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{'agreement': 'false', 'alternative_identifier': '', 'author': None, 'author_email': None, 'creator_user_id': '994bdae6-cbc9-47f5-bd04-27672cfa8648', 'data_classification': 'time series', 'data_collection_organization': 'University of Pretoria', 'data_structure_category': 'structured', 'dataset_language': 'English', 'dataset_license_url': '', 'did_author_or_contact_organization_collect_the_data': 'false', 'email': 'vanderlaanm@arc.agric.za', 'id': '1d42a6d0-fc78-4d04-84a2-6d9c168d94be', 'isopen': False, 'keywords': 'maize, Zea mays L., evapotranspiration, SWB, Cropwat, SAPWAT, SEBAL, Douglas, Northern Cape, soil water balance method', 'license': 'Open (Creative Commons)', 'license_id': None, 'license_title': None, 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2022-05-24T06:55:14.817802', 'metadata_modified': '2022-11-08T05:42:01.871697', 'name': '130-211-222-159-metadata-form-heeet-maize-northern-cape-2012', 'notes': 'This data is linked to a WRC project titled "Water Use Efficiency of Selected Irrigated\r\nCrops Determined with Satellite Imagery" (WRC Report No. TT 602/14). From selected project data a publication was produced focusing on different methods to estimate irrigated maize evapotranspiration and calculate green and blue water footprints (Van der Laan, M., Jarmain, C., Bastidas-Obando, E., Annandale, J. G., Fessehazion, M., & Haarhoff, D. (2019). Are water footprints accurate enough to be useful? A case study for maize (Zea mays L.). Agricultural water management, 213, 512-520).\r\nAbstract\r\nThe application of water footprint accounting is mostly done at large scales, but the estimation of crop- and\r\nregion-specific water footprints for up-scaling is dependent on accurate and representative in-field measurements\r\nof evapotranspiration (ET), yield and irrigation. In a field trial we assessed the influence of maize (Zea\r\nmays L.) ET estimates using soil water balance accounting, remote sensing with satellite imagery (SEBAL model),\r\neddy covariance measurements and three crop models (SWB, CROPWAT, SAPWAT) on water footprint estimates.\r\nWe simultaneously assessed the influence of yield spatial variability as measured by a precision harvester.\r\nSeasonal ET estimations differed by as much as 15% for the different methods, and yield differed by as much as\r\n42%, representing the error which can be introduced as a result of point measurements. Using a combination of\r\nthe highest/lowest ET estimates and the 5th/95th percentile yield, water footprint values differed by as much as\r\n100%, ranging from 338-680m3 t−1. Applying spatially-linked SEBAL ET estimates and precision harvester yield\r\nat the 30×30m scale reduced the range of estimated water footprints to 493-663 m3 t -1, with an average of\r\n547m3 t-1. This was 15% higher than the water footprint estimated using average SEBAL ET and average yield\r\nfor the whole pivot (467m3 t -1). Any error introduced at this stage of water footprint accounting can be\r\ntransferred during up-scaling of the results. For example, based on the minimum and maximum estimated water\r\nfootprints, maize production was expected to consume between 4.4 and 8.3%, respectively, of the Orange River\r\n(South Africa’s largest river) flow during the season in that region. Biophysical scientists have the role of providing\r\nhigh quality data for accurate water consumption estimates. Thereafter, their application by various\r\nstakeholders should be done with caution.', 'num_resources': 92, 'num_tags': 0, 'organization': {'id': '4e8a617a-148a-4440-adde-e503f9346fcd', 'name': 'university-of-pretoria', 'title': 'University of Pretoria', 'type': 'organization', 'description': 'The University of Pretoria (UP) is one of Africa’s top universities and the largest contact university in South Africa. 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<Package id=1d42a6d0-fc78-4d04-84a2-6d9c168d94be name=130-211-222-159-metadata-form-heeet-maize-northern-cape-2012 title=Remotely sensed evapotranspiration of maize in the Northern Cape 2012 version=None url=None author=None author_email=None maintainer=None maintainer_email=None notes=This data is linked to a WRC project titled "Water Use Efficiency of Selected Irrigated
Crops Determined with Satellite Imagery" (WRC Report No. TT 602/14). From selected project data a publication was produced focusing on different methods to estimate irrigated maize evapotranspiration and calculate green and blue water footprints (Van der Laan, M., Jarmain, C., Bastidas-Obando, E., Annandale, J. G., Fessehazion, M., & Haarhoff, D. (2019). Are water footprints accurate enough to be useful? A case study for maize (Zea mays L.). Agricultural water management, 213, 512-520).
Abstract
The application of water footprint accounting is mostly done at large scales, but the estimation of crop- and
region-specific water footprints for up-scaling is dependent on accurate and representative in-field measurements
of evapotranspiration (ET), yield and irrigation. In a field trial we assessed the influence of maize (Zea
mays L.) ET estimates using soil water balance accounting, remote sensing with satellite imagery (SEBAL model),
eddy covariance measurements and three crop models (SWB, CROPWAT, SAPWAT) on water footprint estimates.
We simultaneously assessed the influence of yield spatial variability as measured by a precision harvester.
Seasonal ET estimations differed by as much as 15% for the different methods, and yield differed by as much as
42%, representing the error which can be introduced as a result of point measurements. Using a combination of
the highest/lowest ET estimates and the 5th/95th percentile yield, water footprint values differed by as much as
100%, ranging from 338-680m3 t−1. Applying spatially-linked SEBAL ET estimates and precision harvester yield
at the 30×30m scale reduced the range of estimated water footprints to 493-663 m3 t -1, with an average of
547m3 t-1. This was 15% higher than the water footprint estimated using average SEBAL ET and average yield
for the whole pivot (467m3 t -1). Any error introduced at this stage of water footprint accounting can be
transferred during up-scaling of the results. For example, based on the minimum and maximum estimated water
footprints, maize production was expected to consume between 4.4 and 8.3%, respectively, of the Orange River
(South Africa’s largest river) flow during the season in that region. Biophysical scientists have the role of providing
high quality data for accurate water consumption estimates. Thereafter, their application by various
stakeholders should be done with caution. license_id=None type=metadata-form owner_org=4e8a617a-148a-4440-adde-e503f9346fcd creator_user_id=994bdae6-cbc9-47f5-bd04-27672cfa8648 metadata_created=2022-05-24 06:55:14.817802 metadata_modified=2022-11-08 05:42:01.871697 private=False state=active> |
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From selected project data a publication was produced focusing on different methods to estimate irrigated maize evapotranspiration and calculate green and blue water footprints (Van der Laan, M., Jarmain, C., Bastidas-Obando, E., Annandale, J. G., Fessehazion, M., & Haarhoff, D. (2019). Are water footprints accurate enough to be useful? A case study for maize (Zea mays L.). Agricultural water management, 213, 512-520).\r\nAbstract\r\nThe application of water footprint accounting is mostly done at large scales, but the estimation of crop- and\r\nregion-specific water footprints for up-scaling is dependent on accurate and representative in-field measurements\r\nof evapotranspiration (ET), yield and irrigation. In a field trial we assessed the influence of maize (Zea\r\nmays L.) ET estimates using soil water balance accounting, remote sensing with satellite imagery (SEBAL model),\r\neddy covariance measurements and three crop models (SWB, CROPWAT, SAPWAT) on water footprint estimates.\r\nWe simultaneously assessed the influence of yield spatial variability as measured by a precision harvester.\r\nSeasonal ET estimations differed by as much as 15% for the different methods, and yield differed by as much as\r\n42%, representing the error which can be introduced as a result of point measurements. Using a combination of\r\nthe highest/lowest ET estimates and the 5th/95th percentile yield, water footprint values differed by as much as\r\n100%, ranging from 338-680m3 t−1. Applying spatially-linked SEBAL ET estimates and precision harvester yield\r\nat the 30×30m scale reduced the range of estimated water footprints to 493-663 m3 t -1, with an average of\r\n547m3 t-1. This was 15% higher than the water footprint estimated using average SEBAL ET and average yield\r\nfor the whole pivot (467m3 t -1). Any error introduced at this stage of water footprint accounting can be\r\ntransferred during up-scaling of the results. For example, based on the minimum and maximum estimated water\r\nfootprints, maize production was expected to consume between 4.4 and 8.3%, respectively, of the Orange River\r\n(South Africa’s largest river) flow during the season in that region. Biophysical scientists have the role of providing\r\nhigh quality data for accurate water consumption estimates. 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