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Handheld NDVI sensor-based rice productivity assessment under combinations of fertilizer soil amendment and irrigation water management in lower Moshi irrigation scheme, North Tanzania
Handheld optical sensor was used to measure canopy reflectance at red region (656 nm) and near-infrared region (774 nm) to generate NDVI data for monitoring rice productivity under soil amendment with combinations of fertilizers at two levels of water regime in smallholder Irrigation Scheme, in Lowe...
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| Published in: | Environmental earth sciences 2023-02, Vol.82 (3), p.78, Article 78 |
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| creator | Kimaro, Oforo Didas Gebre, Sintayehu Legesse Hieronimo, Proches Kihupi, Nganga Feger, Karl-Heinz Kimaro, Didas N. |
| description | Handheld optical sensor was used to measure canopy reflectance at red region (656 nm) and near-infrared region (774 nm) to generate NDVI data for monitoring rice productivity under soil amendment with combinations of fertilizers at two levels of water regime in smallholder Irrigation Scheme, in Lower Moshi, North Tanzania. The study was carried out in an experimental design which consisted of two irrigation water levels (flooding and system of rice intensification) with multi-nutrients (NPK) and single nutrient (urea) application replicated three times in a randomized complete block design. Flood irrigation water was applied at 7 cm height throughout the growing season, while SRI treatment irrigation water was applied at 4 cm height under alternate wetting and drying conditions. The annual rates of fertilizers applied was 120 kg N/ha, 20 kg P/ha, and 25 kg K/ha. The variety SARO-5 was used in this experiment. Simple correlation coefficient (
r
) was used to measure the degree of association between field crop performance parameters (plant height, number of tillers, biomass, yield) and NDVI across growth stages and three positions of the sensor above the canopy in the tested fertilizer combinations and water regimes. Results show that at any given fertiliser combinations and water levels, there was no significant correlation between plant height and NDVI except for the plant height at a vegetative stage for 0.6 m above the crop canopy and booting stage at 0.3 m and 0.6 m above the canopy, respectively (
P
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| doi_str_mv | 10.1007/s12665-022-10730-0 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2768941394</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2768941394</sourcerecordid><originalsourceid>FETCH-LOGICAL-c363t-53f2b6e54e52c514b3c4da30cb83b891bc49b1c7116ffa5b463181c275b392fb3</originalsourceid><addsrcrecordid>eNp9kctOwzAQRSMEElXpD7CyxJaAH4mTLFF5FInHBthatuO0rlK7eFxQ-2V8HqZFwIrZzGjm3BlpbpYdE3xGMK7OgVDOyxxTmhNcMZzjvWxAas5zTptm_6eu8WE2ApjjFIywBvNB9jGRrp2ZvkUPly-3CIwDH3IlwbQoWG3QMvh2paN9s3GNJIABWBgX0cq1JiDtF8o6Ga13gHyHOhOi7e0mjcDbHsnEtls-nUE2BDvdwuhdxsQspJNTs51bh3r_nnr3Hmb2Lwp6lpBT9OBDnKEn6TbSWXmUHXSyBzP6zsPs-frqaTzJ7x5vbscXd7lmnMW8ZB1V3JSFKakuSaGYLlrJsFY1U3VDlC4aRXRFCO86WaqCM1ITTatSsYZ2ig2zk93e9IjXlYEo5n4VXDopaMXrpkiPLBJFd5QOHiCYTiyDXciwFgSLL5PEziSRTBJbkwROIrYTQYLd1ITf1f-oPgGDt5k3</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2768941394</pqid></control><display><type>article</type><title>Handheld NDVI sensor-based rice productivity assessment under combinations of fertilizer soil amendment and irrigation water management in lower Moshi irrigation scheme, North Tanzania</title><source>Springer Link</source><creator>Kimaro, Oforo Didas ; Gebre, Sintayehu Legesse ; Hieronimo, Proches ; Kihupi, Nganga ; Feger, Karl-Heinz ; Kimaro, Didas N.</creator><creatorcontrib>Kimaro, Oforo Didas ; Gebre, Sintayehu Legesse ; Hieronimo, Proches ; Kihupi, Nganga ; Feger, Karl-Heinz ; Kimaro, Didas N.</creatorcontrib><description>Handheld optical sensor was used to measure canopy reflectance at red region (656 nm) and near-infrared region (774 nm) to generate NDVI data for monitoring rice productivity under soil amendment with combinations of fertilizers at two levels of water regime in smallholder Irrigation Scheme, in Lower Moshi, North Tanzania. The study was carried out in an experimental design which consisted of two irrigation water levels (flooding and system of rice intensification) with multi-nutrients (NPK) and single nutrient (urea) application replicated three times in a randomized complete block design. Flood irrigation water was applied at 7 cm height throughout the growing season, while SRI treatment irrigation water was applied at 4 cm height under alternate wetting and drying conditions. The annual rates of fertilizers applied was 120 kg N/ha, 20 kg P/ha, and 25 kg K/ha. The variety SARO-5 was used in this experiment. Simple correlation coefficient (
r
) was used to measure the degree of association between field crop performance parameters (plant height, number of tillers, biomass, yield) and NDVI across growth stages and three positions of the sensor above the canopy in the tested fertilizer combinations and water regimes. Results show that at any given fertiliser combinations and water levels, there was no significant correlation between plant height and NDVI except for the plant height at a vegetative stage for 0.6 m above the crop canopy and booting stage at 0.3 m and 0.6 m above the canopy, respectively (
P
< 0.05). A good correlation was also observed between NDVI at booting and full booting stage regardless of the position of the sensor above the canopy and the number of tillers at full booting growth stage (
P
< 0.05). A significant relationship was observed between rice grain yield and NDVI at the vegetative, booting, and full booting stage. The simple linear regression models explained only slightly < 30% of the yield predictions by NDVI at the early stage of the crop growth, decreasing gradually to 5% at the full booting growth stage. Results demonstrate a positive linear relationship between rice grain yield and NDVI for the tested soil fertiliser amendments and irrigation water regimes. Thus, we conclude that handheld NDVI-based sensor can be used in smallholder rice yield predictions for optimising soil fertiliser use and irrigation water management. This allows future multi-functional land management within the soil–water-food nexus.</description><identifier>ISSN: 1866-6280</identifier><identifier>EISSN: 1866-6299</identifier><identifier>DOI: 10.1007/s12665-022-10730-0</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Agricultural production ; Biogeosciences ; Canopies ; Canopy ; Correlation coefficient ; Correlation coefficients ; Crop growth ; Crop yield ; Crops ; Design ; Design of experiments ; Earth and Environmental Science ; Earth Sciences ; Environmental Science and Engineering ; Experimental design ; Fertilizers ; Flood irrigation ; Floods ; Geochemistry ; Geology ; Grain ; Growing season ; Growth stage ; Height ; Hydrology/Water Resources ; Irrigation ; Irrigation water ; Irrigation water management ; Land management ; Moisture content ; Nutrients ; Optical measuring instruments ; Plant cover ; Position sensing ; Productivity ; Reflectance ; Regression analysis ; Regression models ; Rice ; Rice yield ; Sensors ; Soil ; Soil amendment ; Soil testing ; Soil water ; Soils ; Statistical methods ; Terrestrial Pollution ; The Soil-Water-Atmosphere Nexus ; Thematic Issue ; Tillers ; Urea ; Water levels ; Water management ; Water regimes ; Wetting ; Yield forecasting</subject><ispartof>Environmental earth sciences, 2023-02, Vol.82 (3), p.78, Article 78</ispartof><rights>The Author(s) 2023</rights><rights>The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c363t-53f2b6e54e52c514b3c4da30cb83b891bc49b1c7116ffa5b463181c275b392fb3</citedby><cites>FETCH-LOGICAL-c363t-53f2b6e54e52c514b3c4da30cb83b891bc49b1c7116ffa5b463181c275b392fb3</cites><orcidid>0000-0002-8226-133X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Kimaro, Oforo Didas</creatorcontrib><creatorcontrib>Gebre, Sintayehu Legesse</creatorcontrib><creatorcontrib>Hieronimo, Proches</creatorcontrib><creatorcontrib>Kihupi, Nganga</creatorcontrib><creatorcontrib>Feger, Karl-Heinz</creatorcontrib><creatorcontrib>Kimaro, Didas N.</creatorcontrib><title>Handheld NDVI sensor-based rice productivity assessment under combinations of fertilizer soil amendment and irrigation water management in lower Moshi irrigation scheme, North Tanzania</title><title>Environmental earth sciences</title><addtitle>Environ Earth Sci</addtitle><description>Handheld optical sensor was used to measure canopy reflectance at red region (656 nm) and near-infrared region (774 nm) to generate NDVI data for monitoring rice productivity under soil amendment with combinations of fertilizers at two levels of water regime in smallholder Irrigation Scheme, in Lower Moshi, North Tanzania. The study was carried out in an experimental design which consisted of two irrigation water levels (flooding and system of rice intensification) with multi-nutrients (NPK) and single nutrient (urea) application replicated three times in a randomized complete block design. Flood irrigation water was applied at 7 cm height throughout the growing season, while SRI treatment irrigation water was applied at 4 cm height under alternate wetting and drying conditions. The annual rates of fertilizers applied was 120 kg N/ha, 20 kg P/ha, and 25 kg K/ha. The variety SARO-5 was used in this experiment. Simple correlation coefficient (
r
) was used to measure the degree of association between field crop performance parameters (plant height, number of tillers, biomass, yield) and NDVI across growth stages and three positions of the sensor above the canopy in the tested fertilizer combinations and water regimes. Results show that at any given fertiliser combinations and water levels, there was no significant correlation between plant height and NDVI except for the plant height at a vegetative stage for 0.6 m above the crop canopy and booting stage at 0.3 m and 0.6 m above the canopy, respectively (
P
< 0.05). A good correlation was also observed between NDVI at booting and full booting stage regardless of the position of the sensor above the canopy and the number of tillers at full booting growth stage (
P
< 0.05). A significant relationship was observed between rice grain yield and NDVI at the vegetative, booting, and full booting stage. The simple linear regression models explained only slightly < 30% of the yield predictions by NDVI at the early stage of the crop growth, decreasing gradually to 5% at the full booting growth stage. Results demonstrate a positive linear relationship between rice grain yield and NDVI for the tested soil fertiliser amendments and irrigation water regimes. Thus, we conclude that handheld NDVI-based sensor can be used in smallholder rice yield predictions for optimising soil fertiliser use and irrigation water management. This allows future multi-functional land management within the soil–water-food nexus.</description><subject>Agricultural production</subject><subject>Biogeosciences</subject><subject>Canopies</subject><subject>Canopy</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Crop growth</subject><subject>Crop yield</subject><subject>Crops</subject><subject>Design</subject><subject>Design of experiments</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Environmental Science and Engineering</subject><subject>Experimental design</subject><subject>Fertilizers</subject><subject>Flood irrigation</subject><subject>Floods</subject><subject>Geochemistry</subject><subject>Geology</subject><subject>Grain</subject><subject>Growing season</subject><subject>Growth stage</subject><subject>Height</subject><subject>Hydrology/Water Resources</subject><subject>Irrigation</subject><subject>Irrigation water</subject><subject>Irrigation water management</subject><subject>Land management</subject><subject>Moisture content</subject><subject>Nutrients</subject><subject>Optical measuring instruments</subject><subject>Plant cover</subject><subject>Position sensing</subject><subject>Productivity</subject><subject>Reflectance</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Rice</subject><subject>Rice yield</subject><subject>Sensors</subject><subject>Soil</subject><subject>Soil amendment</subject><subject>Soil testing</subject><subject>Soil water</subject><subject>Soils</subject><subject>Statistical methods</subject><subject>Terrestrial Pollution</subject><subject>The Soil-Water-Atmosphere Nexus</subject><subject>Thematic Issue</subject><subject>Tillers</subject><subject>Urea</subject><subject>Water levels</subject><subject>Water management</subject><subject>Water regimes</subject><subject>Wetting</subject><subject>Yield forecasting</subject><issn>1866-6280</issn><issn>1866-6299</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kctOwzAQRSMEElXpD7CyxJaAH4mTLFF5FInHBthatuO0rlK7eFxQ-2V8HqZFwIrZzGjm3BlpbpYdE3xGMK7OgVDOyxxTmhNcMZzjvWxAas5zTptm_6eu8WE2ApjjFIywBvNB9jGRrp2ZvkUPly-3CIwDH3IlwbQoWG3QMvh2paN9s3GNJIABWBgX0cq1JiDtF8o6Ga13gHyHOhOi7e0mjcDbHsnEtls-nUE2BDvdwuhdxsQspJNTs51bh3r_nnr3Hmb2Lwp6lpBT9OBDnKEn6TbSWXmUHXSyBzP6zsPs-frqaTzJ7x5vbscXd7lmnMW8ZB1V3JSFKakuSaGYLlrJsFY1U3VDlC4aRXRFCO86WaqCM1ITTatSsYZ2ig2zk93e9IjXlYEo5n4VXDopaMXrpkiPLBJFd5QOHiCYTiyDXciwFgSLL5PEziSRTBJbkwROIrYTQYLd1ITf1f-oPgGDt5k3</recordid><startdate>20230201</startdate><enddate>20230201</enddate><creator>Kimaro, Oforo Didas</creator><creator>Gebre, Sintayehu Legesse</creator><creator>Hieronimo, Proches</creator><creator>Kihupi, Nganga</creator><creator>Feger, Karl-Heinz</creator><creator>Kimaro, Didas N.</creator><general>Springer Berlin 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in lower Moshi irrigation scheme, North Tanzania</title><author>Kimaro, Oforo Didas ; Gebre, Sintayehu Legesse ; Hieronimo, Proches ; Kihupi, Nganga ; Feger, Karl-Heinz ; Kimaro, Didas N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-53f2b6e54e52c514b3c4da30cb83b891bc49b1c7116ffa5b463181c275b392fb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Agricultural production</topic><topic>Biogeosciences</topic><topic>Canopies</topic><topic>Canopy</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Crop growth</topic><topic>Crop yield</topic><topic>Crops</topic><topic>Design</topic><topic>Design of experiments</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Environmental Science and Engineering</topic><topic>Experimental design</topic><topic>Fertilizers</topic><topic>Flood irrigation</topic><topic>Floods</topic><topic>Geochemistry</topic><topic>Geology</topic><topic>Grain</topic><topic>Growing season</topic><topic>Growth stage</topic><topic>Height</topic><topic>Hydrology/Water Resources</topic><topic>Irrigation</topic><topic>Irrigation water</topic><topic>Irrigation water management</topic><topic>Land management</topic><topic>Moisture content</topic><topic>Nutrients</topic><topic>Optical measuring instruments</topic><topic>Plant cover</topic><topic>Position sensing</topic><topic>Productivity</topic><topic>Reflectance</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Rice</topic><topic>Rice yield</topic><topic>Sensors</topic><topic>Soil</topic><topic>Soil amendment</topic><topic>Soil testing</topic><topic>Soil water</topic><topic>Soils</topic><topic>Statistical methods</topic><topic>Terrestrial Pollution</topic><topic>The Soil-Water-Atmosphere Nexus</topic><topic>Thematic Issue</topic><topic>Tillers</topic><topic>Urea</topic><topic>Water levels</topic><topic>Water management</topic><topic>Water regimes</topic><topic>Wetting</topic><topic>Yield forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kimaro, Oforo Didas</creatorcontrib><creatorcontrib>Gebre, Sintayehu Legesse</creatorcontrib><creatorcontrib>Hieronimo, Proches</creatorcontrib><creatorcontrib>Kihupi, Nganga</creatorcontrib><creatorcontrib>Feger, Karl-Heinz</creatorcontrib><creatorcontrib>Kimaro, Didas N.</creatorcontrib><collection>SpringerOpen</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni 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Didas N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Handheld NDVI sensor-based rice productivity assessment under combinations of fertilizer soil amendment and irrigation water management in lower Moshi irrigation scheme, North Tanzania</atitle><jtitle>Environmental earth sciences</jtitle><stitle>Environ Earth Sci</stitle><date>2023-02-01</date><risdate>2023</risdate><volume>82</volume><issue>3</issue><spage>78</spage><pages>78-</pages><artnum>78</artnum><issn>1866-6280</issn><eissn>1866-6299</eissn><abstract>Handheld optical sensor was used to measure canopy reflectance at red region (656 nm) and near-infrared region (774 nm) to generate NDVI data for monitoring rice productivity under soil amendment with combinations of fertilizers at two levels of water regime in smallholder Irrigation Scheme, in Lower Moshi, North Tanzania. The study was carried out in an experimental design which consisted of two irrigation water levels (flooding and system of rice intensification) with multi-nutrients (NPK) and single nutrient (urea) application replicated three times in a randomized complete block design. Flood irrigation water was applied at 7 cm height throughout the growing season, while SRI treatment irrigation water was applied at 4 cm height under alternate wetting and drying conditions. The annual rates of fertilizers applied was 120 kg N/ha, 20 kg P/ha, and 25 kg K/ha. The variety SARO-5 was used in this experiment. Simple correlation coefficient (
r
) was used to measure the degree of association between field crop performance parameters (plant height, number of tillers, biomass, yield) and NDVI across growth stages and three positions of the sensor above the canopy in the tested fertilizer combinations and water regimes. Results show that at any given fertiliser combinations and water levels, there was no significant correlation between plant height and NDVI except for the plant height at a vegetative stage for 0.6 m above the crop canopy and booting stage at 0.3 m and 0.6 m above the canopy, respectively (
P
< 0.05). A good correlation was also observed between NDVI at booting and full booting stage regardless of the position of the sensor above the canopy and the number of tillers at full booting growth stage (
P
< 0.05). A significant relationship was observed between rice grain yield and NDVI at the vegetative, booting, and full booting stage. The simple linear regression models explained only slightly < 30% of the yield predictions by NDVI at the early stage of the crop growth, decreasing gradually to 5% at the full booting growth stage. Results demonstrate a positive linear relationship between rice grain yield and NDVI for the tested soil fertiliser amendments and irrigation water regimes. Thus, we conclude that handheld NDVI-based sensor can be used in smallholder rice yield predictions for optimising soil fertiliser use and irrigation water management. This allows future multi-functional land management within the soil–water-food nexus.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12665-022-10730-0</doi><orcidid>https://orcid.org/0000-0002-8226-133X</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Environmental earth sciences, 2023-02, Vol.82 (3), p.78, Article 78 |
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| subjects | Agricultural production Biogeosciences Canopies Canopy Correlation coefficient Correlation coefficients Crop growth Crop yield Crops Design Design of experiments Earth and Environmental Science Earth Sciences Environmental Science and Engineering Experimental design Fertilizers Flood irrigation Floods Geochemistry Geology Grain Growing season Growth stage Height Hydrology/Water Resources Irrigation Irrigation water Irrigation water management Land management Moisture content Nutrients Optical measuring instruments Plant cover Position sensing Productivity Reflectance Regression analysis Regression models Rice Rice yield Sensors Soil Soil amendment Soil testing Soil water Soils Statistical methods Terrestrial Pollution The Soil-Water-Atmosphere Nexus Thematic Issue Tillers Urea Water levels Water management Water regimes Wetting Yield forecasting |
| title | Handheld NDVI sensor-based rice productivity assessment under combinations of fertilizer soil amendment and irrigation water management in lower Moshi irrigation scheme, North Tanzania |
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