In 2018, three centers collaborated in the setting up and the realization of the calibration experiments (Table 1). Symbiotic inoculation was performed using a Micosat F ® bio-fertilizer, as coating (1 kg ha^-1) or granulate (10 kg^-1). A total of 52 plots were compared to establish the results on yield. Because some of the litter-bags and leaf samples referred to several plots, 13 Symbiotic inoculated (S) vs. Control (non-inoculated; C) pairwise comparisons were made after a grouping operation to develop the symbiotic model.

Two centers collaborated in the 2019 validation experiments. The inoculation was performed using a Micosat F ® bio-fertilizer and four AFM types as coatings (1 kg ha^-1).

¹Biota composition: finely ground cultivated Sorghum sudanensis roots, containing spores and ifae of Funneliformiscoronatus GO01 and GU53, F. caledonium GM24, F. intraradices GB67 and GG32, F.mosseaeGP11 and GC11, F. viscosum GC41; saprotrophic fungi: Streptomyces spp. ST60, Streptomyces spp. SB14, Streptomyces spp. SA51, Beauveria spp. BB48, Trichoderma viride, Trichoderma harzianum TH01, Trichoderma atroviride TA28, Trichoderma spp.; rhizosphere bacteria: Bacillus subtilis BA41, Pseudomonas fluorescens PN53, Pseudomonas spp. PT65 and Pochoniachlamidosporia, in a relative percentage of 40% crude inoculum and 21.6% bacteria and saprotrophic fungi; ²Rhizophagus intraradicesCA502;³Gigaspora rosea NY328A;⁴Sclerocystis sinuosa MD126; ⁵Claroideoglomus claroideum ON393.

As previously described 18, hay litter-bags were buried along the plant row. After about sixty days, the probes were opened and dried at a temperature of ~ 65 ° C for 36 h and the residues were examined by means of a smart NIR-SCiO^TM (Consumer-Physics, Tel Aviv) operating in the 740-1070 nm NIR band; two scans were carried out on each side of the litter-bags. The chemometric elaborations were carried out by means of the SCiO^TM-Lab software, which operates by means of AKA (As Known As) matrices and provides a percentage recognition of the classification matrix cells. The algorithm used for the classification was the random forest algorithm. The percentages of fingerprinting for the Control (CC) and the Symbiotic (SS) classes were analyzed using an online MedCalc software, and one proportion was tested.

An NIR-SCiO^TM equation, taken from unpublished results of an experimental trial on tomato plants, was used to obtain an indirect estimate of the soil induced respiration (SIR) capacity, which was measured according to Anderson and Domsch23. After having added sugar to the soil sample, it was possible to measure the aerobic activity, stimulated by the microbial consortium, using the infrared meter. The correlation between the estimated and measured data resulted to be sufficiently high (R²_{cross-validated} 0.86) to be considered reliable under comparable conditions.

The NIR spectrum of the leaves was detected using the smart NIR-SCiO^TM in the lower leaf page, in duplicate. Coding samples were obtained and a chemometric elaboration was carried out, as described for the litter-bags.

The raw foliar pH measurements were carried out, according to the acquisitions of previous works192021, on cut leaves, using a BORMAC "XS pH 70" pH meter (www.giorgiobormac.com), with a pH range of 0 ÷ 14, two decimals, and supplied with a Hamilton Peek Double-PoreF, / Knick combined 35 x 6 (LxØ) glass-plastic electrode. The measurement was performed on 15 leaves per sub-group, in the central rib in the lower page, using a screw to slightly dent the rib in order to insert the electrode. Other types of electrodes have been found to not be suitable for this kind of measurement. The pH operation was conducted on fresh or conserved leaves and chained with an NIR scan.

Individual data of the raw foliar pH and of the soil respiratory capacity were analyzed by means of mixed models 24 with the bio-fertilizer and C-S pairwise comparisons as the fixed factors, while the experiment was considered as random.

Yield data from the Control and Symbiotic sub-plots and their effects were analyzed using the Friedman test for paired comparisons 25.

In order to model the dependent variable, as an effect- size, the yield was expressed as the Ln of the response ratio (S/C), where the mean of the inoculated treatment (S) was divided by the mean of the non-inoculated control (C), that is, d_Y = Ln(S/C). This mode of expression is arithmetically equivalent to calculating the relative prevalence of S over C (d _Y= S/C -1), with the result being given in percentage. Seven independent variables, including the squared values of the litter-bag fingerprints, were fitted to the response ratio 25 in a multiple linear regression model. In order to make the relationship positive, the average pH values were transformed into H⁺ as (10^-pH*10^6).

The average results of the six independent parameters issued from the two validation experiments were applied in the model and the dependent predictions were compared to the realized S/C yield deviation. A positive-negative quadrant classification was performed for the more extensive DISAFA 2018-2 experiment.

The average yield measured in the fifty sub-plots of the calibration set was 14.47 t ha^-1 for C vs. 15.53 for S (Table 2 and Figure 1). The relative difference spanned from +33.6% to -15.6%, with a prevalence of +7.3 %, which is globally significant for the Friedman test (P=0.03). In fact, the responses to inoculation were different in the three experiments; the inoculation was not positive in CREA-CI or Maïsadour, while in DISAFA-1, the average yield increased by +11.3% (P=0.01). In the first 2019 validation experiment (Table 3), the yield response was positive (+4%), but the average response to the five types of bio-fertilizers was negative (-3.2%; P 0.11) in the DISAFA-2 experiment and nearly significant for the Shaniya cultivar (P 0.07). Table 4.

The average mixed model solutions were 5.34 for C vs. 5.14 for S (P<0.0001, Table 5) for all the single measurements, and an acidification of 3.7% units of pH (P<0.0001) was observed after inoculation with the BF. Table 6 reports the thirteen pairwise plot yields and the pertinent individual average values of foliar acidity and soil respiration. As far as leaf acidity is concerned,the mean increase as a result of the S treatment was 13.8% (P=0.0066), in terms of H⁺, a result that was derived from four significant increases and one significant decrease (in pairwise 12). The soil respiration activity was significantly increased in two pairs, but it was also decreased in three cases, and on average it was not relevant.

In the 2018 calibration experiments (Figure 2), the regression was only negative for the S plots. As highlighted in Figure 3, which is scaled in the same way as Figure 2, the pH range was more limited in the 2019 DISAFA-2 validation experiment, but a positive regression basically appeared for both the C and the S plots.

The increase in yield was favored where the respiration of the soil increased , as can be seen in Figure 4. This result was clear after the exclusion of two outliers (in red), and a parabolic positive trend was noted.

On average, the leaves of the C and S groupes were classified at a similar sensitivity level, that is, at 73.6 and 72.9%, respectively, for the C and S categories (Table 7). When considering the NIR-Tomoscopy and the yield (Figure 5), the linear regression in the fingerprint % of the leaves of the Control was slightly positive, while that of the Symbiotic was slightly negative. No deviation from linearity was apparent.

A high sensitivity was reached for the AKA classification of the litter-bags (86.0 and 87.6%, respectively, Table 7). When the NIR fingerprint of the litter-bags and the yield were considered together (Figure 6), the regressions in the fingerprints of the Control and of the Symbiotic probes were markedly negative, but a parabolic trend enhanced a maximum of the yield response by around 80-85% for the fingerprinting, and this was further confirmed for the CC litter-bags.

After excluding one outlier, the d_Y model reached an adjusted R2_adj. of 0.78 (P=0.01), with a standard error of ±4% (Table 6; Figure 7). The relationship with the yield increase was driven by the fingerprint in the Control litter-bags (std coef. = 11.85) and by its squared term (-11.77). Table 8

The first experiment in 2019-1, which was based on 75 pH and a leaf examination plus 40 NIR SCiO scans of 20 litterbags, has shown a good agreement between the yield predicted by the model (7.0%) and the yield realized in the field (4.2%). Both values are in the same quadrant of Figure 7 near the diagonal.

In the 2019-2 DISAFA-2 experiment, where a relevant body of measurements were conducted, that is, nearly two thousand (730 leaves for pH and NIR-Tomoscopy plus 350 scans of litter-bags), the average regression response was null (Figure 8), but the classification in the quadrants (Table 9) showed that the general negative response to bio-fertilizers was correctly predicted by 84% in the “bad” quadrant (P 0.0012) and the general means collimated (-2.66% predicted vs. -3.02% realized).

The foliar pH measurements, which on average were 5.23 in 2018 and 5.20 in 2019, confirmed the previously obtained acidic value of 4.84 in the stems1921 and the 5.14 value obtained in Sorghum sudanensis20. The lack of response in the 2019-2 validation experiment has clearly been anticipated, as can be seen from the contrasting trend of the foliar pH, which was increased over the scatter-plots (Figure 3). In the Sorghum sudanensisstudy 20, the foliar pH parameter played a pivotal role in the chemical composition variations and in the plant mass growth. The confirmation of the raw pH fall, as a sign of a probable mycorrhizal modification, is in agreement with previous findings on several species192021. In the limited number of studies conducted up to now, the raw pH in plants has shown signs of some regulation of the thermo-water mechanisms 21, with a variation of -0.07 pH °C^-1 in the aerial temperature increase, while an average reduction of 0.10 units of pH would indicate an increase in water stress of about -0.59 MPa. The pH adjustments of plants to a water-thermal-light environment are active dynamic processes, and not passive ones, which require the presence of light, and they may differ according to the species and the symbiotic relationships. However, only one piece of evidence is so far available concerning the connection between a pH fall and yield increases on a field experiment basis 19. In the present work, a constant relationship cannot be assumed, except for a negative trend which was observed below the core of the expected favorable variations. The comprehension of such a reliable result of pH fall, but a failure in yield response, may be one of the keys for advances in symbiotic corn production. Among the abiotic factors worthy of consideration, the soil pH had no influence on the foliar pH 31, but it was able to modify the rhizosphere 32, and consequently may disturb the beneficial biotas in a similar way as what happens for herbicide residues 33.

The second method used in this work was the NIR-Tomoscopy of leaves. In the study with Sorghum20, the fingerprint of the Control was 96% vs. 81% in the Micosat group. In the present work, the yield response connection to the foliar NIR scans (Figure 5) appeared much lower than the connection to the litter-bags (Figure 6).

Finally, the litter-bag responses appeared to be related to the pH mechanisms but also, and more interestingly, to yield production. The parabolic trend observed in Figure 6 shows similar responses from the C and the S litter-bags, and this new feature may be assimilated to a Mitscherlich curve.

Johnson et al 34 submitted the mycorrhizal factor to a Law of Minimum examination, using the C4 Andropogongerardii species. The results supported the hypothesis that mutualism likely occurs in P-limited systems, while commensalism or parasitism is probable in N-restricted systems. Soil fertility is the key to controlling mycorrhizal costs and benefits, and the Law of Minimum is a useful predictor of the mycorrhizal phenotype. Mycorrhizal fungi can improve the limitation of P in herbage experiments, but not the limitation of N.

In the first experiments conducted to formalize the litter-bag method 18, the eight litter-bag calibration studies, devoid of yield results, showed an average sensitivity of 62% for C and 71% for S, values that now result to clearly be below the range of sensitivity expressed in the present work with reference to intensive corn fields.

A first question arises about the interdependencies of the C and S fingerprints. Are the two closely correlated (r=0.79 in this 2018 experiments)? In theory, if the inoculation has no effect, the threshold of 50% should buffer the AKA classification. Instead, when an effect is present, the differentiation increases, and both the S and C fingerprints should grow.

The question now concerns the band with low litter-bag fingerprints: what does this mean? In our opinion, a minimum of receptivity in the soil is necessary to start reliable mechanisms in the rhizosphere: a “law of the minimum” that must be overcome.

Moreover, what about the soils with too high litter-bag fingerprints? As for all fertilizers, the symbiotic responses are parabolic, and mutualism, or even parasitism, can therefore take place till the yield diminishes. Klironomos 35 compared local and exotic species with both local and exotic AMF and revealed how plant growth responses to inoculation can range from highly mutualistic to parasitic.

In the final model, the soil respiration parameter did not appear to be statistically significant, because of some biased points (Figure 4). It therefore appears necessary to validate the SIR prediction model under several conditions, and especially where the estimated parameter decays significantly in BF soils. It should be pointed out that, in a simultaneous experiment conducted on olive groves in Apulia 36,with the same methodologies, pertaining to the same BF and the plant-soil survey adopted in the present work, SIR and leaf acidity were indicated in the model as being favorable for a reduction of the disease severity degree. In that experiment, the NIR-Tomoscopy of hay-litter-bags from non-inoculated soils was confirmed to be able to forecast the outcome of bio-fertilizer inoculation, and a holistic model that gathered differential and compositional analyses of the leaf (pH, crude protein, water) and of the soil (respiration) was able to explain over 95% of the average mitigation response to microbial inoculation. Two keys for a successful inoculation were identified in the olive experiment: i) a high degree of variability of the soil conditions with an enhancement of microbial activity (lowering the fingerprint of the control litter-bags), and ii) a homogeneity of the leaves (with increases in the fingerprint of the leaves treated with BF), a result that has not been confirmed in the present maize experiment.

The model has proved to be predictive in both directions. Along the positive side, we have obtained encouraging results, while the negative side induces to reflect to a lack of knowledge for the symbiotic mechanisms. The failure to raise the yields observed in the main validation experiment has been forecast, since foliar pH and litter-bags were examined and the model was tested, thus the capacity of the model has been confirmed, even for null or negative outcomes. Therefore, how can we explain the failure of a positive symbiosis on yield in the bio-fertilized plots? The centers and soils were the same as those of the successful 2018 experiments, and the phenotype variability appeared somewhat restricted, but the substantial difference, in our opinion, are due to the genetic background of the cultivars. In 2018, it was the MAS 68K that did not respond, while in 2019 neither MAS DM6318 nor MAS Shaniya responded, thereby reducing the symbiotic relationship to commensalism or even parasitism. Since several AM fungi were involved, a general factor that hinders symbiosis can be envisaged in these genetic lines. The selection for resistance to fungi diseases perhaps also carried over an adversive effect to the beneficial fungi. This hypothesis should be taken into consideration for bio-fertilization advances.