Unveiling spatial heterogeneity of soil available phosphorus for site-specific fertilization in Andisols of Hokkaido, Japan

Unveiling spatial heterogeneity of soil available phosphorus for site-specific fertilization in Andisols of Hokkaido, Japan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unveiling spatial heterogeneity of soil available phosphorus for site-specific fertilization in Andisols of Hokkaido, Japan Elton Amadeus Francisco, Rintaro Kinoshita, Hiroaki Shimada, Daigo Aiuchi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7984946/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Predicting the spatial variability of soil available phosphorus (P) is crucial for efficient site-specific fertilizer management in large-scale fields. In Hokkaido, Japan, Andisols are the dominant cultivated soil type, known for their high P fixation and strong spatial heterogeneity of inherent soil properties. To assess the magnitude and factors controlling the within-field variation in soil available P, a total of 920 surface soil samples were collected from two fields located 300 m apart, designated the East (6.6ha) and West (4.4ha) fields. The samples were analyzed for available P, phosphate absorption coefficient (PAC), total carbon (TC), and oxalate-extracted aluminum (Al o ). The two fields had clear zones of low, medium, and high soil available P; however, the zoning was more pronounced in the East field. The available P was also higher in the East field, averaging 70.1 mg kg -1 , while it averaged 45.7 mg kg -1 in the West. The analysis of the spatial structure of the available P showed moderate spatial dependence (25 < nugget% ≤ 75) in both fields, but a larger range in the East field. PAC was a strong predictor of available P in the West field, whereas both TC and PAC controlled available P in the East field. Despite high Al o concentration in the West field, the PAC was lower than in the East, indicating multiple factors controlling P variability. Overall, this study revealed that site-specific fertilization of P is needed, and management zones should be created based on the variation of soil organic matter and clay minerals. Andisols spatial heterogeneity soil available phosphorus site-specific fertilization management zones. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Phosphorus (P) is an essential element for plant growth, and its limitation negatively affects crop yield and quality (Khan et al., 2023 ; Zuang et al., 2020; Heuer et al., 2017 ). Most agricultural soils are naturally deficient in P, and its availability is improved through the application of P fertilizers (Pang et al., 2018 ). These fertilizers are mainly produced from rock phosphate, a non-renewable resource whose reserves are rapidly declining and may be depleted within the next 300 years (Gilbert, 2009 ). This fact poses a threat to meeting global food demand, which is expected to continue increasing in the coming decades (van Dijk et al., 2021 ). In contrast, global demand for P fertilizers has been increasing due to population growth, rising food demand, and the intensification of agricultural production systems. In 2024, global consumption of P fertilizers (P2O5) was estimated at 47.5 million metric tons and is projected to reach 51.8 million metric tons by 2028, with significant growth in Asia and South America (International Fertilizer Association, 2025 ). However, it is estimated that only 20–25% of P applied as fertilizer is consumed as food. At the same time, the remainder is lost through fixation in the soil, runoff, waste streams, and inefficiencies along the food chain, indicating significant P losses in the food system (van Dijk et al., 2016 ). Furthermore, the application of P fertilizers has been linked to the accumulation of heavy metals, such as cadmium (Cd), in agricultural soils, posing a significant risk to soil health, crop safety, and ultimately human and animal health (Roberts, 2014 ; Nziguheba & Smolders, 2008 ). Therefore, an efficient use of P fertilizers is crucial for sustainable food production. P fertilizer management has become a globally critical issue due to the dual challenge of increasing crop productivity while reducing the environmental impacts. This is particularly important in soils with high P-fixation capacity, such as Andisols, Oxisols, and Ultisols (Parfitt, 1979 ). Among these soils, Andisols have the highest P fixation capacity. Although Andisols cover only 1% of the global land surface, they are very important soils for agricultural production, supporting intensive food production systems in regions such as Japan, New Zealand, and parts of East Africa (Shoji et al., 1993 ; Nanzyo et al., 1993). Although P fertilizer consumption in Japan has been decreasing in recent years, the application rate remains remarkably high. From 2014 to 2023, the average P fertilizer application rate in Japan was 73 kg-P₂O₅ ha − 1 , compared to the global average of around 27 kg-P₂O₅ ha − 1 (FAOSTAT, 2025). This disproportionately high rate is primarily due to the dominance of Andisols, which cover approximately 28% of Japan's cultivated land (Kanda et al., 2017 ) and exhibit a strong P fixation capacity. Maintaining adequate P availability for crops thus requires substantially higher fertilizer inputs than in most other soil types. Hokkaido, the most important agricultural region in Japan, accounts for 47% of the country's upland farming (Ministry of Agriculture, Forestry and Fisheries, Japan, 2020 ), where Andisols dominate, occupying approximately 43% of the surface land and 41% of the agricultural soils; hence, representing a significant soil group for the country's crop production system (Kanda et al., 2016 ). These soils, derived from volcanic parent materials, exhibit a high P fixation ability due to the presence of active aluminum (Al) in non- and para-crystalline clay minerals, as well as Al-humus complexes, making P a major limiting factor (Shoji et al., 1993 ; Hashimoto et al., 2012 ). Consequently, farmers apply high amounts of phosphate fertilizers to optimize crop yields (Gondwe et al., 2017 ). Although in Hokkaido, there is a fertilizer recommendation guideline that indicates the amount of phosphate fertilizer to be applied depending on the crop type, cultivar, cultivation method, soil type, and the available phosphate status in the soil (Department of Agriculture, Hokkaido Government, 2020 ), a long-term application of high levels of P fertilizers has led to excessive accumulation of P in cultivated fields (Tani et al., 2010 ). Several management practices have been developed to optimize P utilization in agriculture, including soil testing to assess available P levels and applying additional amounts to meet crop demand (Wang et al., 2023 ). However, optimizing P management in agricultural soils is particularly more challenging in soils with high P fixation capacity or pronounced spatial variation of available P. In high P-fixation soils, a significant portion of the applied P fertilizers becomes unavailable to plants, reducing fertilizer efficiency and thereby requiring higher P inputs (Poblete-Grant et al., 2021 ). At the same time, spatial heterogeneity within fields creates zones of both P deficiency and excess. These variations result from complex interactions between pedogenic processes and agronomic practices, such as P fertilizer application; therefore, understanding these interactions is crucial for predicting P availability. Although uniform application of P fertilizers is a common practice, it often fails to account for within-field variability, resulting in areas of over- and underapplication of P within the same field (Zhang et al., 2024 ). A variable application rate is recommended to match crop needs and the specific soil nutrient status (Mulla et al., 1992 ; Ndiaye & Yost, 1989 ). There are a few studies specifically addressing the benefits of variable-rate P fertilizer application. However, applying variable P fertilizer resulted in a significant reduction in P fertilizer applied while maintaining sugar cane yield in Oxisols (Sanches et al. 2021 ). Thus, this is a clear indication that mapping soil available P is crucial for implementing a site-specific management approach. Hokkaido's farming system is on a large scale, averaging 45.7 ha per farm, compared to 2.2 ha nationwide (Ministry of Agriculture, Forestry and Fisheries, Japan, 2020 ). The size of farms is expected to increase in the coming decades due to a decline in the number of farm holders (Iida, 2020 ). While this structural shift may improve mechanization efficiency, it is also likely to enhance within-field variability of soil properties, given the greater spatial heterogeneity in large-scale fields and the challenges of uniformly managing inputs across them. Previous studies in this region have reported significant within-field variations in inherent soil properties, such as allophane and phosphate absorption coefficient (PAC), which are closely related to P availability (Francisco et al., 2025 ; Niwa et al., 2019 ). PAC is commonly used in Japan as an index of soil's P-fixation capacity; the higher the PAC value, the greater the P-fixation capacity and, consequently, lower P availability (The Fifth Committee for Soil Classification and Nomenclature, 2017). Despite existing reports on the spatial variability of these inherent properties and the accumulation of P in Andisols of Hokkaido resulting from intensive P fertilizer use, the spatial heterogeneity of available P within fields and the soil properties controlling this variability remain poorly understood. This gap limits the development of effective site-specific P management strategies that account for within-field heterogeneity. Therefore, this study aims to unveil the extent and patterns of spatial heterogeneity in soil-available P and identify the key soil factors driving this variability, thereby providing a scientific basis for precision P management in Andisols-dominated agroecosystems. Materials and methods Study site The study was conducted at the Field Center of Animal Science and Agriculture, Obihiro University of Agriculture and Veterinary Medicine, in Obihiro City, Tokachi district, Hokkaido prefecture, Japan (42°51'43.4"N, 143°09'57.5"E; Fig. 1a). The surface topography of the region is a complex of alluvial fans and floodplains, with terraces found along the abundant river courses. The land surface is covered by volcanic parent materials deposited during the late Pleistocene and Holocene; hence, Andisols are the dominant soil type (Kikuchi, 2008). Two fields, 300 m apart in the east-west direction (Fig. 1b), were selected; therefore, they were accordingly designated as the East and West fields in this study. The East field was 550 m in the N-S direction and 120 m in the E-W direction, while the West field was 550 m N-S and 80 m E-W, making 6.6 and 4.4 ha, respectively (Fig. 1b). Ancient dunes are present in both fields, creating an undulating topography with a 3.2 m and 2.5 m elevation difference in the East and West fields, respectively (Francisco et al., 2025). The ancient dunes are common in the study area, formed from Eniwa-a (En-a) volcanic materials deposited about 19~21 cal. ka BP, . and the Shikotsu pumice fall-1 (Spfa-1) that erupted and fell about 46 cal. ka BP, which were subsequently sedimented by wind (Uesawa et. al, 2016; Machida & Arai, 1992; Kimura et al., 1970). Dent corn for feed ( Zea mays L.) and orchard grass ( Dactylis glomerata ) are intensively grown in both fields (Online Resource 1). Surface soil sampling A total of 920 surface soil samples were collected from the two fields. In the East field, 530 surface soil samples were collected in two periods (265 samples each) in May 2020 and April 2021. In the West field, 390 samples were collected in April 2022. The soil samples were collected by an automated soil sampler (Wintex 1000, Wintex Agro, Thisted, Denmark) installed on an all-terrain vehicle. In both fields, an equilateral triangular grid design with 12 m spacing was used for sampling, and surface soils were collected to a depth of 25 cm (Francisco et al., 2024). A high-precision Global Navigation Satellite System receiver (DG-PRO1RWS; Biz Station Co., Ltd., Nagano, Japan), equipped with a Virtual Reference Station System, was used to precisely identify the sampling point, at a horizontal accuracy of 1 cm. At each sampling point, 10 subsamples were collected every 20-cm linear interval in the N-S direction, starting from a distance of 80 cm from the sampling point, and afterward composited. All samples were air-dried, passed through a 2-mm sieve, and had visible plant residues removed prior to further analysis. Soil analysis Total carbon (TC) concentration was measured by dry combustion (vario EL cube, Elementar Analysensysteme GmbH, Langenselbold, Germany). Acid-oxalate extractable aluminum (Al o ) and silicon (Si o ) were determined using a 0.2 mol L -1 acid ammonium oxalate solution at pH 3.0 after shaking for 4 hours in the dark (Blakemore et al., 1987). The Al o and Si o concentrations were measured by an inductively coupled plasma emission spectrophotometer (ICPE-9820; Shimadzu Corporation, Kyoto, Japan). The phosphate absorption coefficient (PAC), which indicates the soil's capacity to fix phosphate, was estimated by reacting the soil samples with a solution containing 13.44 mg-P 2 O 5 mL -1 at pH 7.0 for 24 hours, and the molybdate yellow method was used to measure the remaining phosphate in the solution through a spectrophotometer (UV mini-1240; Shimadzu Corporation, Kyoto, Japan). The soil available P was estimated by the Truog method, using 0.1 mmol L -1 sulfuric acid (H 2 SO 4 ) and ammonium sulphate ((NH 4 ) 2 SO 4 ) at pH 3.0 (Truog, 1930), followed by the determination of the P concentration in the solution calorimetrically using the molybdate blue method with a spectrophotometer (Murphy & Riley, 1962; UV mini-1240; Shimadzu Corporation, Kyoto, Japan). Statistical and spatial structure analysis The classical statistical analysis for the selected soil properties was conducted using JMP Pro 17.0.0 (SAS Institute Inc., Cary, NC, USA). The coefficient of variation (CV) was calculated as a percentage to describe the variability of the selected soil properties. The variability was classified as low (CVCV35%; Wilding, 1985). The gstat package (Pebesma, 2004) in the R statistical computing environment (Version 1.4.1106, RStudio, PBC) was used for all spatial analysis. Semivariance was calculated according to the equation proposed by Goovaerts (1999): Where (γ(h)) is half of the variance between two locations that are separated by a distance h, called lag, and m(h) is the number of pairs at the distance h. At the maximum value, the semivariance is called the sill. The value of semivariance at lag zero is called the nugget and reflects measurement error or spatial variation on a scale smaller than the sampling grid size. The percent nugget was calculated to show the degree of spatial dependence, as equation 2 (Goovaerts, 1999): The percent nugget (nugget%) ranges from 0 to 100, and is used to classify the spatial dependence. A nugget% less than 25 indicates a strong spatial dependence, between 25 and 75 a moderate spatial dependence, and more than 75 reflects a weak spatial dependence (Cambardella et al., 1994). Variogram models were fitted, and the best-fit model was selected based on the highest R-squared value, which was then used to describe the relationship between semivariance and lag. The lag at which the sill is reached is called the range, which indicates the spatial dependence. Sample distances further than the range are spatially independent. Ordinary kriging was used to map each soil property, and ArcGIS Pro 3.2.0 (Esri Inc., Berkeley, California) was used to display the maps. To delineate P management zones, an unsupervised learning approach was applied. Specifically, k-means clustering was performed on the combined datasets from the East and West fields using JMP Pro 17.0.0 (SAS Institute Inc., Cary, NC, USA). To characterize the clusters, factor analysis was conducted, followed by a decision tree analysis to extract threshold rules defining each cluster (Gavioli et al, 2019). Finally, kriging interpolation was performed in ArcGIS 3.2.0 (Esri Inc., Redlands, CA, USA) to refine the spatial boundaries of the management zones. Results and discussion Soil physicochemical properties of the study fields The results indicate that the within-field variability in soil physicochemical properties differed between the East and West fields, despite their proximity of only 300 m (Table 1). Available P in the soil varied, with higher values in the East field (70.1 mg P kg -1 ) and lower values in the West field (45.0 mg P kg -1 ; Table 1). This difference is inconsistent with the total amount of P fertilizer applied in the two fields over the last 10 years (Online Resource 1), suggesting that P fertilizer management alone cannot explain the differences in available P between the fields. This finding contradicts other studies that reported P variability mainly related to non-uniform management (Mondal et al., 2020). Although soil available P varied significantly in both fields, the variation was slightly greater in the East field, indicating that different factors could be controlling P availability in the two fields (Table 1). Furthermore, the West field generally had less available P than the East field, which may be related to the higher Al o concentration observed in the West field. Poorly crystalline minerals, such as allophane and imogolite, have stronger binding sites that adsorb phosphate ions more strongly than the Al-humus complex, thereby reducing P availability (Parfitt, 1989; Shoji et al., 1993). In the East field, however, the high SOM might have occupied adsorption sites and formed soluble P complexes, thereby increasing availability (Huang & Shenker, 2004). The total carbon (TC) concentration was significantly lower in the West field (46.6 g kg -1 ) than in the East field (61.5 g kg -1 ; Table 1). Moreover, the variation in TC concentration was lower in the West field (CV = 12.1%) than in the East field (CV = 35.2%). The degree of variation observed in the East field was consistent with the findings of Kinoshita et al. (2016), who reported a 33% variation in soil organic carbon (SOC) in an Andisols plot under coffee cultivation. Although their study was conducted on a larger scale (100 ha) in a mountainous landscape, the comparable level of variability suggests that high heterogeneity in TC concentration is primarily controlled by intrinsic soil-forming processes, rather than external factors such as cropping systems or field size. Similarly, Chesworth (2008) found variations in SOC in Andisols, reaching 200 g kg -1 under humid conditions, indicating that this variability is an inherent feature of Andisols across different spatial contexts. Francisco et al. (2024) found variations in organic matter (OM) accumulation in profiles surveyed within the same field, whereas OM accumulated more in soil profiles with poor drainage. Conversely, Francisco et al. (2025) found that well-drained profiles contained less OM. This indicates that in the study area, the TC is controlled mainly by water; therefore, a significant within-field variation of TC concentration in the surface soil could be expected when the field has areas of both good and poor drainage, which was the case in the East field (Francisco et al., 2024). The oxalate-extractable aluminum (Al o ) was higher in the West field (mean = 38.2 g kg -1 ; Table 1). However, it varied more in the East field (CV = 17.4%) than in the West field (14.1%), following the same trend as the variation in TC concentration between the two fields. Allo et al. (2020) also reported an Al o variation of up to 33.4% across 39 Andisol samples, which was greater than our observed variation but still closely correlated with SOC variation. Thus, it is reasonable to conclude that within-field variations of Al o in our study area are less pronounced when SOM varies less, as observed in the West field. The Al o value represents the total amount of Al present in amorphous clay minerals in Andisols, such as allophane and imogolite, as well as Al complexed with humic substances (Blakemore et al., 1987). Therefore, our results also suggest that the concentration of amorphous clay minerals may be higher in the West field. High allophane content is typically found in well-drained Andisols with low organic matter and favorable pH conditions (Shoji et al., 1993), which is consistent with the conditions of the West field (Francisco et al., 2025). However, the formation of allophane could be inhibited in soils rich in humic substances (Inoue & Huang, 1990). This indicates that significant within-field variability in Al o concentration may occur in fields with greater SOM variability, consistent with the higher variability observed in the East field (CV = 17.4%; Table 1). The variation in Al o concentration in this study was also less than that reported by Kinoshita et al. (2016), who observed variations as high as 39.4%. They found a significantly higher SOM content, with a similar variation to that observed in this study (CV = 33.2%); however, this affected the distribution of Al o . Few studies have quantified the within-field variability of Al o , making these observations particularly valuable for understanding the spatial heterogeneity of Andisols and their implications for soil available P management. The PAC, which indicates the soil's ability to fix P, was generally greater than 15.0 g-P 2 O 5 kg -1 in both fields, reflecting the typical characteristics of Andisols (The Fifth Committee for Soil Classification and Nomenclature, 2017). Although both fields had notably high PAC, it was higher in the East field, averaging 18.3 g-P2O5 kg -1 , compared to 17.9 g-P 2 O 5 kg -1 in the West. In the West field, the PAC variation was less at 7.74%, whereas in the East field, it reached 9.73%. When comparing with TC and Al o concentrations, it was found that PAC varied more when the variation of those properties was also high, reflecting the variability observed in the East field and suggesting a complex relationship between PAC, SOM, and clay minerals. In the West field, the PAC could be mainly controlled by amorphous clay minerals such as allophane, imogolite, and other hydrous iron and aluminum oxides (Mizota, 1977). Conversely, the high PAC variability in the East field could be attributed to the high SOM variability (Hashimoto et al., 2012). Spatial distribution of soil available P and the inherent properties In this study, all soil properties exhibited moderate spatial dependence, except for TC in the East field, which showed strong spatial dependence (Table 2). Cambardella et al. (1994) found that spatial dependency varies with the nugget% and that values below 25% are considered strong. A value between 25% and 75% indicates a moderate spatial dependency, while a value above 75% suggests a weak spatial dependency. Although the available P was higher and more variable in the East field (Table 1; Fig. 2a), it showed moderate spatial dependence in both fields (Table 2). The distribution maps were distinct, with a range of 67.3 to 97 m in the West and East fields, respectively. In the West field, the available P map contrasted perfectly with the Al o distribution, suggesting that mapping clay minerals could be sufficient to predict the distribution of available P in this field (Fig. 2a, b). In the East field, however, the available P and Al o maps did not match, eventually because factors other than clay minerals control the available P. Furthermore, the spherical model in the East field can describe the spatial correlation of all the selected soil properties (Table 2), indicating a gradual decrease in spatial correlation with increasing distance until the values are no longer correlated (Cambardela et al., 1994). In the West field, the best-fitting model was dependent on soil properties. The spherical model best fits TC concentration and PAC, whereas for Al o , the exponential model, which indicates a rapid decrease in spatial correlation, is the best fit. The available P in the soil was best fit by a linear model, indicating no or minimal zoning (Table 2; Fig. 2). This difference between the two fields could be attributed to different factors controlling P availability in each field. Additionally, all the soil properties formed clear zones of high and low values in the East field unlike in the West field where only Al o showed clear zones of high and low values (Fig. 2b). As no clear zoning was observed for TC concentration in the West field, we expected that the Al o also could have a similar pattern, but the result was different. The Al o map of the West and East fields showed distinct zones although the size was smaller than the East field, with range of 96.5 and 85.5 m, respectively (Table 2; Fig. 2b). The size of the zones of low and high values in both fields was soil property dependent, however, it was observed that in the West field, the zones were smaller than in the East. Factors affecting available P variability Surprisingly, there was no correlation between PAC and available P in the East field, whereas available P decreased as PAC increased in the West field (r = -0.73, p < 0.001; Fig. 3a), suggesting that different mechanisms control P variability across the study fields. Since the study fields were classified as Andisols (Francisco et al., 2024; Francisco et al., 2025), which are known for their high P-fixation capacity and consequently limited P availability, it was initially hypothesized that the PAC would be the primary predictor of available P. However, our results contradicted this assumption. To further investigate the factors influencing P variability in the East field, a decision tree analysis was conducted using JMP Pro 17.0.0. Unexpectedly it was found that the TC concentration mainly controls the available P in the East field, however, with two different patterns (R 2 =0.31; Fig. 3b). The samples were divided into two groups, where about 77% fitted into TC of less than 80 g kg -1 and the remaining samples had higher TC concentration (Fig. 3a). When the TC concentration is less than 80 g kg -1 , the Al o controlled P variability. This could indicate different factors influencing the available P in the same field. Therefore, Al o and PAC were correlated with the available P in the two TC groups (Fig. 4). The results showed that in low TC, the Al o influenced more negatively the available P (r=-0.54, p< 0.001; Fig. 4a) than PAC (r=-0.28, p <0.001; Fig. 4c). In high TC, the PAC negatively affected the available P more than the Al o (r=-0.38 and r=-0.12, respectively; Fig. 4b, d). The Al o represents the Al from clay amorphous minerals complexed with humic substances (Blakemore et al., 1987). When the TC concentration is low, the Al o mainly represents the Al from the amorphous clay minerals, such as allophane. Therefore, the observed adverse effect of Al o on P availability in low TC could reflect the eventual fixation of P into amorphous clay minerals. On the other hand, PAC is known to be affected by factors such as amorphous clay minerals, the Al-humus complex, and exchangeable Al, as well as base cations like Ca 2+ and Mg 2+ (Hashimoto et al., 2012; Editorial Committee for Methods of Soil Environmental Analysis, 1997). As previously discussed, the soil available P varied more in the East field, where the TC variation was also high. When the TC concentration is high, the PAC may reflect P adsorbed onto organically bound Al. The available P could be enhanced by humus-Al(III)-PO 4 complexes (Hashimoto et al., 2012). Additionally, when the TC is high, the CEC is expected to be high. Consequently, there is a high exchangeable Ca 2+ , which is likely to precipitate with phosphate, thereby limiting P availability (Okruszo et al., 1962). Therefore, in the Andisols field with high SOM content, such as the East field, the estimation of available P variability should account for SOM content variability and the distribution of amorphous clay minerals. Conversely, in the West field and part of the East field, where the TC was below 80 g kg-1, the available P was controlled by Al from non- and poorly crystalline minerals, such as allophane. Although the West field had a low and homogeneous TC concentration, the Al o was relatively higher (mean = 38.2 g kg -1 ; Table 1). Francisco et al. (2025) found variation in the allophane content of topsoils across profiles assessed in the West field, primarily due to natural soil-forming processes, including erosion, the accumulation of allophane-rich materials, and their incorporation into the topsoil. This indicates that even in allophanic Andisols, the variability of available P can be expected due to variations in amorphous clay minerals in the surface soil. Interaction between total C concentration, oxalate-extracted Al, and phosphate absorption coefficient The PAC, which indicates the soil's ability to fix P into the non- and poorly crystalline minerals, crystalline metals, humus complex, and stable minerals (Hiradate & Uchida, 2004), was distinctly influenced by the TC concentration and Al o , indicating a complex relationship among these properties (Fig. 5). The results suggest that in the East field, soil organic matter might mostly control the PAC variability, as indicated by the strong positive correlation with TC concentration (r = 0.66, p < 0.001) and no correlation with Al o (Fig. 5a, b). In the West field, however, the results contradicted, and the PAC was thought to be primarily affected by the clay minerals; hence, there was a strong positive correlation with Al o (r = 0.72, p < 0.001), with no correlation with TC concentration (Fig. 5c, d). Our findings align with those of Hashimoto et al. (2012), who indicated that phosphate retention capacity was strongly associated with the Al-humus complex in non-allophonic Andisols. These results suggest that P retention in the East field may be linked to organically bound Al. In contrast, the West field appears primarily influenced by Al from non-crystalline to paracrystalline clay minerals. This also suggests that the distribution of available P in the surface soil of the two fields may be influenced by different factors. Management zones for available P in the studied fields The results indicated that P availability varied significantly across the studied fields, with PAC contributing more to soil available P variability in the West field and TC in the East field. To further investigate this variability, a cluster analysis was conducted by combining datasets from both fields to identify potential P management zones. This analysis revealed three distinct clusters (Fig. 6a; Table 3). Cluster 3 was notably different from the others, indicating soils with significantly varying levels of P availability, while Clusters 1 and 2 displayed some overlap, suggesting intermediate or transitional P availability conditions. These findings confirm that the spatial distribution of available P is influenced by contrasting soil properties, particularly PAC in the West field and TC in the East field. The overlap between Clusters 1 and 2 suggests that PAC affects some zones strongly, which is commonly observed in Andisols, where high P fixation capacity can reduce plant-available P (Shoji et al., 1993; Parfitt, 1989). In contrast, the separation of Cluster 3 indicates a zone where organic matter dynamics have a more significant impact on P availability. This aligns with research showing that soil organic matter (SOM) can enhance P retention and release through mineral-organic interactions, leading to more variable availability patterns (Tiessen et al., 1984; Hashimoto et al., 2012). The coexistence of sharply separated and overlapping clusters suggests that a combination of mechanisms, including clay minerals and soil organic matter, controls P availability in these fields. This dual control is particularly evident in the East field, where higher levels of TC and its variability contribute to a broader range of P availability. From a management perspective, the clustering results emphasize the need for site-specific fertilization strategies. A decision tree analysis identified three soil-available P zones of low, medium, and high (R² = 0.59; data not shown). Zone 1 was distinctly different, with a mean soil available P of only 43.7 mg-P kg⁻¹. At the same time, Zones 2 and 3 exhibited higher mean P levels, averaging 72.4 mg-P kg ¹ and 89.5 mg-P kg ¹, respectively. Spatial analysis revealed that most of the West field fell within Zone 1, representing the lowest available soil P levels, consistent with the distribution of Al o (Fig. 1, 6b). Meanwhile, all three zones were present in the East field (Fig. 6b). These findings suggest that areas dominated by PAC (Zone 1) may require more frequent or higher applications of P to overcome fixation, while transitional zones influenced by TC (Zones 2 and 3) may need less P fertilizer application. Conclusion The results revealed distinct spatial variability in soil available P between the two fields, which is intrinsically related to their pedogenesis. The East field exhibited higher soil available P concentrations, which did not correspond directly to the total amount of P fertilizer applied over the past decade, indicating that the observed variation is inherent to the field. When examining the factors controlling the spatial distribution of soil-available P, it was found that PAC could clearly explain the variability in available P in the West field. In contrast, in the East field, PAC was not directly related to available P; instead, TC concentration was the best predictor. In this field, when TC concentration was low ( 80 g kg − 1 ), it is predominantly controlled by the complex formed between organically bound aluminum and adsorbed P. These findings highlight the role of humic substances in regulating both the adsorption and desorption of P in Andisols. Both fields had clear zones of low, medium, and high soil-available P; however, the spatial zoning was more pronounced in the East field, which also exhibited greater variation in all soil properties, particularly in TC and Al o concentrations. These variations were associated with differences in TC concentrations. Additionally, it was found that in the East field, the TC concentration was the most influential factor affecting inherent soil properties such as Al o and PAC. In the West field, which had a lower TC concentration and showed less variation, there was no relationship with either Al o or PAC. Despite the higher Al o concentration in the West field, the PAC was lower than in the East field. This suggests that in the East field, where TC concentration was higher, P fixation is influenced by amorphous clay minerals and the humus-Al complex. Conversely, PAC was strongly correlated with allophane in the West field, confirming the hypothesis that different factors control PAC in the two fields. Overall, our results indicate that the spatial heterogeneity of soil available P occurs independently of historical fertilizer application but is mainly controlled by SOM and clay minerals. Therefore, to implement site-specific P fertilization in Andisols, zones should be defined based on SOM variation. If the threshold value of 80 g kg − 1 of TC provides the same relationship in other Andisol-dominated or high P-fixing soils, it can serve as an essential indicator for predicting the P variability and optimizing P fertilizer management; however, validating this hypothesis requires multiple investigations encompassing different soil types, management systems, and environmental conditions. This study is the first to elucidate the mechanisms controlling field-scale variation in soil-available P, paving the way for precision and spatially optimized P fertilizer management. Declarations Author Contribution EAF, RK, and MT designed and conceived this study. EAF and RK performed the measurements, RK, HS, DA, KO, and MT were involved in planning and supervised the work. EAF, RK, and MT processed the experimental data, performed the analysis, drafted the manuscript and designed the figure. MM and MC supported statistical analyses. All authors read and approved the final manuscript. Acknowledgement This work was supported by the Station for Management of Common Equipment, Obihiro University of Agriculture and Veterinary Medicine. This work was partially funded by Loginet Japan CO., Ltd. and Obihiro University of Agriculture and Veterinary Medicine. References Allo, M., Todoroff, P., Jameux, M., Stern, M., Paulin, L. & Albrecht, A. (2020). 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The spatiotemporal variability of soil available phosphorus and potassium in Karst region: The crucial role of socio-geographical factors. Land , 13(6), 882. https://doi.org/10.3390/land13060882 www.fao.org/faostat/en/#data/RFN, accessed August 21, 2025. Tables Table 1. Descriptive statistics of the selected soil properties. Total C concentration Oxalate extracted Al Phosphate absorption coefficient Soil available P † g kg -1 g-P 2 O 5 kg -1 mg-P kg -1 East field (n=525) Mean 61.5 33.4 18.3 70.1 Standard deviation 21.6 5.82 1.78 25.8 Minimum 32.5 20.0 13.1 24.0 Maximum 124 48.8 25.2 167 Coefficient of variation (%) 35.2 17.4 9.73 36.8 West field (n=390) Mean 46.6 38.2 17.9 45.7 Standard deviation 5.64 5.39 1.38 14.3 Minimum 33.0 18.4 13.7 20.1 Maximum 69.4 61.1 22.0 105 Coefficient of variation (%) 12.1 14.1 7.74 31.2 †Soil available P by the Truog method. The values of total C concentration, oxalate extracted Al, and phosphate absorption coefficient in the East field were extracted from Francisco et al. (2024). Table 2. Parameters of the semivariograms of the selected soil properties. Model Nugget (C 0 ) Sill (C 0 +C) Nugget % † Range (m) R 2 Spatial Class‡ East field Total C concentration Spherical 0.00 710 0.00 226 0.97 Strong Oxalate extracted Al Spherical 8.46 29.0 29.2 85.5 0.99 Moderate Phosphate absorption coefficient Spherical 85.3 248 34.4 139 0.99 Moderate Soil available P Spherical 10.3 29.2 35.2 97.0 0.98 Moderate West field Total C concentration Spherical 14.6 35.3 41.4 168 0.97 Moderate Oxalate extracted Al Exponential 15.3 30.8 49.7 96.5 0.84 Moderate Phosphate absorption coefficient Spherical 109 196 55.8 102 0.88 Moderate Soil available P Linear 6.84 10.9 62.8 67.3 0.91 Moderate †Nugget % = C 0 / (C 0 +C) x 100. ‡Strong, Nugget % ≤ 25; Moderate, 25<Nugget % ≤ 75. The semivariogram parameters of the East field were extracted from Francisco et al. (2024). Table 3 . Cluster means for soil available P by the Truog method, total C concentration, and phosphorus absorption coefficient of combined data sets from East and West fields. Cluster Total C concentration Phosphorus absorption coefficient Soil available P g kg -1 - mg-P kg -1 1 47.5 18.5 43.7 2 48.8 16.4 72.4 3 91.2 20.0 89.5 Additional Declarations No competing interests reported. Supplementary Files PAAVPOnlineResources.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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4","display":"","copyAsset":false,"role":"figure","size":242574,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7984946/v1/c3017aee68e5732ba89b52ae.png"},{"id":96240483,"identity":"ef25aa65-9347-4d4b-90eb-7694e6fc95f4","added_by":"auto","created_at":"2025-11-19 07:08:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":234792,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7984946/v1/cc51bd54b201a064710e94e3.png"},{"id":95859715,"identity":"a6f922c4-b500-4442-9045-bbdadc4d8770","added_by":"auto","created_at":"2025-11-13 17:37:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":465580,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7984946/v1/20fb187800751da6eb0d65b8.png"},{"id":101752827,"identity":"4d7e1fa7-9d32-4ded-bfe1-11709a1a4066","added_by":"auto","created_at":"2026-02-03 10:33:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3604105,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7984946/v1/82120af2-dd27-4ec1-a345-442b8d6d17d7.pdf"},{"id":95859710,"identity":"511511d5-3980-4f95-a36a-24010e11f7a4","added_by":"auto","created_at":"2025-11-13 17:37:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17504,"visible":true,"origin":"","legend":"","description":"","filename":"PAAVPOnlineResources.docx","url":"https://assets-eu.researchsquare.com/files/rs-7984946/v1/432ce2ffa13818ca65840e32.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling spatial heterogeneity of soil available phosphorus for site-specific fertilization in Andisols of Hokkaido, Japan","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhosphorus (P) is an essential element for plant growth, and its limitation negatively affects crop yield and quality (Khan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zuang et al., 2020; Heuer et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Most agricultural soils are naturally deficient in P, and its availability is improved through the application of P fertilizers (Pang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These fertilizers are mainly produced from rock phosphate, a non-renewable resource whose reserves are rapidly declining and may be depleted within the next 300 years (Gilbert, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This fact poses a threat to meeting global food demand, which is expected to continue increasing in the coming decades (van Dijk et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, global demand for P fertilizers has been increasing due to population growth, rising food demand, and the intensification of agricultural production systems. In 2024, global consumption of P fertilizers (P2O5) was estimated at 47.5\u0026nbsp;million metric tons and is projected to reach 51.8\u0026nbsp;million metric tons by 2028, with significant growth in Asia and South America (International Fertilizer Association, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, it is estimated that only 20\u0026ndash;25% of P applied as fertilizer is consumed as food. At the same time, the remainder is lost through fixation in the soil, runoff, waste streams, and inefficiencies along the food chain, indicating significant P losses in the food system (van Dijk et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, the application of P fertilizers has been linked to the accumulation of heavy metals, such as cadmium (Cd), in agricultural soils, posing a significant risk to soil health, crop safety, and ultimately human and animal health (Roberts, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Nziguheba \u0026amp; Smolders, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Therefore, an efficient use of P fertilizers is crucial for sustainable food production.\u003c/p\u003e\u003cp\u003eP fertilizer management has become a globally critical issue due to the dual challenge of increasing crop productivity while reducing the environmental impacts. This is particularly important in soils with high P-fixation capacity, such as Andisols, Oxisols, and Ultisols (Parfitt, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Among these soils, Andisols have the highest P fixation capacity. Although Andisols cover only 1% of the global land surface, they are very important soils for agricultural production, supporting intensive food production systems in regions such as Japan, New Zealand, and parts of East Africa (Shoji et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Nanzyo et al., 1993). Although P fertilizer consumption in Japan has been decreasing in recent years, the application rate remains remarkably high. From 2014 to 2023, the average P fertilizer application rate in Japan was 73 kg-P₂O₅ ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, compared to the global average of around 27 kg-P₂O₅ ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (FAOSTAT, 2025). This disproportionately high rate is primarily due to the dominance of Andisols, which cover approximately 28% of Japan's cultivated land (Kanda et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and exhibit a strong P fixation capacity. Maintaining adequate P availability for crops thus requires substantially higher fertilizer inputs than in most other soil types.\u003c/p\u003e\u003cp\u003eHokkaido, the most important agricultural region in Japan, accounts for 47% of the country's upland farming (Ministry of Agriculture, Forestry and Fisheries, Japan, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), where Andisols dominate, occupying approximately 43% of the surface land and 41% of the agricultural soils; hence, representing a significant soil group for the country's crop production system (Kanda et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These soils, derived from volcanic parent materials, exhibit a high P fixation ability due to the presence of active aluminum (Al) in non- and para-crystalline clay minerals, as well as Al-humus complexes, making P a major limiting factor (Shoji et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Hashimoto et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Consequently, farmers apply high amounts of phosphate fertilizers to optimize crop yields (Gondwe et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Although in Hokkaido, there is a fertilizer recommendation guideline that indicates the amount of phosphate fertilizer to be applied depending on the crop type, cultivar, cultivation method, soil type, and the available phosphate status in the soil (Department of Agriculture, Hokkaido Government, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), a long-term application of high levels of P fertilizers has led to excessive accumulation of P in cultivated fields (Tani et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral management practices have been developed to optimize P utilization in agriculture, including soil testing to assess available P levels and applying additional amounts to meet crop demand (Wang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, optimizing P management in agricultural soils is particularly more challenging in soils with high P fixation capacity or pronounced spatial variation of available P. In high P-fixation soils, a significant portion of the applied P fertilizers becomes unavailable to plants, reducing fertilizer efficiency and thereby requiring higher P inputs (Poblete-Grant et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). At the same time, spatial heterogeneity within fields creates zones of both P deficiency and excess. These variations result from complex interactions between pedogenic processes and agronomic practices, such as P fertilizer application; therefore, understanding these interactions is crucial for predicting P availability. Although uniform application of P fertilizers is a common practice, it often fails to account for within-field variability, resulting in areas of over- and underapplication of P within the same field (Zhang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A variable application rate is recommended to match crop needs and the specific soil nutrient status (Mulla et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Ndiaye \u0026amp; Yost, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). There are a few studies specifically addressing the benefits of variable-rate P fertilizer application. However, applying variable P fertilizer resulted in a significant reduction in P fertilizer applied while maintaining sugar cane yield in Oxisols (Sanches et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, this is a clear indication that mapping soil available P is crucial for implementing a site-specific management approach.\u003c/p\u003e\u003cp\u003eHokkaido's farming system is on a large scale, averaging 45.7 ha per farm, compared to 2.2 ha nationwide (Ministry of Agriculture, Forestry and Fisheries, Japan, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The size of farms is expected to increase in the coming decades due to a decline in the number of farm holders (Iida, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While this structural shift may improve mechanization efficiency, it is also likely to enhance within-field variability of soil properties, given the greater spatial heterogeneity in large-scale fields and the challenges of uniformly managing inputs across them. Previous studies in this region have reported significant within-field variations in inherent soil properties, such as allophane and phosphate absorption coefficient (PAC), which are closely related to P availability (Francisco et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Niwa et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). PAC is commonly used in Japan as an index of soil's P-fixation capacity; the higher the PAC value, the greater the P-fixation capacity and, consequently, lower P availability (The Fifth Committee for Soil Classification and Nomenclature, 2017). Despite existing reports on the spatial variability of these inherent properties and the accumulation of P in Andisols of Hokkaido resulting from intensive P fertilizer use, the spatial heterogeneity of available P within fields and the soil properties controlling this variability remain poorly understood. This gap limits the development of effective site-specific P management strategies that account for within-field heterogeneity. Therefore, this study aims to unveil the extent and patterns of spatial heterogeneity in soil-available P and identify the key soil factors driving this variability, thereby providing a scientific basis for precision P management in Andisols-dominated agroecosystems.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy site\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted at the Field Center of Animal Science and Agriculture, Obihiro University of Agriculture and Veterinary Medicine, in Obihiro City, Tokachi district, Hokkaido prefecture, Japan (42\u0026deg;51\u0026apos;43.4\u0026quot;N, 143\u0026deg;09\u0026apos;57.5\u0026quot;E; Fig. 1a). The surface topography of the region is a complex of alluvial fans and floodplains, with terraces found along the abundant river courses. The land surface is covered by volcanic parent materials deposited during the late Pleistocene and Holocene; hence, Andisols are the dominant soil type (Kikuchi, 2008). Two fields, 300 m apart in the east-west direction (Fig. 1b), were selected; therefore, they were accordingly designated as the East and West fields in this study. The East field was 550 m in the N-S direction and 120 m in the E-W direction, while the West field was 550 m N-S and 80 m E-W, making 6.6 and 4.4 ha, respectively (Fig. 1b). Ancient dunes are present in both fields, creating an undulating topography with a 3.2 m and 2.5 m elevation difference in the East and West fields, respectively (Francisco et al., 2025). The ancient dunes are common in the study area, formed from Eniwa-a (En-a) volcanic materials deposited about 19~21 cal. ka BP, . and the Shikotsu pumice fall-1 (Spfa-1) that erupted and fell about 46 cal. ka BP, which were subsequently sedimented by wind (Uesawa et. al, 2016; Machida \u0026amp; Arai, 1992; Kimura et al., 1970). Dent corn for feed (\u003cem\u003eZea mays\u0026nbsp;\u003c/em\u003eL.) and orchard grass (\u003cem\u003eDactylis glomerata\u003c/em\u003e) are intensively grown in both fields (Online Resource 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurface soil sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 920 surface soil samples were collected from the two fields. In the East field, 530 surface soil samples were collected in two periods (265 samples each) in May 2020 and April 2021. In the West field, 390 samples were collected in April 2022. The soil samples were collected by an automated soil sampler (Wintex 1000, Wintex Agro, Thisted, Denmark) installed on an all-terrain vehicle. In both fields, an equilateral triangular grid design with 12 m spacing was used for sampling, and surface soils were collected to a depth of 25 cm (Francisco et al., 2024). A high-precision Global Navigation Satellite System receiver (DG-PRO1RWS; Biz Station Co., Ltd., Nagano, Japan), equipped with a Virtual Reference Station System, was used to precisely identify the sampling point, at a horizontal accuracy of 1 cm. At each sampling point, 10 subsamples were collected every 20-cm linear interval in the N-S direction, starting from a distance of 80 cm from the sampling point, and afterward composited. All samples were air-dried, passed through a 2-mm sieve, and had visible plant residues removed prior to further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoil analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal carbon (TC) concentration was measured by dry combustion (vario EL cube, Elementar Analysensysteme GmbH, Langenselbold, Germany). Acid-oxalate extractable aluminum (Al\u003csub\u003eo\u003c/sub\u003e) and silicon (Si\u003csub\u003eo\u003c/sub\u003e) were determined using a 0.2 mol L\u003csup\u003e-1\u003c/sup\u003e acid ammonium oxalate solution at pH 3.0 after shaking for 4 hours in the dark (Blakemore et al., 1987). The Al\u003csub\u003eo\u003c/sub\u003e and Si\u003csub\u003eo\u003c/sub\u003e concentrations were measured by an inductively coupled plasma emission spectrophotometer (ICPE-9820; Shimadzu Corporation, Kyoto, Japan). The phosphate absorption coefficient (PAC), which indicates the soil\u0026apos;s capacity to fix phosphate, was estimated by reacting the soil samples with a solution containing 13.44 mg-P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e mL\u003csup\u003e-1\u003c/sup\u003e at pH 7.0 for 24 hours, and the molybdate yellow method was used to measure the remaining phosphate in the solution through a spectrophotometer (UV mini-1240; Shimadzu Corporation, Kyoto, Japan). The soil available P was estimated by the Truog method, using 0.1 mmol L\u003csup\u003e-1\u003c/sup\u003e sulfuric acid (H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e) and ammonium sulphate ((NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e) at pH 3.0 (Truog, 1930), followed by the determination of the P concentration in the solution calorimetrically using the molybdate blue method with a spectrophotometer (Murphy \u0026amp; Riley, 1962; UV mini-1240; Shimadzu Corporation, Kyoto, Japan).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical and spatial structure analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe classical statistical analysis for the selected soil properties was conducted using JMP Pro 17.0.0 (SAS Institute Inc., Cary, NC, USA). The coefficient of variation (CV) was calculated as a percentage to describe the variability of the selected soil properties. The variability was classified as low (CV\u0026lt;15%), moderate (15%\u0026gt;CV\u0026lt;35%), and high (CV\u0026gt;35%; Wilding, 1985). The \u003cem\u003egstat\u003c/em\u003e package (Pebesma, 2004) in the R statistical computing environment (Version 1.4.1106, RStudio, PBC) was used for all spatial analysis. Semivariance was calculated according to the equation proposed by Goovaerts (1999):\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\" width=\"517\" height=\"58\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere (\u0026gamma;(h)) is half of the variance between two locations that are separated by a distance h, called lag, and m(h) is the number of pairs at the distance h. At the maximum value, the semivariance is called the sill. The value of semivariance at lag zero is called the nugget and reflects measurement error or spatial variation on a scale smaller than the sampling grid size. The percent nugget was calculated to show the degree of spatial dependence, as equation 2 (Goovaerts, 1999):\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"522\" height=\"52\"\u003e\u003c/p\u003e\n\u003cp\u003eThe percent nugget (nugget%) ranges from 0 to 100, and is used to classify the spatial dependence. A nugget% less than 25 indicates a strong spatial dependence, between 25 and 75 a moderate spatial dependence, and more than 75 reflects a weak spatial dependence (Cambardella et al., 1994). Variogram models were fitted, and the best-fit model was selected based on the highest R-squared value, which was then used to describe the relationship between semivariance and lag. The lag at which the sill is reached is called the range, which indicates the spatial dependence. Sample distances further than the range are spatially independent. Ordinary kriging was used to map each soil property, and ArcGIS Pro 3.2.0 (Esri Inc., Berkeley, California) was used to display the maps.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo delineate P management zones, an unsupervised learning approach was applied. Specifically, k-means clustering was performed on the combined datasets from the East and West fields using JMP Pro 17.0.0 (SAS Institute Inc., Cary, NC, USA). To characterize the clusters, factor analysis was conducted, followed by a decision tree analysis to extract threshold rules defining each cluster (Gavioli et al, 2019). Finally, kriging interpolation was performed in ArcGIS 3.2.0 (Esri Inc., Redlands, CA, USA) to refine the spatial boundaries of the management zones.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003e\u003cstrong\u003eSoil physicochemical properties of the study fields\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results indicate that the within-field variability in soil physicochemical properties differed between the East and West fields, despite their proximity of only 300 m (Table 1). Available P in the soil varied, with higher values in the East field (70.1 mg P kg\u003csup\u003e-1\u003c/sup\u003e) and lower values in the West field (45.0 mg P kg\u003csup\u003e-1\u003c/sup\u003e; Table 1). This difference is inconsistent with the total amount of P fertilizer applied in the two fields over the last 10 years (Online Resource 1), suggesting that P fertilizer management alone cannot explain the differences in available P between the fields. This finding contradicts other studies that reported P variability mainly related to non-uniform management (Mondal et al., 2020). Although soil available P varied significantly in both fields, the variation was slightly greater in the East field, indicating that different factors could be controlling P availability in the two fields (Table 1). Furthermore, the West field generally had less available P than the East field, which may be related to the higher Al\u003csub\u003eo\u003c/sub\u003e concentration observed in the West field. Poorly crystalline minerals, such as allophane and imogolite, have stronger binding sites that adsorb phosphate ions more strongly than the Al-humus complex, thereby reducing P availability (Parfitt, 1989; Shoji et al., 1993). In the East field, however, the high SOM might have occupied adsorption sites and formed soluble P complexes, thereby increasing availability (Huang \u0026amp; Shenker, 2004).\u003c/p\u003e\n\u003cp\u003eThe total carbon (TC) concentration was significantly lower in the West field (46.6 g kg\u003csup\u003e-1\u003c/sup\u003e) than in the East field (61.5 g kg\u003csup\u003e-1\u003c/sup\u003e; Table 1). Moreover, the variation in TC concentration was lower in the West field (CV = 12.1%) than in the East field (CV = 35.2%). The degree of variation observed in the East field was consistent with the findings of Kinoshita et al. (2016), who reported a 33% variation in soil organic carbon (SOC) in an Andisols plot under coffee cultivation. Although their study was conducted on a larger scale (100 ha) in a mountainous landscape, the comparable level of variability suggests that high heterogeneity in TC concentration is primarily controlled by intrinsic soil-forming processes, rather than external factors such as cropping systems or field size. Similarly, Chesworth (2008) found variations in SOC in Andisols, reaching 200 g kg\u003csup\u003e-1\u003c/sup\u003e under humid conditions, indicating that this variability is an inherent feature of Andisols across different spatial contexts. Francisco et al. (2024) found variations in organic matter (OM) accumulation in profiles surveyed within the same field, whereas OM accumulated more in soil profiles with poor drainage. Conversely, Francisco et al. (2025) found that well-drained profiles contained less OM. This indicates that in the study area, the TC is controlled mainly by water; therefore, a significant within-field variation of TC concentration in the surface soil could be expected when the field has areas of both good and poor drainage, which was the case in the East field (Francisco et al., 2024).\u003c/p\u003e\n\u003cp\u003eThe oxalate-extractable aluminum (Al\u003csub\u003eo\u003c/sub\u003e) was higher in the West field (mean = 38.2 g kg\u003csup\u003e-1\u003c/sup\u003e; Table 1). However, it varied more in the East field (CV = 17.4%) than in the West field (14.1%), following the same trend as the variation in TC concentration between the two fields. Allo et al. (2020) also reported an Al\u003csub\u003eo\u003c/sub\u003e variation of up to 33.4% across 39 Andisol samples, which was greater than our observed variation but still closely correlated with SOC variation. Thus, it is reasonable to conclude that within-field variations of Al\u003csub\u003eo\u003c/sub\u003e in our study area are less pronounced when SOM varies less, as observed in the West field. The Al\u003csub\u003eo\u003c/sub\u003e value represents the total amount of Al present in amorphous clay minerals in Andisols, such as allophane and imogolite, as well as Al complexed with humic substances (Blakemore et al., 1987). Therefore, our results also suggest that the concentration of amorphous clay minerals may be higher in the West field. High allophane content is typically found in well-drained Andisols with low organic matter and favorable pH conditions (Shoji et al., 1993), which is consistent with the conditions of the West field (Francisco et al., 2025). However, the formation of allophane could be inhibited in soils rich in humic substances (Inoue \u0026amp; Huang, 1990). This indicates that significant within-field variability in Al\u003csub\u003eo\u003c/sub\u003e concentration may occur in fields with greater SOM variability, consistent with the higher variability observed in the East field (CV = 17.4%; Table 1). The variation in Al\u003csub\u003eo\u003c/sub\u003e concentration in this study was also less than that reported by Kinoshita et al. (2016), who observed variations as high as 39.4%. They found a significantly higher SOM content, with a similar variation to that observed in this study (CV = 33.2%); however, this affected the distribution of Al\u003csub\u003eo\u003c/sub\u003e. Few studies have quantified the within-field variability of Al\u003csub\u003eo\u003c/sub\u003e, making these observations particularly valuable for understanding the spatial heterogeneity of Andisols and their implications for soil available P management.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe PAC, which indicates the soil's ability to fix P, was generally greater than 15.0 g-P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e kg\u003csup\u003e-1\u003c/sup\u003e in both fields, reflecting the typical characteristics of Andisols (The Fifth Committee for Soil Classification and Nomenclature, 2017). Although both fields had notably high PAC, it was higher in the East field, averaging 18.3 g-P2O5 kg\u003csup\u003e-1\u003c/sup\u003e, compared to 17.9 g-P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e kg\u003csup\u003e-1\u003c/sup\u003e in the West. In the West field, the PAC variation was less at 7.74%, whereas in the East field, it reached 9.73%. When comparing with TC and Al\u003csub\u003eo\u0026nbsp;\u003c/sub\u003econcentrations, it was found that PAC varied more when the variation of those properties was also high, reflecting the variability observed in the East field and suggesting a complex relationship between PAC, SOM, and clay minerals. In the West field, the PAC could be mainly controlled by amorphous clay minerals such as allophane, imogolite, and other hydrous iron and aluminum oxides (Mizota, 1977). Conversely, the high PAC variability in the East field could be attributed to the high SOM variability (Hashimoto et al., 2012).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial distribution of soil available P and the inherent properties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, all soil properties exhibited moderate spatial dependence, except for TC in the East field, which showed strong spatial dependence (Table 2). Cambardella et al. (1994) found that spatial dependency varies with the nugget% and that values below 25% are considered strong. A value between 25% and 75% indicates a moderate spatial dependency, while a value above 75% suggests a weak spatial dependency. Although the available P was higher and more variable in the East field (Table 1; Fig. 2a), it showed moderate spatial dependence in both fields (Table 2). The distribution maps were distinct, with a range of 67.3 to 97 m in the West and East fields, respectively. In the West field, the available P map contrasted perfectly with the Al\u003csub\u003eo\u003c/sub\u003e distribution, suggesting that mapping clay minerals could be sufficient to predict the distribution of available P in this field (Fig. 2a, b). In the East field, however, the available P and Al\u003csub\u003eo\u003c/sub\u003e maps did not match, eventually because factors other than clay minerals control the available P. Furthermore, the spherical model in the East field can describe the spatial correlation of all the selected soil properties (Table 2), indicating a gradual decrease in spatial correlation with increasing distance until the values are no longer correlated (Cambardela et al., 1994). In the West field, the best-fitting model was dependent on soil properties. The spherical model best fits TC concentration and PAC, whereas for Al\u003csub\u003eo\u003c/sub\u003e, the exponential model, which indicates a rapid decrease in spatial correlation, is the best fit. The available P in the soil was best fit by a linear model, indicating no or minimal zoning (Table 2; Fig. 2). This difference between the two fields could be attributed to different factors controlling P availability in each field. Additionally, all the soil properties formed clear zones of high and low values in the East field unlike in the West field where only Al\u003csub\u003eo\u003c/sub\u003e showed clear zones of high and low values (Fig. 2b). As no clear zoning was observed for TC concentration in the West field, we expected that the Al\u003csub\u003eo\u003c/sub\u003e also could have a similar pattern, but the result was different. The Al\u003csub\u003eo\u003c/sub\u003e map of the West and East fields showed distinct zones although the size was smaller than the East field, with range of 96.5 and 85.5 m, respectively (Table 2; Fig. 2b). The size of the zones of low and high values in both fields was soil property dependent, however, it was observed that in the West field, the zones were smaller than in the East.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors affecting available P variability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurprisingly, there was no correlation between PAC and available P in the East field, whereas available P decreased as PAC increased in the West field (r = -0.73, p \u0026lt; 0.001; Fig. 3a), suggesting that different mechanisms control P variability across the study fields. Since the study fields were classified as Andisols (Francisco et al., 2024; Francisco et al., 2025), which are known for their high P-fixation capacity and consequently limited P availability, it was initially hypothesized that the PAC would be the primary predictor of available P. However, our results contradicted this assumption. To further investigate the factors influencing P variability in the East field, a decision tree analysis was conducted using JMP Pro 17.0.0. Unexpectedly it was found that the TC concentration mainly controls the available P in the East field, however, with two different patterns (R\u003csup\u003e2\u003c/sup\u003e=0.31; Fig. 3b). The samples were divided into two groups, where about 77% fitted into TC of less than 80 g kg\u003csup\u003e-1\u003c/sup\u003e and the remaining samples had higher TC concentration (Fig. 3a). When the TC concentration is less than 80 g kg\u003csup\u003e-1\u003c/sup\u003e, the Al\u003csub\u003eo\u003c/sub\u003e controlled P variability. This could indicate different factors influencing the available P in the same field. Therefore, Al\u003csub\u003eo\u003c/sub\u003e and PAC were correlated with the available P in the two TC groups (Fig. 4). The results showed that in low TC, the Al\u003csub\u003eo\u003c/sub\u003e influenced more negatively the available P (r=-0.54, \u003cem\u003ep\u0026lt;\u003c/em\u003e0.001; Fig. 4a) than PAC (r=-0.28, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001; Fig. 4c). In high TC, the PAC negatively affected the available P more than the Al\u003csub\u003eo\u003c/sub\u003e (r=-0.38 and r=-0.12, respectively; Fig. 4b, d). The Al\u003csub\u003eo\u003c/sub\u003e represents the Al from clay amorphous minerals complexed with humic substances (Blakemore et al., 1987). When the TC concentration is low, the Al\u003csub\u003eo\u003c/sub\u003e mainly represents the Al from the amorphous clay minerals, such as allophane. Therefore, the observed adverse effect of Al\u003csub\u003eo\u003c/sub\u003e on P availability in low TC could reflect the eventual fixation of P into amorphous clay minerals. On the other hand, PAC is known to be affected by factors such as amorphous clay minerals, the Al-humus complex, and exchangeable Al, as well as base cations like Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e (Hashimoto et al., 2012; Editorial Committee for Methods of Soil Environmental Analysis, 1997). As previously discussed, the soil available P varied more in the East field, where the TC variation was also high. When the TC concentration is high, the PAC may reflect P adsorbed onto organically bound Al. The available P could be enhanced by humus-Al(III)-PO\u003csub\u003e4\u003c/sub\u003e complexes (Hashimoto et al., 2012). Additionally, when the TC is high, the CEC is expected to be high. Consequently, there is a high exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e, which is likely to precipitate with phosphate, thereby limiting P availability (Okruszo et al., 1962). Therefore, in the Andisols field with high SOM content, such as the East field, the estimation of available P variability should account for SOM content variability and the distribution of amorphous clay minerals. Conversely, in the West field and part of the East field, where the TC was below 80 g kg-1, the available P was controlled by Al from non- and poorly crystalline minerals, such as allophane. Although the West field had a low and homogeneous TC concentration, the Al\u003csub\u003eo\u003c/sub\u003e was relatively higher (mean = 38.2 g kg\u003csup\u003e-1\u003c/sup\u003e; Table 1). Francisco et al. (2025) found variation in the allophane content of topsoils across profiles assessed in the West field, primarily due to natural soil-forming processes, including erosion, the accumulation of allophane-rich materials, and their incorporation into the topsoil. This indicates that even in allophanic Andisols, the variability of available P can be expected due to variations in amorphous clay minerals in the surface soil.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction between total C concentration, oxalate-extracted Al, and phosphate absorption coefficient\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PAC, which indicates the soil's ability to fix P into the non- and poorly crystalline minerals, crystalline metals, humus complex, and stable minerals (Hiradate \u0026amp; Uchida, 2004), was distinctly influenced by the TC concentration and Al\u003csub\u003eo\u003c/sub\u003e, indicating a complex relationship among these properties (Fig. 5). The results suggest that in the East field, soil organic matter might mostly control the PAC variability, as indicated by the strong positive correlation with TC concentration (r = 0.66, p \u0026lt; 0.001) and no correlation with Al\u003csub\u003eo\u0026nbsp;\u003c/sub\u003e(Fig. 5a, b). In the West field, however, the results contradicted, and the PAC was thought to be primarily affected by the clay minerals; hence, there was a strong positive correlation with Al\u003csub\u003eo\u003c/sub\u003e (r = 0.72, p \u0026lt; 0.001), with no correlation with TC concentration (Fig. 5c, d). Our findings align with those of Hashimoto et al. (2012), who indicated that phosphate retention capacity was strongly associated with the Al-humus complex in non-allophonic Andisols. These results suggest that P retention in the East field may be linked to organically bound Al. In contrast, the West field appears primarily influenced by Al from non-crystalline to paracrystalline clay minerals. This also suggests that the distribution of available P in the surface soil of the two fields may be influenced by different factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eManagement zones for available P in the studied fields\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results indicated that P availability varied significantly across the studied fields, with PAC contributing more to soil available P variability in the West field and TC in the East field. To further investigate this variability, a cluster analysis was conducted by combining datasets from both fields to identify potential P management zones. This analysis revealed three distinct clusters (Fig. 6a; Table 3). Cluster 3 was notably different from the others, indicating soils with significantly varying levels of P availability, while Clusters 1 and 2 displayed some overlap, suggesting intermediate or transitional P availability conditions. These findings confirm that the spatial distribution of available P is influenced by contrasting soil properties, particularly PAC in the West field and TC in the East field. The overlap between Clusters 1 and 2 suggests that PAC affects some zones strongly, which is commonly observed in Andisols, where high P fixation capacity can reduce plant-available P (Shoji et al., 1993; Parfitt, 1989). In contrast, the separation of Cluster 3 indicates a zone where organic matter dynamics have a more significant impact on P availability. This aligns with research showing that soil organic matter (SOM) can enhance P retention and release through mineral-organic interactions, leading to more variable availability patterns (Tiessen et al., 1984; Hashimoto et al., 2012). The coexistence of sharply separated and overlapping clusters suggests that a combination of mechanisms, including clay minerals and soil organic matter, controls P availability in these fields. This dual control is particularly evident in the East field, where higher levels of TC and its variability contribute to a broader range of P availability. From a management perspective, the clustering results emphasize the need for site-specific fertilization strategies. A decision tree analysis identified three soil-available P zones of low, medium, and high (R² = 0.59; data not shown). Zone 1 was distinctly different, with a mean soil available P of only 43.7 mg-P kg⁻¹. At the same time, Zones 2 and 3 exhibited higher mean P levels, \u0026nbsp;averaging 72.4 mg-P kg ¹ and 89.5 mg-P kg ¹, respectively. Spatial analysis revealed that most of the West field fell within Zone 1, representing the lowest available soil P levels, consistent with the distribution of Al\u003csub\u003eo\u003c/sub\u003e (Fig. 1, 6b). Meanwhile, all three zones were present in the East field (Fig. 6b). These findings suggest that areas dominated by PAC (Zone 1) may require more frequent or higher applications of P to overcome fixation, while transitional zones influenced by TC (Zones 2 and 3) may need less P fertilizer application.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results revealed distinct spatial variability in soil available P between the two fields, which is intrinsically related to their pedogenesis. The East field exhibited higher soil available P concentrations, which did not correspond directly to the total amount of P fertilizer applied over the past decade, indicating that the observed variation is inherent to the field. When examining the factors controlling the spatial distribution of soil-available P, it was found that PAC could clearly explain the variability in available P in the West field. In contrast, in the East field, PAC was not directly related to available P; instead, TC concentration was the best predictor. In this field, when TC concentration was low (\u0026lt;\u0026thinsp;80 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), the variability in available P could be explained by the distribution of amorphous clay minerals. In contrast, at high TC levels (\u0026gt;\u0026thinsp;80 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), it is predominantly controlled by the complex formed between organically bound aluminum and adsorbed P. These findings highlight the role of humic substances in regulating both the adsorption and desorption of P in Andisols. Both fields had clear zones of low, medium, and high soil-available P; however, the spatial zoning was more pronounced in the East field, which also exhibited greater variation in all soil properties, particularly in TC and Al\u003csub\u003eo\u003c/sub\u003e concentrations. These variations were associated with differences in TC concentrations. Additionally, it was found that in the East field, the TC concentration was the most influential factor affecting inherent soil properties such as Al\u003csub\u003eo\u003c/sub\u003e and PAC. In the West field, which had a lower TC concentration and showed less variation, there was no relationship with either Al\u003csub\u003eo\u003c/sub\u003e or PAC. Despite the higher Al\u003csub\u003eo\u003c/sub\u003e concentration in the West field, the PAC was lower than in the East field. This suggests that in the East field, where TC concentration was higher, P fixation is influenced by amorphous clay minerals and the humus-Al complex. Conversely, PAC was strongly correlated with allophane in the West field, confirming the hypothesis that different factors control PAC in the two fields. Overall, our results indicate that the spatial heterogeneity of soil available P occurs independently of historical fertilizer application but is mainly controlled by SOM and clay minerals. Therefore, to implement site-specific P fertilization in Andisols, zones should be defined based on SOM variation. If the threshold value of 80 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of TC provides the same relationship in other Andisol-dominated or high P-fixing soils, it can serve as an essential indicator for predicting the P variability and optimizing P fertilizer management; however, validating this hypothesis requires multiple investigations encompassing different soil types, management systems, and environmental conditions. This study is the first to elucidate the mechanisms controlling field-scale variation in soil-available P, paving the way for precision and spatially optimized P fertilizer management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eEAF, RK, and MT designed and conceived this study. EAF and RK performed the measurements, RK, HS, DA, KO, and MT were involved in planning and supervised the work. EAF, RK, and MT processed the experimental data, performed the analysis, drafted the manuscript and designed the figure. MM and MC supported statistical analyses. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by the Station for Management of Common Equipment, Obihiro University of Agriculture and Veterinary Medicine. This work was partially funded by Loginet Japan CO., Ltd. and Obihiro University of Agriculture and Veterinary Medicine.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllo, M., Todoroff, P., Jameux, M., Stern, M., Paulin, L. \u0026amp; Albrecht, A. (2020). 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Vertical distribution and accumulation of phosphate in virgin soils and arable soils of Tokachi district, Hokkaido. \u003cem\u003eJapanese Journal of Soil Science and Plant Nutrition\u003c/em\u003e, 81(4), 350-359.\u003c/li\u003e\n\u003cli\u003eThe Fifth Committee for Soil Classification and Nomenclature of Japanese Society of Pedology. (2017). Soil classification system of Japan (English translation). \u003c/li\u003e\n\u003cli\u003eTiessen, H., Stewart, J.W.B., \u0026amp; Cole, C.V. (1984). Pathways of phosphorus transformations in soils of differing pedogenesis. \u003cem\u003eSoil Science Society of America Journal\u003c/em\u003e, 48(4), 853\u0026ndash;858. https://doi.org/10.2136/sssaj1984.03615995004800040031x\u003c/li\u003e\n\u003cli\u003eTruog, E. (1930). The determination of readily available phosphorus of soils. \u003cem\u003eAgronomy J.\u003c/em\u003e, 22, 874\u0026ndash;882.\u003c/li\u003e\n\u003cli\u003eUesawa, S., Nakagawa, M., \u0026amp; Umesu, A. (2016). Explosive Eruptive Activity and Temporal Magmatic Changes at Yotei Volcano During the Last 50,000 Years, Southwest Hokkaido, Japan. \u003cem\u003eJournal of Volcanology and Geothermal Research,\u003c/em\u003e 325, 27\u0026ndash;44. https://doi.org/10.1016/j.jvolgeores. 2016.06.008.\u003c/li\u003e\n\u003cli\u003eValle, S.R., Carrasco, J., Pinochet, D., Soto, P., \u0026amp; Mac Donald, R. (2015). Spatial distribution assessment of extractable Al, (NaF) pH, and phosphate retention as tests to differentiate among volcanic soils. \u003cem\u003eCatena\u003c/em\u003e, 127, 17\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eVan Dijk, K.C., Lesschen, J.P., \u0026amp; Oenema, O. (2016). Phosphorus flows and balances of the European Union Member States. \u003cem\u003eScience Total Environment\u003c/em\u003e, 542, 1078-1093. https://doi.org/10.1016/j.scitotenv.2015.08.048\u003c/li\u003e\n\u003cli\u003eVan Dijk, M., Morley, T., Rau, M.L., \u0026amp; Saghai, Y. (2021). A meta-analysis of projected global food demand and population at risk of hunger for the period 2010-2050. \u003cem\u003eNature Food\u003c/em\u003e, 2, 494-501. https://doi.org/10.1038/s43016-021-00322-9. \u003c/li\u003e\n\u003cli\u003eWang, Y., Cui, Y., Wang, K., He, X., Dong, Y., \u0026amp; Li, S. (2023). The agronomic and environmental assessment of soil phosphorus levels for crop production: a meta-analysis. \u003cem\u003eAgronomy for Sustainable Development\u003c/em\u003e, 43(2), 35. https://dx.doi.org/10.1007/s13593-023-00887-8\u003c/li\u003e\n\u003cli\u003eWilding, L.P. (1985). Spatial variability: its documentation, accommodation, and implication to soil surveys. 166-189 ref. 15.\u003c/li\u003e\n\u003cli\u003eZhang, W., Wang, Q., Wu, Q., Zhang, S., Zhu, P., \u0026amp; Chang, P. (2020). The response of soil Olsen-P to the P budgets of three typical cropland soil types under long-term fertilization. \u003cem\u003ePLoS One\u003c/em\u003e, 15(3), https://doi.org/10.1371/journal.pone.0230178\u003c/li\u003e\n\u003cli\u003eZhang, W., Zhang, Y., Zhang, X., Wu, W., \u0026amp; Liu, H. (2024). The spatiotemporal variability of soil available phosphorus and potassium in Karst region: The crucial role of socio-geographical factors. \u003cem\u003eLand\u003c/em\u003e, 13(6), 882. https://doi.org/10.3390/land13060882\u003c/li\u003e\n\u003cli\u003ewww.fao.org/faostat/en/#data/RFN, accessed August 21, 2025.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Descriptive statistics of the selected soil properties.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"89%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eTotal C concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eOxalate extracted Al\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003ePhosphate absorption coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eSoil available P\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 35px;\"\u003e\n \u003cp\u003eg kg\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eg-P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e kg\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003emg-P kg\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eEast field (n=525)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e61.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e33.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e70.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e25.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e32.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e48.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eCoefficient of variation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e35.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e9.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e36.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eWest field (n=390)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e46.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e38.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e5.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e33.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e69.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e61.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eCoefficient of variation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e7.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e31.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026dagger;Soil available P by the Truog method.\u003c/p\u003e\n\u003cp\u003eThe values of total C concentration, oxalate extracted Al, and phosphate absorption coefficient in the East field were extracted from Francisco et al. (2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Parameters of the semivariograms of the selected soil properties.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"578\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eNugget (C\u003csub\u003e0\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eSill (C\u003csub\u003e0\u003c/sub\u003e+C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eNugget %\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eRange (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eSpatial Class\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eEast field\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eTotal C concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eSpherical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eStrong\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eOxalate extracted Al\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eSpherical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e8.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e29.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e85.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003ePhosphate absorption coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eSpherical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e85.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e34.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eSoil available P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eSpherical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e35.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e97.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eWest field\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eTotal C concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eSpherical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e35.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e41.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eOxalate extracted Al\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eExponential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e30.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e49.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e96.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003ePhosphate absorption coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eSpherical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e55.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eSoil available P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eLinear\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e6.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e62.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e67.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026dagger;Nugget % = C\u003csub\u003e0\u003c/sub\u003e / (C\u003csub\u003e0\u003c/sub\u003e+C) x 100.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026Dagger;Strong, Nugget % \u0026le; 25; Moderate, 25\u0026lt;Nugget % \u0026le; 75.\u003c/p\u003e\n\u003cp\u003eThe semivariogram parameters of the East field were extracted from Francisco et al. (2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Cluster means for soil available P by the Truog method, total C concentration, and phosphorus absorption coefficient of combined data sets from East and West fields.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"397\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003eCluster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003eTotal C concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003ePhosphorus absorption coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSoil available P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eg kg\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003emg-P kg\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e47.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e43.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e48.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e72.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e91.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e89.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Andisols, spatial heterogeneity, soil available phosphorus, site-specific fertilization, management zones.","lastPublishedDoi":"10.21203/rs.3.rs-7984946/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7984946/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePredicting the spatial variability of soil available phosphorus (P) is crucial for efficient site-specific fertilizer management in large-scale fields. In Hokkaido, Japan, Andisols are the dominant cultivated soil type, known for their high P fixation and strong spatial heterogeneity of inherent soil properties. To assess the magnitude and factors controlling the within-field variation in soil available P, a total of 920 surface soil samples were collected from two fields located 300 m apart, designated the East (6.6ha) and West (4.4ha) fields. The samples were analyzed for available P, phosphate absorption coefficient (PAC), total carbon (TC), and oxalate-extracted aluminum (Al\u003csub\u003eo\u003c/sub\u003e). The two fields had clear zones of low, medium, and high soil available P; however, the zoning was more pronounced in the East field. The available P was also higher in the East field, averaging 70.1 mg kg\u003csup\u003e-1\u003c/sup\u003e, while it averaged 45.7 mg kg\u003csup\u003e-1 \u003c/sup\u003ein the West. The analysis of the spatial structure of the available P showed moderate spatial dependence (25 \u0026lt; nugget% ≤ 75) in both fields, but a larger range in the East field. PAC was a strong predictor of available P in the West field, whereas both TC and PAC controlled available P in the East field. Despite high Al\u003csub\u003eo\u003c/sub\u003e concentration in the West field, the PAC was lower than in the East, indicating multiple factors controlling P variability. Overall, this study revealed that site-specific fertilization of P is needed, and management zones should be created based on the variation of soil organic matter and clay minerals.\u003c/p\u003e","manuscriptTitle":"Unveiling spatial heterogeneity of soil available phosphorus for site-specific fertilization in Andisols of Hokkaido, Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 17:37:35","doi":"10.21203/rs.3.rs-7984946/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"026068f5-799f-461f-a35e-d6ca583c5808","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-31T09:55:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 17:37:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7984946","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7984946","identity":"rs-7984946","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}