## 4 Discussion  
### Yield Performances and Energetic Output  
The values of the tomato marketable yields in GM were higher by 70 and 123% compared with NO ASC and ECO treatments, respectively. This result was probably due to higher availability of readily available N, which derives from decomposition of the aboveground biomass of the ASC plowed into the soil [4; 11]. Conversely, in the ECO plots, in which the clover was used as living mulch [26; 27], and in the NO ASC, the tomato yield did not reach the standard level of organic production [28]. Moreover, in the ECO system, the cauliflower marketable yield was very low, likely because adverse climatic conditions occurred. In fact, during the growing season there was an extreme adverse event, unusual in the experimental area, showing low mean month temperature (4.5 \({}^{\circ}\)C) and several days of values below \(-4\) \({}^{\circ}\)C on January 2017, associated with high rainfall intensity (117 mm). The zucchini and lettuce marketable yields, which benefited from the residual fertility of the ASC, were comparable with other experimental results on organic crops production [29; 30; 31].  
The total energy output per hectare was higher in GM by 14% and 70% than ECO and NO ASC, respectively (Table 3). In particular, the treatments with the presence of ASC showed similar total energy output productions (about 20,000 MJ ha\({}^{-1}\)), which were substantially different in comparison with the management without cover crops, thus indicating the positive effect of such agro-ecological practice [32]. This result was probably due to the large difference generated in the total tomato output for GM and NO ASC treatments. Conversely, the ECO treatment showed a slightly reduced energy output in comparison with GM, because of the very low productions in the cauliflower cropping cycle [33], even if the four different crops contributed to the overall production by half a hectare. According to the USDA indications [22], the coefficient of equivalent energy of the cauliflower was the highest (value equal to 1), therefore, the low production of this crop influenced the total energy output of the ECO treatment. In any case, we must take into account that the differences among treatments showed a high standard deviation, which was generated by the huge variability of the data.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Very Low** & **Low** & **Medium-Low** & **Medium** & **Medium-High** & **High** & **Very High** \\ \hline
**Overall sustainability** & 0.000 & 0.003 & 0.257 & 0.543 & 0.190 & 0.005 & 0.000 \\
**Production capacity** & - & 0.014 & 0.237 & 0.496 & 0.237 & 0.016 & - \\
**Soil and water** & - & 0.020 & 0.571 & 0.265 & 0.136 & 0.008 & - \\
**Resources preservation** & - & 0.004 & 0.034 & 0.475 & 0.399 & 0.089 & - \\
**Biodiversity** & - & 0.246 & 0.309 & 0.000 & 0.338 & 0.107 & - \\
**conservation** & \multicolumn{1}{c}{} & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relative frequency distributions of the results of 5000 Monte Carlo simulations among the seven different qualitative values (“very low”, “low”, “medium-low”, “medium”, “medium-high”, “high”, “very high”) for the overall sustainability and among the five different qualitative values (“low”, “medium-low”, “medium”, “medium-high”, “high”) for the main aggregate attributes (“production capacity”, “soil and water preservation”, “resources preservation”, and “biodiversity conservation”) obtained with _DEXi-met_.