Influence of Soil Properties on Maize and Wheat Nitrogen Status Assessment from Sentinel-2 Data

Soil properties variability is a factor that greatly influences cereals crops production and interacts with a proper assessment of crop nutritional status, which is fundamental to support site-specific management able to guarantee a sustainable crop production. Several management strategies of precision agriculture are now available to adjust the nitrogen (N) input to the actual crop needs. Many of the methods have been developed for proximal sensors, but increasing attention is being given to satellite-based N management systems, many of which rely on the assessment of the N status of crops. In this study, the reliability of the crop nutritional status assessment through the estimation of the nitrogen nutrition index (NNI) from Sentinel-2 (S2) satellite images was examined, focusing of the impact of soil properties variability for crop nitrogen deficiency monitoring. Vegetation indices (VIs) and biophysical variables (BVs), such as the green area index (GAI_S2), leaf chlorophyll content (Cab_S2), and canopy chlorophyll content (CCC_S2), derived from S2 imagery, were used to investigate plant N status and NNI retrieval, in the perspective of its use for guiding site-specific N fertilization. Field experiments were conducted on maize and on durum wheat, manipulating 4 groups of plots, according to soil characteristics identified by a soil map and quantified by soil samples analysis, with different N treatments. Field data collection highlighted different responses of the crops to N rate and soil type in terms of NNI, biomass (W), and nitrogen concentration (Na%). For both crops, plots in one soil class (FOR1) evidenced considerably lower values of BVs and stress conditions with respect to others soil classes even for high N rates. Soil samples analyses showed for FOR1 soil class statistically significant differences for pH, compared to the other soil classes, indicating that this property could be a limiting factor for nutrient absorption, hence crop growth, regardless of the amount of N distributed to the crop. The correlation analysis between measured crop related BVs and satellite-based products (VIs and S2_BVs) shows that it is possible to: (i) directly derive NNI from CCC_S2 (R2 = 0.76) and either normalized difference red edge index (NDRE) for maize (R2 = 0.79) or transformed chlorophyll absorption ratio index (TCARI) for durum wheat (R2 = 0.61); (ii) indirectly estimate NNI as the ratio of plant nitrogen uptake (PNUa) and critical plant nitrogen uptake (PNUc) derived using CCC_S2 (R2 = 0.77) and GAI_S2 (R2 = 0.68), respectively. Results of this study confirm that NNI is a good indicator to monitor plants N status, but also highlights the importance of linking this information to soil properties to support N site-specific fertilization in the precision agriculture framework. These findings contribute to rational agro-practices devoted to avoid N fertilization excesses and consequent environmental losses, bringing out the real limiting factors for optimal crop growth.

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Status of soil properties in relationship with soil pH in Madhupur tract of Tangail district in Bangladesh

Progressive Agriculture ◽

10.3329/pa.v30i3.45153 ◽

2020 ◽

Vol 30 (3) ◽

pp. 282-287

Author(s):

U Kumar ◽

H Rashid ◽

NH Tithi ◽

MY Mia

Keyword(s):

Organic Matter ◽

Soil Properties ◽

Soil Ph ◽

Soil Samples ◽

The Status ◽

June July ◽

Zn Status ◽

Very High ◽

N Status

The study was intended to investigate the status of soil properties and its relation to soil pH in Madhupur tract soil of Tangail district, Bangladesh. Thirty soil samples were collected during the period from June-July, 2016 covering four types of land as high land, medium high land, medium low land and low land. The interpretative data showed that the range of pH was strongly acidic to slightly acidic (5.27- 5.90), mean pH was slightly acidic (5.61). The organic matter (OM) status was medium (2.11 to 2.33 %) and mean OM was medium (2.24 %). The Nitrogen (N) status was low (0.11 to 0.13 %) and mean N status was medium (0.12 %). The range of the Phosphorus (P) status was found very low to medium (1.63 to 11.06 µg g-1 soil) and mean P status was medium (7.37 µg g-1 soil). The Potassium (K) status was low to very high (0.15 to 0.75meq/100 g soil) and mean K status was low (0.18 meq/100 g soil). The range of the Sulfur (S) status was found from low to medium (11.73 to 16.31 µg g-1 soil), mean S status was low (13.26 µg g-1 soil). The range of the Zinc (Zn) status was found from medium to high (0.96 to 2.23 µg g-1 soil), mean Zn status was optimum (1.55 µg g-1 soil). The range of the Boron (B) status was found from medium to very high (0.39 to 0.86 µg g-1 soil), mean B status was high (0.73 µg g-1 soil). The Calcium (Ca) status was medium to optimum (4.42 to 5.23meq/100 g soil), mean Ca status was optimum (4.83 meq/100 g soil). The Magnesium (Mg) status was optimum to high (1.21 to 1.75meq/100 g soil), mean Mg status was optimum (1.37 meq/100 g soil). No significant correlation of OM and other nutrients with pH. Progressive Agriculture 30 (3): 282-287, 2019

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Optimum size in grid soil sampling for variable rate application in site-specific management

Scientia Agricola ◽

10.1590/s0103-90162011000300017 ◽

2011 ◽

Vol 68 (3) ◽

pp. 386-392 ◽

Cited By ~ 26

Author(s):

Marcos Rafael Nanni ◽

Fabrício Pinheiro Povh ◽

José Alexandre Melo Demattê ◽

Roney Berti de Oliveira ◽

Marcelo Luiz Chicati ◽

...

Keyword(s):

Spatial Variability ◽

Precision Agriculture ◽

Production Efficiency ◽

Sampling Interval ◽

Soil Samples ◽

Soil Sampling ◽

Kappa Statistics ◽

Increase Production Efficiency ◽

Site Specific ◽

Specific Management

The importance of understanding spatial variability of soils is connected to crop management planning. This understanding makes it possible to treat soil not as a uniform, but a variable entity, and it enables site-specific management to increase production efficiency, which is the target of precision agriculture. Questions remain as the optimum soil sampling interval needed to make site-specific fertilizer recommendations in Brazil. The objectives of this study were: i) to evaluate the spatial variability of the main attributes that influence fertilization recommendations, using georeferenced soil samples arranged in grid patterns of different resolutions; ii) to compare the spatial maps generated with those obtained with the standard sampling of 1 sample ha-1, in order to verify the appropriateness of the spatial resolution. The attributes evaluated were phosphorus (P), potassium (K), organic matter (OM), base saturation (V%) and clay. Soil samples were collected in a 100 × 100 m georeferenced grid. Thinning was performed in order to create a grid with one sample every 2.07, 2.88, 3.75 and 7.20 ha. Geostatistical techniques, such as semivariogram and interpolation using kriging, were used to analyze the attributes at the different grid resolutions. This analysis was performed with the Vesper software package. The maps created by this method were compared using the kappa statistics. Additionally, correlation graphs were drawn by plotting the observed values against the estimated values using cross-validation. P, K and V%, a finer sampling resolution than the one using 1 sample ha-1 is required, while for OM and clay coarser resolutions of one sample every two and three hectares, respectively, may be acceptable.

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Establishing management zones using soil electrical conductivity and other soil properties by the fuzzy clustering technique

Scientia Agricola ◽

10.1590/s0103-90162008000600001 ◽

2008 ◽

Vol 65 (6) ◽

pp. 567-573 ◽

Cited By ~ 23

Author(s):

José Paulo Molin ◽

Cesar Nunes de Castro

Keyword(s):

Electrical Conductivity ◽

Soil Fertility ◽

Soil Properties ◽

Precision Agriculture ◽

Principal Component ◽

Site Specific ◽

Management Zones ◽

Physical Attributes ◽

Definition Of ◽

Specific Management

The design of site-specific management zones that can successfully define uniform regions of soil fertility attributes that are of importance to crop growth is one of the most challenging steps in precision agriculture. One important method of so proceeding is based solely on crop yield stability using information from yield maps; however, it is possible to accomplish this using soil information. In this study the soil was sampled for electrical conductivity and eleven other soil properties, aiming to define uniform site-specific management zones in relation to these variables. Principal component analysis was used to group variables and fuzzy logic classification was used for clustering the transformed variables. The importance of electrical conductivity in this process was evaluated based on its correlation with soil fertility and physical attributes. The results confirmed the utility of electrical conductivity in the definition of management zones and the feasibility of the proposed method.

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Using Sentinel-2 for Simplifying Soil Sampling and Mapping: Two Case Studies in Umbria, Italy

Remote Sensing ◽

10.3390/rs13173379 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3379

Author(s):

Francesco Saverio Santaga ◽

Alberto Agnelli ◽

Angelo Leccese ◽

Marco Vizzari

Keyword(s):

Precision Agriculture ◽

Principal Component ◽

Unsupervised Classification ◽

Soil Samples ◽

Soil Sampling ◽

Field Variation ◽

Sample Collection ◽

Sampling Schemes ◽

And Cluster Analysis ◽

Sentinel 2

Soil-sample collection and strategy are costly and time-consuming endeavors, mainly when the goal is in-field variation mapping that usually requires dense sampling. This study developed and tested a streamlined soil mapping methodology, applicable at the field scale, based on an unsupervised classification of Sentinel-2 (S2) data supporting the definition of reduced soil-sampling schemes. The study occurred in two agricultural fields of 20 hectares each near Deruta, Umbria, Italy. S2 images were acquired for the two bare fields. After a band selection based on bibliography, PCA (Principal Component Analysis) and cluster analysis were used to identify points of two reduced-sample schemes. The data obtained by these samplings were used in linear regressions with principal components of the selected S2 bands to produce maps for clay and organic matter (OM). Resultant maps were assessed by analyzing residuals with a conventional soil sampling of 30 soil samples for each field to quantify their accuracy level. Although of limited extent and with a specific focus, the low average errors (Clay ± 2.71%, OM ± 0.16%) we obtained using only three soil samples suggest a wider potential for this methodology. The proposed approach, integrating S2 data and traditional soil-sampling methods could considerably reduce soil-sampling time and costs in ordinary and precision agriculture applications.

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Spatial variability of soil fertility in an integrated crop livestock forest system

Advances in Animal Biosciences ◽

10.1017/s2040470017001145 ◽

2017 ◽

Vol 8 (2) ◽

pp. 590-593 ◽

Cited By ~ 4

Author(s):

A. C. C. Bernardi ◽

G. M. Bettiol ◽

G. G. Mazzuco ◽

S. N. Esteves ◽

P. P. A. Oliveira ◽

...

Keyword(s):

Spatial Variability ◽

Soil Fertility ◽

Soil Properties ◽

Precision Agriculture ◽

Acid Soil ◽

Management Strategies ◽

Fertilizer Application ◽

Management Tools ◽

Site Specific ◽

The Tropics

Knowledge on spatial variability of soil properties is useful for the rational use of inputs, as in the site specific application of lime and fertilizer. Crop-livestock-forest integrated systems (CLFIS) provide a strategy of sustainable agricultural production which integrates annual crops, trees and livestock activities on a same area and in the same season. Since the lime and fertilizer are key factors for the intensification of agricultural systems in acid-soil in the tropics, precision agriculture (PA) is the tool to improve the efficiency of use of these issues. The objective of this research was to map and evaluate the spatial variability of soil properties, liming and fertilizer need of a CLFIS. The field study was carried out in a 30 ha area at Embrapa Pecuária Sudeste in São Carlos, SP, Brazil. Soil samples were collected at 0–0.2 m depth, and each sample represented a paddock. The spatial variability of soil properties and site-specific liming and fertilizer needs were modeled using semi-variograms, the soil fertility information were modeled. Spatial variability soil properties and site specific liming and fertilizer need were modeled by kriging and inverse distance weighting (IDW) techniques. Another approach used was based on lime and fertilizer recommendation considering the paddocks as the minimum management unit. The results showed that geostatistics and GIS were useful tools for revealing soil spatial variability and supporting management strategies. Soil nutrients were used to classify the soil spatial distribution map and design site-specific lime and fertilizer application zones. Spatial analyses of crop needs and requirement can provide management tools for avoiding potential environmental problems, caused by unbalanced nutrient supplies.

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Predictive Mapping of Soil Properties for Precision Agriculture Using Geographic Information System (GIS) Based Geostatistics Models

Modern Applied Science ◽

10.5539/mas.v13n10p60 ◽

2019 ◽

Vol 13 (10) ◽

pp. 60 ◽

Cited By ~ 5

Author(s):

John Kingsley ◽

Solomon Odafe Lawani ◽

Ayito Okon Esther ◽

Kebonye Michael Ndiye ◽

Ogeh Joseph Sunday ◽

...

Keyword(s):

Spatial Variability ◽

Soil Properties ◽

Precision Agriculture ◽

Soil Analysis ◽

Soil Samples ◽

Soil Parameters ◽

Available Phosphorus ◽

Predictive Tool ◽

Large Variability ◽

North East

In precision Agriculture, geostatistical methods as a predictive tool have been extensively utilized. The approach estimates soil properties spatial variability and dependency. This study was carried out in Ovia north east Local Government Area of Edo State of Nigeria in order to map soil properties (Sand, Clay, pH, OC, P, N and CEC) and redict their spatial variability. Twenty-nine (29) soil samples were collected randomly from Typic Kandiudults soil type under three different land use, teak forest plantation, shrub, and arable farm. The soil samples were air-dried and passed through a 2 mm sieve before being analyzed for pH(CaCl2), SOC, Sand, Clay, Phosphorus, Nitrogen, and CEC. Generated data were statistically and geostatistically computed to explain the spatial variability of soil properties. The traditional method of soil analysis and interpretation are tedious, time-consuming with escalating budgets thus geostatical approach. Available phosphorus yielded large variability with CV=57.08% followed by clay content with CV=49.03%. Spherical, Gaussian, Hole Effect model, Stable, Exponential and Circular models were fitted for all the soil parameters. The result revealed that soil pH, Sand content, TN and CEC were moderate spatially autocorrelated with nugget/sill value of 0.32, 0.21, 0.49 and 0.30 respectively.  SOC also gave a moderate spatially autocorrelated with nugget/sill value of 0.44. And Clay and Available phosphorus were strong spatially autocorrelated with nugget/sill value of 0.15 and 0.13 respectively. Cross-validation of the output maps using the semivariogram showed that the interpolation models are superior to assuming mean for any unsampled area. The output maps will help soil users within the area to proffer best management technology to improve crop, fiber and water production.   

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Spatial Variability of Potato Tuber Yield and Plant Nitrogen Uptake Related to Soil Properties

Agronomy Journal ◽

10.2134/agronj13.0468 ◽

2014 ◽

Vol 106 (3) ◽

pp. 851-859 ◽

Cited By ~ 10

Author(s):

Suzanne E. Allaire ◽

Athyna N. Cambouris ◽

Jonathan A. Lafond ◽

Sébastien F. Lange ◽

Bernard Pelletier ◽

...

Keyword(s):

Spatial Variability ◽

Soil Properties ◽

Potato Tuber ◽

Nitrogen Uptake ◽

Tuber Yield ◽

Plant Nitrogen ◽

Plant Nitrogen Uptake

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Terrain forms and spatial variability of soil properties in an area cultivatedwith citrus

Engenharia Agrícola ◽

10.1590/s0100-69162011000400003 ◽

2011 ◽

Vol 31 (4) ◽

pp. 643-651 ◽

Cited By ~ 3

Author(s):

Marisol G. A. de Leão ◽

José Marques Júnior ◽

Zigomar M. de Souza ◽

Diego S. Siqueira ◽

Gener T. Pereira

Keyword(s):

Spatial Variability ◽

Soil Properties ◽

Precision Agriculture ◽

Chemical Properties ◽

Clay Content ◽

Soil Samples ◽

Descriptive Statistics ◽

Geostatistical Modeling ◽

Soil Landscape ◽

Kriging Maps

The technique of precision agriculture and soil-landscape allows delimiting areas for localized management, allowing a localized application of agricultural inputs and thereby may contribute to preservation of natural resources. Therefore, the objective of this work was to characterize the spatial variability of chemical properties and clay content in the context of soil-landscape relationship in a Latosol (Oxisol) under cultivation of citrus. Soil samples were collected at a depth of 0.0-0.2 m in an area of 83.5 ha planted with citrus, as a 50-m intervals grid, with 129 points in concave terrain and 206 points in flat terrain, totaling 335 points. Values for the variables that express the chemical characteristics and clay content of soil properties were analyzed with descriptive statistics and geostatistical modeling of semivariograms for making maps of kriging. The values of range and kriging maps indicated higher variability in the shape of concave topography (top segment) compared with the shape of flat topography (slope and hillside segments below). The identification of different forms of terrain proved to be efficient in understanding the spatial variability of chemical properties and clay content of soil under cultivation of citrus.

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Site‐specific nitrogen balances based on spatially variable soil and plant properties

Precision Agriculture ◽

10.1007/s11119-021-09789-9 ◽

2021 ◽

Author(s):

Martin Mittermayer ◽

August Gilg ◽

Franz-Xaver Maidl ◽

Ludwig Nätscher ◽

Kurt-Jürgen Hülsbergen

Keyword(s):

Soil Properties ◽

N Uptake ◽

Nitrate Content ◽

Site Specific ◽

Nitrate Losses ◽

Wide Range ◽

Reduction Strategies ◽

Combine Harvester ◽

Digital Methods ◽

Collection Methods

AbstractIn this study, site-specific N balances were calculated for a 13.1 ha heterogeneous field. Yields and N uptake as input data for N balances were determined with data from a combine harvester, reflectance measurements from satellites and tractor-mounted sensors. The correlations between the measured grain yields and yields determined by digital methods were moderate. The calculated values for the N surpluses had a wide range within the field. Nitrogen surpluses were calculated from − 76.4 to 91.3 kg ha−1, with a mean of 24.0 kg ha−1. The use of different data sources and data collection methods had an impact on the results of N balancing. The results show the need for further optimization and improvement in the accuracy of digital methods. The factors influencing N uptake and N surplus were determined by analysing soil properties of georeferenced soil samples. Soil properties showed considerable spatial variation within the field. Soil organic carbon correlated very strongly with total nitrogen content (r = 0.97), moderately with N uptake (sensor, r = 0.60) and negatively with N surplus (satellite, r = − 0.46; sensor, r = − 0.56; harvester, r = − 0.60). Nitrate content was analysed in soil cores (0 to 9 m) taken in different yield zones, and compared with the calculated N surplus; there was a strong correlation between the measured nitrate content and calculated N surplus (r = 0.82). Site-specific N balancing can contribute to a more precise identification of the risk of nitrate losses and the development of targeted nitrate reduction strategies.

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Reducing Topdressing N Fertilization with Variable Rates Does Not Reduce Maize Yield

Sustainability ◽

10.3390/su13148059 ◽

2021 ◽

Vol 13 (14) ◽

pp. 8059

Author(s):

Calogero Schillaci ◽

Tommaso Tadiello ◽

Marco Acutis ◽

Alessia Perego

Keyword(s):

Precision Agriculture ◽

Stem Elongation ◽

Maize Yield ◽

Application Rate ◽

N Fertilization ◽

Economic Benefits ◽

Growth Monitoring ◽

Fixed Rate ◽

Positioning Systems ◽

Significant Difference

Proximal sensing represents a growing avenue for precision fertilization and crop growth monitoring. In the last decade, precision agriculture technology has become affordable in many countries; Global Positioning Systems for automatic guidance instruments and proximal sensors can be used to guide the distribution of nutrients such as nitrogen (N) fertilization using real-time applications. A two-year field experiment (2017–2018) was carried out to quantify maize yield in response to variable rate (VR) N distribution, which was determined with a proximal vigour sensor, as an alternative to a fixed rate (FR) in a cereal-livestock farm located in the Po valley (northern Italy). The amount of N distributed for the FR (140 kg N ha−1) was calculated according to the crop requirement and the regional regulation: ±30% of the FR rate was applied in the VR treatment according to the Vigour S-index calculated on-the-go from the CropSpec sensor. The two treatments of N fertilization did not result in a significant difference in yield in both years. The findings suggest that the application of VR is more economically profitable than the FR application rate, especially under the hypothesis of VR application at a farm scale. The outcome of the experiment suggests that VR is a viable and profitable technique that can be easily applied at the farm level by adopting proximal sensors to detect the actual crop N requirement prior to stem elongation. Besides the economic benefits, the VR approach can be regarded as a sustainable practice that meets the current European Common Agricultural Policy.

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