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Optimal Placement of Minimal Number ...

Ko-Madden, Channing Tucker.

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Optimal Placement of Minimal Number of Proximal Sensors for Precision Irrigation Management.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimal Placement of Minimal Number of Proximal Sensors for Precision Irrigation Management./
作者:	Ko-Madden, Channing Tucker.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	153 p.
附註:	Source: Masters Abstracts International, Volume: 57-05.
Contained By:	Masters Abstracts International57-05(E).
標題:	Agricultural engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10742896
ISBN:	9780355969603

Optimal Placement of Minimal Number of Proximal Sensors for Precision Irrigation Management.
Ko-Madden, Channing Tucker.

Optimal Placement of Minimal Number of Proximal Sensors for Precision Irrigation Management. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 153 p.

Source: Masters Abstracts International, Volume: 57-05.

Thesis (M.S.)--University of California, Davis, 2018.

Irrigation can greatly boost agricultural production but consumes the largest amount of water of all agricultural practices. Due to the increasing water scarcity in agricultural regions, the interest in implementation of site-specific irrigation to reduce water consumption is growing. Permanent crops are well suited for the use of proximal sensors that monitor plant water status, which can be used for site-specific irrigation. When constructing a wireless sensor network for managing site-specific irrigation of permanent crops, it is beneficial to know the optimal locations to place proximal sensors. The objectives of this study were: (i) to develop an algorithm for determining the optimal locations for placement of proximal sensors for irrigation management, (ii) to collect stem water potential data to determine optimal locations and validate optimal locations through temporal stability analysis, (iii) to collect stomatal vapor flux and shaded leaf temperature data to determine if one or both of these measurements can be used as a surrogate for stem water potential for determining optimal locations, and (iv) to investigate the efficacy of using apparent soil electrical conductivity measurements to determine optimal locations.

ISBN: 9780355969603Subjects--Topical Terms:

3168406
Agricultural engineering.

Optimal Placement of Minimal Number of Proximal Sensors for Precision Irrigation Management.
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245 1 0 $a Optimal Placement of Minimal Number of Proximal Sensors for Precision Irrigation Management.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 153 p.
500 $a Source: Masters Abstracts International, Volume: 57-05.
500 $a Adviser: Shrinivasa K. Upadhyaya.
502 $a Thesis (M.S.)--University of California, Davis, 2018.
520 $a Irrigation can greatly boost agricultural production but consumes the largest amount of water of all agricultural practices. Due to the increasing water scarcity in agricultural regions, the interest in implementation of site-specific irrigation to reduce water consumption is growing. Permanent crops are well suited for the use of proximal sensors that monitor plant water status, which can be used for site-specific irrigation. When constructing a wireless sensor network for managing site-specific irrigation of permanent crops, it is beneficial to know the optimal locations to place proximal sensors. The objectives of this study were: (i) to develop an algorithm for determining the optimal locations for placement of proximal sensors for irrigation management, (ii) to collect stem water potential data to determine optimal locations and validate optimal locations through temporal stability analysis, (iii) to collect stomatal vapor flux and shaded leaf temperature data to determine if one or both of these measurements can be used as a surrogate for stem water potential for determining optimal locations, and (iv) to investigate the efficacy of using apparent soil electrical conductivity measurements to determine optimal locations.
520 $a In this study, stem water potential, stomatal vapor flux, and shaded leaf temperature data were collected on nonpareil almond trees in an almond orchard at Nickels Soil Laboratory, Arbuckle, CA during the 2017 growing season. This field was previously divided into three management units based on soil and plant characteristics. Field data were interpolated to obtain estimated values for unsampled trees using inverse square distance interpolation, as well as ordinary kriging with an exponential semivariogram model. Results showed that inverse square distance interpolation and ordinary kriging produced similar results when the assumption of second-order stationarity was made and the exponential model fit the experimental data well. Through comparison of their geospatial distributions, it was found that stomatal vapor flux and shaded leaf temperature data did not consistently predict the spatial variability of stem water potential data in the field.
520 $a Moreover, a search algorithm to determine the optimal locations for placement of proximal sensors for irrigation management was proposed, and optimal locations were validated using present and historic data. The multistep search algorithm contained two search constraints. The first constraint required sensors be placed at least a certain distance apart, which was determined through semivariogram analysis. The second constraint required the variability of mean difference for a given combination of locations to be below 10%. Subsequently, to determine from those combination of locations which satisfy the constraints the optimal locations, the combination of locations that had the smallest sample variability of mean difference were selected as the optimal locations. To evaluate the optimal locations determined by the algorithm, temporal stability analysis was used to judge the performance of each group of optimal locations.
520 $a In this study, the proposed algorithm was used to determine the optimal locations for three, four, and five sensors in the three different irrigation management units within the orchard. Results show that the optimal locations determined using stem water potential data were found to be temporally stable, and three sensors for each management zone were sufficient to provide a representative mean value of stem water potential with an estimation error less than 50 kPa. Therefore, we recommend three sensors for each management unit considered in this study. Most of the optimal locations determined using stomatal vapor flux and shaded leaf temperature data were stable. However, due to the difference in spatial variability between stem water potential data and non-stem water potential data, results indicated that use of these non-stem water potential data may not always produce reliable results. The optimal locations determined using shallow apparent soil electrical conductivity data were stable, while those determined from deep apparent soil electrical conductivity data were frequently not stable. While this approach shows promise for determining the optimal placement of minimal number of sensors for precision irrigation based upon optimal control of stem water potential, it needs to be further evaluated for management units with larger areas that are more representative of commercial orchards.
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