Co-Authors:
Hetzroni, A., Agricultural Research Organization, Israel
Zig, U., Hevel Maön enterprises, Israel
Warshavsky, S., Hevel Maön enterprises, Israel
Yosef, S., Agricultural Research Organization, Israel
Abstract:
Attempts to characterize samples of potatoes from field containers revealed large variation in the proportion of market quality potatoes between large containers arriving from the same experimental area. Most of the containers in use hold 10-12 t of potatoes. A sample was drawn from the field containers to determine the shipment quality based on a variety of parameters and rules. With average yield (55 t/ha) and plot size (400-800 m long) a container represents about 2 beds of potatoes (each bed, 1.93 m wide, has two potato rows). To understand the source of quality variation within samples, a survey was conducted, aiming to create spatial maps of potato quality. The fields were mapped using a hand-held GPS to mark ends of rows. During potato digging, we recorded which rows were used to fill each container. Potatoes were harvested by two types of combine harvesters: single-row machines that accumulate the potatoes onboard and two-row harvesters that load the potatoes directly into a wagon pulled alongside. A shipment-identification (ID) was assigned to each container of potatoes, before leaving the field. This ID was retained with the lot until the quality of the lot was evaluated in the packing house, within a few hours. Once the quality assessment of the samples had been made they could be associated with the rows from which they came in order to generate maps of the quality parameters. The maps indicated that potato quality did indeed vary across the plots. The method applied is not practical for mass data collection, which would require automatic association of the rows with the shipment ID. Quality mapping is limited because there is no automatic way to sense potato quality at the moment of harvest. Therefore, a row is considered as uniform in quality, and we have to compromise with unidirectional data. The method was adapted and partially automated over a second year as part of an ongoing effort to develop a recording system for field crop as a tool for management, planning and reporting.