DOI: 10.5593/SGEM2016/B22/S10.134


O.Alipbeki, G.Kabzhanova, C. Alipbekova
Thursday 8 September 2016 by Libadmin2016

References: 16th International Multidisciplinary Scientific GeoConference SGEM 2016, www.sgem.org, SGEM2016 Conference Proceedings, ISBN 978-619-7105-59-9 / ISSN 1314-2704, June 28 - July 6, 2016, Book2 Vol. 2, 1059-1066 pp

Forecasting of crop productivity has great relevance and significance in addressing
issues of ensuring food safety and economic stability of the country. In consideration of
large land resources and agricultural industrial potential of Kazakhstan, only space
monitoring of agricultural production will provide with operational and actual data.
Productivity - a quality, comprehensive index, which depends on many factors. In this
article we was developed the model of forecasting wheat productivity for Nothern
Kazakhstan. In model of forecasting wheat productivity are taken into account the
following factors of formation wheat productivity for the steppe and dry steppe agro
climatic zones: soil moisture reserves, properties of the soil, agro meteorological data,
growth rates and plant phonological phase, biometric characteristics of plants, spatial
analysis of the plant condition on the basis of remote sensing data. In the framework of
the project were conducted set of time series remote sensing data and ground based
observations on the territory covering three regions: Akmola, North Kazakhstan and
Kostanay. It turns out that the Kazakhstan satellite KazEOSat-2 has optimum capacities for regional monitoring of agricultural production according to permission (6,5 m), frequency and coverage of the territory (4 days). Landsat-8 applied as the additional data source. Images were previously processed and georeferenced with the ground based information’s on a condition of plants, biophysical parameters and agro
meteorological observations. The accuracy of determining crop wheat acreages and
forecast of productivity was comparable with data of regional Departments of
agriculture. As a result of were comprised the maps of the condition crop wheat
acreages with forecasted yields.

Keywords: Remote sensing, geoinformation system, agriculture, space monitoring,
KazEOSat-2, wheat, forecasting