## 2 Materials and Methods
### Dataset Preparation and Processing  
The initial dataset consisted of 448 instances with seven features: nitrogen content (NC), volatile solids (VS), season, growth time (T), plant type, fertilizer dose (D), PY, and IENU. Due to a high proportion of missing values (45.5%) in the VS variable, this feature was excluded from further analysis to maintain data quality. Subsequently, records with missing target variables were removed, resulting in 420 complete instances for PY prediction and 342 for IENU prediction. The reduced size for IENU prediction was due to missing nitrogen input data in three studies [29; 30; 37], which were therefore excluded from that analysis.  
To evaluate how preprocessing affected model outcomes, we developed four versions of the dataset:  
Data 1: All records with any missing values were discarded (listwise deletion).  
Data 2: Derived from Data 1, outliers were removed based on the interquartile range (IQR) method. Specifically, a value was considered an outlier if it fell below Q1 - 1.5 \(\times\) IQR or above Q3 + 1.5 \(\times\) IQR, consistent with the boxplot method.  
Data 3: Missing values were imputed (mean for numeric, mode for categorical variables) to preserve sample size.  
Data 4: Based on Data 3, outliers were removed using the same IQR-based method as in Data 2.  
These dataset versions allowed us to assess the trade-offs between completeness and data integrity. While removing missing values (Data 1 and 2) ensures clean input, it reduces the sample size. Conversely, imputation (Data 3 and 4) preserves more data but may introduce uncertainty depending on the distribution of missingness. Each dataset was split into training (80%) and testing (20%) sets using random sampling. We also applied normalization, log transformation, and standardization to test the sensitivity of ML models to different scaling methods.  
The study focused on two output parameters:  
* PY: Represented in kilograms of total solids per hectare (kg/ha), PY reflects the agricultural output per unit area.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Nr** & **Treatment** & **Season** &
\begin{tabular}{c} **Growth Time** \\ **(Months)** \\ \end{tabular} & **Plant** & **References** \\ \hline
16 & Municipal solid FW compost & Cold & 120 & Oat & [38] \\ \hline \multirow{2}{*}{17} & Chemical fertilizer + 100\%, 150\%, and & & & \\  & 300\% FW-livestock manure compost & & & & \\ \hline \multirow{2}{*}{18} & FW digestate, I, II; FW co-digested with & & & & \\  & sewage sludge, I and II & & & & \\ \hline
19 & Acidulous composting FW & Spring & 85, 99 \& 100 & Potato & [41] \\ \hline
20 & Green waste compost & NA & 2.73, 3.00, 3.63, \& 9.37 & Oat & [42] \\ \hline \hline \end{tabular}  
* Notes: Not available (NA); Food waste (FW); Dried digestate sampled from liquid digestate (DD); Liquid digestate (LD); mineral fertilizer (MIN).  
\end{table}
Table 1: _Cont._* IENU: Measured in kilograms of nitrogen per hectare (kg N/ha), IENU evaluates the effectiveness of nitrogen use in supporting crop growth.
* Three explanatory features were analyzed:
* NC: The nitrogen percentage in fertilizer affects crop growth and nitrogen efficiency.
* T: The crop growth duration (in days) influences nutrient uptake and development.
* D: The fertilizer amount applied per hectare impacts yield outcomes.