IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v9y2019i1p6-d194209.html
   My bibliography  Save this article

Modeling Yields Response to Shading in the Field-to-Forest Transition Zones in Heterogeneous Landscapes

Author

Listed:
  • Martin Schmidt

    (Leibniz Centre for Agricultural Landscape Research, Eberswalder Straße 84, 15374 Müncheberg, Germany
    Institute of Earth and Environmental Sciences, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany)

  • Claas Nendel

    (Leibniz Centre for Agricultural Landscape Research, Eberswalder Straße 84, 15374 Müncheberg, Germany)

  • Roger Funk

    (Leibniz Centre for Agricultural Landscape Research, Eberswalder Straße 84, 15374 Müncheberg, Germany)

  • Matthew G. E. Mitchell

    (Institute for Resources, Environment and Sustainability, University of British Columbia, 429-2202 Main Mall, Vancouver, BC V6T 1Z4, Canada)

  • Gunnar Lischeid

    (Leibniz Centre for Agricultural Landscape Research, Eberswalder Straße 84, 15374 Müncheberg, Germany
    Institute of Earth and Environmental Sciences, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany)

Abstract

In crop modeling and yield predictions, the heterogeneity of agricultural landscapes is usually not accounted for. This heterogeneity often arises from landscape elements like forests, hedges, or single trees and shrubs that cast shadows. Shading from forested areas or shrubs has effects on transpiration, temperature, and soil moisture, all of which affect the crop yield in the adjacent arable land. Transitional gradients of solar irradiance can be described as a function of the distance to the zero line (edge), the cardinal direction, and the height of trees. The magnitude of yield reduction in transition zones is highly influenced by solar irradiance—a factor that is not yet implemented in crop growth models on a landscape level. We present a spatially explicit model for shading caused by forested areas, in agricultural landscapes. With increasing distance to forest, solar irradiance and yield increase. Our model predicts that the shading effect from the forested areas occurs up to 15 m from the forest edge, for the simulated wheat yields, and up to 30 m, for simulated maize. Moreover, we estimated the spatial extent of transition zones, to calculate the regional yield reduction caused by shading of the forest edges, which amounted to 5% to 8% in an exemplary region.

Suggested Citation

  • Martin Schmidt & Claas Nendel & Roger Funk & Matthew G. E. Mitchell & Gunnar Lischeid, 2019. "Modeling Yields Response to Shading in the Field-to-Forest Transition Zones in Heterogeneous Landscapes," Agriculture, MDPI, vol. 9(1), pages 1-15, January.
  • Handle: RePEc:gam:jagris:v:9:y:2019:i:1:p:6-:d:194209
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/9/1/6/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/9/1/6/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nendel, C. & Berg, M. & Kersebaum, K.C. & Mirschel, W. & Specka, X. & Wegehenkel, M. & Wenkel, K.O. & Wieland, R., 2011. "The MONICA model: Testing predictability for crop growth, soil moisture and nitrogen dynamics," Ecological Modelling, Elsevier, vol. 222(9), pages 1614-1625.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Paulus, Anne & Hagemann, Nina & Baaken, Marieke C. & Roilo, Stephanie & Alarcón-Segura, Viviana & Cord, Anna F. & Beckmann, Michael, 2022. "Landscape context and farm characteristics are key to farmers' adoption of agri-environmental schemes," Land Use Policy, Elsevier, vol. 121(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hampf, Anna C. & Carauta, Marcelo & Latynskiy, Evgeny & Libera, Affonso A.D. & Monteiro, Leonardo & Sentelhas, Paulo & Troost, Christian & Berger, Thomas & Nendel, Claas, 2018. "The biophysical and socio-economic dimension of yield gaps in the southern Amazon – A bio-economic modelling approach," Agricultural Systems, Elsevier, vol. 165(C), pages 1-13.
    2. Michael Kuhwald & Katja Dörnhöfer & Natascha Oppelt & Rainer Duttmann, 2018. "Spatially Explicit Soil Compaction Risk Assessment of Arable Soils at Regional Scale: The SaSCiA-Model," Sustainability, MDPI, vol. 10(5), pages 1-29, May.
    3. Carauta, Marcelo & Troost, Christian & Guzman-Bustamante, Ivan & Hampf, Anna & Libera, Affonso & Meurer, Katharina & Bönecke, Eric & Franko, Uwe & Ribeiro Rodrigues, Renato de Aragão & Berger, Thomas, 2021. "Climate-related land use policies in Brazil: How much has been achieved with economic incentives in agriculture?," Land Use Policy, Elsevier, vol. 109(C).
    4. Carauta, Marcelo & Parussis, Julia & Hampf, Anna & Libera, Affonso & Berger, Thomas, 2021. "No more double cropping in Mato Grosso, Brazil? Evaluating the potential impact of climate change on the profitability of farm systems," Agricultural Systems, Elsevier, vol. 190(C).
    5. Tadiello, Tommaso & Gabbrielli, Mara & Botta, Marco & Acutis, Marco & Bechini, Luca & Ragaglini, Giorgio & Fiorini, Andrea & Tabaglio, Vincenzo & Perego, Alessia, 2023. "A new module to simulate surface crop residue decomposition: Description and sensitivity analysis," Ecological Modelling, Elsevier, vol. 480(C).
    6. Galmarini, S. & Solazzo, E. & Ferrise, R. & Srivastava, A. Kumar & Ahmed, M. & Asseng, S. & Cannon, A.J. & Dentener, F. & De Sanctis, G. & Gaiser, T. & Gao, Y. & Gayler, S. & Gutierrez, J.M. & Hoogenb, 2024. "Assessing the impact on crop modelling of multi- and uni-variate climate model bias adjustments," Agricultural Systems, Elsevier, vol. 215(C).
    7. Sandra Ledermüller & Marco Lorenz & Joachim Brunotte & Norbert Fröba, 2018. "A Multi-Data Approach for Spatial Risk Assessment of Topsoil Compaction on Arable Sites," Sustainability, MDPI, vol. 10(8), pages 1-22, August.
    8. Tenreiro, Tomás R. & García-Vila, Margarita & Gómez, José A. & Jimenez-Berni, José A. & Fereres, Elías, 2020. "Water modelling approaches and opportunities to simulate spatial water variations at crop field level," Agricultural Water Management, Elsevier, vol. 240(C).
    9. Pasquel, Daniel & Cammarano, Davide & Roux, Sébastien & Castrignanò, Annamaria & Tisseyre, Bruno & Rinaldi, Michele & Troccoli, Antonio & Taylor, James A., 2023. "Downscaling the APSIM crop model for simulation at the within-field scale," Agricultural Systems, Elsevier, vol. 212(C).
    10. Jin, Xiuliang & Li, Zhenhai & Feng, Haikuan & Ren, Zhibin & Li, Shaokun, 2020. "Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the AquaCrop model," Agricultural Water Management, Elsevier, vol. 227(C).
    11. Yin, Xiaogang & Kersebaum, Kurt Christian & Kollas, Chris & Manevski, Kiril & Baby, Sanmohan & Beaudoin, Nicolas & Öztürk, Isik & Gaiser, Thomas & Wu, Lianhai & Hoffmann, Munir & Charfeddine, Monia & , 2017. "Performance of process-based models for simulation of grain N in crop rotations across Europe," Agricultural Systems, Elsevier, vol. 154(C), pages 63-77.
    12. Hampf, Anna C. & Stella, Tommaso & Berg-Mohnicke, Michael & Kawohl, Tobias & Kilian, Markus & Nendel, Claas, 2020. "Future yields of double-cropping systems in the Southern Amazon, Brazil, under climate change and technological development," Agricultural Systems, Elsevier, vol. 177(C).
    13. Carauta, M. & Guzman-Bustamante, I. & Meurer, K. & Hampf, A. & Troost, C. & Rodrigues, R. & Berger, T., 2018. "Assessing the full distribution of greenhouse gas emissions from crop, livestock and commercial forestry plantations in Brazil's Southern Amazon," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277118, International Association of Agricultural Economists.
    14. Xenia Specka & Claas Nendel & Ralf Wieland, 2019. "Temporal Sensitivity Analysis of the MONICA Model: Application of Two Global Approaches to Analyze the Dynamics of Parameter Sensitivity," Agriculture, MDPI, vol. 9(2), pages 1-29, February.
    15. Joseph MacPherson & Carsten Paul & Katharina Helming, 2020. "Linking Ecosystem Services and the SDGs to Farm-Level Assessment Tools and Models," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
    16. Yuexia Sun & Shuai Zhang & Fulu Tao & Rashad Aboelenein & Alia Amer, 2022. "Improving Winter Wheat Yield Forecasting Based on Multi-Source Data and Machine Learning," Agriculture, MDPI, vol. 12(5), pages 1-16, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:9:y:2019:i:1:p:6-:d:194209. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.