REN R 690
Background
Sustainable agriculture is encompassed by “environmental stewardship, farm profitability, and prosperous farming communities” (Wikipedia 2010). One method of achieving this is by incorporating precision farming like variable rate fertilization (VRT) technology into farming practices. The basics of VRT is that fertilizer is applied at different rates throughout the field (I-1). Attached to the tractor and sprayer is a high tech GPS and computer system that automatically does this.
I-1. Image displaying different areas of a field recieving different amounts of nitrogen fertilizer.
Rather than applying a uniform layer of fertilizer to a field, the traditional methods, it makes more environmental and economical sense to apply fertilizer at rates that are site specific within a field. Applying a uniform layer may lead to over or under fertilization . In the case of over fertilization, it is uneconomical, may damage crops, increase greenhouse gas emissions and pollute nearby surface water bodies due to nutrient runoff (TCM 2010). In the case of under fertilization the crop yield will be less than its full potential. Whitley et al. (2000) found that the use of VRT reduced groundwater contamination when using nitrogen fertilizers on coarse textured soils. This is the main premise of VRT, to apply different rates of fertilizers to areas of an agricultural field.
In a typical agricultural field, crop yield is not uniform but varies. There are many factors on the prairie landscapes that affect crop yield, the main ones being soil moisture and fertility (TCM 2010). Soil moisture and nutrients vary depending on factors such as slope position and amount of sunshine (aspect)(Pennock 2003). Lower slope positions typically have higher soil moisture and nutrients due to the proximity of water table resulting in higher yields Higher slope positions are well drained and dry with lower yields. There are other variability’s to be considered in agricultural production besides topography and soil conditions.
Variations in agriculture may be from factors like the crop, management techniques and anomalies. Crop variability includes crop density, crop height, nutrient stress and biophysical properties (Zhang et al 2002). Management variations include tillage practice, seeding rate and crop rotation. Anomalous factors such as weed infestation and wind damage can affect production but are hard to predict and incorporate into a model (Zhang et al 2002). Variable factors like the ones mentioned are incorporated with soil and topographic information when creating VRT application rates.
In a typical agricultural field, crop yield is not uniform but varies. There are many factors on the prairie landscapes that affect crop yield, the main ones being soil moisture and fertility (TCM 2010). Soil moisture and nutrients vary depending on factors such as slope position and amount of sunshine (aspect)(Pennock 2003). Lower slope positions typically have higher soil moisture and nutrients due to the proximity of water table resulting in higher yields Higher slope positions are well drained and dry with lower yields. There are other variability’s to be considered in agricultural production besides topography and soil conditions.
Variations in agriculture may be from factors like the crop, management techniques and anomalies. Crop variability includes crop density, crop height, nutrient stress and biophysical properties (Zhang et al 2002). Management variations include tillage practice, seeding rate and crop rotation. Anomalous factors such as weed infestation and wind damage can affect production but are hard to predict and incorporate into a model (Zhang et al 2002). Variable factors like the ones mentioned are incorporated with soil and topographic information when creating VRT application rates.
A common problem with VRT technology is how to determine these fertility zones receiving different amounts of fertilizer. Soil physical and chemical properties from manual soil sampling of the field yields the most precise information but is laborious and costly (Zhang et al. 2010). Recent precision farming technology has focused on using remote sensing (Zhang et al. 2010). Remote sensing for precision agriculture “is based on the relationships of surface spectral reflectance with various soil properties and crop characteristics“ (Moran et al. 1997). Spectral reflectance is measured using various techniques like aerial photography and satellite observations. Drawbacks to this methods is that it requires considerable technical knowledge about computing and remote sensing (Moreenthaler et al 2003; Zhang et al. 2002).
An alternative method of determining different fertility zones is presented. As mentioned earlier, soil moisture and fertility are major factors affecting crop yield. Directly measuring soil moisture and fertility of an entire field is time consuming, costly and destructive. These parameters can be indirectly estimated using a digital elevation model (DEM) approach. The input data for the DEM is the elevation and position (northing and easting) of a points in a field. This information can be easily obtained as every tractor with VRT has a Differential GPS with precision +-2 m (Zhang et al 2002). From the model, topographic attributes like slope position are determined which are an indirect measure of soil moisture and fertility.
An alternative method of determining different fertility zones is presented. As mentioned earlier, soil moisture and fertility are major factors affecting crop yield. Directly measuring soil moisture and fertility of an entire field is time consuming, costly and destructive. These parameters can be indirectly estimated using a digital elevation model (DEM) approach. The input data for the DEM is the elevation and position (northing and easting) of a points in a field. This information can be easily obtained as every tractor with VRT has a Differential GPS with precision +-2 m (Zhang et al 2002). From the model, topographic attributes like slope position are determined which are an indirect measure of soil moisture and fertility.
Objectives
The general objective of the study is to use multi variate statistics to gain an understanding of which topographic attributes are related to soil moisture and fertility. More specifically, the goal is to create a model using observed data from electrical conductivity survey of a field. The resulting model will predict zones using a digital elevation model (DEM) determined topographic outputs. A minor aspect of the study is to create a model predicting the zones based on satellite imagery data.
Expected Results
It is expected that the zones created based on the DEM will be similar to the satellite on the large scale. But at the smaller scale, there will be differences because the DEM based on EC will be more precise and site specific.
