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Irrigation Scheduling by C. J. Brouwer

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1NASA Technical Reports Server (NTRS) 19740003060: Irrigation Scheduling, Freeze Warning And Soil Salinity Detecting. [starr And Cameron Counties, Texas

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2NASA Technical Reports Server (NTRS) 19740017746: Irrigation Scheduling, Freeze Warning And Soil Salinity Detecting

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3NASA Technical Reports Server (NTRS) 19750004342: Irrigation Scheduling, Freeze Warning And Soil Salinity Detecting. [Cameron County, Texas

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4NASA Technical Reports Server (NTRS) 19780006648: Airborne Monitoring Of Crop Canopy Temperatures For Irrigation Scheduling And Yield Prediction

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Airborne and ground measurements were made on April 1 and 29, 1976, over a USDA test site consisting mostly of wheat in various stages of water stress, but also including alfalfa and bare soil. These measurements were made to evaluate the feasibility of measuring crop temperatures from aircraft so that a parameter termed stress degree day, SDD, could be computed. Ground studies have shown that SDD is a valuable indicator of a crop's water needs, and that it can be related to irrigation scheduling and yield. The aircraft measurement program required predawn and afternoon flights coincident with minimum and maximum crop temperatures. Airborne measurements were made with an infrared line scanner and with color IR photography. The scanner data were registered, subtracted, and color-coded to yield pseudo-colored temperature-difference images. Pseudo-colored images reading directly in daily SDD increments were also produced. These maps enable a user to assess plant water status and thus determine irrigation needs and crop yield potentials.

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5NASA Technical Reports Server (NTRS) 19740024648: Irrigation Scheduling, Freeze Warning And Soil Salinity Detecting. [Starr And Cameron County, Texas

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6Scheduling Irrigation With A Neutron Probe

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7NASA Technical Reports Server (NTRS) 20040046141: Remote Sensing Of Vineyard FPAR, With Implications For Irrigation Scheduling

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Normalized difference vegetation index (NDVI) data, acquired at two-meter resolution by an airborne ADAR System 5500, were compared with fraction of photosynthetically active radiation (FPAR) absorbed by commercial vineyards in Napa Valley, California. An empirical line correction was used to transform image digital counts to surface reflectance. "Apparent" NDVI (generated from digital counts) and "corrected" NDVI (from reflectance) were both strongly related to FPAR of range 0.14-0.50 (both r(sup 2) = 0.97, P < 0.01). By suppressing noise, corrected NDVI should form a more spatially and temporally stable relationship with FPAR, reducing the need for repeated field support. Study results suggest the possibility of using optical remote sensing to monitor the transpiration crop coefficient, thus providing an enhanced spatial resolution component to crop water budget calculations and irrigation management.

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8NASA Technical Reports Server (NTRS) 19750015986: Irrigation Scheduling, Freeze Warning And Soil Salinity Detecting. [in Cameron County Texas

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The author has identified the following significant results. Correlations of multispectral scanner (MSS) digital data differences between vegetated and bare soil areas with salinity levels from the eight saline areas using MSS bands seven and ten in the infrared region were significant. Correlations were derived for Cameron County, Texas. Detection of saline soils may be possible, using either film density readings or multispectral scanner data, when the lower reflectance of vegetation on highly saline soil and the higher reflectance of vegetation on lower saline soil are considered by using film on MSS contrasts between vegetation and bare soil.

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9NASA Technical Reports Server (NTRS) 19740008987: Irrigation Scheduling, Freeze Warning And Soil Salinity Detecting. [Texas

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10NASA Technical Reports Server (NTRS) 19740026644: Irrigation Scheduling, Freeze Warning And Soil Salinity Detecting. [Cameron County, Texas

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11NASA Technical Reports Server (NTRS) 19750007053: Irrigation Scheduling, Freeze Warning And Soil Salinity Detecting. [Cameron And Starr Counties, Texas

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12Irrigation Scheduling/Auditing

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13Evapotranspiration And Irrigation Scheduling : Proceedings Of The International Conference, November 3-6, 1996, San Antonio Convention Center, San Antonio, Texas

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14NASA Technical Reports Server (NTRS) 19720004627: Thermal Scanner Data For Studying Freeze Conditions And For Aiding Irrigation Scheduling

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The use of thermal imagery in connection with plant water stress appears to hold considerable potential, particularly with scanners such as the RS-14 where internal calibration sources can be adjusted to bracket the temperatures of interest. The data from the thermal film are fairly easy to reduce; however, data reduction in 3 days or less would be required before it could be used in irrigation timing.

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15Scheduling Irrigation For Corn In The Southeast

Includes references

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16NASA Technical Reports Server (NTRS) 19750011715: Irrigation Scheduling, Freeze Warning And Soil Salinity Detecting

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17NASA Technical Reports Server (NTRS) 19750006130: Irrigation Scheduling, Freeze Warning And Soil Salinity Detecting. [Cameron And Starr County, Texas

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18Scheduling Irrigation To Meet Crop Demands.

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ii, 10, 8 p

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19NASA Technical Reports Server (NTRS) 19740026622: Irrigation Scheduling, Freeze Warning And Soil Salinity Detecting. [Cameron And Starr County, Texas

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20Dynamic Domain Transformation Resource Scheduling Approach: Water Irrigation Scheduling For Urban Farming

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Scheduling resources under limited resources using tailored approaches can be done successfully. However, there are situations and problems that require a schedule to handle uncertainties dynamically. The changes in the environment could lead to a non-optimal schedule, which could lead to the wastage of resources. The infeasible schedule could also be an outcome of changes that would render the schedule obsolete, and a new schedule must be generated. The majority of the scheduling problems are solved by a heuristic approach that utilizes a random number generator, thus the outcome is not guaranteed to be optimal. Domain transformation approach (DTA) is a scheduling methodology that has confirmed its expressive power in producing feasible and good quality schedules through avoidance of randomness elements as highly used in heuristic approaches. DTA has been employed in this study to solve the water irrigation scheduling for urban farming. The proposed model was tested on three different datasets. It was observed that the costs obtained on all datasets without utilizing the dynamic DTA are higher in all instances, which indicates that the solution produced by DTA is of higher quality. Thus, dynamic DTA is a more effective way of scheduling resources with considering ad-hoc changes.

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21Irrigation Scheduling For Water & Energy

Scheduling resources under limited resources using tailored approaches can be done successfully. However, there are situations and problems that require a schedule to handle uncertainties dynamically. The changes in the environment could lead to a non-optimal schedule, which could lead to the wastage of resources. The infeasible schedule could also be an outcome of changes that would render the schedule obsolete, and a new schedule must be generated. The majority of the scheduling problems are solved by a heuristic approach that utilizes a random number generator, thus the outcome is not guaranteed to be optimal. Domain transformation approach (DTA) is a scheduling methodology that has confirmed its expressive power in producing feasible and good quality schedules through avoidance of randomness elements as highly used in heuristic approaches. DTA has been employed in this study to solve the water irrigation scheduling for urban farming. The proposed model was tested on three different datasets. It was observed that the costs obtained on all datasets without utilizing the dynamic DTA are higher in all instances, which indicates that the solution produced by DTA is of higher quality. Thus, dynamic DTA is a more effective way of scheduling resources with considering ad-hoc changes.

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2229 Agri IJASR Weather Based Irrigation Scheduling In Summer Groundnut In Odisha Condition

An experiment was conducted at the Central Research Station of Orissa University of Agriculture and Technology, Bhubaneswar during summer season of 2015 to study the growth, yieldand moisture use of three groundnut varieties under different levels of irrigation (IW/CPE ratio of 1.0,0.8 and 0.6 ). The experiment was laid out in a split-plot design with three replications. Three Irrigation scheduling IW/CPE ratio (I1- 1.0, I2-0.8 and I3- 0.6) were taken in the main plot and three groundnut varieties (V1- Kadri -6, V2- Devi ,V3- Smruti ) were assigned to sub-plots. The soil was silty clay having pH 5.45, available N, P and K of (150, 18.5, 107.5)kg/ha, respectively. There was significant variation among irrigation levels with regards to shoot length, branch number, LAI, dry matter production and yield attributes viz. test weight of pod and kernel and shelling percentage were affected by both the factor which in turn influenced the yield. The irrigation level IW/CPE ratio 0.8 recorded significantly higher pod yield of 23.35 q/ha than that of 0.6 and 1.0 due to more number of filled pods (25 per plant) 100 pod weight (59.9g )and 100 kernel weight (50.0 g). Variety Devi recorded significantly higher pod yield of 23.33 q/ha.which is 28.2 % higher than that ofvariety AKP-7 and 11.5 % higher over variety Smruti.

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234 Irrigation Scheduling Rdi

An experiment was conducted at the Central Research Station of Orissa University of Agriculture and Technology, Bhubaneswar during summer season of 2015 to study the growth, yieldand moisture use of three groundnut varieties under different levels of irrigation (IW/CPE ratio of 1.0,0.8 and 0.6 ). The experiment was laid out in a split-plot design with three replications. Three Irrigation scheduling IW/CPE ratio (I1- 1.0, I2-0.8 and I3- 0.6) were taken in the main plot and three groundnut varieties (V1- Kadri -6, V2- Devi ,V3- Smruti ) were assigned to sub-plots. The soil was silty clay having pH 5.45, available N, P and K of (150, 18.5, 107.5)kg/ha, respectively. There was significant variation among irrigation levels with regards to shoot length, branch number, LAI, dry matter production and yield attributes viz. test weight of pod and kernel and shelling percentage were affected by both the factor which in turn influenced the yield. The irrigation level IW/CPE ratio 0.8 recorded significantly higher pod yield of 23.35 q/ha than that of 0.6 and 1.0 due to more number of filled pods (25 per plant) 100 pod weight (59.9g )and 100 kernel weight (50.0 g). Variety Devi recorded significantly higher pod yield of 23.33 q/ha.which is 28.2 % higher than that ofvariety AKP-7 and 11.5 % higher over variety Smruti.

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24NASA Technical Reports Server (NTRS) 19750013651: Irrigation Scheduling, Freeze Warning, And Soil Salinity Detecting

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25NASA Technical Reports Server (NTRS) 19740020700: Irrigation Scheduling, Freeze Warning And Soil Salinity Detecting. [Cameron And Starr Counties, Texas

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26Remote Sensing Of Vineyard FPAR, With Implications For Irrigation Scheduling

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Normalized difference vegetation index (NDVI) data, acquired at two-meter resolution by an airborne ADAR System 5500, were compared with fraction of photosynthetically active radiation (FPAR) absorbed by commercial vineyards in Napa Valley, California. An empirical line correction was used to transform image digital counts to surface reflectance. ''Apparent'' NDVI (generated from digital counts) and ''corrected'' NDVI (from reflectance) were both strongly related to FPAR of range 0.14-0.50 (both r(sup 2) = 0.97, P ( 0.01). By suppressing noise, corrected NDVI should form a more spatially and temporally stable relationship with FPAR, reducing the need for repeated field support. Study results suggest the possibility of using optical remote sensing to monitor the transpiration crop coefficient, thus providing an enhanced spatial resolution component to crop water budget calculations and irrigation management.

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27NASA Technical Reports Server (NTRS) 19750005278: Irrigation Scheduling, Freeze Warning And Soil Salinity Detecting. [Cameron County, Texas

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28The IoT As A Tool To Combine The Scheduling Of The Irrigation With The Geostatistics Of The Soils

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Persistent droughts, population growth, and consequences of the climate change put severeconstraints on agriculture in many Regions. This emergence can be ameliorated by: (i) either increasing the water supply, or (ii) reducing the water demand. One viable avenue is to enhance the irrigation's efficiency by increasing on one hand the water supply (via the use of recycled waters), and on the other by decreasing the water consumption (via the use of drip irrigation, autonomous network of sensors and predictive models). To this aim, we propose an IoT-framework to assess and control the environmental risks associated with the use of recycled water(s). The IoT-framework is organized along the following main streams:(i) design an autonomous network of sensors that collect data about soil moisture and concentration of dissolved contaminants; (ii) assimilate these data together with precipitation forecast into predictive models of soil moisture dynamics and contaminant migration; (iii) use these data-driven models to optimize the irrigation practices while minimizing their environmental impact. The fundamental pre-requisite common to (i)–(iii) is the proper processing of the soil data. The present paper will focus on such a topic by showing how it works within the IoT-framework.

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29Scheduling Irrigation To Meet Crop Demands

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Persistent droughts, population growth, and consequences of the climate change put severeconstraints on agriculture in many Regions. This emergence can be ameliorated by: (i) either increasing the water supply, or (ii) reducing the water demand. One viable avenue is to enhance the irrigation's efficiency by increasing on one hand the water supply (via the use of recycled waters), and on the other by decreasing the water consumption (via the use of drip irrigation, autonomous network of sensors and predictive models). To this aim, we propose an IoT-framework to assess and control the environmental risks associated with the use of recycled water(s). The IoT-framework is organized along the following main streams:(i) design an autonomous network of sensors that collect data about soil moisture and concentration of dissolved contaminants; (ii) assimilate these data together with precipitation forecast into predictive models of soil moisture dynamics and contaminant migration; (iii) use these data-driven models to optimize the irrigation practices while minimizing their environmental impact. The fundamental pre-requisite common to (i)–(iii) is the proper processing of the soil data. The present paper will focus on such a topic by showing how it works within the IoT-framework.

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