Yield estimation based on agronomic traits in vegetables under different biochar levels

• Biochar from guinea pig manure improve yield in spinach, chard, and cabbage. • The best performance yield was observed at the 30 t/ha biochar dose in all crops. • UAV-based spectral indices enhanced the prediction of aerial biomass. • CART and SVM models achieved high accuracy in yield prediction. • Integration of biochar and ML supports sustainable Andean vegetable farming. 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.

Authors

Institutions

Publication Details

Journal
Scientia Horticulturae
Published
2025-10-01
DOI
https://doi.org/10.1016/j.scienta.2025.114425
Primary Topic
Leaf Properties and Growth Measurement
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Yield estimation based on agronomic traits in vegetables under different biochar levels

Samuel Pizarro, Edilson J. Requena‐Rojas, Alberto Arias-Arredondo, Dennis Ccopi-Trucios et al.
Scientia Horticulturae
Leaf Properties and Growth Measurement
article

Yield estimation based on agronomic traits in vegetables under different biochar levels

Samuel Pizarro, Edilson J. Requena‐Rojas, Alberto Arias-Arredondo, Dennis Ccopi-Trucios, João Marcelo, Maglorio Taipe
article en

Abstract

• Biochar from guinea pig manure improve yield in spinach, chard, and cabbage. • The best performance yield was observed at the 30 t/ha biochar dose in all crops. • UAV-based spectral indices enhanced the prediction of aerial biomass. • CART and SVM models achieved high accuracy in yield prediction. • Integration of biochar and ML supports sustainable Andean vegetable farming. 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.

Scientia HorticulturaeVol. 352
Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (ES)
Openalex Percentile: Top 80%
Leaf Properties and Growth Measurement
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.