation scheduling for improving water use efficiency in agricultural settings. Data on utilization of an array of sensors including soil moisture, weather and crop-specific indicators., real-time data was collected and analyzed to determine the optimum irrigation timing and volume. The procedure integrated these sensor-derived insights with irrigation scheduling algorithms to dynamically adjust water delivery, aligning with the crop's actual water needs. This study reviews the importance of soil moisture sensors for irrigation, as well as sensor technology and its uses irrigation management and irrigation scheduling.
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Taghvaeian, Saleh, Allan A. Andales, L. Niel Allen, Isaya Kisekka, Susan A. O’Shaughnessy, Dana O. Porter, Ruixiu Sui, Suat Irmak, Allan Fulton, and Jonathan Aguilar. "Irrigation Scheduling for Agriculture in the United States: The Progress Made and the Path Forward." Transactions of the ASABE 63, no. 5 (2020): 1603–18. http://dx.doi.org/10.13031/trans.14110.
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HighlightsThe progress made in agricultural irrigation scheduling in the past ten years and the current challenges are discussed.The main scientific scheduling strategies are based on soil water status, plant characteristics, and crop modeling.Challenges include large time and data requirements and availability of decision support systems.Opportunities include integration of scheduling strategies and demonstrating their effectiveness through local studies.Abstract. Irrigation scheduling is the process of determining the appropriate amount and timing of water application to achieve desired crop yield and quality, maximize water conservation, and minimize possible negative effects on the environment, such as nutrient leaching below the crop root zone. Effective irrigation scheduling has been shown to save water, save energy, and help agricultural producers achieve improved yields and quality. However, scientific irrigation scheduling methods generally have remained limited to mostly research applications with relatively low adoption by irrigators. There are several main approaches to irrigation scheduling, including those based on soil water status, plant characteristics, and/or crop modeling. Each of these approaches has advantages as well as limitations and sources of uncertainty and variability, depending on application conditions. This article summarizes progress made in the U.S. in each of the main scheduling approaches in the past ten years (since the 2010 Decennial Irrigation Symposium) and existing challenges and opportunities that should be considered moving forward. This article is intended to guide future research and extension projects in improving adoption of scientific irrigation scheduling approaches. Keywords: Computer modeling, Plant characteristics, Soil water status.
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Criscione, Kristopher S., Jeb S. Fields, and James S. Owen. "Root Exploration, Initial Moisture Conditions, and Irrigation Scheduling Influence Hydration of Stratified and Non-Stratified Substrates." Horticulturae 8, no. 9 (September 8, 2022): 826. http://dx.doi.org/10.3390/horticulturae8090826.
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Soilless substrate stratification (i.e., layering unique substrates within a single container) is an emerging substrate management strategy that may provide opportunities to augment nursery resource use. As such, this research aimed to analyze water movement through containers during hydration events under different initial moisture conditions. The results indicated substrate stratification had minimal influence on water movement compared to non-stratified systems (uniformly filled nursery containers). Cyclic irrigation significantly increased the stratified substrates’ ability to retain water when irrigated at 20% volumetric water content (p < 0.0001) and significantly decreased the total volume leached (p < 0.0001). Moreover, irrigating the substrate profile with shallow and more frequent irrigations facilitated stratified substrates ty reach effective container capacity conditions (p < 0.0001n compared to non-stratified systems. The stratified systems took longer to leach all gravitational pores (p = 0.0266). In dry moisture conditions, non-stratified substrates were more hydrated when cyclic irrigation applications were applied compared to single applications (p = 0.0492). This study demonstrated that cyclic irrigation scheduling enhanced water retention in both non-stratified and stratified profiles under different initial moisture conditions and can be used as an irrigation strategy when dry substrate conditions prevail.
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Bryla, David R., Elizabeth Dickson, Robert Shenk, R. Scott Johnson, Carlos H. Crisosto, and Thomas J. Trout. "Influence of Irrigation Method and Scheduling on Patterns of Soil and Tree Water Status and Its Relation to Yield and Fruit Quality in Peach." HortScience 40, no. 7 (December 2005): 2118–24. http://dx.doi.org/10.21273/hortsci.40.7.2118.
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A 3-year study was done to determine the effects of furrow, microspray, surface drip, and subsurface drip irrigation on production and fruit quality in mature `Crimson Lady' peach [Prunus persica (L.) Batsch] trees. Furrow and microspray irrigations were scheduled weekly or biweekly, which is common practice in central California, while surface and subsurface drip irrigations were scheduled daily. Trees were maintained at similar water potentials following irrigation by adjusting water applications as needed. Tree size and fruit number were normalized among treatments by pruning and thinning each season. Surface and subsurface drip produced the largest fruit on average and the highest marketable yields among treatments. Drip benefits appeared most related to the ability to apply frequent irrigations. Whether water was applied above or below ground, daily irrigations by drip maintained higher soil water content within the root zone and prevented cycles of water stress found between less-frequent furrow and microspray irrigations. With furrow and microsprays, midday tree water potentials reached as low as –1.4 MPa between weekly irrigations and –1.8 MPa between biweekly irrigations, which likely accounted for smaller fruit and lower yields in these treatments. To reduce water stress, more frequent irrigation is probably impractical with furrow systems but is recommended when irrigating during peak water demands by microspray.
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Al-Ghobari, Hussein M., Fawzi S. Mohammad, and Mohamed S. A. El Marazky. "Evaluating two irrigation controllers under subsurface drip irrigated tomato crop." Spanish Journal of Agricultural Research 14, no. 4 (December 2, 2016): e1206. http://dx.doi.org/10.5424/sjar/2016144-8615.
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Smart systems could be used to improve irrigation scheduling and save water under Saudi Arabia’s present water crisis scenario. This study investigated two types of evapotranspiration-based smart irrigation controllers, SmartLine and Hunter Pro-C2, as promising tools for scheduling irrigation and quantifying plants’ water requirements to achieve water savings. The effectiveness of these technologies in reducing the amount of irrigation water was compared with the conventional irrigation scheduling method as a control treatment. The two smart irrigation sensors were used for subsurface irrigation of a tomato crop (cv. Nema) in an arid region. The results showed that the smart controllers significantly reduced the amount of applied water and increased the crop yield. In general, the Hunter Pro-C2 system saved the highest amount of water and produced the highest crop yield, resulting in the highest water irrigation efficiency compared with the SmartLine controller and the traditional irrigation schedule. It can be concluded that the application of advanced scheduling irrigation techniques such as the Hunter controller under arid conditions can realise economic benefits by saving large amounts of irrigation water.
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Miller, Luke, George Vellidis, and Timothy Coolong. "Comparing a Smartphone Irrigation Scheduling Application with Water Balance and Soil Moisture-based Irrigation Methods: Part II—Plasticulture-grown Watermelon." HortTechnology 28, no. 3 (June 2018): 362–69. http://dx.doi.org/10.21273/horttech04014-18.
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A new smartphone irrigation scheduling application (VegApp) was compared with current irrigation scheduling recommendations and an automated soil moisture sensor (SMS)–based irrigation system in southern Georgia during Spring 2016 and 2017. Plants were grown using plastic mulch and drip irrigation following standard production practices for watermelon (Citrullus lanatus) in Georgia. The VegApp irrigation regime was based on evapotranspiration (ETo) values calculated from real-time data collected from a nearby weather station. Current irrigation scheduling recommendations use a water balance (WB) method. The WB method uses historic averages for determining ETo rates for the season. Water applied, soil water tension at 6-, 10-, and 14-inch depths, yield, and fruit quality were evaluated. In 2016, the SMS-based irrigation plots applied the least water. In 2017, the lowest amount of water was applied to plants grown using the VegApp. Total marketable yields were not significantly affected by irrigation regime. However, 45-count fruit yields were affected by irrigation in 2017. Plants grown using SMS-based irrigation had significantly higher yields of 45-count fruit than those grown using the WB method. Irrigation water use efficiency (IWUE) was affected by irrigation treatment and year. The SMS-irrigated plants had the greatest IWUE, although it was not significantly different from plants grown using the VegApp irrigation program. Internal quality parameters including, firmness, hollow heart, and total soluble solids (TSS) were not significantly affected by irrigation scheduling during the study. The results suggest that overall water applications may be reduced and yields maintained when using VegApp compared with traditional WB methods of irrigation scheduling.
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Braunworth, William S., and Harry J. Mack. "Evaluation of Irrigation Scheduling Methods for Sweet Corn." Journal of the American Society for Horticultural Science 112, no. 1 (January 1987): 29–32. http://dx.doi.org/10.21273/jashs.112.1.29.
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Abstract Accurate irrigation scheduling for sweet corn can reduce irrigation costs and ensure meeting of yield goals. Three scheduling methods, evaluated in a 2-year study, included: a) irrigation when 46% and 57% of available water was depleted in 1984 and 1985, respectively, as measured by a neutron meter; b) irrigation when 50% of available water was depleted as estimated by the Food and Agriculture Organization modified Penman equation; and c) irrigation at three growth stages. Irrigation water applied for the neutron meter, modified Penman, and growth stage method was 367, 279, and 269 mm, respectively, in 1984 while in 1985 these methods resulted in application of 500, 368, and 366 mm of irrigation water. Yields of total unhusked ears in 1984 for the growth stage and modified Penman methods were significantly lower than the yields of the neutron meter method but were not significantly different from one another. In 1985, there were no significant differences in total unhusked or husked processable ear yields among the three scheduling methods. Quality factors, which included ear length, kernel moisture content, and ear weight did not vary significantly with irrigation scheduling methods. Since total unhusked, husked processable yields, and quality differences were minor, irrigation scheduling by any of these methods would appear to be satisfactory.
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Al-Ghobari, H. M., F. S. Mohammad, and M. S. A. El Marazky. "Assessment of smart irrigation controllers under subsurface and drip-irrigation systems for tomato yield in arid regions." Crop and Pasture Science 66, no. 10 (2015): 1086. http://dx.doi.org/10.1071/cp15065.
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Here, two types of smart irrigation controllers intended to reduce irrigation water are investigated under Saudi Arabia’s present water crisis scenario. These controllers are specially made for scheduling irrigation and management of landscaping. Consequently, the aim of this study is to adapt the efficient automated controllers to tomato crops, and for extension to other similar agricultural crops. The controllers are based on evapotranspiration and have been shown to be promising tools for scheduling irrigation and quantifying the water required by plants to achieve water savings. In particular, the study aims to evaluate the effectiveness of these technologies (SmartLine SL 1600and Hunter Pro-C) in terms of the amount of irrigation applied and compare them with conventional irrigation scheduling methods. The smart irrigation systems were implemented and tested under drip irrigation and subsurface irrigation for tomato (cv. Nema) in an arid region. The results revealed significant differences between the three irrigation-scheduling methods in both the amount of applied water and yield. For example, each 1 mm water depth applied to the tomato crop via subsurface (or drip) irrigation by SmartLine, Hunter Pro-C, and the control system yielded 129.70 kg (70.33 kg), 161.50 kg (93.47 kg), and 109.78 kg (108.32 kg), respectively. Generally, the data analysis indicates that the Hunter Pro-C system saves water and produces a higher yield with the greatest irrigation water-use efficiency (IWUE) of the irrigation scheduling methods considered. Moreover, the results indicate that the subsurface irrigation system produced a higher yield and IWUE than the drip system.
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Stone, Kenneth C., Phil J. Bauer, Susan O’Shaughnessy, Alejandro Andrade-Rodriguez, and Steven Evett. "A Variable-Rate Irrigation Decision Support System for Corn in the U.S. Eastern Coastal Plain." Transactions of the ASABE 63, no. 5 (2020): 1295–303. http://dx.doi.org/10.13031/trans.13965.
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HighlightsA decision support system using the USDA-ARS Irrigation Scheduling and Supervisory Control and Data Acquisition (ISSCADA) system was evaluated for spatially managing corn irrigation in the U.S. Eastern Coastal Plain.The ISSCADA system was compared to traditional scheduling methods based on measured soil water potentials.The ISSCADA system with feedback on allowable soil water depletion shows potential as a tool for growers for managing variable-rate irrigation systems.Abstract. Variable-rate irrigation (VRI) systems are capable of applying different water depths both in the direction of travel and along the length of the irrigation system. VRI systems maybe useful for improving crop water management and efficiency. Although VRI technology is available and has high grower interest, it has had limited adoption. To address this, researchers have developed a decision support system that uses remote sensing of plant, soil, and microclimate to schedule VRI irrigations. In this research, we evaluated the use of the USDA-ARS Irrigation Scheduling and Supervisory Control and Data Acquisition (ISSCADA) system for spatially managing corn irrigation in the U.S. Eastern Coastal Plain. The ISSCADA system consists of center pivot mounted infrared thermometers (IRT) to measure crop canopy temperatures and in situ soil water sensors. An integrated crop water stress index (iCWSI) was calculated from the canopy temperatures. The ISSCADA system analyzes the iCWSI and soil water measurements to provide an irrigation recommendation. The ISSCADA system was evaluated using (1) iCWSI values and (2) a hybrid ISSCADA system that incorporated both iCWSI values and soil water depletion criteria. These ISSCADA treatments were compared to traditional irrigation management using measured soil water potentials. The ISSCADA system was evaluated for four years. In 2016 and 2017, corn yields and water use efficiency were not significantly different between the irrigation treatments due to adequate rainfall during the growing season. In 2018 and 2019, mid-season drought conditions and sporadic rainfall patterns required frequent irrigations. In both years, the irrigation treatment corn yields were not significantly different from each other but were greater than the rainfed yields. In 2018, the irrigation treatments produced corn yields of 10.7, 10.4, and 10.1 Mg ha-1 for the hybrid, ISSCADA, and SWP treatments, respectively. Over the four-year study, the water use efficiencies of the irrigation treatments were not significantly different from each other or the rainfed treatment and ranged from 16.6 to 22.7 kg ha-1 mm-1. In the two years that the hybrid ISSCADA system was used for managing irrigations, it produced higher corn yields and required less irrigation than the standard ISSCADA treatments. Results from this experiment will help to evaluate and refine the ISSCADA system to provide a tool for growers to use in managing spatial irrigation with VRI systems. Keywords: Crop water stress, Decision support system, Variable rate irrigation.
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Al-Haddad, Amer Hassan, and Tamara Sideeq Bakr. "Irrigation Scheduling Effect on Water Requirements." Journal of Engineering 19, no. 1 (May 10, 2023): 96–145. http://dx.doi.org/10.31026/j.eng.2013.01.07.
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Irrigation scheduling techniques is one of the suggested solutions for water scarcity problem. The study aims to show the possibility of using practical and applicable irrigation scheduling program which was designed by Water Resources Department at the University of Baghdad by using Spreadsheet Formulas for Microsoft Excel program, version 2007, with some modification to generalize it and made it applicable to various climatic zone and different soil types, as a salvation for the shortage of irrigation water inside the irrigation projects. Irrigation projects which incidence of Tigris River basin will be taken as an applicable example. This program was based on water budgeting and programmed depending on scientific concepts which facilitate irrigation structures operation and ease the use by farmers. By using the abilities of this program, the monthly and annually water requirements and drainage water were estimated. Finally a comparison is made between the calculated discharges with the designers suggested ones. This comparisons showed that the use of this type of irrigation scheduling (i.e. predicted irrigation scheduling) with itsapplicable constrains require high attention when choosing the cropping pattern for each climate zone. Also it found that this irrigation program is a useful tool for saving water if cropping pattern has been chosen carefully.
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Kirnak, H., C. Kaya, D. Higgs, I. Bolat, M. Simsek, and A. Ikinci. "Effects of preharvest drip-irrigation scheduling on strawberry yield, quality and growth." Australian Journal of Experimental Agriculture 43, no. 1 (2003): 105. http://dx.doi.org/10.1071/ea02045.
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Strawberry (Fragaria × ananassa Duch) cultivars, Oso Grande and Camarosa were grown in the field from July 1999 to May 2000 in order to investigate the effectiveness of preharvest drip-irrigation management on fruit yield, quality (i.e. soluble dry matter, fruit size), leaf macro-nutrient composition and normal growth parameters. All plots were irrigated uniformly until 2 weeks before harvest. Differential treatments were then imposed ranging from a complete cut-off of irrigation to full irrigation through the harvest period. Preharvest drip-irrigation management treatments were (i) complete irrigation cut-off, dry (D), (ii) normal irrigation based on class A pan and percentage cover (C), (iii) 75% of normal irrigation, C (IR1), (iv) 50% of normal irrigation, C (IR2), and (v) 25% of normal irrigation, C (IR3). Normal irrigation (control, C) was created by irrigating plants once every 2 days at 100% A pan (Epan) evaporation. No irrigation (D) and IR3 treatments caused reductions in most parameters measured, except water-soluble dry matter concentrations (SDM) in fruit compared with other treatments. There were no significant differences between C, IR1, and IR2 treatments in normal growth parameters or leaf nutrient composition. Fruit size and SDM were both significantly affected by late-season irrigation management; individual fruit weight was significantly reduced and SDM increased even in the IR2 and IR3 treatments compared with control values. Fruit yield was not affected significantly by reduced water application except in the D treatment. These results clearly indicate that reduced preharvest irrigation was partially detrimental; a small reduction in irrigation (IR1) had little or no effect but 50% or less of normal irrigation, while not reducing overall fruit yield, resulted in smaller fruits.