IDEAS home Printed from
   My bibliography  Save this article

Interpretation of commercial production information: A case study of lulo (Solanum quitoense), an under-researched Andean fruit


  • Jiménez, Daniel
  • Cock, James
  • Jarvis, Andy
  • Garcia, James
  • Satizábal, Héctor F.
  • Damme, Patrick Van
  • Pérez-Uribe, Andrés
  • Barreto-Sanz, Miguel A.


Every time a farmer plants and harvests a crop represents a unique event or experiment. Our premise is that if it were possible to characterize the production system in terms of management and the environmental conditions, and if information on the harvested product were collected from a large number of harvesting events under varied conditions, it should be possible to develop data-driven models that describe the production system. These models can then be used to identify appropriate growing conditions and improved management practices for crops that have received little attention from researchers. The analysis and interpretation of commercial production data in the context of naturally occurring variation in environmental and management, as opposed to controlled experimental data, requires novel approaches. Information was available on both variation in commercial production of the tropical fruit, lulo (Solanum quitoense), and the associated environmental conditions in Colombia. This information was used to develop and evaluate procedures for the interpretation of the variation in commercial production of lulo. The most effective procedures depended on expert guidance: it was not possible to develop a simple effective one step procedure, but rather an iterative approach was required. The most effective procedure was based on the following steps. First, highly correlated independent variables were evaluated and those that were effectively duplicates were eliminated. Second, regression models identified those environmental factors most closely associated with the dependent variable of fruit yield. The environmental factors associated with variation in fruit yield were then used for more in depth analysis, and those environmental variables not associated with yield were excluded from further analysis. Linear regression and multilayer perceptron regression models explained 65-70% of the total variation in yield. Both models identified three of the same factors but the multilayer perceptron based on a neural network identified one location as an additional factor. Third, the three environmental factors common to both regression models were used to define three Homogeneous Environmental Conditions (HECs) using Self-Organizing Maps (SOM). Fourth, yield was analyzed with a mixed model with the categorical variables of HEC, location, as a proxy for cultural factors associated with a geographic region, and farm as proxy for management skills. The mixed model explained more than 80% of the total variation in yield with 61% associated with the HECs and 19% with farm. Location had minimal effects. The results of this model can be used to determine the appropriate environmental conditions for obtaining high yields for crops where only commercial data are available, and also to identify those farms that have superior management practices for given environmental conditions.

Suggested Citation

  • Jiménez, Daniel & Cock, James & Jarvis, Andy & Garcia, James & Satizábal, Héctor F. & Damme, Patrick Van & Pérez-Uribe, Andrés & Barreto-Sanz, Miguel A., 2011. "Interpretation of commercial production information: A case study of lulo (Solanum quitoense), an under-researched Andean fruit," Agricultural Systems, Elsevier, vol. 104(3), pages 258-270, March.
  • Handle: RePEc:eee:agisys:v:104:y:2011:i:3:p:258-270

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Thomas, Duncan & Strauss, John & Henriques, Maria-Helena, 1990. "Child survival, height for age and household characteristics in Brazil," Journal of Development Economics, Elsevier, vol. 33(2), pages 197-234, October.
    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. Kannan, Vijay R. & Tan, Keah Choon, 2005. "Just in time, total quality management, and supply chain management: understanding their linkages and impact on business performance," Omega, Elsevier, vol. 33(2), pages 153-162, April.
    4. Deon Filmer & Lant Pritchett, 1999. "The Effect of Household Wealth on Educational Attainment: Evidence from 35 Countries," Population and Development Review, The Population Council, Inc., vol. 25(1), pages 85-120.
    5. Richard H. Steckel, 1995. "Stature and the Standard of Living," Journal of Economic Literature, American Economic Association, vol. 33(4), pages 1903-1940, December.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Cock, J. & Kam, S.P. & Cook, S. & Donough, C. & Lim, Y.L. & Jines-Leon, A. & Lim, C.H. & Primananda, S. & Yen, B.T. & Mohanaraj, S.N. & Samosir, Y.M.S. & Oberthür, T., 2016. "Learning from commercial crop performance: Oil palm yield response to management under well-defined growing conditions," Agricultural Systems, Elsevier, vol. 149(C), pages 99-111.
    2. 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.


    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:eee:agisys:v:104:y:2011:i:3:p:258-270. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.