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ISHS Acta Horticulturae 619: XXVI International Horticultural Congress: Potatoes, Healthy Food for Humanity: International Developments in Breeding, Production, Protection and Utilization

PRECISION MANAGEMENT OF NITROGEN AND WATER IN POTATO PRODUCTION THROUGH MONITORING AND MODELLING

Authors:   A.J. Haverkort, J. Vos, R. Booij
Keywords:   Crop nitrogen content, crop reflection, soil water content, growing season, growth cycle
DOI:   10.17660/ActaHortic.2003.619.24
Abstract:
Nitrogen and water application rates and timing depend on the final yield a grower expects the potato crop to achieve. Therefore precise measurements of 1) the nitrogen status of the crop and 2) the water status of the soil are needed. A crop growth model LINTUL-Potato calculates yields and the associated resource requirements based on the temperature dependent length of the growing season, the amount of solar radiation (potential yield) and on the soil moisture availability (achievable yields). Irrigation has to take place before a critical soil moisture level is reached and monitoring soil moisture may be done with several devices. Soil fluxes such as capillary rise, drainage and rainfall need not be monitored as they are accounted for by monitoring the soil for moisture depletion. Timing and amount of irrigation follow from the depletion rate, which depends on the proportion of the ground covered by green foliage and on the forecasted evapotranspiration rate by weather services of green foliage. To support decisions on supplemental nitrogen dressing knowledge is required on how much nitrogen is present in the soil at planting. Experimental data incorporated in the crop growth model have shown how much nitrogen a potato crop needs to contain before the end of a crucial time window to achieve the desired yield level. Several sampling techniques showing the crop nitrogen content presently exist. A nitrogen and water decision support system based on model and sensing techniques is nearing completion and already partly ready for world wide web application. Its usefulness for field specific application increases for each individual field, as more reinforcing data become available and are used in the decision process. Such a self-learning system becomes more powerful when more growers join the scheme for more years.
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