Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression
Soybean grain yield has regularly been impaired by drought periods, and the future climatic scenarios for soybean production might drastically impact yields worldwide. In this context, the knowledge of soybean yield is extremely important to subsidize government and corporative decisions over techni...
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MDPI AG
2021-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/5/977 |
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| author | Luís Guilherme Teixeira Crusiol Marcos Rafael Nanni Renato Herrig Furlanetto Rubson Natal Ribeiro Sibaldelli Everson Cezar Liang Sun José Salvador Simonetto Foloni Liliane Marcia Mertz-Henning Alexandre Lima Nepomuceno Norman Neumaier José Renato Bouças Farias |
| author_facet | Luís Guilherme Teixeira Crusiol Marcos Rafael Nanni Renato Herrig Furlanetto Rubson Natal Ribeiro Sibaldelli Everson Cezar Liang Sun José Salvador Simonetto Foloni Liliane Marcia Mertz-Henning Alexandre Lima Nepomuceno Norman Neumaier José Renato Bouças Farias |
| author_sort | Luís Guilherme Teixeira Crusiol |
| collection | DOAJ |
| description | Soybean grain yield has regularly been impaired by drought periods, and the future climatic scenarios for soybean production might drastically impact yields worldwide. In this context, the knowledge of soybean yield is extremely important to subsidize government and corporative decisions over technical issues. This paper aimed to predict grain yield in soybean crop grown under different levels of water availability using reflectance spectroscopy and partial least square regression (PLSR). Field experiments were undertaken at Embrapa Soja (Brazilian Agricultural Research Corporation) in the 2016/2017, 2017/2018 and 2018/2019 cropping seasons. The data collected were analyzed following a split plot model in a randomized complete block design, with four blocks. The following water conditions were distributed in the field plots: irrigated (IRR), non-irrigated (NIRR) and water deficit induced at the vegetative (WDV) and reproductive stages (WDR) using rainout shelters. Soybean genotypes with different responses to water deficit were distributed in the subplots. Soil moisture and weather data were monitored daily. A total of 7216 leaf reflectance (from 400 to 2500 nm, measured by the FieldSpec 3 Jr spectroradiometer) was collected at 24 days in the three cropping seasons. The PLSR (<i>p</i> ≤ 0.05) was performed to predict soybean grain yield by its leaf-based reflectance spectroscopy. The results demonstrated the highest accuracy in soybean grain yield prediction at the R5 phenological stage, corresponding to the period when grains are being formed (R<sup>2</sup> ranging from 0.731 to 0.924 and the RMSE from 334 to 403 kg ha<sup>−1</sup>—7.77 to 11.33%). Analyzing the three cropping seasons into a single PLSR model at R5 stage, R<sup>2</sup> equal to 0.775, 0.730 and 0.688 were obtained at the calibration, cross-validation and external validation stages, with RMSE lower than 634 kg ha<sup>−1</sup> (13.34%). The PLSR demonstrated higher accuracy in plants submitted to water deficit both at the vegetative and reproductive periods in comparison to plants under natural rainfall or irrigation. |
| first_indexed | 2024-03-09T05:31:17Z |
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| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:31:17Z |
| publishDate | 2021-03-01 |
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| spelling | doaj.art-62c7e9757a85458982d6cfab5f1d78a02023-12-03T12:32:23ZengMDPI AGRemote Sensing2072-42922021-03-0113597710.3390/rs13050977Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares RegressionLuís Guilherme Teixeira Crusiol0Marcos Rafael Nanni1Renato Herrig Furlanetto2Rubson Natal Ribeiro Sibaldelli3Everson Cezar4Liang Sun5José Salvador Simonetto Foloni6Liliane Marcia Mertz-Henning7Alexandre Lima Nepomuceno8Norman Neumaier9José Renato Bouças Farias10Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture/CAAS-CIAT Joint Laboratory in Advanced Technologies for Sustainable Agriculture—Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaDepartment of Agronomy, State University of Maringá, Maringá, PR 87020-900, BrazilDepartment of Agronomy, State University of Maringá, Maringá, PR 87020-900, BrazilMathematician, Statistical Specialist, Londrina, PR 86001-970, BrazilDepartment of Agronomy, State University of Maringá, Maringá, PR 87020-900, BrazilKey Laboratory of Agricultural Remote Sensing, Ministry of Agriculture/CAAS-CIAT Joint Laboratory in Advanced Technologies for Sustainable Agriculture—Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaEmbrapa Soja (National Soybean Research Center—Brazilian Agricultural Research Corporation), Londrina, PR 86001-970, BrazilEmbrapa Soja (National Soybean Research Center—Brazilian Agricultural Research Corporation), Londrina, PR 86001-970, BrazilEmbrapa Soja (National Soybean Research Center—Brazilian Agricultural Research Corporation), Londrina, PR 86001-970, BrazilEmbrapa Soja (National Soybean Research Center—Brazilian Agricultural Research Corporation), Londrina, PR 86001-970, BrazilEmbrapa Soja (National Soybean Research Center—Brazilian Agricultural Research Corporation), Londrina, PR 86001-970, BrazilSoybean grain yield has regularly been impaired by drought periods, and the future climatic scenarios for soybean production might drastically impact yields worldwide. In this context, the knowledge of soybean yield is extremely important to subsidize government and corporative decisions over technical issues. This paper aimed to predict grain yield in soybean crop grown under different levels of water availability using reflectance spectroscopy and partial least square regression (PLSR). Field experiments were undertaken at Embrapa Soja (Brazilian Agricultural Research Corporation) in the 2016/2017, 2017/2018 and 2018/2019 cropping seasons. The data collected were analyzed following a split plot model in a randomized complete block design, with four blocks. The following water conditions were distributed in the field plots: irrigated (IRR), non-irrigated (NIRR) and water deficit induced at the vegetative (WDV) and reproductive stages (WDR) using rainout shelters. Soybean genotypes with different responses to water deficit were distributed in the subplots. Soil moisture and weather data were monitored daily. A total of 7216 leaf reflectance (from 400 to 2500 nm, measured by the FieldSpec 3 Jr spectroradiometer) was collected at 24 days in the three cropping seasons. The PLSR (<i>p</i> ≤ 0.05) was performed to predict soybean grain yield by its leaf-based reflectance spectroscopy. The results demonstrated the highest accuracy in soybean grain yield prediction at the R5 phenological stage, corresponding to the period when grains are being formed (R<sup>2</sup> ranging from 0.731 to 0.924 and the RMSE from 334 to 403 kg ha<sup>−1</sup>—7.77 to 11.33%). Analyzing the three cropping seasons into a single PLSR model at R5 stage, R<sup>2</sup> equal to 0.775, 0.730 and 0.688 were obtained at the calibration, cross-validation and external validation stages, with RMSE lower than 634 kg ha<sup>−1</sup> (13.34%). The PLSR demonstrated higher accuracy in plants submitted to water deficit both at the vegetative and reproductive periods in comparison to plants under natural rainfall or irrigation.https://www.mdpi.com/2072-4292/13/5/977<i>Glycine max</i> (L.) Merrilldrought stresssoybean genotypesleaf-based datahyperspectral reflectance |
| spellingShingle | Luís Guilherme Teixeira Crusiol Marcos Rafael Nanni Renato Herrig Furlanetto Rubson Natal Ribeiro Sibaldelli Everson Cezar Liang Sun José Salvador Simonetto Foloni Liliane Marcia Mertz-Henning Alexandre Lima Nepomuceno Norman Neumaier José Renato Bouças Farias Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression Remote Sensing <i>Glycine max</i> (L.) Merrill drought stress soybean genotypes leaf-based data hyperspectral reflectance |
| title | Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression |
| title_full | Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression |
| title_fullStr | Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression |
| title_full_unstemmed | Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression |
| title_short | Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression |
| title_sort | yield prediction in soybean crop grown under different levels of water availability using reflectance spectroscopy and partial least squares regression |
| topic | <i>Glycine max</i> (L.) Merrill drought stress soybean genotypes leaf-based data hyperspectral reflectance |
| url | https://www.mdpi.com/2072-4292/13/5/977 |
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