Yield estimation based on agronomic traits in vegetables under different biochar levels
Biochar, a carbon-rich material produced through oxygen-limited pyrolysis of organic biomass, demonstrates exceptional potential as a soil amendment due to its porous structure and stability. This research investigated the impact of guinea pig manure biochar on three vegetable species cultivated in...
| Autores principales: | , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier B.V.
2025
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12955/2935 https://doi.org/10.1016/j.scienta.2025.114425 |
| _version_ | 1855490517803991040 |
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| author | Ccopi Trucios, Dennis Requena Rojas, Edilson Jimmy Arias Arredondo, Alberto Taipe Crispin, Maglorio Marcelo Matero, Jhonny Demis Pizarro Carcausto, Samuel Edwin |
| author_browse | Arias Arredondo, Alberto Ccopi Trucios, Dennis Marcelo Matero, Jhonny Demis Pizarro Carcausto, Samuel Edwin Requena Rojas, Edilson Jimmy Taipe Crispin, Maglorio |
| author_facet | Ccopi Trucios, Dennis Requena Rojas, Edilson Jimmy Arias Arredondo, Alberto Taipe Crispin, Maglorio Marcelo Matero, Jhonny Demis Pizarro Carcausto, Samuel Edwin |
| author_sort | Ccopi Trucios, Dennis |
| collection | Repositorio INIA |
| description | Biochar, a carbon-rich material produced through oxygen-limited pyrolysis of organic biomass, demonstrates exceptional potential as a soil amendment due to its porous structure and stability. This research investigated the impact of guinea pig manure biochar on three vegetable species cultivated in high Andean conditions: spinach (Spinacia oleracea L.), cabbage (Brassica oleracea var.), and chard (Beta vulgaris var.). The study implemented four biochar application rates (0, 10, 20, and 30 t/ha) and measured comprehensive agronomic parameters including leaf count, leaf length, and fresh/dry biomass of both leaves and roots. Simultaneously, UAV-captured multispectral imagery provided spectral indices that were integrated with agronomic data into machine learning models: linear regression, support vector machines (SVM), and regression trees (CART). Results demonstrated significant vegetative growth enhancement and yield increases across all crops, with the 30 t ha-1 application rate producing optimal outcomes. Predictive modeling exhibited remarkable accuracy: spinach analysis via SVM achieved R² = 0.94 and RMSE = 0.32 g; chard analysis through CART delivered R² = 0.92 and RMSE = 0.35 g; and cabbage assessment using CART yielded R² = 0.91 and RMSE = 0.38 g. This research substantiates biochar’s effectiveness as an organic amendment while establishing a reliable framework for crop yield prediction using machine learning algorithms integrated with spectral data. These findings position biochar as a valuable component in sustainable agricultural systems, particularly for vegetable production in challenging high-altitude environments. |
| format | Artículo |
| id | INIA2935 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier B.V. |
| publisherStr | Elsevier B.V. |
| record_format | dspace |
| spelling | INIA29352025-12-03T15:51:47Z Yield estimation based on agronomic traits in vegetables under different biochar levels Ccopi Trucios, Dennis Requena Rojas, Edilson Jimmy Arias Arredondo, Alberto Taipe Crispin, Maglorio Marcelo Matero, Jhonny Demis Pizarro Carcausto, Samuel Edwin Biochar Vegetables Machine learning Spectral índices Sustainable agricultura Yield prediction Biocarbón Hortalizas Aprendizaje automático Índices espectrales Agricultura sostenible Predicción de rendimiento. https://purl.org/pe-repo/ocde/ford#4.01.01 Espinaca; Basella alba; Repollo; Cabbages; Acelga; Chard; Rendimiento de cultivos; Crop yield; Región andina; Andean region Biochar, a carbon-rich material produced through oxygen-limited pyrolysis of organic biomass, demonstrates exceptional potential as a soil amendment due to its porous structure and stability. This research investigated the impact of guinea pig manure biochar on three vegetable species cultivated in high Andean conditions: spinach (Spinacia oleracea L.), cabbage (Brassica oleracea var.), and chard (Beta vulgaris var.). The study implemented four biochar application rates (0, 10, 20, and 30 t/ha) and measured comprehensive agronomic parameters including leaf count, leaf length, and fresh/dry biomass of both leaves and roots. Simultaneously, UAV-captured multispectral imagery provided spectral indices that were integrated with agronomic data into machine learning models: linear regression, support vector machines (SVM), and regression trees (CART). Results demonstrated significant vegetative growth enhancement and yield increases across all crops, with the 30 t ha-1 application rate producing optimal outcomes. Predictive modeling exhibited remarkable accuracy: spinach analysis via SVM achieved R² = 0.94 and RMSE = 0.32 g; chard analysis through CART delivered R² = 0.92 and RMSE = 0.35 g; and cabbage assessment using CART yielded R² = 0.91 and RMSE = 0.38 g. This research substantiates biochar’s effectiveness as an organic amendment while establishing a reliable framework for crop yield prediction using machine learning algorithms integrated with spectral data. These findings position biochar as a valuable component in sustainable agricultural systems, particularly for vegetable production in challenging high-altitude environments. This research was funded by the INIA project “Mejoramiento de los servicios de investigación y transferencia tecnológica en el manejo y recuperación de suelos agrícolas degradados y aguas para riego en la pequeña y mediana agricultura en los departamentos de Lima, Áncash, San Martín, Cajamarca, Lambayeque, Junín, Ayacucho, Arequipa, Puno y Ucayali” CUI 2487112, of the Ministry of Agrarian Development and Irrigation (MIDAGRI) of the Peruvian Government. 2025-11-12T20:21:06Z 2025-11-12T20:21:06Z 2025-09-29 info:eu-repo/semantics/article Ccopi, D., Requena-Rojas, E., Arias-Arredondo, A., Taipe, M., Marcelo, J., & Pizarro, S. (2025). Yield estimation based on agronomic traits in vegetables under different biochar levels. Scientia Horticulturae, 352, 114425. https://doi.org/10.1016/j.scienta.2025.114425 1879-1018 http://hdl.handle.net/20.500.12955/2935 https://doi.org/10.1016/j.scienta.2025.114425 eng urn:issn:0304-4238 Scientia Horticulturae info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ application/pdf application/pdf Elsevier B.V. NL Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | Biochar Vegetables Machine learning Spectral índices Sustainable agricultura Yield prediction Biocarbón Hortalizas Aprendizaje automático Índices espectrales Agricultura sostenible Predicción de rendimiento. https://purl.org/pe-repo/ocde/ford#4.01.01 Espinaca; Basella alba; Repollo; Cabbages; Acelga; Chard; Rendimiento de cultivos; Crop yield; Región andina; Andean region Ccopi Trucios, Dennis Requena Rojas, Edilson Jimmy Arias Arredondo, Alberto Taipe Crispin, Maglorio Marcelo Matero, Jhonny Demis Pizarro Carcausto, Samuel Edwin Yield estimation based on agronomic traits in vegetables under different biochar levels |
| title | Yield estimation based on agronomic traits in vegetables under different biochar levels |
| title_full | Yield estimation based on agronomic traits in vegetables under different biochar levels |
| title_fullStr | Yield estimation based on agronomic traits in vegetables under different biochar levels |
| title_full_unstemmed | Yield estimation based on agronomic traits in vegetables under different biochar levels |
| title_short | Yield estimation based on agronomic traits in vegetables under different biochar levels |
| title_sort | yield estimation based on agronomic traits in vegetables under different biochar levels |
| topic | Biochar Vegetables Machine learning Spectral índices Sustainable agricultura Yield prediction Biocarbón Hortalizas Aprendizaje automático Índices espectrales Agricultura sostenible Predicción de rendimiento. https://purl.org/pe-repo/ocde/ford#4.01.01 Espinaca; Basella alba; Repollo; Cabbages; Acelga; Chard; Rendimiento de cultivos; Crop yield; Región andina; Andean region |
| url | http://hdl.handle.net/20.500.12955/2935 https://doi.org/10.1016/j.scienta.2025.114425 |
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