Use of on-ground gamma-ray spectrometry to measure plant-available potassium and other topsoil attributes

Soil Research ◽  
1999 ◽  
Vol 37 (2) ◽  
pp. 267 ◽  
Author(s):  
M. T. F. Wong ◽  
R. J. Harper
Keyword(s):  
Western Australia ◽  
Precision Agriculture ◽  
Strong Relationship ◽  
Soil Sampling ◽  
Gamma Ray Spectrometry ◽  
Separate Analysis ◽  
Organic Carbon Content ◽  
Soil Attributes ◽  
Γ Ray ◽  
Strong Relationships

The incidence of potassium (K) deficiency is increasing in crops, pastures, and forestry in south-western Australia. Although soil K can be measured using soil sampling and analysis, γ-ray spectrometry offers a potentially cheaper and spatially more precise alternative. This could be particularly useful in precision agriculture, where inputs are applied according to need rather than by general prescription. In a study of topsoils near Jerramungup, Western Australia, strong relationships (r2 = 0·9) were found between on-ground counts of γ-rays derived from 40K (γ-K) and both total K and plant-available K. The success of γ-ray spectrometry in predicting available K relied on a strong relationship (r2 = 0·9) between total K and available K which may not hold in all areas. Although the relationship between γ-K and available K held over the range of 36–1012 mg/kg, crop response to K fertilisers is only expected when the available K content is <100 mg/kg. Estimates of available K from γ-K were unreliable at this lower end of the regression curve. Separate analysis with a subset of the data with available K <100 mg/kg showed a poor relationship between γ-K and available K (r2 = 0·05; d.f. 11). The usefulness of γ-ray spectrometry may therefore be restricted to defining areas where response to fertiliser K may occur, and where further soil sampling and analysis are required to predict the fertiliser requirement. Strong relationships (r2 = 0·9) were also found between γ-K and a range of other soil attributes, including clay, silt, and organic carbon content. These relationships depended on the locally strong relationship between total K and these soil attributes. Since such relationships do not hold everywhere, the utility of γ-ray spectrometry will likewise be limited. Site-specific calibrations are required if γ-ray spectrometry is to be used for soil property mapping.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

The Distribution of Caesium-137 in Rangeland Soils at Three Sites in Western Australia.

1993 ◽  
Vol 15 (1) ◽  
pp. 24 ◽  
Author(s):  
RJ Loughman ◽  
DJ Mcfarlane ◽  
BL Campbell ◽  
R Shepherd
Keyword(s):  
Soil Erosion ◽  
Western Australia ◽  
Significant Relationship ◽  
Soil Sampling ◽  
Annual Rainfall ◽  
Department Of Agriculture ◽  
Erosion And Deposition ◽  
North East

Soil sampling for the fallout isotope caesium-137 (137Cs) was canied out on three pastoral properties in Western Australia to assess its suitability for estimating soil erosion status. The sites were situated east and north-east of Geraldton in a region receiving an annual rainfall of approximately 200 rnm. It was hypothesised that 137Cs levels would be lower outside Department of Agriculture exclosures, established in the early 1950s, because of higher rates of soil erosion due to pastoral activities. The exclosures are areas of fenced-off rangeland which have the purpose of excludin'g in11 herbivores. It was further hypothesised that 137Cs levels would be related to microtopography in this rangeland-plains landscape. Soil erosion and deposition have given rise to scalds and soil mounds under shrubs, respectively, and amounts of 137Cs at these sites could reflect these processes. The Mann-Whitney U test showed that there were no significant differences in 137Cs levels between samples collected inside and outside the exclosures. At one property there was a significant relationship between 137Cs and microtopography (U test: sig. 0.025 level), suggesting that soil erosion was more severe on open and scald sites than under shrubs. No detectable 137Cs was found at 23% of all sites, but there was evidence that localized deposition of sediments had occurred. Because the total number of samples used in this survey was small, further work will be required to confirm the utility of the 13'Cs technique for measuring erosion status in arid Australia.

Changes in chemical properties of 48 intensively grazed, rain-fed dairy paddocks on sandy soils over 11 years of liming in south-western Australia

Soil Research ◽  
2010 ◽  
Vol 48 (8) ◽  
pp. 682 ◽  
Author(s):  
M. D. A. Bolland ◽  
W. K. Russell
Keyword(s):  
Western Australia ◽  
Root Zone ◽  
Sandy Loam ◽  
Chemical Properties ◽  
Soil Testing ◽  
Soil Test ◽  
Organic Carbon Content ◽  
Pasture Production ◽  
Soil P ◽  
Soil Test P

Soil testing was conducted during 1999–2009 to determine lime and fertiliser phosphorus (P), potassium (K), and sulfur (S) requirements of intensively grazed, rain-fed, ryegrass dairy pastures in 48 paddocks on sand to sandy loam soils in the Mediterranean-type climate of south-western Australia. The study demonstrated that tissue testing was required in conjunction with soil testing to confirm decisions based on soil testing, and to assess management decisions for elements not covered by soil testing. Soil testing for pH was reliable for indicating paddocks requiring lime to ameliorate soil acidity, and to monitor progress of liming. Soil P testing proved reliable for indicating when P fertiliser applications were required, with no P being required when soil-test P was above the critical value for that soil, and when no P was applied, tissue testing indicated that P remained adequate for ryegrass production. Soil testing could not be used to determine paddocks requiring fertiliser K and S, because both elements can leach below the root-zone, with rainfall determining the extent of leaching and magnitude of the decrease in pasture production resulting from deficiency, which cannot be predicted. The solution is to apply fertiliser K and S each year, and use tissue testing to improve fertiliser K and S management. Research has shown that, for dairy and other grazing industries in the region, laboratories need measure and report every year soil pH and soil-test P only, together with measuring every 3–5 years the P-buffering index (estimating P sorption of soil), organic carbon content, and electrical conductivity.

scholarly journals GPS receivers for georeferencing of spatial variability of soil attributes

2011 ◽  
Vol 31 (6) ◽  
pp. 1162-1169 ◽  
Author(s):  
David L Rosalen ◽  
Marcos S Rodrigues ◽  
Carlos A Chioderoli ◽  
Flavia J. C Brandão ◽  
Diego S Siqueira
Keyword(s):  
Spatial Variability ◽  
Precision Agriculture ◽  
Agricultural Practices ◽  
Gps Receiver ◽  
Usual Practice ◽  
Soil Attributes ◽  
Structure Relationship ◽  
Kinematic Relative Positioning ◽  
Relationship Of

The characterization of the spatial variability of soil attributes is essential to support agricultural practices in a sustainable manner. The use of geostatistics to characterize spatial variability of these attributes, such as soil resistance to penetration (RP) and gravimetric soil moisture (GM) is now usual practice in precision agriculture. The result of geostatistical analysis is dependent on the sample density and other factors according to the georeferencing methodology used. Thus, this study aimed to compare two methods of georeferencing to characterize the spatial variability of RP and GM as well as the spatial correlation of these variables. Sampling grid of 60 points spaced 20 m was used. For RP measurements, an electronic penetrometer was used and to determine the GM, a Dutch auger (0.0-0.1 m depth) was used. The samples were georeferenced using a GPS navigation receiver, Simple Point Positioning (SPP) with navigation GPS receiver, and Semi-Kinematic Relative Positioning (SKRP) with an L1 geodetic GPS receiver. The results indicated that the georeferencing conducted by PPS did not affect the characterization of spatial variability of RP or GM, neither the spatial structure relationship of these attributes.

scholarly journals TRANSITION FROM SYSTEMATIC TO DIRECTED SOIL SAMPLING DESIGNS IN AN AREA MANAGED WITH PRECISION AGRICULTURE

2019 ◽  
Vol 39 (3) ◽  
pp. 400-409
Author(s):  
Osmar H. de C. Pias ◽  
Maurício R. Cherubin ◽  
Antônio L. Santi ◽  
Claudir J. Basso ◽  
Cimélio Bayer
Keyword(s):  
Precision Agriculture ◽  
Soil Sampling

Farm’s Sequence of Adoption of Information-intensive Precision Agricultural Technology

2017 ◽  
Vol 33 (4) ◽  
pp. 521-527 ◽  
Author(s):  
Terry W. Griffin ◽  
Noah J. Miller ◽  
Jason Bergtold ◽  
Aleksan Shanoyan ◽  
Ajay Sharda ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Management Practices ◽  
Transition Probability ◽  
Soil Sampling ◽  
Agricultural Technology ◽  
Current Status ◽  
Variable Rate ◽  
Yield Monitor ◽  
Financial Variables ◽  
Time Period

Abstract. Precision agriculture (PA) has been commercially available for decades, however only specific technologies have been readily adopted. The overall goal of this study was to provide information of the historical changes (from 2000 to 2016), current status of PA utilization, and sales expectations in the next time period. Within this overarching objective, specific goals included 1) determining the specific technologies that farmers adopt and 2) estimating the probability of transitioning from one bundle of PA technologies to another. The three information-intensive technologies included: 1) yield monitor (YM) with or without GNSS 2) variable rate (VR) application of inputs, and 3) precision soil sampling (PSS). Combinations of these three technologies in addition to a possible “no technology adopted” response resulted in eight categories of PA technology bundles. Each year, farms were classified as having one of these eight possible bundles of PA technology. Adoption of PA technologies has increased over time, with the use of only YMs and the bundle of all three PA technologies (YM, PSS, and VR) as the two primary bundles being adopted. When only VR was adopted, there was a 47% probability that the farm would add a YM by next year. When a farm used YM, VR, and PSS, there was a 99% probability that a farm would continue using the bundle in the following year. The results are useful for farmers, extension professionals, and policymakers to understand prior adoption paths for bundles of PA technology. Future steps can connect this database on adoption of PA technology with farm meta-descriptors such as acreage, type of crop, rotation, other relevant management practices, and financial variables so to better understand how farmers are integrating technologies into their farming operations. Keywords: Adoption, Information-intensive, Markov chain, Precision agriculture, Sequential, Site specific, Soil sampling, Transition probability, Variable rate, Yield monitor.

scholarly journals Vegetation indexes and delineation of management zones for soybean1

2017 ◽  
Vol 47 (2) ◽  
pp. 168-177 ◽  
Author(s):  
Amanda Carolina Marx Bacellar Kuiawski ◽  
José Lucas Safanelli ◽  
Eduardo Leonel Bottega ◽  
Antonio Mendes de Oliveira Neto ◽  
Naiara Guerra
Keyword(s):  
Crop Yield ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Growth Stages ◽  
Soybean Yield ◽  
Management Zones ◽  
Soil Attributes ◽  
Vegetation Indexes ◽  
Simple Ratio ◽  
Yield Map

ABSTRACT The delimitation of site-specific management zones may be an operational and economically feasible approach in precision agriculture. This study aimed at investigating the spatial correlations between spectral indexes sampled during different growth stages of soybean and crop yield. Soil attributes stratified in each zone and the influence of altitude were also assessed. The simple ratio index, normalized difference vegetation index and soil-adjusted vegetation index were calculated for soybean at the V6, R5 and R5.5 stages. Spatial dependence analysis via semivariogram was performed for the vegetation indexes, soybean yield and terrain elevation. The crop yield map was taken as a reference to assess the spatial agreement with the different maps generated from the spectral indexes. The average values for chemical and granulometric soil attributes were calculated and analyzed by their means among the zones delineated. The field division into two management zones, due to the combination of altitude, simple ratio index of the V6 stage and soil-adjusted vegetation index of the R5.5 stage, showed the highest agreement with the soybean yield map. Differences between the delineated zones were identified for the phosphorus, clay and silt contents.

Soil properties: their prediction and feature extraction from the LUCAS spectral library using deep convolutional neural networks

2021 ◽  
Author(s):  
Liang Zhong ◽  
Xi Guo ◽  
Zhe Xu ◽  
Meng Ding
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Soil Properties ◽  
Convolutional Neural Networks ◽  
Soil Texture ◽  
Precision Agriculture ◽  
Organic Carbon Content ◽  
Spectral Library ◽  
Deep Convolutional Neural Networks ◽  
Large Soil

&lt;p&gt;Soil, as a non-renewable resource, should be monitored continuously to prevent its degradation and promote sustainable agricultural management. Soil spectroscopy in the visible-near infrared range is a fast and cost-effective analytical technique to predict soil properties. The use of large soil spectral libraries can reduce the work needed to reliably estimate soil properties and obtain robust models capable of widespread applicability. Deep learning is apt for big data analysis, and this approach could herald a profound change in the way we model soil spectral data generally. Accordingly, we explored the modeling potential of deep convolutional neural networks (DCNNs) for soil properties based on a large soil spectral library. The European topsoil dataset provided by the Land Use/Cover Area frame Survey (LUCAS) was used without any pre-processing of spectra or covariates added. Two 16-layer DCNN models (ResNet-16 and VGGNet-16) were successfully used to make regression predictions of seven soil properties and classification predictions of soil texture into four groups and 12 levels. Our results showed that the ResNet-16 and VGGNet-16 models produced highly accurate predictions for most soil properties, being superior to either a shallow convolutional neural network and&amp;#160;traditional machine learning approaches. Soil organic carbon content, nitrogen content, cation exchange capacity, pH, and calcium carbonate content were well predicted, having a ratio of performance to deviation (RPD)&amp;#160;&gt; 2.0. Soil potassium content was adequately predicted (1.4 &amp;#8804; RPD&amp;#160;&amp;#8804; 2.0) and phosphorous content was poorly predicted (RPD&amp;#160;&lt; 1.4). The overall classification accuracy of soil texture was 0.749&amp;#160;(four groups) and 0.566&amp;#160;(12 levels). The position of feature wavelengths differed among the soil properties, for which multiple characteristic peaks were common. This study fully demonstrates the modeling potential of deep learning with soil hyperspectral data, which could bring us closer to achieving precision agriculture.&lt;/p&gt;

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