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Transferability of Models for Estimating Paddy Rice Biomass from Spatial Plant Height Data

Author

Listed:
  • Nora Tilly

    () (ICASD-International Center for Agro-Informatics and Sustainable Development, Institute of Geography (GIS & Remote Sensing Group), University of Cologne, 50923 Cologne, Germany)

  • Dirk Hoffmeister

    () (ICASD-International Center for Agro-Informatics and Sustainable Development, Institute of Geography (GIS & Remote Sensing Group), University of Cologne, 50923 Cologne, Germany)

  • Qiang Cao

    () (ICASD-International Center for Agro-Informatics and Sustainable Development, Department of Plant Nutrition, China Agricultural University, 100193 Beijing, China)

  • Victoria Lenz-Wiedemann

    () (ICASD-International Center for Agro-Informatics and Sustainable Development, Institute of Geography (GIS & Remote Sensing Group), University of Cologne, 50923 Cologne, Germany)

  • Yuxin Miao

    () (ICASD-International Center for Agro-Informatics and Sustainable Development, Department of Plant Nutrition, China Agricultural University, 100193 Beijing, China)

  • Georg Bareth

    () (ICASD-International Center for Agro-Informatics and Sustainable Development, Institute of Geography (GIS & Remote Sensing Group), University of Cologne, 50923 Cologne, Germany)

Abstract

It is known that plant height is a suitable parameter for estimating crop biomass. The aim of this study was to confirm the validity of spatial plant height data, which is derived from terrestrial laser scanning (TLS), as a non-destructive estimator for biomass of paddy rice on the field scale. Beyond that, the spatial and temporal transferability of established biomass regression models were investigated to prove the robustness of the method and evaluate the suitability of linear and exponential functions. In each growing season of two years, three campaigns were carried out on a field experiment and on a farmer’s conventionally managed field. Crop surface models (CSMs) were generated from the TLS-derived point clouds for calculating plant height with a very high spatial resolution of 1 cm. High coefficients of determination between CSM-derived and manually measured plant heights ( R 2 : 0.72 to 0.91) confirm the applicability of the approach. Yearly averaged differences between the measurements were ~7% and ~9%. Biomass regression models were established from the field experiment data sets, based on strong coefficients of determination between plant height and dry biomass ( R 2 : 0.66 to 0.86 and 0.65 to 0.84 for linear and exponential models, respectively). The spatial and temporal transferability of the models to the farmer’s conventionally managed fields is supported by strong coefficients of determination between estimated and measured values ( R 2 : 0.60 to 0.90 and 0.56 to 0.85 for linear and exponential models, respectively). Hence, the suitability of TLS-derived spatial plant height as a non-destructive estimator for biomass of paddy rice on the field scale was verified and the transferability demonstrated.

Suggested Citation

  • Nora Tilly & Dirk Hoffmeister & Qiang Cao & Victoria Lenz-Wiedemann & Yuxin Miao & Georg Bareth, 2015. "Transferability of Models for Estimating Paddy Rice Biomass from Spatial Plant Height Data," Agriculture, MDPI, Open Access Journal, vol. 5(3), pages 1-23, July.
  • Handle: RePEc:gam:jagris:v:5:y:2015:i:3:p:538-560:d:53043
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    References listed on IDEAS

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    1. Unknown, 1998. "Can Agriculture And Growth Coexist? Proceedings," Special Reports 14810, Virginia Tech, Rural Economic Analysis Program (REAP).
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    More about this item

    Keywords

    terrestrial laser scanning; plant height; biomass; rice; precision agriculture; field level;

    JEL classification:

    • Q1 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture
    • Q10 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - General
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance
    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services
    • Q17 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agriculture in International Trade
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy

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