A Spatial Data Infrastructure Approach for the Characterization of New Zealand's Groundwater Systems

Kmoch, Alexander; Klug, Hermann; Ritchie, Alistair; Schmidt, Jochen; White, Paul

doi:10.1111/tgis.12171

Cited by 10 publications

(4 citation statements)

References 21 publications

(22 reference statements)

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“…Water-use data were processed using Hilltop Software (Kmoch et al, 2015), a Microsoft (Redmond, WA) Windows-based database and reporting application suite for hydrology-related time series data. Visual quality checks were performed using the graphing functions of Hilltop Software.…”

Section: Data Management and Calculationsmentioning

confidence: 99%

Water use on nonirrigated pasture-based dairy farms: Combining detailed monitoring and modeling to set benchmarks

Higham

Horne

Singh

et al. 2017

Journal of Dairy Science

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Water use in intensively managed, confinement dairy systems has been widely studied, but few reports exist regarding water use on pasture-based dairy farms. The objective of this study was to quantify the seasonal pattern of water use to develop a prediction model of water use for pasture-based dairy farms. Stock drinking, milking parlor, and total water use was measured on 35 pasture-based, seasonal calving dairy farms in New Zealand over 2 yr. Average stock drinking water was 60 L/cow per day, with peak use in summer. We estimated that, on average, 26% of stock drinking water was lost through leakage from water-distribution systems. Average corrected stock drinking water (equivalent to voluntary water intake) was 36 L/cow per day, and peak water consumption was 72 L/cow per day in summer. Milking parlor water use increased sharply at the start of lactation (July) and plateaued (August) until summer (February), after which it decreased with decreasing milk production. Average milking parlor water use was 58 L/cow per day (between September and February). Water requirements were affected by parlor type, with rotary milking parlor water use greater than herringbone parlor water use. Regression models were developed to predict stock drinking and milking parlor water use. The models included a range of climate, farm, and milk production variables. The main drivers of stock drinking water use were maximum daily temperature, potential evapotranspiration, radiation, and yield of milk and milk components. The main drivers for milking parlor water use were average per cow milk production and milking frequency. These models of water use are similar to those used in confinement dairy systems, where milk yield is commonly used as a variable. The models presented fit the measured data more accurately than other published models and are easier to use on pasture-based dairy farms, as they do not include feed and variables that are difficult to measure on pasture-based farms.

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Section: Data Management and Calculationsmentioning

confidence: 99%

Water use on nonirrigated pasture-based dairy farms: Combining detailed monitoring and modeling to set benchmarks

Higham

Horne

Singh

et al. 2017

Journal of Dairy Science

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“…Water use data were imported into Hilltop software (Kmoch et al, 2015). The water use data from data loggers were compared with the manual readings of the water meters.…”

Section: Data Management and Calculationsmentioning

confidence: 99%

Temporal and spatial water use on irrigated and nonirrigated pasture-based dairy farms

Higham

Horne

Singh

et al. 2017

Journal of Dairy Science

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Robust information for water use on pasture-based dairy farms is critical to farmers' attempts to use water more efficiently and the improved allocation of freshwater resources to dairy farmers. To quantify the water requirements of dairy farms across regions in a practicable manner, it will be necessary to develop predictive models. The objectives of this study were to compare water use on a group of irrigated and nonirrigated farms, validate existing water use models using the data measured on the group of nonirrigated farms, and modify the model so that it can be used to predict water use on irrigated dairy farms. Water use data were collected on a group of irrigated dairy farms located in the Canterbury, New Zealand, region with the largest area under irrigation. The nonirrigated farms were located in the Manawatu region. The amount of water used for irrigation was almost 52-fold greater than the amount of all other forms of water use combined. There were large differences in measured milking parlor water use, stock drinking water, and leakage rates between the irrigated and nonirrigated farms. As expected, stock drinking water was lower on irrigated dairy farms. Irrigation lowers the dry matter percentage of pasture, ensuring that the amount of water ingested from pasture remains high throughout the year, thereby reducing the demand for drinking water. Leakage rates were different between the 2 groups of farms; 47% of stock drinking water was lost as leakage on nonirrigated farms, whereas leakage on the irrigated farms equated to only 13% of stock drinking water. These differences in leakage were thought to be related to regional differences rather than differences in irrigated versus nonirrigated farms. Existing models developed to predict milking parlor, corrected stock drinking water, and total water use on nonirrigated pasture-based dairy farms in a previous related study were tested on the data measured in the present research. As expected, these models performed well for nonirrigated dairy farms but provided poor predictive power for irrigated farms. Partial least squares regression models were developed specifically to simulate corrected stock drinking water, milking parlor water, and total water use on irrigated dairy farms.

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“…The definition of a SDI and the components a SDI should contain have been explored and discussed many times [6,16,17]. A SDI can be defined more simply as the collection of software, hardware, and custom code needed to provide basic data services such as storage, management, analysis, and distribution to both data users and data producers.…”

Section: Architecture and Implementation Of The Ops Infrastructurementioning

confidence: 99%

Open Polar Server (OPS)—An Open Source Infrastructure for the Cryosphere Community

Liu

Purdon²,

Stafford

et al. 2016

IJGI

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Abstract:The Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas has collected approximately 1000 terabytes (TB) of radar depth sounding data over the Arctic and Antarctic ice sheets since 1993 in an effort to map the thickness of the ice sheets and ultimately understand the impacts of climate change and sea level rise. In addition to data collection, the storage, management, and public distribution of the dataset are also primary roles of the CReSIS. The Open Polar Server (OPS) project developed a free and open source infrastructure to store, manage, analyze, and distribute the data collected by CReSIS in an effort to replace its current data storage and distribution approach. The OPS infrastructure includes a spatial database management system (DBMS), map and web server, JavaScript geoportal, and MATLAB application programming interface (API) for the inclusion of data created by the cryosphere community. Open source software including GeoServer, PostgreSQL, PostGIS, OpenLayers, ExtJS, GeoEXT and others are used to build a system that modernizes the CReSIS data distribution for the entire cryosphere community and creates a flexible platform for future development. Usability analysis demonstrates the OPS infrastructure provides an improved end user experience. In addition, interpolating glacier topography is provided as an application example of the system.

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A Spatial Data Infrastructure Approach for the Characterization of New Zealand's Groundwater Systems

Cited by 10 publications

References 21 publications

Water use on nonirrigated pasture-based dairy farms: Combining detailed monitoring and modeling to set benchmarks

Water use on nonirrigated pasture-based dairy farms: Combining detailed monitoring and modeling to set benchmarks

Temporal and spatial water use on irrigated and nonirrigated pasture-based dairy farms

Open Polar Server (OPS)—An Open Source Infrastructure for the Cryosphere Community

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