My research is focused on developing a web-based forage species selection tool and estimating potential yield of key forage species grown in Oregon and Sichuan Province.
Our goal is to match appropriate species with each eco-region. Related to this class, the problem is how we can use the GIS spatial analyst tools to define and display a workable number of forage production eco-regions based on topography, climate, soil characteristics, land-use and land-cover, and agricultural systems.
Although there have been several important studies directed at defining ecoregions (Bailey, 1996; Thorson, 2003; Omernick, 2004), these have been based primarily on the Köppen Climate Classification System (1936) and broad groupings of species suitable for each zone. They are not helpful in quantifying potential annual forage yield or seasonal production profiles required for rational management of forage-livestock systems.
To provide useful guidance to Oregon and Sichuan Province farmers and ranchers, our agroecozone classification systems will use a hierarchical approach beginning with climate, with modifications due to physiography and land-use/land-cover, and soil characteristics.
Level I: Climate (Thermal Units and Precipitation)
Climate was chosen as the foundational level of the classification system due to the essential nature of temperature and precipitation in plant growth and development. Base spatial layers for climate factors will include extreme monthly cold and hot temperature, mean monthly maximum and minimum temperature, mean annual temperature, and mean annual, seasonal, and monthly precipitation. Climate-based indices will be developed to predict forage crop growth and development. These will include solar radiation and photosynthetically active radiation, accumulated thermal units (with various base temperatures), growing season length, and vernalization chilling days. For agricultural systems that include irrigation a soil water balance model will be applied.
Level II: Physiography, Land-use/Land-cover [Topography (DEM), MODIS Images]
The second level of the classification systems will involve physiography and land-use and land-cover. A DEM will be used to underlay the climate layers and identify terrain slope, with the following rankings: >60°, not useful; 60°— 50°, 30% can be useful for livestock; 50°— 40°50% can be useful; 40°— 30°, can be used as pasture; and >30°, useful as grassland (Zhang, Grassland management, 2010). Current land-use and land-cover will be characterized from current and historical MODIS satellite images, with particular focus on cropland, pastureland, and rangeland areas.
Level III: Soil Characteristics (pH, Drainage, Salinity)
Soil characteristics will be the final level of the hierarchy, since, to a large degree, these can be modified in agricultural systems. Spatial data layers will be obtained for soil type, pH, drainage classification, and salinity. Site specific data will be obtained for more detailed fertility information.
One area of my research is chicken forage systems. A lot of farmers here are converting to free range chickens, and Organic standards require chickens to have access to the outdoors. However, farmers typically range the hens on pasture, which is not optimal. Chickens originated in jungle-like forests, where they scratched for insects in the debris of the forest floor, and flew into the branches to sleep at night. Their diet was 60-80% insects, supplemented by greens, fruit, and some seeds. So they are not well adapted to eating grass, and they also get hot easily in the summer and prefer the shade.
A proper chicken forage would consist of fruit- and nut-bearing trees to provide food and shade. Then, rather than grasses, the understory should consist of bushes and herbs of the variety that leaf out aggressively in response to pruning, like orach, lambs-quarters, kale, and collards, what we call “cut and come again” greens. Then you would also want to add a variety of flowers to attract additional insects to the area, the goal being to maximize insect biomass, while supplementing the chickens’ diet with greens, fruits, and nuts.
I mention this so that you will think carefully about how what you plant can change the suitability and carrying capacity of the forage. In an area like the Pacific Northwest, which is wet much of the year but very dry in the summer, choosing the appropriate species of plants makes a huge difference in net production. Also, the manager must judge when to rotate the animals off the forage and let the land rest and heal, so management also affects the carrying capacity of the land.
Sorry, this may not be useful since it has nothing to do with GIS!
Will you use STATSGO or SSURGO (or both) for your soil characteristics in the U.S.? Does Sichuan Province have these soil characteristics mapped?
Thank you for your comment, we will use the STATSGO FOR soil characteristics in Oregon. But for Sichuan we just have polygon data for soil characteristics.
I think you did a good job with the hierarchical structuring of your forage suitability index. One question I have is about the range and variability of forage species you are going to include. I wonder if the species’ requirements are so similar that you could end up with an interactive map that has all the species’ ranges overlapping.
In addition to the soil characteristics you have listed, I think it could be just as helpful to download a shapefile (http://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx) of the soil survey for Oregon. That should give you the characteristics and soil type throughout the state. Soil characteristics are a function of your Level 1 and Level 2 analyses and may refine the spatial extent and productivity of a particular forage species.
Very interesting project – The interactive web mapping application will be very helpful for farmers/ranchers.
Thank you for your soil information. we will chose the key species for each place. Species’ requirements are not similar, but they can be divided into four groups according on the species type, so I try to map the distribution area for the four groups.