Soil Community Structure, Function, and Spatial Variation in an Organic Agroecosystem

The research portion of this project was focused at addressing objectives one through four:

 Objective 1. Use geostatistics to develop maps for soil organisms and edaphic properties

Soils within the area of the farm sampled varied greatly in texture. There was a strong gradient of increasing sand and decreasing clay from west to east (Figure 4a-c). Sand ranged from 5-54 % while clay ranged from 9-27% and silt ranged from 35-76%. Four texture classes were identified: sandy loam, loam, silt loam, and silty clay loam. Silt loam was the predominant texture class (Figure 5).

The proportion of soil composed of aggregates was higher in the western, more clay rich, area of the farm (Figure 6).

Collembola (Figure 7) and nematode (Figure 8) populations were highly aggregated at the farm scale but areas of highest populations did not overlap.

Total C (Figure 9), N-mineralization potential (Figure 10), and microbial biomass (Figure 11) were all highest in an area of the farm managed as a meadow. The ratio of bacteria to fungi ranged from 1.3-3.7 (Figure 12) and pH ranged from 4.86 to 6.84 (Figure 13).

 Objective 2. Develop biological indicators that correlate to N mineralization potential and aggregate stability

Correlation analysis indicated that N mineralization potential was most strongly positively correlated with total C (R=0.79), microbial biomass (R=0.74) the nematode maturity index (R=0.57), and weeks since tillage (R=0.58) and negatively correlated with bulk density (R=-0.65) and pH (R=-0.40). Regression tree analysis is capable of finding non-linear relationships among many different variables. The regression tree for N mineralization potential indicated that variance in laboratory nitrate mineralization was largely explained by soil C; the highest mineralization rates were associated with soil C greater than 7.9% and in less C-rich soils the amount of C also explained the most variance (Figure 14). The nematode EI explained some of the variance in soils with C between 3 and 7.9%, with EI values less than 80.4 being associated with higher mineralization rates. The regression tree model explained 67% of the variance, while soil C alone explained 59% of the variance (100 X the square of the correlation, 0.79, between soil C and mineralization).

Much of the variation in total nematode density across the farm was explained by the time since the last tillage. Sites tilled less than 2 weeks before sampling had the lowest nematode populations (mean = 2.37 log nematodes 100 cc-1). Among the sites that had not been tilled recently, nematode populations were higher in soils with pH values greater than 6.11. These soils had mean concentrations of 3.21 log nematodes 100 cc-1 while the more acid soils had mean nematode concentrations of 2.84 log nematodes 100 cc-1 (Figure 15).

The proportion of aggregates greater than 0.25mm (PROP AGG) was negatively correlated with sand (R=-0.70) and total C (R=-0.45), and positively correlated with silt (R=0.70) and clay (R=0.61). The regression tree analysis of PROP AGG indicated that soil texture parameters explained most of the variation in aggregate stability (Figure 16). Soils with clay content less than 13.5% had a lower proportion of aggregates (mean = 0.67) than sites in more clay-rich areas (mean = 0.89). Within these clay-rich sites, silt also influenced aggregation; sites with silt content greater than 63.5% had higher proportion of aggregates (mean =0.91) than sites with less silt content (mean =0.84).

Though biological indices were not the best predictors of PROP AGG, regression tree analysis of the nematode structure index indicated that areas with PROP AGG greater than 0.93 were associated with the highest nematode structure index (SI, Figure 17). The many different physical parameters that affect PROP AGG may also be affecting farm management and SI. For example, the eastern, sandier area of the farm is more intensively farmed and has received more historical manure application and cultivation than the more clay-rich areas to the west. Both of these forms of agricultural intensification can negatively affect nematode taxa that are indicators of community structure. These nematodes are omnivores and predators that have larger body sizes and longer life cycles than the bacterivore and fungivore nematodes that are favored by physical disturbance and nutrient enrichment.

 Objective 3. Recommend general biological sampling methods

In the field-scale analysis we found clay content ranged from 10- 17 % in field 3 and from 16 – 31 % in field 5 (Figure 18). Clay was significantly negatively correlated with nematodes in field 3 (R = -0.57) but not in field 5 (R= -0.20). The mean nematode populations from the georeferenced sites were 1155 and 131 nematodes 100cc-1 (3.06 and 2.11 log nematodes 100cc-1) for fields 3 and 5 respectively. The bulked samples overestimated nematode populations at both sites; there were 1400 and 158 nematodes 100cc-1 (3.14 and 2.19 log nematodes 100cc-1) for fields 3 and 5 respectively.

Semivariogram analysis of the nematode distribution in field 3 indicated that nematode populations were strongly autocorrelated – meaning that the nematode density at a specific site is predictive of nematode densities at nearby sites. On the other hand, there was little spatial structure in the nematode populations in field 5. The range of autocorrelation, or patch size, in field 3 was 20 m. The range was not different for bacterial-feeding and fungal-feeding nematodes.

Two contrasting strategies for sampling nematodes to characterize agroecosystems are described in the literature (Yeates, 1999). The first is to select 5 or 10 patches each of 1-2 m2, which in total reflect the contribution of plant, soil, and topographic variation in the area, and in each to collect 5 soil cores. The second is to take composite cores at 20 equally spaced sites along a serpentine transect of a random 2-ha area than mix thoroughly by hand. We approximated this comparison by taking both geo-referenced samples and bulking samples within the same area, though our site was only 0.09ha. Bulking provides only an average value and, as we demonstrated, these values overestimated nematode population means by 17% from not bulking. Selecting smaller patches to sample provides information about local variability. If coupled with information about other edaphic properties it can also provide context about effects of edaphic properties on populations within the local area.

The current study emphasized that physical and chemical parameters can vary greatly at the farm and even field scale. We found, as others have, that the effects of physical and chemical properties on biological populations were not consistent from field to field (Wyse-Pester et al., 2002). We conclude that soil physical and chemical data are not a priori evidence of soil biological populations, but within certain ranges the variation in these properties can affect biological variation. Thus, careful mapping of inherent soil quality parameters should be the first step in monitoring soil biological populations.

 Objective 4. Make recommendations to optimize farm productivity and profitability

The geostatistical approach employed in this project yielded valuable information about spatial variation of chemical and physical properties across Full Circle Farm. These properties are not easily affected by management so the maps may provide utility for years to come. For example, the detailed texture maps provided by this project may aid in future farm planning. Similarly, the pockets of soil acidity and trend in soil carbon content that were discovered should also be considered in soil management. Specific management recommendations follow:

1. Address soil acidity.
Through mapping soil parameters we discovered that large areas of the farm have soils with pH less than 5.7 and some areas are as low as 4.8. The grower, Andrew Stout, had done some recent soil testing of his own so our sampling reinforced his inclination to apply lime to the most acid fields. We met with Andrew and his farm manager Amy Sills in early February 2008 to discuss soil management. We recommended 2 applications of agricultural lime plus Sulfa-Mag

2. Smooth organic matter distribution across farm.
As with many small farms in this area, Full Circle Farm was once home to a small dairy. Though we have no historical record of manure applications beyond the 5-year period of current management, it is logical that manure would have been spread both closely to the barn and also in areas that were not flooded and were workable earlier in the spring, such as the areas of higher elevation near the barn. The result is C-enriched soil near the barn. The less C-rich and heavier soils in the western area of the farm could benefit from applications of compost to increase their organic matter content. Organic matter levels are sufficient, or perhaps even too high, at the eastern edge of the farm. Amendment applications in these areas should be done with nitrogen-rich materials that do not increase soil carbon to the degree that compost or manure does (e.g. fish meal).

3. Rest the intensively farmed eastern side of the farm.
Fields located on the sandy loam and loam soils on the eastern edge of the farm (figure 5) are the most intensively cultivated. They drain better, are workable earlier in the spring and later in the fall, and are the most desirable fields for the salad greens often planted there. Taking an opportunity to rotate with a cereal-legume mix cover crop would provide several benefits: rotation can help break pathogen cycles; the growth could be harvested for making compost; and the lack of tillage can enhance larger-bodied soil organisms and increase soil aggregation.

To encourage farm managers at Full Circle Farm to think about the spatial variation on their farm and to use this information in management decisions we framed a 2X3 ft. poster of the background aerial photo in figure 1 and gave it to Andrew Stout.