Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models

One important issue faced by wheat producers is temporal and spatial yield variation management at a within-field scale. Vegetation indices derived from remote-sensing platforms, such as Landsat, can provide vital information characterising this variability and allow crop yield indicators developmen...
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Autor*in:	Fathiyya Ulfa [verfasserIn] Thomas G. Orton [verfasserIn] Yash P. Dang [verfasserIn] Neal W. Menzies [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	wheat yield prediction within-field variation vegetation index long-term average yields

Übergeordnetes Werk:	In: Agronomy - MDPI AG, 2012, 12(2022), 2, p 384
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:2, p 384

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/agronomy12020384

Katalog-ID:	DOAJ013809393

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allfields	10.3390/agronomy12020384 doi (DE-627)DOAJ013809393 (DE-599)DOAJ748b3e573f3141c592d02ea876ee7871 DE-627 ger DE-627 rakwb eng Fathiyya Ulfa verfasserin aut Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier One important issue faced by wheat producers is temporal and spatial yield variation management at a within-field scale. Vegetation indices derived from remote-sensing platforms, such as Landsat, can provide vital information characterising this variability and allow crop yield indicators development to map productivity. However, the most appropriate vegetation index and crop growth stage for use in yield mapping is often unclear. This study considered vegetation indices and growth stages selection and built and tested models to predict within-field yield variation. We used 48 wheat yield monitor maps to build linear-mixed models for predicting yield that were tested using leave-one-field-out cross-validation. It was found that some of the simplest models were not improved upon (by more complex models) for the prediction of the spatial pattern of the high and low yielding areas (the within-field yield ranking). In addition, predictions of longer-term average yields were generally more accurate than predictions of yield for single years. Therefore, the predictions over multiple years are valuable for revealing consistent spatial patterns in yield. The results demonstrate the potential and limitations of tools based on remote-sensing data that might provide growers with better knowledge of within-field variation to make more informed management decisions. wheat yield prediction within-field variation vegetation index long-term average yields Agriculture S Thomas G. Orton verfasserin aut Yash P. Dang verfasserin aut Neal W. Menzies verfasserin aut In Agronomy MDPI AG, 2012 12(2022), 2, p 384 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:12 year:2022 number:2, p 384 https://doi.org/10.3390/agronomy12020384 kostenfrei https://doaj.org/article/748b3e573f3141c592d02ea876ee7871 kostenfrei https://www.mdpi.com/2073-4395/12/2/384 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 2, p 384
spelling	10.3390/agronomy12020384 doi (DE-627)DOAJ013809393 (DE-599)DOAJ748b3e573f3141c592d02ea876ee7871 DE-627 ger DE-627 rakwb eng Fathiyya Ulfa verfasserin aut Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier One important issue faced by wheat producers is temporal and spatial yield variation management at a within-field scale. Vegetation indices derived from remote-sensing platforms, such as Landsat, can provide vital information characterising this variability and allow crop yield indicators development to map productivity. However, the most appropriate vegetation index and crop growth stage for use in yield mapping is often unclear. This study considered vegetation indices and growth stages selection and built and tested models to predict within-field yield variation. We used 48 wheat yield monitor maps to build linear-mixed models for predicting yield that were tested using leave-one-field-out cross-validation. It was found that some of the simplest models were not improved upon (by more complex models) for the prediction of the spatial pattern of the high and low yielding areas (the within-field yield ranking). In addition, predictions of longer-term average yields were generally more accurate than predictions of yield for single years. Therefore, the predictions over multiple years are valuable for revealing consistent spatial patterns in yield. The results demonstrate the potential and limitations of tools based on remote-sensing data that might provide growers with better knowledge of within-field variation to make more informed management decisions. wheat yield prediction within-field variation vegetation index long-term average yields Agriculture S Thomas G. Orton verfasserin aut Yash P. Dang verfasserin aut Neal W. Menzies verfasserin aut In Agronomy MDPI AG, 2012 12(2022), 2, p 384 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:12 year:2022 number:2, p 384 https://doi.org/10.3390/agronomy12020384 kostenfrei https://doaj.org/article/748b3e573f3141c592d02ea876ee7871 kostenfrei https://www.mdpi.com/2073-4395/12/2/384 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 2, p 384
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allfieldsGer	10.3390/agronomy12020384 doi (DE-627)DOAJ013809393 (DE-599)DOAJ748b3e573f3141c592d02ea876ee7871 DE-627 ger DE-627 rakwb eng Fathiyya Ulfa verfasserin aut Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier One important issue faced by wheat producers is temporal and spatial yield variation management at a within-field scale. Vegetation indices derived from remote-sensing platforms, such as Landsat, can provide vital information characterising this variability and allow crop yield indicators development to map productivity. However, the most appropriate vegetation index and crop growth stage for use in yield mapping is often unclear. This study considered vegetation indices and growth stages selection and built and tested models to predict within-field yield variation. We used 48 wheat yield monitor maps to build linear-mixed models for predicting yield that were tested using leave-one-field-out cross-validation. It was found that some of the simplest models were not improved upon (by more complex models) for the prediction of the spatial pattern of the high and low yielding areas (the within-field yield ranking). In addition, predictions of longer-term average yields were generally more accurate than predictions of yield for single years. Therefore, the predictions over multiple years are valuable for revealing consistent spatial patterns in yield. The results demonstrate the potential and limitations of tools based on remote-sensing data that might provide growers with better knowledge of within-field variation to make more informed management decisions. wheat yield prediction within-field variation vegetation index long-term average yields Agriculture S Thomas G. Orton verfasserin aut Yash P. Dang verfasserin aut Neal W. Menzies verfasserin aut In Agronomy MDPI AG, 2012 12(2022), 2, p 384 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:12 year:2022 number:2, p 384 https://doi.org/10.3390/agronomy12020384 kostenfrei https://doaj.org/article/748b3e573f3141c592d02ea876ee7871 kostenfrei https://www.mdpi.com/2073-4395/12/2/384 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 2, p 384
allfieldsSound	10.3390/agronomy12020384 doi (DE-627)DOAJ013809393 (DE-599)DOAJ748b3e573f3141c592d02ea876ee7871 DE-627 ger DE-627 rakwb eng Fathiyya Ulfa verfasserin aut Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier One important issue faced by wheat producers is temporal and spatial yield variation management at a within-field scale. Vegetation indices derived from remote-sensing platforms, such as Landsat, can provide vital information characterising this variability and allow crop yield indicators development to map productivity. However, the most appropriate vegetation index and crop growth stage for use in yield mapping is often unclear. This study considered vegetation indices and growth stages selection and built and tested models to predict within-field yield variation. We used 48 wheat yield monitor maps to build linear-mixed models for predicting yield that were tested using leave-one-field-out cross-validation. It was found that some of the simplest models were not improved upon (by more complex models) for the prediction of the spatial pattern of the high and low yielding areas (the within-field yield ranking). In addition, predictions of longer-term average yields were generally more accurate than predictions of yield for single years. Therefore, the predictions over multiple years are valuable for revealing consistent spatial patterns in yield. The results demonstrate the potential and limitations of tools based on remote-sensing data that might provide growers with better knowledge of within-field variation to make more informed management decisions. wheat yield prediction within-field variation vegetation index long-term average yields Agriculture S Thomas G. Orton verfasserin aut Yash P. Dang verfasserin aut Neal W. Menzies verfasserin aut In Agronomy MDPI AG, 2012 12(2022), 2, p 384 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:12 year:2022 number:2, p 384 https://doi.org/10.3390/agronomy12020384 kostenfrei https://doaj.org/article/748b3e573f3141c592d02ea876ee7871 kostenfrei https://www.mdpi.com/2073-4395/12/2/384 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022 2, p 384
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author	Fathiyya Ulfa
spellingShingle	Fathiyya Ulfa misc wheat yield prediction misc within-field variation misc vegetation index misc long-term average yields misc Agriculture misc S Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models
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topic_title	Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models wheat yield prediction within-field variation vegetation index long-term average yields
topic	misc wheat yield prediction misc within-field variation misc vegetation index misc long-term average yields misc Agriculture misc S
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title	Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models
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title_sort	developing and testing remote-sensing indices to represent within-field variation of wheat yields: assessment of the variation explained by simple models
title_auth	Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models
abstract	One important issue faced by wheat producers is temporal and spatial yield variation management at a within-field scale. Vegetation indices derived from remote-sensing platforms, such as Landsat, can provide vital information characterising this variability and allow crop yield indicators development to map productivity. However, the most appropriate vegetation index and crop growth stage for use in yield mapping is often unclear. This study considered vegetation indices and growth stages selection and built and tested models to predict within-field yield variation. We used 48 wheat yield monitor maps to build linear-mixed models for predicting yield that were tested using leave-one-field-out cross-validation. It was found that some of the simplest models were not improved upon (by more complex models) for the prediction of the spatial pattern of the high and low yielding areas (the within-field yield ranking). In addition, predictions of longer-term average yields were generally more accurate than predictions of yield for single years. Therefore, the predictions over multiple years are valuable for revealing consistent spatial patterns in yield. The results demonstrate the potential and limitations of tools based on remote-sensing data that might provide growers with better knowledge of within-field variation to make more informed management decisions.
abstractGer	One important issue faced by wheat producers is temporal and spatial yield variation management at a within-field scale. Vegetation indices derived from remote-sensing platforms, such as Landsat, can provide vital information characterising this variability and allow crop yield indicators development to map productivity. However, the most appropriate vegetation index and crop growth stage for use in yield mapping is often unclear. This study considered vegetation indices and growth stages selection and built and tested models to predict within-field yield variation. We used 48 wheat yield monitor maps to build linear-mixed models for predicting yield that were tested using leave-one-field-out cross-validation. It was found that some of the simplest models were not improved upon (by more complex models) for the prediction of the spatial pattern of the high and low yielding areas (the within-field yield ranking). In addition, predictions of longer-term average yields were generally more accurate than predictions of yield for single years. Therefore, the predictions over multiple years are valuable for revealing consistent spatial patterns in yield. The results demonstrate the potential and limitations of tools based on remote-sensing data that might provide growers with better knowledge of within-field variation to make more informed management decisions.
abstract_unstemmed	One important issue faced by wheat producers is temporal and spatial yield variation management at a within-field scale. Vegetation indices derived from remote-sensing platforms, such as Landsat, can provide vital information characterising this variability and allow crop yield indicators development to map productivity. However, the most appropriate vegetation index and crop growth stage for use in yield mapping is often unclear. This study considered vegetation indices and growth stages selection and built and tested models to predict within-field yield variation. We used 48 wheat yield monitor maps to build linear-mixed models for predicting yield that were tested using leave-one-field-out cross-validation. It was found that some of the simplest models were not improved upon (by more complex models) for the prediction of the spatial pattern of the high and low yielding areas (the within-field yield ranking). In addition, predictions of longer-term average yields were generally more accurate than predictions of yield for single years. Therefore, the predictions over multiple years are valuable for revealing consistent spatial patterns in yield. The results demonstrate the potential and limitations of tools based on remote-sensing data that might provide growers with better knowledge of within-field variation to make more informed management decisions.
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title_short	Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models
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