Spatial Relationships of Landscape Attributes and Wheat Yield Patterns

Abstract

Success of precision farming practices requires knowledge of fields such as soil type, topography, soil nutrients, spatial variability effects, yield patterns and their spatial relationships. A three year (2008-09 to 2010-11) field experimental study was conducted at Postgraduate Agricultural Research Station, University of Agriculture, Faisalabad, Pakistan, to identify the influencing landscape parameters and their spatial distribution, having effects on wheat yield patterns using artificial neural network (ANN) and GIS map overlay techniques. A total of 48 soil samples were collected from top 30 cm of the soil, before sowing, at center of each grid of 24 x 67 m in size along with position data using Global Positioning System receiver (GARMIN, GPS60). Landscape attributes such as elevation, %sand, %silt, %clay, soil electrical conductivity (EC), pH, soil nitrogen (N) and soil phosphorus were included in the analysis. ANN analysis revealed that urea fertilizer treatments, followed by %sand, %silt, % clay, elevation, soil nitrogen and EC were ranked as the most influencing parameters. The yield data, however, were normalized to remove fertilizer treatments effects and then were used in the subsequent analysis. The map overlay analysis showed that the areas having lower elevation, lower soil EC and higher levels of soil N produced higher yields. Whereas the areas having higher elevation, higher soil EC and moderate soil N produced lower yields, establishing the cause-effect relationships. These results indicated that ANN and GIS techniques were helpful in identifying the influencing parameters affecting wheat yield, which can be managed under precision farming practices.

Delineating site-specific management zones for precision agriculture

“…2007; Farid et al . 2013). The normalized grain yield data were used for subsequent analysis, to prepare GIS data layers for spatial analysis and to delineate site-specific management zones using MZA software.…”

“…Therefore, a total of 48 yield samples were collected in each year. The yield samples were threshed manually and wheat grain per unit area determined (Farid et al 2013). The measured grain yield data was used to evaluate the delineated site-specific management zones.…”

“…Many researchers have also used the same approach for data normalization and to remove treatment effects (Colvin et al 1997;Jaynes et al 2005;Bakhsh et al 2007;Farid et al 2013). The normalized grain yield data were used for subsequent analysis, to prepare GIS data layers for spatial analysis and to delineate site-specific management zones using MZA software.…”

Calibration and Validation of CERES-Wheat (Triticum Aestivum) Model for Simulating Fertilizer Application Rates in Management Zones

“…Each zone was of 2 ha in size having 12 experimental units. The size of each experimental unit was of 24 × 67 m (0.161 ha) (Farid et al, 2013a(Farid et al, , 2013b. Wheat variety of AS-2002 was grown within each site-specific MZ during growing seasons of 2010-11 and 2011-12.…”