## 2 Materials and Methods
### Data Sources and Processing  
The data collected in this study included indicators related to land use, stand conditions, soil properties, and cropland utilization (Table 1). To ensure data consistency, all raster data were adjusted to a scale of 1 km \(\times\) 1 km using the resampling tool of ArcGIS 10.7 software to ensure consistency and comparability between different datasets. The point data were processed into raster data by spatial interpolation techniques to ensure the completeness of spatial coverage and facilitate subsequent analyses.  
The rate of agroforestry networks was evaluated through the analysis of 30 m \(\times\) 30 m land use and cover change (1985-2022). Farmland forest networks play a significant role in the protection of cultivated land, the prevention of wind erosion, and the provision of ecological benefits. In this study, the farmland forest reticulation rate was determined by calculating the ratio of the area of cultivated land protected by the forest network to the total area of cultivated land. Based on the prevalence of certain tree species (e.g., poplar and elm) in the northeast region, an average tree height of 40 m was established, with a forest network spacing of 800 m. This configuration was designed to effectively safeguard farmland while simultaneously facilitating optimal ventilation and light conditions for crops. An 800 m buffer zone was created around the forested land, and the cultivated land data were superimposed to determine the area of cultivated land protected by the forest network [8]. The proportion of this area to the total cultivated land area was calculated to derive the rate of farmland forest reticulation in each municipality.  
The average production cost per mu is a significant element in the assessment of CLQ. This was calculated as the sum of the average fertilizer cost per mu and the cost of machinery. The extensive utilization of mechanization in contemporary agricultural practices and the prevalent overuse of fertilizers in Chinese agriculture have been identified as the primary determinants influencing production costs [33]. By integrating the costs associated with fertilizers and machinery, this study effectively demonstrates the spatial variability of production costs to a great extent.  
Figure 1: Location of the study area and the digital elevation model. All figures were produced on the basis of the Ministry of Natural Resources Standard Map Service System GS (2022) 1873 ([[http://bzdt.ch.mnr.gov.cn/](http://bzdt.ch.mnr.gov.cn/)]([http://bzdt.ch.mnr.gov.cn/](http://bzdt.ch.mnr.gov.cn/)), 3 July 2024), with no modifications to the base map boundaries.  
The estimation of the thickness of the black soil layer was based on the United Nations' definition of black soil, specifically where the soil organic matter content exceeds 2% [34]. Data were obtained from the 250 m Global Soil Organic Carbon dataset, which provides  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
the soil organic matter content at different depths (0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm). It was assumed that the midpoint of each layer represented the organic matter content at that depth (e.g., the 0-5 cm layer represents a depth of 2.5 cm). The thickness of the black soil layer was then estimated by identifying the depth intervals at which the organic matter content exceeded 2% and applying linear interpolation. If the organic matter content fell below 2% in the shallowest layer (2.5 cm), the thickness of the black soil layer was set to 0. Conversely, if the content remained above 2% at a depth of 150 cm, the thickness was set to 150 cm. While the method relies on certain assumptions, it provides a reasonable estimation of the black soil layer's thickness and effectively reflects its approximate distribution, particularly when direct measurement data are unavailable.