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Estimation of Dry Matter Production and Yield Prediction in Greenhouse Cucumber without Destructive Measurements

Author

Listed:
  • Kazuya Maeda

    (National Agricultural and Food Research Organization, Tsukuba 305-8519, Japan)

  • Dong-Hyuk Ahn

    (National Agricultural and Food Research Organization, Tsukuba 305-8519, Japan)

Abstract

In this study, we aimed to estimate dry matter (DM) production and fresh fruit yield in “Fresco-dash” (FD) and “Project X” (PX) cucumber cultivars using an empirical model developed for tomatoes. First, we cultivated the two cucumber cultivars under a hydroponic system for about six months. Also, parameters related to DM production such as light use efficiency (LUE), light extinction coefficient ( k ), DM distribution of fruits (DMD), and fruit dry matter content (DMC) were measured via destructive measurements. The k, DMD and DMC values were 0.99 and 0.93, 46.0 and 45.2, 3.84 and 3.78 in “Fresco Dash” and “Project X”, respectively. Second, we cultivated cucumbers to estimate DM production and fruit fresh yield using the model without destructive measurement for about eight months and validated the model’s effectiveness. The predicted DM fell within the range of the observed DM ± standard error at 51 and 132 d after transplantation (DAT) in PX as well as 51 (DAT) in FD. The predicted and observed DM at 163 DAT were 2.08 and 1.82 kg m −2 , 2.09 and 1.87 kg m −2 in “Fresco Dash” and “Project X”, respectively. The predicted and observed fruit yield at 200 DAT were 30.3 and 31.7, 30.5 and 29.1 in “Fresco Dash” and “Project X”, respectively, which were 4.4% lower than the observed fruit yield in FD and 4.9% higher than that in PX. These results suggest that the model applies to cucumbers in predicting dry matter production and fresh fruit yield.

Suggested Citation

  • Kazuya Maeda & Dong-Hyuk Ahn, 2021. "Estimation of Dry Matter Production and Yield Prediction in Greenhouse Cucumber without Destructive Measurements," Agriculture, MDPI, vol. 11(12), pages 1-10, November.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:12:p:1186-:d:686946
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    References listed on IDEAS

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    1. Kelvin López-Aguilar & Adalberto Benavides-Mendoza & Susana González-Morales & Antonio Juárez-Maldonado & Pamela Chiñas-Sánchez & Alvaro Morelos-Moreno, 2020. "Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter," Agriculture, MDPI, vol. 10(4), pages 1-14, April.
    2. Dayan, E. & van Keulen, H. & Jones, J. W. & Zipori, I. & Shmuel, D. & Challa, H., 1993. "Development, calibration and validation of a greenhouse tomato growth model: II. Field calibration and validation," Agricultural Systems, Elsevier, vol. 43(2), pages 165-183.
    3. Dayan, E. & van Keulen, H. & Jones, J. W. & Zipori, I. & Shmuel, D. & Challa, H., 1993. "Development, calibration and validation of a greenhouse tomato growth model: I. Description of the model," Agricultural Systems, Elsevier, vol. 43(2), pages 145-163.
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