3 Results
In the studied region, the average soybean yield in the 2015-2017
three-year period was 4.13 Mg ha-1 at 65 plots. The
minimal and maximal values of soybean yield were 3.23 Mg
ha-1 and 4.97 Mg ha-1, respectively.
The box plots of the SOM, FLF, OLF and HF properties contain only the
data for the 0.0-0.10-m (L1) layer. The presented data in figure 1 for
the other variables are at L1 (0.0-0.10-m), L2 (0.10-0.20-m) and L3
(0.20-0.40-m). The classification of the 65 studied plots revealed that
50, eight, five and two plots formed the textural classes, including
clay, sandy clay loam, sandy clay and sandy loam, respectively.
Although, the clay exhibited an incremental function with increasing
depth, this increment was not sufficient to constitute a textural
gradient, and the soils in all the tested areas were Oxisols
(Latosols;Fig. 1).
The relationships between the soil physical properties at each layer
(L1, L2 and L3) and the soybean yield indicated that seven significant
response variables explained the soybean yield. Specifically, the PR,
BD, n parameter, I, TP, S-index and macro-porosity presented significant
relationships (Table 1). Of these properties, positive and negative
relationships were obtained with the S index, macro-porosity, TP and n
parameter and with PR, I and BD, respectively (Table 1).
The analysis of the relationships of the SOM,FLF, OLF and HF with the
clay, sand, PAW, S and PR properties showed significant values between
the clay and sand contents and all forms of SOM studied. Among the
properties affected by the soil management (i.e., PAW, S and PR), the
significant relationships were only detected between OLF and PR, and
between FLF and S index (Table 2).
The RP and BD values obtained at L1 showed a significant negative
relationship with the soybean yield. In contrast, PD did not show
significant relationship with productivity at L1 the most relevant among
the three studied layers, with p=0.32 (Fig. 2).
The α parameter of the Van Genuchten equation also showed
non-significant relationship with the soybean yield. However, the n
parameter and the inflection point (I) of the SWRC showed positive and
negative significant associations with the soybean yield, respectively
(Fig. 3).
The total porosity, macro-porosity and S-index properties at L1
exhibited a significant positive relationship with the soybean yield
(Fig. 4). The AWC, water content at saturation (θs) and residual water
content (θr) showed no significant relationship with the soybean yield
in any of the studied layers (Fig. 5). The AWC and θs obtained at L3 had
a stronger relationship with productivity, with p=0.53 and p=0.56,
respectively, than those obtained for the other layers, whereas the
relationship with the θr value obtained for L2 was stronger, with p =
0.60, compared with those found for the other layers.
The clay and sand contents did not show a significant relationship with
the soybean yield. However, L1 was found to be the most relevant for
clay content, with p=0.16, and L2 was the most important for the sand
content, with p=0.22 (Fig. 6). The field capacity that describes the
soil water content for the potential matrices obtained at the SWRC
inflection point was also not significant at all layers, although a
stronger relationship was found at L3, with p=0.52, than in the other
layers.
The clay content of the soil exhibited significant positive
relationships with the SOM,FLF, OLF, and HF, whereas significant
negative relationships of SOM and its fraction (FLF, OLF and HF) were
also found with sand contents. The SOM did not present a significant
association with the PAW (p=0.46), PR (p=0.19) and S (p=0.20). The FLF
was also not significantly related to the PAW (p=0.66), PR (p=0.76) and
S index (p=0.14). Whereas, the OLF presented a significant positive
relation with PR, but was not significantly related to the PAW (p=0.62)
and S (p=0.57). The HF did not show a significant relationship with the
PAW (p=57%), PR (p=89%) or S index (p = 71%;Fig. 7).