Spatial Distribution Of Soil Nutrients Research Articles

Spatial Distribution Characteristics of Soil Carbon and Nitrogen in Citrus Orchards on the Slope of Purple Soil Hilly Area

Since the 1990s, a large area of sloping farmland in a purple soil hilly region of southwest China was converted into an orchard to prevent soil erosion, increase soil fertility, and elevate economic benefits for farmers. In order to explore the spatial distribution of soil carbon (C) and nitrogen (N) fractions on the slope of returning arable lands to citrus orchards in purple soil hilly areas, a soil sampling event was carried out in a citrus orchard at the Yanting Agro-ecological Experimental Station of Purple Soil, Chinese Academy of Sciences, to examine the differences in soil C and N fractions and their influencing factors. The results showed that the slope position had significant effects on the contents of soil total nitrogen (TN), nitrate nitrogen (NO3--N), and dissolved organic carbon (DOC) (P < 0.05), but the effects were not obvious regarding the total organic carbon (SOC) and ammonia nitrogen (NH4+-N) of the soil (P > 0.05). For topsoil (0-30 cm), the variation trend of soil NO3--N content along the slope was upper slope < middle slope < lower slope, whereas the TN and DOC contents along the slope exhibited the trend of upper slope > middle slope > lower slope. The contents of soil C and N in each slope position generally showed a downward trend with increasing soil depth (0-30 cm). The contents of soil TN, SOC, NO3--N, and DOC were significantly affected by soil depth (P < 0.05). The TN storage (0-30 cm) significantly decreased from the top to the bottom within the soil slope, with a value of 2.37, 1.89, and 1.62 t·hm-2 (reported as N) for the upper slope, middle slope, and lower slope, respectively. There was no significant difference in SOC reserves along the slope, with a range from 56.12 to 58.48 t·hm-2 (reported as C). Our results provide scientific basis for understanding the spatial distribution of soil nutrients of the restored farmland in purple soil hilly areas. Our research suggests that the spatial distribution of soil carbon and nitrogen storage should not be ignored when predicting the response of soil nutrients to land use change.

Spatial differentiation of soil nutrients and their ecological chemometrics based on geographic detector in National Agricultural Park of Tangchang, Southwest China.

In order to analyze the spatial variability of soil nutrients and their ecological chemometrics in Tangchang Town, National Agricultural Park, 20 influencing factors were selected: soil pH, Cd, Hg, As, Cu, Pb, Cr, Zn, Ni, Se, elevation, slope, aspect, land use type, distance from industrial land, distance from commercial land, distance from railway, distance from residential area, distance from highway and distance from river. The effects of various influencing factors on the spatial variability of total organic carbon (TOC), total nitrogen (N), total phosphorus (P), total potassium (K) and ecological stoichiometry were analyzed by means of geographic detector. The results showed that average contents of soil TOC, N, P and K in the study area are 10.24 g kg-1, 1.33 g kg-1, 1.14 g kg-1 and 23.60 g kg-1, respectively, and there were significant differences in the spatial distribution of soil nutrients and their eco-stoichiometry in the study area, and TOC, N, P, K, C/N, C/P, C/K, N/P, N/K and P/K has a significant correlation with each other and most correlation coefficients are above 0.5 or below -0.5. Factor detection showed that soil properties, distance from railway and distance from residential area had the most significant explanatory power to the spatial heterogeneity of soil nutrients and eco-stoichiometry. Interaction detection showed that the interaction between soil properties with other factors was the most important factor affecting the spatial differentiation of soil nutrients and their ecological chemometrics, and elevation, distance from railway and distance from residential area were also important factors. Risk detection showed that the differences of soil nutrients and their ecological stoichiometry were most significant in the subregions of soil properties (pH, Cd, Hg, As, Cu, Pb, Cr, Zn, Ni and Se).

County-Scale Spatial Distribution of Soil Nutrients and Driving Factors in Semiarid Loess Plateau Farmland, China

Characterized by a topography of thousands of ravines, the Loess Plateau has highly complex spatial variability in terms of soil nutrients. Therefore, it is of considerable importance to study the soil nutrient spatial distribution, driving factors of precise fertilizer management, and the strategic use of soil nutrient resources. In 2017, 242 soil samples were taken from the semiarid Anding district farming region in northern China. The spatial variability and factors influencing soil nutrients were studied using statistical and geostatistical methods. The results showed that the mean soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), available potassium (AK), and pH values were averaged at 12.64 g·kg−1, 0.84 g·kg−1, 23.20 mg·kg−1, 188.87 mg·kg−1, and 8.60, respectively. The nugget-to-sill ratios for the semi-variograms of SOM, TN, AP, and AK varied from 25.84 to 49.93%, while the coefficients of variation varied from 24.53 to 69.44%, revealing that all four indicators exhibited considerable variability, and that the samples’ geographical variability was produced by a combination of random and structural factors. Overall increasing trends were exhibited from the middle to the northeast and southwest in the distributions of SOM, TN, and AP. The spatial distribution of AK displayed the opposite trend, increasing from the southwest to north and southeast. The texture of the tillage layer was the main factor directly affecting SOM, and explained 8% of its variation. The distribution of TN was mainly influenced by the irrigation method and water source type. AP and AK contents differed significantly between the two parent materials, three textures, and three topography types at the level of p < 0.01. In conclusion, the regional soil fertility was poor, spatial heterogeneity was moderate, and influencing factors were complex, highlighting the need to adopt precise fertilization management and adopting land management measures according to the actual influencing factors of each nutrient, thereby contributing to the enhancement of regional fertility.

Variations of soil quality in the southern Qinghai-Tibet Plateau during 1980s to 2020s.

Climate warming and thawing of permafrost in the Qinghai-Tibet Plateau have resulted in soil erosion and the decline of soil quality. Determining the decadal variation of soil quality in the Qinghai-Tibet Plateau is the basis for scientific understanding of soil resources and the key to vegetation restoration and ecological reconstruction. In this study, we used eight indicators (including soil organic matter, total nitrogen, and total phosphorus) to eva-luate soil quality of montane coniferous forest zone (Tibet's natural geographical division zone Ⅱ) and montane shrubby steppe zone (zone Ⅳ) by calculating soil quality index (SQI) in the southern Qinghai-Tibet Plateau in the 1980s and 2020s. Variation partitioning (VPA) was used to examine the drivers for the heterogeneity of the spatial-temporal distribution of soil quality. The results showed that soil quality in each natural zone showed a downward trend in the past 40 years, with SQI of zone Ⅱ decreasing from 0.505 to 0.484 and that of zone Ⅳ decreasing from 0.458 to 0.425. The spatial distribution of soil nutrients and quality was heterogeneous, while soil nutrient conditions and quality in zone Ⅱ were better than those in zone Ⅳ in different periods. The VPA results indicated that the interaction of climate change, land degradation, and vegetation differences was the major cause of temporal variation in soil quality. Differences in climate and vegetation could better explain the spatial variation of SQI.

Drought Shapes Photosynthetic Production Traits and Water Use Traits along with Their Relationships with Leaves of Typical Desert Shrubs in Qaidam

Leaf functional traits in drylands are sensitive to environmental changes, which are closely related to plant growth strategies and resource utilization ability and can reflect the balance of substance synthesis and water loss. However, the influence of environmental factors on photosynthetic production traits and water use traits is still unclear in drylands. In this study, nine environmental factors (climatic characteristics and soil physical and chemical properties), leaf net photosynthetic rate (A), transpiration rate (E), and stomatal conductance (GSW) were measured via 60 plant samples and 45 soil samples, which were collected at five sampling sites according to rainfall gradient. Redundancy analysis (RDA), structural equation model (SEM), and regression analysis were used to analyze the influencing mechanism of drought on photosynthetic production traits and water use traits. The results provided the following conclusions: (i) The hydrothermal condition determined A, E, and GSW by affecting the spatial distribution of soil nutrients (SN) and soil salinity (SS); meanwhile, temperature was able to affect A, E, and GSW directly. (ii) The water content (WC) was the key driver of the strength of the synergistic relationship between photosynthetic production traits and water use traits; soil salinity (SS) was the main driver of the synergistic relationship between E and GSW.

Disentangling effects of upslope runoff and sediment on soil nutrients on the hilly slopes of the Loess Plateau, China

Upslope runoff and sediment critically influence soil erosion, while their impact on the subsequent downslope nutrient distribution has not been examined, consequently hindering the exact assessment of the effects of erosion on soil biogeochemical cycles. Herein we selected two groups of wide and narrow slopes on the middle Loess Plateau, with each slope group including slopes with and without upslope runoff and sediment. For each slope, we collected soil samples from topsoil along the slope at different distances from the upper slope to the lower slope, and collected samples from depths of 0–60 cm at the upper, middle and lower positions of the slope. The contents of soil organic matter and nutrients were analysed. The purpose of this study was to disentangle the effects of upslope runoff and sediment on spatial distribution of soil nutrients on slopes of different widths. We showed that compared with slopes without upslope runoff and sediment, upslope runoff and sediment increased the content of most nutrients in topsoil on the wide slope, but had a contrasting effect on the narrow slope. Upslope runoff and sediment reduced the random spatial heterogeneity of soil nutrients on both the wide and narrow slopes, with greater effects observed on the narrow slope. The influence of upslope runoff and sediment on the profile distribution of soil nutrients varied with slope position and slope width, and was nutrient type-dependent. These results indicated that slope topographical conditions interact with upslope runoff and sediment in influencing vertical and horizontal patterns of soil nutrients on slopes.

Spatial distribution of soil nutrients and evaluation of cultivated land in Xuwen county.

It is of great significance to promote the quantitative research of soil science and the implementation of precision agriculture. On this basis, taking Xuwen County as the research object, this paper comprehensively analyzed the soil characteristics of cultivated land in Xuwen County and clarify the soil nutrient content and spatial distribution characteristics of cultivated land in Xuwen County, this paper comprehensively applied the methods of geostatistics, geographic information system (GIS) and fuzzy mathematics, and referred to the cultivated land quality grade standard (GB/T 33469-2016), to analyze the soil characteristics and evaluate the soil fertility of this region. The results show that the optimal interpolation model of soil pH and available phosphorus (AP) is a Gaussian model, and the optimal interpolation model of soil organic matter (SOM), available nitrogen (AN) and available potassium (AK) is a J-Bessel model. In addition, the spatial correlation of AK is weak, whereas pH, SOM, AN and AP show moderate spatial correlation. The proportion of excellent, good, average, medium and poor comprehensive fertility index are 26.00%, 32.67%, 19.33%, 19.00% and 3.00%, respectively. The overall level of soil fertility in Xuwen County is above the average, and the fertility quality presents an obvious trend of high in the South and low in the North. Areas that above average fertility are mainly distributed in Maichen Town, Qujie Town, Nanshan Town and Chengbei Town. The results can provide theoretical basis for improving the utilization rate of chemical fertilizer, fine management of cultivated land and ecological environment in this region, which can help in decision-making of precision fertilization.

Analysis of Spatial Variability and Influencing Factors of Soil Nutrients in Western China: A Case Study of the Daliuta Mining Area

An understanding of the spatial variation and influence factors of soil nutrients in mining areas can provide a reference for land reclamation and ecological restoration. Daliuta was used as the study area. The spatial variability of soil nutrients was analyzed using traditional statistics and geostatistics. The effects of topography, mining history, and soil erosion were discussed. The results indicate that the soil pH of the Daliuta mining area is slightly acidic to slightly alkaline, and the soil organic matter, available nitrogen, available phosphorus, and available potassium belonged to the five levels (very low), six (extremely low), five (extremely low), and four (moderately low), respectively. The soil water and salt content indicated that the soil environment in the mining area is arid and has normal levels of salinity. The organic matter, available nitrogen, available phosphorus, available potassium, and soil salt varied moderately, and the pH did not change much, while the soil water varied strongly. The organic matter, pH, and soil salinity are moderately spatially autocorrelated, and the available nitrogen, available phosphorus, available potassium, and soil water are weakly spatially autocorrelated. Each nutrient index had a certain spatial trend effect. The slope, aspect, elevation, and topographic wetness index are the primary topographic factors that control the spatial distribution of soil nutrients. The organic matter, pH, and soil salinity are moderately spatially autocorrelated, and the available nitrogen, available phosphorus, available potassium, and soil water are weakly spatially autocorrelated. Each nutrient index had a certain spatial trend effect. The slope, aspect, elevation, and topographic wetness index are the primary topographic factors that control the spatial distribution of soil nutrients. Soil erosion and mining history are also important factors that lead to the spatial variation of soil nutrients.

Effects of compost tea on the spatial distribution of soil nutrients and growth of cotton under different fertilization strategies

Compost tea comes from a wide range of sources and can provide nutrients for crops and improve soil quality. In order to explore the effects of fertilization frequencies of compost tea on the spatial distribution of nutrients and the uptake by cotton, a trial comprised no fertilization treatment (CK) and three fertilizer treatments, that is, the application of a fixed amount of fertilizer at four (F3), two (F5), or one (F9) irrigation cycle, and each irrigation cycle was 10 days. The total amount of nutrients (N, P2O5, K2O) in fertilization treatment were all 18 g, and their amount of fertilization each time was 1/3, 1/5 and 1/9 of the total fertilizer, respectively. The results showed that in 0–10 cm soil layers, high-frequency fertilization (F9) reduced the soil pH by 5.6% compared with F3, the soil nutrient content of F3 treatment at seedling stage was significantly higher than that of the other two treatments by 71.9%–272.3%, however, the 20–40 cm soil layer was hardly affected by fertilization; The nutrient accumulation of cotton in F3 treatment was 17.1%–32.4% higher than that in the other two treatments, and the highest dry matter and recovery efficiency were also obtained under the F3 treatment. In conclusion, increasing the proportion of fertilization in seedling stage (F3) can increase the uptake and utilization of nutrients by cotton; While high-frequency fertilization (F9) can reduce the pH of calcareous soil. This study provides a basis for fertilizer optimization and soil quality improvement of composted tea.

Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data

The study on the spatial distribution of forest soil nutrients is important not only as a reference for understanding the factors affecting soil variability, but also for the rational use of soil resources and the establishment of a virtuous cycle of forest ecosystems. The rapid development of remote sensing satellites provides an excellent opportunity to improve the accuracy of forest soil prediction models. This study aimed to explore the utility of the Gaofen-1 (GF-1) satellite in the forest soil mapping model in Luoding City, Yunfu City, Guangdong Province, Southeast China. We used 1000 m resolution coarse-resolution soil map to represent the overall regional soil nutrient status, 12.5 m resolution terrain-hydrology variables to reflect the detailed spatial distribution of soil nutrients, and 8 m resolution remote sensing variables to reflect the surface vegetation status to build terrain-hydrology artificial neural network (ANN) models and full variable ANNs, respectively. The prediction objects were alkali-hydro-nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). The results showed that the full-variable ANN accuracy at five soil depths was better than the terrain-hydrology ANNs, indicating that remote sensing variables reflecting vegetation status can improve the prediction of forest soil nutrients. The remote sensing variables had different effectiveness for different soil nutrients and different depths. In upper soil layers (0–20 and 20–40 cm), remote sensing variables were more useful for AN, AP, and OM, and were between 10%–14% (R2), and less effective for AK at only 8% and 6% (R2). In deep soil layers (40–60, 60–80, and 80–100 cm), the improvement of all soil nutrient models was not significant, between 3 and 6% (R2). RMSE and ROA ± 5% also decreased with the depth of soil. Remote sensing ANNs (coarse resolution soil maps + remote sensing variables) further demonstrated that the predictive power of remote sensing data decreases with soil depth. Compared to terrain-hydrological variables, remote sensing variables perform better at 0–20 cm, but the predictive power decreased rapidly with depth. In conclusion, the results of the study showed that the integration of remote sensing with coarse-resolution soil maps and terrain-hydrology variables could strongly improve upper forest soil (0–40 cm) nutrients prediction and NDVI, green band, and forest types were the best remote sensing predictors. In addition, the study area is rich in AN and OM, while AP and AK are scarce. Therefore, to improve forest health, attention should be paid to monitoring and managing AN, AP, AK, and OM levels.

The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes

Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.

Large-scale digital mapping of topsoil total nitrogen using machine learning models and associated uncertainty map

Understanding the spatial distribution of soil nutrients and factors affecting their concentration and availability is crucial for soil fertility management and sustainable land utilization while quantifying factors affecting soil nitrogen distribution in Qorveh-Dehgolan plain is mostly lacking. This study, thus, aimed at digital modeling and mapping the spatial distribution of topsoil total nitrogen (TN) in Qorveh-Dehgolan plain with an area of 150,000 ha using random forest (RF), decision tree (DT), and cubist (CB) algorithms. A total of 130 observation points were collected from a depth of 0 to 30cm from topsoil surfaces based on a random sampling pattern. Then, soil physicochemical properties, calcium carbonate equivalent, organic carbon, and topsoil total nitrogen were measured. A number of 51 environmental variables including 31 geomorphometric attributes derived from a digital elevation model with 12.5-m spatial resolution, 13 spectral indices and reflectance from SENTINEL-2 satellite (MSIsensor), and five soil properties and two spatial variables of latitude and longitude were used as covariates for digital mapping of topsoil total nitrogen. The most appropriate covariates were then selected by the Boruta algorithm in the R software environment. A standard deviation map was produced to show model uncertainty. The covariate selection resulted in the separation of 14 effective covariates in the spatial prediction of topsoil total nitrogen by using the data mining algorithms. The validation of digital mapping of topsoil total nitrogen by RF, DT, and CB models using 20% of independent data showed root mean square error (RMSE) of 0.032, 0.035, and 0.043%; mean absolute error (MAE) of 0.0008, 0.001, and 0.002%; and based on the coefficients of determination of 0.42, 0.38, 0.35, respectively. Relative importance (RI) of environmental covariates using the %IncMSE index indicated the importance of two geomorphometric variables of midslope position and normalized height along with SAVI and NDVI remote sensing variables in the spatial modeling and distribution of total nitrogen in the studied lands. The RF prediction and associated uncertainty maps, with show high accuracy and low standard deviation in the most part of study area, reveled low overfitting and overtraining in soil-landscape modeling; so, this model can lead to the development of a digital map of soil surface properties with acceptable accuracy for sustainable land utilization.

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

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

Spatial variability of soil nutrients and GIS-based nutrient management in upland of Tamil Nadu: A case study from Valapadi Block, Salem district

Spatial distribution of soil nutrients is not normally considered for small holder farms in Tamil Nadu resulting in blanket fertilizer application which leads to low efficiency of some applied nutrients. This study focuses on applying geospatial analyses to map from 39 revenue villages of Valapadi block of Salem district, Tamil Nadu to identify nutrient distribution. Soil samples were taken from these 640locations and analysed soil reaction, EC, OC, major nutrients viz. nitrogen (N), phosphorus (P) and potassium (K) and micro nutrients (Fe, Mn, Cu and Zn). Spatial models of the contents were generated through geo statistical analysis to map the status of N, Pand K nutrients across the locations. Study results indicated that proportion of area of soils are neutral (56.1%) and non saline. Low OC was distributed in 68.2% area and low in available N (91.5%), P (39%) and K (52.8%) respectively. The soils are deficient in Zn (37.3%) and sufficient in Fe (75.6%), Cu (80.7%) and Mn (97.0%).Distribution pattern for P, K, Cu and Mn were rated of low to high and mapped. Outcome of this study could enhance site-specific nutrient recommendation in upland of Tamil Nadu.

Spatial distribution of soil organic matter in a coal mining subsidence area

ABSTRACTUnderground mining has caused drastic disturbances to regional ecosystems and soil nutrients. Understanding the three-dimensional (3D) spatial distribution of soil nutrients in mining area farmland is crucial for agricultural production and environmental management. However, few studies have reported the 3D spatial distribution of soil organic matter (SOM) in coal mining subsidence area. In our study, a sequential Gaussian simulation (SGS) algorithm was used to analyse the spatial distribution of SOM based on observations of 180 soil samples in the Zhaogu mine in China. The results showed that the SOM content had considerable variation in spatial distribution at different soil depths (0–20, 20–40, 40–60 cm) and decreased with the increase in soil depth. The spatial variability of surface organic matter was the largest, and the coefficient of variation was 29.38%, which was moderately mutated. The spatial distribution of SOM also varied among slope locations. The SOM content was higher upslope and downslope than on the middle slope. In addition, given a threshold, SGS can be used to calculate the probability that the organic matter content at any position is lower or higher than the given value. The research results provide a reference for land reclamation and precision agriculture.

38 Enhancing production efficiency on southern beef farms with an interactive extension approach using forage management

Abstract Production efficiency in beef grazing systems can be improved by several relatively simple changes in forage management practices. During a 4-year project period funded by the NRCS Conservation Innovation Grant Program (68-3A75-14–251) and in collaboration with the University of Georgia, the Amazing Grazing Program in North Carolina utilized several innovative forage management approaches on 6 private farms and 3 public research stations to educate beef producers. On-farm grid sampling illustrated spatial distribution of soil nutrients as influenced by long-term historical management, particularly of winter hay feeding stations. Side-by-side testing of simple and complex annual forage mixes on three private farms demonstrated the opportunities to enhance forage nutritive value, capture the value of diverse botanical composition, and to defer grazing on perennial pastures. Smallplot testing of yield response to fall-stockpiled tall fescue nitrogen fertilizer rate allowed producers to reap the economic benefits of improved soil health and to help improve the environment surrounding their farm. At each private farm, summer, and winter forage management workshops explored innovative approaches to beef farm management, including the nature and importance of dung beetles, value of simple compared with complex annual forages, how to approach winter grazing, benefits of hay unrolling, need for mineral supplementation, utilizing temporary fencing, and frost-seeding clovers. As the project progressed, interest and attendance at workshops grew and cooperating farmer engagement increased. We found that many small-scale beef farmers were interested in simple practices such as hay unrolling and frost seeding when demonstrated by their peers. Using a variety of educational approaches, workshop audiences responded positively to hands-on activities and time for open discussion. Attendees were interested, attentive, and motivated to adopt new practices based on these practical demonstrations on peer farms.

Spatial Distribution of Soil Nutrients in Berambadi Watershed of Chamarajanagar District, Karnataka for Sustainable Crop Production

The spatial distribution of soil nutrients was assessed in Berambadi watershed of Chamarajanagar district, Karnataka based on geo-statistics model. A total of 447 surface soil samples (0-20 cm) were collected at 250 m grid interval and analyzed. Semi variogram parameters (nugget, sill and range) for each soil property with best fitted models were identified based on minimum root mean square error (RMSE). Exponential, spherical and gaussian models were fit well for the soil properties. The nugget-sill ratio showed that the spatial variability of soil nutrients moderate spatial dependence (25-75 %) except soil pH and zinc (<25 % spatial dependence). The results of spatial distribution of nutrients showed that available phosphorus content was medium (23-56 kg ha-1) in 57% of total geographical area and available potassium content was high (>330 kg ha-1) in more than 60% of total geographical area of Berambadi watershed. Spatial distribution of soil nutrients is most important to know the status of soil fertility, which helps in adjusting the agricultural management measures for better crop production.

Soil mixing and redistribution by strategic deep tillage in a sandy soil

Strategic deep tillage (SDT) is proposed as a single or occasional practice to help sustain the long-term productivity of the no-till system. Soil water repellence, herbicide resistant weeds, subsoil acidity and compaction are constraints that can evolve in long-term no-till systems and SDT may provide an effective solution for these. While the effect of SDT on the lateral and vertical movement of soil is understood, soil heterogeneity and mixing caused by SDT is not. Soil mixing may be an important factor for the availability of soil nutrients after SDT or for the effectiveness of soil amendments. Soil mixing and redistribution were quantified using coloured soil tracers and digital image analysis. Trenches of synthetic coloured soil were installed at four depths at a field site and four tillage treatments were applied; offset disc harrow and deep ripping (DHDR), disc plough (DP), mouldboard plough (MP) and rotary spader (RS). Digital images were acquired for soil pit faces at 5 cm intervals to capture the location of the coloured soil after SDT, these images were segmented and the 3-dimensional distribution of the coloured soil tracers was reconstructed. The mixing indices for the tillage treatments were low level (0.04–0.12). The vertical movement of soil differed between treatments; approximately 80, 60, 60 and 10% of soil from 0 to 10 cm was redistributed to deeper soil layers by MP, DP, RS and DHDR respectively. The soil profiles after the SDT treatments were composed of layered, or rotated soil that had been translocated within the working depth of the implement; however, there was some mixing at the interface between layers, or seams of soil that originated from different depths. The impact of a change in the spatial distribution of soil nutrients created by SDT on crop growth is an important knowledge gap.

Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale.

Rapid acquisition of the spatial distribution of soil nutrients holds great implications for farmland soil productivity safety, food security and agricultural management. To this end, we collected 1297 soil samples and measured the content of soil total nitrogen (TN), soil available phosphorus (AP) and soil available potassium (AK) in Zengcheng, north of the Pearl River Delta, China. Hyperspectral remote sensing images (115 bands) of the Chinese Environmental 1A satellite were used as auxiliary variables and dimensionality reduction was performed using Pearson correlation analysis and principal component analysis. The TN, AP and AK of soil were predicted in the study area based on auxiliary variables after dimensionality reduction, along with stepwise linear regression (SLR), support vector machine (SVM), random forest (RF) and back-propagation neural network (BPNN) models; 324 independent points were used to verify the predictive performance. The BPNN model, which demonstrated the best predictive accuracy among all methods, combined ordinary kriging (OK) with mapping the spatial variations of soil nutrients. Results show that the BPNN model with double hidden layers had better predictive accuracy for soil TN (root mean square error (RMSE) = 0.409 mg kg−1, R2 = 44.24%), soil AP (RMSE = 40.808 mg kg−1, R2 = 42.91%) and soil AK (RMSE = 67.464 mg kg−1, R2 = 48.53%) compared with the SLR, SVM and RF models. The back propagation neural network-ordinary kriging (BPNNOK) model showed the best predictive results of soil TN (RMSE = 0.292 mg kg−1, R2 = 68.51%), soil AP (RMSE = 29.62 mg kg−1, R2 = 69.30%) and soil AK (RMSE = 49.67 mg kg−1 and R2 = 70.55%), indicating the best fitting ability between hyperspectral remote sensing bands and soil nutrients. According to the spatial mapping results of the BPNNOK model, concentrations of soil TN (north-central), soil AP (central and southwest) and soil AK (central and southeast) were respectively higher in the study area. The most important bands (464–517 nm) for soil TN (b10, b14, b20 and b21), soil AP (b3, b19 and b22) and soil AK (b4, b11, b12 and b25) exhibited the best response and sensitivity according to the SLR, SVM, RF and BPNN models. It was concluded that the application of hyperspectral images (visible-near-infrared data) with BPNNOK model was found to be an efficient method for mapping and monitoring soil nutrients at the regional scale.

Geospatial approach to study the spatial distribution of major soil nutrients in the Northern region of Ghana

Spatial distribution of soil nutrients is not normally considered for smallholder farms in Ghana resulting in blanket fertilizer application which leads to low efficiencies of some applied nutrient...