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Spatial validation of crop models for precision agriculture

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  • Basso, B.
  • Ritchie, J. T.
  • Pierce, F. J.
  • Braga, R. P.
  • Jones, J. W.

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  • Basso, B. & Ritchie, J. T. & Pierce, F. J. & Braga, R. P. & Jones, J. W., 2001. "Spatial validation of crop models for precision agriculture," Agricultural Systems, Elsevier, vol. 68(2), pages 97-112, May.
    • Handle: RePEc:eee:agisys:v:68:y:2001:i:2:p:97-112
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    References listed on IDEAS

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    1. Verhagen, A. & Booltink, H. W. G. & Bouma, J., 1995. "Site-specific management: Balancing production and environmental requirements at farm level," Agricultural Systems, Elsevier, vol. 49(4), pages 369-384.
    2. Paz, J. O. & Batchelor, W. D. & Babcock, B. A. & Colvin, T. S. & Logsdon, S. D. & Kaspar, T. C. & Karlen, D. L., 1999. "Model-based technique to determine variable rate nitrogen for corn," Agricultural Systems, Elsevier, vol. 61(1), pages 69-75, July.
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    1. Schlindwein, Sandro L. & Eulenstein, Frank & Lana, Marcos & Sieber, Stefan & Boulanger, Jean-Philippe & Guevara, Edgardo & Meira, Santiago & Gentile, Elvira & Bonatti, Michelle, 2015. "What Can Be Learned about the Adaptation Process of Farming Systems to Climate Dynamics Using Crop Models?," Sustainable Agriculture Research, Canadian Center of Science and Education, vol. 4(4).
    2. Kaul, Monisha & Hill, Robert L. & Walthall, Charles, 2005. "Artificial neural networks for corn and soybean yield prediction," Agricultural Systems, Elsevier, vol. 85(1), pages 1-18, July.
    3. Perielathu Mathunny Mathews Author-Workplace- Scientist, Soil Chemistry, Division of Agronomy and Soil Sciences, India, 2016. "Soil Chemistry: A Way Ahead," Agricultural Research & Technology: Open Access Journal, Juniper Publishers Inc., vol. 2(4), pages 115-122, September.
    4. Cock, James & Oberthür, Thomas & Isaacs, Camilo & Läderach, Peter Roman & Palma, Alberto & Carbonell, Javier & Victoria, Jorge & Watts, Geoff & Amaya, Alvaro & Collet, Laure & Lema, Germán & Anderson,, 2011. "Crop management based on field observations: Case studies in sugarcane and coffee," Agricultural Systems, Elsevier, vol. 104(9), pages 755-769.
    5. Leo, Stephen & De Antoni Migliorati, Massimiliano & Nguyen, Trung H. & Grace, Peter R., 2023. "Combining remote sensing-derived management zones and an auto-calibrated crop simulation model to determine optimal nitrogen fertilizer rates," Agricultural Systems, Elsevier, vol. 205(C).
    6. 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).
    7. Fei Li & Shuwen Zhang & Xinliang Xu & Jiuchun Yang & Qing Wang & Kun Bu & Liping Chang, 2015. "The Response of Grain Potential Productivity to Land Use Change: A Case Study in Western Jilin, China," Sustainability, MDPI, vol. 7(11), pages 1-16, November.
    8. Pedersen, Michael Friis & Gyldengren, Jacob Glerup & Pedersen, Søren Marcus & Diamantopoulos, Efstathios & Gislum, René & Styczen, Merete Elisabeth, 2021. "A simulation of variable rate nitrogen application in winter wheat with soil and sensor information - An economic feasibility study," Agricultural Systems, Elsevier, vol. 192(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. Morrison, Kathryn T. & Nelson, Trisalyn A. & Ostry, Aleck S., 2011. "Methods for mapping local food production capacity from agricultural statistics," Agricultural Systems, Elsevier, vol. 104(6), pages 491-499, July.

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