The cost of accuracy in crop area estimation |
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| Institution: | 1. International Maize and Wheat Improvement Center (CIMMYT), P.O. Box 25171, Nairobi, Kenya;2. International Crops Research Institute for the Semi-Arid Tropics, BP 320 Bamako Mali;1. Institut de l’Environnement et de Recherches Agricoles, CREAF de Kamboinsé, 01 BP 476 Ouagadougou 01, Burkina Faso;2. Laboratoire d’Entomologie Fondamentale dt Appliquée, Unité de Formation et de Recherches en Sciences de la Vie et de la Terre, Université Ouaga I Pr Joseph Ki-Zerbo, 06 BP 9499 Ouagadougou 06, Burkina Faso;3. International Crops Research Institute for the Semi-Arid Tropics, BP 12404 Niamey, Niger.;1. Department of Clinical Radiology, Kuopio University Hospital, Kuopio, P.O. Box 100, Puijonlaaksontie 2, 70029 KYS, Finland;2. Department of Plastic Surgery, Kuopio University Hospital, Kuopio, P.O. Box 100, Puijonlaaksontie 2, 70029 KYS, Finland;3. Department of Clinical Pathology, Kuopio University Hospital, Kuopio, P.O. Box 100, Puijonlaaksontie 2, 70029 KYS, Finland;4. Unit of Radiology, Biocenter Kuopio, University of Eastern Finland, P.O.B. 1627, FI-70211 Kuopio, Finland;5. Unit of Pathology and Forensic Medicine at Institute of Clinical Medicine, Biocenter Kuopio, University of Eastern Finland, P.O.B. 1627, FI-70211 Kuopio, Finland;6. Cancer Center of Eastern Finland, University of Eastern Finland, P.O.B. 1627, FI-70211 Kuopio, Finland;1. Flow and Image Cytometry Shared Resource, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, NY, United States;2. Dept of Urology, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, NY, United States;3. Dept of Immunology, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, NY, United States;1. Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands;2. Department of Gynecology, Leiden University Medical Center, Leiden, the Netherlands;3. Department of Surgery, Washington University School of Medicine, St. Louis, Missouri;4. Department of Gynecology, Reinier de Graaf Gasthuis Delft, Delft, the Netherlands;1. Immunopathology and Infection Research Grouping (IRG), EA4517, UMR PIMIT, Université de la Réunion, Processus Infectieux en Milieu Insulaire Tropical, INSERM 1187, CNRS 9192, IRD 249, 97405 St Denis Cedex, La Réunion, France;2. Internal Medicine and Dermatology Unit, CHU Félix Guyon, St Denis, 97400 La Reunion, France;3. Microbiology/Virology Laboratory, Félix Guyon University Hospital of La Reunion, 97400 St Denis, La Reunion, France;4. Internal Medicine Unit, GHER Hospital, 97470 St Benoit, La Reunion, France;5. Infectious Disease Unit, CHU Félix Guyon, 97400 St Denis, La Reunion, France |
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| Abstract: | Accuracy and cost of direct crop area measurement are compared with those of farmers’ estimates after visual inspection, in this methodological study conducted in southern Mali. The observational error, the difference between the area measured and the area estimated, was first studied at the plot level. Average observational error or bias was ?11% of the average area measured, indicating an average underestimation of plots by 11%. This observational error is strongly related to plot size, with smaller plots being overestimated and larger plots underestimated, in an approximately negative linear relationship. The observational error is also smaller for cotton fields than for cereals. The analysis was repeated at the farm level, where the bias in estimating the total area per farm was ?8%. At this level, total error or accuracy was calculated by the relative total error (RTE), the square root of the mean square error, divided by the mean. The farmers’ estimate was found to be less accurate (RTE = 9.4% of the mean) than physical measurement (RTE = 6.6%), but at a cost of only $370 as compared with $2328 (for a sample of 96 farms in 11 villages). The coefficient of variation (CV) of most surface variables was found to lie between 60% and 100%, and their relative bias (average observational error divided by the mean area) between 2% and 10%. For crop area per farm, the physical measurement of plots resulted in a gain of accuracy of 2–4%, as compared with the farmers’ estimate after visual inspection. A general model was developed in which these calculated parameters are used to predict the accuracy in future surveys and to compare the accuracy with the survey’s cost. It is shown how the survey design can be optimized based on acceptable error, sample size and cost for each measurement technique. Simulations demonstrate that the total error for biased estimators, even for variables with small CVs, hardly decreases above sample sizes of 100–150 farmers. |
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