Landscape Management of Agronomic Processes for Site-specific Farming
The objective of this project is to measure and model landscape dynamics for a hummocky topography in the Black Soil Zone of east central Alberta. Then evaluate the potential benefit of variable rate application.
IPNI-1999-CAN-AB19
15 Mar 2001
Justification
- Site specific management of agricultural land has the potential desired outcome of improving crop production and protecting the environment by matching inputs with requirements at the sub-field level. The goals of site-specific farming are four fold:
- to identify the variation in productivity within a soil landscape,
- to determine the factors contributing to the variability,
- to delineate areas with similar productive potential, and
- to develop a management system that enables producers to apply the right amount of inputs in the right places to obtain the best economic returns.
The landscape features of a field have a strong influence on soil processes and crop growth. Topography plays a critical role in modifying both microclimate and the hydrological conditions within a landscape. In an undulating or hummocky terrain the redistribution of soil organic matter and plant nutrients is controlled largely by the influence of surface features on the redistribution of water (Pennock et al, 1994). Topography exerts a principle control on the basic hydrological conditions within the landscape, within a field sharing similar topographical features also share similar hydrology. Therefore, dynamic soil processes and properties that are controlled directly or indirectly by water similarly are expected to reflect the basic site topography (van Kessel and Wally, 1995). In situ measurement of plant nutrients using ion exchange membranes (Schoenau et al., 1993; Qian et al., 1993) along with soil moisture movement can be used to model dynamic soil process related to crop nutrition based on landscape properties.
Developing agronomic models for making soil and crop management decisions is critical for site specific management. Agronomic models are valuable tools for integrating components of complex soil and crop systems. The role of agronomic simulation models includes (1) an aid in interpreting experimental results, (2) an agronomic research tool, (3) an agronomic production tool for crop system decision management and (4) policy analysis. Agronomic models are valuable for synthesizing research results and for integrating up from a basic process research. Crop models can be used to examine a specific scientific hypothesis. For a model to provide reliable results, it must go through a process of calibration, testing and validation using external data from sources outside that used to develop the model.
Recently, agronomic models have been combined with global positioning sensors (GPS) and geographic information systems (GIS) to optimize site-specific field management. The addition of agronomic models linked with historical weather patterns plus soil and crop management data can provide information on the long-term consequences and risks associated with a range of management options, including fertilizer use. Agronomic models allow the user to test and compare the effects of various short-term decisions such as how much fertilizer to use for a particular crop or cultivation practice, and long-term decisions which could achieve sustainability. Agronomic modeling is a technique to allow experimental results to be extended to a wider area because the models are processed based. As a result, models allow investigators to examine more interactions or integration of processes than they could with individual small-plot research projects. Integrating an agronomic model with GIS for site-specific farming allows the investigator to evaluate agronomic and conservation planning for variable field conditions. It would provide a means of predicting field productivity and environmental impacts of various farm practices in relation to field variability.
The objective of agricultural crop production is to improve the efficiency of crop production by increasing crop yields, improving crop quality and reducing environmental impact. A reliable agronomic model can contribute to enlarging our knowledge, provide guidance for efficient methods of soil.