Multidisciplinary Journal Epistemology of the Sciences
Volume 3, Issue 1, 2026, January–March
DOI: https://doi.org/10.71112/7s3xfc80
CLASSIFICATION OF PRODUCTIVE ENVIRONMENTS FOR VARIABLE RATE
FERTILIZATION IN THE PRECISION AGRICULTURE SYSTEM
CLASIFICACIÓN DE AMBIENTES PRODUCTIVOS PARA FERTILIZACIÓN A TASA
VARIABLE EN EL SISTEMA DE AGRICULTURA DE PRECISION
Bladimir Fernández Orellana
Bolivia
DOI: https://doi.org/10.71112/7s3xfc80
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Classification of productive environments for variable rate fertilization in the
precision agriculture system
Clasificación de ambientes productivos para fertilización a tasa variable en el
sistema de agricultura de precision
Bladimir Fernández Orellana
ing.fernandez.bladimir@gmail.com
https://orcid.org/0009-0005-5077-8210
Universidad Gabriel René Moreno, Faculty of Agricultural Sciences.
Santa Cruz - Bolivia.
ABSTRACT
Precision agriculture is essential in today's agricultural production. It allows fields to be classified
according to the productive potential of each environment, providing personalized management,
improving production costs, and increasing yields.
The methodology used in this research is descriptive and applied, classifying productive
environments based on the physical and chemical attributes of soils and their geospatial
availability in order to recommend variable rate fertilization prescriptions for soybean cultivation
on an agricultural property in the Cuatro Cañadas Municipality in the eastern zone of Santa
Cruz. Georeferenced sampling was carried out by environments, and as a result, it was
determined that 54% of the study area corresponds to environment A (high potential), 22%
corresponds to environment B (medium potential), and 24% comprises environment C (low
potential). In addition, sustainable management measures were suggested.
Keywords: Precision agriculture; Yields; Remote sensing; Variable rate fertilization; Geographic
Information Systems.
DOI: https://doi.org/10.71112/7s3xfc80
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RESUMEN
La agricultura de precisión es esencial en la producción agrícola actual, permite clasificar un
campo de acuerdo a la potencialidad productiva de cada ambiente brindando un manejo
personalizado, mejorando los costos de producción y aumentando los rendimientos.
La metodología empleada en esta investigación es de tipo descriptivo y aplicativo donde se
clasificó los ambientes productivos en base a los atributos físicos – químicos de suelos y su
disponibilidad geoespacial para poder recomendar prescripciones de fertilización a tasa
variable en el cultivo de soya, ubicado en una propiedad agrícola del Municipio Cuatro Cañadas
Zona Este de Santa Cruz. Se realizó el tipo de muestreo georreferenciado por ambientes y
como resultado se determinó que del área de estudio un 54% corresponde a un ambiente A
(potencialidad Alta), el 22% corresponde a un ambiente B (potencialidad media), y el 24%
comprende al ambiente C (potencialidad baja). Además, se sugirieron medidas de gestión
sostenibles.
Palabras clave: Agricultura de precisión; Rendimientos; Teledetección; Fertilización a tasa
variable; Sistemas de información Geográfica.
Received: December 29, 2025 | Accepted: January 21, 2026 | Published: January 22, 2026
INTRODUCTION
Thanks to technological innovations, precision agriculture now allows fields to be
classified according to the productive potential of each environment. The stratification of these
environments is crucial for determining variable rate fertilization, setting achievable yield targets
according to the productive potential of each environment and its nutritional requirements.
DOI: https://doi.org/10.71112/7s3xfc80
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Figure 1
To do this, at least three things must be acknowledged: (I) that the heterogeneity of
elements and/or detect the differences that exist in a plot and, eventually, their causes and the
processes affected by that heterogeneity; (II) that this heterogeneity modifies responses to
management practices, that is, it interacts with them; and (III) crops can be managed within the
limits of this heterogeneity; that is, units can be defined on which to make decisions and
implement their management. In environment-based agriculture, unlike the plot, the
management unit has similar agroecological attributes, which modulate crop performance and
make it advisable to adjust a technical approach that is different from that of units belonging to
another environment, both to improve yield and the efficient use of input resources, and to
reduce variability and production risk. (Satorre & Bert, 2014).
Detailed knowledge of the chemical and physical variability of soils allows for the
adjustment of nutrition plans to a varied rate. Its correlation with productivity can have a greater
or lesser impact depending on the soil: therefore, knowing the soil type helps to complement
recommendations and interpret productivity maps (Mosquera, 2012).
Potentially productive environments
DOI: https://doi.org/10.71112/7s3xfc80
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Santa Cruz, Bolivia, has the right conditions to develop precision agriculture technology
for the benefit of producers. The adoption of variable rates in Santa Cruz will require the
development of yield maps, topographic maps, satellite images, aerial photographs, real-time
remote sensors, etc. (Rodríguez, 2012).
Precision agriculture (PA) allows for the management of plots based on the variability of
agricultural production and the factors involved in it (Bragachini et al., 2006).
Zoning is a process of simplifying the production variability that exists in a plot,
establishment, or region. The delimitation of these zones consists of dividing the plot into
homogeneous subunits based on characteristics that are stable over time, others that are
dynamic, and others that change over time and with management. (Bermudez, 2012).
Geographic information systems
Geographic Information Systems (GIS) play a crucial role in modern agriculture, as they
provide tools for visualizing, analyzing, and managing spatial data in order to improve crop
productivity and sustainability. (Astate, 2024; Esri, 2021). This technology helps farmers make
informed decisions, optimize resource use, and address various challenges in agricultural
practices. (Muhammad, 2023; gisnavigator, 2025).
The adoption of GIS in agriculture offers numerous advantages, resulting in more
efficient, sustainable, and profitable agriculture. (Butora et al., 2022).
GIS enables farmers to create detailed maps of vegetation and productivity, allowing
them to make informed decisions about seeds, nutrients, herbicides, and fertilizer amounts for
each plot. This data-driven approach helps identify productive and unproductive areas,
facilitating the implementation of targeted fertilization strategies.
Precision Agriculture
Precision agriculture, also known as prescription agriculture, uses GIS to optimize
resource use and minimize environmental impact by focusing on site-specific management
(Kumar et al., 2024).
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(INTA, 2013)The National Institute of Agricultural Technology defines precision
agriculture as the use of information technology to make economically and environmentally
sound decisions for crop production, which has the potential to increase production efficiency
and reduce environmental impact.
To this end, techniques such as remote sensing are used, which is generally defined as
the measurement of energy emitted from the Earth's surface. If the source of the measured
energy is the sun, then it is called passive remote sensing, and the result of this measurement
can be a digital image (Richards & Jia, 2006) . This technology uses the electromagnetic
spectrum as its main measurement variable, which is "the system that classifies, according to
wavelength, all energy (from short cosmic to long radio) that moves harmoniously at the
constant speed of light" (NASA, 2011) .
The Normalized Difference Vegetation Index (NDVI) is a widely used metric for
quantifying vegetation health and density through sensor data. (projects., 2006) It is an
important indicator in agricultural organizations and environmental studies, as it measures
greenness, vegetation health, and predicts agricultural productivity, as well as mapping
desertification. ( NDVI is commonly used in remote sensing to monitor seasonal, interannual,
and long-term variations in the structural, phenological, and biophysical parameters of land
surface vegetation cover. (Falk, 2004) .
The results obtained through remote sensing were corroborated in situ through soil
sampling. Accurate soil analyses are based on representative samples. Obtaining
representative samples requires care and skill. In most cases, the sample represents a quantity
of soil that is more than ten million times greater than the portion sent to the laboratory.
Therefore, regardless of whether the sample represents a small or large area, it is important to
take multiple samples covering the entire surface of the area, which are then combined and
mixed well to obtain a sample for analysis that truly represents the entire area sampled (Jensen
et al., 2013).
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Mention three ways of taking samples, which are detailed below:
Zigzag sampling method
Subsamples will be collected in a zigzag pattern (Figure 2), at equidistant points 15 to 20
steps apart. Another way is to divide the plots into homogeneous lots, and for each division
made, sampling is carried out in a zigzag pattern, with 20 to 30 subsamples of equal size
considered an adequate number.
Figure 2
Zigzag sampling
Georeferenced sampling (grid method)
This is done with the help of GPS (Global Positioning System) equipment. The
methodology is more technical in nature: the plot is subdivided into grids with a distance of 50 to
100 meters between one point and another (Figure 3). Each individual sample is collected and
placed in individual plastic bags. The collected samples are arranged on a table in the same
way as they were found in the plot, and then touch tests are performed to determine similar
textures, which are mixed and identified.
DOI: https://doi.org/10.71112/7s3xfc80
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Figure 3
Zigzag sampling
Georeferenced sampling method Potentially productive environments
Sampling is carried out with the help of GPS equipment and georeferenced data such as
environment or productivity maps obtained from overlaying systems of various layers of spatial
information, i.e., soil sampling is carried out based on the spatial variability detected within a
productive plot. This involves the use of various technologies such as: multi-temporal satellite
and drone images, GPS, yield monitors, variable rate applicators, altimetric levels, green
indices, waterlogging, compaction, and the main physical, chemical, and biological
characteristics of soils that affect agricultural production. (Figure 4)
DOI: https://doi.org/10.71112/7s3xfc80
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Figure 4.
Physical Soil Attribute
Good physical soil quality determines a suitable environment for plant root development,
as well as optimal water intake and storage necessary for plant growth (Taboada & Álvarez,
2008).
Humans modify the physical quality of the soil through agricultural or livestock
management. The decline in physical quality has serious consequences for chemical and
biological conditions (Dexter, 2004).
Soil Profile
(Bruulsema et al., 2012) defines the soil profile as a vertical section of soil extending
from the surface through all its horizons to the parent material.
Soil Texture
Texture is the relative size portion of soil particles.
The most common classification separates these sizes into sand, silt, and clay with the
following limits: sand from 2 to 0.02 mm, silt from 0.02 to 0.002 mm, and clay less than 0.002
Georeferenced Sampling by Production Environments
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mm (Canedo, 2008) . Texture is the element that best characterizes soil from a physical-
structural point of view; permeability, ease of tillage, cation exchange capacity, water retention
capacity, and structure are some of the characteristics of soil that depend largely on texture.
(Darwich, 2005) .
Apparent Density
Apparent density is the mass of soil per unit volume (g.cm
-3
or t.m
-3
). It describes soil
compaction, representing the relationship between solids and pore spaces (Keller & Hákansson,
2010)
The apparent density value is a necessary parameter in various soil-related calculations,
such as: Calculating soil suitability; Calculating the weight of a given volume of soil; Calculating
the amount of nutrients in kg/ha; Converting the gravimetric moisture content of the soil to
volumetric content.
In addition to the above, it is an estimator of the degree of soil compaction, since if this
problem is occurring, the bulk density increases; it is also an indicator of high organic matter
(OM) content in the soil, since OM reduces the value of this density. In other words, the higher
the bulk density, the lower the organic matter content. Apparent density is also a parameter for
estimating the degree of soil deterioration, bearing in mind that as its value increases, the
structure of the soil is degrading, either due to compaction or the loss of organic matter.
Chemical Attributes of Soil
pH
Canedo (2008) mentions that soil pH, directly or indirectly, can be responsible for the
availability of almost all soil nutrients to plants. Phosphorus, iron, and zinc, among the main
ones, are insolubilized by chemical reactions that occur at very acidic or very alkaline pH levels.
pH is defined as the negative logarithm of the concentration of hydrogen ions or as the
logarithm of the inverse of the activity of the hydrogen ion. Soil pH simply measures the activity
of hydrogen ions and is expressed, as already mentioned, in logarithmic terms (Darwich, 2005).
DOI: https://doi.org/10.71112/7s3xfc80
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Organic Matter
Canedo (2008) mentions that organic matter is an active and important part of soil,
although most cultivated soils contain 1 to 5% organic matter, especially in the top 25 cm of soil.
This small amount can modify the physical characteristics of the soil and strongly affect its
chemical and biological properties.
Nitrogen
According to , nitrogen (N) is one of the most widely distributed elements in nature. The
main reservoir of N is the atmosphere.
In the soil, it is found in three forms:
Nitrates: This is a form of N that is assimilable or available to plant roots.
Ammoniacal: This is a transitional form of N and is not abundant in the soil.
Organic: Found in organic matter and the only permanent source or reserve of N in the
soil.
In addition to its role in protein formation, nitrogen is an integral part of the chlorophyll
molecule, which absorbs the energy from solar radiation necessary for photosynthesis
(Echeverría & Saínz Rozas, Nitrogen, 2006).
Phosphorus
Plants absorb phosphorus, preferably as an anion (H
2
PO
4
-
) and to a lesser extent
(HPO
4
=
). The first organic compounds formed with phosphorus in the plant are phosphohexoses
and uridine diphosphate, the precursors of ATP. Phosphate occurs in plants in inorganic form as
orthophosphate and, to a lesser extent, as pyrophosphate.
The organic forms of phosphate are compounds in which orthophosphate is esterified
with hydroxyl groups of sugars and alcohols or linked by a pyrophosphate bonded to another
phosphate group. The most important compound in which the phosphate group is linked by
pyrophosphate bonds is ATP or adenosine triphosphate, which plays an important role in plants
as a constituent of high-energy storage compounds (Mengel & Kirkby, 2012).
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Phosphorus is part of enzymes, nucleic acids, and proteins and is involved in virtually all
energy transfe r processes. Plants with phosphorus deficiencies have less expansion and leaf
area and fewer leaves. The greater effect on leaf growth than on chlorophyll growth explains the
darker green colors observed in phosphorus-deficient plants (García et al., 2006).
Sulfur
The soil contains sulfur in organic matter in varying amounts, and it is only available to
plants through mineralization. Sulfur is absorbed by plant roots almost exclusively in the form of
sulfate ion (SO
4
=
). Although small amounts are absorbed in the form of sulfur dioxide (SO
2
)
through the leaves of plants. A sulfur deficiency, which has a pronounced effect of slowing plant
growth, is characterized by uniformly chlorotic plants and thin stems (Canedo, 2008).
Sulfur is an essential nutrient for plants with requirements similar to phosphorus.
However, for a long time, it did not receive much attention because crops did not respond to its
addition. This situation has changed in many crop and pasture production areas on five
continents, where improvements in yields and the quality of agricultural products have been
observed with the addition of sulfur. Sulfur is also a constituent of the amino acids methionine
and cysteine, and its deficiency causes serious malnutrition problems in humans (Echeverría,
Azufre, 2006) .
Potassium
Potassium is one of the three main inorganic mineral nutrients, along with N and P. Not
only is it important quantitatively, as it is the most abundant element along with the four main
components of proteins and carbohydrates (N, O, and H), but its importance also derives from
its multiple functions. It is the most important cation due to its physiological and biochemical
functions (Melgar et al., 2011).
Potassium is absorbed as a monovalent cation (K
+
) present in the soil solution and
remains without forming part of any molecule throughout the life cycle, unlike other mineral
elements. (Melgar et al., 2011).
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Potassium is part of organic molecules and is also involved in numerous physiological
and biochemical functions in plants, which makes it an essential element. Plants require
relatively large amounts of potassium, sometimes more than nitrogen. (Conti & García, 2006) .
Calcium
Calcium is an element required by all higher plants; absorbed in the form of Ca
++
ions, it
is found in abundant quantities in plant leaves and, in some species, in plant cells as calcium
oxalate precipitate. It can also be present in the sap of cells in ionic form. The specific
physiological functions of calcium in plants are not clearly defined. Traditionally, calcium has
been considered necessary for the formation of the middle lamella of cells because of its
important role in the synthesis of calcium pectate. It has also been suggested that calcium
promotes the formation and increase of the protein contained in mitochondria. (Canedo, 2008).
Magnesium
Magnesium is one of the constituent elements of plant chlorophyll, so it is actively
involved in photosynthesis. Most Mg is found in chlorophyll. Magnesium participates in
phosphate metabolism in plant respiration, in the activation of various enzyme systems, and
indirectly in protein synthesis (Darwich, 2005).
Cation exchange capacity
CEC is the property of soils to retain and exchange ions on the surface of organic and
inorganic colloids in the soil. The ions retained in the exchange complex maintain a dynamic
equilibrium with the ions in the soil solution (Conti and García, 2006).
Iron
Iron is absorbed by the roots as a divalent cation Fe
2+
(ferrous iron) or as a chelate, with
absorption in trivalent form being irrelevant (ferric iron) due to the low solubility of the latter at
normal soil pH. The rate of iron reduction is dependent on pH, being higher at lower pH (Mengel
& Kirkby, 2012; Melgar et al., 2011).
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(Darwich, 2005) He mentions that iron is a catalyst that aids in the formation of
chlorophyll and acts as an oxygen carrier. Iron also contributes to the formation of certain
respiratory enzyme systems. Iron deficiency produces pale green leaves (interveinal chlorosis).
With a marked distinction between green veins and yellow interveins, symptoms usually appear
first on the green leaves at the top of the plant.
Micronutrients
Manganese
According to , the manganese present in the soil comes from oxides, carbonates,
silicates, and sulfates. Due to its different degrees of oxidation (II, III, and IV) and its ability to
change from one form to another, the behavior of manganese in the soil is complex.
The forms in which it can occur are:
Manganous ion Mn
2+
(divalent) in soil solution. It is interchangeable and available to
plants.
Oxides and hydroxides (MnO
2
Mn(OH)) or associated with iron hydroxides. In some soils, it can
occur in layers on the planes of separation of aggregates or in the form of fern leaves. In these
states, manganese is neither exchangeable nor available.
Poorly soluble salts (Mn(II) and Mn(III) phosphates, Mn(II) carbonates), especially in
calcareous or alkaline soils.
Zinc
Zn absorption occurs mainly as a divalent Zn
2+
cation from the soil solution, aided by a
protein with a high affinity for Zn at low pH values, and probably as Zn(OH)
2
at high values.
Transport in plants occurs both as Zn
2
+ and bound to organic acids. The latter, in the form of
low molecular weight compounds, are the most physiologically active fraction. It accumulates in
the roots but is translocated to the shoots when necessary. It is relatively mobile, translocating
from mature leaves to developing organs. (Alloway, 2008).
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Copper
Copper is an immobile element that accumulates in surface horizons due to
bioaccumulation and anthropogenic contamination. Most of this element is absorbed specifically
by organic matter and, to a lesser extent, by silicate clay surfaces, generating forms that are not
readily available. (Torri et al., 2006).
Copper is absorbed in very small quantities through a metabolically assisted process
and competes strongly with Zn absorption and vice versa (Bowen, 1969) . However, copper
absorption is mainly related to the levels of copper available in the soil. Nevertheless, there is
debate as to whether copper is absorbed as a Cu2+ ion or as a chelate, (Graham, 1981) .
Boron
Boron is absorbed by the plant as boric acid (H
3
BO
3
) and perhaps as borate anion at
high pH, both through the roots and through the leaves. Boron is an element with low mobility
within the plant. It has also been proven that young plants absorb it more intensely than older
ones, with low mobility from old to young tissues. There may even be a boron deficiency in one
leaf while another on the same stem has adequate levels. (Canedo, 2008).
Variable Rate
Variable rate is a technology that allows for the rationalization of input use without
affecting productivity. On the contrary, by balancing the amounts of active ingredient or nutrient,
lowering costs, and creating more environmentally balanced conditions, after a few crop cycles
it is possible to better adjust the system and increase productivity (Mosquera, 2012).
Variable Rate Technology (VRT) allows farmers, once they know the behavior of the crop in
each sector of the plot, to calculate the input requirements in each smaller homogeneous area
or subunit on the ground and apply them in a site-specific manner ( ) (Bragachini, 2004 cited by
Bragachini et al., 2009). See Figure 5.
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Figure 5.
Variable rate fertilization
The potential for improving profitability through variable application of these inputs
depends on 1) identifying areas in the field where additional inputs will increase income on a
larger scale than the additional costs generated by such inputs and/or; 2) the identification of
areas where reducing inputs will decrease costs on a scale that is greater than the potential
reduction in income correlated with lower grain yield (Koch, 2004, cited in Bragachini et al.,
2009).
Fertilization for soybean cultivation
(García & Correndo, 2013) In the FUNDACRUZ technical dissemination manual on
soybeans, they mention that soybean cultivation needs to absorb a significant amount of
nutrients to achieve adequate growth and grain yield. Nutrient harvest rates (the ratio between
the amount of nutrients in the grain and the amount of nutrients absorbed) are high (Table 1).
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Table 1.
Nutritional requirements for soybean cultivation
Nutrients
Requirements
Harvest indices
Soybean 3000 kg/ha
Absorption
Extraction
-
Kg/ton grain
%
kg/ha
kg/ha
N
75
0.73
225
164.25
P
7
0.88
21
18.48
K
39
0.49
117
57.33
Ca
16
0.19
48
9.12
Mg
9
0.39
27
10.53
S
4.5
0.72
13.5
9.72
B
0.025
0.31
0.075
0.02325
Cl
0.237
0.47
0.711
0.33417
Cu
0.025
0.53
0.075
0.03975
Fe
0.3
0.25
0.9
0.225
Mn
0.15
0.33
0.45
0.1485
Mo
0.005
0.85
0.015
0.01275
Zn
0.06
0.7
0.18
0.126
This research stems from the need for farmers to know the
the productive potential and fertility of their soils when making decisions, including fertilization,
taking into account the unknowns: ¿How much to apply? What to apply? And where to apply it?
Based on this objective, we will proceed with the classification of productive environments
based on the physical and chemical attributes of the soil and its geospatial distribution, and then
set achievable yield goals, recommending variable rate fertilizer prescriptions.
DOI: https://doi.org/10.71112/7s3xfc80
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METHODOLOGY
The research was carried out on the San Carlos property in the Cuatro Cañadas
Municipality, Ñuflo de Chávez Province, Department of Santa Cruz. Geographically located at
17°18'35.69" south latitude and 62° 0'57.00" west longitude, at an altitude of 265 meters above
sea level.
The materials used are: sampling drill, sample bags, shovel, pickaxe, tape measure,
metal cylinder, precision scale, oven, customized computers (16–32 GB RAM), Garmin 64S
GPS, CHC i70 Centimeter GPS RTK, John Deere Axial Combine Performance Monitor, and
Kacife laboratory physical-chemical analysis – Brazil.
The geographic information systems used are: ArcGIS (10.6) (ArcMap) Trial Version
software, Global Mapper 18 software, Russian SAS Planet software, Google Earth Pro
software, and Apex John Deere software.
The geospatial materials used are satellite and aerial images:
Landsat 5,7: Multitemporal images from 1990 > 2014.
Landsat 8: Multitemporal images from 2014 to 2017, flooding and NDVI
Sentinel 2: Multitemporal images > 2018, Flooding risk and NDVI
Aster: For the Digital Elevation Model (DEM)
Google Maps/Earth, Bing Maps (Bird's Eye), Yandex Maps, OpenStreetMap,
DigitalGlobe, and eAtlas: High-precision measurement for detecting net cultivable area.
Fixed-wing drone with "Micasense" multispectral camera: Very high-precision
measurement by plot and NDVI, Chlorophyll.
Classification of soils by productive environments
To classify the soils in the study area according to their productive potential, factors
causing their heterogeneity were identified, which made it possible to classify them into three
types of productive environments. The characteristics used for each of them are detailed below:
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High Environment (A): Loamy to medium-heavy soils: Loose, predominantly loamy-silty
texture, with less than 25% clay, good productive potential, high green index (NDVI), and no risk
of waterlogging.
Medium Environment (B): Medium-heavy soils with acceptable productivity, clay
content between 25–35%, medium green index, with slight surface unevenness and minor
waterlogging problems;
Low Environment (C): Heavy soils, predominantly clayey in texture, with clay content
above 35%, and other soils with high sand content >80%, low productive potential, abundant
depressed areas, low green index, areas with un r waterlogging and salinity problems.
Sampling Points
On the map of productive environments, with the intention of identifying the aptitudes of
each productive environment and searching for its deficits or virtues, georeferenced points
(GPS – X and Y coordinates) were positioned.
Soil sampling was carried out with the help of GPS equipment, a drill, plastic bags, and
labels. Twenty subsamples were taken around each georeferenced point, at a depth of 0–20
cm, which were homogenized to obtain a representative sample. To determine soil compaction,
apparent density samples were taken using the cylinder method. Three replicates were taken
from each layer identified at the following depths: 0–10, 10–20, and 20–30 cm, corresponding to
the topsoil layer.
These samples were properly labeled and placed in an oven for drying, obtaining the dry
weight of the soil. The apparent density (g/cm3) was calculated using the following formula.
𝐃𝐀𝐏 =
𝑴𝑺
𝑽𝑪
Where:
DAP: Apparent density (g/cm³); MS: Dry soil mass (g); VC: Cylinder volume (cm³).
The following parameters were used to interpret the pH. (Table 2)
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Table 2.
Interpretation of active soil acidity class (pH)
Chemical classification
Very high
acidity
High
acidity
Medium
acidity
Low acidity
Neutral
Weak
alkalinity
High
alkalinity
> 4.5
4.5 - 5.0
5.1 - 6.0
6.1- 6.9
7
7.1 - 7.8
> 7.8
Agronomic assessment
Very low
Low
Good
High
Very high
<4.5
04.05 - 05.04
5.5 - 6.0
6.1 - 7.0
> 7.0
With regard to the interpretation of macro- and micronutrients, soil parameters according
to Kacife Laboratories were used (Table 3):
Table 3.
Sufficiency levels of soil analysis parameters (KACIFE)
Analysis parameters
Sufficiency levels
Very low
Low
Moderate
High
Very high
Organic matter (%)
< 0.71
0.71 - 2.00
2.01 - 4.00
4.01 - 7.00
> 7.00
Total, nitrogen (%)
< 0.05
0.05 - 0.12
0.12 - 0.22
0.23 - 0.28
> 0.28
Available nitrogen (mg/kg)
< 12.00
12.40 - 29.00
29.10 - 53.00
54.00 - 70.00
> 70.00
Phosphorus Resin (mg/l)
< 6.0
7.00 - 15.00
16.00 - 40.00
41.00 - 80.00
> 80.00
Potassium (mg/l)
---
< 150
150 - 250
250 - 800
> 800
Calcium (cmol
c
/l)
< 0.41
0.41 - 1.20
1.21 - 2.40
2.41 - 4.00
> 4.00
Magnesium (cmol
c
/l)
< 0.16
0.16 - 0.45
0.46 - 0.90
0.91 - 1.50
> 1.50
Int. Capacity Cat. Effective (cmolc/l)
< 1.61
1.61 - 4.30
4.31 - 8.60
8.61 - 15.00
> 15.00
Total, Int. Bases (cmolc/l)
< 0.81
0.81 - 2.30
2.31 - 4.60
4.61 - 8.00
> 8.00
Base saturation (%)
< 20
20 - 40
40 - 60
60 - 80
> 80
DOI: https://doi.org/10.71112/7s3xfc80
541 Multidisciplinary Journal Epistemology of the Sciences | Vol. 3, Issue 1, 2026, January–March
Nutrient management and requirements in soybean cultivation
The nutritional balance was calculated for each georeferenced point that was sampled,
considering the essential nutritional requirements and needs for soybean cultivation. Taking into
account the laboratory results, these were converted to kilograms/hectare at a depth of 20 cm
using the soil weight calculated from the apparent density. The difference in nutrients required
to be incorporated into the soil was calculated and converted into forms of commercial products
available on the market.
The following tables show the absorption and extraction values of different nutrients: For
environments A and B (High and Medium Potential), yield targets of 3.5 t/ha were set. (See
Table 4).
Remaining
P (mg/l)
Analysis parameters
Sufficiency levels
Very low
Low
Moderate
High
Very high
0 – 4
Sulfur (mg/l)
< 1.80
1.80 - 2.50
2.60 - 3.60
3.70 - 5.40
> 5.40
Oct 4
< 2.50
2.50 - 3.60
3.70 - 5.00
5.10 - 7.50
> 7.50
Oct-19
< 3.40
3.40 - 5.00
5.10 - 6.90
7.00 - 10.30
> 10.30
19 – 30
< 4.70
4.70 - 6.90
7.00 - 9.40
9.50 - 14.20
> 14.20
30 – 44
< 6.50
6.50 - 9.40
9.50 - 13.00
13.10 - 19.60
> 19.60
44 – 60
< 8.90
9.00 - 13.00
13.10 - 18.00
18.10 - 27.00
> 27.00
DOI: https://doi.org/10.71112/7s3xfc80
542 Multidisciplinary Journal Epistemology of the Sciences | Vol. 3, Issue 1, 2026, January–March
Table 4.
Nutritional Balance (Environment A and B)
Nutrients
Requirements
Harvest
indices
Absorption
Extraction
-
Kg/t
%
kg/ha
kg/ha
N
75
0.73
262.5
191.625
P
7
0.88
24.5
21.56
K
39
0.49
136.5
66.885
Ca
16
0.19
56
10.64
Mg
9
0.39
31.5
12.285
S
4.5
0.72
15.75
11.34
B
0.025
0.31
0.0875
0.027125
Cl
0.237
0.47
0.8295
0.389865
Cu
0.025
0.53
0.0875
0.046375
Fe
0.3
0.25
1.05
0.2625
Mn
0.15
0.33
0.525
0.17325
Mo
0.005
0.85
0.0175
0.014875
Zn
0.06
0.7
0.21
0.147
And for environment C (low potential), a yield target of 2.5 t/ha was set (See Table 5)
Table 5.
Nutritional Balance (Environment C
Nutrients
Requirements
Harvest
Indices
Absorption
Extraction
-
Kg/t
%
kg/ha
kg/ha
N
75
0.73
187.5
136.875
P
7
0.88
17.5
15.4
K
39
0.49
97.5
47.775
Ca
16
0.19
40
7.6
Mg
9
0.39
22.5
8.775
S
4.5
0.72
11.25
8.1
B
0.025
0.31
0.0625
0.019375
Cl
0.237
0.47
0.5925
0.278475
Cu
0.025
0.53
0.0625
0.033125
Fe
0.3
0.25
0.75
0.1875
Mn
0.15
0.33
0.375
0.12375
Mo
0.005
0.85
0.0125
0.010625
Zn
0.06
0.7
0.15
0.105
DOI: https://doi.org/10.71112/7s3xfc80
543 Multidisciplinary Journal Epistemology of the Sciences | Vol. 3, Issue 1, 2026, January–March
RESULTS
Survey Map
The total area under study on the San Carlos property is 1835.64 ha, of which 1467.63
ha (3626.59 acres) is usable for agricultural plots; 267.86 ha is windbreaks; 56.98 ha is water
ponds; 12.25 ha are corrals; 8.76 ha are water channels; 8.71 ha are roads; 5.49 ha are
campgrounds; 3.48 ha are defensive; 2.53 ha are water barriers; and 1.95 ha are for the track.
(see Figure 6)
Figure 6
Survey map
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545 Multidisciplinary Journal Epistemology of the Sciences | Vol. 3, Issue 1, 2026, January–March
Figure 8
Georeferenced sampling points placed on the map of potentially productive environments.
Soil texture according to the texture of the soils of San Carlos, presents textural contrast,
they can be grouped as follows (Figure 9).
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Figure 9
Intermediate soils predominate at 52% (loamy, silty, and silty loam), followed by heavy
soils at 37% (silty clay and clay), and finally sandy soils at 11% (sandy loam and loam).
The Textural Number-"Y+(0.5)L" is the combined representation of the clay and silt content of a
given soil in a given production environment and ranges from 24 to 82%.
Soils in environment A have complex textures, with an average clay content of 23%, but
with values up to 40%, being predominantly silty loam soils, with an average of 61% silt and
18% sand.
The high silt content gives the soil a weak structure, it flattens very quickly, and tends to
form a crust after rain, so it needs to be managed with a lot of stubble cover to minimize its
negative effect. It also causes a reduction in water infiltration into the soil, so there must be
permanent soil cover.
In environment B, clay increases to an average of 45% with ranges up to 56%. These
are soils where silt particles predominate, with an average of 52% and values between 40% and
68%. In environment C, the soils are heavier, with an average of 57% clay.
Clay correlates with the textural number and ranges from 4 to 74%. There is a noticeable
increase in clay in environments B and C. (see Figure 10).
Soil texture percentage.
Sandy soil…
Loam
8%
Silty loam
30%
Loamy
1%
Silty clay
loam…
Silty clay
25%
Clay
12%
TEXTURE IN SAN CARLOS
DOI: https://doi.org/10.71112/7s3xfc80
547 Multidisciplinary Journal Epistemology of the Sciences | Vol. 3, Issue 1, 2026, January–March
Figure 10
Figure 11
Clay map
Note: This is directly related to the NDVI index. (Figure 12)
Relationship between clay and textural number
22
32
8
24
28
22
12
36
26
24
10
24
22
14
18
16
28
30
22
32
22
16
16
20
26
22
6
26
30
4
22
26
20
36
34
22
38
20
32
28
20
32
18
14
16
36
12
10
40
32
8
48
40
44
44
50
40
30
32
50
44
50
54
52
56
50
42
42
42
54
54
50
52
72
72
74
70
40
60
48
66
52
46
54
48
38
62
52
60
62
68
0
10
20
30
40
50
60
70
80
90
100
0
10
20
30
40
50
60
70
80
A1
A2
A3
A4
A5
A6
A7
A8
A10
A11
A12
A13
A14
A15
A16
A17
A18
A19
A20
A21
A22
A23
A24
A25
A26
A27
A28
A29
A30
A31
A32
A33
A34
A35
A36
A37
A38
A40
A41
A43
A44
A45
A46
A47
A48
A49
A50
A51
A52
A53
A54
B1
B2
B3
B4
B5
B7
B8
B9
B10
B11
B12
B13
B14
B15
B16
B17
B18
B19
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
C20
C21
C22
HIGH MEDIUM LOW
Y+(0.5)L
Clay %
Clay % Y+(0.5)L
DOI: https://doi.org/10.71112/7s3xfc80
549 Multidisciplinary Journal Epistemology of the Sciences | Vol. 3, Issue 1, 2026, January–March
Figure 13
Yield Map for the 2018/2019 Summer Harvest, San Carlos Property.
(John Deere Axial Combine Yield Monitor, Dry Matter Yield T/ha.)
Nutritional Management
Fertilization for soybean cultivation
A productivity level for soybean cultivation has been considered with a yield target of 3.5
t/ha in environments A and B, while for environment C it is 2.5 t/ha and, based on the demand
for macronutrients through a nutritional balance, only phosphorus and sulfur fertilization is
required. The following maps detail the fertilizer doses that need to be applied at variable rates
and their relationship to the geospatial distribution of these nutrients.
DOI: https://doi.org/10.71112/7s3xfc80
553 Multidisciplinary Journal Epistemology of the Sciences | Vol. 3, Issue 1, 2026, January–March
Figure 17
Variable Dosage Map for Sulfur Bentonite
DISCUSSION
In the classification of potentially productive environments, according to the total usable
area of agricultural plots in San Carlos, which is 1467.63 ha, 54% corresponds to an
environment A, with high productivity (775.68 ha); 22% corresponds to an environment B, with
moderate productive potential (342.47 ha), and 24% comprises environment C, with low
potential (349.48 ha).
The soil profile of environment A indicates that these are intermediate to semi-heavy
soils with high productive potential, high-altitude soils without waterlogging problems. The soils
of environment B have a higher clay content, especially the soils of environment C, which are
DOI: https://doi.org/10.71112/7s3xfc80
554 Multidisciplinary Journal Epistemology of the Sciences | Vol. 3, Issue 1, 2026, January–March
clay soils with waterlogging problems during rainy seasons, the presence of oxidation spots,
and low productive potential.
The apparent density indicates that approximately 40% of the property has compaction
problems (mild and severe), which occur at a depth of 20 cm or in the second layer of the soil
profile in the test pits.
According to the texture of the soils in San Carlos, there is a textural contrast, and they
can be grouped as follows: 3% of the soils are sandy loam (FA), 8% are loam (F), 30% are silty
loam (FL), 1% are silty (L), 21% are loamy clay loam (FYL), 25% are clay loam (YL), and 12%
are clay loam (Y).
Soils with low acidity, with an average pH of 6.59, the three productive environments
have values between 4.12 (very high acidity) and 8.75 (weak alkalinity).
The organic matter in the soils studied is generally at an average of 2.42%, considered a
moderate level, with ranges between 1.26% and 4.10%, with the highest values in the low-
productivity environment (C) and the lowest values in the high-productivity environment (A).
Total, nitrogen is generally at an average of 0.12% (moderate). The highest
concentration is found in environment C and the lowest in environment A.
Phosphorus availability averages 90.00 mg/l (very high), with slight variations: in the high
environment, the average is 83.89 mg/l; in environment B, it is 103.80 mg/l; and in environment
C, it is 92.86 mg/l.
Sulfur averages 12.89 mg/l (moderate), but its concentration is highly variable, ranging
from 3.53 mg/l to 65.67 mg/l, which is common in Santa Cruz soils.
The effective cation exchange capacity in these soils ranges from 7.86 cmolc/l
(moderate) to 22.42 cmolc/l (very high) and averages 14.63 cmolc/l, which is considered high.
Soils with very high total exchangeable bases. Potassium averages 504.42 mg/l, which
is considered a high level. Calcium and magnesium are at very high levels.
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555 Multidisciplinary Journal Epistemology of the Sciences | Vol. 3, Issue 1, 2026, January–March
The soils of San Carlos have a predominantly low potential acidity, with an average of
2.34 cmol/l classified as low acidity. However, there are ranges from 0.98 cmol/l (very low
acidity) to 6.32 cmol/l (high acidity).
The soils under study have an overall average base saturation of 83%, which is
considered very high saturation, meaning that they will provide sufficient cations to the plant.
The Ca/Mg ratio is predominantly below equilibrium and also at equilibrium in some
areas, while the K/Mg ratio is predominantly above equilibrium, with som t equilibrium and below
equilibrium.
In terms of micronutrients, iron and copper are present in high concentrations
throughout; zinc is predominantly at moderate levels, with some low and high values;
manganese and boron are present at low to high levels.
Physiographically, the soils of San Carlos are deep soils in the elevated areas and
depressions of the fluvial-lacustrine plains, with medium to slightly heavy textures, slightly acidic
to slightly alkaline pH, and moderate fertility.
CONCLUSIONS
Given that areas of the San Carlos property show a certain degree of compaction in the
top 30 cm of soil, where root development occurs, it is recommended to establish a natural soil
rotation scheme or, failing that, using a subsoiler to a depth of 30 cm to improve root
development and rainwater infiltration, and then establishing grasses that maintain
macroporosity in the soil.
Subsoiling is not recommended for soils with greater clay contraction (expansive), as
they crack in the dry season.
Avoid soybean monoculture, which, due to its poor root exploration, will increase the
problem of compaction, in addition to the low supply of stubble it offers, both in quantity and
quality.
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In loamy soils, avoid conventional tillage, thus avoiding a negative effect on the physical
properties of the soil, since these soils are easily compacted and their structure is quickly
destroyed.
Avoid bringing machinery onto the land when the soil profile is very wet to prevent
compaction, especially in light to medium soils, as they are more susceptible to compaction.
Practice direct seeding with permanent stubble cover. Use a production system based on no-till
farming with surface residue management and crop rotation.
In areas with alkaline pH, it is possible to lower the pH by applying calcium sulfate in
combination with a good supply of organic matter and the elimination of alkalizing fertilizers
(urea, DAP, etc.). It is preferable to use ammonium sulfate and MAP, which have an acidic
reaction, as well as to rotate soils and provide coverage and fix N2. Black velvet bean also
provides a lot of green material that decomposes to produce organic acids, which neutralize
alkaline soils.
In soils with sodicity problems, correction with the application of large amounts of
gypsum is recommended, as this is the most commonly used method to minimize the effect of
soil sodicity. This amendment is used as a source of Ca2+ to displace Na+ from the soil
exchange complex, allowing the soil to recover its structure, improve its porosity, and allow
water to flow through the soil horizons again.
In specific sites with nitrogen deficiencies for soybean cultivation, ensure good seed
inoculation before planting. It is possible to double inoculate the seed to ensure nodulation and
N2 fixation.
Likewise, green manure, especially legumes such as winter pigeon peas, can be used to
fix N and improve crop residue input, applying conservation techniques.
In nutritionally deficient soils, correction with mineral fertilizers at variable rates is
recommended, based on nutritional balance criteria, as described in the preceding chapters.
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557 Multidisciplinary Journal Epistemology of the Sciences | Vol. 3, Issue 1, 2026, January–March
mineral fertilizers, at variable rates, based on nutritional balance criteria, as described in the
preceding chapters.
Monitor yields obtained under variable rate fertilization criteria to obtain higher yields,
according to each productive environment.
Conflict of interest statement
The authors declare that they have no conflicts of interest related to this research.
Declaration of contribution to authorship
Author Bladimir Fernandez Orellana: conceptualization, data curation, formal analysis,
fund acquisition, research, methodology, project management, resources, free and trial
software, supervision, validation, visualization, writing of the original draft, revision, and editing
of the manuscript.
Declaration of use of artificial intelligence
No artificial intelligence was used in any part of the manuscript.
REFERENCES
Abdusamea, R. M. (2024). The Importance of the Normalized Difference Vegetation Index
(NDVI) and the Use of the ArcGIS to create NDVI Maps. Revista de Ciencias y Estudios
Humanísticos - Facultad de Letras y Ciencias - Al-Marj.
https://www.semanticscholar.org/paper/4a92a36f61fa2bf90494199c8acf1d4327ebcafd
Alloway, B. J. (2008). Zinc in Soils and Crop Nutrition. IFA. Paris.
Astate. (2024). B.S.A. in GIS and Precisión Agriculture Online at A-state.
https://degree.astate.edu/online-programs/undergraduate/agriculture-studies/bachelors-
gis-precision-agriculture/
Bermudez, M. (2012). Nitrógeno. En A. Quiroga, & A. Bonos, Manual de Fertilidad y evaluacion
de suelos (págs. 101-105). La Pampa, Argentina: Editorial INTA.
DOI: https://doi.org/10.71112/7s3xfc80
558 Multidisciplinary Journal Epistemology of the Sciences | Vol. 3, Issue 1, 2026, January–March
Bono, A., & Romano, N. (2012). Nitrógeno. En A. Quiroga, & A. Bono, Manual de fertilidad y
evaluación de suelos. (págs. 101-105). La Pampa, Argentina: Editorial INTA.
Bowen, J. E. (1969). Absorption of Copper, Zinc, and Manganese by Sugarcane Leaf Tissue.
En Plant Physiology (Vol. 44, págs. 255-261). https://doi.org/
https://doi.org/10.1104/pp.44.2.255
Bragachini, M., Méndez, A., Scaramuzza, F., & Prioetti, F. (2006). Sistema de Posicionamiento
Global y su Aplicación en Agricultura. En R. Bongiovanni, E. Chartuni Mantovani, S.
Best, & A. Roel, Agricultura de Precision: Programa Cooperativo para el desarrollo
Técnológico Agroalimentario y Agroindustrial del Cono Sur (págs. 45-54). PROCISUR.
Bruulsema, T. W., Fixen, P. E., & Sulewski, G. D. (2012). 4R plant nutrition manual: A manual
for improving the management of plant nutrition. Georgia, USA: International Plant
Nutrition Institute (IPNI), Norcross.
Butora, A., Soloniewicz, B., Schwartz, C., Su, S., & Mahmoud, M. (2022). The Practical use of
GIS in Agriculture. International Conference on Computational Science and
Computational Intelligence (CSCI).
https://www.semanticscholar.org/paper/ce04b652be1145e4c86cd10e6516213a187ef47e
Canedo, E. (2008). Edafología y Fertilidad de Suelo. Texto Guía, 228. Santa Cruz.
Conti, M. E., & García, F. O. (2006). Potasio. En H. E. Echeverría, & F. O. García, Fertilidad de
suelo y fertilización de cultivos. (págs. 123-137). Buenos Aires, Argentina: Ediciones
INTA.
Darwich, N. (2005). Manual de Fertilidad de Suelos y uso de fertilizantes. 2°edición, p.289.
Dexter, A. R. (2004). Soil physical quality: Part I. Theory, effects of soil texture, density, and
organic matter, and effects on root growth. Geoderma, 120((3-4)), 201-214.
Echeverría, H. E., & Saínz Rozas, H. R. (2006). Nitrógeno. En H. E. Echeverría, & F. O. García,
Fertilidad de suelo y Fertilización de cultivos. (págs. 69-97). Buenos Aires, Argentina:
Ediciones INTA.
DOI: https://doi.org/10.71112/7s3xfc80
559 Multidisciplinary Journal Epistemology of the Sciences | Vol. 3, Issue 1, 2026, January–March
Echeverría, H. E. (2006). Azufre. En H. E. Echeverría, & F. O. García, Fertilidad de suelo y
fertilización de cultivos. (págs. 139-160). Buenos Aires, Argentina: Ediciones INTA.
Esri. (2021). GIS for Agriculture | Precision Agriculture & Farm Management - Esri.
https://www.esri.com/en-us/industries/agriculture/overview
Falk, M. (2004). Seasonal course of a normalized differential vegetation index ‘NDVI’ derived
from tower data. Semantic Scholar.
https://www.semanticscholar.org/paper/fd3c447466546c11b0fe625dddbd2154732a2feb
García, F. O., Picone, L. I., & Berardo, A. (2006). Fósforo. En H. E. Echeverría , & F. O. García,
Fertilidad de suelo y fertilización de cultivos. (págs. 99-121). Buenos Aires, Argentina:
Ediciones INTA.
García, F., & Correndo, A. (2013). Cálculo de requerimientos nutricionales. Acassuso ,
Argentina: Instituto Internacional de nutrición vegetal - IPNI Cono Sur. .
gisnavigator. (2025). GIS in Agriculture - Everything You Need to Know. .
https://gisnavigator.co.uk/gis-in-agriculture/
Graham, R. D. (1981). Abosorption of copper by plant roots. (J. F. Loneragan, A. D. Robson, &
R. D. Graham, Edits.) Sídney, Australia: Academic Press.
Guaygua, G., & Espinoza, D. (2013). Muestreo e interpretación de análisis de suelo. En R.
Nakasato, & R. Sandoval, Manual de Difusión Técnica de Soya (págs. 171-183).
Fundación de Desarollo Agrícola Santa Cruz - Fundacruz.
INTA. (2013). Curso Internacional de Agricultura de Precision. Expo de Maquinas Precisas,
recopilación de presentaciones técnicas. Argentina.
Jensen, T., Norton, R., & Singh, H. (2013). Prácticas de Apoyo. En T. Brusselma , P. Fixen, &
G. Sulewski, Manual de Nutrición de Plantas 4R (págs. 8-1 a 8-12). EE.UU.:
International Plant Nutrition Institute - IPNI.
Keller, T., & Hákansson, I. (2010). Estimation of reference bulk density from soil particle size
distribution and soil organic matter content. (Vol. 154). Geoderma.
DOI: https://doi.org/10.71112/7s3xfc80
560 Multidisciplinary Journal Epistemology of the Sciences | Vol. 3, Issue 1, 2026, January–March
Kumar, P., Sangeetha, C., Damor, J. S., S, K. P., & J. S., M., R. G., M., R. G., Moond, V.,
Moond, V. (2024). Remote Sensing and Geographic Information Systems for Precision
Agriculture: A Review. International Journal of Environment and Climate Change.
https://doi.org/https://doi.org/10.9734/ijecc/2024/v14i23945
Melgar, R., Vitti, G., & Benites, V. M. (2011). Fertilizando para los altos rendimientos - Soja en
Latinoamérica. Instituto Nacional de la Potasa - IIP Boletín N° 20 Suiza.
Mengel, K., & Kirkby, E. A. (2012). Principles of plant nutrition. Springer Science & Business
Media.
Mosquera, C. (2012). Fertilización en tasa variada. Revista Tecnicaña N°28:
https://www.siiba.conadesuca.gob.mx/siiaca/Consulta/verDoc.aspx?num=654
Muhammad, F. (2023). Applications of (GIS) Geoinformatics in Agriculture.
https://geopard.tech/blog/applications-of-gis-geoinformatics-in-agriculture/
NASA. (2011). Landsat 7 Science Data users Handbook Lnadsat Project Science Office at
NASA´s Goddard Space Flight Center in Grebelt.
projects., C. t. (2006). Normalized difference vegetation index.
https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index
Richards, J., & Jia, X. (2006). An Introduction. Remote Sensing Digital Image Analysis.
Germany: Springer.
Rodríguez, F. (2012). Agricultura de Precisión. En N. R. R., Manual de difusión técnica de soya
(págs. 157-161). Fundación de Desarollo Agrícola -FUNDACRUZ.
Satorre, E. H., & Bert, F. E. (2014). Agricultura por ambientes: Conceptos para su incorporación
eficaz al manejo de nuestros campos. Revista Cultivar decisiones, 13(5).
Taboada, M., & Álvarez, C. (2008). Fertilidad Física de Suelos 2da Edición. 2 Ed. Editorial
Facultad de Agronomía. Universidad de Buenos Aires.
DOI: https://doi.org/10.71112/7s3xfc80
561 Multidisciplinary Journal Epistemology of the Sciences | Vol. 3, Issue 1, 2026, January–March
Torri, S. I., Urricariet, S., & Lavado, S. (2006). Micronutrientes y otros elementos traza. En H. E.
Echeverría, & F. O. García, Fertilidad de suelo y fertilización de cultivos (págs. 189-205).
Buenos Aires, Argentina: Ediciones INTA.
