Spatial Distribution of Soil Nutrients in Farmland in a Hilly Region of the Pearl River Delta in China Based on Geostatistics and the Inverse Distance Weighting Method

Abstract

Soil nutrients are essential factors that reflect farmland quality. Nitrogen, phosphorus, and potassium are essential elements for plants, while silicon is considered a “quasi-essential” element. This study investigated the spatial distribution of plant nutrients in soil in a hilly region of the Pearl River Delta in China. A total of 201 soil samples were collected from farmland topsoil (0–20 cm) for the analysis of total nitrogen (TN), available phosphorus (AP), available potassium (AK), and available silicon (ASi). The coefficients of variation ranged from 47.88% to 76.91%. The NSRs of TN, AP, AK, and ASi were 0.15, 0. 07, 0.12, and 0.13, respectively. The NSRs varied from 0.02 to 0.20. All variables exhibited weak spatial dependence (R2 < 0.5), except for TN (R2 = 0.701). After comparing the prediction accuracy of the different methods, we used the inverse distance weighting method to analyze the spatial distribution of plant nutrients in soil. The uniform spatial distribution of AK, TN overall showed a trend of increasing from northeast to southwest, and the overall spatial distribution of AP and ASi showed that the northeast was higher than the southwest. This study provides support for the delimitation of basic farmland protection areas, the formulation of land use spatial planning, and the formulation of accurate farmland protection policies.

Geostatistical Assessment of Heavy Metals and Nutrients Availability in Soil of Oil Palm Plantation Affected by Bauxite Mining

“…The significant positive correlation between the HMs explained that the contamination of the HMs in the soil could be possibly from similar sources of contamination (Doabi et al, 2019). The correlation coefficients between Cu-Zn, Cu-Mn, Cu-Fe, Zn-Mn, Zn-Fe and Mn-Fe were relatively larger than 0.5, explaining a strong correlation among the HMs, indicating a homologous sources of contamination (Wang et al, 2021). However, Pb did not show any significant correlation with the HMs.…”

“…where Z(x i ) is the value of the variable Z at location of x i , h the lag and N(h) the number of pairs of sample points separated by h. Theoretical semivariograms were used to fit the empirical semivariogram models to generate geostatistical parameters, such as structured variance (C 1 ), nugget variance (C 0 ), and sill variance (C 0 + C 1 ) and distance parameter (h)The fitted model was adopted based on high R 2 (> 50%) and low residual sum square (RSS) (Wang et al, 2021). Ordinary kriging interpolation was employed after semivariogram models were constructed.…”

Spatial Prediction of Total Nitrogen in Soil Surface Layer Based on Machine Learning

“…Soil nutrients are an important factor in measuring soil fertility, and traditional farm management and agricultural systems have led to polarization of soil nutrients in farmland, i.e., excess nutrients in fertile soils reduce the utilization of chemical fertilizers, and nutrientpoor soil vegetation does not receive adequate nutrients. Soil nutrient monitoring has evolved from qualitative to quantitative studies [35], and the determination of TN inversion models plays an important role in understanding their spatial distribution characteristics, guiding the implementation of precision agriculture, and promoting the development of agricultural production [2,36]. Monitoring of total nitrogen content has further improved and updated its inversion means from traditional laboratory analysis to large-scale spatial prediction based on regional scale.…”

“…As the main material basis of land resources, the inherently non-renewable nature of soil determines the limited carrying capacity [1]. One of the critical factors reflecting the quality of farmland is the soil nutrient content, and the normal growth and development of plants and soil nutrients are closely related [2]. In the era of rapid development of digital agriculture, accurate, fast and dynamic acquisition of soil information on demand is the guarantee of modern precision agriculture and understanding the spatial distribution characteristics of nutrients is a basic requirement for sustainable agriculture, which plays an important role in sustainable agro-ecological development [2][3][4].…”

Spatial Variation and Influencing Factors of Trace Elements in Farmland in a Lateritic Red Soil Region of China

“…This model can determine the degree of spatial correlation among regionalized variables using the nugget-to-sill ratio (NSR), which reflects the spatial dependence of soil attributes. According to the grading standard for such analyses [34], an NSR less than 25% indicates a strong correlation; a rate between 25% and 75% is moderate, and >75% is weak.…”