## 3 Results and Discussion  
### Exploratory Data Analysis  
Table 2 encompasses six variables, each capturing distinct dimensions of agricultural research. The targets, PY and IENU, exhibit high variability with coefficients of variation (CV) of 136.61% and 286.76%, respectively. PY, with a mean of 2268.42 kg TS/ha and a range up to 18,008.11 kg TS/ha, and IENU, averaging 32.26 kg N/ha with a range extending to 1285.71 kg N/ha, underscore the broad spectrum of outcomes in agricultural productivity and efficiency. Predictor variables NC, T, and D also show significant variability. Table 3 outlines the frequency of various crops studied for growth using kitchen waste as fertilizer, reflecting multiple agricultural interests. Rvegrass dominates the dataset with a substantial frequency of 73.77%. \"Grass\" (unspecified species) accounted for 10.76%, while oats and lettuce followed with frequencies of 4.87% and 2.02%, respectively. Other  
crops, including barley, corn, and specific varieties of potato and rice, were less frequently represented (<2%), suggesting limited research attention on these species. The correlation analysis was conducted using all available data. Due to the non-normal distribution and the presence of a significant number of ties, Kendall's Tau-b was employed as it better accounts for tied ranks and has less influence on it. Among the predictors, D exhibited significant positive correlations with both PY and IENU, with r-values of 0.42 and 0.44, respectively (\(p\) < 0.01) (Figure 1). In contrast, VS and T showed negative correlations (r \(\approx\) \(-\)0.24, \(p\) < 0.01), while NC was not significantly associated with the targets. The low intercorrelation among predictors suggests their independence, supporting their inclusion in predictive models. The positive relationship between D and both PY and IENU suggests that longer decomposition allows for more complete nutrient mineralization and humification, enhancing nutrient availability and soil conditioning. As organic matter decomposes, labile nutrients are released and complex organic compounds are transformed into more stable humic substances, improving nutrient retention and soil structure [58]. From a practical perspective, these findings imply that optimizing decomposition time before application can improve \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Unit** & **Mean \(\pm\) SD** & **Range (Min–Max)** & **CV (\%)** \\ \hline N content (NC) & g/kg TS & 24.05 \(\pm\) 15.54 & 1.78–57.10 & 64.62 \\ \hline Volatile solids (VS) & \% & 55.38 \(\pm\) 31.65 & 1.23–95.00 & 57.15 \\ \hline Growth time (T) & Month & 8.17 \(\pm\) 16.00 & 0.50–100.00 & 195.84 \\ \hline Dosage (D) & kg N/ ha & 189.27 \(\pm\) 181.05 & 0.10–1020 & 95.7 \\ \hline Plant yield (PY) & kg/ ha & 2268.42 \(\pm\) 3099.00 & 0.00–18,008.11 & 136.61 \\ \hline Internal efficiency of nitrogen utilization (IENU) & kg N/ ha & 32.26 \(\pm\) 92.51 & 0.00–1285.71 & 286.76 \\ \hline \hline \end{tabular}  
* TS: Total solids; CV: Coefficient of Variation.  
\end{table} 
Table 2: Summary statistics of key variables in the original data. \begin{table}
\begin{tabular}{c c} \hline \hline
**Crop** & **Frequency (\%)** \\ \hline Barley (_Hordeum vulgare_ L.) & 0.45 \\ \hline Corn & 0.45 \\ \hline Grass & 10.76 \\ \hline Grass—_Festuca arundinaca_ & 0.90 \\ \hline _Lactuva sativa_ & 1.79 \\ \hline Lettuce & 2.02 \\ \hline Oats combined & 4.87 \\ \hline Potato (_Solanum tuberosum_ L. ‘Dansyakuimo’) & 1.35 \\ \hline Rice _Oryzaza sativa_ L. cv: Saechucheong & 0.67 \\ \hline Ryegrass & 73.77 \\ \hline Shoots Kai choy (_Brassica juncea_, var. [PERSON]) & 0.67 \\ \hline Triple mix (a mixture of 70\% timothy (_Pileum pretense_) + 15\% red \\ clover + 15\% alsike) TM & 0.22 \\ \hline Wheat combined & 1.34 \\ \hline Oat combined with hairy vetch OHV & 0.22 \\ \hline Oat combined with red clover Trifolium pretense ORC & 0.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Distribution of crop studies utilizing kitchen waste as fertilizer.