Influence of hill slope on biological pools of carbon, nitrogen, and phosphorus in acidic alfisols of citrus orchard

Wanshnong, Rangeh K.; Thakuria, Dwipendra; Sangma, Christy B.K.; Ram, Vishram; Bora, P. K.

doi:10.1016/j.catena.2013.07.009

Cited by 20 publications

(7 citation statements)

References 29 publications

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“…This greatly alters the spatial variability of soil nutrients from the field level to the national scale and changes the relationships between soil nutrients (Jia et al 2011;Liu et al 2013;Roger et al 2014;Shuklaa et al 2016). Thus, considerable effort has been expended to assess the spatial variability of soil nutrients and the affecting factors in different areas in recent years (Wang et al 2009;Wanshnong et al 2013;Elbasiouny et al 2014;Roger et al 2014;Li et al 2016a). Due to the complexity of terrestrial ecosystems, the spatial variability of soil nutrients varies in different regions (Yang et al 2007;Wanshnong et al 2013;Roger et al 2014;Xin et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, considerable effort has been expended to assess the spatial variability of soil nutrients and the affecting factors in different areas in recent years (Wang et al 2009;Wanshnong et al 2013;Elbasiouny et al 2014;Roger et al 2014;Li et al 2016a). Due to the complexity of terrestrial ecosystems, the spatial variability of soil nutrients varies in different regions (Yang et al 2007;Wanshnong et al 2013;Roger et al 2014;Xin et al 2016). These variations are caused by many environmental factors including climate, parent material, topography, soil type and human activity (Walter et al 2003;Wang et al 2009;Yang et al 2013;Basso et al 2016;Li et al 2016a).…”

Section: Introductionmentioning

confidence: 99%

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Spatial variability of soil total nitrogen, phosphorus and potassium in Renshou County of Sichuan Basin, China

Gao

Xiao

Deng

et al. 2019

Journal of Integrative Agriculture

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Understanding soil nutrient distributions and the factors affecting them are crucial for fertilizer management and environmental protection in vulnerable ecological regions. Based on 555 soil samples collected in 2012 in Renshou County, located in the purple soil hilly area of Sichuan Basin, China, the spatial variability of soil total nitrogen (TN), total phosphorus (TP) and total potassium (TK) was studied with geostatistical analysis and the relative roles of the affecting factors were quantified using regression analysis. The means of TN, TP and TK contents were 1.12, 0.82 and 9.64 g kg-1 , respectively. The coefficients of variation ranged from 30.56 to 38.75% and the nugget/sill ratios ranged from 0.45 to 0.61, indicating that the three soil nutrients had moderate variability and spatial dependence. Two distribution patterns were observed. TP and TK were associated with patterns of obvious spatial distribution trends while the spatial distribution of TN was characterized by higher variability. Soil group, land use type and topographic factors explained 26.5, 35.6 and 8.4% of TN variability, respectively, with land use being the dominant factor. Parent material, soil group, land use type and topographic factors explained 17.5, 10.7, 12.0 and 5.0% of TP variability, respectively, and both parent material and land use type played important roles. Only parent material and soil type contributed to TK variability and could explain 25.1 and 13.7% of TK variability, respectively. More attention should focus on adopting reasonable land use types for the purposes of fertilizer management and consider the different roles of the affecting factors at the landscape scale in this purple soil hilly area.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial variability of soil total nitrogen, phosphorus and potassium in Renshou County of Sichuan Basin, China

Gao

Xiao

Deng

et al. 2019

Journal of Integrative Agriculture

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“…Some studies have shown that the spatial distribution of soil nutrients are influenced by elevation, slope, and landscape positions, by soil use and management (Wang et al, 2009; Zhang et al, 2011), and by variations in microtopography such as surface roughness and tortuosity (Moser et al, 2009). Even under a uniform management, hill slope position determined the spatial–temporal variations in soil C, N, and P distribution in a citrus orchard (Wanshnong et al, 2013). Different geospatial techniques are used to quantify the spatial relationship between soils and the terrain attributes, and digital soil mapping (DSM) (McBratney et al, 2003)—a novel approach in soil mapping—describes such tools and techniques in soil mapping and in environmental applications (Boettinger et al, 2010; Lagacherie et al, 2007).…”

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confidence: 99%

Topographic Controls on Soil Nutrient Variations in a Silvopasture System

Adhikari

Owens

Ashworth

et al. 2018

Agrosystems Geosci & Env

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Core Ideas Topographic variation influenced soil nutrient distribution in a silvopasture system. High‐resolution digital maps of soil nutrients were generated. Terrain attributes identified topographic functional units as management zones. Level of soil nutrients in topographic functional units were different. Topography plays a crucial role in spatial distribution of nutrients in soils; however, studies to quantify topographic influence on soil nutrient distribution from a silvopasture system are mostly lacking. To address this question, a 4.3‐ha silvopasture site in northwest Arkansas was selected and a total of 51 topsoil (0–15 cm thickness) samples were collected and analyzed for primary (total N [TN], P, K), secondary (Ca, Mg, S), and micronutrients (Fe, Zn, Cu, Mn, B, Na). Topographic information was acquired from 12 terrain attributes derived from a 1‐m digital elevation model. The prediction model was based on random forest. Results showed TN, S, and P were best predicted, whereas Cu, Ca, and Mn had the lowest prediction performance. Levels of S, Ca, Zn, Fe, and TN increased with SAGA wetness index, valley depth, flow accumulation, and multi‐resolution valley bottom flatness index. Normalized height and slope height were positively related to Na but negatively to B and Cu distribution. Aspect had a positive influence on P and Mg concentrations. Based on terrain attributes, the study site could be divided into four topographic functional units (TFU), namely A, B, C, and D; TFU A had the highest nutrients present, whereas TFU B had the lowest P, K, Zn, Cu, Fe, and Ca but highest Na content. However, Mn, Mg, and B did not vary among TFUs. This study affirmed topographic influences on soil nutrient distribution, and the resulting continuous soil nutrient maps are useful for fine‐tuning production systems through optimum nutrient and pasture management.

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“…The major role of topography is how it controls the (re)distribution of runoff and energy across landscapes and therefore could explain the spatial variability of soil P content and P index value across a farm (Adhikari et al, 2018;I. D. Moore et al, 1993;Wanshnong et al, 2013;Zhang et al, 2014). The spatial relationship between terrain attributes and soil P measurements can be quantified, and a continuous P map can be generated using digital soil mapping (DSM) techniques (McBratney et al, 2003).…”

Section: Core Ideasmentioning

confidence: 99%

Relating topography and soil phosphorus distribution in litter‐amended pastures in Arkansas

Adhikari

Braden

Owens

et al. 2021

Agrosystems Geosci & Env

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Poultry producers in northwestern Arkansas fertilize pastures with litter, leading to excessive P buildup on surface soils with risk of contaminating nearby surface waters. Information on the influence of pasture topography on P runoff is limited. Objectives were to assess soil P and P index status in pastures, quantify topographic influence on P distribution, and generate high-resolution P maps for site-specific nutrient management. Soil samples were collected from a commercial farm in a grid design and analyzed for Mehlich-3 P (STP), and dissolved reactive P (DRP). Gburek (GPI) and Sims P indices (SPI) were calculated by considering soil erosion and runoff potentials, STP, and P fertilizer application rate and source. A machine-learning algorithm, based on a random forest model, quantified spatial relationships of STP, DRP, and P indices with topography. The study area was highly variable in topography and soil P levels. High-slope areas bordering a stream and flat areas with lower elevation had greater GPI and SPI. Topography explained up to 50% of variation in STP and DRP distribution and >70% variation in GPI and SPI, which was linked to litter application sites and cattle feeding sites. Key terrain attributes for STP, DRP, GPI, and SPI distribution were elevation, slope position, slope height, valley depth, and valley bottom flatness. Predicted P maps illustrated areas along a stream had lower STP and DRP levels, but greater GPI and SPI. This analysis linked topographic relationships with P distribution, as topography controls the flow and distribution of water and influences farm management practices; therefore, future P management strategies should explicitly incorporate topographic risks.

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Influence of hill slope on biological pools of carbon, nitrogen, and phosphorus in acidic alfisols of citrus orchard

Cited by 20 publications

References 29 publications

Spatial variability of soil total nitrogen, phosphorus and potassium in Renshou County of Sichuan Basin, China

Spatial variability of soil total nitrogen, phosphorus and potassium in Renshou County of Sichuan Basin, China

Topographic Controls on Soil Nutrient Variations in a Silvopasture System

Relating topography and soil phosphorus distribution in litter‐amended pastures in Arkansas

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