# Promoting Urban Farming for Creating Sustainable Cities in Nepal
## 5 Empirical Analysis
### Results: Linear Regression Models  
We used regression models to examine the relationships between agricultural areas exclusively within the urban boundaries and total urban built-up areas within the same administrative urban boundaries. These data were derived from the analysis of remotely sensed data downloaded from the Esri website. We set the urban land area as the dependent variable and agriculture land area, forest, and shrublands as independent variables for years 2017 until 2021. Regression outputs are given in Appendix A. Our hypothesis is that, despite urban growth, there was no decrease in agricultural activity in years 2017 until 2021. During the field visits by one of the authors in 2017, 2018, and 2019, we saw that several agricultural lands in Nepal were being plotted for buildings. However, satellite observations for the years of 2017 to 2021 reveal no negative relationship between urban growth and agricultural land within the urban territory (Figures 10 and 11a-e). The statistics revealed from the analysis of satellite imagery taken from 2017 to 2021 show no decrease in overall urban farm areas despite the expansion of urban built-up area. Simply put, there is no decrease in overall urban farm areas (Figure 10) in all the years of observations within the urban delineated boundaries (Figure 8), and a positive correlation (r = 0.999) exists between urban and farm areas within the urban territories. Correlations between urban areas and agriculture, forest, and shrubland are in decreasing order. With agriculture, it is higher, but the correlation between urban and forest and shrubland decreased in successive years. These results reveal that, as urban areas increase in successive years, the forest and shrub areas decrease, whereas large agricultural areas shrink, but the agricultural crops' greenery, as depicted by satellite imagery, has increased along with urban areas (Figure 11a-e). The same sensor, however, recorded a decrease in forest and shrublands between 2017 and 2021 (Figure 10). This is also evidenced from the negative coefficients for forest and shrubland for the years 2017 to 2021, and the positive coefficients for agriculture for the corresponding years, with t-values higher than 8 and \(p\)-values < 0.00001 for all observation years, suggest that the urban farming area has not decreased despite its low rate of increase; however, forest and shrubland has decreased, showing \(p\leq 0.00001\) and t-value being larger than negative 10 and negative \(\beta\) coefficients. Detailed outputs of the regression analyses for years 2017 until 2021 are given in Appendix A. \begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Crops**} & \multicolumn{2}{c}{**Production (kg. per sq. Meter per Cycle)**} \\ \cline{2-3}  & **Urban Farm** & **Traditional Farm** \\ \hline Cabbages & 3.6 & 2.9 \\ \hline Cauliflowers & 0.57 & 0.55 \\ \hline Broccoli & 0.61 & 0.46 \\ \hline Maize & 2.5 & 2.22 \\ \hline Paddy & 0.44 & 0.35 \\ \hline Roots and tubers & 3.8 & 1.3 \\ \hline Oil crops & 0.85 & 0.34 \\ \hline Vegetables & 4.6 & 1.9 \\ \hline Primary fruits & 2.2 & 1.4 \\ \hline Cereals & 0.62 & 0.40 \\ \hline Sugar & 5.3 & 6.9 \\ \hline Fiber crops & 0.42 & 0.01 \\ \hline Average production in kg per sq. meter & 2.84 & 1.68 \\ \hline Average production in kg per hectare & 28.400 & 16,800 \\ \hline \end{tabular} Source: [48].  
\end{table} 
Table 4: _Cont._Figure 10 clearly depicts the changes (reduction) in forest and shrubland, but agricultural land and urban built-up areas are increasingly showing a perfect Pearson correlation (r = 0.9999). During our field visits between 2017 and 2019, we observed that, in several places, forested areas were degraded due to human activities turning such areas into shrublands, and when human interferences were reduced, after some time, the same areas experienced a comeback with forest stands. These are the reasons why there are changes between forest and shrubland.