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Dergiler / Black Sea Journal of Agriculture / 2026 / Cilt: 9 Sayı: 5

Non-destructive Estimation of Leaf Area in Sainfoin (Onobrychis viciifolia Scop.) Using Plant Height and Canopy Width: A Comparative Evaluation of Four Regression Models

Tefide Kızıldeniz(NIGDE OMER HALISDEMIR UNIVERSITY)0000-0002-5627-1307
Abass Issaka Mohammed(NIGDE OMER HALISDEMIR UNIVERSITY)0009-0006-8327-6310
Sayfa
754–762
Yayın Tarihi
2026-09-15
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

These results pave the way for practical morphological models that could assist plant growth monitoring and agronomic studies while limiting the number of destructive samplings.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Leaf area (LA) is one of the most important morphological traits for the assessment of plant growth, biomass production and physiological performance.
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Temel Bulgular & Veri:For forage crops, rapid, accurate and non-destructive methods of LA estimation are particularly important to allow repeated measurements throughout the growing season.
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Sonuç & Katkı:These results pave the way for practical morphological models that could assist plant growth monitoring and agronomic studies while limiting the number of destructive samplings.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Leaf area estimation · Non-destructive measurement · Multiple linear regression

Özet

Leaf area (LA) is one of the most important morphological traits for the assessment of plant growth, biomass production and physiological performance. For forage crops, rapid, accurate and non-destructive methods of LA estimation are particularly important to allow repeated measurements throughout the growing season. The aim of this study was to compare the leaf area estimation of sainfoin (Onobrychis viciifolia Scop.) by four regression models (linear, logarithmic, polynomial and multiple linear regression) based on simple plant morphological measurements. Ninety sainfoin plants were evaluated at the flowering stage under controlled conditions. Plant height and canopy width were measured manually and actual leaf area was measured with a LI-COR LI-3100C Leaf Area Meter. Regression analyses were performed using SPSS software to construct prediction models and compare the performance of the models. Of the models tested, the multiple linear regression model had the highest prediction accuracy (R²=0.981), which was significantly better than the polynomial (R²=0.413), linear (R²=0.399) and logarithmic (R²=0.361) models. The results demonstrated that the combination of plant height and canopy width greatly improved the leaf area estimation over the use of a single predictor. The predictive performance of simple regression models was low. Multiple linear regression was a reliable, rapid and non-destructive method to estimate the leaf area of sainfoin. These results pave the way for practical morphological models that could assist plant growth monitoring and agronomic studies while limiting the number of destructive samplings.

Abstract

Leaf area (LA) is one of the most important morphological traits for the assessment of plant growth, biomass production and physiological performance. For forage crops, rapid, accurate and non-destructive methods of LA estimation are particularly important to allow repeated measurements throughout the growing season. The aim of this study was to compare the leaf area estimation of sainfoin (Onobrychis viciifolia Scop.) by four regression models (linear, logarithmic, polynomial and multiple linear regression) based on simple plant morphological measurements. Ninety sainfoin plants were evaluated at the flowering stage under controlled conditions. Plant height and canopy width were measured manually and actual leaf area was measured with a LI-COR LI-3100C Leaf Area Meter. Regression analyses were performed using SPSS software to construct prediction models and compare the performance of the models. Of the models tested, the multiple linear regression model had the highest prediction accuracy (R²=0.981), which was significantly better than the polynomial (R²=0.413), linear (R²=0.399) and logarithmic (R²=0.361) models. The results demonstrated that the combination of plant height and canopy width greatly improved the leaf area estimation over the use of a single predictor. The predictive performance of simple regression models was low. Multiple linear regression was a reliable, rapid and non-destructive method to estimate the leaf area of sainfoin. These results pave the way for practical morphological models that could assist plant growth monitoring and agronomic studies while limiting the number of destructive samplings.

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Bu Makaleye Atıf Yap

Kızıldeniz, T., Mohammed, A. I. (2026). Non-destructive Estimation of Leaf Area in Sainfoin (Onobrychis viciifolia Scop.) Using Plant Height and Canopy Width: A Comparative Evaluation of Four Regression Models. Black Sea Journal of Agriculture, Cilt: 9 Sayı: 5, 754–762. https://doi.org/10.47115/bsagriculture.1922557

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